In the past, number of methods have been developed for predicting single label subcellular localization of mRNA in a cell. Only limited methods had been built to predict multi-label subcellular localization of mRNA. Most of the existing methods are slow and cannot be implemented at transcriptome scale. In this study, a fast and reliable method had been developed for predicting multi-label subcellular localization of mRNA that can be implemented at genome scale. Firstly, deep learning method based on convolutional neural network method have been developed using one-hot encoding and attained an average AUROC - 0.584 (0.543 - 0.605). Secondly, machine learning based methods have been developed using mRNA sequence composition, our XGBoost classifier achieved an average AUROC - 0.709 (0.668 - 0.732). In addition to alignment free methods, we also developed alignment-based methods using similarity and motif search techniques. Finally, a hybrid technique has been developed that combine XGBoost models and motif-based searching and achieved an average AUROC 0.742 (0.708 - 0.816). Our method - MRSLpred, developed in this study is complementary to the existing method. One of the major advantages of our method over existing methods is its speed, it can scan all mRNA of a transcriptome in few hours. A publicly accessible webserver and a standalone tool has been developed to facilitate researchers (Webserver: https://webs.iiitd.edu.in/raghava/mrslpred/).

In the current era, one of the major challenges is to manage the treatment of drug/antibiotic-resistant strains of bacteria. Phage therapy, a century-old technique, may serve as an alternative to antibiotics in treating bacterial infections caused by drug-resistant strains of bacteria. In this review, a systematic attempt has been made to summarize phage-based therapy in depth. This review has been divided into the following two sections: general information and computer-aided phage therapy (CAPT). In the case of general information, we cover the history of phage therapy, the mechanism of action, the status of phage-based products (approved and clinical trials) and the challenges. This review emphasizes CAPT, where we have covered primary phage-associated resources, phage prediction methods and pipelines. This review covers a wide range of databases and resources, including viral genomes and proteins, phage receptors, host genomes of phages, phage–host interactions and lytic proteins. In the post-genomic era, identifying the most suitable phage for lysing a drug-resistant strain of bacterium is crucial for developing alternate treatments for drug-resistant bacteria and this remains a challenging problem. Thus, we compile all phage-associated prediction methods that include the prediction of phages for a bacterial strain, the host for a phage and the identification of interacting phage–host pairs. Most of these methods have been developed using machine learning and deep learning techniques. This review also discussed recent advances in the field of CAPT, where we briefly describe computational tools available for predicting phage virions, the life cycle of phages and prophage identification. Finally, we describe phage-based therapy’s advantages, challenges and opportunities.

This study focuses on the development of in silico models for predicting antibacterial peptides as a potential solution for combating antibiotic-resistant strains of bacteria. Existing methods for predicting antibacterial peptides are mostly designed to target either gram-positive or gram-negative bacteria. In this study, we introduce a novel approach that enables the prediction of antibacterial peptides against several bacterial groups, including gram-positive, gram-negative, and gram-variable bacteria. Firstly, we developed an alignment-based approach using BLAST to identify antibacterial peptides and achieved poor sensitivity. Secondly, we employed a motif-based approach to predict antibacterial peptides and obtained high precision with low sensitivity. To address the similarity issue, we developed machine learning-based models using a variety of compositional and binary features. Our machine learning-based model developed using the amino acid binary profile of terminal residues achieved maximum AUC 0.93, 0.98 and 0.94 for gram-positive, gram-negative, and gram-variable bacteria, respectively, when evaluated on a validation/independent dataset. Our attempts to develop hybrid or ensemble methods by merging machine learning models with similarity and motif-based techniques did not yield any improvements. To ensure robust evaluation, we employed standard techniques such as five-fold cross-validation, internal validation, and external validation. Our method performs better than existing methods when we compare our method with existing approaches on an independent dataset. In summary, this study makes significant contributions to the field of antibacterial peptide prediction by providing a comprehensive set of methods tailored to different bacterial groups. As part of our contribution, we have developed the AntiBP3 web server and standalone package, which will assist researchers in the discovery of novel antibacterial peptides for combating bacterial infections (https://webs.iiitd.edu.in/raghava/antibp3/).Key Points⍰BLAST-based similarity for annotating antibacterial peptides.⍰Machine learning-based models developed using composition and binary profiles.⍰Identification and mapping of motifs exclusively found in antibacterial peptides⍰Improved version of AntiBP and AntiBP2 for predicting antibacterial peptides.⍰Web server for predicting/designing/scanning antibacterial peptides for all groups of bacteriaAuthor’s BiographyNisha Bajiya is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.Shubham Choudhury is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.Anjali Dhall is currently working as Ph.D. in Computational Biology from Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.Gajendra P. S. Raghava is currently working as Professor and Head of Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.