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Principal Investigator: KRZYSZTOF A FIDELIS
Organization: UNIVERSITY OF CALIFORNIA AT DAVIS
Fiscal Year: 2024
Award: $638,990
Funding agency: National Institute of General Medical Sciences
PROJECT SUMMARY
Experimental determination of protein structure often provides atomic accuracy models, but is inherently time-
consuming, often costly, and not always possible. Computational modeling is currently less accurate, but offers an
alternative approach when experimental results are not available. The goal of CASP (Critical Assessment of Structure
Prediction) is to advance the protein structure modeling field by conducting community-wide experiments that objectively
determine the strengths and weaknesses of current methods and so foster progress. Approximately 100 research groups
world-wide participate. In the most recent experiment (2018), there were 57,000 submissions in nine modeling categories,
including over 35,000 tertiary structure models. The Center provides the infrastructure for CASP and Aim 1 is the continued
development and operation of this resource. Principal tasks include registration and communication with participants;
solicitation, characterization, and management of modeling targets; collection and validation of models; and extensive
numerical analysis of submissions. These operations are supported by a secure and robust data infrastructure. The Center
also develops evaluation, analysis, and display software, and provides access to models and evaluation results. CASP relies
on independent assessors, experts in modeling or a related experimental field, to interpret the results. The Center
coordinates this process, providing evaluation data and, when necessary, implementing new evaluation methods.
Recent CASPs have shown dramatic improvements in model accuracy, especially for the most difficult cases where
homology modeling cannot be used. A major factor driving progress is the use of new machine learning approaches,
particularly convolutional neural networks. These and related methods appear poised to make further major advances in
a number of key modeling areas. The plan for the next period of the project is designed to capitalize on these and other
opportunities for progress. Greater success with modeling small proteins and domains dictates a shift in emphasis to the
still challenging area of large multi-domain proteins and complexes (Aim 2), where progress is expected both from the
machine learning developments and from the incorporation of sparse experimental data. Although accuracy of models
has improved, it is still seldom competitive with experiment. Aim 3 is to pursue strategies that will make models more
accurate and useful, by nurturing further progress in refining initial models, better methods for estimating model accuracy,
and assessment of the utility of models. Aim 4 introduces new ways of strengthening interactions between CASP and the
broader research community, providing models that directly address contemporary problems (for example, for CoV-2
protein structures) and boosting communications through meetings, webinars, publications and other means.
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