PBITV3 : A robust and comprehensive tool for screening pathogenic proteomes for drug targets and prioritizing vaccine candidates.

Rise of life-threatening superbugs, pandemics and epidemics warrants the need for cost-effective and novel pharmacological interventions. Availability of publicly available proteomes of pathogens supports development of high-throughput discovery platforms to prioritize potential drug-targets and develop testable hypothesis for pharmacological screening. The pipeline builder for identification of target (PBIT) was developed in 2016 and updated in 2021, with the purpose of accelerating the search for drug-targets by integration of methods like comparative and subtractive genomics, essentiality/virulence and druggability analysis. Since then, it has been used for identification of drugs and vaccine targets, safety profiling of multiepitope vaccines and mRNA vaccine construction against a broad-spectrum of pathogens. This tool has now been updated with functionalities related to systems biology and immuno-informatics and validated by analyzing 48 putative antigens of Mycobacterium tuberculosis documented in literature. PBITv3 available as both online and offline tools will enhance drug discovery against emerging drug-resistant infectious agents. PBITv3 can be freely accessed at http://pbit.bicnirrh.res.in/.

CAMPR4: a database of natural and synthetic antimicrobial peptides.

There has been an exponential increase in the design of synthetic antimicrobial peptides (AMPs) for its use as novel antibiotics. Synthetic AMPs are substantially enriched in residues with physicochemical properties known to be critical for antimicrobial activity; such as positive charge, hydrophobicity, and higher alpha helical propensity. The current prediction algorithms for AMPs have been developed using AMP sequences from natural sources and hence do not perform well for synthetic peptides. In this version of CAMP database, along with updating sequence information of AMPs, we have created separate prediction algorithms for natural and synthetic AMPs. CAMPR4 holds 24243 AMP sequences, 933 structures, 2143 patents and 263 AMP family signatures. In addition to the data on sequences, source organisms, target organisms, minimum inhibitory and hemolytic concentrations, CAMPR4 provides information on N and C terminal modifications and presence of unusual amino acids, as applicable. The database is integrated with tools for AMP prediction and rational design (natural and synthetic AMPs), sequence (BLAST and clustal omega), structure (VAST) and family analysis (PRATT, ScanProsite, CAMPSign). The data along with the algorithms of CAMPR4 will aid to enhance AMP research. CAMPR4 is accessible at http://camp.bicnirrh.res.in/.

Enrichment analyses of diseases and pathways associated with precocious puberty using PrecocityDB.

Precocious puberty (PP) is an important endocrine disorder affecting children globally. Several genes, SNPs and comorbidities are reported to be associated with PP; however, this data is scattered across scientific literature and has not been systematically collated and analysed. In this study, we present PrecocityDB as the first manually curated online database on genes and their ontology terms, SNPs, and pathways associated with PP. A tool for visualizing SNP coordinates and allelic variation on each chromosome, for genes associated with PP is also incorporated in PrecocityDB. Pathway enrichment analysis of PP-associated genes revealed that endocrine and cancer-related pathways are highly enriched. Disease enrichment analysis indicated that individuals with PP seem to be highly likely to suffer from reproductive and metabolic disorders such as PCOS, hypogonadism, and insulin resistance. PrecocityDB is a useful resource for identification of comorbid conditions and disease risks due to shared genes in PP. PrecocityDB is freely accessible at http://www.precocity.bicnirrh.res.in . The database source code and content can be downloaded through GitHub ( https://github.com/bic-nirrh/precocity ).

Exploring the druggable proteome of Candida species through comprehensive computational analysis.

Candida albicans and non-albicans Candida spp. are major cause of systemic mycoses. Antifungal drugs such as azoles and polyenes are not efficient to successfully eradicate Candida infection owing to their fungistatic nature or low bioavailability. Here, we have adopted a comprehensive computational workflow for identification, prioritization and validation of targets from proteomes of Candida albicans and Candida tropicalis. The protocol involves identification of essential drug-target candidates using subtractive genomics, protein-protein interaction network properties and systems biology based methods. The essentiality of the novel metabolic and non-metabolic targets was established by performing in silico gene knockouts, under aerobic as well as anaerobic conditions, and in vitro drug inhibition assays respectively. Deletion of twelve genes that are involved in amino acid, secondary metabolite, and carbon metabolism showed zero growth in metabolic model under simulated conditions. The algorithm, used in this study, can be downloaded from http://pbit.bicnirrh.res.in/offline.php and executed locally.

PCOSKBR2: a database of genes, diseases, pathways, and networks associated with polycystic ovary syndrome.

PolyCystic Ovary Syndrome KnowledgeBase (PCOSKBR2) is a manually curated database with information on 533 genes, 145 SNPs, 29 miRNAs, 1,150 pathways, and 1,237 diseases associated with PCOS. This data has been retrieved based on evidence gleaned by critically reviewing literature and related records available for PCOS in databases such as KEGG, DisGeNET, OMIM, GO, Reactome, STRING, and dbSNP. Since PCOS is associated with multiple genes and comorbidities, data mining algorithms for comorbidity prediction and identification of enriched pathways and hub genes are integrated in PCOSKBR2, making it an ideal research platform for PCOS. PCOSKBR2 is freely accessible at http://www.pcoskb.bicnirrh.res.in/ .

PBIT: Pipeline Builder for Identification of drug Targets for infectious diseases.

