Abstract
Many protein-protein docking protocols are based on a shotgun approach, in which thousands of independent random-start trajectories minimize the rigid-body degrees of freedom. Another strategy is enumerative sampling as used in ZDOCK. Here, we introduce an alternative strategy, ReplicaDock, using a small number of long trajectories of temperature replica exchange. We compare replica exchange sampling as low-resolution stage of RosettaDock with RosettaDock's original shotgun sampling as well as with ZDOCK. A benchmark of 30 complexes starting from structures of the unbound binding partners shows improved performance for ReplicaDock and ZDOCK when compared to shotgun sampling at equal or less computational expense. ReplicaDock and ZDOCK consistently reach lower energies and generate significantly more near-native conformations than shotgun sampling. Accordingly, they both improve typical metrics of prediction quality of complex structures after refinement. Additionally, the refined ReplicaDock ensembles reach significantly lower interface energies and many previously hidden features of the docking energy landscape become visible when ReplicaDock is applied.
Highlights
Protein-protein interactions are one of the fundamental molecular mechanisms of life, and to investigate them it is important to know the atomic structures of the formed complexes
We address the following questions in this study: First, does Importance Sampling in the form of Replica Exchange have a benefit over shotgun sampling for the low-resolution stage of protein-protein docking despite the potentially misleading lowenergy function
Shotgun sampling consists of a random initial placement followed by a short energy optimization procedure using 500 steps of Monte Carlo sampling with adjusted step-sizes and constant inverse temperature of b~0:8{1kcal-1:mol(Methods)
Summary
Protein-protein interactions are one of the fundamental molecular mechanisms of life, and to investigate them it is important to know the atomic structures of the formed complexes. Computational protein docking describes any in silico methodology for combining structural knowledge of individual protein components with general knowledge about protein complexes (often in form of a potential energy function) and, if available, sparse data of the complex [4,5,6,7]. Many popular docking programs employ a two-stage approach: First, conformational space is sampled broadly, keeping partner structures rigid. To account for possible side-chain or loop motion, many docking methods employ a low-resolution model during their initial rigid-body search to create the required level of softness without adding extra degrees of freedom [13,15,16]. Whereas some methods use solely a low-resolution representation, others refine structures in an all-atom representation, often allowing side-chain and loop motion [17,18,19,20]. The highresolution stage generally serves only to discriminate conformations, not to generate them
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