Antimicrobial peptides (AMPs) have been identified as a potentially new class of antibiotics to combat bacterial resistance to conventional drugs. The design of de novo AMPs with high therapeutic indexes, low cost of synthesis, high resistance to proteases and high bioavailability remains a challenge. Such design requires computational modeling of antimicrobial properties. Currently, most computational methods cannot accurately calculate antimicrobial potency against particular strains of bacterial pathogens. We developed a tool for AMP prediction (Special Prediction (SP) tool) and made it available on our Web site (https://dbaasp.org/prediction). Based on this tool, a simple algorithm for the design of de novo AMPs (DSP) was created. We used DSP to design short peptides with high therapeutic indexes against gram-negative bacteria. The predicted peptides have been synthesized and tested in vitro against a panel of gram-negative bacteria, including drug resistant ones. Predicted activity against Escherichia coli ATCC 25922 was experimentally confirmed for 14 out of 15 peptides. Further improvements for designed peptides included the synthesis of D-enantiomers, which are traditionally used to increase resistance against proteases. One synthetic D-peptide (SP15D) possesses one of the lowest values of minimum inhibitory concentration (MIC) among all DBAASP database short peptides at the time of the submission of this article, while being highly stable against proteases and having a high therapeutic index. The mode of anti-bacterial action, assessed by fluorescence microscopy, shows that SP15D acts similarly to cell penetrating peptides. SP15D can be considered a promising candidate for the development of peptide antibiotics. We plan further exploratory studies with the SP tool, aiming at finding peptides which are active against other pathogenic organisms.

Antimicrobial peptides (AMPs) are anti-infectives that have the potential to be used as a novel and untapped class of biotherapeutics. Modes of action of antimicrobial peptides include interaction with the cell envelope (cell wall, outer- and inner-membrane). A comprehensive understanding of the peculiarities of interaction of antimicrobial peptides with the cell envelope is necessary to perform a rational design of new biotherapeutics, against which working out resistance is hard for microbes. In order to enable de novo design with low cost and high throughput, in silico predictive models have to be invoked. To develop an efficient predictive model, a comprehensive understanding of the sequence-to-function relationship is required. This knowledge will allow us to encode amino acid sequences expressively and to adequately choose the accurate AMP classifier. A shared protective layer of microbial cells is the inner, plasmatic membrane. The interaction of AMP with a biological membrane (native and/or artificial) has been comprehensively studied. We provide a review of mechanisms and results of interactions of AMP with the cell membrane, relying on the survey of physicochemical, aggregative, and structural features of AMPs. The potency and mechanism of AMP action are presented in terms of amino acid compositions and distributions of the polar and apolar residues along the chain, that is, in terms of the physicochemical features of peptides such as hydrophobicity, hydrophilicity, and amphiphilicity. The survey of current data highlights topics that should be taken into account to come up with a comprehensive explanation of the mechanisms of action of AMP and to uncover the physicochemical faces of peptides, essential to perform their function. Many different approaches have been used to classify AMPs, including machine learning. The survey of knowledge on sequences, structures, and modes of actions of AMP allows concluding that only possessing comprehensive information on physicochemical features of AMPs enables us to develop accurate classifiers and create effective methods of prediction. Consequently, this knowledge is necessary for the development of design tools for peptide-based antibiotics.

The evolution of drug-resistant pathogenic microbial species is a major global health concern. Naturally occurring, antimicrobial peptides (AMPs) are considered promising candidates to address antibiotic resistance problems. A variety of computational methods have been developed to accurately predict AMPs. The majority of such methods are not microbial strain specific (MSS): they can predict whether a given peptide is active against some microbe, but cannot accurately calculate whether such peptide would be active against a particular MS. Due to insufficient data on most MS, only a few MSS predictive models have been developed so far. To overcome this problem, we developed a novel approach that allows to improve MSS predictive models (MSSPM), based on properties, computed for AMP sequences and characteristics of genomes, computed for target MS. New models can perform predictions of AMPs for MS that do not have data on peptides tested on them. We tested various types of feature engineering as well as different machine learning (ML) algorithms to compare the predictive abilities of resulting models. Among the ML algorithms, Random Forest and AdaBoost performed best. By using genome characteristics as additional features, the performance for all models increased relative to models relying on AMP sequence-based properties only. Our novel MSS AMP predictor is freely accessible as part of DBAASP database resource at http://dbaasp.org/prediction/genome

