Dataset Information

In Silico Approach for Prediction of Antifungal Peptides.

ABSTRACT: This paper describes in silico models developed using a wide range of peptide features for predicting antifungal peptides (AFPs). Our analyses indicate that certain types of residue (e.g., C, G, H, K, R, Y) are more abundant in AFPs. The positional residue preference analysis reveals the prominence of the particular type of residues (e.g., R, V, K) at N-terminus and a certain type of residues (e.g., C, H) at C-terminus. In this study, models have been developed for predicting AFPs using a wide range of peptide features (like residue composition, binary profile, terminal residues). The support vector machine based model developed using compositional features of peptides achieved maximum accuracy of 88.78% on the training dataset and 83.33% on independent or validation dataset. Our model developed using binary patterns of terminal residues of peptides achieved maximum accuracy of 84.88% on training and 84.64% on validation dataset. We benchmark models developed in this study and existing methods on a dataset containing compositionally similar antifungal and non-AFPs. It was observed that binary based model developed in this study preforms better than any model/method. In order to facilitate scientific community, we developed a mobile app, standalone and a user-friendly web server 'Antifp' (http://webs.iiitd.edu.in/raghava/antifp).

SUBMITTER: Agrawal P

PROVIDER: S-EPMC5834480 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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<i>In Silico</i> Approach for Prediction of Antifungal Peptides.

Agrawal Piyush P Bhalla Sherry S Chaudhary Kumardeep K Kumar Rajesh R Sharma Meenu M Raghava Gajendra P S GPS

Frontiers in microbiology 20180226

This paper describes <i>in silico</i> models developed using a wide range of peptide features for predicting antifungal peptides (AFPs). Our analyses indicate that certain types of residue (e.g., C, G, H, K, R, Y) are more abundant in AFPs. The positional residue preference analysis reveals the prominence of the particular type of residues (e.g., R, V, K) at N-terminus and a certain type of residues (e.g., C, H) at C-terminus. In this study, models have been developed for predicting AFPs using a ...[more]

PMID: 29535692

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Project description:BackgroundOver the past few decades, scientific research has been focused on developing peptide/protein-based therapies to treat various diseases. With the several advantages over small molecules, including high specificity, high penetration, ease of manufacturing, peptides have emerged as promising therapeutic molecules against many diseases. However, one of the bottlenecks in peptide/protein-based therapy is their toxicity. Therefore, in the present study, we developed in silico models for predicting toxicity of peptides and proteins.DescriptionWe obtained toxic peptides having 35 or fewer residues from various databases for developing prediction models. Non-toxic or random peptides were obtained from SwissProt and TrEMBL. It was observed that certain residues like Cys, His, Asn, and Pro are abundant as well as preferred at various positions in toxic peptides. We developed models based on machine learning technique and quantitative matrix using various properties of peptides for predicting toxicity of peptides. The performance of dipeptide-based model in terms of accuracy was 94.50% with MCC 0.88. In addition, various motifs were extracted from the toxic peptides and this information was combined with dipeptide-based model for developing a hybrid model. In order to evaluate the over-optimization of the best model based on dipeptide composition, we evaluated its performance on independent datasets and achieved accuracy around 90%. Based on above study, a web server, ToxinPred has been developed, which would be helpful in predicting (i) toxicity or non-toxicity of peptides, (ii) minimum mutations in peptides for increasing or decreasing their toxicity, and (iii) toxic regions in proteins.ConclusionToxinPred is a unique in silico method of its kind, which will be useful in predicting toxicity of peptides/proteins. In addition, it will be useful in designing least toxic peptides and discovering toxic regions in proteins. We hope that the development of ToxinPred will provide momentum to peptide/protein-based drug discovery (http://crdd.osdd.net/raghava/toxinpred/).

Project description:Candida albicans, a ubiquitous opportunistic fungal pathogen, plays a pivotal role in human health and disease. As a commensal organism, it normally resides harmlessly within the human microbiota. However, under certain conditions, C. albicans can transition into a pathogenic state, leading to various infections collectively known as candidiasis. With the increasing prevalence of immunocompromised individuals and the widespread use of invasive medical procedures, candidiasis has become a significant public health concern. The emergence of drug-resistant strains further complicates treatment options, highlighting the urgent need for alternative therapeutic strategies. Antifungal peptides (AFPs) have gained considerable attention as potential candidates for combating Candida spp. infections. These naturally occurring peptides possess broad-spectrum antimicrobial activity, including specific efficacy against C. albicans. AFPs exhibit several advantageous properties, such as rapid killing kinetics, low propensity for resistance development, and diverse mechanisms of action, making them promising alternatives to conventional antifungal agents. In recent years, extensive research has focused on discovering and developing novel AFPs with improved efficacy and selectivity against Candida species. Advances in biotechnology and synthetic peptide design have enabled the modification and optimization of natural peptides, enhancing their stability, bioavailability, and therapeutic potential. Nevertheless, several challenges must be addressed before AFPs can be widely implemented in clinical practice. These include optimizing peptide stability, enhancing delivery methods, overcoming potential toxicity concerns, and conducting comprehensive preclinical and clinical studies. This commentary presents a short overview of candidemia and AFP; articles and reviews published in the last 10 years were searched on The National Library of Medicine (National Center for Biotechnology Information-NIH-PubMed). The terms used were C. albicans infections, antimicrobial peptides, antifungal peptides, antifungal peptides mechanisms of action, candidemia treatments and guidelines, synthetic peptides and their challenges, and antimicrobial peptides in clinical trials as the main ones. Older publications were cited if they brought some relevant concept or helped to bring a perspective into our narrative. Articles older than 20 years and those that appeared in PubMed but did not match our goal to bring updated information about using antifungal peptides as an alternative to C. albicans infections were not considered.

Dataset Information

In Silico Approach for Prediction of Antifungal Peptides.

Publications

<i>In Silico</i> Approach for Prediction of Antifungal Peptides.

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