iAMPCN: a deep-learning approach for identifying antimicrobial peptides and their functional activities

Abstract

Antimicrobial peptides (AMPs) are short peptides that play crucial roles in diverse biological processes and have various functional activities against target organisms. Due to the abuse of chemical antibiotics and microbial pathogens’ increasing resistance to antibiotics, AMPs have the potential to be alternatives to antibiotics. As such, the identification of AMPs has become a widely discussed topic. A variety of computational approaches have been developed to identify AMPs based on machine learning algorithms. However, most of them are not capable of predicting the functional activities of AMPs, and those predictors that can specify activities only focus on a few of them. In this study, we first surveyed ten predictors that can identify AMPs and their functional activities in terms of the features they employed and the algorithms they utilized. Then, we constructed comprehensive AMP datasets and proposed a new deep learning-based framework, iAMPCN (identification of AMPs based on CNNs), to identify AMPs and their related 22 functional activities. Our experiments demonstrate that iAMPCN significantly improved the prediction performance of AMPs and their corresponding functional activities based on four types of sequence features. Benchmarking experiments on the independent test datasets showed that iAMPCN outperformed a number of state-of-the-art approaches for predicting AMPs and their functional activities. Further, we analyzed the amino acid preferences of different AMP activities and evaluated the model on datasets of varying sequence redundancy thresholds. To facilitate the community-wide identification of AMPs and their corresponding functional types, we have made the source codes of iAMPCN publicly available at https://github.com/joy50706/iAMPCN/tree/master. We anticipate that iAMPCN can be explored as a valuable tool for identifying potential AMPs with specific functional activities for further experimental validation.

Full text

Jing Xu is currently a PhD student in the Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Australia.
Her research interests are bioinformatics, computational biology, machine learning and deep learning.
Fuyi Li received his PhD in Bioinformatics from Monash University, Australia. He is currently a Professor at the College of Information Engineering, Northwest
A&F University, China. His research interests are bioinformatics, computational biology, machine learning and deep learning.
Chen Li is a research fellow in the Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University. His research
interests include systems proteomics, immunopeptidomics, personalized medicine and experimental bioinformatics.
Xudong Guo received his MEng degree from Ningxia University, China. He is currently a research assistant at the College of Information Engineering, Northwest
A&F University. His research interests are bioinformatics and data mining.
Cornelia Landersdorfer is an associate professor at the Monash Institute of Pharmaceutical Sciences, Monash University, Australia.Her research interests include
antibiotic mono- and combination therapy to achieve synergistic bacterial killing and prevent resistance, mechanism-based mathematical modeling, population
pharmacokinetics, pharmacodynamics, and optimized dosage regimens of anti-infectives and other agents.
Hsin-Hui Shen is an NHMRC Career Development Fellow at Monash University. She completed her PhD in Physical Chemistry at Oxford University in 2008. Her
research interests include interaction of polymyxins with bacterial membranes and synergistic effects of antibiotics against Gram-positive bacteria.
Anton Y. Peleg is a professor of Infectious Diseases and Microbiology and Director of the Department of Infectious Diseases at the Alfred Hospital and Monash
University. He is also a research group leader in the Department of Microbiology, Monash University. His research spans clinical to basic research, with a focus on
hospital-acquired infections, antimicrobial resistance, infections in immunocompromised hosts and understanding mechanisms of disease caused by hospital
pathogens.
Jian Li is a professor and an NHMRC Principal Research Fellow in the Biomedicine Discovery Institute and Department of Microbiology, Monash University,
Australia. His research interests include the pharmacology of polymyxins and the discovery of novel, safer polymyxins.
Seiya Imoto is professor and director of the Human Genome Center, Institute of Medical Science, The University of Tokyo,Japan. His research interests include
genome sequence data analysis, metagenome data analysis, immunogenomics, big data analysis, disease risk prediction and supercomputing in bioinformatics
and computational biology.
Jianhua Yao is an expert fellow and head of the Medical Algorithm Group in Tencent AI Lab, China. He received his PhD degree from John Hopkins University. His
research interests include bioinformatics, computational biology, medical imaging and pattern recognition.
Tatsuya Akutsu received his DEng degree in Information Engineering in 1989 from the University of Tokyo, Japan. Since 2001, he has been a professor in the
Bioinformatics Centre, Institute for Chemical Research, Kyoto University, Japan. His research interests include bioinformatics and discrete algorithms.
Jiangning Song is currently an associate professor and group leader in the Monash Biomedicine Discovery Institute, Monash University. He is also a member of the
Monash Data Futures Institute. His research interests include bioinformatics, computational biology, machine learning, medical imaging and pattern recognition.
Received: October 26, 2022. Revised: May 30, 2023. Accepted: June 8, 2023
© The Author(s) 2023. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which
permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Briefings in Bioinformatics, 2023, 24(4),1–20
https://doi.org/10.1093/bib/bbad240
Advance access publication date 27 June 2023
Problem Solving Protocol
iAMPCN: a deep-learning approach for identifying
antimicrobial peptides and their functional activities
Jing Xu,Fuyi Li,Chen Li ,Xudong Guo, Cornelia Landersdorfer, Hsin-Hui Shen, Anton Y. Peleg,Jian Li, Seiya Imoto, Jianhua Yao,
Tatsuya Akutsu and Jiangning Song
Corresponding authors. J. Song,. Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Victoria 3800,
Australia. E-mail: Jiangning.Song@monash.edu; T. Akutsu, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 611-0011, Japan.
E-mail: takutsu@kuicr.kyoto-u.ac.jp; J. Yao, Tencent AI Lab, Tencent, China. E-mail: jianhua.yao@gmail.com
Abstract
Antimicrobial peptides (AMPs) are short peptides that play crucial roles in diverse biological processes and have various functional
activities against target organisms. Due to the abuse of chemical antibiotics and microbial pathogens’ increasing resistance to
antibiotics, AMPs have the potential to be alternatives to antibiotics. As such, the identification of AMPs has become a widely discussed
topic. A variety of computational approaches have been developed to identify AMPs based on machine learning algorithms. However,
most of them are not capable of predicting the functional activities of AMPs, and those predictors that can specify activities only focus on
a few of them. In this study, we first surveyed 10 predictors that can identify AMPs and their functional activities in terms of the features
they employed and the algorithms they utilized. Then, we constructed comprehensive AMP datasets and proposed a new deep learning-
based framework, iAMPCN (identification of AMPs based on CNNs), to identify AMPs and their related 22 functional activities. Our
experiments demonstrate that iAMPCN significantly improved the prediction performance of AMPs and their corresponding functional
activities based on four types of sequence features. Benchmarking experiments on the independent test datasets showed that iAMPCN
outperformed a number of state-of-the-art approaches for predicting AMPs and their functional activities.Furthermore, we analyzed the
amino acid preferences of different AMP activities and evaluated the model on datasets of varying sequence redundancy thresholds.
To facilitate the community-wide identification of AMPs and their corresponding functional types, we have made the source codes
of iAMPCN publicly available at https://github.com/joy50706/iAMPCN/tree/master. We anticipate that iAMPCN can be explored as a
valuable tool for identifying potential AMPs with specific functional activities for further experimental validation.
Keywords:antimicrobial peptides; bioinformatics; sequence analysis; machine learning; deep learning; functional activities
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2|Xu et al.
INTRODUCTION
Due to the microbial pathogens’ increasing resistance to chemical
antibiotics, it is urgent to develop novel infectious therapeutics
[1,2]. Over the past decade,there have been several developments
in utilizing antimicrobial peptides (AMPs) as potential alternatives
to treat infections since most natural AMPs are particular
polypeptide substances in living organisms and are critical
components of the innate immune system which protects the
host against invading pathogens [3]. AMPs are generally small-
molecule polypeptides and have diverse functional activities
against target organisms such as bacteria, yeasts, fungi, viruses
and cancer cells. Compared with traditional chemical antibiotics,
AMPs have higher antibacterial activities, broader antibacterial
spectrums and fewer possibilities resulting in target strains’
resistance mutation [4]. Therefore, AMPs have a wide range of
application prospects in the pharmaceutical industry and have
become a hotspot in biomedical research [4].
Based on existing research data about AMPs, many efforts
have been made to construct various databases containing exper-
imentally validated AMPs. To date, a number of databases have
been developed to provide comprehensive experimentally veri-
fied annotations of AMPs. For example, the antimicrobial peptide
database (APD3) [5] includes various AMPs with different func-
tional activities such as antibacterial, antifungal, antiviral and
anticancer. It also provides a user-friendly web page for peptide
classification, search and prediction. dbAMP [6,7]isanothercom-
prehensive AMP database specifically focusing on AMPs’ func-
tional and physicochemical characteristics in high-throughput
transcriptome and proteome data. It also provides relevant anno-
tations of AMP–protein interactions and targeting species of AMPs.
