CAP - Catabolite gene Activating Protein (1BER)
DNA - GATTACAGATTACAGATTACA Endonuclease PvuII bound to palindromic DNA recognition site CAGCTG (1PVI) DNA - GATTACAGATTACAGATTACA TBP - TATA box Binding Protein (1C9B)
CAP - Catabolite gene Activating Protein (1BER)
GCN4 - leucine zipper transcription factor bound to palindromic DNA recognition site ATGAC(G)TCAT (1YSA)
GCN4 - leucine zipper transcription factor bound to palindromic DNA recognition site ATGAC(G)TCAT (1YSA)
GCN4 - leucine zipper transcription factor bound to palindromic DNA recognition site ATGAC(G)TCAT (1YSA)
GCN4 - leucine zipper transcription factor bound to palindromic DNA recognition site ATGAC(G)TCAT (1YSA)
GCN4 - leucine zipper transcription factor bound to palindromic DNA recognition site ATGAC(G)TCAT (1YSA)
GCN4 - leucine zipper transcription factor bound to palindromic DNA recognition site ATGAC(G)TCAT (1YSA)
TBP - TATA box Binding Protein (1C9B)


CASP ('Critical Assessment of Structure Prediction') is a biennial evaluation of today's many approaches to protein structure prediction, organized by the Prediction Center since 1994. During each CASP season (lasting ~4 months), about 200 research groups try to predict the structures of ~100 proteins (the CASP targets), applying all combinations of prediction methods, ranging from experimental homegrown ones to well established molecular modeling packages. The target sequences are provided to CASP by structural biology labs (mostly structural genomics projects) just before the corresponding structures are solved. The predictions are thus real 'blind predictions', which makes CASP a unique opportunity to judge if a certain method really works, or is mainly based on wishful thinking and a lot of advertisement. Interestingly, the most expensive solutions hardly ever perform well at CASP, showing that users of molecular modeling software must be careful not to waste their resources.

Figure 1 : Screen recording of the YASARA presentation at the CASP8 meeting on Sardinia (homology modeling session, December 4, 2008). To avoid the bad YouTube video quality, download the original video here and watch it with your own media player. But since YASARA creates these animations in real-time using OpenGL, best download the corresponding macro (GNU GPL licensed) from the YASARA movie page to watch it in highest resolution with up to 60 frames per second.

CASP8 Refinement Section - The last mile of the protein folding problem

One of YASARA's main functions has always been the atomistic simulation of proteins. Unfortunately today's computers are much too slow to run a full molecular dynamics simulation of a folding protein for all but the simplest peptides. CASP came to the rescue by introducing the refinement section: predictors are provided with the best model submitted for a certain target (created with any other method), and can then use their high-resolution refinement simulations to improve the model and move it closer to the target (the 'last mile of the protein folding problem'). As it turns out, this problem is already hard enough: while it is trivial to shake a protein around by molecular dynamics simulation, most of the shaking goes in the wrong direction, usually due to the limited accuracy of today's empirical force fields.

Given the difficulty of the problem, we are happy to report that YASARA's molecular dynamics simulations won 3 of the 12 refinement targets, and nobody else won more targets than YASARA (using the CASP Model_1-only ranking for targets TR429 , TR454 and TR469 , see table 1 below). We congratulate the Baker Lab for also having won three targets (others won at most one target). Since their Monte-Carlo based Rosetta program has regularly disqualified molecular dynamics based methods during the previous CASPs, it is great news to see that eight years of research on increasing the accuracy of YASARA's force fields [1,2] has made molecular dynamics competitive again, at least in the refinement section. Predictions were made fully automatically without human intervention. Additional help came from WHAT IF in the Twinset to provide a second opinion on model quality , and from CONCOORD to speed up sampling under certain conditions. It should be noted that despite these successes, there still exists no method today that can consistently improve every single model, some still go into the wrong direction. Work to open this bottleneck is in progress.

CASP8 refinement example

Figure 2: The one and only refinement success example shown by Dr. Justin MacCallum during his assessment at the CASP8 meeting. Obtained by molecular dynamics simulation in explicit solvent using the YASARA force field. The text has been added by us, the RMSD is the average over all target structures (target R469 is an NMR ensemble, PDB entry 2K5E).

CASP8 refinement results
Table 1: YASARA refinement success targets, listing model_1 only (predictors are allowed to submit up to four additional guesses, which are scanned for interesting cases, but traditionally ignored for the ranking).

CASP8 Homology Modeling Section

YASARA's homology modeling module was registered as a CASP server and thus had to provide predictions within three days after target release, fully automatically and without human intervention. Models were submitted for 61 targets, whenever Psi-BLAST found a useful homology modeling template and the resulting model was of acceptable quality. YASARA won two homology modeling targets (T445 and T488 , see table 2 below), and more importantly, was overall ranked first in the category 'high-resolution server accuracy' by the assessors. This is shown along the vertical axis in Figure 3 on the right. Since it involves especially side-chains and hydrogen bonds, YASARA obviously benefited from its side-chain modeling and high-resolution refinement algorithms . There is still room for improvement along the horizontal low-resolution C-alpha accuracy axis, requiring tuned alignments and improved fusion of multiple templates. Again, this topic is currently being worked on. Additional help came from the PDB-Redo database , which supplied re-refined templates.

CASP8 modeling server comparison

Figure 3: Comparison of CASP8 modeling servers, courtesy of Jane S. Richardson at Duke University, assessor of CASP8 homology modeling targets (YASARA logo, arrows and explanatory text added by us). Each dot corresponds to one predictor group, those marked with a star have been selected to present their methods.

CASP8 Homology Modeling Targets

Table 2: YASARA homology modeling success targets.

The YASARA results have been published in the CASP8 special issue of the journal Proteins: Structure, Function and Bioinformatics , including the YASARA force field with knowledge-based dihedral potentials , that has been optimized to yield stable energy minima close to native X-ray structures. YASARA's homology modeling module is also outlined briefly. The journal cover shows how the various dihedrals in an arginine residue are combined by YASARA's multi-dimensional potentials.

Improving physical realism, stereochemistry, and side-chain accuracy in homology modeling: Four approaches that performed well in CASP8
Krieger E, Joo K, Lee J, Lee J, Raman S, Thompson J, Tyka M, Baker D, Karplus K
Proteins. 2009;77 Suppl 9:114-22
CASP8 Special Issue

[1] Increasing the precision of comparative models with YASARA NOVA - a self-parameterizing force field
Krieger E, Koraimann G, Vriend G (2002) Proteins 47 , 393-402.
[2] Making optimal use of empirical energy functions: Force-field parameterization in crystal space
Krieger E, Darden T, Nabuurs SB, Finkelstein A, Vriend G (2004) Proteins 57 , 678-683