Computational Resource for Drug Discovery

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Computational Resources for Drug Discovery (CRDD) is one of the important silico modules of Open Source for Drug Discovery (OSDD). The CRDD web portal provides computer resources related to drug discovery on a single platform. It caters to researchers of computer-aided drug design, providing computational resources, a discussion forum, and wiki resources related to drug discovery, predicting inhibitors, and predicting the ADME-Tox properties of molecules. One of the major objectives of CRDD is to promote open source software in the field of cheminformatics and pharmacoinformatics.

Contents

Features

Under CRDD, mumerous resources related to computer-aided drug design have been collected and compiled. These resources are organized and presented on CRDD so users may locate resources from a single source.

Community contribution

CRDD developed a platform where the community may contribute to the process of drug discovery.

Indigenous development: software and web services

Beside collecting and compiling resources, CRDD members develop new software and web services. All services developed are free for academic use. The following are a few major tools developed at CRDD.[ citation needed ]

Development of databases

Software developed

Resources created

Web services for cheminformatics

CRDD developed an open source platform which allows users to predict inhibitors against novel M. Tuberculosis drug targets and other important properties of drug molecules like ADMET. Following are list of few servers.

Prediction and analysis of drug targets

Related Research Articles

<span class="mw-page-title-main">Bioinformatics</span> Computational analysis of large, complex sets of biological data

Bioinformatics is an interdisciplinary field of science that develops methods and software tools for understanding biological data, especially when the data sets are large and complex. Bioinformatics uses biology, chemistry, physics, computer science, computer programming, information engineering, mathematics and statistics to analyze and interpret biological data. The subsequent process of analyzing and interpreting data is referred to as computational biology.

<span class="mw-page-title-main">Membrane topology</span>

Topology of a transmembrane protein refers to locations of N- and C-termini of membrane-spanning polypeptide chain with respect to the inner or outer sides of the biological membrane occupied by the protein.

<span class="mw-page-title-main">Structural alignment</span> Aligning molecular sequences using sequence and structural information

Structural alignment attempts to establish homology between two or more polymer structures based on their shape and three-dimensional conformation. This process is usually applied to protein tertiary structures but can also be used for large RNA molecules. In contrast to simple structural superposition, where at least some equivalent residues of the two structures are known, structural alignment requires no a priori knowledge of equivalent positions. Structural alignment is a valuable tool for the comparison of proteins with low sequence similarity, where evolutionary relationships between proteins cannot be easily detected by standard sequence alignment techniques. Structural alignment can therefore be used to imply evolutionary relationships between proteins that share very little common sequence. However, caution should be used in using the results as evidence for shared evolutionary ancestry because of the possible confounding effects of convergent evolution by which multiple unrelated amino acid sequences converge on a common tertiary structure.

<span class="mw-page-title-main">Structural bioinformatics</span> Bioinformatics subfield

Structural bioinformatics is the branch of bioinformatics that is related to the analysis and prediction of the three-dimensional structure of biological macromolecules such as proteins, RNA, and DNA. It deals with generalizations about macromolecular 3D structures such as comparisons of overall folds and local motifs, principles of molecular folding, evolution, binding interactions, and structure/function relationships, working both from experimentally solved structures and from computational models. The term structural has the same meaning as in structural biology, and structural bioinformatics can be seen as a part of computational structural biology. The main objective of structural bioinformatics is the creation of new methods of analysing and manipulating biological macromolecular data in order to solve problems in biology and generate new knowledge.

An epitope, also known as antigenic determinant, is the part of an antigen that is recognized by the immune system, specifically by antibodies, B cells, or T cells. The part of an antibody that binds to the epitope is called a paratope. Although epitopes are usually non-self proteins, sequences derived from the host that can be recognized are also epitopes.

Protein subcellular localization prediction involves the prediction of where a protein resides in a cell, its subcellular localization.

In academia, computational immunology is a field of science that encompasses high-throughput genomic and bioinformatics approaches to immunology. The field's main aim is to convert immunological data into computational problems, solve these problems using mathematical and computational approaches and then convert these results into immunologically meaningful interpretations.

Internal Coordinate Mechanics (ICM) is a software program and algorithm to predict low-energy conformations of molecules by sampling the space of internal coordinates defining molecular geometry. In ICM each molecule is constructed as a tree from an entry atom where each next atom is built iteratively from the preceding three atoms via three internal variables. The rings kept rigid or imposed via additional restraints. ICM is used for modelling peptides and interactions with substrates and coenzymes.