DRAMP [8–10] offers access to minimum inhibitory concentration
values and structure information of AMPs, and LAMP [11,12]
crosslinks existing AMP databases into account and provides
the related information. In addition to comprehensive databases,
some disease-specific databases integrate AMPs with specific
functional activities, such as AntiTbPdb Field [13], a database
for antitubercular (anti-TB) peptides. With the continuous expan-
sion and development of AMP databases, identifying AMPs and
their functional types accurately by computational methods has
become increasingly important for AMP research, due to the time-
consuming, expensive and laborious wet-laboratory experiments.
In the last decade, a plethora of computational predictors
have been developed for identifying AMPs [13–16]; a few attempts
have also been made to review, benchmark and evaluate these
approaches [17,18]. However, the majority of these predictors
only focus on identifying AMPs. As such, they cannot be used to
predict the functional activities of particular interest to biomedi-
cal researchers. Several predictors have been proposed to predict
AMPs with one specific functional activity, such as DeepAVP for
predicting antiviral peptides [19], Deep-AFPpred for predicting
antiviral peptides [20], and StaBle-ABPpred [21] and Deep-ABPpred
[22] for predicting antibacterial peptides. However, these predic-
tors have limited predictive capability and cannot provide com-
prehensive functional activity annotations of AMPs. Furthermore,
only a few predictors can predict multiple functional activities
of AMPs, given the significance of AMP functional activities and
the fact that none of the work systematically summarized and
evaluated these computational approaches for predicting AMPs
and their functional activities. Herein, we reviewed these com-
putational approaches comprehensively, including the involved
functional activities, benchmark datasets, machine learning algo-
rithms, feature selection algorithms, and performance evaluation
strategies and metrics. Then, we developed a predictive frame-
work named iAMPCN (identification of AMPs and their functional
activities based on convolutional neural networks), which is com-
posed of multiple single-class models. We evaluated its ability to
identify different kinds of functional activities of AMPs. The per-
formance evaluation results demonstrated that iAMPCN achieved
superior performances in identifying AMPs and their functional
types compared with available predictive tools. We anticipate that
iAMPCN can serve as a prominent tool for identifying potential
AMPs and their specific functions that can be experimentally
validated.
MATERIALS AND METHODS
Existing approaches for predicting AMPs and
their functional types
In the present study, we comprehensively reviewed 10 compu-
tational predictors for AMPs and their activities regarding the
predicted functional activities, data sources, algorithms and per-
formance evaluation strategies in Table 1 and Figure 1. We also
provided a summary of the key features used in these approaches
in Table 2. We grouped these tools into traditional machine learn-
ing (ML)-based and deep learning (DL)-based according to the
algorithms they applied.
Machine learning-based approaches
To our best knowledge, iAMP-2L [23] is the first AMP predictor,
which utilizes the fuzzy K-nearest neighbor (FKNN) algorithm
to identify both AMPs and their functional activities, including
antibacterial, anticancer, antiviral, antifungal and anti-human
immunodeficiency virus (anti-HIV). The first-level prediction of
iAMP-2L is to determine whether a peptide is an AMP, for which
the FKNN [24] algorithm was utilized. The second-level prediction
of iAMP-2L was designed to characterize the functional activity
types of the queryAMP, for which the multi-label fuzzy K-nearest
neighbor (ML-FKNN) classifier was applied. Both levels of predic-
tion utilized the pseudo amino acid composition (PseAAC) [25]to
represent sequences. In another work, Lin et al.proposedMLAMP
[26] by mainly focusing on addressing the unbalanced labeled
problem in the two-level AMP prediction. ML-SMOTE was devel-
oped by modifying the synthetic minority oversampling technique
(SMOTE) [27] based on the multi-label classification. In addition,
MLAMP converted the PseAAC [25]intonewvectorsassequence
representations with the grey model [28]. Another approach uti-
lizing oversampling technique was proposed by Zhang et al. [29],
which employed adaptive synthetic sampling (ADASYN) [30]to
handle the data imbalance issue. Both MLAMP and Zhang et al.’s
work applied the ensemble classifier chain (ECC) [31] to classify
the AMP functional activities where the relationship between
labels was considered. The only difference is that Zhang et al.
utilized the gradient boosting decision tree (GBDT) [32]astheclas-
sifier for the first stage of prediction and the extra tree (ET) [33]for
the second stage. In contrast, both stages of MLAMP applied the
random forest (RF) as the classifier. Besides, Zhang et al. adopted
Lasso [34] for feature selection to further improve the perfor-
mance. AMAP [35] utilized the support vector machine (SVM) [36]
and extreme gradient boosting (XGBoost) [37] as the classifiers,
trained with amino acid composition (AAC) [38] and physico-
chemical properties to identify AMPs. Compared with other pre-
dictors, AMAP can identify the most functional types. AMPfun
[39] is also based on RF but was built on a more comprehensive
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Predicting AMPs and functional activities |3
Tab l e 1: A comprehensive summary of the reviewed approaches for AMP prediction
Category Tool Ye ar Prediction of functional activities Positive data
source
CD-HIT cut-off Algorithm Sampling
strategy
Feature
selection
Evaluation strategy Evaluation metrics Web se rve r/
software
availability
Machine
learning based
iAMP-2L 2013 Antibacterial, anticancer,
antiviral, antifungal, anti-HIV
APD2 1st: 40%; 2nd:
40%
FKNN N.A . N.A . Jack-knife validation
test, independent test
Acc,Sp,Sn,MCC,
macro Pr,macroSn,
macro Acc,SA,HL
Yes
MLAMP 2016 Antibacterial, anticancer,
antifungal, anti-HIV, antiviral
APD2 1st: 40%; 2nd:
40%
RF, ECC SMOTE N.A. Jack-knife validation
test, independent test
Acc,Sp,Sn,MCC,
macro Pr,macroSn,
macro Acc,SA,HL
Yes
AMAP 2019 Antibacterial, anticancer,
antifungal, anti-HIV, antiviral,
antibiofilm, antiparasitic,
chemotactic, insecticidal,
antimalarial, antioxidant,
spermicidal, anti-protist
APD3 1st: N.A.; 2nd:
N.A .
SVM, XGB,
one-vs-rest
classifier fusion
N.A . N.A . LOCO, 5-fold CV,
independent test
AUC, AUPR, MCC Yes
AMPfun 2019 Antiparasitic, anticancer,
antifungal, antiviral, targeting
mammals, targeting
Gram-positive bacteria, targeting
Gram-positive bacteria
APD3, ADAM,
ParaP ep, AVPdb,
CancerPPD, MLACP,
AntiCP, AntiFP,
DRAMP
1st: 50%; 2nd:
N.A .
DT, RF, SVM N.A . SFS 10-fold CV,
independent test
Acc,Sp,Sn,MCC,AUC Ye s
Zhang et al.’s
work
2020 Anti-MRSA, antibacterial,
antiviral, antifungal, anticancer,
anti-HIV, antiparasitic
APD3, APD2 1st: 40%; 2nd:
40%
GBDT, ET, ECC ADASYN Lasso 10-fold CV,
independent test
Acc,Sp,Sn,MCC,
macro Pr,macroSn,
macro Acc,HL,AP
No
iAMP-RAAC 2021 Antiparasitic, anticancer,
antifungal, antiviral, targeting
mammals, targeting
Gram-positive bacteria, targeting
Gram-positive bacteria
APD3, ADAM,
ParaP ep, AVPdb,
CancerPPD, MLACP,
AntiCP, AntiFP,
DRAMP
1st: 50%; 2nd:
N.A .
SVM N.A . ANOVA, IFS 10-fold
CV,independent test
Acc,Sp,Sn,MCC Ye s
dbAMP 2.0 2021 Anticancer, antifungal, antiviral,
targeting mammals, targeting
Gram-positive bacteria, targeting
Gram-positive bacteria
dbAMP 2.0, APD2,
CAMP
1st: 50%; 2nd:
N.A .
GBDT N.A . N.A . 5-fold CV Sn,Sp, balanced Acc Yes
Deep learning
based
iAMP-CA2L 2021 Antibacterial, anticancer,
antifungal, anti-HIV, antiviral,
antibiofilm, antiparasitic,
chemotactic, anti-MRSA,
antiendotoxin
APD3, AMPer,
ADAM
1st: 90%; 2nd:
90%
DL N.A . N.A . Jack-knife validation,
independent test
Acc,Pr,Sp,Sn,MCC,
F1, SA, AP, coverage,
HL, RL, OE, macro Pr,
macro Sn, macro F1,
micro Pr,microSn,
micro F1
Yes
Multi-AMP 2021 Anti-MRSA, antibacterial,
antiviral, antifungal, anticancer,
anti-HIV, antiparasitic
APD3, APD2 1st: 40%; 2nd:
40%
DL N.A . N.A . 5-fold CV,
independent test
Acc,Sp,Sn, MCC, F1,
macro Sp,macroSn,
macro Acc,HL,macro
MCC, macro F1
No
AMPDiscover 2021 Antibacterial, antifungal, antiviral,
antiparasitic
starPepDB 1st: 50%; 2nd:
N.A .
