Complete protein–protein association kinetics in atomic detail revealed by molecular dynamics simulations and Markov modelling

317 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Deep Generative Markov State Models (2018, methodology): “This makes the method promising for performing active learning in MD [2, 21], and to predict the future evolution of the system in other contexts.”
VAMPnets for deep learning of molecular kinetics (2017, background): “(2)– (4) and A k k(2)F1⁄4 n 1 P i;j A 2 ij is the Frobenius norm of n × n matrix A.”
Scalable emulation of protein equilibrium ensembles with generative deep learning (2025, unknown): “Due to the vast computational costs, exhaustive MD sampling of biomolecules has only been achieved in few cases, either with special purpose supercomputers [27] or via large-scale distributed simulations integrated via statistical models [9, 28].”
Constructing Markov State Models to elucidate the functional conformational changes of complex biomolecules (2018, methodology): “HMM has been successfully applied to study the dynamics of Ubiquitin,(128) and the recognition process between ribonuclease barnase and its inhibitor barstar.(39) For these two cases, where a clear separation of timescale exists, the HMM demonstrated better performance over MSMs.”
Coarse graining molecular dynamics with graph neural networks. (2020, background): “However, even the longest protein simulations still fail to reach total times exceeding milliseconds, and dedicated analysis methods are required to infer dynamics at longer timescales 5,6 .”