Literature DB >> 21974955

Protein binding site analysis by means of structural interaction fingerprint patterns.

Stefan Mordalski1, Tomasz Kosciolek, Kurt Kristiansen, Ingebrigt Sylte, Andrzej J Bojarski.   

Abstract

We introduce a new approach to the known concept of interaction profiles, based on Structural Interaction Fingerprints (SIFt), for precise and rapid binding site description. A set of scripts for batch generation and analysis of SIFt were prepared, and the implementation is computationally efficient and supports parallelization. It is based on a 9-digit binary interaction pattern that describes physical ligand-protein interactions in structures and models of ligand-protein complexes. The tool performs analysis and identifies binding site residues (crucial and auxiliary) and classifies interactions according to type (hydrophobic, aromatic, charge, polar, side chain, and backbone). It is convenient and easy to use, and gives manageable output data for both, interpretation and further processing. In the presented Letter, SIFts are applied to analyze binding sites in models of antagonist-5-HT7 receptor complexes and structures of cyclin dependent kinase 2-ligand complexes.
Copyright © 2011 Elsevier Ltd. All rights reserved.

Mesh:

Substances:

Year:  2011        PMID: 21974955     DOI: 10.1016/j.bmcl.2011.09.027

Source DB:  PubMed          Journal:  Bioorg Med Chem Lett        ISSN: 0960-894X            Impact factor:   2.823


  11 in total

1.  Proteochemometric modeling of the antigen-antibody interaction: new fingerprints for antigen, antibody and epitope-paratope interaction.

Authors:  Tianyi Qiu; Han Xiao; Qingchen Zhang; Jingxuan Qiu; Yiyan Yang; Dingfeng Wu; Zhiwei Cao; Ruixin Zhu
Journal:  PLoS One       Date:  2015-04-22       Impact factor: 3.240

2.  Delineation of Polypharmacology across the Human Structural Kinome Using a Functional Site Interaction Fingerprint Approach.

Authors:  Zheng Zhao; Li Xie; Lei Xie; Philip E Bourne
Journal:  J Med Chem       Date:  2016-03-17       Impact factor: 7.446

3.  From Homology Models to a Set of Predictive Binding Pockets-a 5-HT1A Receptor Case Study.

Authors:  Dawid Warszycki; Manuel Rueda; Stefan Mordalski; Kurt Kristiansen; Grzegorz Satała; Krzysztof Rataj; Zdzisław Chilmonczyk; Ingebrigt Sylte; Ruben Abagyan; Andrzej J Bojarski
Journal:  J Chem Inf Model       Date:  2017-01-18       Impact factor: 4.956

4.  Bioinformatics and computational biology in Poland.

Authors:  Janusz M Bujnicki; Jerzy Tiuryn
Journal:  PLoS Comput Biol       Date:  2013-05-02       Impact factor: 4.475

5.  An Algorithm to Identify Target-Selective Ligands - A Case Study of 5-HT7/5-HT1A Receptor Selectivity.

Authors:  Rafał Kurczab; Vittorio Canale; Paweł Zajdel; Andrzej J Bojarski
Journal:  PLoS One       Date:  2016-06-07       Impact factor: 3.240

6.  Practical application of the Average Information Content Maximization (AIC-MAX) algorithm: selection of the most important structural features for serotonin receptor ligands.

Authors:  Dawid Warszycki; Marek Śmieja; Rafał Kafel
Journal:  Mol Divers       Date:  2017-02-09       Impact factor: 2.943

7.  Ligand-guided homology modelling of the GABAB2 subunit of the GABAB receptor.

Authors:  Thibaud Freyd; Dawid Warszycki; Stefan Mordalski; Andrzej J Bojarski; Ingebrigt Sylte; Mari Gabrielsen
Journal:  PLoS One       Date:  2017-03-21       Impact factor: 3.240

8.  Prediction of sensitivity to gefitinib/erlotinib for EGFR mutations in NSCLC based on structural interaction fingerprints and multilinear principal component analysis.

Authors:  Bin Zou; Victor H F Lee; Hong Yan
Journal:  BMC Bioinformatics       Date:  2018-03-07       Impact factor: 3.169

Review 9.  Structure-based protein-ligand interaction fingerprints for binding affinity prediction.

Authors:  Debby D Wang; Moon-Tong Chan; Hong Yan
Journal:  Comput Struct Biotechnol J       Date:  2021-11-25       Impact factor: 7.271

10.  Characterization of the PH1704 protease from Pyrococcus horikoshii OT3 and the critical functions of Tyr120.

Authors:  Dongling Zhan; Aixi Bai; Lei Yu; Weiwei Han; Yan Feng
Journal:  PLoS One       Date:  2014-09-05       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.