Emerging bacterial resistance to the existing antibiotics makes the development of new types of antibiotics an increasingly important challenge. Antimicrobial peptides (AMPs) can be considered as novel and efficient type of antibiotics that are hard to acquire resistance against. We have developed an algorithm to design peptides that are active against certain species. The prediction is based on clusterization of peptides with known biological activities by physicochemical properties. The Database of Antimicrobial Activity and Structure of Peptides (DBAASP, https://dbaasp.org) now includes Special Prediction (SP) tool, which allows to apply this algorithm to any amino acid sequence to predict whether this peptide is active against particular microbes. To verify the efficiency of the algorithm, we designed several variants of active peptides and tested them in vitro against two strains Escherichia coli ATCC 25922 and Staphylococcus aureus 25923. Prediction precision for the designed peptides against Escherichia coli ATCC was 95% and against Staphylococcus aureus was 68%. To improve prediction precision against Staphylococcus aureus, we applied the linear regression analysis based on binary classification. This approach allows us to improve the prediction precision of the peptides designed for Staphylococcus aureus 25923 up to 92%.

Sequence alignment is a standard method for the estimation of the evolutionary, structural, and functional relationships among amino acid sequences. The quality of alignments depends on the used similarity matrix. Statistical contact potentials (CPs) contain information on contact propensities among residues in native protein structures. Substitution matrices (SMs) based on CPs are applicable for the comparison of distantly related sequences. Here, contact between amino acids was estimated on the basis of the evaluation of the distances between side-chain terminal groups (SCTGs), which are defined as the group of the side-chain heavy atoms with fixed distances between them. In this paper, two new types of CPs and similarity matrices have been constructed: one based on fixed cutoff distance obtained from geometric characteristics of the SCTGs (TGC1), while the other is distance-dependent potential (TGC2). These matrices are compared with other popular SMs. The performance of the matrices was evaluated by comparing sequence with structural alignments. The obtained results show that TGC2 has the best performance among contact-based matrices, but on the whole, contact-based matrices have slightly lower performance than other SMs except fold-level similarity.

Antimicrobial peptides (AMPs) have been identified as a potentially new class of antibiotics. There are a lot of computational methods of AMP prediction. Although most of them can predict antimicrobial potency against any microbe (microbe is not identified) with rather high accuracy, prediction quality of these tools against particular bacterial strains is low [1,2]. Special prediction is a tool for the prediction of antimicrobial potency of peptides against particular target species with high accuracy. This tool is included into the Database of Antimicrobial Activity and Structure of Peptides (DBAASP, https://dbaasp.org [3]). In this presentation we describe this tool and predictive models for some Gram positive bacterial strains (Staphylococcus aureus ATCC 25923 and Bacillus subtilis) and a model for the prediction of hemolytic activity. Predictive model for Gram negative Escherichia coli ATCC 25922 was presented earlier [2,4]. Special prediction tool can be used for the design of peptides being active against particular strain. To demonstrate the capability of the tool, peptides predicted as active against E-coli ATCC 25922 and Staphylococcus aureus ATCC 25923 have been synthesized, and tested in vitro. The results have shown the justification of using special prediction tool for the design of new AMPs

The evolution of drug-resistant pathogenic microbial species is a major global health concern. Naturally occurring, antimicrobial peptides (AMPs) are considered promising candidates to address antibiotic resistance problems. A variety of computational methods have been developed to accurately predict AMPs. The majority of such methods are not microbial strain-specific (MSS): they can predict whether a given peptide is active against some microbe, but cannot accurately calculate whether such peptide would be active against a particular microbial strain. Due to insufficient data on most microbial strains, only a few MSS predictive models have been developed so far. To overcome this problem, we developed a novel approach that allows to improve MSS predictive models (MSSPM), based on properties, computed for AMP sequences and characteristics of genomes, computed for target microbial strains. New models can perform predictions of AMPs for microbial strains that do not have data on peptides tested on them. We tested various types of feature engineering as well as different machine learning (ML) algorithms to compare the predictive abilities of resulting models. Among the ML algorithms, Random Forest and AdaBoost performed best. By using genome characteristics as additional features, the performance for all models increased significantly (on average by 7% relative to models relying on AMP sequence-based properties only). Our novel MSS AMP predictor is freely accessible as part of DBAASP database resource at https://dbaasp.org/tools?page=genome-prediction

Design and in-vitro testing of new antimicrobial peptides based on QSAR modelling 2 Designed peptides In-vitro testing DBAASP database AMP predictive model based on the clasterization AMP training sets optimized clusters of AMP In-silico verification AMP test sets