Development of a four-body statistical pseudo-potential to discriminate native from non-native protein conformations

Bala Krishnamoorthy; Alexander Tropsha

doi:10.1093/bioinformatics/btg186

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Development of a four-body statistical pseudo-potential to discriminate native from non-native protein conformations

Journal article

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Peer reviewed

Development of a four-body statistical pseudo-potential to discriminate native from non-native protein conformations

Bala Krishnamoorthy and Alexander Tropsha

Bioinformatics (Oxford, England), Vol.19(12), pp.1540-1548

08/12/2003

DOI: https://doi.org/10.1093/bioinformatics/btg186

Handle:

https://hdl.handle.net/2376/112326

PMID: 12912835

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Abstract

Amino Acid Sequence

Sequence Analysis, Protein - methods

Algorithms

Crystallography - methods

Models, Molecular

Molecular Sequence Data

Protein Conformation

Proteins - chemistry

Protein Folding

Quantitative Structure-Activity Relationship

Most scoring functions used in protein fold recognition employ two-body (pseudo) potential energies. The use of higher-order terms may improve the performance of current algorithms. Proteins are represented by the side chain centroids of amino acids. Delaunay tessellation of this representation defines all sets of nearest neighbor quadruplets of amino acids. Four-body contact scoring function (log likelihoods of residue quadruplet compositions) is derived by the analysis of a diverse set of proteins with known structures. A test protein is characterized by the total score calculated as the sum of the individual log likelihoods of composing amino acid quadruplets. The scoring function distinguishes native from partially unfolded or deliberately misfolded structures. It also discriminates between pre- and post-transition state and native structures in the folding simulations trajectory of Chymotrypsin Inhibitor 2 (CI2).

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Title: Development of a four-body statistical pseudo-potential to discriminate native from non-native protein conformations
Creators: Bala Krishnamoorthy - Department of Operations Research, CB 3180, UNC Chapel Hill, NC 27599, USA
Alexander Tropsha
Publication Details: Bioinformatics (Oxford, England), Vol.19(12), pp.1540-1548
Academic Unit: Mathematics and Statistics, Department of
Publisher: England
Identifiers: 99900547730301842
Language: English
Resource Type: Journal article