Protein Structure Prediction Center
Success Stories From Recent CASPs
 
template-based
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template-based modeling
Models based on templates identified by sequence similarity remain the most accurate. Over the course of the CASP experiments there have been enormous improvements in this area. However, the overall accuracy improvements that we have seen in the first 10 years of CASP remained unmatched until CASP12 (2016), when a new burst of progress happened [Kryshtafovych et al, 2018]. In two years from 2014 to 2016, the backbone accuracy of the submitted models improved more than in the preceeding 10 years. The next CASP continued the trend [Croll et al, 2019], and the 2014-2018 model accuracy improvement doubled that of 2004-2014 (see left plot). Several factors contributed to this, including more accurate alignment of the target sequence to that of available templates, combining multiple templates, improved accuracy of regions not covered by templates, successful refinement of models, and better selection of models from decoy sets due to improved methods for estimation of model accuracy.

CASP14 marked an extraordinary increase in the accuracy of the computed three-dimensional protein structures with the emergence of the advanced deep learning method AlphaFold2. Models built with this method proved to be competitive with the experimental accuracy (GDT_TS>90) for ~2/3 of the targets and of high accuracy (GDT_TS>80) for almost 90% of the targets (middle plot). The accuracy of CASP14 models for TBM targets significally superseeded accuracy of models that can be built by simple transcription of information from templates, and reached the level of GDT_TS=92 on average, which is significantly higher than the corresponding averages in previous two CASPs (right plot).

Welcome to the Protein Structure Prediction Center!

Our goal is to help advance the methods of identifying protein structure from sequence. The Center has been organized to provide the means of objective testing of these methods via the process of blind prediction. The Critical Assessment of protein Structure Prediction (CASP) experiments aim at establishing the current state of the art in protein structure prediction, identifying what progress has been made, and highlighting where future effort may be most productively focused.

There have been fourteen previous CASP experiments. The fifteenth experiment is planned to start in Spring 2022. Description of these experiments and the full data (targets, predictions, interactive tables with numerical evaluation results, dynamic graphs and prediction visualization tools) can be accessed following the links:

CASP1 (1994) | CASP2 (1996) | CASP3 (1998) | CASP4 (2000) | CASP5 (2002) | CASP6 (2004) | CASP7 (2006) | CASP8 (2008) | CASP9 (2010) | CASP10 (2012) | CASP11 (2014) | CASP12 (2016) | CASP13 (2018) | CASP14 (2020)

Raw data for the experiments held so far are archived and stored in our data archive.


Details of the experiments have been published in a scientific journal Proteins: Structure, Function and Bioinformatics. CASP proceedings include papers describing the structure and conduct of the experiments, the numerical evaluation measures, reports from the assessment teams highlighting state of the art in different prediction categories, methods from some of the most successful prediction teams, and progress in various aspects of the modeling.
Prediction methods are assessed on the basis of the analysis of a large number of blind predictions of protein structure. Summary of numerical evaluation of the tertiary structure prediction methods tested in the latest CASP experiment can be found on this web page. The main numerical measures used in evaluations, data handling procedures, and guidelines for navigating the data presented on this website are described in [1] .

Some of the best performing methods are implemented as fully automated servers and therefore can be used by public for protein structure modeling.


To proceed to the pages related to the latest CASP experiments click on the logo below:

CASP Commons Home CASP14 Home



Related experiments:

Prediction
of docking interactions
Continuous Automated Model EvaluatiOn
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Protein Structure Prediction Center
Sponsored by the US National Institute of General Medical Sciences (NIH/NIGMS)
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