Learning Cellular Sorting Pathways
Using Protein Interactions and Sequence Motifs
TIEN-HO LIN,1ZIV BAR-JOSEPH,2and ROBERT F. MURPHY2,3
Proper subcellular localization is critical for proteins to perform their roles in cellular
functions. Proteins are transported by different cellular sorting pathways, some of which
take a protein through several intermediate locations until reaching its final destination. The
pathway a protein is transported through is determined by carrier proteins that bind to
specific sequence motifs. In this article, we present a new method that integrates protein
interaction and sequence motif data to model how proteins are sorted through these sorting
pathways. We use a hidden Markov model (HMM) to represent protein sorting pathways.
The model is able to determine intermediate sorting states and to assign carrier proteins and
motifs to the sorting pathways. In simulation studies, we show that the method can accu-
rately recover an underlying sorting model. Using data for yeast, we show that our model
leads to accurate prediction of subcellular localization. We also show that the pathways
learned by our model recover many known sorting pathways and correctly assign proteins
to the path they utilize. The learned model identified new pathways and their putative
carriers and motifs and these may represent novel protein sorting mechanisms. Supple-
mentary results and software implementation are available from http://murphylab
Key words: gene expression, HMM, machine learning, pathways, protein motifs, subcellular
localization, protein sorting.
pathways involving carrier proteins. Disruption of these pathways leading to inaccurate localization plays an
important role in several diseases, including cancer (Cohen et al., 2008; Kau et al., 2004; Gladden and Diehl,
2005), Alzheimer’s disease (De Strooper et al., 1997), hyperoxaluria (Purdue et al., 1990), and cystic fibrosis
(Skach, 2000). Thus, an important problem in systems biology is to determine how proteins are localized to
their target compartments, the carriers and motifs that govern this localization, and the pathways that are
o perform their function(s), proteins usually need to be localized to the specific compart-
ment(s) in which they operate. Subcellular localization of proteins is typically achieved by sorting
1Language Technology Institute,
School of Computer Science, and3Departments of Biological Sciences and Biomedical Engineering, Carnegie Mellon
University, Pittsburgh, Pennsylvania.
2Lane Center for Computational Biology and Machine Learning Department,
JOURNAL OF COMPUTATIONAL BIOLOGY
Volume 18, Number 11, 2011
# Mary Ann Liebert, Inc.
Recent advances in fluorescent microscopy coupled with automated image-based analysis methods
provide rich information about the compartments to which proteins are localized in yeast (Huh et al., 2003;
Chen et al., 2007) and human (Osuna et al., 2007; Barbe et al., 2008; Newberg et al., 2009). Several
computational methods have been developed to predict subcellular localization by integrating sequence
data with other types of high-throughput data (Chou and Shen, 2008; Horton et al., 2007; Emanuelsson
et al., 2007, 2000; Nair and Rost, 2005; Scott et al., 2005; Rashid et al., 2007; Bannai et al., 2002). These
methods either treat the problem as a one versus all classification problem (Chou and Shen, 2008; Ema-
nuelsson et al., 2007, 2000; Horton et al., 2007) or utilize a tree that corresponds to the current knowledge
regarding intermediate compartments, for example, LOCtree (Nair and Rost, 2005), BaCelLo (Pierleoni
et al., 2006), and discriminative HMMs (Lin et al., 2011). The tree-based methods were shown to be
superior to the one versus all methods; however, these methods do not attempt to learn the sorting
pathways, relying instead on current (partial) knowledge of protein sorting mechanism.
A number of methods have learned decision trees for predicting subcellular localization. These include
PSLT2 (Scott et al., 2005), which refines the location into sub-compartments using a decision tree learned
from data, and YimLOC (Shen and Burger, 2007), which learns a decision tree for the mitochondrion
compartment only using features that include predictions from SherLoc (Shatkay et al., 2007), an abstract-
based localization classifier. While the decision trees generated by these methods are often quite accurate,
they are not intended to reflect sorting pathways, and they utilize features that, while useful for classifi-
cation, are not related to the biochemical process of protein sorting.
