Protein contact map

A protein contact map represents the distance between all possible amino acid residue pairs of a three-dimensional protein structure using a binary two-dimensional matrix. For two residues i {displaystyle i} and j {displaystyle j} , the i j {displaystyle ij} element of the matrix is 1 if the two residues are closer than a predetermined threshold, and 0 otherwise. Various contact definitions have been proposed: The distance between the Cα-Cα atom with threshold 6-12 Å; distance between Cβ-Cβ atoms with threshold 6-12 Å (Cα is used for Glycine); and distance between the side-chain centers of mass. A protein contact map represents the distance between all possible amino acid residue pairs of a three-dimensional protein structure using a binary two-dimensional matrix. For two residues i {displaystyle i} and j {displaystyle j} , the i j {displaystyle ij} element of the matrix is 1 if the two residues are closer than a predetermined threshold, and 0 otherwise. Various contact definitions have been proposed: The distance between the Cα-Cα atom with threshold 6-12 Å; distance between Cβ-Cβ atoms with threshold 6-12 Å (Cα is used for Glycine); and distance between the side-chain centers of mass. Contact maps provide a more reduced representation of a protein structure than its full 3D atomic coordinates. The advantage is that contact maps are invariant to rotations and translations. They are more easily predicted by machine learning methods. It has also been shown that under certain circumstances (e.g. low content of erroneously predicted contacts) it is possible to reconstruct the 3D coordinates of a protein using its contact map. Contact maps are also used for protein superimposition and to describe similarity between protein structures. They are either predicted from protein sequence or calculated from a given structure. With the availability of high numbers of genomic sequences it becomes feasible to analyze such sequences for coevolving residues. The effectiveness of this approach results from the fact that a mutation in position i of a protein is more likely to be associated with a mutation in position j than with a back-mutation in i if both positions are functionally coupled (e.g. by taking part in an enzymatic domain, or by being adjacent in a folded protein, or even by being adjacent in an oligomer of that protein). Several statistical methods exist to extract from a multiple sequence alignment such coupled residue pairs: observed versus expected frequencies of residue pairs (OMES); the McLachlan Based Substitution correlation (McBASC); statistical coupling analysis; Mutual Information (MI) based methods; and recently direct coupling analysis (DCA). Machine learning algorithms have been able to enhance MSA analysis methods, especially for non-homologous proteins (ie. shallow MSA's). Predicted contact maps have been used in the prediction of membrane proteins where helix-helix interactions are targeted. Knowledge of the relationship between a protein's structure and its dynamic behavior is essential for understanding protein function. The description of a protein three dimensional structure as a network of hydrogen bonding interactions (HB plot) was introduced as a tool for exploring protein structure and function. By analyzing the network of tertiary interactions the possible spread of information within a protein can be investigated. HB plot offers a simple way of analyzing protein secondary structure and tertiary structure. Hydrogen bonds stabilizing secondary structural elements (secondary hydrogen bonds) and those formed between distant amino acid residues - defined as tertiary hydrogen bonds - can be easily distinguished in HB plot, thus, amino acid residues involved in stabilizing protein structure and function can be identified.