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pmid: 29166086
A detailed understanding of the conformational dynamics of biological molecules is difficult to obtain by experimental techniques due to resolution limitations in both time and space. Computer simulations avoid these in theory but are often too short to sample rare events reliably. Here we show that the progress index-guided sampling (PIGS) protocol can be used to enhance the sampling of rare events in selected parts of biomolecules without perturbing the remainder of the system. The method is very easy to use as it only requires as essential input a set of several features representing the parts of interest sufficiently. In this feature space, new states are discovered by spontaneous fluctuations alone and in unsupervised fashion. Because there are no energetic biases acting on phase space variables or projections thereof, the trajectories PIGS generates can be analyzed directly in the framework of transition networks. We demonstrate the possibility and usefulness of such focused explorations of biomolecules with two loops that are part of the binding sites of bromodomains, a family of epigenetic “reader” modules. This real-life application uncovers states that are structurally and kinetically far away from the initial crystallographic structures and are also metastable. Representative conformations are intended to be used in future high-throughput virtual screening campaigns.
Binding Sites, Protein Conformation, Proteins, 610 Medicine & health, Molecular Dynamics Simulation, 3100 General Physics and Astronomy, High-Throughput Screening Assays, Kinetics, 10019 Department of Biochemistry, 570 Life sciences; biology, 1606 Physical and Theoretical Chemistry
Binding Sites, Protein Conformation, Proteins, 610 Medicine & health, Molecular Dynamics Simulation, 3100 General Physics and Astronomy, High-Throughput Screening Assays, Kinetics, 10019 Department of Biochemistry, 570 Life sciences; biology, 1606 Physical and Theoretical Chemistry
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 9 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |