Protein Structure Prediction and Structural Genomics
Abstract
Genome sequencing projects are producing linear amino acid sequences, but full understanding of the biological role of these proteins will require knowledge of their structure and function. Although experimental structure determination methods are providing high-resolution structure information about a subset of the proteins, computational structure prediction methods will provide valuable information for the large fraction of sequences whose structures will not be determined experimentally. The first class of protein structure prediction methods, including threading and comparative modeling, rely on detectable similarity spanning most of the modeled sequence and at least one known structure. The second class of methods, de novo or ab initio methods, predict the structure from sequence alone, without relying on similarity at the fold level between the modeled sequence and any of the known structures. In this Viewpoint, we begin by describing the essential features of the methods, the accuracy of the models, and their application to the prediction and understanding of protein function, both for single proteins and on the scale of whole genomes. We then discuss the important role that protein structure prediction methods play in the growing worldwide effort in structural genomics.
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AlphaFold does not resolve the protein folding problem and does not identify folding pathways.
“AlphaFold does not resolve the decades-long protein folding challenge, nor does it identify the folding pathways.”
From AlphaFold, Artificial Intelligence (AI), and Allostery (2022) · cites this paper as [53]
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- Authors
- Baker, D., & Sali, A.
- Affiliations
- Howard Hughes Medical Institute (US)
- Laboratory of Molecular Biophysics, Pels Family Center for Biochemistry and Structural Biology, The Rockefeller University, New York, NY 10021, USA..
- ORCID
- Baker, David
- Venue
- Science
- Published
- 2001-10-05
- Volume
- 294
- Issue
- 5540
- Pages
- 93-96
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2170471837
- Cited by
- 1,587
- References
- 38
- Citations by year
- 2026: 12 · 2025: 30 · 2024: 30 · 2023: 47 · 2022: 37 · 2021: 46 · 2020: 47 · 2019: 49 · 2018: 57 · 2017: 74 · 2016: 62 · 2015: 62 · 2014: 63 · 2013: 76 · 2012: 72
- FWCI
- 25.84
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of General Medical Sciences (GM54762)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- protein structure prediction, structural genomics, homology modeling, threading, ab initio structure prediction, protein function, model accuracy
- MeSH terms
- Amino Acid Sequence, Animals, Binding Sites, Computer Simulation, Humans, Models, Molecular, Protein Conformation, Proteins, Software, Templates, Genetic, Databases, Factual, Sequence Alignment, Protein Structure, Tertiary, Protein Folding, Computational Biology, Internet, Genomics
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