Descriptors for antimicrobial peptides.
ABSTRACT Introduction: A frightening increase in the number of isolated multidrug resistant bacterial strains linked to the decline in novel antimicrobial drugs entering the market is a great cause for concern. Cationic antimicrobial peptides (AMPs) have lately been introduced as a potential new class of antimicrobial drugs, and computational methods utilizing molecular descriptors can significantly accelerate the development of new peptide drug candidates. Areas covered: This paper gives a broad overview of peptide and amino-acid scale descriptors available for AMP modeling and highlights which of these are currently being used in quantitative structure-activity relationship (QSAR) studies for AMP optimization. Additionally, some key commercial computational tools are discussed, and both successful and less successful studies are referenced, illustrating some of the challenges facing AMP scientists. Through examples of different peptide QSAR studies, this review highlights some of the missing links and illuminates some of the questions that would be interesting to challenge in a more systematic fashion. Expert opinion: Computer-aided peptide QSAR using molecular descriptors may provide the necessary edge to peptide drug discovery, enabling successful design of a new generation anti-infective drug molecules. However, if this wonderful scenario is to play out, computational chemists and peptide microbiologists would need to start playing together and not just side by side.
- SourceAvailable from: Natália D. S. Cordeiro[Show abstract] [Hide abstract]
ABSTRACT: Today, emerging and increasing resistance to antibiotics has become a threat to public health worldwide. Antimicrobial peptides have unique action mechanisms making them an attractive therapeutic prospect to be applied against resistant bacteria. However, the major drawback is related with their high hemolytic activity which cancels out the safety requirements for a human antibiotic. Therefore, additional efforts are needed to develop new antimicrobial peptides that possess a greater potency for bacterial cells and less or no toxicity over erythrocytes. In this paper, we introduce a practical approach to simultaneously deal with these two conflicting properties. The convergence of machine learning techniques and desirability theory allowed us to derive a simple, predictive, and interpretable multicriteria classification rule for simultaneously handling the antibacterial and hemolytic properties of a set of cyclic β-hairpin cationic peptidomimetics (Cβ-HCPs). The multicriteria classification rule exhibited a prediction accuracy of about 80% on training and external validation sets. Results from an additional concordance test have shown an excellent agreement between the multicriteria classification rule predictions and the predictions from independent classifiers for complementary antibacterial and hemolytic activities, respectively, evidencing the reliability of the multicriteria classification rule. The rule was also consistent with the general mode of action of cationic peptides pointing out its biophysical relevance. We also propose a multicriteria virtual screening strategy based on the joint use of the multicriteria classification rule, desirability, similarity, and chemometrics concepts. The ability of such a virtual screening strategy to prioritize selective (nonhemolytic) antibacterial Cβ-HCPs was assessed and challenged for their predictivity regarding the training, validation, and overall data. In doing so, we were able to rank a selective antibacterial Cβ-HCP earlier than a biologically inactive or nonselective antibacterial Cβ-HCP with a probability of ca. 0.9. Our results thus indicate that promising chemoinformatics tools were obtained by considering both the multicriteria classification rule and the virtual screening strategy, which could, for instance, be used to aid the discovery and development of potent and nontoxic antimicrobial peptides.Journal of Chemical Information and Modeling 11/2011; 51(12):3060-77. DOI:10.1021/ci2002186 · 4.07 Impact Factor
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ABSTRACT: Multidrug-resistant bacteria are a severe threat to public health. Conventional antibiotics are becoming increasingly ineffective as a result of resistance, and it is imperative to find new antibacterial strategies. Natural antimicrobials, known as host defence peptides or antimicrobial peptides, defend host organisms against microbes but most have modest direct antibiotic activity. Enhanced variants have been developed using straightforward design and optimization strategies and are being tested clinically. Here, we describe advanced computer-assisted design strategies that address the difficult problem of relating primary sequence to peptide structure, and are delivering more potent, cost-effective, broad-spectrum peptides as potential next-generation antibiotics.dressNature Reviews Drug Discovery 12/2011; 11(1):37-51. DOI:10.1038/nrd3591 · 37.23 Impact Factor
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ABSTRACT: We employed segmented principal component analysis and regression, as a new methodology in quantitative structure-activity relationship (QSAR), to define new amino acid indices. The descriptors are first classified into different groups (based on the similarity in the information contents they are possessing) and then each group is subjected to principal component analysis (PCA), separately. The extracted principal components (PC) from the descriptor data matrix of each group can be considered as new sources of amino acid indices. These indices were used as input variables for QSAR study of two dipeptide data sets (58 angiotensin-converting enzyme (ACE) inhibitor activity, and 48 bitter tasting threshold (BTT) activity). Modeling between the indices and biological activity was achieved utilizing segmented principal component regression (SPCR) and segmented partial least squares (SPLS) methods. Both methods resulted in reliable QSAR models. In comparison with conventional principal component regression (PCR) and partial least square (PLS), the segmented ones produced more predictive models. In addition, the developed models showed better performances with respect to the previously reported models for the same data sets. It can be concluded that by segmentation of variables and partitioning of the information into informative and redundant parts, it is possible to discard the redundant part of variables and to obtain more appropriate models.Journal of Theoretical Biology 04/2012; 305:37-44. DOI:10.1016/j.jtbi.2012.03.028 · 2.30 Impact Factor