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Protein structure prediction: when is it useful?
Open accessAbstract
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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“Very high confidence predictions in the metagenomic atlas are expected to often be reliable at a resolution sufficient for insight similar to experimentally determined structures, such as into the biochemistry of active sites (61);”
From Evolutionary-scale prediction of atomic level protein structure with a language model (2022) · cites this paper as [61]
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Self-citation
Metadata
- 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)
- 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
- 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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