Binary Response Models for Recognition of Antimicrobial Peptides

There is now great urgency in developing new antibiotics to combat bacterial resistance. Recent attention has turned to naturally-occurring antimicrobial peptides (AMPs) that can serve as templates for antibacterial drug research. As natural AMPs have a wide range of activity against various bacteria, current research is focusing on modifying existing peptides or designing new ones to increase potency. This paper presents a computational approach to further our understanding of what physicochemical properties or features confer to a peptide antimicrobial activity. One of the contributions of this paper is the ability to rigorously test the relevance of features obtained by biological or computational researchers in the context of AMP recognition. A second contribution is the construction of a predictive model that employs relevant features and their combinations to associate with a novel peptide sequence a probability to have antimicrobial activity. Taken together, the work in this paper seeks to help researchers elucidate features of importance for antimicrobial activity. This is an important first step towards modification or design of novel AMPs for treatment. With this goal in mind, we provide access to the proposed methodology through a web server, which allows users to replicate the findings here or evaluate their own feature set.

[1]  Kenneth A. De Jong,et al.  An evolutionary-based approach for feature generation: Eukaryotic promoter recognition , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[2]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[3]  H. Akaike.  A new look at the statistical model identification , 1974 .

[4]  Shreyas Karnik,et al.  CAMP: a useful resource for research on antimicrobial peptides , 2009, Nucleic Acids Res..

[5]  K. Chou,et al.  iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types. , 2013, Analytical biochemistry.

[6]  M. N. Melo,et al.  Antimicrobial peptides: linking partition, activity and high membrane-bound concentrations , 2009, Nature Reviews Microbiology.

[7]  C. Fjell,et al.  Identification of novel antibacterial peptides by chemoinformatics and machine learning. , 2009, Journal of medicinal chemistry.

[8]  D. Hoskin,et al.  Studies on anticancer activities of antimicrobial peptides. , 2008, Biochimica et biophysica acta.

[9]  Song S. Qian,et al.  Two statistical methods for the detection of environmental thresholds , 2003 .

[10]  G. Schneider,et al.  Designing antimicrobial peptides: form follows function , 2011, Nature Reviews Drug Discovery.

[11]  Amarda Shehu,et al.  Systematic analysis of global features and model building for recognition of antimicrobial peptides , 2013, 2013 IEEE 3rd International Conference on Computational Advances in Bio and medical Sciences (ICCABS).

[12]  Daniel J Rigden,et al.  Prediction of antimicrobial peptides based on the adaptive neuro-fuzzy inference system application. , 2012, Biopolymers.

[13]  Haruki Nakamura,et al.  Announcing the worldwide Protein Data Bank , 2003, Nature Structural Biology.

[14]  Kenneth A. De Jong,et al.  An Evolutionary Algorithm Approach for Feature Generation from Sequence Data and Its Application to DNA Splice Site Prediction , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[15]  J. Habbema,et al.  Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. , 2001, Journal of clinical epidemiology.

[16]  Gerhard Tutz,et al.  Regression for Categorical Data , 2011 .

[17]  Kristopher Hall,et al.  Surface plasmon resonance analysis of antimicrobial peptide-membrane interactions: affinity & mechanism of action , 2003, Letters in Peptide Science.

[18]  Artem Cherkasov,et al.  BIOINFORMATICS ORIGINAL PAPER doi:10.1093/bioinformatics/btm068 Databases and ontologies AMPer: a database and an automated discovery tool for antimicrobial peptides , 2022 .

[19]  H. G. Boman,et al.  Antibacterial peptides: basic facts and emerging concepts , 2003, Journal of internal medicine.

[20]  Carl T. Bergstrom,et al.  The ecology and evolution of antibiotic-resistant bacteria , 2007 .

[21]  Artem Cherkasov,et al.  Application of 'inductive' QSAR descriptors for quantification of antibacterial activity of cationic polypeptides. , 2004, Molecules.

[22]  John A. Robinson,et al.  Protein epitope mimetics as anti-infectives. , 2011, Current opinion in chemical biology.

