Abstract
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.
Highlights
The current experimental approaches to determine the native structure of proteins are too costly to keep pace with the wealth of protein sequences that genome sequencing projects generate
For example, quality assessment is typically applied to large sets of decoys, all of which were generated by the same method
WeFold provides a flexible infrastructure for the creation of prediction pipelines (e.g. Fig. 2 shows the pipelines that start with Rosetta decoys), into which researchers may insert components of their methods such as refinement and quality assessment
Summary
The current experimental approaches to determine the native structure of proteins are too costly to keep pace with the wealth of protein sequences that genome sequencing projects generate. These tracks: contact prediction, quality assessment, and refinement, each addressing a major sub-problem, have a considerable impact on research They evaluate the performance of methods in an objective manner, and most importantly, they provide developers with large data sets that can be used to improve them. WeFold provides a flexible infrastructure for the creation of prediction pipelines (e.g. Fig. 2 shows the pipelines that start with Rosetta decoys), into which researchers may insert components of their methods such as refinement and quality assessment These pipelines participate as groups in CASP allowing their overall performance to be evaluated in an objective and coherent manner along with all the other groups. We refer to these events as WeFold[1], WeFold[2], and WeFold[3], respectively
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