AniAMPpred: artificial intelligence guided discovery of novel antimicrobial peptides in animal kingdom

Abstract

With advancements in genomics, there has been substantial reduction in the cost and time of genome sequencing and has resulted in lot of data in genome databases. Antimicrobial host defense proteins provide protection against invading microbes. But confirming the antimicrobial function of host proteins by wet-lab experiments is expensive and time consuming. Therefore, there is a need to develop an in silico tool to identify the antimicrobial function of proteins. In the current study, we developed a model AniAMPpred by considering all the available antimicrobial peptides (AMPs) of length $\in $[10 200] from the animal kingdom. The model utilizes a support vector machine algorithm with deep learning-based features and identifies probable antimicrobial proteins (PAPs) in the genome of animals. The results show that our proposed model outperforms other state-of-the-art classifiers, has very high confidence in its predictions, is not biased and can classify both AMPs and non-AMPs for a diverse peptide length with high accuracy. By utilizing AniAMPpred, we identified 436 PAPs in the genome of Helobdella robusta. To further confirm the functional activity of PAPs, we performed BLAST analysis against known AMPs. On detailed analysis of five selected PAPs, we could observe their similarity with antimicrobial proteins of several animal species. Thus, our proposed model can help the researchers identify PAPs in the genome of animals and provide insight into the functional identity of different proteins. An online prediction server is also developed based on the proposed approach, which is freely accessible at https://aniamppred.anvil.app/.

An Efficient Evaluation System Accelerates α-Helical Antimicrobial Peptide Discovery and Its Application to Global Human Genome Mining

“…Although several computational methods have been taken to screen AMPs from various samples ( Li et al, 2018 ; Porto et al, 2018 ; Tan et al, 2019 ; Sharma et al, 2021 ), the systematic screening of encrypted AMP from the global human genome has not been performed. In this study, 337 peptides with 15–25 amino acid residues were screened out from the entire human genome including intron and exon with a high MD score (≥65) by the MultiDS system, and 60 entities were synthesized to test the antimicrobial activities.…”

Antimicrobial Peptides: An Update on Classifications and Databases

“…SVM combined with deep learning-based features identified 436 possible antimicrobial proteins in the genome of Helobdella robusta [ 548 ]. Discriminant analysis (DA), which is a multivariate approach [ 549 ], quadratic discriminate analysis [ 550 ], and conditional random fields [ 551 ] may also be used for AMP prediction.…”

Recent Progress in the Discovery and Design of Antimicrobial Peptides Using Traditional Machine Learning and Deep Learning

“…AMP discovery from large-scale natural known peptide libraries is based on the antimicrobial activity prediction from traditional ML models in a screening manner. Traditional ML techniques, such as SVM [ 92 , 93 , 94 , 95 , 96 ], discriminant analysis (DA) [ 97 ], RF [ 98 , 99 , 100 , 101 ], kNN [ 95 , 102 , 103 ], and ensemble learning [ 104 , 105 , 106 , 107 , 108 ] have been applied to discover AMPs by classification. Among these methods, SVM non-linearly transforms the original input space into a higher-dimensional feature space by means of kernel functions [ 109 , 110 ].…”

“…DL methods can be used to extract features of peptides and then learned from traditional ML methods. For example, Sharma et al [ 102 ] proposed AniAMPpred, in which a one-dimensional CNN with Word2vec [ 81 ] embedding was used to encode features from peptide sequences and an SVM was used to develop the classifier based on the datasets considering only all available AMPs from the animal kingdom with lengths ranging from 10 to 200 for identifying probable AMPs in the animal genomes.…”