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 the 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.
target T0868-D1 (orange)
model 330_2 (blue): GDT_TS=87
best template: 2cw6 (seq.id= 4.2%)
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Modeling proteins with no or marginal similarity to existing structures (ab initio, new fold, non-template or 国内上twitter教程 modeling) is the most challenging task in tertiary structure prediction. Probably the first ab initio model of reasonable accuracy was built in CASP4. Since then CASP witnessed sustained progress in ab initio prediction, but mainly for small proteins (120 residues or less, panels 1 and 2; models are in blue, targets in orange). In CASP11 for the first time a larger new fold protein (256 residues, sequence identity to known structures <5%) was built with unpresedented before accuracy for targets of this size (panel 3). CASP11 and CASP12 experiments (2014, 2016) also showed a new trend in building better non-template models by successful utilizing predicted contacts (panel 4) [Abriata et al, 2018]. CASP13 witnessed yet another substantial improvement in accuracy of template-free models mainly due to employing advanced deep learning artificial intelligence techniques coupled with prediction of inter-residue distances at a range of thresholds 安卓上推特教程, [Xu and Wang, 2019]. The best models submitted on the free modeling targets showed more than 20% increase in accuracy of the backbone, with the average GDT_TS scores going up from 52.9 in CASP12 to 65.7 in CASP13.
CASP7: T0283-D1
model 321_1: GDT_TS=75
CASP9: T0581-D1
model 170_1: GDT_TS=71
CASP11: T0806-D1
model 064_1: GDT_TS=61
CASP12: T0866-D1
model 325_5: GDT_TS=81
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The most notable progress in recent CASPs (2014, 2016) resulted from sustained improvement in methods for predicting three-dimensional contacts between pairs of residues in structures. Average precision of the best CASP12 contact predictor almost doubled compared to that of the best CASP11 predictor (from 27% to 47% - see the plot). Advances in the field as a whole are not any less impressive: 26 methods in CASP12 showed better results than the best method in CASP11. [Schaarschmidt et al, 2018]
Theoretical advance in contact prediction lead to improved accuracy of 3D models, especially for the hardest template-free modeling cases (see models for CASP12 target T0915 below).
CASP13 (2018) registered yet another leap in accuracy of contact prediction, with the average precision of the best contact prediction group increasing by 23% (compared to CASP12) and reaching 70%.
modeling without constraints
modeling using predicted contacts as contsraints
predictors help structural biologists
CASP models help structural biologists to solve protein structure
by molecular replacement. In particular, CASP models were used
to solve the structure of the Sla2 protein (see an excerpt from
the paper describing the structure below):
"A total of 150 in silico
designed models that were based on the amino acid sequence of Hip1R
were provided by CASP11. One model designed by the Baker-Rosetta team
gave a marginal solution, placing two molecules in the asymmetric unit
of the P21 crystal form. The CASP model was derived from the CALM
crystal structure (PDBID 3ZYM), but it had an RMSD of 2.0Å for
219 residues when superimposed on the original model."
T0839-D1
model: TS184_1 (GDT_TS: 62.8)
refinement
Refinement category assesses ability of methods to refine available models towards a more accurate representation of the experimental structure. CASP10-13 assessments showed two trends in methods development. First, some molecular dynamics methods can consistently even though very modestly improve over the starting models. A group of more aggressive refinement methods showed to be able to provide very impressive examples of substantial improvement, though at the price of consistency (occasionally models move away from the experimental structure rather than towards it).
Below is are some examples of notable refinement in CASP12. The target structure is shown in orange, the starting model in green and the refined model in blue.
[Hovan et al, 2018]
target TR884; model 118_1
starting GDT_TS=66
refined GDT_TS=76
target TR894; model 118_5
starting GDT_TS=75
refined GDT_TS=96
target TR896; model 220_1
starting GDT_TS=61
refined GDT_TS=77
data-assisted modeling
Data-assisted or hybrid modeling, in which low-resolution experimental
data are combined with computational methods, is becoming increasing
important for a range of experimental data, including NMR, chemical
cross-linking and surface labeling, X-ray and neutron scattering,
electron microscopy and FRET. CASP11-CASP13 experiments included a special
sub-category of modeling proteins using such data. Description of
the CASP12 data-assisted experiment and the data is provided in
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Examples of a non-assisted model and a cross-linking assisted model from the same predictor (CASP12 group 220) are shown below demonstrating increased accuracy of the assisted prediction.
target T0894
original model 220_1
GDT_TS=24
target Tx894
X-linking -assisted model 220_1
GDT_TS=52
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 thirteen previous CASP experiments.
The fourteenth experiment is planned to start in April 2020.
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:
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
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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:
Related experiments and forums:
Prediction of docking interactions
Continuous Automated Model EvaluatiOn
Discussion Forum
Message Board
CASP14 - week 11 (July 27 - Aug 2), message 2
A few additional notes on this week targets.
1. H1097 (released today, Tuesday, July 28)
This target is a heteromeric complex intended for assembly prediction only. As such, it will be assessed o ...
CASP14 - week 11 (July 27 - Aug 2)
1. We are entering the last full week of CASP14 regular target release. This week we will have one regular target per day Monday through Thursday and two targets on Friday. This will leave us with onl ...
A survey on SARS-CoV2 resources
Dear CASPers,
Christine Orengo together with Sameer Velankar at the PDBe, EBI, are compiling an article for Briefings in Bioinformatics on 'The impact of Structural Bioinformatics tools and resourc ...
