The classification of amino acid conservation.

By William R. Taylor. 1986

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Generating functional protein variants with variational autoencoders (2020, background): “…(K, R and H) cluster tightly together (Fig 2), while the second principal component seems to separate the hydrophobic amino acids (both the hydrophobic and aromatic groups in the legend) from the polar amino acids, recapit-ulating the major groupings in traditional classification schemes [47].”
PhosContext2vec: a distributed representation of residue-level sequence contexts and its application to general and kinase-specific phosphorylation site prediction (2018, background): “Each amino acid is encoded into 10 bits representing 10 physiochemical properties respectively 32 ; 6.”
Clustered Sequence Representation for Fast Homology Search (2007, unknown): “We used the amino acid classiﬁcations described in Taylor (1986) to deﬁne the set of seven wildcards shown in the right-hand column of Table 1.”
Prediction of protein secondary structure and active sites using the alignment of homologous sequences. (1987, methodology): “More than 3700 protein sequences are known (e.g. see Barker et al., 1986) and this wealth of biological information has highlighted the need for accurate and automatic methods to predict protein conformation and function from primary structure (for reviews, see Sternberg, lg86; Taylor, 1986a).”
Artificial neural networks for computer-based molecular design. (1998, background): “A useful grouping of amino acid residues has been conceived by Taylor (1986) (Fig.”
Specificity prediction of adenylation domains in nonribosomal peptide synthetases (NRPS) using transductive support vector machines (TSVMs) (2005, background): “Chemical properties chosen were the number of hydrogen bond donors, (49).”
BMGE (Block Mapping and Gathering with Entropy): a new software for selection of phylogenetic informative regions from multiple sequence alignments (2010, background): “…then formula (1) with matrix I r returns h ( c 1 ) = h ( c 2 ) = -4× 0.25 log 20 0.25 ≈ 0.462, and then indicates the same level of variability for both characters c 1 and c 2 , while residues in character c 1 are much more likely to be substituted than those in character c 2 [27,37,38].”
Protein database searches using compositionally adjusted substitution matrices (2005, background): “Over time, many different rationales were advanced for constructing such matrices [2–8], based on a variety of considerations, such as the genetic code and amino acid physico-chemical properties.”
Amino‐Acid Properties and Consequences of Substitutions (2003, background): “Taylor presented a classification that explains mutation data through correlation with the physical, chemical and structural properties of amino acids (Taylor, 1986).”
A candidate gene for fire blight resistance in Malus ×robusta 5 is coding for a CC–NBS–LRR (2013, background): “istics: aliphatic (I and L) as well as hydrophobic and nonpolar (I, C, L) (Kyte and Doolittle 1982; Taylor 1986).”