This project provides the enabling infrastructure for the Critical Assessment of Structure Prediction (CASP) program, dedicated to the objective evaluation of protein structure modeling methods. Knowledge of protein structure significantly aids in the investigation of macromolecular function, interactions, and biochemical pathways. It also has a major impact on the understanding of human disease, and on the development of therapeutics. Experimental determination of structure is inherently time-consuming, costly, and often meets with insurmountable obstacles. Computational modeling offers an alternative approach but modeling methods vary in their effectiveness and typically do not directly provide measures of accuracy.
CASP aims at answering these concerns by objective evaluation of methods, so driving progress. CASP is a community-wide program, with over 100 research groups world-wide submitting over 60,000 predictions in the last round (2014). The Center is the primary infrastructure resource for CASP, and handles processing of predictions, develops evaluation software, performs model assessment, develops analysis and display tools, and facilitates access to models and evaluation data. The current proposal includes two major new initiatives, extending the impact of CASP to important new areas of structural biology. First, we will collaborate with appropriate experimental groups to develop modeling methods that maximally leverage sparse and low resolution data. Preliminary results with NMR and crosslinking demonstrate the potential in this area. Second, in partnership with the CAPRI initiative (Critical Assessment of Protein Interactions), we will include modeling of biological assemblies. We will also continue the successful CASP strategy of focusing on overcoming specific bottleneck problems, such as model refinement, estimation of model accuracy, and prediction of three dimensional contacts; adding modeling of non-template regions in homology models. In addition, we will place special emphasis on evaluating the usefulness of models in specific life science applications. Finally, we will interact with teachers and researchers, with te goal of disseminating the insights on modeling and the wealth of data gained through CASP.
Knowledge of macromolecular structure plays a crucial role in biology and medicine, allowing for detailed studies and understanding of biological processes and disease mechanisms. Yet, relatively few structures are obtained experimentally - the rest must be modeled. The Critical Assessment of Structure Prediction program (CASP), provides the primary means of evaluating performance of the methods dedicated to this task.
|Moult, John; Fidelis, Krzysztof; Kryshtafovych, Andriy et al. (2018) Critical assessment of methods of protein structure prediction (CASP)-Round XII. Proteins 86 Suppl 1:7-15|
|Schaarschmidt, Joerg; Monastyrskyy, Bohdan; Kryshtafovych, Andriy et al. (2018) Assessment of contact predictions in CASP12: Co-evolution and deep learning coming of age. Proteins 86 Suppl 1:51-66|
|Lafita, Aleix; Bliven, Spencer; Kryshtafovych, Andriy et al. (2018) Assessment of protein assembly prediction in CASP12. Proteins 86 Suppl 1:247-256|
|Kryshtafovych, Andriy; Monastyrskyy, Bohdan; Fidelis, Krzysztof et al. (2018) Evaluation of the template-based modeling in CASP12. Proteins 86 Suppl 1:321-334|
|Ogorzalek, Tadeusz L; Hura, Greg L; Belsom, Adam et al. (2018) Small angle X-ray scattering and cross-linking for data assisted protein structure prediction in CASP 12 with prospects for improved accuracy. Proteins 86 Suppl 1:202-214|
|Kryshtafovych, Andriy; Monastyrskyy, Bohdan; Fidelis, Krzysztof et al. (2018) Assessment of model accuracy estimations in CASP12. Proteins 86 Suppl 1:345-360|
|Kryshtafovych, Andriy; Albrecht, Reinhard; Baslé, Arnaud et al. (2018) Target highlights from the first post-PSI CASP experiment (CASP12, May-August 2016). Proteins 86 Suppl 1:27-50|
|Moult, John; Fidelis, Krzysztof; Kryshtafovych, Andriy et al. (2016) Critical assessment of methods of protein structure prediction: Progress and new directions in round XI. Proteins 84 Suppl 1:4-14|
|Kryshtafovych, Andriy; Barbato, Alessandro; Monastyrskyy, Bohdan et al. (2016) Methods of model accuracy estimation can help selecting the best models from decoy sets: Assessment of model accuracy estimations in CASP11. Proteins 84 Suppl 1:349-69|
|Kinch, Lisa N; Li, Wenlin; Schaeffer, R Dustin et al. (2016) CASP 11 target classification. Proteins 84 Suppl 1:20-33|
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