In contrast to the global localization prediction methods, several experimental researchers have focused
on trying to assign a specific sorting pathway to a small number of proteins. For example, proteins
containing a signal peptide are exported through the secretory pathway (Lodish et al., 2003), while some
proteins without a classical N-terminal signal peptide are found to be exported via the non-classical
secretory pathway (Rubartelli and Sitia, 1997). A number of computational methods were developed to
use this information to predict, for a given pathway, whether a protein goes through that pathway or not
based on its sequence—for example, SignalP (Bendtsen et al., 2004b) and SecretomeP (Bendtsen et al.,
2004a). However, these methods rely on the pathway as an input and cannot be used to infer new
There are many methods developed for reconstruction of pathways of other types, for example, for
signaling pathways (Ruths et al., 2008; Bebek and Yang, 2007; Scott et al., 2006) and metabolic pathways
(Dale et al., 2010; Fischer and Sauer, 2005; Covert et al., 2004). These pathways are used to describe
information flow: one protein senses the environments and by activating a signaling or regulatory pathway
passes that information along so that the cells can mount a response. We focused on a completely different
meaning of pathway: physical movement of a specific protein. When referring to sorting pathways, we
mean that a single protein is being carried from one location to another. Unlike information flow pathways,
which involve different molecules along the way, physical sorting pathways always involve the same
proteins interacting with a set of different proteins. This makes it much more complicated to infer the order
in which this is performed (since it is always the same protein). In addition, the outcome of an information
flow pathway is often a change in genes expression which can be readily measured using microarrays. In
contrast, the outcome of a sorting pathway is the localization of a single (or a few) proteins to a com-
partment. Again, this requires different methods for inference. We are not aware of any prior article
discussing computational methods for large scale inference of pathways describing physical movement of a
While the above experimental methods provide some information on sorting pathways, no method exists
to try and infer global sorting pathways from current localization information. In this article, we show that,
by integrating sequence, motif, and protein interaction data, we can develop global models for the process
in which proteins are localized to subcellular compartments. We use a hidden Markov model (HMM) to
represent sorting pathways. Carrier proteins and motifs are used to define internal states in this model and
the compartments serve as the final (goal) state. Using this model, we identified several sorting pathways,
the carrier proteins that govern them, and the proteins that are being sorted according to these pathways.
Simulation data indicates that the models learned are accurate (leading to 81% prediction accuracy with a
noise level of 5%; see Fig. 3 below). Using data from yeast, we show that our model leads to accurate
classification of protein compartments while at the same time enabling us to recover many known pathways
and the proteins that govern these pathways. Several new predictions are provided by the model re-
presenting new putative sorting pathways.
1710 LIN ET AL.
2.1. Input data
Our input data is composed of the localization of all proteins, their interactions, and their sequences.
Each protein is labeled with one or more locations. Generative HMM search for motifs present in one
compartment and discriminative HMM search for motifs present in one compartment but absent in other
compartments. We also collected all interacting partners of the protein and the occurrences of a set of
known motifs from public databases (denoted as deterministic motifs to distinguish from novel motifs
extracted from sequence described below), specifically InterPro (Mulder et al., 2003) domains and three
signal sequence feature from UniProt (Bairoch et al., 2005): signal peptides, transmembrane region, and
GPI anchor (for more detail, see Section 3.2). We perform feature selection by a hypergeometric test to
identify features with a significant association with a location before learning our model.
We extract novel motifs associated with a location using the generative and discriminative HMM motif
finder we have previously described (Lin et al., 2011). We will compare two approaches to convert each
sequence to motif features: sequence likelihood and binary occurrence. The first approach uses the se-
quence likelihood given the motif as feature, Pr(Srkk) where kkis the profile HMM of the motif. It
represents how strong the instance matches the motif. Note that what really matters is the likelihood ratio of
motif versus background, as described below. The second approach uses a binary value to represent
whether a motif occurs in a sequence instead of a real value. Binary motif occurrence are determined by
posterior decoding as described in our previous article (Lin et al., 2011).
2.2. Modeling sorting pathway by hidden Markov models
We used a HMM to model the process of sorting proteins to their compartments, determined by the
interactions and sequence motifs. HMM is a generative model and thus provides the set of events that lead
to the observed localization of the proteins (Fig. 1). An allowed pathway through the HMM state space
structure represents a possible protein sorting pathway. All proteins start at the same start state, representing
their translation in the cytoplasm. (While those few proteins that are translated in mitochondria would not
begin in the cytoplasm, there were no mitochondrially-encoded proteins in our datasets and we can ignore
this possibility.) The assigned (final) compartment of a protein is represented by a state in the model that
unobserved intermediate sorting states at each level or each step. Z1???Z3are the emission responsible for protein sorting
at each step. S is the sequence and F corresponds to the binary feature observations. (B) The simplified HMM that
maintains conditional independence between steps. (C) A sample state space: The top block is the root and its outgoing
arrows correspond to initial probabilities. Bottom nodes are compartment states. The blocks are states and the arrows are
transitions, with transition probabilities labeled. The items listed inside a blocks are top features emitted by the states, and
emission probabilities are given on the left. Diamond-shaped blocks are silent states that emit the background feature only.