RF, DL N.A . Correlation
subset, SE,
Relif-F, IG, gain
ratio,
symmetrical
uncertainty
5-fold CV, 10-fold CV,
15-fold CV, 20-fold
CV, 25-fold CV,
independent test
Acc,Sp,Sn,MCC,AUC Ye s
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4|Xu et al.
Figure 1. A summary of current computational approaches for predicting AMPs and their functional activities. The blue dots indicate the functional
activities that the predictors can predict.
Tab l e 2: Different types of features employed by the reviewed approaches for AMP prediction
Feature type Feature Tools
Composition features Amino acid composition (AAC) AMAP, AMPfun, dbAMP 2.0
Dipeptide composition (DPC) dbAMP 2.0
N-gram composition found by counting (NCC) AMPfun
N-gram composition found by t-test (NTC) AMPfun
Motifs composition (MC) AMPfun
Position features N-gram binary profiling of position found by
counting (NCB)
AMPfun
N-gram binary profiling of position found by
t-test (NTB)
AMPfun
Motifs binary profiling of position (MB) AMPfun
Structural and physicochemical properties Physicochemical properties dbAMP 2.0
Composition/transition/distribution (CTD) AMPfun
Pseudo amino acid composition (PseAAC) iAMP-2L, MLAMP, AMPfun, dbAMP 2.0
Conjoint triad (CTriad) AMAP
Reduced amino acid clusters (RAAC) iAMP-RAAC
Protein Descriptors Calculation (ProtDCal) AMPDiscover
Evolutionary information Cellular automata images (CAIs) iAMP-CA2L
training dataset by integrating multiple AMP databases. AMPfun
employed abundant sequence-based features, including compo-
sitional information, physicochemical descriptions and binary
profiles, for model training. It can classify a range of functional
activities, including antiparasitic, antiviral, anticancer, antifungal
and anti-mammalian cells, anti-Gram positive and anti-Gram
negative. iAMP-RAAC [40] is also a two-stage AMP predictor based
on SVM and reduced amino acid cluster (RAAC) [41]features.
iAMP-RAAC was trained on the same datasets as AMPfun and
can predict the same functional activities as AMPfun. In addition,
dbAMP 2.0 [6], an AMP database, has a user interface developed
based on the GBDT algorithm for identifying AMPs and their
functional types.
Deep learning-based approaches
With the advances and applications of DL techniques in bioinfor-
matics, several DL-based predictors have been recently developed.
Among them, iAMP-CA2L [42] is the first DL-based predictor
for AMPs and their functional activities. iAMP-CA2L used the
ANTIALIAS [43] technology to extract features from cellular
automata images (CAIs) [44] of sequences and convolutional
neural networks (CNNs) [45] and long short-term memory (LSTM)
[46] to further extract features. Then, two SVMs were trained with
these features to distinguish AMPs from non-AMPs and annotate
10 different functional activities, including antibacterial, antiviral,
antifungal, antibiofilm, antiparasitic, anti-HIV, anticancer, chemo-
tactic, anti-Methicillin-resistant Staphylococcus aureus (anti-
MRSA) and antiendotoxin. Another recent approach, multi-AMP
[47], has also been developed based on DL. However, different from
iAMP-CA2L, multi-AMP considered the AMP identification as a
multi-task problem. Accordingly, the final output layers of multi-
AMP are used to address two tasks: AMP identification and their
functional activity prediction. The models for these tasks shared
identical parameters in the front layers (CNNs) for feature extrac-
tion but were structured with different dense layers for the final
prediction. Multi-AMP used the position-specific scoring matrix
[48] to represent peptide sequences. AMPDiscover [49]isanother
recently developed webserver, which provides two predictive
models for AMPs: RF models with six different feature selection
strategies, and the RNNs (recurrent neural networks) [50].
Sequence similarity threshold
To remove the redundant sequences from the training datasets
to avoid the overfitting issue during training machine learning
models, most studies employed CD-HIT [51–53] with a relatively
stringent sequence similarity threshold to reduce the sequence
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Predicting AMPs and functional activities |5
redundancy. For example, iAMP-2L [23] and Zhang et al.’s work [29]
set the sequence identity threshold at 40%. AMPfun [39], dbAMP
2.0 [6] and AMPDiscover [49] set the sequence identity threshold
at 50%. However, in the case of functional activity prediction of
AMPs, most studies set a relatively loosened threshold or did not
remove redundant sequences, which is probably because of the
rather limited annotations of functional activities. For example,
iAMP-CA2L [42] set the sequence identity threshold at 90%, while
AMAP [35], AMPfun [39], dbAMP 2.0 [6]andAMPDiscover[49] did
not remove the sequence redundancy.
Model development
Although several approaches have been proposed to characterize
AMPs and their functional activities, they have some drawbacks
or limitations. First, the majority of the existing approaches
only focused on limited functional activities. Although other
approaches can cover more functional types, they were developed
based on relatively out-of-date databases with fewer AMPs,
resulting in the inaccurate annotation of the functional activities.
AMAP can predict several activities that other methods cannot,
such as antibiofilm, insecticidal, antimalarial, antioxidant,
spermicidal and anti-protist. However, its models for identifying
these functional activities were trained with less than 50 positive
samples, which was particularly the case that only four positive
samples were used to identify the ‘anti-protist’ activity of AMPs.
Accordingly, we assume that the predictive results of these models
could be less reliable, given the fact that many publicly available
AMP databases have been updated recently. Second, different AMP
databases may have some differences in AMPs that they include.
Nevertheless, most existing AMP predictors were developed based
on the data collected from one or only a few databases. As a result,
the training datasets on which these predictors were built could
be biased and poorly annotated. In view of these shortcomings,
it is necessary to curate a comprehensive training dataset by
integrating the available databases and developing more accurate
predictors for identifying AMPs and their functional activities.
In this study, we introduce a two-stage computational frame-
work, ter med identification of AMPs and their functional activities
based on Convolutional Neural networks (iAMPCN), to address
the aforementioned shortcomings and improve the predictive
performance of AMPs and their functional activities. Specifically,
iAMPCN employs the one-hot encoding scheme to encode the
peptide sequences and a two-stage framework coupled with CNN
to train the model and conduct the prediction.
Dataset construction
iAMPCN is a two-stage computational framework for identifying
AMPs and their functional types. Accordingly, we constructed
comprehensive benchmark training and independent test
datasets for both tasks. Our detailed procedures are described
below.
In the f irst stage, for the positive dataset, we collected the AMPs
with functional activity annotations from several databases,
including APD3 [5], dbAMP [6,7], DRAMP [8–10], dbaasp [54–
56], CAMP [57,58], LAMP [11,12], ParaPep [59], phytAMP [60],
AVPdb [ 61], CancerPPD [62], AntiTbPdb [63], Hemolytik [64]and
milkAMP [65], as well as the training datasets of 24 AMP classifiers,
including ADAM [66], iAMP-2L [23], AMPfun [39], iAMP-CA2L [42],
MLACP [67], AntiCP 2.0 [68], ACPred [69], ACPred-FL [70], ACPred-
Fuse [71], ACP-DL [72], iACP-DRLF [73], mACPpred [74], AntiFP
[75], AVPpred [76], AVPIden [77], AntiTbPred [78], AtbPpred [78],
BIOFIN [79], BIPEP [80], dPABBs [81], HemoPI [82], HLPpred-Fuse
[83], iAntiTB [84], AnOxPePred [85] and HAPPENN [86]. Then,
we eliminated those sequences with non-standard residues
such as ‘B’, ‘J’, ‘O’, ‘U’, ‘X’ or ‘Z’. Finally, 49 115 experimentally
validated AMP sequences were extracted from these resources.
We performed the following procedures to generate the negative
dataset: (i) peptide sequences were downloaded from UniProt
[87–90](http://www.uniprot.org), and all entries containing the
keyword ‘antimicrobial’ and related keywords (e.g. ‘antibacterial’,
‘antifungal’, ‘anticancer’, ‘antiviral’, ‘antiparasitic’, ‘anticancer’,
‘antibiotic’, ‘antibiofilm’ or ‘effector’) were removed; (ii) the
sequences with the length greater than 200 or less than 10 amino
acid residues and with non-standard residues ‘B’, ‘J’, ‘O’, ‘U’, ‘X’ or
‘Z’ were discarded; (iii) the CD-HIT program [51–53] was applied
to remove the sequences that had 40% pairwise sequence identity
to those in the positive dataset. Finally, 195 525 negative samples
were obtained.
Here, we applied the same strategy as AMPfun [39]tocon-
struct the training and independent test datasets for each func-
tional type. We collected positive and negative samples from
49 115 experimentally validated AMP sequences for each func-
tional activity. The authors of AMPfun [39] and iAMP-CA2L [42]
also constructed a relatively large AMP dataset from several dif-
ferent databases. As mentioned in AMPfun [39], a peptide with a
specific functional activity would be used as a positive sample of
this activity; otherwise, it would be treated as a negative sample.
We integrated the positive and negative datasets for 22 different
functional types in the same way. It is known that the dataset
containing instances with missing labels is an unavoidable prob-
lem. Even though all AMP sequences are obtained from only
one database, it is very likely that these sequences need to be
completely annotated since different AMP databases have been
updated several times. In addition, there are a large number
of overlapping AMPs between different AMP databases, and the
linking antimicrobial peptides (LAMP) [11,12] database was con-
structed to reflect the crosslink between several AMP databases
[11,12]. Therefore, combining peptides from multiple databases
can make the activity annotations more complete. Irrespective of
the combinations or not, instances with missing labels must exist.
Because of the large number of overlapping AMPs, combining
peptides from multiple datasets will be better than using peptides
from only one dataset.
A detailed summary of positive and negative datasets for the
first and second stages is shown in Table 3. For training and
evaluating predictive models for AMP and functional activity
predictions, we randomly selected 20% samples from positive
and negative datasets to construct test datasets. We also used
the remaining 80% of the samples as training datasets. We then
filtered those sequences used in the training datasets of other
predictive tools and split the remaining datasets to construct the
independent datasets and training datasets. Detailed information
on training, test and independent test datasets is shown in Sup-
plementary Tables S1 and S2.
Model construction
An overview of the architecture of iAMPCN is illustrated in
Figures 2 and 3. Here, we utilized four different types of amino
acid information to represent peptide sequences, including one-
hot encoding, BLOSUM62 encoding, AAIndex [91,92] encoding
and PAAC encoding. When using the one-hot encoding scheme,
a peptide sequence is converted into a numerical matrix with
the dimension of 200∗21, where 200 represents the length
of a peptide sequence and a 21-dimensional binary vector
represents each amino acid type. For example, the one-hot
encoding of A is [1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0], C is
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6|Xu et al.
Tab l e 3: The numbers of positive and negative samples for predicting AMP and their functional activities
Positive samples Negative samples
AMPs 49 115 195 525
Functional activity
Antibacterial 16 058 13 551
Antibiofilm 372 29 202
Anticancer 4698 24 876
Anticandidal 669 28 905
Antifungal 5955 23 619
Anti-Gram negative 8537 21 044
Anti-Gram positive 8053 21 539
Anti-HIV 812 28 762
Antimalarial 73 29 501
Anti-MRSA 267 29 307
Antiparasitic 457 29 117
Antiplasmodial 63 29 511
Antiprotozoal 53 29 521
Anti-TB 275 29 299
Antiviral 6495 23 093
Anti-mammalian cells 4345 25 229
Anuran defense 779 28 795
Chemotactic 85 29 489
Cytotoxic 180 29 394
Endotoxin 82 29 492
Hemolytic 2383 27 191
Insecticidal 415 29 159
[0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] and the one-hot encoding
of the non-standard amino acids is [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,
0,0,0,1]. The model with one-hot vector encoding with 21 bits and
the model with one-hot vector encoding with 20 bits are the same
when training and testing the sequences with usual amino acids
(a detailed analysis is provided in the Supplementary Methods).
We padded the vectors with ‘0’ for those sequences shorter than
200 and removed parts of sequences for those longer than 200
to obtain fixed dimensional matrices. For BLOSUM62 encoding,
a peptide sequence is converted into a numerical matrix with
the dimension of 200∗23, where 200 again represents the length
of a peptide sequence and a 23-dimensional vector represents
each amino acid type. This encoding ref lects the evolutionary
information of amino acid residues. For the AAIndex encoding, a
peptide sequence is converted into a numerical matrix with the
dimension of 200∗531. Each amino acid type is represented by a
531-dimensional vector that presents various physicochemical
and biochemical properties of amino acids. Lastly, for the PAAC
encoding, a peptide sequence is converted into a numerical
matrix with a dimension of 200∗3. Each amino acid type is
represented by a three-dimensional vector that indicates the
original hydrophobicity, hydrophilicity and side-chain masses
of amino acid residues. Recently, Otovi´
cet al.[
93]proposeda
model based on the recursive neural network (RNN) to predict
therapeutic peptides. This model also used physicochemical
encodings to represent sequences. Without using the AAIndex
[91,92], it used the ‘AAdata’ from the R package ‘Peptides’ [94].
Each amino acid of ‘AAdata’ has 94 physicochemical properties
that reflect hydrophobicity, electronegativity, alpha and turn
propensities, etc. Most of these physicochemical properties were
calculated using some formulas, such as principal components
and Z-scale. Compared with ‘AAdata’, ‘AAIndex’ contains more
enriched and detailed physicochemical properties. For example,
‘AAIndex’ includes the ‘short and medium range non-bonded
energy per residue’, which are not included in ‘AAdata’. In
addition, ‘AAIndex’ also provides other original, informative
physicochemical properties.
The one-dimensional CNNs (i.e. Conv1d) with different filter
lengths ranging from two to six were utilized to extract feature
information of varying sequence lengths from each type of encod-
ing. First, the features extracted by each CNN were normalized
and inputted into a pooling layer. Then, the extracted features
from the same encoding type were combined and inputted into
a pooling layer to obtain the final features. After combining these
features, a final dense layer was used to give the final predictive
output. The parameters of this framework are described in detail
in the Supplementary Methods.
In this study, we utilized the transfer learning strategy—
specifically, we applied the model for predicting AMPs and non-
AMPs trained at the first stage of AMP prediction, as the pretrained
model to initialize all parameters’ weights of the models for
predicting functional activities. The learning rates of model
training for both the AMP prediction and AMP functional activity
prediction were set as the same,i.e. 0.0001.Due to the imbalanced
datasets of certain functional activities, the focal loss [95]was
applied as follows:
FL pt=−at1−ptγlog pt,
where atcontrols the weights of the samples belonging to positive
and negative classes, and ptis an estimated probability by the
model, 1−ptγis considered to be a modulating factor that con-
trols the effects of easy-classified samples. Here,atand γwere set
to 1 and 2, respectively. In addition, the early stopping strategy was
applied during the 10-fold stratified cross-validation test to avoid
overfitting.
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Predicting AMPs and functional activities |7
Figure 2. The architecture of iAMPCN. Four kinds of sequence representations were utilized, and different CNN models with different filter lengths
ranging from two to six were utilized to extract feature information of varying sequence lengths from each type of encodings.
Performance evaluation strategies and metrics
Performance evaluation strategies, including the K-fold stratified
cross-validation test, jack-knife validation test and independent
test, are commonly used to evaluate predictors and optimize the
parameters of models. In this study, we employed the 10-fold
stratified cross-validation test on training data sets for model
selection and optimization. In addition, we used the independent
test to compare the predictive performance of different tools. To
objectively assess and compare the model’s performance with dif-
ferent tools and web servers, six performance metrics were used,
including sensitivity, specificity, precision, accuracy, Matthew’s
correlation coefficient (MCC) [96] and area under the ROC curve
(AUROC or AUC), which are defined as follows:
⎧
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎩
Sensitivity =TP
TP+FN 0≤Sn ≤1
Specificity =TN
TN+FP 0≤Sp ≤1
Precision =TP
TP+FP 0≤Pr ≤1
Accuracy =TP+TN
TP+FN+TN+FP 0≤Acc ≤1
MCC =TP×TN−FP×FN
√(TP+FP)(
TP+FN)(
TN+FP)(
TN+FN)−1≤MCC ≤1
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8|Xu et al.
Figure 3. Sequence analysis of AMPs collected for this study. (A) Amino acid distributions of AMP sequences versus non-AMP sequences. (B)AnUpSet
plot demonstrating the statistics of AMPs with overlapping and/or unique functional activities.(C) A Venn diagram illustrating the distributions of AMPs
with five main functional activities, including antibacterial, anticancer, antifungal, antiviral and anti-mammalian cells. (D) Amino acid distributions of
the AMPs with the five major functional activities. (E) Amino acid distributions of anti-Gram-positive and anti-Gram-negative AMPs.
where TP and TN represent the numbers of correctly predicted
positive and negative samples, respectively, while FP and FN rep-
resent the numbers of incorrectly predicted positive and negative
samples, respectively.
