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Protein Structure Prediction Center



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Success Stories From Recent CASPs
 


assembly
modeling template-based
modeling ab initio
modeling contact
prediction help structural
biologists refinement data-assisted
modeling ||

assembly modeling

CASP15 (2022) showed enormous progress in modeling multimolecular protein
complexes.
The assembly modeling (a.k.a. quaternary structure modeling, oligomeric
modeling, multimeric modeling) has been assessed in CASP since 2016 (CASP12).
Typically, models were of good accuracy when templates were available for the
structure of the whole target complex. After the success of AlphaFold2 in CASP14
(2020), it was expected that deep learning methodology that brought monomeric
modeling to qualitatively new level will be extended to multimeric modeling.
Indeed, CASP15 showed that newly developed methods are capable of accurate
reproducing structures of oligomeric complexes and outperform CASP14 methods by
a large margin. In particular, the accuracy of models almost doubled in terms of
the Interface Contact Score (ICS a.k.a. F1) and increased by 1/3 in terms of the
overall fold similarity score LDDTo (left panel). An impressive example of
multimeric modeling is shown in the right panel below.

CASP15: T1113o 
model 239_2: F1=92.2; LDDTo=0.913

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).

ab initio modeling

Modeling proteins with no or marginal similarity to existing structures (ab
initio, new fold, non-template or free 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, panel 1;
model is in blue, target 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. CASP11 and CASP12
experiments (2014, 2016) also showed a new trend in building better non-template
models by successful utilizing predicted contacts (panel 3) [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 [Senior et al, 2019], [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.

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. The CASP14 trend line in the historical progress
plot (panel 2, black trendline) starts at a GDT_TS of about 95, and finishes at
about 85 for difficult targets. Because of experimental errors and artifacts, a
GDT_TS of 100 is highly unlikely. In CASP14, about 2/3 of the 96 targets reached
GDT_TS values greater than that, and so are considered competitive with
experiment in backbone accuracy.

CASP7: T0283-D1 
model 321_1: GDT_TS=75

CASP12: T0866-D1 
model 325_5: GDT_TS=81

contact prediction

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%. There has been no noticeable increase
in the accuracy of predicted contacts between CASP13 and CASP14 (left graph).



modeling without constraints

modeling using predicted contacts as constraints

predictors help
structural biologists

In early CASPs, generated models have occasionally helped solve structures. For
example, the crystal structure of Sla2 ANTH domain of Chaetomium thermophilum
(CASP11 target T0839 - see the image below) was determined by molecular
replacement using CASP models, but these have been exceptions.

In CASP14, four structures were solved with the aid of AlphaFold2 models. A
post- CASP analysis has shown that models from other groups would also have been
effective in some cases. These are all hard targets with limited or no homology
information available for at least some domains, demonstrating the power of the
new methods for all classes of modeling difficulty. For one other target,
provision of the models resulted in correction of a local experimental error. A
detailed account of these cases is provided in the Proteins paper [Kryshtafovych
et al, 2021]

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. CASP10-14
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 (CASP14 did not include data-assisted category due to the
COVID-19-associated difficulties in obtaining experimental data).
Description of the CASP12 data-assisted experiment and the data is provided in
[Ogorzalek et al, 2018]
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 fifteen previous CASP experiments. The sixteenth experiment is
planned to start in May 2024. 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) | CASP15 (2022) |
CASP16 (2024)

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

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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.

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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.


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To proceed to the latest CASP experiment click on the logo below:





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Related experiments:



Prediction
of docking interactions Continuous Automated Model EvaluatiOn

Message Board

Junior CASP Hi everyone, We are trying to incorporate an opportunity for junior
CASP16 attendees who are earlier in their academic careers to connect and engage
within the main conference setting. If there’ ...

CASP16 conference in Punta Cana Dear CASP Participants, We encourage those of
you attending the Dec. 1-4 CASP16 conference in Punta Cana, Dominican Republic,
to present a poster. We will provide specific format details and pos ...

CASP conference early registration rates extended Dear CASPers, In response to a
number of requests, we are extending the pre-registration rates until October 7,
2024. CASP Organizers ...





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