(A) The graphical model representation of a sample HMM for sorting pathways. Variables X1???X4 are
LEARNING CELLULAR SORTING PATHWAYS1711
does not have any outgoing transitions. Intermediate states correspond to intermediate compartments or to
sorting events (e.g., interaction with a carrier protein). These internal states emit observed features that are
related to the sorting events, namely motifs (implying that the targeted protein uses that motif to direct it to
that state) and carrier proteins that target proteins to the state. The emitted features of a protein are observed
and determine its path in the state space. Emission is probabilistic, and so certain proteins can pass through
states even if they do not contain any of the motifs and do not interact with any of the carriers for that state.
Note that while the compartment information is available during training, we do not know how many
intermediate states should be included in the model (some sorting pathways may be short and others long,
and several compartments can share parts of the pathways). Thus, unlike traditional HMM learning tasks
that focus on learning the transition and emission probabilities, for our model we also need to learn the set
of states that are used in the sorting HMM.
2.3. A HMM for the sorting pathways problem
We will discuss the likelihood of our HMM in detail here (Fig. 1). The following description applies to
using likelihood for motif features, but can be easily adapted to the case of binary motif features by
removing the sequence variable S and include motif occurrences in the binary feature variables F. As
discussed above, in our HMM model all proteins move from a single start state to their final compartment.
For reasons that will become clear when talking about learning the parameters of the model, we associate
each state in our model with a specific level. The root state is level 0, all compartment states are associated
with the final level (T) and each intermediate state is associated with a specific level t (0 < t < T). The
number of levels T is inferred from the data during structure initialization as described in section 2.6. We
require that a state at level t can be reached from the root after exactly t transitions; connections that are
more than one level apart move through several ‘‘silent’’ states so that transitions are only between adjacent
levels (diamond-shaped states in Figure 1). Silent states only emit a ‘‘background’’ feature. Let Xtdenote a
hidden state at level t, t¼1‚2‚ ???‚T in a T-level model. The value of Xtcan be one of J possible states,
Xt2 f1‚2‚ ???‚Jg.
In addition to transition probabilities states are associated with emission probabilities. State Xtemits a
feature index Zt. Ztcan either be one of M motifs (represented as a likelihood score for each protein), or
one of K binary features which include interactions with selected carriers, selected deterministic motif
occurrences based on UniProt, or the background feature emitted by silent states. Hence Zt2 f1‚2‚ ???
M þK þ1g, where the motifs are indexed from 1 to M and the features are indexed from M + 1 to
M + K.
Let S denote the sequence observed for each protein, F be the binary features from interaction databases
and UniProt, and Y be the compartment assignments for a protein. The data likelihood of our HMM model
(Fig. 1), is defined as:
Pr(S‚F‚Y‚X1‚ ???XT‚Z1‚ ???ZT ?1jY)
These joint probabilities can be decomposed based on the HMM independence assumptions as follows:
Pr(S‚F‚Y‚X1‚ ???XT‚Z1‚ ???ZT ?1jY)
Pr(FjZ1‚ ???ZT ?1)Pr(YjZT):
Pr(Xtþ1jXt)Pr(ZtjXt)Pr(SjZ1‚ ???ZT ?1)
The parameters of our HMM are the initial, transition and emission probabilities, Y = (p, A, B), defined
pi¼ Pr(X1¼i)‚ Aij¼ Pr(Xtþ1¼jjXt¼i)‚ Bik¼ Pr(Zt¼kjXt¼i):
where piis the initial probability of transition from the root to state i, Aijis the transition probability
between state i and state j, and Bikis the emission probabilities from state i to emission k. Since each
state only transits to a small number of states and emits a small number of features, these matrices are
1712LIN ET AL.
2.4. Defining the emission and transition probabilities for our model
As indicated above the feature observation includes the sequences and interactions selected carriers in-
ferred by feature selection described above. Note that these observations are static and so may depend on all
levels in the HMM. The emission probability for the sequence S is thus Pr(SjZ1‚ ???ZT ?1). Since probability
depends on several motif models (one per level), which may be dependent (for example for overlapping
motifs) and is thus computationally intractable given many combinations of motifs. As is commonly done
(Sinha, 2006), we approximate this term by the product of the conditional probabilities of the sequence given
an individual emission at each level:QT ?1
(unlike for the sequence data this computation is exact since they are provided as independent events):
motif, Pr(Srkk), where kkis the motif mode. We use a profile HMM model in this article, but any other
probabilistic models would also work, for example, a position weight matrix (PWM) which specifies a
weight for each amino acid at each motif position, assuming independence between positions. This like-
lihood is termed the motif score and indicates how well the sequence agrees with the motif model. For
states emitting one of the binary features or the background feature, the likelihood of the sequence is
Pr(Srk0), where k0is the background model for which we use a 0th-order Markov model, which assumes
that each position in the sequence are generated independently according to amino acid frequencies.
