Knowledgebase of potential multifaceted solutions to antimicrobial resistance.

Antimicrobial resistance (AMR), a top threat to global health, challenges preventive and treatment strategies of infections. AMR strains of microbial pathogens arise through multiple mechanisms. The underlying "antibiotic resistance genes" (ARGs) spread through various species by lateral gene transfer thereby causing global dissemination. Human methods also augment this process through inappropriate use, non-compliance to treatment schedule, and environmental waste. Worldwide significant efforts are being invested to discover novel therapeutic solutions for tackling resistant pathogens. Diverse therapeutic strategies have evolved over recent years. In this work we have developed a comprehensive knowledgebase by collecting alternative antimicrobial therapeutic strategies from literature data. Therapeutic strategies against bacteria, virus, fungus and parasites were extracted from PubMed literature using text mining. We have used a subjective (sentimental) approach for data mining new strategies, resulting in broad coverage of novel entities and subsequently add objective data like entity name (including IUPAC), potency, and safety information. The extracted data was organized in a freely accessible web platform, KOMBAT. The KOMBAT comprises 1104 Chemical compounds, 220 of newly identified antimicrobial peptides, 42 bacteriophages, 242 phytochemicals, 106 nanocomposites, and 94 novel entities for phototherapy. Entities tested and evaluated on AMR pathogens are included. We envision that this database will be useful for developing future therapeutics against AMR pathogens. The database can be accessed through http://kombat.igib.res.in/.

Vaxi-DL: A web-based deep learning server to identify potential vaccine candidates.

The development of a new vaccine is a challenging exercise involving several steps including computational studies, experimental work, and animal studies followed by clinical studies. To accelerate the process, in silico screening is frequently used for antigen identification. Here, we present Vaxi-DL, web-based deep learning (DL) software that evaluates the potential of protein sequences to serve as vaccine target antigens. Four different DL pathogen models were trained to predict target antigens in bacteria, protozoa, fungi, and viruses that cause infectious diseases in humans. Datasets containing antigenic and non-antigenic sequences were derived from known vaccine candidates and the Protegen database. Biological and physicochemical properties were computed for the datasets using publicly available bioinformatics tools. For each of the four pathogen models, the datasets were divided into training, validation, and testing subsets and then scaled and normalised. The models were constructed using Fully Connected Layers (FCLs), hyper-tuned, and trained using the training subset. Accuracy, sensitivity, specificity, precision, recall, and AUC (Area under the Curve) were used as metrics to assess the performance of these models. The models were benchmarked using independent datasets of known target antigens against other prediction tools such as VaxiJen and Vaxign-ML. We also tested Vaxi-DL on 219 known potential vaccine candidates (PVC) from 37 different pathogens. Our tool predicted 175 PVCs correctly out of 219 sequences. We also tested Vaxi-DL on different datasets obtained from multiple resources. Our tool has demonstrated an average sensitivity of 93% and will thus be a useful tool for prioritising PVCs for preclinical studies.

Identification of vaccine targets in pathogens and design of a vaccine using computational approaches.

Antigen identification is an important step in the vaccine development process. Computational approaches including deep learning systems can play an important role in the identification of vaccine targets using genomic and proteomic information. Here, we present a new computational system to discover and analyse novel vaccine targets leading to the design of a multi-epitope subunit vaccine candidate. The system incorporates reverse vaccinology and immuno-informatics tools to screen genomic and proteomic datasets of several pathogens such as Trypanosoma cruzi, Plasmodium falciparum, and Vibrio cholerae to identify potential vaccine candidates (PVC). Further, as a case study, we performed a detailed analysis of the genomic and proteomic dataset of T. cruzi (CL Brenner and Y strain) to shortlist eight proteins as possible vaccine antigen candidates using properties such as secretory/surface-exposed nature, low transmembrane helix (< 2), essentiality, virulence, antigenic, and non-homology with host/gut flora proteins. Subsequently, highly antigenic and immunogenic MHC class I, MHC class II and B cell epitopes were extracted from top-ranking vaccine targets. The designed vaccine construct containing 24 epitopes, 3 adjuvants, and 4 linkers was analysed for its physicochemical properties using different tools, including docking analysis. Immunological simulation studies suggested significant levels of T-helper, T-cytotoxic cells, and IgG1 will be elicited upon administration of such a putative multi-epitope vaccine construct. The vaccine construct is predicted to be soluble, stable, non-allergenic, non-toxic, and to offer cross-protection against related Trypanosoma species and strains. Further, studies are required to validate safety and immunogenicity of the vaccine.