ArticleBioinformatics (Oxford, England)2020
A knowledge-based scoring function to assess quaternary associations of proteins.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Crystal structures of the sweet-tasting protein honey truffle active component from Mattirolomyces terfezioides.Acta crystallographica. Section F, Structural biology communications · 2026Article
- Protein engineering: status report.Protein engineering, design & selection : PEDS · 2026Review
- A comprehensive survey of scoring functions for protein docking models.BMC bioinformatics · 2025Article
- Structure-based learning to predict and model protein-DNA interactions and transcription-factor co-operativity inNAR genomics and bioinformatics · 2024Article
- Characterizing and explaining the impact of disease-associated mutations in proteins without known structures or structural homologs.Briefings in bioinformatics · 2022Article
- Identifying vaccine escape sites via statistical comparisons of short-term molecular dynamics.Biophysical reports · 2022Article
- Interplay between p300 and HDAC1 regulate acetylation and stability of Api5 to regulate cell proliferation.Scientific reports · 2021Article
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Authors and funding
3 authors.
Funding
Abstract
motivationThe elucidation of all inter-protein interactions would significantly enhance our knowledge of cellular processes at a molecular level. Given the enormity of the problem, the expenses and limitations of experimental methods, it is imperative that this problem is tackled computationally. In silico predictions of protein interactions entail sampling different conformations of the purported complex and then scoring these to assess for interaction viability. In this study, we have devised a new scheme for scoring protein-protein interactions.
resultsOur method, PIZSA (Protein Interaction Z-Score Assessment), is a binary classification scheme for identification of native protein quaternary assemblies (binders/nonbinders) based on statistical potentials. The scoring scheme incorporates residue-residue contact preference on the interface with per residue-pair atomic contributions and accounts for clashes. PIZSA can accurately discriminate between native and non-native structural conformations from protein docking experiments and outperform other contact-based potential scoring functions. The method has been extensively benchmarked and is among the top 6 methods, outperforming 31 other statistical, physics based and machine learning scoring schemes. The PIZSA potentials can also distinguish crystallization artifacts from biological interactions. AVAILABILITY AND IMPLEMENTATION: PIZSA is implemented as a web server at http://cospi.iiserpune.ac.in/pizsa and can be downloaded as a standalone package from http://cospi.iiserpune.ac.in/pizsa/Download/Download.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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