Literature DB >> 24852252

PredHS: a web server for predicting protein-protein interaction hot spots by using structural neighborhood properties.

Lei Deng1, Qiangfeng Cliff Zhang2, Zhigang Chen3, Yang Meng3, Jihong Guan4, Shuigeng Zhou5.   

Abstract

Identifying specific hot spot residues that contribute significantly to the affinity and specificity of protein interactions is a problem of the utmost importance. We present an interactive web server, PredHS, which is based on an effective structure-based hot spot prediction method. The PredHS prediction method integrates many novel structural and energetic features with two types of structural neighborhoods (Euclidian and Voronoi), and combines random forest and sequential backward elimination algorithms to select an optimal subset of features. PredHS achieved the highest performance identifying hot spots compared with other state-of-the-art methods, as benchmarked by using an independent experimentally verified dataset. The input to PredHS is protein structures in the PDB format with at least two chains that form interfaces. Users can visualize their predictions in an interactive 3D viewer and download the results as text files. PredHS is available at http://www.predhs.org.
© The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

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Year:  2014        PMID: 24852252      PMCID: PMC4086081          DOI: 10.1093/nar/gku437

Source DB:  PubMed          Journal:  Nucleic Acids Res        ISSN: 0305-1048            Impact factor:   16.971


  33 in total

1.  PiQSi: protein quaternary structure investigation.

Authors:  Emmanuel D Levy
Journal:  Structure       Date:  2007-11       Impact factor: 5.006

2.  Fragment-based identification of druggable 'hot spots' of proteins using Fourier domain correlation techniques.

Authors:  Ryan Brenke; Dima Kozakov; Gwo-Yu Chuang; Dmitri Beglov; David Hall; Melissa R Landon; Carla Mattos; Sandor Vajda
Journal:  Bioinformatics       Date:  2009-01-28       Impact factor: 6.937

3.  Identification of computational hot spots in protein interfaces: combining solvent accessibility and inter-residue potentials improves the accuracy.

Authors:  Nurcan Tuncbag; Attila Gursoy; Ozlem Keskin
Journal:  Bioinformatics       Date:  2009-04-08       Impact factor: 6.937

4.  An automated decision-tree approach to predicting protein interaction hot spots.

Authors:  Steven J Darnell; David Page; Julie C Mitchell
Journal:  Proteins       Date:  2007-09-01

5.  PCRPi: Presaging Critical Residues in Protein interfaces, a new computational tool to chart hot spots in protein interfaces.

Authors:  Salam A Assi; Tomoyuki Tanaka; Terence H Rabbitts; Narcis Fernandez-Fuentes
Journal:  Nucleic Acids Res       Date:  2009-12-11       Impact factor: 16.971

6.  Prediction of protein-protein interaction sites using an ensemble method.

Authors:  Lei Deng; Jihong Guan; Qiwen Dong; Shuigeng Zhou
Journal:  BMC Bioinformatics       Date:  2009-12-16       Impact factor: 3.169

7.  Protein-protein interaction hotspots carved into sequences.

Authors:  Yanay Ofran; Burkhard Rost
Journal:  PLoS Comput Biol       Date:  2007-07       Impact factor: 4.475

8.  A feature-based approach to modeling protein-protein interaction hot spots.

Authors:  Kyu-il Cho; Dongsup Kim; Doheon Lee
Journal:  Nucleic Acids Res       Date:  2009-03-09       Impact factor: 16.971

9.  HotSprint: database of computational hot spots in protein interfaces.

Authors:  Emre Guney; Nurcan Tuncbag; Ozlem Keskin; Attila Gursoy
Journal:  Nucleic Acids Res       Date:  2007-10-24       Impact factor: 16.971

10.  Identification of hot-spot residues in protein-protein interactions by computational docking.

Authors:  Solène Grosdidier; Juan Fernández-Recio
Journal:  BMC Bioinformatics       Date:  2008-10-21       Impact factor: 3.169

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  21 in total

1.  Rapid prediction of crucial hotspot interactions for icosahedral viral capsid self-assembly by energy landscape atlasing validated by mutagenesis.

Authors:  Ruijin Wu; Rahul Prabhu; Aysegul Ozkan; Meera Sitharam
Journal:  PLoS Comput Biol       Date:  2020-10-20       Impact factor: 4.475

2.  The FTMap family of web servers for determining and characterizing ligand-binding hot spots of proteins.

Authors:  Dima Kozakov; Laurie E Grove; David R Hall; Tanggis Bohnuud; Scott E Mottarella; Lingqi Luo; Bing Xia; Dmitri Beglov; Sandor Vajda
Journal:  Nat Protoc       Date:  2015-04-09       Impact factor: 13.491

3.  Methods for Discovering and Targeting Druggable Protein-Protein Interfaces and Their Application to Repurposing.

Authors:  E Sila Ozdemir; Farideh Halakou; Ruth Nussinov; Attila Gursoy; Ozlem Keskin
Journal:  Methods Mol Biol       Date:  2019

4.  Computational Methods and Deep Learning for Elucidating Protein Interaction Networks.

Authors:  Dhvani Sandip Vora; Yogesh Kalakoti; Durai Sundar
Journal:  Methods Mol Biol       Date:  2023

5.  Virtual screening for inhibitors of the human TSLP:TSLPR interaction.

Authors:  Dries Van Rompaey; Kenneth Verstraete; Frank Peelman; Savvas N Savvides; Koen Augustyns; Pieter Van Der Veken; Hans De Winter
Journal:  Sci Rep       Date:  2017-12-08       Impact factor: 4.379

6.  Accurate prediction of functional effects for variants by combining gradient tree boosting with optimal neighborhood properties.

Authors:  Yuliang Pan; Diwei Liu; Lei Deng
Journal:  PLoS One       Date:  2017-06-14       Impact factor: 3.240

7.  Discovery of peptide inhibitors targeting human programmed death 1 (PD-1) receptor.

Authors:  Qiao Li; Lina Quan; Jiankun Lyu; Zenghui He; Xia Wang; Jiajia Meng; Zhenjiang Zhao; Lili Zhu; Xiaofeng Liu; Honglin Li
Journal:  Oncotarget       Date:  2016-10-04

8.  PredRSA: a gradient boosted regression trees approach for predicting protein solvent accessibility.

Authors:  Chao Fan; Diwei Liu; Rui Huang; Zhigang Chen; Lei Deng
Journal:  BMC Bioinformatics       Date:  2016-01-11       Impact factor: 3.169

9.  A boosting approach for prediction of protein-RNA binding residues.

Authors:  Yongjun Tang; Diwei Liu; Zixiang Wang; Ting Wen; Lei Deng
Journal:  BMC Bioinformatics       Date:  2017-12-01       Impact factor: 3.169

Review 10.  Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery.

Authors:  Stephani Joy Y Macalino; Shaherin Basith; Nina Abigail B Clavio; Hyerim Chang; Soosung Kang; Sun Choi
Journal:  Molecules       Date:  2018-08-06       Impact factor: 4.411

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