scholarly journals CATH: increased structural coverage of functional space

2020 ◽  
Vol 49 (D1) ◽  
pp. D266-D273
Author(s):  
Ian Sillitoe ◽  
Nicola Bordin ◽  
Natalie Dawson ◽  
Vaishali P Waman ◽  
Paul Ashford ◽  
...  
Keyword(s):  
Sequence Data ◽  
Protein Structures ◽  
Functional Space ◽  
Web Pages ◽  
Functional Annotations ◽  
Functional Sites ◽  
Domain Structures ◽  
Functional Families ◽  
Structural Coverage ◽  
Coherent Sequence

Abstract CATH (https://www.cathdb.info) identifies domains in protein structures from wwPDB and classifies these into evolutionary superfamilies, thereby providing structural and functional annotations. There are two levels: CATH-B, a daily snapshot of the latest domain structures and superfamily assignments, and CATH+, with additional derived data, such as predicted sequence domains, and functionally coherent sequence subsets (Functional Families or FunFams). The latest CATH+ release, version 4.3, significantly increases coverage of structural and sequence data, with an addition of 65,351 fully-classified domains structures (+15%), providing 500 238 structural domains, and 151 million predicted sequence domains (+59%) assigned to 5481 superfamilies. The FunFam generation pipeline has been re-engineered to cope with the increased influx of data. Three times more sequences are captured in FunFams, with a concomitant increase in functional purity, information content and structural coverage. FunFam expansion increases the structural annotations provided for experimental GO terms (+59%). We also present CATH-FunVar web-pages displaying variations in protein sequences and their proximity to known or predicted functional sites. We present two case studies (1) putative cancer drivers and (2) SARS-CoV-2 proteins. Finally, we have improved links to and from CATH including SCOP, InterPro, Aquaria and 2DProt.

scholarly journals A community proposal to integrate structural bioinformatics activities in ELIXIR (3D-Bioinfo Community)

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 278
Author(s):  
Christine Orengo ◽  
Sameer Velankar ◽  
Shoshana Wodak ◽  
Vincent Zoete ◽  
Alexandre M.J.J. Bonvin ◽  
...  
Keyword(s):  
Structural Biology ◽  
Protein Design ◽  
Sequence Data ◽  
Protein Structures ◽  
Structural Bioinformatics ◽  
Data Sets ◽  
Educational Training ◽  
Scientific Methods ◽  
Functional Sites ◽  
Common Interests

Structural bioinformatics provides the scientific methods and tools to analyse, archive, validate, and present the biomolecular structure data generated by the structural biology community. It also provides an important link with the genomics community, as structural bioinformaticians also use the extensive sequence data to predict protein structures and their functional sites. A very broad and active community of structural bioinformaticians exists across Europe, and 3D-Bioinfo will establish formal platforms to address their needs and better integrate their activities and initiatives. Our mission will be to strengthen the ties with the structural biology research communities in Europe covering life sciences, as well as chemistry and physics and to bridge the gap between these researchers in order to fully realize the potential of structural bioinformatics. Our Community will also undertake dedicated educational, training and outreach efforts to facilitate this, bringing new insights and thus facilitating the development of much needed innovative applications e.g. for human health, drug and protein design. Our combined efforts will be of critical importance to keep the European research efforts competitive in this respect. Here we highlight the major European contributions to the field of structural bioinformatics, the most pressing challenges remaining and how Europe-wide interactions, enabled by ELIXIR and its platforms, will help in addressing these challenges and in coordinating structural bioinformatics resources across Europe. In particular, we present recent activities and future plans to consolidate an ELIXIR 3D-Bioinfo Community in structural bioinformatics and propose means to develop better links across the community. These include building new consortia, organising workshops to establish data standards and seeking community agreement on benchmark data sets and strategies. We also highlight existing and planned collaborations with other ELIXIR Communities and other European infrastructures, such as the structural biology community supported by Instruct-ERIC, with whom we have synergies and overlapping common interests.

scholarly journals CATH functional families predict functional sites in proteins

2020 ◽  
Author(s):  
Sayoni Das ◽  
Harry M Scholes ◽  
Neeladri Sen ◽  
Christine Orengo
Keyword(s):  
Functional Characterization ◽  
Functional Site ◽  
Training Data ◽  
Supplementary Information ◽  
Conserved Residues ◽  
Functional Sites ◽  
Protein Protein Interaction ◽  
Evolutionary Features ◽  
Functional Families