<span class="mw-page-title-main">Trefoil knot fold</span>

The trefoil knot fold is a protein fold in which the protein backbone is twisted into a trefoil knot shape. "Shallow" knots in which the tail of the polypeptide chain only passes through a loop by a few residues are uncommon, but "deep" knots in which many residues are passed through the loop are extremely rare. Deep trefoil knots have been found in the SPOUT superfamily. including methyltransferase proteins involved in posttranscriptional RNA modification in all three domains of life, including bacterium Thermus thermophilus and proteins, in archaea and in eukaryota.

Structural and physical properties of DNA provide important constraints on the binding sites formed on surfaces of DNA-binding proteins. Characteristics of such binding sites may be used for predicting DNA-binding sites from the structural and even sequence properties of unbound proteins. This approach has been successfully implemented for predicting the protein–protein interface. Here, this approach is adopted for predicting DNA-binding sites in DNA-binding proteins. First attempt to use sequence and evolutionary features to predict DNA-binding sites in proteins was made by Ahmad et al. (2004) and Ahmad and Sarai (2005). Some methods use structural information to predict DNA-binding sites and therefore require a three-dimensional structure of the protein, while others use only sequence information and do not require protein structure in order to make a prediction.

Anders Krogh is a bioinformatician at the University of Copenhagen, where he leads the university's bioinformatics center. He is known for his pioneering work on the use of hidden Markov models in bioinformatics, and is co-author of a widely used textbook in bioinformatics. In addition, he also co-authored one of the early textbooks on neural networks. His current research interests include promoter analysis, non-coding RNA, gene prediction and protein structure prediction.

<span class="mw-page-title-main">Gajendra Pal Singh Raghava</span>

Gajendra Pal Singh Raghava is an Indian bio-informatician and head of computational biology at the Indraprastha Institute of Information Technology.

<span class="mw-page-title-main">Short linear motif</span>

In molecular biology short linear motifs (SLiMs), linear motifs or minimotifs are short stretches of protein sequence that mediate protein–protein interaction.

Protein function prediction methods are techniques that bioinformatics researchers use to assign biological or biochemical roles to proteins. These proteins are usually ones that are poorly studied or predicted based on genomic sequence data. These predictions are often driven by data-intensive computational procedures. Information may come from nucleic acid sequence homology, gene expression profiles, protein domain structures, text mining of publications, phylogenetic profiles, phenotypic profiles, and protein-protein interaction. Protein function is a broad term: the roles of proteins range from catalysis of biochemical reactions to transport to signal transduction, and a single protein may play a role in multiple processes or cellular pathways.

αr9 is a family of bacterial small non-coding RNAs with representatives in a broad group of α-proteobacteria from the order Hyphomicrobiales. The first member of this family (Smr9C) was found in a Sinorhizobium meliloti 1021 locus located in the chromosome (C). Further homology and structure conservation analysis have identified full-length Smr9C homologs in several nitrogen-fixing symbiotic rhizobia, in the plant pathogens belonging to Agrobacterium species as well as in a broad spectrum of Brucella species. αr9C RNA species are 144-158 nt long and share a well defined common secondary structure consisting of seven conserved regions. Most of the αr9 transcripts can be catalogued as trans-acting sRNAs expressed from well-defined promoter regions of independent transcription units within intergenic regions (IGRs) of the α-proteobacterial genomes.

αr35 is a family of bacterial small non-coding RNAs with representatives in a reduced group of Alphaproteobacteria from the order Hyphomicrobiales. The first member of this family (Smr35B) was found in a Sinorhizobium meliloti 1021 locus located in the symbiotic plasmid B (pSymB). Further homology and structure conservation analysis have identified full-length SmrB35 homologs in other legume symbionts, as well as in the human and plant pathogens Brucella anthropi and Agrobacterium tumefaciens, respectively. αr35 RNA species are 139-142 nt long and share a common secondary structure consisting of two stem loops and a well conserved rho independent terminator. Most of the αr35 transcripts can be catalogued as trans-acting sRNAs expressed from well-defined promoter regions of independent transcription units within intergenic regions of the Alphaproteobacterial genomes.

Single nucleotide polymorphism annotation is the process of predicting the effect or function of an individual SNP using SNP annotation tools. In SNP annotation the biological information is extracted, collected and displayed in a clear form amenable to query. SNP functional annotation is typically performed based on the available information on nucleic acid and protein sequences.

<span class="mw-page-title-main">SHLD1</span> Protein-coding gene in the species Homo sapiens

SHLD1 or shieldin complex subunit 1 is a gene on chromosome 20. The C20orf196 gene encodes an mRNA that is 1,763 base pairs long, and a protein that is 205 amino acids long.

