Құрылымдық қатарластыру: Когнитивтік ғылым және молекулалық тізбектерді салыстыру

structural alignment in cognitive science
Aligning molecular sequences using sequence and structural information

Structural alignment attempts to establish homology between two or more polymer structures based on their shape and three dimensional conformation. This process is usually applied to protein tertiary structures but can also be used for large RNA molecules. In contrast to simple structural superposition, where at least some equivalent residues of the two structures are known, structural alignment requires no a priori knowledge of equivalent positions. Structural alignment is a valuable tool for the comparison of proteins with low sequence similarity, where evolutionary relationships between proteins cannot be easily detected by standard sequence alignment techniques. Structural alignment can therefore be used to imply evolutionary relationships between proteins that share very little common sequence. However, caution should be used in using the results as evidence for shared evolutionary ancestry because of the possible confounding effects of convergent evolution by which multiple unrelated amino acid sequences converge on a common tertiary structure. Structural alignments can compare two sequences or multiple sequences. Because these alignments rely on information about all the query sequences' three dimensional conformations, the method can only be used on sequences where these structures are known. These are usually found by X ray crystallography or NMR spectroscopy. It is possible to perform a structural alignment on structures produced by structure prediction methods. Indeed, evaluating such predictions often requires a structural alignment between the model and the true known structure to assess the model's quality. Choosing a representation to facilitate computation is critical to developing an efficient alignment mechanism.

Structural alignment techniques have been used in comparing individual structures or sets of structures as well as in the production of "all to all" comparison databases that measure the divergence between every pair of structures present in the Protein Data Bank (PDB). Such databases are used to classify proteins by their fold.

A common and popular structural alignment method is the DALI, or Distance matrix ALIgnment method, which breaks the input structures into hexapeptide fragments and calculates a distance matrix by evaluating the contact patterns between successive fragments. A number of similarity metrics are possible; the original definition of the CE method included only structural superpositions and inter residue distances but has since been expanded to include local environmental properties such as secondary structure, solvent exposure, hydrogen bonding patterns, and dihedral angles. approaches the alignment problem from a different objective than almost all other methods. Rather than trying to find an alignment that maximally superimposes the largest number of residues, it seeks the subset of the structural alignment least likely to occur by chance. To do this it marks a local motif alignment with flags to indicate which residues simultaneously satisfy more stringent criteria: 1) Local structure overlap 2) regular secondary structure 3) 3D superposition 4) same ordering in primary sequence. It converts the statistics of the number of residues with high confidence matches and the size of the protein to compute an Expectation value for the outcome by chance. It excels at matching remote homologs, particularly structures generated by ab initio structure prediction to structure families such as SCOP, because it emphasizes extracting a statistically reliable sub alignment and not in achieving the maximal sequence alignment or maximal 3D superposition. For every overlapping window of 7 consecutive residues it computes the set of displacement direction unit vectors between adjacent C alpha residues. All against all local motifs are compared based on the URMS score. These values becomes the pair alignment score entries for dynamic programming which produces a seed pair wise residue alignment. The second phase uses a modified MaxSub algorithm: a single 7 reside aligned pair in each proteins is used to orient the two full length protein structures to maximally superimpose these just these 7 C alpha, then in this orientation it scans for any additional aligned pairs that are close in 3D. It re orients the structures to superimpose this expanded set and iterates until no more pairs coincide in 3D. This process is restarted for every 7 residue window in the seed alignment. The output is the maximal number of atoms found from any of these initial seeds. This statistic is converted to a calibrated E value for the similarity of the proteins. Mammoth makes no attempt to re iterate the initial alignment or extend the high quality sub subset. Therefore, the seed alignment it displays can't be fairly compared to DALI or TM align as it was formed simply as a heuristic to prune the search space. (It can be used if one wants an alignment based solely on local structure motif similarity agnostic of long range rigid body atomic alignment.) Because of that same parsimony, it is well over ten times faster than DALI, CE and TM align. It is often used in conjunction with these slower tools to pre screen large data bases to extract the just the best E value related structures for more exhaustive superposition or expensive calculations. It has been particularly successful at analyzing "decoy" structures from ab initio structure prediction.

The SSAP (Sequential Structure Alignment Program) method uses double dynamic programming to produce a structural alignment based on atom to atom vectors in structure space. Instead of the alpha carbons typically used in structural alignment, SSAP constructs its vectors from the beta carbons for all residues except glycine, a method which thus takes into account the rotameric state of each residue as well as its location along the backbone. SSAP works by first constructing a series of inter residue distance vectors between each residue and its nearest non contiguous neighbors on each protein. A series of matrices are then constructed containing the vector differences between neighbors for each pair of residues for which vectors were constructed. Dynamic programming applied to each resulting matrix determines a series of optimal local alignments which are then summed into a "summary" matrix to which dynamic programming is applied again to determine the overall structural alignment. SSAP originally produced only pairwise alignments but has since been extended to multiple alignments as well. against binding sites or whole protein structural databases. The MultiBind and MAPPIS servers allow the identification of common spatial arrangements of physicochemical properties such as H bond donor, acceptor, aliphatic, aromatic or hydrophobic in a set of user provided protein binding sites defined by interactions with small molecules (MultiBind) or in a set of user provided protein–protein interfaces (MAPPIS). Others provide comparison of entire protein structures against a number of user submitted structures or against a large database of protein structures in reasonable time (ProBiS). Unlike global alignment approaches, local structural alignment approaches are suited to detection of locally conserved patterns of functional groups, which often appear in binding sites and have significant involvement in ligand binding. a local structure alignment tool, with TM align, a global structure alignment based method. While G Losa predicts drug like ligands’ positions in single chain protein targets more precisely than TM align, the overall success rate of TM align is better. However, as algorithmic improvements and computer performance have erased purely technical deficiencies in older approaches, it has become clear that there is no one universal criterion for the 'optimal' structural alignment. TM align, for instance, is particularly robust in quantifying comparisons between sets of proteins with great disparities in sequence lengths, but it only indirectly captures hydrogen bonding or secondary structure order conservation which might be better metrics for alignment of evolutionarily related proteins. Thus recent developments have focused on optimizing particular attributes such as speed, quantification of scores, correlation to alternative gold standards, or tolerance of imperfection in structural data or ab initio structural models. An alternative methodology that is gaining popularity is to use the consensus of various methods to ascertain proteins structural similarities.

Structural alignment techniques have traditionally been applied exclusively to proteins, as the primary biological macromolecules that assume characteristic three dimensional structures. However, large RNA molecules also form characteristic tertiary structures, which are mediated primarily by hydrogen bonds formed between base pairs as well as base stacking. Functionally similar noncoding RNA molecules can be especially difficult to extract from genomics data because structure is more strongly conserved than sequence in RNA as well as in proteins,