Cited by 277Protein Structure and DynamicsEnzyme Structure and FunctionMachine Learning in Bioinformatics

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Protein structure prediction: when is it useful?

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Zhang, Yang·2009·Current Opinion in Structural Biology

DOI 10.1016/j.sbi.2009.02.005PMC PMC2673339

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Abstract

Computationally predicted three-dimensional structure of protein molecules has demonstrated the usefulness in many areas of biomedicine, ranging from approximate family assignments to precise drug screening. For nearly 40 years, however, the accuracy of the predicted models has been dictated by the availability of close structural templates. Progress has recently been achieved in refining low-resolution models closer to the native ones; this has been made possible by combining knowledge-based information from multiple sources of structural templates as well as by improving the energy funnel of physics-based force fields. Unfortunately, there has been no essential progress in the development of techniques for detecting remotely homologous templates and for predicting novel protein structures.

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Authors: Zhang, Y.
Affiliations: University of Kansas (US)
Corresponding author: Zhang, Yang
ORCID: Zhang, Yang
Venue: Current Opinion in Structural Biology
Publisher: Elsevier BV
Published: 2009-04
Volume: 19
Issue: 2
Pages: 145-155
ISSN: 0959-440X (ISSN-L)
DOI: 10.1016/j.sbi.2009.02.005
PMC: PMC2673339
OpenAlex: W2140564438
Cited by: 277
References: 57
Citations by year: 2026: 3 · 2025: 6 · 2024: 1 · 2023: 7 · 2022: 9 · 2021: 17 · 2020: 16 · 2019: 21 · 2018: 14 · 2017: 22 · 2016: 16 · 2015: 22 · 2014: 18 · 2013: 23 · 2012: 24
FWCI: 2.09
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Current Opinion in Structural Biology (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
KU ScholarWorks (University of Kansas) (submittedVersion, cc-by-nc-nd)
Funders: National Institute of General Medical Sciences (R01GM083107)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Keywords: protein structure prediction, template-based structure prediction, structural templates, knowledge-based potentials, force fields, remote homology detection, drug screening
MeSH terms: Algorithms, Computer Simulation, Humans, Models, Molecular, Protein Conformation, Proteins, Structure-Activity Relationship, Computational Biology, Databases, Protein

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