Chapter 22: A model of evolutionary change in proteins

1978

Citations in the corpus

• A Survey on Metric Learning for Feature Vectors and Structured Data (2013, neutral): “For some applications, such as protein alignment or handwritten digit recognition, handtuned cost matrices may be available (Dayhoff et al., 1978; Henikoff and Henikoff, 1992; Micó and Oncina, 1998).”
• Phylogenetic Analysis by Maximum Likelihood (PAML) (2002, methodology): “The empirical matrices of Dayhoff et al. (1978) and Jones et al.”
• Improving the accuracy of PSI-BLAST protein database searches with composition-based statistics and other refinements. (2001, methodology): “For the PAM matrices (16,17) we inferred these values from other published data, while for the BLOSUM matrices (18) we obtained these values from the authors, S.”
• TREE-PUZZLE: maximum likelihood phylogenetic analysis using quartets and parallel computing (2002, methodology): “Models for protein sequences: Dayhoff (Dayhoff et al., 1978), JTT (Jones et al., 1992), mtREV24 (Adachi and Hasegawa, 1996), BLOSUM 62 (Henikoff and Henikoff, 1992), VT (Müller and Vingron, 2000), WAG (Whelan and Goldman, 2001).”
• ConSurf: an algorithmic tool for the identification of functional regions in proteins by surface mapping of phylogenetic information. (2001, background): “acid replacement matrices, such as that by Dayhoff et al.(45) Notwithstanding the use of physicochemical”
• A model of substitution trajectories in sequence space and long-term protein evolution. (2015, methodology): “Amino acid sequence divergence models often use a precomputed matrix of amino acid substitution rates, such as BLOSUM or PAM (Dayhoff et al. 1978; Henikoff S and Henikoff JG 1992; Whelan and Goldman 2001).”
• A general empirical model of protein evolution derived from multiple protein families using a maximum-likelihood approach. (2001, methodology): “Dayhoff and colleagues (Dayhoff, Eck, and Park 1972; Dayhoff, Schwartz, and Orcutt 1978) used a parsimony-based counting method to generate accepted point mutation (PAM) matrices from the limited amount of protein sequence data available at the time.”
• ProbCons: Probabilistic consistency-based multiple sequence alignment. (2005, methodology): “Quantitatively, scores for aligned residues are given by log-odds (Altschul 1991) substitution matrices such as PAM (Dayhoff 1978), GONNET (Gonnet et al. 1992), or BLOSUM (Henikoff and Henikoff 1992).”
• Tools for integrated sequence-structure analysis with UCSF Chimera (2006, background): “Alignment scores can include residue similarity, secondary structure information, and gap penalties: Residue similarity Any of several common substitution matrices (BLOSUM [11] or PAM [12]) can be used.”