RESULTS AND DISCUSSION
Sequence analysis of collected AMPs
The amino acid distributions of AMPs and non-AMPs in the
constructed datasets are shown in Figure 3A. Compared with
non-AMPs, cysteine (C), lysine (K) and arginine (R) residues
appeared to be abundant in AMP sequences. This observation
is consistent with previous studies, which show that most active
AMP sequences are abundant in hydrophilic or positively charged
amino acids [3,97]. An overview of the extracted functional
activities of the AMP sequences is shown in Figure 3B–C.We
compared the amino acid distributions of AMPs in terms of five
main functional activities (Figure 3D). We found that the AMPs
with the activity of targeting mammalian cells have more lysine
(K) and leucine (L) residues, and the AMPs with antifungal activity
have more cysteine residues. We also compared the amino acid
distributions of AMPs with anti-Gram-negative and anti-Gram-
positive activity, given that most AMPs with anti-Gram-negative
activity can also usually perform the anti-Gram-positive activity
(Figure 3B). We found that the amino acids of these two types
of AMPs have similar compositions (Figure 3E). The detailed
amino acid distributions of the other activities are shown in
Supplementary Figure S1. In addition, we compared the length
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Predicting AMPs and functional activities |9
Figure 4. Performances of AMP prediction. (A) The numbers of AMPs and non-AMPs in the training datasets with different sequence identity cut-off
thresholds. (B–C) AUCs and accuracy values of iAMPCN built on the training datasets with different sequence identity cut-off thresholds based on the
10-fold stratified cross-validation test. (D) ROC curves and the corresponding AUC values of various modes using the independent test dataset. (E)AUCs
of iAMPCN built on the training datasets with different identity thresholds via the independent test dataset. (F) The accuracy values of identifying short
AMPs (5–30 AAs) on the independent test dataset.
distributions of AMPs and non-AMPs (Supplementary Figure 2), as
well as the length distributions of positive samples and negative
samples of different functional activity datasets (Supplementary
Figure 3). As a result, we noticed no significant length differences
between the AMPs with various activities.
Predictive performance for AMP identification
Most studies usually utilized sequence cluster programs, such
as CD-HIT, to remove highly similar sequences from the train-
ing dataset to reduce homology bias and redundancy. However,
some studies indicated that this step would affect the predictive
performance of the trained model. Therefore, we first evaluated
the predictive performance of the models using training datasets
with different sequence identity cut-offs ranging from 40 to 100%.
Figure 4A shows the numbers of positive and negative samples
of the training datasets with varying thresholds of identity, and
the performance evaluation results based on 10-fold stratified
cross-validation tests on these training datasets are shown in
Figure 4B–C and Table 4. The performance comparison results
indicate that the AUC and accuracy of the model trained on a
dataset pre-processed with a higher identity cut-off are better
than that with a lower identity cut-off value. In addition, we
compared our model with an identity cut-off of 40% with several
available webserver/tools, including AMAP [35], AMPDiscover [49],
AMPfun [39], AMPlify [98], AMPscanner [15], APIN [16], iAMP-
CA2L [42], iAMP-RAAC [40] and dbAMP [6], using the independent
test dataset. Furthermore, we compared the performance of our
model trained by training datasets with different identity cut-
off thresholds ranging from 40 to 100% on the independent test
dataset and provided the results in Figure 4D–E. iAMPCN trained
on the dataset with an identity cut-off of 40% achieved the AUC
value of 0.9973, outperforming other approaches. Along with the
identity thresholds increasing, the AUC values increased slightly.
We also evaluated the predictive probability distributions and
performance metrics of different computational approaches on
the independent validation dataset and demonstrated the results
in Figure 5 and Table 5. These distributions also suggest that the
iAMPCN has much better prediction performance. Moreover, we
selected those AMPs with lengths ranging from 5 to 30 amino acids
from the independent test dataset to construct a new test dataset
to further evaluate the performance of iAMPCN in identifying
short AMPs. The performance comparison results in terms of
the accuracy of iAMPCN and two state-of-the-art approaches for
predicting short AMPs, RF-AMPEP30 [99] and sAMPEP-PFPDeep
[100], are provided in Figure 4F. All these performance evaluation
results demonstrated that iAMPCN achieved a highly competitive
predictive performance and outperformed state-of-the-art pre-
dictors for AMP identification.
Performance on identifying functional activities
of AMPs
To preliminarily assess the performance of iAMPCN using the
extracted features, we evaluated the predictive performances of
identifying functional activities based on three existing datasets
constructed by previous studies [23,39,42] and provided the per-
formance comparison results in Supplementary Tables S3–S5.The
results indicate that the models built on the combination of four
types of sequence features usually outperformed those models
based on one type of sequence representation. Furthermore, on
the iAMP-2L benchmark dataset, iAMPCN achieved the best per-
formance in terms of all performance measures except hamming
loss, which might be caused by the small dataset. Besides, iAMPCN
performed best on the iAMP-CA2L benchmark dataset in terms
of accuracy. These results indicate that iAMPCN is a competitive
predictor for AMPs and their functional activities.
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10 |Xu et al.
Tab l e 4: Performances of 10-fold cross-validation test based on the AMP training dataset
Sequence identity Sensitivity Specificity Accuracy MCC AUC
40% 0.5065 (±0.0648) 0.9929 (±0.0032) 0.7497 (±0.0309) 0.6312 (±0.0276) 0.9541 (±0.0070)
50% 0.8265 (±0.0493) 0.9874 (±0.0048) 0.9070 (±0.0227) 0.8492 (±0.0191) 0.9841 (±0.0023)
60% 0.8960 (±0.0145) 0.9890 (±0.0031) 0.9425 (±0.0065) 0.9040 (±0.0085) 0.9910 (±0.0011)
70% 0.9034 (±0.0158) 0.9914 (±0.0026) 0.9474 (±0.0068) 0.9147 (±0.0053) 0.9930 (±0.0012)
80% 0.8972 (±0.0207) 0.9942 (±0.0022) 0.9457 (±0.0094) 0.9187 (±0.0071) 0.9941 (±0.0006)
90% 0.9270 (±0.0122) 0.9942 (±0.0011) 0.9606 (±0.0060) 0.9370 (±0.0075) 0.9958 (±0.0009)
100% 0.9409 (±0.0124) 0.9961 (±0.0021) 0.9685 (±0.0053) 0.9518 (±0.0032) 0.9972 (±0.0003)
To explore the effects of different sequence identity thresholds
in predicting functional activities, we also evaluated the predictive
models trained on the benchmark datasets with different CD-HIT
thresholds ranging from 40 to 100% (Supplementary Tables S6–
S27,Supplementary Figure S4). For most functional activities, the
higher the threshold of sequence identity, the higher the accuracy
and robustness of the models achieved. Therefore, we utilized
the trained models based on the training datasets with a CD-HIT
threshold of 100% for the rest of the analysis.
Performance comparison of AMP function prediction on the
independent test datasets
We first compared the performance of iAMPCN with that of
several state-of-the-art approaches for predicting AMP functional
types, including AMAP [35], iAMP-CA2L [42], AMPfun [39], iAMP-
RAAC [40] and AMPDiscover [49], based on both balanced and
imbalanced independent test datasets, respectively. A statisti-
cal summary of the balanced and imbalanced independent test
datasets is provided in Supplementary Table S2.Figure 6 shows
the ROC curves of the compared methods. Other performance
evaluation metrics of these methods on the balanced indepen-
dent test datasets are provided in Table 6. The corresponding
pairwise comparisons (based on the Wilcoxon signed-rank test)
of the accuracy, F1, MCC and AUC for measuring the performance
differences between these methods are provided in Table 7.The
ROC curves and other performance evaluation metrics on the
imbalanced independent test datasets are shown in Supplemen-
tary Figure S5 and Table S28, respectively. Altogether, we conclude
that except for ‘endotoxin activity’, iAMPCN outperforms other
compared methods for predicting all the functional activities with
the highest accuracy and AUC values. As can be seen from Table 6,
several methods including AMAP, AMPDiscover-RF, AMPDiscover-
RNN, AMPfun, dbAMP and iAMP-RAAC achieved low precision
and specificity values, which indicates that the numbers of their
predicted false positive (FP) samples were much higher than those
of the predicted true negative (TN) samples since the number of
tested negative samples was fixed (Supplementary Table S2). In
addition, iAMP-CA2L also achieved low specificity values, which
means that the numbers of its predicted false negative (FN)
samples were higher than those of the predicted true positive (TP)
samples since the number of tested positive samples was fixed
(Supplementary Table S2). Higher FN or FP values often result
in lower MCC values. When both FN and FP values were very
high, the MCC value would tend to be very low, which is the case
for iAMP-CA2L for predicting the antibacterial activity (Table 6).
Compared with these methods, iAMPCN achieved lower FN or FP
values for predicting most functional activities.
Nevertheless, it is challenging to improve the performance of
iAMPCN in predicting some specific functional activities, includ-
ing antibacterial, antifungal, anticancer, antiparasitic, endotoxin
and anti-mammalian cell activities, as indicated by the low MCC
values in Table 6. Previous studies using traditional machine-
learning algorithms also demonstrated a similar challenge (as
illustrated using the MCC values) in predicting such functional
activities based on the constructed datasets of these studies
[39,40]. As such, we would like to argue that predicting these
functional activities is difficult, regardless of machine-learning
models, feature engineering strategies applied or the training
data. Here, we explain the possible reasons: First, the predic-
tions for those functional activities with low MCC values may
be related to the killing targets and the mechanism of action.
