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

Evaluating GPCR modeling and docking strategies in the era of deep learning-based protein structure prediction

While deep learning (DL) has brought a revolution in the protein structure prediction field, still an important question remains how the revolution can be transferred to advances in structure-based drug discovery. Because the lessons from the recent GPCR dock challenge were inconclusive primarily due to the size of the dataset, in this work we further elaborated on 70 diverse GPCR complexes bound to either small molecules or peptides to investigate the best-practice modeling and docking strategies for GPCR drug discovery. From our quantitative analysis, it is shown that substantial improvements in docking and virtual screening have been possible by the advance in DL-based protein structure predictions with respect to the expected results from the combination of best pre-DL tools. The success rate of docking on DL-based model structures approaches that of cross-docking on experimental structures, showing over 30% improvement from the best pre-DL protocols. This amount of performance could be achieved only when two modeling points were considered properly: 1) correct functional-state modeling of receptors and 2) receptor-flexible docking. Best-practice modeling strategies and the model confidence estimation metric suggested in this work may serve as a guideline for future computer-aided GPCR drug discovery scenarios

Protein Loop Modeling Using a New Hybrid Energy Function and Its Application to Modeling in Inaccurate Structural Environments

<div><p>Protein loop modeling is a tool for predicting protein local structures of particular interest, providing opportunities for applications involving protein structure prediction and <i>de novo</i> protein design. Until recently, the majority of loop modeling methods have been developed and tested by reconstructing loops in frameworks of experimentally resolved structures. In many practical applications, however, the protein loops to be modeled are located in inaccurate structural environments. These include loops in model structures, low-resolution experimental structures, or experimental structures of different functional forms. Accordingly, discrepancies in the accuracy of the structural environment assumed in development of the method and that in practical applications present additional challenges to modern loop modeling methods. This study demonstrates a new strategy for employing a hybrid energy function combining physics-based and knowledge-based components to help tackle this challenge. The hybrid energy function is designed to combine the strengths of each energy component, simultaneously maintaining accurate loop structure prediction in a high-resolution framework structure and tolerating minor environmental errors in low-resolution structures. A loop modeling method based on global optimization of this new energy function is tested on loop targets situated in different levels of environmental errors, ranging from experimental structures to structures perturbed in backbone as well as side chains and template-based model structures. The new method performs comparably to force field-based approaches in loop reconstruction in crystal structures and better in loop prediction in inaccurate framework structures. This result suggests that higher-accuracy predictions would be possible for a broader range of applications. The web server for this method is available at <a href="http://galaxy.seoklab.org/loop" target="_blank">http://galaxy.seoklab.org/loop</a> with the PS2 option for the scoring function.</p></div

Distributions of environmental errors for the three types of test sets employed in the study.

<p>(A) for the test set of crystal structures with perturbed side chains, (B) for the crystal structures with both backbone and side chains perturbed, and (C) for the template-based models. The gray curve behind the histogram represents an interpolation. The average E-RMSD values are 0.9 Å, 2.1 Å, and 2.8 Å for the side chain-perturbed set (A), the backbone-perturbed set (B), and the template-based model set (C), respectively. E-RMSD represents the all-atom RMSD of environment residues for which any atoms are within 10 Å from any loop C_β atoms.</p

Examples of loops modeled in inaccurate environmental structures.

<p>In all panels, the crystal structures are colored in green and the models in magenta. Framework structures are shown transparent for clarity. (A) Two examples of tolerating errors in surrounding side chains, 1oyc (left; RMSD = 0.4 Å) and 1c5e (right; RMSD = 0.5 Å). The loop-framework salt bridges in the crystal structures are indicated with black dotted lines. High-accuracy modeling is possible even though the salt bridges cannot be recovered owing to the perturbed arginine orientations in the framework. (B) An example of unsuccessful modeling in the framework of perturbed side-chains, 1oth (RMSD = 2.3 Å), showing the necessity of additional sampling. The perturbed Arg66 and Tyr345 side chains (magenta) would clash with the two leucine residues in the loop if the crystal loop structure were to be placed. (C) Two examples of tolerating additional backbone errors, 1my7 (left; RMSD = 1.0 Å) and 1cb0 (right; RMSD = 0.9 Å). The overall backbone trace and key side-chain interactions are well reproduced.</p

Sampling results of GalaxyLoop-PS2 on the three test sets.

1)<p>Number of loop targets for which at least one structure among the 30 loop conformations (or 50 conformations for 12-residue loops) in the final CSA bank is within a given RMSD value.</p><p>Sampling results of GalaxyLoop-PS2 on the three test sets.</p

Comparison of loop modeling results on the test set of template-based models.

<p>The average RMSD and its standard deviation are reported in Å. The Loop RMSD is calculated as the root-mean-square deviation of the main-chain atoms N, C_α, C, and O.</p>1)<p>Loop conformations generated by MODELLER <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0113811#pone.0113811-Sali1" target="_blank">[30]</a>.</p>2)<p>Loop conformations generated by loop refinement using ModLoop of MODELLER <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0113811#pone.0113811-Fiser1" target="_blank">[1]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0113811#pone.0113811-Fiser2" target="_blank">[27]</a>.</p>3)<p>Results of the best-score models sampled by Next-generation KIC (NGK) using the protocol provided by Stein <i>et al.</i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0113811#pone.0113811-Stein1" target="_blank">[18]</a>.</p><p>500 models were generated for each target as in Stein <i>et al.</i> The Rosetta program v3.5 was used.</p>4)<p>Loop set constructed in this study. See <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0113811#pone.0113811.s008" target="_blank">Table S7</a></b> for the list of loops.</p><p>Comparison of loop modeling results on the test set of template-based models.</p