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 ] 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

Korth (2011) Empirical hydrogen-bond potential functions--an old hat reconditioned. Chemphyschem 12:3131-42. (pmid: 22038888)

[ PubMed ] [ DOI ] The accurate description of hydrogen-bond interactions is of vital importance for the computational modeling of biological systems. Standard force field (FF) as well as semiempirical quantum mechanical (SQM) methods are now known to have considerable problems with the accurate description of hydrogen bonds. It was found that the performance of SQM methods can be greatly improved with empirical hydrogen-bond correction terms. In the first part of this work we review the improvements developed during the recent revival of dedicated hydrogen-bond terms, also in the light of earlier FF-related work. The second part presents new findings connected to open questions in this field, namely, a study on the importance of angular and torsional information, a scheme how to avoid atom-type-defined target angles and a reduced version of our DH(+) model for the application to force-field methods and physically motivated protein-ligand scoring functions. Our results highlight the importance of using a complete geometric description (including angular and torsional coordinates) for the accurate treatment of hydrogen bonding. The reduced DH(+) model-applied to a modified version of the UFF force field-shows a much improved accuracy for non-covalent interactions also with FF methods, with gains in accuracy by more than one order of magnitude.

Morozov & Kortemme (2005) Potential functions for hydrogen bonds in protein structure prediction and design. Adv Protein Chem 72:1-38. (pmid: 16581371)

[ PubMed ] [ DOI ] Hydrogen bonds are an important contributor to free energies of biological macromolecules and macromolecular complexes, and hence an accurate description of these interactions is important for progress in biomolecular modeling. A simple description of the hydrogen bond is based on an electrostatic dipole-dipole interaction involving hydrogen-donor and acceptor-acceptor base dipoles, but the physical nature of hydrogen bond formation is more complex. At the most fundamental level, hydrogen bonding is a quantum mechanical phenomenon with contributions from covalent effects, polarization, and charge transfer. Recent experiments and theoretical calculations suggest that both electrostatic and covalent components determine the properties of hydrogen bonds. Likely, the level of rigor required to describe hydrogen bonding will depend on the problem posed. Current approaches to modeling hydrogen bonds include knowledge-based descriptions based on surveys of hydrogen bond geometries in structural databases of proteins and small molecules, empirical molecular mechanics models, and quantum mechanics-based electronic structure calculations. Ab initio calculations of hydrogen bonding energies and geometries accurately reproduce energy landscapes obtained from the distributions of hydrogen bond geometries observed in protein structures. Orientation-dependent hydrogen bonding potentials were found to improve the quality of protein structure prediction and refinement, protein-protein docking, and protein design.

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 ] 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.