The killing targets of the AMPs with some functional activities
are not cells, such as antiviral AMPs, but the targets of the AMPs
with antibacterial, antifungal, anticancer, antiparasitic,endotoxin
and anti-mammalian cell activities are typically cells, and as
such, in order to kill the cells, the cell membrane structure
needs to be destroyed. To achieve this, the AMPs tend to pos-
sess similar physicochemical properties (e.g. positively charged),
structures (e.g. α-helical) and mechanisms of action [101,102].
Due to their similar physicochemical properties, the AMPs with
such functional activities may have high sequence similarities
[101,102]. Our analysis has indeed demonstrated that the amino
acid distributions of the positive and negative data of the antibac-
terial, antifungal, anticancer, antiparasitic, endotoxin and anti-
mammalian cell activities are similar (Supplementary Figure 1),
thereby making it difficult to distinguish specific functional activ-
ities from each other. Second, the dataset imbalance of some
activities could also negatively affect the predictive performance.
Although we tried several strategies to reduce the impact (e.g.
focal loss) and improve the TP and TF values, such improve-
ment was limited. Therefore, some advanced strategies to better
handle the imbalanced data need to be developed in the future
work. Third, we also utilized the PHOENIX (https://phoenix.arize.
com/) tool to explore the embedding performance of represent-
ing each peptide sequence. In particular, the embeddings were
extracted from the nodes preceding the final dense layer. The
training and test datasets of specific functional activities (e.g.
antibacterial, antifungal, anticancer and anti-mammalian cells)
were utilized as the inputs to the PHOENIX tool. As a result,
we observed that some clusters of specific functional activities
only contained the points representing the peptides from the
test datasets, indicating that the representations of the training
data could not provide sufficient information for distinguishing
these peptide sequences (Supplementary Figures 6–9). Further-
more, we tried the t-distributed stochastic neighbor embedding
(t-SNE) [103] algorithm to visualize the embedding representa-
tions of the test data. For example, although the peptides with
antibacterial activities could be roughly clustered into two groups,
some peptides still could not be clearly clustered (Supplemen-
tary Figure 10). Taken together, the experiments indicate that (i)
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Predicting AMPs and functional activities |11
Figure 5. Predictive probability distributions of different computational approaches based on the independent test dataset. These approaches include
(A)AMAP,(B) AMPDiscover-RF, (C) AMPDiscover-RNN, (D) AMPfun, (E) AMPlify, (F) AMPscanner, (G)APIN,(H) APIN-fusion, (I) iAMP-CA2L, (J) iAMP-RAAC,
(K) dbAMP, (L) iAMPCN (CD-HIT 40%), (M) iAMPCN (CD-HIT 50%), (N) iAMPCN (CD-HIT 60%), (O) iAMPCN (CD-HIT 70%), (P) iAMPCN (CD-HIT 80%), (Q)
iAMPCN (CD-HIT 90%) and (R) iAMPCN (CD-HIT 100%).
the sequences between the positive and negative datasets have
similarities to a certain extent, (ii) the samples from the training
datasets cannot provide enough information for distinguishing
the samples of test datasets and (iii) the sequence embedding
module (i.e. CNNs and peptide encodings) of iAMPCN needs to
be optimized in order to improve the peptide embedding repre-
sentations in the future work. Lastly, the peptide structure also
influences its functional activity. For example, peptides with an
α-helical structure are more likely to have antimicrobial activ-
ities [101,102]. In the present study, we only incorporated the
physicochemical properties of peptides to encode the sequences
to improve the predictive performance, but did not explicitly
explore the structural information of peptides. In the future work,
we will explore effective strategies to integrate their structural
information to further improve the predictive performance of
those specific functional activities with low MCC values.
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12 |Xu et al.
Tab l e 5: Performances of different predictors based on the AMP independent test dataset
Tool Precision Sensitivity Specificity Accuracy F1 MCC AUC
AMAP 0.8908 0.6875 0.9668 0.8271 0.776 0.7134 0.8271
AMPDiscover-RF 0.3671 0.6882 0.5326 0.6104 0.4788 0.1992 0.8407
AMPDiscover-RNN 0.4740 0.6894 0.6986 0.6940 0.5617 0.3551 0.6940
AMPfun 0.9784 0.9687 0.9916 0.9801 0.9735 0.9632 0.9950
AMPlify 0.9611 0.6162 0.9902 0.8032 0.7510 0.7089 0.9613
AMPscanner 0.3045 0.5885 0.4707 0.5296 0.4014 0.0535 0.5436
APIN 0.6618 0.5868 0.8819 0.7343 0.6220 0.4870 0.7393
APIN_fusion 0.5229 0.5585 0.7993 0.6789 0.5401 0.3509 0.6584
iAMP-CA2L 0.6123 0.7283 0.8184 0.7733 0.6653 0.5211 0.7733
iAMP-RAAC 0.9736 0.9299 0.9901 0.9600 0.9513 0.9332 0.9600
dbAMP 0.9835 0.9473 0.9937 0.9705 0.9650 0.9519 0.9923
iAMPCN (40%) 0.9809 1.0000 0.9923 0.9962 0.9904 0.9866 0.9973
iAMPCN (50%) 0.9869 1.0000 0.9948 0.9974 0.9934 0.9908 0.9976
iAMPCN (60%) 0.9892 1.0000 0.9957 0.9978 0.9946 0.9924 0.9977
iAMPCN (70%) 0.9922 1.0000 0.9969 0.9985 0.9961 0.9946 0.9980
iAMPCN (80%) 0.9957 1.0000 0.9983 0.9992 0.9979 0.9970 0.9981
iAMPCN (90%) 0.9943 1.0000 0.9978 0.9989 0.9972 0.9960 0.9982
iAMPCN (100%) 0.9969 1.0000 0.9988 0.9994 0.9985 0.9979 0.9982
Adaptability and stability analysis
To further assess the adaptability in AMP identification, we com-
pared the predictive performance of iAMPCN with that of other
existing predictors. We employed the datasets on which these
existing predictors were trained and evaluated to evaluate the
performance. These predictors include García-Jacas et al.’s work
[104], BERT-based [105], ACEP [106], APIN-fusion [16], APIN [16],
AMPScannerV2 [15], DeepAVP [19], UnidLSTM [19], MultiLSTM
[19], DynEvo [19], StaEvo [19], Deep-AmPEP30 [99], Deep-ABPpred
[22], AniAMPpred [13], Deep-AFPpred [20], Deep-AVPpred [107]and
StaBle-ABPpred [21]. Based on the datasets from Veltri et al.[15],
Nishant et al.[
76], Li et al.[19], Yan et al.[99], Sharma et al.[13,20,
22,107] and Singh et al.[21], the performance evaluation metrics
of iAMPCN and these predictors were calculated and provided
in Supplementary Tables S29 and S30. It is shown that iAMPCN
achieved the highest MCC values on 5 out of 11 test datasets,
including the Nishant et al. AVP dataset [76], Nishant et al.AVP
∗
dataset [76], Sharma et al.AMPdataset[13], Sharma et al.AFP
dataset [20], Singh et al. evaluation dataset [21] and Singh et al.test
dataset [21]. Moreover, iAMPCN also achieved the second-highest
MCC values on 2 of 11 test datasets, including Li et al.[
19]and
Sharma et al.AVP[
107]. These results show that iAMPCN is a very
competitive predictor for identifying AMPs and AMPs with one
specific functional activity. To further evaluate the performance,
iAMPCN was compared with some latest state-of-the-art predic-
tors, including the consensus model [14]andGarcía-Jacaset al.’s
model [108], based on the Pinacho-Castellanos et al.’s general AMP
datasets [49](Supplementary Table S31). The consensus model
[14] is an ensemble model combining several predictive results
from RNN and BERT [14,109,110] models. As a result, iAMPCN
achieved an accuracy which was only 0.004 lower than that
of the consensus model when being evaluated on the external
test dataset, while iAMPCN also achieved a higher accuracy on
the test dataset. All these results indicate that iAMPCN has the
outstanding adaptability for identifying AMPs or AMPs with one
specific functional activity.
As the predictive performance of different models might be
affected by the selection of negative data sampling strategies [98],
we applied the same datasets and evaluation strategy provided
by Sidorczuk et al. [98] to further measure the effect caused by
the selection of negative data. The corresponding AUC values
are listed in Supplementary Table S32.WecanseethatiAMPCN
achieved a much better performance than the other compared
methods, with only one average AUC of <0.8. In addition, all
the AUC’s standard deviations of iAMPCN were relatively low.
In summary, these results suggest that iAMPCN has excellent
stability.