References

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  2. Singla D, Sharma A, Kaur J, Panwar B, Raghava GP (March 2010). "BIAdb: a curated database of benzylisoquinoline alkaloids". BMC Pharmacology. 10: 4. doi: 10.1186/1471-2210-10-4 . PMC   2844369 . PMID   20205728.
  3. Ansari HR, Flower DR, Raghava GP (January 2010). "AntigenDB: an immunoinformatics database of pathogen antigens". Nucleic Acids Research. 38 (Database issue): D847–D853. doi:10.1093/nar/gkp830. PMC   2808902 . PMID   19820110.
  4. Aithal A, Sharma A, Joshi S, Raghava GP, Varshney GC (2012-04-11). Kaufmann GF (ed.). "PolysacDB: a database of microbial polysaccharide antigens and their antibodies". PLOS ONE. 7 (4): e34613. Bibcode:2012PLoSO...734613A. doi: 10.1371/journal.pone.0034613 . PMC   3324500 . PMID   22509333.
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  9. Mishra NK, Agarwal S, Raghava GP (July 2010). "Prediction of cytochrome P450 isoform responsible for metabolizing a drug molecule". BMC Pharmacology. 10: 8. doi: 10.1186/1471-2210-10-8 . PMC   2912882 . PMID   20637097.
  10. Garg A, Tewari R, Raghava GP (March 2010). "KiDoQ: using docking based energy scores to develop ligand based model for predicting antibacterials". BMC Bioinformatics. 11: 125. doi: 10.1186/1471-2105-11-125 . PMC   2841597 . PMID   20222969.
  11. Singla D, Anurag M, Dash D, Raghava GP (July 2011). "A web server for predicting inhibitors against bacterial target GlmU protein". BMC Pharmacology. 11: 5. doi: 10.1186/1471-2210-11-5 . PMC   3146400 . PMID   21733180.
  12. Kumar M, Gromiha MM, Raghava GP (2010). "SVM based prediction of RNA-binding proteins using binding residues and evolutionary information". Journal of Molecular Recognition. 24 (2): 303–313. doi:10.1002/jmr.1061. PMID   20677174. S2CID   12677753.
  13. Rashid, M. and Raghava, G. P. S. (2010) A simple approach for predicting protein–protein interactions. Current Protein & Peptide Science (In Press).
  14. Chauhan JS, Mishra NK, Raghava GP (December 2009). "Identification of ATP binding residues of a protein from its primary sequence". BMC Bioinformatics. 10: 434. doi: 10.1186/1471-2105-10-434 . PMC   2803200 . PMID   20021687.
  15. Mishra NK, Raghava GP (January 2010). "Prediction of FAD interacting residues in a protein from its primary sequence using evolutionary information". BMC Bioinformatics. 11 (Suppl 1): S48. doi: 10.1186/1471-2105-11-S1-S48 . PMC   3009520 . PMID   20122222.
  16. Chauhan JS, Mishra NK, Raghava GP (June 2010). "Prediction of GTP interacting residues, dipeptides and tripeptides in a protein from its evolutionary information". BMC Bioinformatics. 11: 301. doi: 10.1186/1471-2105-11-301 . PMC   3098072 . PMID   20525281.
  17. Ansari HR, Raghava GP (March 2010). "Identification of NAD interacting residues in proteins". BMC Bioinformatics. 11: 160. doi: 10.1186/1471-2105-11-160 . PMC   2853471 . PMID   20353553.
  18. Agarwal S, Mishra NK, Singh H, Raghava GP (2011). "Identification of mannose interacting residues using local composition". PLOS ONE. 6 (9): e24039. Bibcode:2011PLoSO...624039A. doi: 10.1371/journal.pone.0024039 . PMC   3172211 . PMID   21931639.
  19. Panwar B, Raghava GP (September 2010). "Prediction and classification of aminoacyl tRNA synthetases using PROSITE domains". BMC Genomics. 11: 507. doi: 10.1186/1471-2164-11-507 . PMC   2997003 . PMID   20860794.
  20. Ansari HR, Raghava GP (October 2010). "Identification of conformational B-cell Epitopes in an antigen from its primary sequence". Immunome Research. 6: 6. doi: 10.1186/1745-7580-6-6 . PMC   2974664 . PMID   20961417.
  21. Ahmed F, Raghava GP (2011). "Designing of highly effective complementary and mismatch siRNAs for silencing a gene". PLOS ONE. 6 (8): e23443. Bibcode:2011PLoSO...623443A. doi: 10.1371/journal.pone.0023443 . PMC   3154470 . PMID   21853133.

Further reading