Abstract
BackgroundDevelopment of effective scoring functions is a critical component to the success of protein structure modeling. Previously, many efforts have been dedicated to the development of scoring functions. Despite these efforts, development of an effective scoring function that can achieve both good accuracy and fast speed still presents a grand challenge.ResultsBased on a coarse-grained representation of a protein structure by using only four main-chain atoms: N, Cα, C and O, we develop a knowledge-based scoring function, called NCACO-score, that integrates different structural information to rapidly model protein structure from sequence. In testing on the Decoys'R'Us sets, we found that NCACO-score can effectively recognize native conformers from their decoys. Furthermore, we demonstrate that NCACO-score can effectively guide fragment assembly for protein structure prediction, which has achieved a good performance in building the structure models for hard targets from CASP8 in terms of both accuracy and speed.ConclusionsAlthough NCACO-score is developed based on a coarse-grained model, it is able to discriminate native conformers from decoy conformers with high accuracy. NCACO is a very effective scoring function for structure modeling.
Highlights
Development of effective scoring functions is a critical component to the success of protein structure modeling
A central stage at the protein structure modeling is to develop an effective energy function, called potential or scoring function, which generally fall into two categories: physical-based and knowledge-based energy functions
Knowledge-based energy functions are based on statistical analysis of experimentally determined protein structures, which provide an excellent shortcut towards a powerful energy function [4]
Summary
Development of effective scoring functions is a critical component to the success of protein structure modeling. A central stage at the protein structure modeling is to develop an effective energy function, called potential or scoring function, which generally fall into two categories: physical-based and knowledge-based energy functions. Compared to physical-based energy functions, knowledge-based energy functions have become more and more popular in protein structure prediction due to the relatively easy generation and manipulation of model structures and the lower computational cost. This can be seen from recent CASPs Assessment of Techniques for Protein Structure Prediction), in which the most successful prediction methods use knowledge-based energy functions [5,6]. Knowledge-based approaches have been widely used in protein design [7], validation of experimentally determined protein structures [8,9] and protein-protein and protein-ligand interactions [10]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