Combined, the sequence likelihood is given by
The binary features observations, F ¼(F1‚F2‚ ???‚FK)‚Fk2 f0‚1g correspond to observed protein
interactions and deterministic motifs as discussed above. As mentioned above, we assume independence in
noisy observation of these features, which is a necessary simplification. This leads to
t¼1Pr(SjZt). Similarly we calculate the conditional probability of the
binary features Pr(FjZ1‚ ???ZT ?1) using the product of the conditional probabilities of individual emissions
t¼1Pr(FjZt). This leads to the more typical HMM model shown in Figure 1B.
To translate the sequence information to a probability we use the likelihood of the sequence given the
if M þ1pkpM þK þ1
The conditional probability of observing a feature Fjgiven an emission Ztis
if k 6¼ M þj
if k¼M þj
where mjis the probability of observing this interaction across all proteins in our dataset (background
distribution), and 1 - m0is the probability of false negatives (i.e., proteins that should go through this state
but do not have this interaction/motif). Note that we need to use mjsince an interaction or a motif may be
observed even if the corresponding feature is not emitted by one of the states since many interactions are
not related to protein sorting but rather to another pathway in which this protein is a member.
The conditional probability of the compartment given the final state is denoted by: Pr(YrXT). If a single
compartment is given for a protein, the bottom state XTis known for that protein and so this probability is 1
for that compartment and 0 for others. If the training data contains multiple compartments for a protein, it is
reflected by the given compartment likelihood Pr(Y = yrXT= c), which is assumed to be uniform for all
compartments listed for that protein. In other words, we consider multiple localization as uncertainty. For
example, a protein might be considered to be 50% certain as one compartment and 50% certain as another
2.5. Approximation and feature levels
Unlike a typical HMM learning problem, the emission data we observe (sequence and interaction data) is
static and so cannot be directly associated with any sequence of events. In addition, since our features are
static, they can be emitted multiple times along the same path. However, if this happens the independence
assumptionsofHMMsare violated. Specifically,ifafeatureisemittedbya state inleveltand then againby a
LEARNING CELLULAR SORTING PATHWAYS1713
state in level t + 1 then it is not true anymore that the probability of emitting the feature given the state is
independent of any emission events in previous states (since ifit was emitted before, the proteincan still emit
it again). We thus constrain all features in our model so that each is only associated with a specific level and
can only be emitted by states on that level. The level is determined in the initial structure estimation step
discussed in the next section. Since no transitions are allowed betweenstates on the same level no feature can
thus be emitted more than once along the path and so the independence assumption holds. This requirement
guarantees that the likelihood function obtained from the model presented in Figure 1B is a constant factor
approximation of the likelihood function of our original model (Fig. 1A). See detailed proof at http://
2.6. Structure learning
In addition to learning the parameters (emission and transition probabilities) we also need to learn the set
of states that should be included in our model. The learning algorithm is formally presented in Figure 2. We
start by associating potential features (protein interactions and known motifs) with compartments. For a
potential feature, we use the hypergeometric distribution to determine the significance of this association
(by looking at the overlap between proteins assigned to each compartment and proteins that are associated
with each of the features). We next identify a set of significantly associated compartments (p-value < 0.01
with Bonferroni correction) for each potential feature. Features that are significantly associated with at least
one compartment are selected and the remaining features are removed.
After feature selection, we estimate an initial structure by using the association between features and
compartments. All features that correspond to the same set of associated compartments are grouped and
assigned to a single state, such that this state emits these features with uniform probability. These features are
fixedto the level correspondingtothe numberof compartments they are significantly associated with andcan
onlybeemittedby states onthat level (we tried optimizing thesefeature levels aspartof the iterative learning
states is determined from the inclusion relationship of the set of compartments (states for which features are
associated with more compartments are assigned to higher levels). We initially only allow transitions
between two states where the second state contains features that are associated with a subset of the com-
partments of the first state. That is, the initial structure resembles a partially ordered set when the states are
ordered by inclusion. The transition probability out of a state is also set to the uniform distribution. The
number of levels of this structure, T, will be fixed throughout the structure search process.
