Molecular kinetics underlies all biological phenomena and, like many other biological processes, may best be understood in terms of networks. These networks, called Markov state models (MSMs), are typically built from physical simulations. Thus, they are capable of quantitative prediction of experiments and can also provide an intuition for complex conformational changes. Their primary application has been to protein folding; however, these technologies and the insights they yield are transferable. For example, MSMs have already proved useful in understanding human diseases, such as protein misfolding and aggregation in Alzheimer's disease
The conformational and thermodynamic properties of disordered proteins are commonly described in ter...
A new class of rare event acceleration techniques based on steered molecular dynamics (SMD) simulati...
Motivation: Network inference approaches are widely used to shed light on regulatory interplay betwe...
Markov State Modelling as a concept for a coarse grained description of the essential kinetics of a ...
Conformational changes of proteins are an*Author contributed equally with all other contributors. es...
Molecular recognition, the process by which biological macromolecules selectively bind, plays an imp...
Markov State Modelling as a concept for a coarse grained description of the essential kinetics of a ...
With recent advances in structural biology, including experi-mental techniques and deep learning-ena...
Molecular dynamic (MD) simulations are animportant tool for studying protein aggregation processes, ...
CONSPECTUS: Protein function is inextricably linked to protein dynamics. As we move from a static st...
Mechanistic (also called kinetic) models quantitatively describe dynamic and steady states of bioche...
Markov State Models (MSMs) are constructed from Molecular Dynamics (MD) simulation data, high-resolu...
A new class of rare event acceleration techniques based on steered molecular dynamics (SMD) simulati...
BackgroundGene regulatory networks with dynamics characterized by multiple stable states underlie ce...
Markov state models (MSMs) and other related kinetic network models are frequently used to study the...
The conformational and thermodynamic properties of disordered proteins are commonly described in ter...
A new class of rare event acceleration techniques based on steered molecular dynamics (SMD) simulati...
Motivation: Network inference approaches are widely used to shed light on regulatory interplay betwe...
Markov State Modelling as a concept for a coarse grained description of the essential kinetics of a ...
Conformational changes of proteins are an*Author contributed equally with all other contributors. es...
Molecular recognition, the process by which biological macromolecules selectively bind, plays an imp...
Markov State Modelling as a concept for a coarse grained description of the essential kinetics of a ...
With recent advances in structural biology, including experi-mental techniques and deep learning-ena...
Molecular dynamic (MD) simulations are animportant tool for studying protein aggregation processes, ...
CONSPECTUS: Protein function is inextricably linked to protein dynamics. As we move from a static st...
Mechanistic (also called kinetic) models quantitatively describe dynamic and steady states of bioche...
Markov State Models (MSMs) are constructed from Molecular Dynamics (MD) simulation data, high-resolu...
A new class of rare event acceleration techniques based on steered molecular dynamics (SMD) simulati...
BackgroundGene regulatory networks with dynamics characterized by multiple stable states underlie ce...
Markov state models (MSMs) and other related kinetic network models are frequently used to study the...
The conformational and thermodynamic properties of disordered proteins are commonly described in ter...
A new class of rare event acceleration techniques based on steered molecular dynamics (SMD) simulati...
Motivation: Network inference approaches are widely used to shed light on regulatory interplay betwe...