TY - JOUR
T1 - A physics-based scoring function for protein structural decoys
T2 - Dynamic testing on targets of CASP-ROLL
AU - Ruiz-Blanco, Yasser B.
AU - Marrero-Ponce, Yovani
AU - García, Yamila
AU - Puris, Amilkar
AU - Bello, Rafael
AU - Green, James
AU - Sotomayor-Torres, Clivia M.
N1 - Funding Information:
Yasser B. Ruiz-Blanco, Yamila García and Clivia M. Sotomayor-Torres acknowledge the partial support of the Spanish MINECO grant MAT-2012-31392 (TAPHOR). Yasser B. Ruiz-Blanco acknowledges the ELAP scholarship to conduct research at Carleton University. Yovani Marrero-Ponce thanks the ‘ International Professor’ program for a fellowship to work at Cartagena University in 2013–2014. Appendix A
PY - 2014/8/28
Y1 - 2014/8/28
N2 - Most successful structure prediction strategies use knowledge-based functions for global optimization, in spite of their intrinsic limited potential to create new folds, while physics-based approaches are often employed only during structure refinement steps. We here propose a physics-based scoring potential intended to perform global searches of the conformational space. We introduce a dynamic test to evaluate the discrimination power of our function, and compare it with predictions of targets from the CASP-ROLL competition. Results demonstrate that this dynamic test is able to generate 3D models which outrank 59% (according GDT-TS score) of models generated with ab initio structure prediction servers.
AB - Most successful structure prediction strategies use knowledge-based functions for global optimization, in spite of their intrinsic limited potential to create new folds, while physics-based approaches are often employed only during structure refinement steps. We here propose a physics-based scoring potential intended to perform global searches of the conformational space. We introduce a dynamic test to evaluate the discrimination power of our function, and compare it with predictions of targets from the CASP-ROLL competition. Results demonstrate that this dynamic test is able to generate 3D models which outrank 59% (according GDT-TS score) of models generated with ab initio structure prediction servers.
UR - https://www.scopus.com/pages/publications/84905166328
U2 - 10.1016/j.cplett.2014.07.014
DO - 10.1016/j.cplett.2014.07.014
M3 - Artículo
AN - SCOPUS:84905166328
SN - 0009-2614
VL - 610-611
SP - 135
EP - 140
JO - Chemical Physics Letters
JF - Chemical Physics Letters
ER -