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Correlated mutations in models of protein sequences: phylogenetic and structural effects
Abstract
Covariation analysis of sets of aligned sequences for RNA molecules is relatively successful in elucidating RNA secondary structure, as well as some aspects of tertiary structure [Gutell et al. (1992)].Covariation analysis of sets of aligned sequences for protein molecules is successful in certain instances in elucidating certain structural and functional links [Korber et al. (1993)], but in general, pairs of sites displaying highly covarying mutations in protein sequences do not necessarily correspond to sites that are spatially close in the protein structure [Gobel et al. (1994), Clarke (1995), Shindyalov et al. (1994), Thomas et al. (1996), Taylor &;Hatrick (1994), Neher (1994)].In this paper we identify two reasons why naive use of covariation analysis for protein sequences fails to reliably indicate sequence positions that are spatially proximate.The first reason involves the bias introduced in calculation of covariation measures due to the fact that biological sequences are generally related by a non-trivial phylogenetic tree.We present a null-model approach to solve this problem.The second reason involves linked chains of covariation which can result in pairs of sites displaying significant covariation even though they are not spatially proximate.We present a maximum entropy solution to this classic problem of "causation versus correlation".The methodologies are validated in simulation.
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“Within a protein family, structure and function can be inferred from the patterns in sequences (4, 5).”
From Evolutionary-scale prediction of atomic level protein structure with a language model (2022) · cites this paper as [4]
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Evolutionary-scale prediction of atomic level protein structure with a language model
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Protein 3D Structure Computed from Evolutionary Sequence Variation
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Large language models generate functional protein sequences across diverse families
Madani, A., Krause, B., Greene, E. R., Subramanian, S., Mohr, B. P., Holton, J. M., Olmos, J. L., Xiong, C., Sun, Z. Z., Socher, R., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01618-2 · Cited by 1,078
Mutation effects predicted from sequence co-variation
Hopf, T. A., Ingraham, J. B., Poelwijk, F. J., Schärfe, C. P. I., Springer, M., Sander, C., & Marks, D. S. (2017) · Nature Biotechnology · doi:10.1038/nbt.3769 · Cited by 775
Metadata
- Authors
- Lapedes, A. S., Giraud, B., Liu, L., & Stormo, G. D.
- Affiliations
- Santa Fe Institute (US)
- Venue
- Institute of Mathematical Statistics Lecture Notes - Monograph Series
- Publisher
- Institute of Mathematical Statistics
- Published
- 1999
- Pages
- 236-256
- ISSN
- 0749-2170 (ISSN-L)
- OpenAlex
- W1514077080
- Cited by
- 124
- References
- 30
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 6 · 2023: 7 · 2022: 4 · 2021: 13 · 2020: 9 · 2019: 16 · 2018: 9 · 2017: 8 · 2016: 9 · 2015: 7 · 2014: 14 · 2013: 7 · 2012: 3
- FWCI
- 0.00
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Lecture notes-monograph series (publishedVersion)
- Project Euclid (Cornell University) (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Keywords
- correlated mutations, covariation analysis, protein sequences, phylogenetic bias, null models, correlation versus causation, maximum entropy, protein structure, protein tertiary structure
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