arXiv subjects

Malak Pirtskhalava

Publications and source records attributed to Malak Pirtskhalava.

2 recordsLinked to original sources

arXiv2020

Physicochemical Features and Peculiarities of Interaction of Antimicrobial Peptides with the Membrane

Antimicrobial peptides (AMPs) are anti-infectives that have potential as a novel and untapped class of biotherapeutics. Modes of action of antimicrobial peptides imply interaction with cell envelope. Comprehensive understanding of peculiarities of interactions of antimicrobial peptides with cell envelope is necessary to perform the task-oriented design of new biotherapeutics, against which for microbes it is hard to work out resistance. In order to enable a de novo design with low costs and in high throughput, in silico predictive models have to be required. To develop the performant predictive model, comprehensive knowledge on mechanisms of action of AMPs has to be possessed. The last knowledge will allow us to encode amino acid sequences expressively and to get success to the choosing of the accurate classifier of AMPs. A shared protective layer of microbial cells is inner, plasmatic membrane. The interaction of AMP with a biological membrane (native and/or artificial) is the most comprehensively studied. We provide a review of mechanisms and results of interaction of AMP with the cell membrane, relying on the survey of physicochemical, aggregative and structural features of AMPs. Potency and mechanism of action of AMP have presented in the terms of amino acid compositions and distributions of the polar and apolar residues along the chain, that is in such physicochemical features of peptides as the hydrophobicity, hydrophilicity, and amphiphilicity. Many different approaches were used to classify AMPs. The survey of the knowledge on sequences, structures, and modes of actions of AMP, allows concluding that, only the physicochemical features of AMPs give the capability to perform the unambiguous classification. Comprehensive knowledge of physicochemical features of AMP is necessary to develop task-oriented methods of design of peptide-based antibiotics de novo.

q-bio.BM↗

arXiv2013

Prediction of linear cationic antimicrobial peptides based on characteristics responsible for their interaction with the membranes

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 which are 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, propensity to disordered structure 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 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 sensitivity obtained by our method gives even higher value than the one obtained by CAMP prediction tools.

q-bio.BM↗