Recognizing protein-protein interfaces with empirical potentials and reduced amino acid alphabets.

International audienceBACKGROUND: In structural genomics, an important goal is the detection and classification of protein-protein interactions, given the structures of the interacting partners. We have developed empirical energy functions to identify native structures of protein-protein complexes among sets of decoy structures. To understand the role of amino acid diversity, we parameterized a series of functions, using a hierarchy of amino acid alphabets of increasing complexity, with 2, 3, 4, 6, and 20 amino acid groups. Compared to previous work, we used the simplest possible functional form, with residue-residue interactions and a stepwise distance-dependence. We used increased computational resources, however, constructing 290,000 decoys for 219 protein-protein complexes, with a realistic docking protocol where the protein partners are flexible and interact through a molecular mechanics energy function. The energy parameters were optimized to correctly assign as many native complexes as possible. To resolve the multiple minimum problem in parameter space, over 64000 starting parameter guesses were tried for each energy function. The optimized functions were tested by cross validation on subsets of our native and decoy structures, by blind tests on series of native and decoy structures available on the Web, and on models for 13 complexes submitted to the CAPRI structure prediction experiment. RESULTS: Performance is similar to several other statistical potentials of the same complexity. For example, the CAPRI target structure is correctly ranked ahead of 90% of its decoys in 6 cases out of 13. The hierarchy of amino acid alphabets leads to a coherent hierarchy of energy functions, with qualitatively similar parameters for similar amino acid types at all levels. Most remarkably, the performance with six amino acid classes is equivalent to that of the most detailed, 20-class energy function. CONCLUSION: This suggests that six carefully chosen amino acid classes are sufficient to encode specificity in protein-protein interactions, and provide a starting point to develop more complicated energy functions

Computational Protein Design: Validation and Possible Relevance as a Tool for Homology Searching and Fold Recognition

International audienceBACKGROUND: Protein fold recognition usually relies on a statistical model of each fold; each model is constructed from an ensemble of natural sequences belonging to that fold. A complementary strategy may be to employ sequence ensembles produced by computational protein design. Designed sequences can be more diverse than natural sequences, possibly avoiding some limitations of experimental databases. METHODOLOGY/PRINCIPAL FINDINGS: WE EXPLORE THIS STRATEGY FOR FOUR SCOP FAMILIES: Small Kunitz-type inhibitors (SKIs), Interleukin-8 chemokines, PDZ domains, and large Caspase catalytic subunits, represented by 43 structures. An automated procedure is used to redesign the 43 proteins. We use the experimental backbones as fixed templates in the folded state and a molecular mechanics model to compute the interaction energies between sidechain and backbone groups. Calculations are done with the Proteins@Home volunteer computing platform. A heuristic algorithm is used to scan the sequence and conformational space, yielding 200,000-300,000 sequences per backbone template. The results confirm and generalize our earlier study of SH2 and SH3 domains. The designed sequences ressemble moderately-distant, natural homologues of the initial templates; e.g., the SUPERFAMILY, profile Hidden-Markov Model library recognizes 85% of the low-energy sequences as native-like. Conversely, Position Specific Scoring Matrices derived from the sequences can be used to detect natural homologues within the SwissProt database: 60% of known PDZ domains are detected and around 90% of known SKIs and chemokines. Energy components and inter-residue correlations are analyzed and ways to improve the method are discussed. CONCLUSIONS/SIGNIFICANCE: For some families, designed sequences can be a useful complement to experimental ones for homologue searching. However, improved tools are needed to extract more information from the designed profiles before the method can be of general use

Virtual reality based approach to protein-protein docking problem

Proteins are large molecules that are vital for all living organisms and they are essential components of many industrial products. Protein-protein docking is the evaluation of binding of a protein to another via computer simulations. Many automated algorithms have been proposed to find docking configurations that might yield promising protein-protein complexes. However, these automated methods are likely to come up with false positives and have high computational costs. Consequently, Virtual Reality has been used to take advantage of user's experience on the problem. Haptic devices have been used for molecular docking problems; but they are inappropriate for protein-protein docking due to their workspace limitations and lack of sufficient information from force feedback. Instead of haptic rendering of forces, we provide two novel visual feedback methods for simulating physicochemical forces of proteins. We propose an interactive 3D application, DockPro, which enables domain experts to come up with dockings of protein-protein couples by using magnetic trackers and gloves in front of a large display

Recognizing protein–protein interfaces with empirical potentials and reduced amino acid alphabets-3

<p><b>Copyright information:</b></p><p>Taken from "Recognizing protein–protein interfaces with empirical potentials and reduced amino acid alphabets"</p><p>http://www.biomedcentral.com/1471-2105/8/270</p><p>BMC Bioinformatics 2007;8():270-270.</p><p>Published online 27 Jul 2007</p><p>PMCID:PMC2034607.</p><p></p>tein length (number of amino acids). The corresponding energy functions are those derived for fold recognition, using the Monomeric Optimization Set. The mean number of decoys is shown vs. protein length (grey bars; righthand graduations)

Recognizing protein–protein interfaces with empirical potentials and reduced amino acid alphabets-1

<p><b>Copyright information:</b></p><p>Taken from "Recognizing protein–protein interfaces with empirical potentials and reduced amino acid alphabets"</p><p>http://www.biomedcentral.com/1471-2105/8/270</p><p>BMC Bioinformatics 2007;8():270-270.</p><p>Published online 27 Jul 2007</p><p>PMCID:PMC2034607.</p><p></p>tein length (number of amino acids). The corresponding energy functions are those derived for fold recognition, using the Monomeric Optimization Set. The mean number of decoys is shown vs. protein length (grey bars; righthand graduations)

Recognizing protein–protein interfaces with empirical potentials and reduced amino acid alphabets-0

<p><b>Copyright information:</b></p><p>Taken from "Recognizing protein–protein interfaces with empirical potentials and reduced amino acid alphabets"</p><p>http://www.biomedcentral.com/1471-2105/8/270</p><p>BMC Bioinformatics 2007;8():270-270.</p><p>Published online 27 Jul 2007</p><p>PMCID:PMC2034607.</p><p></p>imized, 20-class energy matrix (left). The Pearson Correlation coefficent of each cluster is given for the lefthand tree

Recognizing protein–protein interfaces with empirical potentials and reduced amino acid alphabets-2

<p><b>Copyright information:</b></p><p>Taken from "Recognizing protein–protein interfaces with empirical potentials and reduced amino acid alphabets"</p><p>http://www.biomedcentral.com/1471-2105/8/270</p><p>BMC Bioinformatics 2007;8():270-270.</p><p>Published online 27 Jul 2007</p><p>PMCID:PMC2034607.</p><p></p>imized, 20-class energy matrix (left). The Pearson Correlation coefficent of each cluster is given for the lefthand tree