Dataset Information

Optimized atomic statistical potentials: assessment of protein interfaces and loops.

ABSTRACT:

Motivation

Statistical potentials have been widely used for modeling whole proteins and their parts (e.g. sidechains and loops) as well as interactions between proteins, nucleic acids and small molecules. Here, we formulate the statistical potentials entirely within a statistical framework, avoiding questionable statistical mechanical assumptions and approximations, including a definition of the reference state.

Results

We derive a general Bayesian framework for inferring statistically optimized atomic potentials (SOAP) in which the reference state is replaced with data-driven 'recovery' functions. Moreover, we restrain the relative orientation between two covalent bonds instead of a simple distance between two atoms, in an effort to capture orientation-dependent interactions such as hydrogen bonds. To demonstrate this general approach, we computed statistical potentials for protein-protein docking (SOAP-PP) and loop modeling (SOAP-Loop). For docking, a near-native model is within the top 10 scoring models in 40% of the PatchDock benchmark cases, compared with 23 and 27% for the state-of-the-art ZDOCK and FireDock scoring functions, respectively. Similarly, for modeling 12-residue loops in the PLOP benchmark, the average main-chain root mean square deviation of the best scored conformations by SOAP-Loop is 1.5 Å, close to the average root mean square deviation of the best sampled conformations (1.2 Å) and significantly better than that selected by Rosetta (2.1 Å), DFIRE (2.3 Å), DOPE (2.5 Å) and PLOP scoring functions (3.0 Å). Our Bayesian framework may also result in more accurate statistical potentials for additional modeling applications, thus affording better leverage of the experimentally determined protein structures.

Availability and implementation

SOAP-PP and SOAP-Loop are available as part of MODELLER (http://salilab.org/modeller).

SUBMITTER: Dong GQ

PROVIDER: S-EPMC3842762 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature

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Publications

Optimized atomic statistical potentials: assessment of protein interfaces and loops.

Dong Guang Qiang GQ Fan Hao H Schneidman-Duhovny Dina D Webb Ben B Sali Andrej A

Bioinformatics (Oxford, England) 20130927 24

<h4>Motivation</h4>Statistical potentials have been widely used for modeling whole proteins and their parts (e.g. sidechains and loops) as well as interactions between proteins, nucleic acids and small molecules. Here, we formulate the statistical potentials entirely within a statistical framework, avoiding questionable statistical mechanical assumptions and approximations, including a definition of the reference state.<h4>Results</h4>We derive a general Bayesian framework for inferring statisti ...[more]

PMID: 24078704

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Project description:BackgroundIn 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.ResultsPerformance 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.ConclusionThis 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.

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Project description:Understanding protein-protein interactions (PPIs) at the molecular level may lead to innovations in medicine and biochemistry. The assumption that there are certain "hot spots" on protein surfaces that mediate their interactions with other proteins has led to a search for specific sequences involved in protein-protein contacts. In this work, we analyze sequential amino acid motifs, both at the single motif and at the motif-motif level, across a large and diverse dataset of biologically relevant protein-protein interfaces retrieved from the PDB, comparing their presence at interfaces and surfaces in a statistically rigorous manner. At the single motif level, our results indicate statistically significant over-presence of hydrophobic and in particular aromatic residues and under-presence of charged residues at protein-protein interfaces. Certain PPI-mediating motifs reported in the literature (e.g., the Tyrosine-based Motif YxxΦ and the PDZ-Binding Motif X-S/T-X-V/I) were confirmed to have a significant presence at interfaces. In addition, multiple PPI-mediating motifs were reported in the ELM database and from those present in our dataset, half were confirmed to have a statistically significant presence at interfaces whereas others were not. At the single residue, motif-motif level, Cysteine-Cysteine contacts were found to be the most abundant ones followed by interactions involving aromatic/hydrophobic residues. Top ranking, longer motif-motif pairs show predominance of Leucine and aromatic residues. Finally, preliminary energy calculations (using the MM/GBSA procedure) indicate a partial correlation between the probability of motifs-pair to be a part of a protein-protein interface and the strength of the interactions between the motifs. In conclusion, this study points to specific characteristics of motifs that have a higher probability to mediate protein-protein interactions. Prominent motifs identified in this study may be used in the future as possible components in protein engineering.

Dataset Information

Optimized atomic statistical potentials: assessment of protein interfaces and loops.

Motivation

Results

Availability and implementation

Publications

Optimized atomic statistical potentials: assessment of protein interfaces and loops.

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