Protein structure interpretation

Protein structure interpretation

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Summary ...

 

Contents

Superposition

Mechelke & Habeck (2010) Robust probabilistic superposition and comparison of protein structures. BMC Bioinformatics 11:363. (pmid: 20594332)

[ PubMed ] [ DOI ] Abstract BACKGROUND: Protein structure comparison is a central issue in structural bioinformatics. The standard dissimilarity measure for protein structures is the root mean square deviation (RMSD) of representative atom positions such as alpha-carbons. To evaluate the RMSD the structures under comparison must be superimposed optimally so as to minimize the RMSD. How to evaluate optimal fits becomes a matter of debate, if the structures contain regions which differ largely--a situation encountered in NMR ensembles and proteins undergoing large-scale conformational transitions. RESULTS: We present a probabilistic method for robust superposition and comparison of protein structures. Our method aims to identify the largest structurally invariant core. To do so, we model non-rigid displacements in protein structures with outlier-tolerant probability distributions. These distributions exhibit heavier tails than the Gaussian distribution underlying standard RMSD minimization and thus accommodate highly divergent structural regions. The drawback is that under a heavy-tailed model analytical expressions for the optimal superposition no longer exist. To circumvent this problem we work with a scale mixture representation, which implies a weighted RMSD. We develop two iterative procedures, an Expectation Maximization algorithm and a Gibbs sampler, to estimate the local weights, the optimal superposition, and the parameters of the heavy-tailed distribution. Applications demonstrate that heavy-tailed models capture differences between structures undergoing substantial conformational changes and can be used to assess the precision of NMR structures. By comparing Bayes factors we can automatically choose the most adequate model. Therefore our method is parameter-free. CONCLUSIONS: Heavy-tailed distributions are well-suited to describe large-scale conformational differences in protein structures. A scale mixture representation facilitates the fitting of these distributions and enables outlier-tolerant superposition.

http://www.ks.uiuc.edu/Research/vmd/script_library/scripts/splitmultiframepdb/splitmultiframepdb.tcl

   

Further reading and resources

Wang (2005) Covariation analysis of local amino acid sequences in recurrent protein local structures. J Bioinform Comput Biol 3:1391-409. (pmid: 16374913)

[ PubMed ] [ DOI ] Abstract Local structural information is supposed to be frequently encoded in local amino acid sequences. Previous research only indicated that some local structure positions have specific residue preferences in some particular local structures. However, correlated pairwise replacements for interacting residues in recurrent local structural motifs from unrelated proteins have not been studied systematically. We introduced a new method fusing statistical covariation analysis and local structure-based alignment. Systematic analysis of structure-based multiple alignments of recurrent local structures from unrelated proteins in representative subset of Protein Databank indicates that covarying residue pairs with statistical significance exist in local structural motifs, in particular beta-turns and helix caps. These residue pairs are mostly linked through polar functional groups with direct or indirect hydrogen bonding. Hydrophobic interaction is also a major factor in constraining pairwise amino acid residue replacement in recurrent local structures. We also found correlated residue pairs that are not clearly linked with through-space interactions. The physical constrains underlying these covariations are less clear. Overall, covarying residue pairs with statistical significance exist in local structures from unrelated proteins. The existence of sequence covariations in local structural motifs from unrelated proteins indicates that many relics of local relations are still retained in the tertiary structures after protein folding. It supports the notion that some local structural information is encoded in local sequences and the local structural codes could play important roles in determining native state protein folding topology.