Summary: PBIT (Pipeline Builder for Identification of drug Targets) is an online webserver that has been developed for screening of microbial proteomes for critical features of human drug targets such as being non-homologous to human proteome as well as the human gut microbiota, essential for the pathogen's survival, participation in pathogen-specific pathways etc. The tool has been validated by analyzing 57 putative targets of Candida albicans documented in literature. PBIT integrates various in silico approaches known for drug target identification and will facilitate high-throughput prediction of drug targets for infectious diseases, including multi-pathogenic infections. Availability and Implementation: PBIT is freely accessible at http://www.pbit.bicnirrh.res.in/ . Contact: thomass@nirrh.res.in. Supplementary information: Supplementary data are available at Bioinformatics online.

Leveraging family-specific signatures for AMP discovery and high-throughput annotation.

Antimicrobial peptides (AMPs) are diverse, biologically active, essential components of the innate immune system. As compared to conventional antibiotics, AMPs exhibit broad spectrum antimicrobial activity, reduced toxicity and reduced microbial resistance. They are widely researched for their therapeutic potential, especially against multi-drug resistant pathogens. AMPs are known to have family-specific sequence composition, which can be mined for their discovery and rational design. Here, we present a detailed family-based study on AMP families. The study involved the use of sequence signatures represented by patterns and hidden Markov models (HMMs) present in experimentally studied AMPs to identify novel AMPs. Along with AMPs, peptides hitherto lacking antimicrobial annotation were also retrieved and wet-lab studies on randomly selected sequences proved their antimicrobial activity against Escherichia coli. CAMPSign, a webserver has been created for researchers to effortlessly exploit the use of AMP family signatures for identification of AMPs. The webserver is available online at www.campsign.bicnirrh.res.in. In this work, we demonstrate an optimised and experimentally validated protocol along with a freely available webserver that uses family-based sequence signatures for accelerated discovery of novel AMPs.

PCOSKB: A KnowledgeBase on genes, diseases, ontology terms and biochemical pathways associated with PolyCystic Ovary Syndrome.

Polycystic ovary syndrome (PCOS) is one of the major causes of female subfertility worldwide and ≈ 7-10% of women in reproductive age are affected by it. The affected individuals exhibit varying types and levels of comorbid conditions, along with the classical PCOS symptoms. Extensive studies on PCOS across diverse ethnic populations have resulted in a plethora of information on dysregulated genes, gene polymorphisms and diseases linked to PCOS. However, efforts have not been taken to collate and link these data. Our group, for the first time, has compiled PCOS-related information available through scientific literature; cross-linked it with molecular, biochemical and clinical databases and presented it as a user-friendly, web-based online knowledgebase for the benefit of the scientific and clinical community. Manually curated information on associated genes, single nucleotide polymorphisms, diseases, gene ontology terms and pathways along with supporting reference literature has been collated and included in PCOSKB (http://pcoskb.bicnirrh.res.in).

CAMPR3: a database on sequences, structures and signatures of antimicrobial peptides.

Antimicrobial peptides (AMPs) are known to have family-specific sequence composition, which can be mined for discovery and design of AMPs. Here, we present CAMPR3; an update to the existing CAMP database available online at www.camp3.bicnirrh.res.in. It is a database of sequences, structures and family-specific signatures of prokaryotic and eukaryotic AMPs. Family-specific sequence signatures comprising of patterns and Hidden Markov Models were generated for 45 AMP families by analysing 1386 experimentally studied AMPs. These were further used to retrieve AMPs from online sequence databases. More than 4000 AMPs could be identified using these signatures. AMP family signatures provided in CAMPR3 can thus be used to accelerate and expand the discovery of AMPs. CAMPR3 presently holds 10247 sequences, 757 structures and 114 family-specific signatures of AMPs. Users can avail the sequence optimization algorithm for rational design of AMPs. The database integrated with tools for AMP sequence and structure analysis will be a valuable resource for family-based studies on AMPs.

CAMP: Collection of sequences and structures of antimicrobial peptides.

Antimicrobial peptides (AMPs) are gaining importance as anti-infective agents. Here we describe the updated Collection of Antimicrobial Peptide (CAMP) database, available online at http://www.camp.bicnirrh.res.in/. The 3D structures of peptides are known to influence antimicrobial activity. Although there exists databases of AMPs, information on structures of AMPs is limited in these databases. CAMP is manually curated and currently holds 6756 sequences and 682 3D structures of AMPs. Sequence and structure analysis tools have been incorporated to enhance the usefulness of the database.

CAMP: a useful resource for research on antimicrobial peptides.

Antimicrobial peptides (AMPs) are gaining popularity as better substitute to antibiotics. These peptides are shown to be active against several bacteria, fungi, viruses, protozoa and cancerous cells. Understanding the role of primary structure of AMPs in their specificity and activity is essential for their rational design as drugs. Collection of Anti-Microbial Peptides (CAMP) is a free online database that has been developed for advancement of the present understanding on antimicrobial peptides. It is manually curated and currently holds 3782 antimicrobial sequences. These sequences are divided into experimentally validated (patents and non-patents: 2766) and predicted (1016) datasets based on their reference literature. Information like source organism, activity (MIC values), reference literature, target and non-target organisms of AMPs are captured in the database. The experimentally validated dataset has been further used to develop prediction tools for AMPs based on the machine learning algorithms like Random Forests (RF), Support Vector Machines (SVM) and Discriminant Analysis (DA). The prediction models gave accuracies of 93.2% (RF), 91.5% (SVM) and 87.5% (DA) on the test datasets. The prediction and sequence analysis tools, including BLAST, are integrated in the database. CAMP will be a useful database for study of sequence-activity and -specificity relationships in AMPs. CAMP is freely available at http://www.bicnirrh.res.in/antimicrobial.