J Chem Inf Model - Prediction of linear cationic antimicrobial peptides based on characteristics responsible for their interaction with the membranes.

Tópicos

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Resumo

Most available antimicrobial peptides (AMP) prediction methods use common approach for different classes of AMP. Contrary to available approaches, we suggest that a strategy of prediction should be based on the fact that there are several kinds of AMP that vary in mechanisms of action, structure, mode of interaction with membrane, etc. According to our suggestion for each kind of AMP, a particular approach has to be developed in order to get high efficacy. Consequently, in this paper, a particular but the biggest class of AMP, linear cationic antimicrobial peptides (LCAP), has been considered and a newly developed simple method of LCAP prediction described. The aim of this study is the development of a simple method of discrimination of AMP from non-AMP, the efficiency of which will be determined by efficiencies of selected descriptors only and comparison the results of the discrimination procedure with the results obtained by more complicated discriminative methods. As descriptors the physicochemical characteristics responsible for capability of the peptide to interact with an anionic membrane were considered. The following characteristics such as hydrophobicity, amphiphaticity, location of the peptide in relation to membrane, charge density, propensities to disordered structure and aggregation were studied. On the basis of these characteristics, a new simple algorithm of prediction is developed and evaluation of efficacies of the characteristics as descriptors performed. The results show that three descriptors, hydrophobic moment, charge density and location of the peptide along the membranes, can be used as discriminators of LCAPs. For the training set, our method gives the same level of accuracy as more complicated machine learning approaches offered as CAMP database service tools. For the test set accuracy obtained by our method gives even higher value than the one obtained by CAMP prediction tools. The AMP prediction tool based on the considered method is available at http://www.biomedicine.org.ge/dbaasp/.

Resumo Limpo

avail antimicrobi peptid amp predict method use common approach differ class amp contrari avail approach suggest strategi predict base fact sever kind amp vari mechan action structur mode interact membran etc accord suggest kind amp particular approach develop order get high efficaci consequ paper particular biggest class amp linear cation antimicrobi peptid lcap consid newli develop simpl method lcap predict describ aim studi develop simpl method discrimin amp nonamp effici will determin effici select descriptor comparison result discrimin procedur result obtain complic discrimin method descriptor physicochem characterist respons capabl peptid interact anion membran consid follow characterist hydrophob amphiphat locat peptid relat membran charg densiti propens disord structur aggreg studi basi characterist new simpl algorithm predict develop evalu efficaci characterist descriptor perform result show three descriptor hydrophob moment charg densiti locat peptid along membran can use discrimin lcap train set method give level accuraci complic machin learn approach offer camp databas servic tool test set accuraci obtain method give even higher valu one obtain camp predict tool amp predict tool base consid method avail httpwwwbiomedicineorggedbaasp

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