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Correlated Mutations in Models of Protein Sequences: Phylogenetic and Structural Effects

Alan S. Lapedes, Bertrand G. Giraud, LonChang Liu and Gary D. Stormo
Lecture Notes-Monograph Series
Vol. 33, Statistics in Molecular Biology and Genetics (1999), pp. 236-256
Stable URL: http://www.jstor.org/stable/4356049
Page Count: 21
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Correlated Mutations in Models of Protein Sequences: Phylogenetic and Structural Effects
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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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