Scoring Functions for De Novo Protein Structure Prediction Revisited Export

@incollection{citeulike:5795260, abstract = {De novo protein structure prediction methods attempt to predict tertiary structures from sequences based on general principles that govern protein folding energetics and/or statistical tendencies of conformational features that native structures acquire, without the use of explicit templates. A general paradigm for de novo prediction involves sampling the conformational space, guided by scoring functions and other sequence-dependent biases, such that a large set of candidate ( ” decoy”) structures are generated, and then selecting native-like conformations from those decoys using scoring functions as well as conformer clustering. High-resolution refinement is sometimes used as a final step to fine-tune native-like structures. There are two major classes of scoring functions. Physics-based functions are based on mathematical models describing aspects of the known physics of molecular interaction. Knowledge-based functions are formed with statistical models capturing aspects of the properties of native protein conformations. We discuss the implementation and use of some of the scoring functions from these two classes for de novo structure prediction in this chapter.}, author = {Ngan, Shing-Chung and Hung, Ling-Hong and Liu, Tianyun and Samudrala, Ram}, citeulike-article-id = {5795260}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/978-1-59745-574-9_10}, citeulike-linkout-1 = {http://www.springerlink.com/content/nh17003547036011}, doi = {10.1007/978-1-59745-574-9_10}, journal = {Protein Structure Prediction}, keywords = {folding, function, protein, scoring}, pages = {243--281}, posted-at = {2009-09-17 13:51:40}, priority = {0}, title = {Scoring Functions for De Novo Protein Structure Prediction Revisited}, url = {http://dx.doi.org/10.1007/978-1-59745-574-9_10}, year = {2008} }

TY - CHAP ID - citeulike:5795260 L3 - citeulike-article-id:5795260 N2 - De novo protein structure prediction methods attempt to predict tertiary structures from sequences based on general principles that govern protein folding energetics and/or statistical tendencies of conformational features that native structures acquire, without the use of explicit templates. A general paradigm for de novo prediction involves sampling the conformational space, guided by scoring functions and other sequence-dependent biases, such that a large set of candidate (“decoy”) structures are generated, and then selecting native-like conformations from those decoys using scoring functions as well as conformer clustering. High-resolution refinement is sometimes used as a final step to fine-tune native-like structures. There are two major classes of scoring functions. Physics-based functions are based on mathematical models describing aspects of the known physics of molecular interaction. Knowledge-based functions are formed with statistical models capturing aspects of the properties of native protein conformations. We discuss the implementation and use of some of the scoring functions from these two classes for de novo structure prediction in this chapter. JF - Protein Structure Prediction EP - 281 TI - Scoring Functions for De Novo Protein Structure Prediction Revisited SP - 243 KW - folding KW - function KW - protein KW - scoring AU - Ngan, Shing-Chung AU - Hung, Ling-Hong AU - Liu, Tianyun AU - Samudrala, Ram PY - 2008/// UR - http://dx.doi.org/10.1007/978-1-59745-574-9_10 ER -