Scoring Functions for De Novo Protein Structure Prediction Revisited

  • Shing-Chung Ngan
  • Ling-Hong Hung
  • Tianyun Liu
  • Ram Samudrala
Part of the Methods in Molecular Biology™ book series (MIMB, volume 413)


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.


De novo physics-based knowledge-based potential protein folding 



We thank Drs. Enoch Huang and Britt Park for their earlier edition on scoring functions for de novo protein structure prediction and the anonymous reviewer for the many helpful suggestions. This work is supported in part by a Searle Scholar Award, NSF Grant DBI-0217241, an NSF CAREER award, and NIH Grant GM068152 to R.S. and the University of Washington’s Advanced Technology Initiative in Infectious Diseases.


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Copyright information

© Humana Press Inc 2008

Authors and Affiliations

  • Shing-Chung Ngan
  • Ling-Hong Hung
  • Tianyun Liu
  • Ram Samudrala

There are no affiliations available

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