Evidence map›Paper›PMID 25232727›Full record

ArticlePLoS computational biology2014

eMatchSite: sequence order-independent structure alignments of ligand binding pockets in protein models.

Michal Brylinski

Abstract read
In one paragraph

Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

  1. Beyond sequence: Structure-based machine learning.Computational and structural biotechnology journal · 2023
    Review
  2. Article
  3. Estimating the Similarity between Protein Pockets.International journal of molecular sciences · 2022
    Review
  4. Review
  5. Review
  6. Article
  7. 3D-PP: A Tool for Discovering Conserved Three-Dimensional Protein Patterns.International journal of molecular sciences · 2019
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Michal BrylinskiDepartment of Biological Sciences, Louisiana State University, Baton Rouge, Louisiana, United States of America; Center for Computation & Technology, Louisiana State University, Baton Rouge, Louisiana, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Detecting similarities between ligand binding sites in the absence of global homology between target proteins has been recognized as one of the critical components of modern drug discovery. Local binding site alignments can be constructed using sequence order-independent techniques, however, to achieve a high accuracy, many current algorithms for binding site comparison require high-quality experimental protein structures, preferably in the bound conformational state. This, in turn, complicates proteome scale applications, where only various quality structure models are available for the majority of gene products. To improve the state-of-the-art, we developed eMatchSite, a new method for constructing sequence order-independent alignments of ligand binding sites in protein models. Large-scale benchmarking calculations using adenine-binding pockets in crystal structures demonstrate that eMatchSite generates accurate alignments for almost three times more protein pairs than SOIPPA. More importantly, eMatchSite offers a high tolerance to structural distortions in ligand binding regions in protein models. For example, the percentage of correctly aligned pairs of adenine-binding sites in weakly homologous protein models is only 4-9% lower than those aligned using crystal structures. This represents a significant improvement over other algorithms, e.g. the performance of eMatchSite in recognizing similar binding sites is 6% and 13% higher than that of SiteEngine using high- and moderate-quality protein models, respectively. Constructing biologically correct alignments using predicted ligand binding sites in protein models opens up the possibility to investigate drug-protein interaction networks for complete proteomes with prospective systems-level applications in polypharmacology and rational drug repositioning. eMatchSite is freely available to the academic community as a web-server and a stand-alone software distribution at http://www.brylinski.org/ematchsite.

Indexed as

SoftwareAlgorithmsDatabases, ProteinProtein BindingProteinsProteomicsSequence AlignmentSequence Analysis, ProteinProteins

Identifiers

PMID25232727
PMCPMC4168975

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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.