Abstract
The method for protein-structure prediction, which combines the physics-based coarse-grained UNRES force field with knowledge-based modeling, has been developed further and tested in the 13th Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP13). The method implements restraints from the consensus fragments common to server models. In this work, the server models to derive fragments have been chosen on the basis of quality assessment; a fully automatic fragment-selection procedure has been introduced, and Dynamic Fragment Assembly pseudopotentials have been fully implemented. The Global Distance Test Score (GDT_TS), averaged over our “Model 1” predictions, increased by over 10 units with respect to CASP12 for the free-modeling category to reach 40.82. Our “Model 1” predictions ranked 20 and 14 for all and free-modeling targets, respectively (upper 20.2% and 14.3% of all models submitted to CASP13 in these categories, respectively), compared to 27 (upper 21.1%) and 24 (upper 18.9%) in CASP12, respectively. For oligomeric targets, the Interface Patch Similarity (IPS) and Interface Contact Similarity (ICS) averaged over our best oligomer models increased from 0.28 to 0.36 and from 12.4 to 17.8, respectively, from CASP12 to CASP13, and top-ranking models of 2 targets (H0968 and T0997o) were obtained (none in CASP12). The improvement of our method in CASP13 over CASP12 was ascribed to the combined effect of the overall enhancement of server-model quality, our success in selecting server models and fragments to derive restraints, and improvements of the restraint and potential-energy functions.
Highlights
Modeling protein structures becomes increasingly important with the progress of biological and medical sciences, the main reason for this importance being an insufficient supply of experimental structures
Physics-based modeling is guided by the energy function of choice,[8−10] the engine being the selected method of conformational-space search, while sequence-structure similarity, which is justified by evolutionary relationship, is the basis of knowledge-based modeling.[1]
Recently,[21,22] we developed a hybrid approach to proteinstructure modeling, in which a restrained conformational search is carried out with the coarse-grained physics-based UNRES force field developed in our laboratory,[23] the geometry restraints being taken from the fragments extracted from the knowledge-based models produced by servers
Summary
Modeling protein structures becomes increasingly important with the progress of biological and medical sciences, the main reason for this importance being an insufficient supply of experimental structures. The accuracy of theoretical models has greatly improved over the years.[1] relatively inexpensive experiments such as small-angle X-ray/neutron scattering (SAXS/SANS)[2−4] and chemical cross-link/mass spectrometry (XLMS)[5,6] enable us to guide modeling for difficult targets. Protein-structure modeling used to be divided into knowledge-based and physics-based categories,[7] which were thought to be clearly separated from each other. The knowledge-based methods underwent significant progress in recent years, owing to improved contact prediction[11−15] and the introduction of deep-learning algorithms.[16,17] because there are at least 10% of targets for which no reliable template can be found,[18] the knowledge-based methods routinely use energy functions in such important tasks as model selection and refinement as well as in a limited search of the conformational space in the famous fragment method developed by the Baker group.[19,20] On the other hand, the physics-based methods use knowledge-based information, Received: October 2, 2019 Published: January 30, 2020
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