Advancing Loop Prediction to Ultra-High Resolution Sampling

Homology modeling is integral to structure-based drug discovery. Robust homology modeling to atomic-level accuracy requires in the general case successful prediction of protein loops containing small segments of secondary structure. For loops identified to possess α-helical segments, an alternative dihedral library is employed composed of (phi,psi) angles commonly found in helices. Even with imperfect knowledge coming from sequence-based secondary structure, helix or hairpin embedded loops, up to 17 residues in length, are successfully predicted to median sub-angstrom RMSD. Having demonstrated success with these cases, performance costs for these and other similar long loop predictions will be discussed. Dramatic improvements in both speed and accuracy are possible through the development of a Cβ-based scoring function, applicable to hydrophobic residues, that can be applied as early as half-loop buildup. With this scoring function, up to a 30-fold reduction in the cost to produce competitive sub-2 A loops are observed. Through the use of this scoring function, an efficient method will be presented to achieve ultra-high resolution buildup that restrains combinatorial explosion and offers an alternative to the current approach to full-loop buildup. This novel method is designed to be inherently suitable for homology model refinement.