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Proceedings of the 31 st European Peptide SymposiumMichal Lebl, Morten Meldal, Knud J. Jensen, Thomas Hoeg-Jensen (Editors)European Peptide Society, 2010AntiMicrobial Protein Analyzer (AMPA): A ComputationalTool to Screen Antimicrobial Domains in Proteins and PeptidesMarc Torrent and David AndreuDepartment of Experimental and Health Sciences, Pompeu Fabra University,Barcelona Biomedical Research Park, 08003, Barcelona, SpainIntroductionHost defense antimicrobial peptides (AMPs) or proteins are important effectors of theinnate immune system and play a vital role in the prevention of bacterial infections.Computational algorithms are useful tools for predicting active fragments in proteins andpeptides that can potentially be developed as therapeutic agents to combat bacterialinfections. Automated systems for prediction of short AMP sequences have beendeveloped, but a methodology for full protein scanning is not available. Here we presentAMPA, a new algorithm that can identify antimicrobial proteins and successfully locatetheir active regions. AMPA is able to perform a fast screening analysis over large proteinsets in order to identify potentially active AMPs derived from large protein molecules.Results and DiscussionThe AMPA algorithm uses an antimicrobial propensity scale to generate an antimicrobialprofile by means of a sliding window system. The propensity scale has been derived usinghigh-throughput screening results from the AMP bactenecin 2A, a 12-residue peptide forwhich antimicrobial IC 50 values for all amino acid replacements at each position have beendetermined [1]. From the IC 50 for each substitution, propensity values (PV) for individualresidues can be calculated (Table 1) that provide a fair assessment of the tendency of suchamino acid to be found within an AMP sequence. As low IC 50 values correspond to highactivity, amino acids with a low PV are the most favoured to be part of an AMP. Cationic(R, K) residues, necessary to direct AMPs to negatively charged bacterial surfaces, areespecially favoured. Some hydrophobic residues (W, Y, I, V), needed to destabilize lipidbilayers and eventually cause cell death, also display low PVs. Anionic residues, asexpected, are unfavoured and thus have high PVs.The prediction algorithm was applied to a training set of 100 proteins (50 bactericidaland 50 non-bactericidal) including representative members of the main antimicrobialprotein families in the literature. A 7-residue sliding window was chosen for the screening.To improve accuracy, 3 predictive lengths (10, 12 and 14 residues) were evaluated and, foreach length, the optimal number of allowed gaps (2, 3 or 4) was determined. For eachlength/gap combination, a receiver-operating curve (ROC) was then constructed, and theaccuracy, sensitivity and selectivity evaluated in order to identify the best parameters.Optimal results were obtained using a predictive length of 12 amino acids, with 2 gapsallowed. For these parameters, the average propensity value (avPV) ensuring the bestpredictive accuracy was 0.225; residues with avPV0.225 were considered unfavourable.Table 1. Antimicrobial propensity values (PV) of amino acid residuesResidue Arg Lys Cys Trp Tyr Ile ValPV 0.106 0.111 0.165 0.172 0.185 0.198 0.200Residue His Asn Thr Phe Leu Gln GlyPV 0.202 0.240 0.242 0.246 0.246 0.248 0.265Residue Met Ser Ala Pro Glu AspPV 0.265 0.281 0.307 0.327 0.449 0.479418