Literature DB >> 18036612

Molecular docking for substrate identification: the short-chain dehydrogenases/reductases.

Angelo D Favia1, Irene Nobeli, Fabian Glaser, Janet M Thornton.   

Abstract

Protein ligand docking has recently been investigated as a tool for protein function identification, with some success in identifying both known and unknown substrates of proteins. However, identifying a protein's substrate when cross-docking a large number of enzymes and their cognate ligands remains a challenge. To explore a more limited yet practically important and timely problem in more detail, we have used docking for identifying the substrates of a single protein family with remarkable substrate diversity, the short-chain dehydrogenases/reductases. We examine different protocols for identifying candidate substrates for 27 short-chain dehydrogenase/reductase proteins of known catalytic function. We present the results of docking >900 metabolites from the human metabolome to each of these proteins together with their known cognate substrates and products, and we investigate the ability of docking to (a) reproduce a viable binding mode for the substrate and (b) to rank the substrate highly amongst the dataset of other metabolites. In addition, we examine whether our docking results provide information about the nature of the substrate, based on the best-scoring metabolites in the dataset. We compare two different docking methods and two alternative scoring functions for one of the docking methods, and we attempt to rationalise both successes and failures. Finally, we introduce a new protocol, whereby we dock only a set of representative structures (medoids) to each of the proteins, in the hope of characterising each binding site in terms of its ligand preferences, with a reduced computational cost. We compare the results from this protocol with our original docking experiments, and we find that although the rank of the representatives correlates well with the mean rank of the clusters to which they belong, a simple structure-based clustering is too naive for the purpose of substrate identification. Many clusters comprise ligands with widely varying affinities for the same protein; hence important candidates can be missed if a single representative is used.

Entities:  

Mesh:

Substances:

Year:  2007        PMID: 18036612     DOI: 10.1016/j.jmb.2007.10.065

Source DB:  PubMed          Journal:  J Mol Biol        ISSN: 0022-2836            Impact factor:   5.469


  25 in total

1.  Homology models guide discovery of diverse enzyme specificities among dipeptide epimerases in the enolase superfamily.

Authors:  Tiit Lukk; Ayano Sakai; Chakrapani Kalyanaraman; Shoshana D Brown; Heidi J Imker; Ling Song; Alexander A Fedorov; Elena V Fedorov; Rafael Toro; Brandan Hillerich; Ronald Seidel; Yury Patskovsky; Matthew W Vetting; Satish K Nair; Patricia C Babbitt; Steven C Almo; John A Gerlt; Matthew P Jacobson
Journal:  Proc Natl Acad Sci U S A       Date:  2012-03-05       Impact factor: 11.205

Review 2.  Protein promiscuity and its implications for biotechnology.

Authors:  Irene Nobeli; Angelo D Favia; Janet M Thornton
Journal:  Nat Biotechnol       Date:  2009-02       Impact factor: 54.908

3.  Missing in action: enzyme functional annotations in biological databases.

Authors:  Nicholas Furnham; John S Garavelli; Rolf Apweiler; Janet M Thornton
Journal:  Nat Chem Biol       Date:  2009-08       Impact factor: 15.040

Review 4.  Exploring the structure and function paradigm.

Authors:  Oliver C Redfern; Benoit Dessailly; Christine A Orengo
Journal:  Curr Opin Struct Biol       Date:  2008-06       Impact factor: 6.809

Review 5.  Leveraging structure for enzyme function prediction: methods, opportunities, and challenges.

Authors:  Matthew P Jacobson; Chakrapani Kalyanaraman; Suwen Zhao; Boxue Tian
Journal:  Trends Biochem Sci       Date:  2014-07-02       Impact factor: 13.807

Review 6.  Virtual screening applications in short-chain dehydrogenase/reductase research.

Authors:  Katharina R Beck; Teresa Kaserer; Daniela Schuster; Alex Odermatt
Journal:  J Steroid Biochem Mol Biol       Date:  2017-03-09       Impact factor: 4.292

7.  Protein pockets: inventory, shape, and comparison.

Authors:  Ryan G Coleman; Kim A Sharp
Journal:  J Chem Inf Model       Date:  2010-04-26       Impact factor: 4.956

8.  Assignment of pterin deaminase activity to an enzyme of unknown function guided by homology modeling and docking.

Authors:  Hao Fan; Daniel S Hitchcock; Ronald D Seidel; Brandan Hillerich; Henry Lin; Steven C Almo; Andrej Sali; Brian K Shoichet; Frank M Raushel
Journal:  J Am Chem Soc       Date:  2013-01-02       Impact factor: 15.419

9.  Relating the shape of protein binding sites to binding affinity profiles: is there an association?

Authors:  Zoltán Simon; Margit Vigh-Smeller; Agnes Peragovics; Gábor Csukly; Gergely Zahoránszky-Kohalmi; Anna A Rauscher; Balázs Jelinek; Péter Hári; István Bitter; András Málnási-Csizmadia; Pál Czobor
Journal:  BMC Struct Biol       Date:  2010-10-05

Review 10.  Exploiting structural classifications for function prediction: towards a domain grammar for protein function.

Authors:  Benoît H Dessailly; Oliver C Redfern; Alison Cuff; Christine A Orengo
Journal:  Curr Opin Struct Biol       Date:  2009-04-22       Impact factor: 6.809

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.