Abstract Motivation Identification of functional sites in proteins is essential for functional characterization, variant interpretation and drug design. Several methods are available for predicting either a generic functional site, or specific types of functional site. Here, we present FunSite, a machine learning predictor that identifies catalytic, ligand-binding and protein–protein interaction functional sites using features derived from protein sequence and structure, and evolutionary data from CATH functional families (FunFams). Results FunSite’s prediction performance was rigorously benchmarked using cross-validation and a holdout dataset. FunSite outperformed other publicly available functional site prediction methods. We show that conserved residues in FunFams are enriched in functional sites. We found FunSite’s performance depends greatly on the quality of functional site annotations and the information content of FunFams in the training data. Finally, we analyze which structural and evolutionary features are most predictive for functional sites. Availabilityand implementation https://github.com/UCL/cath-funsite-predictor. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Structure-Guided Computational Approaches to Unravel Druggable Proteomic Landscape of Mycobacterium leprae

2021 ◽  
Vol 8 ◽  
Author(s):  
Sundeep Chaitanya Vedithi ◽  
Sony Malhotra ◽  
Marta Acebrón-García-de-Eulate ◽  
Modestas Matusevicius ◽  
Pedro Henrique Monteiro Torres ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Schwann Cells ◽  
Protein Structures ◽  
Mycobacterium Leprae ◽  
Data Bank ◽  
Nerve Damage ◽  
Structural Proteomics ◽  
Bacterial Survival ◽  
Functional Sites

Leprosy, caused by Mycobacterium leprae (M. leprae), is treated with a multidrug regimen comprising Dapsone, Rifampicin, and Clofazimine. These drugs exhibit bacteriostatic, bactericidal and anti-inflammatory properties, respectively, and control the dissemination of infection in the host. However, the current treatment is not cost-effective, does not favor patient compliance due to its long duration (12 months) and does not protect against the incumbent nerve damage, which is a severe leprosy complication. The chronic infectious peripheral neuropathy associated with the disease is primarily due to the bacterial components infiltrating the Schwann cells that protect neuronal axons, thereby inducing a demyelinating phenotype. There is a need to discover novel/repurposed drugs that can act as short duration and effective alternatives to the existing treatment regimens, preventing nerve damage and consequent disability associated with the disease. Mycobacterium leprae is an obligate pathogen resulting in experimental intractability to cultivate the bacillus in vitro and limiting drug discovery efforts to repositioning screens in mouse footpad models. The dearth of knowledge related to structural proteomics of M. leprae, coupled with emerging antimicrobial resistance to all the three drugs in the multidrug therapy, poses a need for concerted novel drug discovery efforts. A comprehensive understanding of the proteomic landscape of M. leprae is indispensable to unravel druggable targets that are essential for bacterial survival and predilection of human neuronal Schwann cells. Of the 1,614 protein-coding genes in the genome of M. leprae, only 17 protein structures are available in the Protein Data Bank. In this review, we discussed efforts made to model the proteome of M. leprae using a suite of software for protein modeling that has been developed in the Blundell laboratory. Precise template selection by employing sequence-structure homology recognition software, multi-template modeling of the monomeric models and accurate quality assessment are the hallmarks of the modeling process. Tools that map interfaces and enable building of homo-oligomers are discussed in the context of interface stability. Other software is described to determine the druggable proteome by using information related to the chokepoint analysis of the metabolic pathways, gene essentiality, homology to human proteins, functional sites, druggable pockets and fragment hotspot maps.

scholarly journals Thera-SAbDab: the Therapeutic Structural Antibody Database

2019 ◽  
Author(s):  
Matthew I. J. Raybould ◽  
Claire Marks ◽  
Alan P. Lewis ◽  
Jiye Shi ◽  
Alexander Bujotzek ◽  
...  
Keyword(s):  
Sequence Data ◽  
Data Bank ◽  
World Health Organisation ◽  
Variable Domain ◽  
World Health ◽  
Sequence Identity ◽  
Single File ◽  
Domain Sequence ◽  
Structural Coverage ◽  
New Protein

The Therapeutic Structural Antibody Database (Thera-SAbDab; http://opig.stats.ox.ac.uk/webapps/therasabdab) tracks all antibody- and nanobody-related therapeutics recognised by the World Health Organisation (WHO), and identifies any corresponding structures in the Structural Antibody Database (SAbDab) with near-exact or exact variable domain sequence matches. Thera-SAbDab is synchronised with SAbDab to update weekly, reflecting new Protein Data Bank entries and the availability of new sequence data published by the WHO. Each therapeutic summary page lists structural coverage (with links to the appropriate SAbDab entries), alignments showing where any near-matches deviate in sequence, and accompanying metadata, such as intended target and investigated conditions. Thera-SAbDab can be queried by therapeutic name, by a combination of metadata, or by variable domain sequence - returning all therapeutics that are within a specified sequence identity over a specified region of the query. The sequences of all therapeutics listed in Thera-SAbDab (461 unique molecules, as of 5th August 2019) are downloadable as a single file with accompanying metadata.

