New tools and expanded data analysis capabilities at the protein structure prediction center

Kryshtafovych, Andriy; Prlić, Andreas; Dmytriv, Zinoviy; Daniluk, Paweł; Miłostan, Maciej; Eyrich, Volker A.; Hubbard, Tim; Fidelis, Krzysztof

doi:10.1002/prot.21653

Cited by 22 publications

(28 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The best TBM predictors are already able to add value, on average, to the single best template [17]. Here, predictors are trying to take this to the next level.…”

Section: Introductionmentioning

confidence: 99%

Assessment of protein structure refinement in CASP9

et al. 2011

View full text Add to dashboard Cite

We assess performance in the structure refinement category in CASP9. Two years after CASP8, the performance of the best groups has not improved. There are few groups that improve any of our assessment scores with statistical significance. Some predictors, however, are able to consistently improve the physicality of the models. Although we cannot identify any clear bottleneck to improving refinement, several points arise: (1) The refinement portion of CASP has too few targets to make many statistically meaningful conclusions. (2) Predictors are usually very conservative, limiting the possibility of large improvements in models. (3) No group is actually able to correctly rank their five submissions—indicating that potentially better models may be discarded. (4) Different sampling strategies work better for different refinement problems; there is no single strategy that works on all targets. In general, conservative strategies do better, while the greatest improvements come from more adventurous sampling–at the cost of consistency. Comparison with experimental data reveals aspects not captured by comparison to a single structure. In particular, we show that improvement in backbone geometry does not always mean better agreement with experimental data. Finally, we demonstrate that even given the current challenges facing refinement, the refined models are useful for solving the crystallographic phase problem through molecular replacement.

show abstract

“…The best TBM predictors are already able to add value, on average, to the single best template [17]. Here, predictors are trying to take this to the next level.…”

Section: Introductionmentioning

confidence: 99%

Assessment of protein structure refinement in CASP9

et al. 2011

View full text Add to dashboard Cite

show abstract

“…Our approach was to define a measure of correlation for predicted quality versus actual quality that captures many of the properties desirable for model quality assessment and to optimize weights of the 73 individual Undertaker cost functions, maximizing correlation. This approach differs from the typical approach of directly predicting a model quality measure (such as GDT_TS6 or GDT_HA7) using neural networks,4, 8 support vector regression,2 or a similar technique.…”

Section: Methodsmentioning

confidence: 99%

Applying undertaker cost functions to model quality assessment

Archie

Karplus

2008

Proteins

View full text Add to dashboard Cite

Undertaker is a program designed to help predict protein structure using alignments to proteins of known structure and fragment assembly. The program generates conformations and uses cost functions to select the best structures from among the generated conformations. This paper describes the use of Undertaker's cost functions for model quality assessment (MQA). We achieve an accuracy that is similar to other methods, without using consensus-based techniques. Adding consensus-based features further improves our approach substantially. We report several correlation measures, including a new weighted version of Kendall's τ (τ 3 ) and show MQA results superior to previously published results on all correlation measures when using only models with no missing atoms.

show abstract

“…Specially, it is extensively applied for assessment of similarity for protein prediction in Critical Assessment of Techniques for Protein Structure Prediction (CASP) (Kryshtafovych et al, 2007;Moult et al, 2009). The 25 conformer of 1M2F and its average model of 1M2E are compared by both of PFSA approach and LGA method respectively.…”

Section: Pfsa Vs Lgamentioning

confidence: 99%