Knowledge-Based Potential for Positioning Membrane-Associated Structures and Assessing Residue-Specific Energetic Contributions

The complex hydrophobic and hydrophilic milieus of membrane-associated proteins pose experimental and theoretical challenges to their understanding. Here we produce a non-redundant database to compute knowledge-based asymmetric cross-membrane potentials from the per-residue distributions of Cβ, Cγ and functional group atoms. We predict transmembrane and peripherally associated regions from genomic sequence and position peptides and protein structures relative to the bilayer (available at http://www.degradolab.org/ez). The pseudo-energy topological landscapes underscore positional stability and functional mechanisms demonstrated here for antimicrobial peptides, transmembrane proteins, and viral fusion proteins. Moreover, experimental effects of point mutations on the relative ratio changes of dual-topology proteins are quantitatively reproduced. The functional group potential and the membrane-exposed residues display the largest energetic changes enabling to detect native-like structures from decoys. Hence, focusing on the uniqueness of membrane-associated proteins and peptides, we quantitatively parameterize their cross-membrane propensity thus facilitating structural refinement, characterization, prediction and design

An analysis and evaluation of the WeFold collaborative for protein structure prediction and its pipelines in CASP11 and CASP12

Every two years groups worldwide participate in the Critical Assessment of Protein Structure Prediction (CASP) experiment to blindly test the strengths and weaknesses of their computational methods. CASP has significantly advanced the field but many hurdles still remain, which may require new ideas and collaborations. In 2012 a web-based effort called WeFold, was initiated to promote collaboration within the CASP community and attract researchers from other fields to contribute new ideas to CASP. Members of the WeFold coopetition (cooperation and competition) participated in CASP as individual teams, but also shared components of their methods to create hybrid pipelines and actively contributed to this effort. We assert that the scale and diversity of integrative prediction pipelines could not have been achieved by any individual lab or even by any collaboration among a few partners. The models contributed by the participating groups and generated by the pipelines are publicly available at the WeFold website providing a wealth of data that remains to be tapped. Here, we analyze the results of the 2014 and 2016 pipelines showing improvements according to the CASP assessment as well as areas that require further adjustments and research

An analysis and evaluation of the WeFold collaborative for protein structure prediction and its pipelines in CASP11 and CASP12

Every two years groups worldwide participate in the Critical Assessment of Protein Structure Prediction (CASP) experiment to blindly test the strengths and weaknesses of their computational methods. CASP has significantly advanced the field but many hurdles still remain, which may require new ideas and collaborations. In 2012 a web-based effort called WeFold, was initiated to promote collaboration within the CASP community and attract researchers from other fields to contribute new ideas to CASP. Members of the WeFold coopetition (cooperation and competition) participated in CASP as individual teams, but also shared components of their methods to create hybrid pipelines and actively contributed to this effort. We assert that the scale and diversity of integrative prediction pipelines could not have been achieved by any individual lab or even by any collaboration among a few partners. The models contributed by the participating groups and generated by the pipelines are publicly available at the WeFold website providing a wealth of data that remains to be tapped. Here, we analyze the results of the 2014 and 2016 pipelines showing improvements according to the CASP assessment as well as areas that require further adjustments and research

A novel approach to decoy set generation: Designing a physical energy function having local minima with native structure characteristics

Ab initio protein structure prediction: global optimization versus divide and conquer After almost four decades of intense research, the knights of computational structural biolog

Empirical modifications to the Amber/OPLS potential for predicting the solution conformations of cyclic peptides by vacuum calculations

Background: Peptides have ubiquitous roles in all biological systems and are thus of interest in both basic and applied research. The rational design of bioactive peptides could be greatly enhanced by an efficient method for accurately predicting the conformations that these molecules can adopt in solution. As a design process inevitably requires testing numerous molecules, an efficient method would require the calculations to be performed in vacuum.Results: Attempts to predict the conformations of cyclic peptides using a simulated annealing protocol with the Amber/OPLS potential in vacuum resulted, not unexpectedly, in overly packed, non-native conformations. We therefore empirically modified the potential by several cycles of structure prediction and function refinement until a good fit between experimental and predicted conformations was obtained. Three major modifications to the potential were required in order to reproduce the solution structures of cyclic peptides: explicit torsional energies for the peptide backbone torsional angles; explicit hydrogen-bonding energies for backbone hydrogen bonds; and a penalty for close approaches between uncharged and charged atoms.Conclusions:Using the modified potential, we predicted the solution conformations of cyclic peptides in the size range of 5–10 residues with reasonable accuracy

Prediction of structural stability of short beta-hairpin peptides by molecular dynamics and knowledge-based potentials

<p>Abstract</p> <p>Background</p> <p>The structural stability of peptides in solution strongly affects their binding affinities and specificities. Thus, in peptide biotechnology, an increase in the structural stability is often desirable. The present work combines two orthogonal computational techniques, Molecular Dynamics and a knowledge-based potential, for the prediction of structural stability of short peptides (< 20 residues) in solution.</p> <p>Results</p> <p>We tested the new approach on four families of short β-hairpin peptides: TrpZip, MBH, bhpW and EPO, whose structural stabilities have been experimentally measured in previous studies. For all four families, both computational techniques show considerable correlation (r > 0.65) with the experimentally measured stabilities. The consensus of the two techniques shows higher correlation (r > 0.82).</p> <p>Conclusion</p> <p>Our results suggest a prediction scheme that can be used to estimate the relative structural stability within a peptide family. We discuss the applicability of this predictive approach for in-silico screening of combinatorial peptide libraries.</p

Redundancy-weighting for better inference of protein structural features

ABSTRACT Motivation: Structural knowledge, extracted from the Protein Data Bank (PDB), underlies numerous potential functions and prediction methods. The PDB, however, is highly biased: many proteins have more than one entry, while entire protein families are represented by a single structure, or even not at all. The standard solution to this problem is to limit the studies to non-redundant subsets of the PDB. While alleviating biases, this solution hides the many-to-many relations between sequences and structures. That is, non-redundant data-sets conceal the diversity of sequences that share the same fold and the existence of multiple conformations for the same protein. A particularly disturbing aspect of non-redundant subsets is that they hardly benefit from the rapid pace of protein structure determination, as most newly solved structures fall within existing families. Results: In this study we explore the concept of redundancyweighted data-sets, originally suggested by Miyazawa and Jernigan. Redundancy-weighted data-sets include all available structures and associate them (or features thereof) with weights that are inversely proportional to the number of their homologs. Here, we provide the first systematic comparison of redundancy-weighted data-sets with non-redundant ones. We test three weighting schemes and show that the distributions of structural features that they produce are smoother (having higher entropy) compared with the distributions inferred from non-redundant data-sets. We further show that these smoothed distributions are both more robust and more correct than their non-redundant counterparts. We suggest that the better distributions, inferred using redundancyweighting, may improve the accuracy of knowledge-based potentials, and increase the power of protein structure prediction methods. Consequently, they may enhance model-driven molecular biology. Contact