Literature DB >> 23432543

Encoding protein-ligand interaction patterns in fingerprints and graphs.

Jérémy Desaphy1, Eric Raimbaud, Pierre Ducrot, Didier Rognan.   

Abstract

We herewith present a novel and universal method to convert protein-ligand coordinates into a simple fingerprint of 210 integers registering the corresponding molecular interaction pattern. Each interaction (hydrophobic, aromatic, hydrogen bond, ionic bond, metal complexation) is detected on the fly and physically described by a pseudoatom centered either on the interacting ligand atom, the interacting protein atom, or the geometric center of both interacting atoms. Counting all possible triplets of interaction pseudoatoms within six distance ranges, and pruning the full integer vector to keep the most frequent triplets enables the definition of a simple (210 integers) and coordinate frame-invariant interaction pattern descriptor (TIFP) that can be applied to compare any pair of protein-ligand complexes. TIFP fingerprints have been calculated for ca. 10,000 druggable protein-ligand complexes therefore enabling a wide comparison of relationships between interaction pattern similarity and ligand or binding site pairwise similarity. We notably show that interaction pattern similarity strongly depends on binding site similarity. In addition to the TIFP fingerprint which registers intermolecular interactions between a ligand and its target protein, we developed two tools (Ishape, Grim) to align protein-ligand complexes from their interaction patterns. Ishape is based on the overlap of interaction pseudoatoms using a smooth Gaussian function, whereas Grim utilizes a standard clique detection algorithm to match interaction pattern graphs. Both tools are complementary and enable protein-ligand complex alignments capitalizing on both global and local pattern similarities. The new fingerprint and companion alignment tools have been successfully used in three scenarios: (i) interaction-biased alignment of protein-ligand complexes, (ii) postprocessing docking poses according to known interaction patterns for a particular target, and (iii) virtual screening for bioisosteric scaffolds sharing similar interaction patterns.

Entities:  

Mesh:

Substances:

Year:  2013        PMID: 23432543     DOI: 10.1021/ci300566n

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  37 in total

1.  A benchmark driven guide to binding site comparison: An exhaustive evaluation using tailor-made data sets (ProSPECCTs).

Authors:  Christiane Ehrt; Tobias Brinkjost; Oliver Koch
Journal:  PLoS Comput Biol       Date:  2018-11-08       Impact factor: 4.475

2.  Binding site characterization - similarity, promiscuity, and druggability.

Authors:  Christiane Ehrt; Tobias Brinkjost; Oliver Koch
Journal:  Medchemcomm       Date:  2019-06-06       Impact factor: 3.597

3.  Binding site matching in rational drug design: algorithms and applications.

Authors:  Misagh Naderi; Jeffrey Mitchell Lemoine; Rajiv Gandhi Govindaraj; Omar Zade Kana; Wei Pan Feinstein; Michal Brylinski
Journal:  Brief Bioinform       Date:  2019-11-27       Impact factor: 11.622

4.  The role of drug profiles as similarity metrics: applications to repurposing, adverse effects detection and drug-drug interactions.

Authors:  Santiago Vilar; George Hripcsak
Journal:  Brief Bioinform       Date:  2017-07-01       Impact factor: 11.622

5.  Binding mode similarity measures for ranking of docking poses: a case study on the adenosine A2A receptor.

Authors:  Andrew Anighoro; Jürgen Bajorath
Journal:  J Comput Aided Mol Des       Date:  2016-06-22       Impact factor: 3.686

6.  Docking pose selection by interaction pattern graph similarity: application to the D3R grand challenge 2015.

Authors:  Inna Slynko; Franck Da Silva; Guillaume Bret; Didier Rognan
Journal:  J Comput Aided Mol Des       Date:  2016-08-01       Impact factor: 3.686

7.  AlphaSpace 2.0: Representing Concave Biomolecular Surfaces Using β-Clusters.

Authors:  Joseph Katigbak; Haotian Li; David Rooklin; Yingkai Zhang
Journal:  J Chem Inf Model       Date:  2020-02-11       Impact factor: 4.956

8.  Determining Cysteines Available for Covalent Inhibition Across the Human Kinome.

Authors:  Zheng Zhao; Qingsong Liu; Spencer Bliven; Lei Xie; Philip E Bourne
Journal:  J Med Chem       Date:  2017-04-04       Impact factor: 7.446

9.  Targeting the Side-Chain Convergence of Hydrophobic α-Helical Hot Spots To Design Small-Molecule Mimetics: Key Binding Features for i, i + 3, and i + 7.

Authors:  Zhen Wang; Haitao Ji
Journal:  J Med Chem       Date:  2019-10-22       Impact factor: 7.446

10.  The Development of Target-Specific Pose Filter Ensembles To Boost Ligand Enrichment for Structure-Based Virtual Screening.

Authors:  Jie Xia; Jui-Hua Hsieh; Huabin Hu; Song Wu; Xiang Simon Wang
Journal:  J Chem Inf Model       Date:  2017-06-01       Impact factor: 4.956

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.