Evidence map›Paper›PMID 25638036›Full record

ArticleBMC bioinformatics2015

PCalign: a method to quantify physicochemical similarity of protein-protein interfaces.

Shanshan Cheng, Yang Zhang, Charles L Brooks

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Article in BMC bioinformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

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11citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Shanshan ChengDepartment of Computational Medicine and Bioinformatics, Medical School, University of Michigan, Ann Arbor, MI, USA. sscheng@umich.edu.
Yang ZhangDepartment of Computational Medicine and Bioinformatics, Medical School, University of Michigan, Ann Arbor, MI, USA. zhng@umich.edu.
Charles L BrooksDepartment of Computational Medicine and Bioinformatics, Medical School, University of Michigan, Ann Arbor, MI, USA. brookscl@umich.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundStructural comparison of protein-protein interfaces provides valuable insights into the functional relationship between proteins, which may not solely arise from shared evolutionary origin. A few methods that exist for such comparative studies have focused on structural models determined at atomic resolution, and may miss out interesting patterns present in large macromolecular complexes that are typically solved by low-resolution techniques.

resultsWe developed a coarse-grained method, PCalign, to quantitatively evaluate physicochemical similarities between a given pair of protein-protein interfaces. This method uses an order-independent algorithm, geometric hashing, to superimpose the backbone atoms of a given pair of interfaces, and provides a normalized scoring function, PC-score, to account for the extent of overlap in terms of both geometric and chemical characteristics. We demonstrate that PCalign outperforms existing methods, and additionally facilitates comparative studies across models of different resolutions, which are not accommodated by existing methods. Furthermore, we illustrate potential application of our method to recognize interesting biological relationships masked by apparent lack of structural similarity.

conclusionsPCalign is a useful method in recognizing shared chemical and spatial patterns among protein-protein interfaces. It outperforms existing methods for high-quality data, and additionally facilitates comparison across structural models with different levels of details with proven robustness against noise.

Indexed as

AlgorithmsSoftwareChemical PhenomenaHumansModels, MolecularProtein BindingProteinsProteins

Identifiers

PMID25638036
PMCPMC4339745

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.