BCL::Fold-Protein topology determination from limited NMR restraints

• Published in: Proteins, structure, function, and bioinformatics Vol. 82; no. 4; pp. 587 - 595
• Main Authors: Weiner, Brian E., Alexander, Nathan, Akin, Louesa R., Woetzel, Nils, Karakas, Mert, Meiler, Jens
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01.04.2014; Wiley Subscription Services, Inc
• Subjects: Algorithms; Models, Chemical; Models, Molecular; Monte Carlo Method; NOE; Nuclear Magnetic Resonance, Biomolecular - methods; Protein Conformation; Protein Folding; protein structure prediction; Protein Structure, Secondary; Proteins; Proteins - chemistry; Proteins - metabolism; Proteins - ultrastructure; RDC; sparse data; Topology; topology prediction; RDC; sparse data; NOE; protein structure prediction; topology prediction
• ISSN: 0887-3585; 1097-0134; 1097-0134
• DOI: 10.1002/prot.24427

ABSTRACT When experimental protein NMR data are too sparse to apply traditional structure determination techniques, de novo protein structure prediction methods can be leveraged. Here, we describe the incorporation of NMR restraints into the protein structure prediction algorithm BCL::Fold. The method assembles discreet secondary structure elements using a Monte Carlo sampling algorithm with a consensus knowledge‐based energy function. New components were introduced into the energy function to accommodate chemical shift, nuclear Overhauser effect, and residual dipolar coupling data. In particular, since side chains are not explicitly modeled during the minimization process, a knowledge based potential was created to relate experimental side chain proton–proton distances to Cβ–Cβ distances. In a benchmark test of 67 proteins of known structure with the incorporation of sparse NMR restraints, the correct topology was sampled in 65 cases, with an average best model RMSD100 of 3.4 ± 1.3 Å versus 6.0 ± 2.0 Å produced with the de novo method. Additionally, the correct topology is present in the best scoring 1% of models in 61 cases. The benchmark set includes both soluble and membrane proteins with up to 565 residues, indicating the method is robust and applicable to large and membrane proteins that are less likely to produce rich NMR datasets. Proteins 2014; 82:587–595. © 2013 Wiley Periodicals, Inc.