Search PubMed⌕ Search
Home/Archive/PubMed

PubMed · 10091668

Protein structure comparison using iterated double dynamic programming.

W R Taylor
View original source ↗Export metadata ↓

Abstract

A protein structure comparison method is described that allows the generation of large populations of high-scoring alternate alignments. This was achieved by incorporating a random element into an iterative double dynamic programming algorithm. The maximum scores from repeated comparisons of a pair of structures converged on a value that was taken as the global maximum. This lay 15% over the score obtained from the single fixed (unrandomized) calculation. The effect of the gap penalty was observed through the shift of the alignment populations, characterized by their alignment length and root-mean-square deviation (RMSD). The best (lowest RMSD) values found in these populations provided a base-line against which other methods were compared.

Explore related subjects

Protein ConformationSequence AlignmentSoftwareComparative StudyJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

W R Taylor. 1999. Protein structure comparison using iterated double dynamic programming.. https://doi.org/10.1110/ps.8.3.654

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed1999

A physical basis for protein secondary structure.

A physical theory of protein secondary structure is proposed and tested by performing exceedingly simple Monte Carlo simulations. In essence, secondary structure propensities are predominantly a consequence of two competing local effects, one favoring hydrogen bond formation in helices and turns, the other opposing the attendant reduction in sidechain conformational entropy on helix and turn formation. These sequence specific biases are densely dispersed throughout the unfolded polypeptide chain, where they serve to preorganize the folding process and largely, but imperfectly, anticipate the native secondary structure.

Protein Conformation↗
PubMed1999

Atom density in protein structures.

The residue environment in protein structures is studied with respect to the density of carbon (C), oxygen (O), and nitrogen (N) atoms within a certain distance (say 5 A) of each residue. Two types of environments are evaluated: one based on side-chain atom contacts (abbreviated S-S) and the other based on all atom (side-chain + backbone) contacts (abbreviated A-A). Different atom counts are observed about nine-residue structural categories defined by three solvent accessibility levels and three secondary structure states. Among the structural categories, the S-S atom count ratios generally vary more than the A-A atom count ratios because of the fact that the backbone (O) and (N) atoms contribute equal counts. Secondary structure affects the (C) density for the A-A contacts whereas secondary structure has little influence on the (C) density for the S-S contacts. For S-S contacts, a greater density of (O) over (N) atom neighbors stands out in the environment of most amino acid types. By contrast, for A-A contacts, independent of the solvent accessibility levels, the ratio (O)/(N) is approximately 1 in helical states, consistent with the geometry of alpha-helical residues whose side-chains tilt oppositely to the amino to carboxy alpha-helical axis. The highest ratio of neighbor (O)/(N) is achieved under solvent exposed conditions. This (O) vs. (N) prevalence is advantageous at the protein surface that generally exhibits an acid excess that helps to enhance protein solubility in the cell and to avoid nonspecific interactions with phosphate groups of DNA, RNA, and other plasma constituents.

Protein Conformation↗
PubMed1999

Hierarchies and logarithmic oscillations in the temporal relaxation patterns of proteins and other complex systems.

Logarithmic oscillations superimposed on the temporal relaxation patterns of complex systems are considered from the standpoint of their hierarchical origin. We propose that a closer examination of experimental data should reveal logarithmic oscillations in systems that are characterized by a hierarchical structure of their dynamical degrees of freedom. On that footing, a new methodology of data analysis is proposed that may prove important for the dynamics of protein folding and of conformational fluctuations in proteins in which the relevant time scales of the dynamical evolution underlying the relaxation kinetics can be deduced from these oscillations.

Protein Conformation↗