Ke Tang1, Samuel W K Wong2, Jun S Liu3, Jinfeng Zhang4, Jie Liang1. 1. Richard and Loan Hill Department of Bioengineering, University of Illinois at Chicago, Chicago, IL. 2. Department of Statistics, University of Florida, Gainesville, FL. 3. Department of Statistics, Harvard University, Science Center, Cambridge, MA and. 4. Department of Statistics, Florida State University, Tallahassee, FL, USA.
Abstract
MOTIVATION: Loops in proteins are often involved in biochemical functions. Their irregularity and flexibility make experimental structure determination and computational modeling challenging. Most current loop modeling methods focus on modeling single loops. In protein structure prediction, multiple loops often need to be modeled simultaneously. As interactions among loops in spatial proximity can be rather complex, sampling the conformations of multiple interacting loops is a challenging task. RESULTS: In this study, we report a new method called multi-loop Distance-guided Sequential chain-Growth Monte Carlo (M-DiSGro) for prediction of the conformations of multiple interacting loops in proteins. Our method achieves an average RMSD of 1.93 Å for lowest energy conformations of 36 pairs of interacting protein loops with the total length ranging from 12 to 24 residues. We further constructed a data set containing proteins with 2, 3 and 4 interacting loops. For the most challenging target proteins with four loops, the average RMSD of the lowest energy conformations is 2.35 Å. Our method is also tested for predicting multiple loops in β-barrel membrane proteins. For outer-membrane protein G, the lowest energy conformation has a RMSD of 2.62 Å for the three extracellular interacting loops with a total length of 34 residues (12, 12 and 10 residues in each loop). AVAILABILITY AND IMPLEMENTATION: The software is freely available at: tanto.bioe.uic.edu/m-DiSGro. CONTACT: jinfeng@stat.fsu.edu or jliang@uic.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Loops in proteins are often involved in biochemical functions. Their irregularity and flexibility make experimental structure determination and computational modeling challenging. Most current loop modeling methods focus on modeling single loops. In protein structure prediction, multiple loops often need to be modeled simultaneously. As interactions among loops in spatial proximity can be rather complex, sampling the conformations of multiple interacting loops is a challenging task. RESULTS: In this study, we report a new method called multi-loop Distance-guided Sequential chain-Growth Monte Carlo (M-DiSGro) for prediction of the conformations of multiple interacting loops in proteins. Our method achieves an average RMSD of 1.93 Å for lowest energy conformations of 36 pairs of interacting protein loops with the total length ranging from 12 to 24 residues. We further constructed a data set containing proteins with 2, 3 and 4 interacting loops. For the most challenging target proteins with four loops, the average RMSD of the lowest energy conformations is 2.35 Å. Our method is also tested for predicting multiple loops in β-barrel membrane proteins. For outer-membrane protein G, the lowest energy conformation has a RMSD of 2.62 Å for the three extracellular interacting loops with a total length of 34 residues (12, 12 and 10 residues in each loop). AVAILABILITY AND IMPLEMENTATION: The software is freely available at: tanto.bioe.uic.edu/m-DiSGro. CONTACT: jinfeng@stat.fsu.edu or jliang@uic.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Matthew P Jacobson; David L Pincus; Chaya S Rapp; Tyler J F Day; Barry Honig; David E Shaw; Richard A Friesner Journal: Proteins Date: 2004-05-01
Authors: Nicholas Noinaj; Adam J Kuszak; James C Gumbart; Petra Lukacik; Hoshing Chang; Nicole C Easley; Trevor Lithgow; Susan K Buchanan Journal: Nature Date: 2013-09-01 Impact factor: 49.962