Visible volume: a robust measure for protein structure characterization

Effective knowledge-based potentials

Empirical or knowledge-based potentials have many applications in structural biology such as the prediction of protein structure, protein-protein, and protein-ligand interactions and in the evaluation of stability for mutant proteins, the assessment of errors in experimentally solved structures, and the design of new proteins. Here, we describe a simple procedure to derive and use pairwise distance-dependent potentials that rely on the definition of effective atomic interactions, which attempt to capture interactions that are more likely to be physically relevant. Based on a difficult benchmark test composed of proteins with different secondary structure composition and representing many different folds, we show that the use of effective atomic interactions significantly improves the performance of potentials at discriminating between native and near-native conformations. We also found that, in agreement with previous reports, the potentials derived from the observed effective atomic interactions in native protein structures contain a larger amount of mutual information. A detailed analysis of the effective energy functions shows that atom connectivity effects, which mostly arise when deriving the potential by the incorporation of those indirect atomic interactions occurring beyond the first atomic shell, are clearly filtered out. The shape of the energy functions for direct atomic interactions representing hydrogen bonding and disulfide and salt bridges formation is almost unaffected when effective interactions are taken into account. On the contrary, the shape of the energy functions for indirect atom interactions (i.e., those describing the interaction between two atoms bound to a direct interacting pair) is clearly different when effective interactions are considered. Effective energy functions for indirect interacting atom pairs are not influenced by the shape or the energy minimum observed for the corresponding direct interacting atom pair. Our results suggest that the dependency between the signals in different energy functions is a key aspect that need to be addressed when empirical energy functions are derived and used, and also highlight the importance of additivity assumptions in the use of potential energy functions.

A substitution matrix for structural alphabet based on structural alignment of homologous proteins and its applications

Analysis of protein structures based on backbone structural patterns known as structural alphabets have been shown to be very useful. Among them, a set of 16 pentapeptide structural motifs known as protein blocks (PBs) has been identified and upon which backbone model of most protein structures can be built. PBs allows simplification of 3D space onto 1D space in the form of sequence of PBs. Here, for the first time, substitution probabilities of PBs in a large number of aligned homologous protein structures have been studied and are expressed as a simplified 16 x 16 substitution matrix. The matrix was validated by benchmarking how well it can align sequences of PBs rather like amino acid alignment to identify structurally equivalent regions in closely or distantly related proteins using dynamic programming approach. The alignment results obtained are very comparable to well established structure comparison methods like DALI and STAMP. Other interesting applications of the matrix have been investigated. We first show that, in variable regions between two superimposed homologous proteins, one can distinguish between local conformational differences and rigid-body displacement of a conserved motif by comparing the PBs and their substitution scores. Second, we demonstrate, with the example of aspartic proteinases, that PBs can be efficiently used to detect the lobe/domain flexibility in the multidomain proteins. Lastly, using protein kinase as an example, we identify regions of conformational variations and rigid body movements in the enzyme as it is changed to the active state from an inactive state.

Assessing functional divergence in EF-1alpha and its paralogs in eukaryotes and archaebacteria

A number of methods have recently been published that use phylogenetic information extracted from large multiple sequence alignments to detect sites that have changed properties in related protein families. In this study we use such methods to assess functional divergence between eukaryotic EF-1alpha (eEF-1alpha), archaebacterial EF-1alpha (aEF-1alpha) and two eukaryote-specific EF-1alpha paralogs-eukaryotic release factor 3 (eRF3) and Hsp70 subfamily B suppressor 1 (HBS1). Overall, the evolutionary modes of aEF-1alpha, HBS1 and eRF3 appear to significantly differ from that of eEF-1alpha. However, functionally divergent (FD) sites detected between aEF-1alpha and eEF-1alpha only weakly overlap with sites implicated as putative EF-1beta or aminoacyl-tRNA (aa-tRNA) binding residues in EF-1alpha, as expected based on the shared ancestral primary translational functions of these two orthologs. In contrast, FD sites detected between eEF-1alpha and its paralogs significantly overlap with the putative EF-1beta and/or aa-tRNA binding sites in EF-1alpha. In eRF3 and HBS1, these sites appear to be released from functional constraints, indicating that they bind neither eEF-1beta nor aa-tRNA. These results are consistent with experimental observations that eRF3 does not bind to aa-tRNA, but do not support the 'EF-1alpha-like' function recently proposed for HBS1. We re-assess the available genetic data for HBS1 in light of our analyses, and propose that this protein may function in stop codon-independent peptide release.

Inferring functional constraints and divergence in protein families using 3D mapping of phylogenetic information

Comparative sequence analysis has been used to study specific questions about the structure and function of proteins for many years. Here we propose a knowledge-based framework in which the maximum likelihood rate of evolution is used to quantify the level of constraint on the identity of a site. We demonstrate that site-rate mapping on 3D structures using datasets of rhodopsin-like G-protein receptors and alpha- and beta-tubulins provides an excellent tool for pinpointing the functional features shared between orthologous and paralogous proteins. In addition, functional divergence within protein families can be inferred by examining the differences in the site rates, the differences in the chemical properties of the side chains or amino acid usage between aligned sites. Two novel analytical methods are introduced to characterize rate- independent functional divergence. These are tested using a dataset of two classes of HMG-CoA reductases for which only one class can perform both the forward and reverse reaction. We show that functionally divergent sites occur in a cluster of sites interacting with the catalytic residues and that this information should facilitate the design of experimental strategies to directly test functional properties of residues.

The directional atomic solvation energy: an atom-based potential for the assignment of protein sequences to known folds

The Directional Atomic Solvation EnergY (DASEY) is an atom-based description of the environment of an amino acid position within a known 3D protein structure. The DASEY has been developed to align and score a probe amino acid sequence to a library of template protein structures for fold assignment. DASEY is computed by summing the atomic solvation parameters of atoms falling within a tetrahedral sector, or petal, extending 16 A along each of the four bond axes of each alpha-carbon atom of the protein. The DASEY discriminates between pairs of structurally equivalent positions and random pairs in protein structures sharing a fold but belonging to different superfamilies, unlike some previous descriptors of protein environments, such as buried area. Furthermore, the DASEY values have characteristic patterns of residue replacement, an essential feature of a successful fold assignment method. Benchmarking fold assignment with DASEY achieves coverage of 56% of sequences with 90% accuracy when probe sequences are matched to protein structural templates belonging to the same fold but to a different superfamily, an improvement of greater than 200% over a previous method.

Performance of threading scoring functions designed using new optimization method

We present a new procedure for optimization of a threading scoring function. A scoring function is usually formulated in terms of the structural environment states that describe the protein fold model. We propose a method for the optimal selection of those structural environment states that naturally follows from the probabilistic description of the threading problem and is done prior to threading experiments. We demonstrate the selection of the optimal structural environment states for the solvent exposure of the amino acid position, and present the results of threading experiments performed using scoring functions designed with and without the optimization of the structural environment states. These results confirm that the optimal scoring function predicts the sequence-to-structure alignments most accurately. Threading experiments performed with 15 optimally designed scoring functions show that the correlation coefficient between the information content of the amino acid distribution that determines the scoring function and the accuracy of the optimal sequence-to-structure alignment is 0.94.