Generation of accurate protein loop conformations through low-barrier molecular dynamics

Prediction and refinement of protein loop structures are important and challenging tasks for which no general solution has been found. In addition to the accuracy of scoring functions, the main problems reside in (1) insufficient statistical sampling and (2) crossing energy barriers that impede conformational rearrangements of the loop. We approach these two issues by using "low-barrier molecular dynamics," a combination of energy smoothing techniques. To address statistical sampling, locally enhanced sampling (LES) is used to produce multiple copies of the loop, thus improving statistics and reducing energy barriers. We introduce a novel extension of LES that can improve local sampling even further through hierarchical subdivision of copies. Even though LES reduces energy barriers, it cannot provide for crossing infinite barriers, which can be problematic when substantial rearrangement of residues is necessary. To permit this kind of loop residue repacking, a "soft-core" potential energy function is introduced, so that atomic overlaps are temporarily allowed. We tested this new combined methodology to a loop in anti-influenza antibody Fab 17/9 (7 residues long) and to another loop in the antiprogesterone antibody DB3 (8 residues). In both cases, starting from random conformations, we were able to locate correct loop structures (including sidechain orientations) with heavy-atom root-mean-square deviation (fit to the nonloop region) of ∼1.1 Å in Fab 17/9 and ∼1.8 Å in DB3. We show that the combination of LES and soft-core potential substantially improves sampling compared to regular molecular dynamics. Moreover, the sampling improvement obtained with this combined approach is significantly better than that provided by either of the two methods alone.