Starting with this initial model, we use a greedy search algorithm which attempts to optimize the
Bayesian information criterion (BIC), which is the negative data log likelihood plus a penalty term for
Algorithm for structure
1714LIN ET AL.
where S, F, Y are the collection of sequences, feature observations, and compartments of the proteins in the
training data. Y = p, A, B) denote the parameters of the HMM. rYr is the number of parameters according to
the structure, which is a function of the number of states and the number of transitions and emissions of each
of proteins in our training data. BIC is asymptotically consistent while Akaike information criterion (AIC) is
not, and BIC is chosen particularly because we prefer sparser structures (Hastie et al., 2003). Since use of BIC
can sometimes lead to overfitting, we compared the use of BIC to fourfold internal cross-validation for model
selection. BIC is fasterthan internal cross validation and performed better on simulated data (see Section3.1).
To improve the initial structure described above we perform two types of local moves at each search
iteration: adding a new state and splitting the largest state. For each level, we try adding a state which is
fully connected to all states in levels above and below it and emits all features on that level. We run
standard EM algorithm (Dempster et al., 1977) to optimize the parameters of the model for all states
(transition and emission probabilities). Transitions and emissions with probabilities lower than a specific
threshold are pruned. Features not emitted by any states are also pruned, so the feature set becomes smaller
and smaller. Then we run EM algorithm again because the parameters are changed. A candidate model and
structure is created by this process for each level. We also try splitting the largest state, defined as the state
with the largest number of out-transitions. A randomly chosen half of the out-transitions will be moved to a
newly created state which shares the same in-transitions and emissions. As above we run EM algorithm,
prune transitions and emission, and run EM algorithm again to obtain a candidate structure. We try this for
a fixed number of times, usually the number of levels so that half of the local moves are adding and half are
splitting. Among all candidate structures obtained by adding and splitting, the one with the highest BIC
score is chosen. This procedure is repeated until the BIC score no longer improves.
3.1. Simulated data
We first tested our method using simulated data in order to determine how well it can recover a known
underlying structure given only information on destinations, carriers and motifs. We manually created
structures with 7, 14, 23, 25, and 31 states with multiple emitted features per state (for the structure of these
models, see http://murphylab.web.cmu.edu/software/2010_RECOMB_pathway/). For each structure we
simulate the probabilistic generative procedure and record the emitted features. 1,200 proteins are gen-
erated from the model, with varying levels of noise (leading to false positive and false negative features for
proteins). We also tested various sizes of input sets with a fixed noise level.
Noise (sample size 1400)
Sample size (noise 0.02)
Noise (sample size 1400)
Sim25 overlap ratio
Sample size (noise 0.02)
Sim25 overlap ratio
(false positive and false negative in features). The training sample size was fixed at 1400. (B) Testing error versus
different training sample sizes. The noise level was fixed at 2%. (C) The ratio of overlapping nodes and edges between
the learned model and the true model with varying levels of noise. The training sample size was fixed at 1400. (D) The
ratio of overlapping nodes and edges with varying training sample sizes. The noise level was fixed at 2%.
(A) Testing error of simulated dataset generated from a structure with 25 states with varying levels of noise
LEARNING CELLULAR SORTING PATHWAYS 1715
3.1.1. Predicting protein locations.
dictions regarding the final localization of each protein. For each training dataset, we therefore generated a
test dataset with 4,000 proteins from the same model and evaluated the accuracy of predicting protein
localization for the test data using the structure and model learned by our method. Our method is compared
to predictions made by the true model (note that due to noise, the true model can make mistakes as well) and
by a linear support vector machine (SVM) learned from the training data using the features associated with
each protein. Prediction accuracy on the 25-states dataset is shown in Figure 3, and the accuracy of other
simulated datasets are available at http://murphylab.web.cmu.edu/software/2010_RECOMB_pathways/. As
can be seen, when noise levels are low, our model performs well and its accuracy is similar to that obtained
by the true model for both simple and more complicated models. Both the learned model and the true model
outperform SVM which does not try to model the generative process in which proteins are sorted in cells
relying instead on a one versus all classification strategy. We compare model selection based on BIC versus
fourfold internal cross validation. BIC achieved similar accuracy with less computation and matched the true
While it is not its primary goal, our method can provide pre-
3.1.2. Recovering the true structure.
sembles the true structure, we use the graph edit distance to measure their topological similarity (Gao et al.,
2010). First we need to match the nodes in a learned structure to a node in the true structure. We run the
Viterbi algorithm on proteins in the testing data, and count the state co-occurrence matrix W whose
elements Wijis the co-occurrence of state i in the learned model and state j in the true model (i.e.,
the number of proteins in which the two states i and j occur in the Viterbi path inferred by the two models).