[23]  Amarda Shehu,et al.  Physicochemical Determinants of Antimicrobial Activity , 2012 .

[24]  Michael J. Crawley,et al.  The R book , 2022 .

[25]  David Andreu,et al.  AMPA: an automated web server for prediction of protein antimicrobial regions , 2012, Bioinform..

[26]  C. Gualerzi,et al.  How to cope with the quest for new antibiotics , 2011, FEBS letters.

[27]  Michael Zasloff.  Antibiotic peptides as mediators of innate immunity , 1992, Current Biology.

[28]  R. Doolittle,et al.  A simple method for displaying the hydropathic character of a protein. , 1982, Journal of molecular biology.

[29]  Eric D Brown,et al.  Antibiotics as probes of biological complexity. , 2011, Nature chemical biology.

[30]  Guangshun Wang,et al.  Antimicrobial peptides: discovery, design and novel therapeutic strategies. , 2010 .

[31]  R. Hancock,et al.  Host defence peptides from invertebrates--emerging antimicrobial strategies. , 2006, Immunobiology.

[32]  Erik L. L. Sonnhammer,et al.  Advantages of combined transmembrane topology and signal peptide prediction—the Phobius web server , 2007, Nucleic Acids Res..

[33]  Kristopher Hall,et al.  Surface plasmon resonance analysis of antimicrobial peptide–membrane interactions: affinity & mechanism of action , 2003, Letters in Peptide Science.

[34]  R. Dawson,et al.  Cathelicidin peptide SMAP‐29: comprehensive review of its properties and potential as a novel class of antibiotics , 2009 .

[35]  Gajendra P. S. Raghava,et al.  AntiBP2: improved version of antibacterial peptide prediction , 2010, BMC Bioinformatics.

[36]  Walter L. Smith.  Probability and Statistics , 1959, Nature.

[37]  Fabiano C. Fernandes,et al.  An SVM Model Based on Physicochemical Properties to Predict Antimicrobial Activity from Protein Sequences with Cysteine Knot Motifs , 2010, BSB.

[38]  L. Serrano,et al.  Prediction of sequence-dependent and mutational effects on the aggregation of peptides and proteins , 2004, Nature Biotechnology.

[39]  M. Zasloff.  Antimicrobial peptides of multicellular organisms , 2002, Nature.

[40]  Vassilios Ioannidis,et al.  ExPASy: SIB bioinformatics resource portal , 2012, Nucleic Acids Res..

[41]  Artem Cherkasov,et al.  Identification of novel host defense peptides and the absence of α‐defensins in the bovine genome , 2008, Proteins.

[42]  K. Chou,et al.  Prediction of Antimicrobial Peptides Based on Sequence Alignment and Feature Selection Methods , 2011, PloS one.

[43]  Amarda Shehu,et al.  Evolutionary-inspired probabilistic search for enhancing sampling of local minima in the protein energy surface , 2012, Proteome Science.

[44]  Gajendra P. S. Raghava,et al.  Analysis and prediction of antibacterial peptides , 2007, BMC Bioinformatics.

[45]  Floyd E Romesberg,et al.  Combating bacteria and drug resistance by inhibiting mechanisms of persistence and adaptation , 2007, Nature Chemical Biology.

[46]  Gerhard Tutz.  Regression for Categorical Data: Preface , 2011 .

[47]  María Dolores Ugarte,et al.  Probability and Statistics with R , 2008 .

[48]  Kenneth A. De Jong,et al.  Selecting predictive features for recognition of hypersensitive sites of regulatory genomic sequences with an evolutionary algorithm , 2010, GECCO '10.

[49]  Francesc X. Avilés,et al.  AGGRESCAN: a server for the prediction and evaluation of "hot spots" of aggregation in polypeptides , 2007, BMC Bioinform..

[50]  Graham Bell,et al.  Experimental evolution of resistance to an antimicrobial peptide , 2006, Proceedings of the Royal Society B: Biological Sciences.