Performance comparison of alternative
machine-learning algorithms
To further analyze and evaluate the feature extraction capac-
ity of iAMPCN and the prediction capacity of the final dense
layer of iAMPCN, we extracted the feature representation used
as inputs into the final dense layer to conduct the compari-
son. We then replaced the final dense layer with other state-
of-the-art machine-learning algorithms, including random forest
(RF), adaptive boosting (AdaBoost), XGBoost, logistic regression
(LR) and GBDT. In addition, we utilized two oversampling strate-
gies, SMOTE [27] and ADASYN [30], to generate balance datasets.
Finally, we compared the AUC values of these algorithms on
different functional activity training datasets by 10-fold strati-
fied cross-validation tests and plotted the ROC curves on test
datasets. The corresponding performance comparison results are
provided in Supplementary Figures S11 and S12, respectively.
These results illustrated no significant differences in terms of
predictive performance by these machine-learning algorithms.
From another perspective, these results also indicate that the
feature extraction part by CNN modules of iAMPCN can already
learn appropriate feature representations from AMP sequences,
leading to outstanding and robust performance, regardless of any
machine-learning algorithm in the final dense layer. Furthermore,
we conclude that the entire deep-learning framework of iAMPCN
outperformed other machine-learning algorithms for the predic-
tion of most functional activities and that the last part of iAMPCN
has an excellent predictive capability for the AMP functional
activities and accordingly, the trained models have the potential
to be applied as effective feature extraction modules in future
studies.
Apart from the performance comparison between iAMPCN
with several different classic algorithms, we also modified the
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Predicting AMPs and functional activities |13
Tab l e 6: Performances of different computational approaches for predicting AMP functional activities based on balanced independent
test datasets
Activity Method Precision Sensitivity Specificity Accuracy F1 MCC AUC
Antibacterial AMAP 0.5788 0.8750 0.3633 0.6192 0.6967 0.2774 0.6192
AMPDiscover-RF 0.6162 0.8803 0.4518 0.6660 0.7250 0.3675 0.6322
AMPDiscover-RNN 0.6132 0.8727 0.4495 0.6611 0.7203 0.3557 0.6611
iAMP-CA2L 0.4990 0.6826 0.3148 0.4987 0.5766 -0.0028 0.4987
iAMPCN 0.6709 0.7914 0.6118 0.7016 0.7262 0.4099 0.7621
Antifungal AMAP 0.5354 0.8228 0.2860 0.5544 0.6487 0.1289 0.5544
AMPDiscover-RF 0.5385 0.7851 0.3272 0.5561 0.6388 0.1263 0.5398
AMPDiscover-RNN 0.5316 0.7895 0.3044 0.5469 0.6354 0.1073 0.5469
AMPfun 0.5901 0.5746 0.6009 0.5877 0.5822 0.1755 0.6256
dbAMP 0.5524 0.8044 0.3482 0.5763 0.6550 0.1715 0.6082
iAMP-CA2L 0.5000 0.2140 0.7860 0.5000 0.2998 0.0000 0.5000
iAMP-RAAC 0.5850 0.6763 0.5202 0.5982 0.6273 0.1989 0.5982
iAMPCN 0.7347 0.5368 0.8061 0.6715 0.6204 0.3561 0.7483
Antiviral AMAP 0.3042 0.2336 0.4656 0.3496 0.2643 -0.3092 0.3496
AMPDiscover-RF 0.4283 0.5432 0.2749 0.4091 0.4790 -0.1888 0.4999
AMPDiscover-RNN 0.4447 0.5683 0.2902 0.4293 0.4989 -0.1473 0.4293
AMPfun 0.5862 0.7502 0.4705 0.6103 0.6582 0.2299 0.6653
dbAMP 0.5392 0.8618 0.2635 0.5627 0.6633 0.1564 0.6006
iAMP-CA2L 0.4615 0.0243 0.9717 0.4980 0.0461 -0.0126 0.4980
iAMP-RAAC 0.6156 0.6653 0.5845 0.6249 0.6395 0.2506 0.6249
iAMPCN 0.8248 0.7308 0.8448 0.7878 0.7750 0.5794 0.8865
Anticancer AMAP 0.3973 0.4118 0.3753 0.3935 0.4044 -0.2130 0.3935
AMPfun 0.4450 0.4172 0.4796 0.4484 0.4306 -0.1034 0.4444
dbAMP 0.4828 0.6344 0.3204 0.4774 0.5483 -0.0476 0.4759
iAMP-RAAC 0.4658 0.1172 0.8656 0.4914 0.1873 -0.0259 0.4914
iAMPCN 0.6830 0.7667 0.6441 0.7054 0.7224 0.4139 0.7698
Anti-Gram positive AMPfun 0.5607 0.8865 0.3054 0.5960 0.6869 0.2359 0.6304
dbAMP 0.5513 0.8265 0.3272 0.5768 0.6614 0.1774 0.6107
iAMP-RAAC 0.5590 0.8879 0.2995 0.5937 0.6860 0.2317 0.5937
iAMPCN 0.6372 0.7995 0.5449 0.6722 0.7092 0.3561 0.7246
Anti-Gram negative AMPfun 0.5683 0.8868 0.3262 0.6065 0.6927 0.2572 0.6390
dbAMP 0.5603 0.8515 0.3317 0.5916 0.6758 0.2144 0.6232
iAMP-RAAC 0.5635 0.8582 0.3354 0.5968 0.6803 0.2271 0.5968
iAMPCN 0.6335 0.8058 0.5338 0.6698 0.7093 0.3529 0.7212
Anti-mammalian cells AMPfun 0.6024 0.3574 0.7641 0.5608 0.4486 0.1330 0.6163
dbAMP 0.5374 0.6486 0.4416 0.5451 0.5878 0.0923 0.5585
iAMP-RAAC 0.6364 0.1769 0.8989 0.5379 0.2768 0.1096 0.5379
iAMPCN 0.6748 0.7942 0.6173 0.7058 0.7297 0.4181 0.7665
Anti-HIV AMAP 0.3879 0.2830 0.5535 0.4182 0.3273 -0.1699 0.4182
iAMP-CA2L 0.3333 0.0063 0.9874 0.4969 0.0123 -0.0325 0.4969
iAMPCN 0.8489 0.7421 0.8679 0.8050 0.7919 0.6149 0.8633
Anti-MRSA iAMP-CA2L 0.9565 0.4151 0.9811 0.6981 0.5789 0.4806 0.6981
iAMPCN 0.8600 0.8113 0.8679 0.8396 0.8350 0.6803 0.8597
Antiparasitic AMAP 0.2692 0.1609 0.5632 0.3621 0.2014 -0.3013 0.3621
AMPDiscover-RF 0.5000 0.7241 0.2759 0.5000 0.5915 0.0000 0.6092
AMPDiscover-RNN 0.4841 0.7011 0.2529 0.4770 0.5728 -0.0514 0.4770
AMPfun 0.4750 0.4368 0.5172 0.4770 0.4551 -0.0461 0.4970
iAMP-CA2L 0.4000 0.0230 0.9655 0.4943 0.0435 -0.0344 0.4943
iAMP-RAAC 0.5517 0.1839 0.8506 0.5172 0.2759 0.0463 0.5172
iAMPCN 0.7534 0.6322 0.7931 0.7126 0.6875 0.4309 0.7861
Antibiofilm AMAP 0.5965 0.4595 0.6892 0.5743 0.5191 0.1527 0.5743
iAMP-CA2L 0.8571 0.0811 0.9865 0.5338 0.1481 0.1592 0.5338
iAMPCN 0.8243 0.8243 0.8243 0.8243 0.8243 0.6486 0.8667
Chemotactic AMAP 0.4000 0.1429 0.7857 0.4643 0.2105 -0.0933 0.4643
iAMP-CA2L 0.0000 0.0000 1.0000 0.5000 0.0000 0.0000 0.5000
iAMPCN 1.0000 0.5000 1.0000 0.7500 0.6667 0.5774 0.8163
Endotoxin iAMP-CA2L 0.9167 0.6875 0.9375 0.8125 0.7857 0.6455 0.8125
iAMPCN 0.7059 0.7500 0.6875 0.7188 0.7273 0.4384 0.7617
Insecticidal AMAP 0.5588 0.2289 0.8193 0.5241 0.3248 0.0597 0.5241
iAMPCN 0.8514 0.7590 0.8675 0.8133 0.8025 0.6302 0.8936
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14 |Xu et al.