The optimal one-to-one matching M, denoted as a set containing pairs of matched state indexes, can be
found by running the Hungarian algorithm on the co-occurrence matrix W optimizing the objective function
With the optimal matching, we use the maximum common subgraph (MCS) and minimum common
supergraph in the graph edit distance methodology to quantify similarity between two structures. Given two
graphs G1and G2, let^G and?G be the MCS and minimum common supergraph of G1and G2. Denote rGr as
the size, or the number of edges and nodes of a graph, we define the overlap rate as j^Gj=j?Gj (i.e., the
percentage of overlapping edges and nodes). The overlap rate comparing to the true model on the 25-states
dataset is shown in Figure 3C. Structural comparison on other datasets is available on the supporting
website. As can be seen, our algorithm successfully recovers the correct structure in all cases with 0%
noise. As the noise increases the accuracy decreases. However, even for very high levels of noise the two
models share a substantial overlap (around 40% of states and transitions could be matched).
To quantitatively evaluate how well a learned structure re-
3.2. Yeast data
We next evaluated our method using subcellular locations of yeast proteins derived from fluorescence
microscopy (the UCSF yeast GFP dataset [Huh et al., 2003]). This dataset contains 3,914 proteins that were
manually annotated, based on imaging data, to 22 compartments. We collected the features from the
following sources. Protein-protein interaction (PPI) data was downloaded from BioGRID (BiG) (Stark
et al., 2006). For deterministic motifs we use the annotated occurrences of InterPro (Mulder et al., 2003)
domains and the following three signal sequences listed on UniProt (Bairoch et al., 2005):
1. Signal peptides: UniProt defines this sequence feature based on the literature or consensus vote of
four programs, SignalP, TargetP, Phobius and Predotar.
2. Transmembrane region: UniProt annotates a sequence with this feature either based on literature or
consensus vote of four programs, TMHMM, Memsat, Phobius and Eisenberg.
3. GPI anchor: UniProt annotation for this feature either relies on literature or prediction by the program
The above features are filtered by a hypergeometric test to identify features with a significant association
with a final destination (p-value < 0.01 with Bonferroni correction) before learning the model.
To extract novel motifs associated with localization, we downloaded protein sequences from UniProt
(Bairoch et al., 2005) and ran generative and discriminative HMM motif finder (Lin et al., 2011). We
extract 20 motifs for each compartment, and compared setting all to length 4 versus setting the length to
range from 3 to 7. The performance in all following evaluations are similar and we show results based on
1716LIN ET AL.
motif length as 4. We will compare using likelihood and binary occurrence for motif features. For binary
motif occurrence, a motif is considered present if posterior probabilities of the begin state and the end state
of the motif are both greater than 0.9 (detail in Lin et al., 2011).
3.2.1. Predicting protein locations.
predicting the final subcellular location for each protein. This provides a useful benchmark for comparison
to all other computational methods for which this is the end result. The performance is evaluated by 10-fold
cross-validation. In each fold both feature selection and motif finding are restricted to the training data
without accessing the testing data. We use three conventional measure in information retrieval: the accuracy,
micro-averaging F1 and macro-averaging F1 (Yang and Liu, 1999). For the accuracy, a prediction is con-
sidered correct if it matches any of the true locations. The F1 score is the harmonic mean of precision and
recall (Van Rijsbergen, 1979). Micro-averaging takes the average of the F1 score over all proteins, giving
each protein an equal weight; in other words, the classes are weighted by their sizes. Macro-averaging takes
the average of the score over classes, giving each class an equal weight. Including macro-averaging F1
ensures smaller classes are not ignored since other measures are dominated by large classes. The result is
shown in Figure 4. We compared our method with the k-Nearest Neighbors (kNN) from Lee et al. (2008)
which was shown by the authors to outperform other methods. As can be seen in Figure 4, PPI information
(BiG) provides the major contribution for accurate predictions while InterPro motifs do not contribute as
much. This agrees with previous studies (Scott et al., 2005; Lee et al., 2008). When adding more features, the
performance improves and the best result is achieved using all features. Note that the accuracy of our method
is very close to that of the kNN method. However, it is important to note that our method performs the much
of the method that is achieved while not compromising prediction accuracy.