scholarly journals Thera-SAbDab: the Therapeutic Structural Antibody Database

2019 ◽  
Vol 48 (D1) ◽  
pp. D383-D388 ◽  
Author(s):  
Matthew I J Raybould ◽  
Claire Marks ◽  
Alan P Lewis ◽  
Jiye Shi ◽  
Alexander Bujotzek ◽  
...  
Keyword(s):  
Sequence Data ◽  
Data Bank ◽  
World Health Organisation ◽  
Variable Domain ◽  
World Health ◽  
Sequence Identity ◽  
Single File ◽  
Domain Sequence ◽  
Structural Coverage ◽  
New Protein

Abstract The Therapeutic Structural Antibody Database (Thera-SAbDab; http://opig.stats.ox.ac.uk/webapps/therasabdab) tracks all antibody- and nanobody-related therapeutics recognized by the World Health Organisation (WHO), and identifies any corresponding structures in the Structural Antibody Database (SAbDab) with near-exact or exact variable domain sequence matches. Thera-SAbDab is synchronized with SAbDab to update weekly, reflecting new Protein Data Bank entries and the availability of new sequence data published by the WHO. Each therapeutic summary page lists structural coverage (with links to the appropriate SAbDab entries), alignments showing where any near-matches deviate in sequence, and accompanying metadata, such as intended target and investigated conditions. Thera-SAbDab can be queried by therapeutic name, by a combination of metadata, or by variable domain sequence - returning all therapeutics that are within a specified sequence identity over a specified region of the query. The sequences of all therapeutics listed in Thera-SAbDab (461 unique molecules, as of 5 August 2019) are downloadable as a single file with accompanying metadata.

RECOGNIZING COMPLEX, ASYMMETRIC FUNCTIONAL SITES IN PROTEIN STRUCTURES USING A BAYESIAN SCORING FUNCTION

2003 ◽  
Vol 01 (01) ◽  
pp. 119-138 ◽  
Author(s):  
LIPING WEI ◽  
RUSS B. ALTMAN
Keyword(s):  
Binding Sites ◽  
Calcium Binding ◽  
Active Sites ◽  
Large Scale ◽  
Protein Structures ◽  
Scoring Function ◽  
Chemical Properties ◽  
Data Bank ◽  
Functional Sites ◽  
Calcium Binding Sites

The increase in known three-dimensional protein structures enables us to build statistical profiles of important functional sites in protein molecules. These profiles can then be used to recognize sites in large-scale automated annotations of new protein structures. We report an improved FEATURE system which recognizes functional sites in protein structures. FEATURE defines multi-level physico-chemical properties and recognizes sites based on the spatial distribution of these properties in the sites' microenvironments. It uses a Bayesian scoring function to compare a query region with the statistical profile built from known examples of sites and control nonsites. We have previously shown that FEATURE can accurately recognize calcium-binding sites and have reported interesting results scanning for calcium-binding sites in the entire Protein Data Bank. Here we report the ability of the improved FEATURE to characterize and recognize geometrically complex and asymmetric sites such as ATP-binding sites and disulfide bond-forming sites. FEATURE does not rely on conserved residues or conserved residue geometry of the sites. We also demonstrate that, in the absence of a statistical profile of the sites, FEATURE can use an artificially constructed profile based on a priori knowledge to recognize the sites in new structures, using redoxin active sites as an example.

scholarly journals UET: a database of evolutionarily-predicted functional determinants of protein sequences that cluster as functional sites in protein structures

2015 ◽  
Vol 44 (D1) ◽  
pp. D308-D312 ◽  
Author(s):  
Rhonald C. Lua ◽  
Stephen J. Wilson ◽  
Daniel M. Konecki ◽  
Angela D. Wilkins ◽  
Eric Venner ◽  
...  
Keyword(s):  
Protein Structures ◽  
Protein Sequences ◽  
Functional Sites

scholarly journals Matching of PDB chain sequences to information in public databases as a prerequisite for 3D functional site visualization

2004 ◽  
Vol 1 (1) ◽  
pp. 80-89
Author(s):  
Guido Dieterich ◽  
Dirk W. Heinz ◽  
Joachim Reichelt
Keyword(s):  
Genetic Disorders ◽  
Protein Structures ◽  
3D Structure ◽  
Data Bank ◽  
Mendelian Inheritance ◽  
Biological Information ◽  
Structure Comparison ◽  
3D Structures ◽  
Functional Sites ◽  
And Function

Abstract The 3D structures of biomacromolecules stored in the Protein Data Bank [1] were correlated with different external, biological information from public databases. We have matched the feature table of SWISS-PROT [2] entries as well InterPro [3] domains and function sites with the corresponding 3D-structures. OMIM [4] (Online Mendelian Inheritance in Man) records, containing information of genetic disorders, were extracted and linked to the structures. The exhaustive all-against-all 3D structure comparison of protein structures stored in DALI [5] was condensed into single files for each PDB entry. Results are stored in XML format facilitating its incorporation into related software. The resulting annotation of the protein structures allows functional sites to be identified upon visualization.

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