Protein structure sequence alignments

C. Dodge, R. Schneider, C. Sander. The HSSP database of protein structure-sequence alignments and family profiles. Nucleic Acids Res. 1998, 26, 313-315. 

Key Words Sequence alignment propensity protein structures sequence pattern secondary structure. 

Figure 6. A stereoview showing how bound cobalamin is displaced in the activation complex. The protein structures were aligned by matching atoms from the B]2-binding domains (displayed as ribbons). The cofactor from the activation conformation is represented by the thicker bonds, and the peptide sequence Ala-Met-Trp-Pro-Gly-Ala from the activation domain is drawn in ball-and-stick mode at the left. Overlaps between the corrin in its cap-on conformation (thin bonds) and atoms of Ala 1170 and Gly 1174 are avoided by the upward movement of the corrin macrocycle.

A sequence alignment establishes the correspondences between the amino adds in th unknown protein and the template protein (or proteins) from wliich it will be built. Th three-dimensional structures of two or more related proteins are conveniently divided int structurally conserved regions (SCRs) and structurally variable regions (SVRs). Ihe structural conserved regions correspond to those stretches of maximum sequence identity or sequenc... 

Barton G J1996. Protein Sequence Alignment and Database Scanning. In Sternberg M E (Editor) Prote Structure Prediction - A Practical Approach. Oxford, IRL Press, pp. 31-63. 

GJ Barton. Protein sequence alignment and database scanning. In MJE Sternberg, ed. Protein Structure Prediction A Practical Approach. Oxford, UK IRE Press at Oxford Univ Press, 1998. 

TF Flavel, ME Snow. A new method for building protein conformations from sequence alignments with homologues of known structure. J Mol Biol 217 1-7, 1991. 

C Sander, R Schneider. Database of homology-derived protein structures and the structural meaning of sequence alignment. Proteins 9 56-68, 1991. 

A prior distribution for sequence profiles can be derived from mixtures of Dirichlet distributions [16,51-54]. The idea is simple Each position in a multiple alignment represents one of a limited number of possible distributions that reflect the important physical forces that determine protein structure and function. In certain core positions, we expect to get a distribution restricted to Val, He, Met, and Leu. Other core positions may include these amino acids plus the large hydrophobic aromatic amino acids Phe and Trp. There will also be positions that are completely conserved, including catalytic residues (often Lys, GIu, Asp, Arg, Ser, and other polar amino acids) and Gly and Pro residues that are important in achieving certain backbone conformations in coil regions. Cys residues that form disulfide bonds or coordinate metal ions are also usually well conserved. 

Thompson and Goldstein [89] improve on the calculations of Stolorz et al. by including the secondary structure of the entire window rather than just a central position and then sum over all secondary strucmre segment types with a particular secondary structure at the central position to achieve a prediction for this position. They also use information from multiple sequence alignments of proteins to improve secondary structure prediction. They use Bayes rule to fonnulate expressions for the probability of secondary structures, given a multiple alignment. Their work describes what is essentially a sophisticated prior distribution for 6 i(X), where X is a matrix of residue counts in a multiple alignment in a window about a central position. The PDB data are used to form this prior, which is used as the predictive distribution. No posterior is calculated with posterior = prior X likelihood. 

MAS Saqi, PA Bates, MJE Sternberg. Towards an automatic method of predicting protein structure by homology An evaluation of suboptimal sequence alignments. Protein Eng 5 305-311, 1992. 

For each fold one searches for the best alignment of the target sequence that would be compatible with the fold the core should comprise hydrophobic residues and polar residues should be on the outside, predicted helical and strand regions should be aligned to corresponding secondary structure elements in the fold, and so on. In order to match a sequence alignment to a fold, Eisenberg developed a rapid method called the 3D profile method. The environment of each residue position in the known 3D structure is characterized on the basis of three properties (1) the area of the side chain that is buried by other protein atoms, (2) the fraction of side chain area that is covered by polar atoms, and (3) the secondary stmcture, which is classified in three states helix, sheet, and coil. The residue positions are rather arbitrarily divided into six classes by properties 1 and 2, which in combination with property 3 yields 18 environmental classes. This classification of environments enables a protein structure to be coded by a sequence in an 18-letter alphabet, in which each letter represents the environmental class of a residue position. 

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