As with the simulated data, we first evaluated the accuracy of
kNN, we use the reported accuracy
based on PPI information from BiG,
deterministic InterPro motif anno-
tation from UniProt, and amino acid
composition of different length,
gaps, and chemical properties using
leave one out cross validation (Lee
et al., 2008). For HMM, we also
show micro-averaging and macro-
The accuracy of predict-
and binary features (GenHMM/DiscHMM b).
collected from the literature. Each
pathway is a path from cytosol to a
compartment at the bottom, con-
sisting of one or more steps (the
links) that transport proteins be-
tween intermediate locations. Each
step has a list of carriers and motifs
responsible for the transportation
by which we can verify whether the
pathway is recovered. Shaded links
denote steps whose carriers are
underrepresented on BiG (covering
less than 5% of proteins transported
Protein sorting pathways
to the corresponding compartment in the GFP dataset). Dashed lines denote steps taken by default without specific
carriers. The percentage under pathway name is the protein sorting precision when the pathway is recovered, as
described in Table 2.
LEARNING CELLULAR SORTING PATHWAYS1717
3.2.2. Evaluation of the learned structure.
collected information about known sorting pathways from the literature. We were able to find information
regarding 13 classical and non-classical sorting pathways (pathways followed by a minor fraction of
proteins or that differ from the first discovered pathway are often referred to as non-classical pathways). For
each of these pathways, we identified a set of carriers or motifs that govern the pathway and, when
available, the set of proteins that are predicted to use this pathway. Figure 5 presents the pathways we
collected from the literature. For example, the classical HDEL pathway into ER has two steps. In the first,
proteins with signal peptide (SP) are introduced into this pathway by the SRP complex. In the second,
proteins with the HDEL motif are retained in ER by interaction with proteins Erd1 and Erd2. The full list
of carriers and motifs for these pathways is available at http://murphylab.web.cmu.edu/software/
We first wanted to check if the databases we used for obtaining features contain the carrier information
for the literature pathway. We filtered pathways for which carrier information in the BIG database did not
contain the genes associated with the pathway (and thus no method can identify this pathway based in this
input data) leaving 10 pathways that could, in principal, be recovered by computational models. Sorting
steps that were filtered out in this way are represented as shaded links in Figure 5.
To determine whether we accurately recovered a pathway in our model, we looked at the carriers and
motifs that are associated with that pathway in the literature. A step in a literature pathway can be matched
to a state if the state emits any carrier or motif in that step. A known pathway is considered recovered in a
learned structure if its steps can be matched to the states along a path from the root to the compartment to
which it leads. A pathway is partially recovered if only some of its steps can be matched. For example, the
MVB pathway (Fig. 5) is only partially recovered (66.7%) because the third step does not have a well-
represented carrier in the data sources. The numbers of recovered pathways for different sets of features are
listed in Table 1. The ranges correspond to the different folds in our cross validation analysis. Fractions
represent partial matches as discussed above. When using the full set of input features our algorithm is able
To evaluate the accuracy of the learned structure, we
Pathway Recovery Results of Structure Learned from Different Feature Sets
FeaturesPathway recoveryInferred protein path
HMM BiG + Ipr + Signals
HMM BiG + Ipr + Signals + GenHMM b
HMM BiG + Ipr + Signals + DiscHMM b
HMM BiG + Ipr + Signals + GenHMM
HMM BiG + Ipr + Signals + DiscHMM
The precision of inferred protein path is also listed here. Mean, minimum, and maximum among the 10 folds are
Recovery and Protein Sorting Results of Each Pathway
Using the Features BiG + InterPro + Signals + DiscHMM 4
CompartmentPathway (#proteins)Recovery (folds)Steps Sorting
ER SP + HDEL
SP + MVB
1718 LIN ET AL.
The HMM state space structure learned by our method that corresponds to potential protein sorting pathways. A state is represented by a block; its transitions are shown as arrows
and its top three emitting features are listed inside the block. The sparse transition and emission probabilities are omitted here. The initial state probabilities are denoted as arrows from the
root block at the top. The bottom states are the final destination compartments. Some transitions are shaded only because of visual clarity, including transitions across levels or from and to
the highly connected state (state 58). While silent states are not explicitly displayed (to remove clutter) they are actually implicitly present. Any time an edge jumps more than one level it is
going through silent state(s). For example, the right most edge coming out of the root goes through a silent state in the first level. Carriers and motifs that matches our literature pathway
collection are shown in boldface; other features potentially related to protein trafficking according to SGD are marked with an asterisk.
to recover roughly 80% of known pathways. Most of these pathways are recovered in all 10 folds (Table 1).