Figure 6. ROC curves and the corresponding AUC values of different tools for predicting 14 AMP functional activities on independent test datasets. These
functional activities are (A) antibacterial, (B) antifungal, (C)antiviral,(D) anticancer, (E) anti-Gram positive, (F) anti-Gram negative, (G) anti-mammalian
cells, (H) anti-HIV, (I) anti-MRSA, (J) antiparasitic, (K) antibiofilm, (L) chemotactic, (M) endotoxin and (N) insecticidal.
final dense layers of iAMPCN to evaluate the effect of dense
layers on the predictive performance. In particular, we selected
several different hidden dense layers, including three hidden
layers with the node numbers [128, 64, 32], two hidden layers
with the node numbers [128, 64], [64, 32], and one hidden layer
with the node number [128], [64] and [32], to compare the per-
formance of iAMPCN which connected all the extracted fea-
ture representations to a single final output dense layer without
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Predicting AMPs and functional activities |15
Tab l e 7: The Wilcoxon signed-rank test for measuring the statistical significance of the performance between different approaches in
terms of accuracy, F1, MCC and AUC
Accuracy
Wilcoxon P-value AMAP AMPDiscover-RF AMPDiscover-RNN AMPfun iAMP-CA2L iAMP-RAAC dbAMP
AMPDiscover-RF –
AMPDiscover-RNN – –
AMPfun – – –
iAMP-CA2L 0.008362 – – –
iAMP-RAAC – – – 0.006714 –
dbAMP – – – 0.030151 –0.035339
iAMPCN 6.10E−05 – – 6.10E−05 0.000122 6.10E−05 6.10E−05
F1
Wilcoxon P-value AMAP AMPDiscover-RF AMPDiscover-RNN AMPfun iAMP-CA2L iAMP-RAAC dbAMP
AMPDiscover-RF –
AMPDiscover-RNN – –
AMPfun – – –
iAMP-CA2L 0.187622 – – –
iAMP-RAAC – – – 0.106995 –
dbAMP – – – 0.002014 –0.00061
iAMPCN 0.000122 – – 6.10E−05 0.000122 0.000122 0.000305
MCC
Wilcoxon P-value AMAP AMPDiscover-RF AMPDiscover-RNN AMPfun iAMP-CA2L iAMP-RAAC dbAMP
AMPDiscover-RF –
AMPDiscover-RNN – –
AMPfun – – –
iAMP-CA2L 0.008362 – – –
iAMP-RAAC – – – 0.008362 –
dbAMP – – – 0.035339 –0.047913
iAMPCN 6.10E−05 6.10E−05 6.10E−05 6.10E−05 0.000183 6.10E−05 6.10E−05
AUC
Wilcoxon P-value AMAP AMPDiscover-RF AMPDiscover-RNN AMPfun iAMP-CA2L iAMP-RAAC dbAMP
AMPDiscover-RF –
AMPDiscover-RNN – –
AMPfun – – –
iAMP-CA2L 0.008362 – – –
iAMP-RAAC – – – 0.072998 –
dbAMP – – – 0.035339 –0.00116
iAMPCN 6.10E−05 – – 6.10E−05 0.000122 6.10E−05 6.10E−05
‘–’ means that the number of functional activities that both tools can predict is less than 5.
any hidden dense layers by performing 10-fold stratified cross-
validation tests. Five datasets were selected for performance eval-
uation, including the AMP, antibacterial, antifungal, antiviral and
antiparasitic datasets. Among these datasets, the antibacterial
dataset is nearly balanced for positive and negative samples,
while the antiviral and antifungal datasets are imbalanced, and
the antiparasitic dataset is very imbalanced. Thus, these selected
datasets for the performance comparison are representative. The
corresponding AUCs are calculated and listed in Supplemen-
tary Table S33. It can be seen that the deep learning structures
with more straightforward dense layers achieved higher AUCs
and performed better when being tested on the AMP, antibac-
terial, antifungal and antiviral datasets, while the deep learning
structures with different dense layers achieved similar AUC val-
ues when being tested on the antiparasitic dataset. Considering
that training more complex structures is more time consuming
and computationally expensive, the deep learning structure of
iAMPCN without the hidden dense layers is more suitable for AMP
identification.
Performance comparison with the unsupervised
pretrained model
In addition, we also compared the predictive performance of
our proposed iAMPCN method with an unsupervised pretrained
model. Particularly, we applied the Evolutionary Scale Modeling
(ESM-2) [111] model to extract the sequence features and utilized
the RF and XGBoost models to predict AMPs and their functional
activities. To make a fair comparison, the same training datasets
of iAMPCN were utilized. The predictive performance is shown
in Supplementary Tables S34 and S35. As a result, we can see
that compared with the other two models, iAMPCN achieved the
best MCC value for AMP identification (Supplementary Table S34)
and also achieved the best MCC values in predicting 12 out of 14
functional activities (Supplementary Table S35). In addition, we
also performed the pairwise comparisons (based on the Wilcoxon
signed-rank test), and the results showed that the accuracies
and MCCs between our method and the other two methods were
significantly different (Supplementary Tables S36), with the only
exception for the AUC score between iAMPCN and XGBoost,which
was statistically insignificant with the P-value of 0.3028.
Model interpretability analysis
Model interpretability is critically important for researchers to
better understand machine learning-based models and make
informed decisions regarding the discovery of new potential
AMPs. Here, we analyzed the AMPs with the six most common
functional activities (i.e. anti-Gram positive, anti-Gram negative,
antifungal, antiviral, anti-mammalian cells and anticancer)
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16 |Xu et al.
Figure 7. Model interpretability analysis using SHAP values for selected AMPs. (A) Anti-Gram negative, (B) anti-Gram positive, (C) antifungal, (D) antiviral,
(E) anti-mammalian cells and (F) anticancer. The value on the color scale of each panel indicates the relative importance. The higher the value, the
more important the corresponding amino acid residue for the functional activity of AMPs.
and applied the ‘GradientExplainer’ method from the SHapley
Additive exPlanations (SHAP) algorithm to conduct the inter-
pretability analysis. In particular, we selected three AMPs for
each functional activity, and displayed the amino acids’ relative
importance for each AMP sequence in Figure 7.Wecanseethat
lysine and arginine residues play a relatively important role
in antimicrobial activities, presumably due to their positively
charged characteristics (Figure 7). This observation is consistent
with many previous studies [112,113]. In addition, leucine,
tryptophan and phenylalanine also contribute to antimicrobial
activities because of their hydrophobicity (Figure 7)[114,115].
Furthermore, the aspartic acid was also identified to be significant
for anticancer activity (Figure 7), which is also suggested in
previous studies [116,117].
CONCLUSIONS
Accurate identification of AMPs and their functional activities is
critical for functional peptide design and antimicrobial therapy
development. In this study, based on a systematic community-
wide assessment of computational methods for AMP and their
function prediction, we constructed a comprehensive AMP
benchmark dataset by integrating a number of public databases
and developed a novel deep learning-based framework, iAMPCN,
to accurately identify AMPs and their 22 functional activities.
Extensive stratified cross-validation tests based on the training
datasets and independent test results demonstrated that iAMPCN
achieved superior performance for predicting AMPs and most
AMP functional types. In addition, the model structure analysis
indicates that iAMPCN can be utilized as a feature extraction
tool for AMP prediction. The superior predictive performance of
iAMPCN can be attributed to three major factors: (i) the reliable
dataset curation of the up-to-date annotations of AMPs and their
functional activity to provide the most comprehensive training
data; (ii) the highly accurate deep-learning framework of iAMPCN
learns from the effective feature representations to build robust
predictive power for AMP and functional activity prediction; (iii)
the selection of appropriate sequence identity thresholds for AMP
and function prediction, respectively. Our analysis indicates that
a threshold of 40% is sufficient for identifying AMPs, while it is
suggested that a higher threshold should be chosen for predicting
AMP functional activities. Taken together, we anticipate iAMPCN
will be a practical approach for identifying AMPs and their
functional activities. In our future work, we will focus on tackling
the label imbalance problem to improve model performance in
predicting AMP functional activities.
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Predicting AMPs and functional activities |17
Key Points
• We provided a comprehensive summary of existing tools
for predicting antimicrobial peptides (AMPs) and their
functional activities.
•We constructed comprehensive benchmark datasets
of AMPs and 22 functional activities and accordingly
developed a two-stage deep learning-based framework,
termed iAMPCN, for the identification of AMPs and their
functional activities based on four different types of
sequence encodings.
•Performance benchmarking based on the independent
test datasets suggested that iAMPCN outperformed
other available state-of-the-art tools for the prediction
of AMPs and the majority of functional activities.
• The source codes of iAMPCN are publicly available
at https://github.com/joy50706/iAMPCN/tree/master for
the wider research community to use.
SUPPLEMENTARY DATA
Supplementary data are available online at https://academic.oup.
com/bib.
FUNDING
National Health and Medical Research Council of Australia
(NHMRC) (APP1127948, APP1144652), Australian Research Council
(ARC) (LP110200333, DP120104460), National Institute of Allergy
and Infectious Diseases of the National Institutes of Health
(R01 AI111965), Major and Seed Inter-Disciplinary Research (IDR)
projects awarded by Monash University. This work was also
partially supported by the International Collaborative Research
Program of Institute for Chemical Research, Kyoto University
and a Grant from the International Joint Usage/Research Center,
Institute of Medical Science, The University of Tokyo (K23-2074).
DATA AVAILABILITY
The source codes and data are available at https://github.com/
joy50706/iAMPCN/tree/master.
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