iAlign: a method for the structural comparison of protein–protein interfaces
Open accessAbstract
Motivation: Protein–protein interactions play an essential role in many cellular processes. The rapid accumulation of protein–protein complex structures provides an unprecedented opportunity for comparative studies of protein–protein interactions. To facilitate such studies, it is necessary to develop an accurate and efficient computational algorithm for the comparison of protein–protein interaction modes. While there are many structural comparison approaches developed for individual proteins, very few methods are available for protein–protein complexes. Results: We present a novel interface alignment method, iAlign, for the structural alignment of protein–protein interfaces. New scoring schemes for measuring interface similarity are introduced, and an iterative dynamic programming algorithm is implemented. We find that the similarity scores follow extreme value distributions. Using statistical models, we empirically estimate their statistical significance, which is in good agreement with manual classifications by human experts. Large-scale tests of iAlign were conducted on both artificial docking models and experimental structures. In a benchmark test on 1517 dimers, iAlign successfully detects biologically related, structurally similar protein–protein interfaces at a coverage percentage of 90% and an error per query of 0.05. When compared against previously published methods, iAlign is substantially more accurate and efficient. Availability: The iAlign software package is freely available at http://cssb.biology.gatech.edu/iAlign Contact: skolnick@gatech.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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The structural space of protein-protein interfaces is small (<1000 structurally distinct interfaces), with nothing special about protein-protein interfaces.
“In practice, the structural space of protein–protein interfaces is small (<1000 structurally distinct interfaces),41,42 with nothing special about protein–protein interfaces.43”
From AlphaFold 2: Why It Works and Its Implications for Understanding the Relationships of Protein Sequence, Structure, and Function (2021) · cites this paper as [42]
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- Authors
- Gao, M., & Skolnick, J.
- Affiliations
- Georgia Institute of Technology (US)
- Corresponding author
- Skolnick, Jeffrey
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2010-07-11
- Volume
- 26
- Issue
- 18
- Pages
- 2259-2265
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC2935406
- OpenAlex
- W2160902835
- Cited by
- 98
- References
- 41
- Citations by year
- 2026: 6 · 2025: 4 · 2024: 8 · 2023: 5 · 2022: 4 · 2021: 5 · 2020: 4 · 2019: 5 · 2018: 5 · 2017: 3 · 2016: 10 · 2015: 7 · 2014: 4 · 2013: 12 · 2012: 7
- FWCI
- 3.65
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health
- National Institute of General Medical Sciences (R01GM037408, GM-48835, R01GM048835)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Materials Science › Materials Chemistry › Enzyme Structure and Function
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- protein-protein interfaces, structural alignment, protein-protein interaction, protein-protein complex, extreme value distribution, dynamic programming alignment
- MeSH terms
- Algorithms, Humans, Male, Protein Conformation, Proteins, Software, Models, Statistical, Sequence Alignment, Benchmarking
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