Note that because some carriers do not appear in our database not all steps in all pathways can be matched
and the best possible recovery is 8.7. Thus, the 7.7 recovery obtained is very close to optimal.
For example, because of lack of evidence (the motif and carrier detection steps did not find the Vam3,
Vam7, or the Vps41 features), the classical vacuole import pathway (Vac in Fig. 5) and the alternative
Vps41 pathway can only be 50% recovered (each missing a step). For both, the step of signal peptide (SP)
is accurately found, but alternative motifs/carriers are selected to route proteins to the vacuole or cell
We further collected lists of proteins indicated as following specific pathways in the literature for
four of the pathways, NLS, HDEL, Sec, and MVB, and tested whether the recovered pathways indeed
sort proteins on the correct path to the correct destination (allowing close compartments as above). For
each protein, we use the Viterbi algorithm to infer the highest probability path of states the protein is
expected to follow according to our learned model, and compare the Viterbi path to the known
pathways. Again counting partial match of a multi-step pathway as above, on average using all features
results in correctly assigning 21% of 63 proteins. Focusing on a representative feature set, detailed
protein path results for each pathway are also given in Table 2. The recovered NLS pathway sorted 39%
of proteins correctly, and the recovered HDEL pathway sorted 33% correctly but sorted the other 25%
via SP. Similarly the recovered MVB pathway sorted 23% to go through two of the three steps (SP and
MVB) and other 9% to one of the three steps. The recovered Sec pathway only sorted 2% of the
proteins to go through SP and end at cell periphery. However, this was due to the fact that while 17 of
the 28 proteins collected from literature as being secreted were included in the GFP dataset, the
majority are labeled as ER and vacule, and none are labeled as cell periphery. Overall the GFP dataset
include 40 out of the 63 proteins whose pathway is known, of which only 28% are labeled in agreement
with our lierature survey.
It is important to note that our analysis of the learned structure may underestimate its accuracy, since it
may have recovered correct pathways that could not be verified due to insufficient detection of relevant
motifs or carriers in the input data.
Figure 6 shows one of the learned structures obtained using all features. Besides carriers and motifs
included in our literature pathway collection (marked as boldface), many other features were found that are
also known to participate in protein trafficking as curated in SGD (Cherry et al., 1998) (marked with an
asterisk). For those compartments not covered by our collection of known pathways, the general topology
of this structure agrees with our basic understanding of cell biology. For example, microtubule share a step
with spindle pole, which in turn share a step with nuclear periphery, and cell periphery share steps with bud
neck, which in turn share steps with bud and actin.
The goal of this research is to propose hypotheses about protein sorting mechanisms, not just to make
predictions. We propose, for what we believe is the first time, a method to learn sorting pathways from
protein localization annotation, based on co-occurrence of interacting partner and sequence motif. Our
method is able to recover a significant part of known pathways collected from the literature, and to infer the
correct path of proteins known to follow these pathways.
Using a HMM, naturally simulates the transportation path of a protein among unobserved intermediate
states. Although the path is unobserved, the most likely one can be inferred by the Viterbi algorithm of the
HMM based on observed features. The model is probabilistic and returns a distribution of possible com-
partments, instead of a single predicted compartment. Proteins that are targeted to more than one com-
partment in the training data can be handled by treating multiple localization as uncertainty. While the
method has been successful, an HMM-based approach also suffers from a number of limitations. The input
data used by our method is static while HMM expects sequential data. This requires us to rely on a number
of assumptions including limiting each of the features to a unique level, and assuming independence
between the features. The structure search algorithm requires substantial computation, since the EM
algorithm must be run every time a candidate structure is being tried. Improving the search strategy is a
direction for future work. Another issue we would like to address is the inference of the actual location
of the intermediate states. For example, we might associate an internal state with the ER or Golgi.
1720 LIN ET AL.
To determine such locations, we would need to tie the model to literature and try to identify overlaps which
can be generalized.
Given that the sorting routes taken by many proteins are currently unknown, the most important part of our
work is the potential to identify novel pathways. In this regard, we note that, just like hand-constructed
perturbing motifs and/or carriers. An additional advantage of building comprehensive sorting models is that
potential inconsistencies in canonical models can be identified and experiments performed to resolve them.
We would like to thank Jennifer Bakal for programming support. This work was supported in part by the
NIH (grant R01 GM075205).
No competing financial interests exist.
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Address correspondence to:
Dr. Robert F. Murphy
Lane Center for Computational Biology
Carnegie Mellon University
Pittsburgh, PA 15213
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