Advances in enhanced sampling molecular dynamics simulations for biomolecules

Wang, Anhui; Zhang, Zhichao; Li, Guohui

doi:10.1063/1674-0068/cjcp1905091

Cited by 33 publications

(30 citation statements)

References 157 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Enhanced sampling techniques generally accelerate the crossing of energy barriers to achieve better sampling efficiency, such as by introducing bias potentials, modifying the potential energy itself, and changing the effective temperature. These techniques have proven essential in atomistic simulations of IDPs [70,71], yielding levels of convergence that could not be achieved even with drastically longer standard constant-temperature MD simulations [13]. The central idea of biased MD simulations is similar to importance sampling in MC simulations, where a biased potential is introduced to construct a flat free energy landscape along single or multiple collective variables of interest, such that many states can be readily sampled due to the removal of free energy barriers.…”

Section: Enhanced Sampling Methods For Sampling Idp Conformational Ensemblesmentioning

confidence: 99%

Advanced Sampling Methods for Multiscale Simulation of Disordered Proteins and Dynamic Interactions

Gong

Chen

2021

Biomolecules

View full text Add to dashboard Cite

Intrinsically disordered proteins (IDPs) are highly prevalent and play important roles in biology and human diseases. It is now also recognized that many IDPs remain dynamic even in specific complexes and functional assemblies. Computer simulations are essential for deriving a molecular description of the disordered protein ensembles and dynamic interactions for a mechanistic understanding of IDPs in biology, diseases, and therapeutics. Here, we provide an in-depth review of recent advances in the multi-scale simulation of disordered protein states, with a particular emphasis on the development and application of advanced sampling techniques for studying IDPs. These techniques are critical for adequate sampling of the manifold functionally relevant conformational spaces of IDPs. Together with dramatically improved protein force fields, these advanced simulation approaches have achieved substantial success and demonstrated significant promise towards the quantitative and predictive modeling of IDPs and their dynamic interactions. We will also discuss important challenges remaining in the atomistic simulation of larger systems and how various coarse-grained approaches may help to bridge the remaining gaps in the accessible time- and length-scales of IDP simulations.

show abstract

Section: Enhanced Sampling Methods For Sampling Idp Conformational Ensemblesmentioning

confidence: 99%

Advanced Sampling Methods for Multiscale Simulation of Disordered Proteins and Dynamic Interactions

Gong

Chen

2021

Biomolecules

View full text Add to dashboard Cite

show abstract

“…This fact leads to the trapping of such systems in energy wells of the conformational space, thus hindering the exploration of new states ( Figure 3 b). 36 , 41 , 55 , 56 In order to surmount this limitation and widen the sampling time scales that are typically accessed by MD, several enhanced sampling approaches have been developed. 36 , 56 Furthermore, an appropriate sampling enables the calculation of the free energy of the processes under study.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“… 36 , 41 , 55 , 56 In order to surmount this limitation and widen the sampling time scales that are typically accessed by MD, several enhanced sampling approaches have been developed. 36 , 56 Furthermore, an appropriate sampling enables the calculation of the free energy of the processes under study. 32 Therefore, in the following, we briefly explain the fundamentals of enhanced sampling methods used in LPS research for both exploring slow events and computing the free energy of the phenomena of interest.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

See 1 more Smart Citation

Fighting Against Bacterial Lipopolysaccharide-Caused Infections through Molecular Dynamics Simulations: A Review

González-Fernández

Basauri

Fallanza

et al. 2021

J. Chem. Inf. Model.

View full text Add to dashboard Cite

Lipopolysaccharide (LPS) is the primary component of the outer leaflet of Gram-negative bacterial outer membranes. LPS elicits an overwhelming immune response during infection, which can lead to life-threatening sepsis or septic shock for which no suitable treatment is available so far. As a result of the worldwide expanding multidrug-resistant bacteria, the occurrence and frequency of sepsis are expected to increase; thus, there is an urge to develop novel strategies for treating bacterial infections. In this regard, gaining an in-depth understanding about the ability of LPS to both stimulate the host immune system and interact with several molecules is crucial for fighting against LPS-caused infections and allowing for the rational design of novel antisepsis drugs, vaccines and LPS sequestration and detection methods. Molecular dynamics (MD) simulations, which are understood as being a computational microscope, have proven to be of significant value to understand LPS-related phenomena, driving and optimizing experimental research studies. In this work, a comprehensive review on the methods that can be combined with MD simulations, recently applied in LPS research, is provided. We focus especially on both enhanced sampling methods, which enable the exploration of more complex systems and access to larger time scales, and free energy calculation approaches. Thereby, apart from outlining several strategies for surmounting LPS-caused infections, this work reports the current state-of-the-art of the methods applied with MD simulations for moving a step forward in the development of such strategies.

show abstract

“…The content will be organized as follows. Part 2 describes fundamental challenges in molecular modeling; Part 3 summarizes application of these two fundamental algorithmic principles in two lines of methodological research, coarse graining (CG) [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ] and enhanced sampling (ES) [ 28 , 29 , 30 , 31 ]; Part 4 covers how machine learning, particularly deep learning, facilitates DC and “caching” in CG and ES [ 29 , 30 , 32 , 33 , 34 , 35 ], Part 5 introduces local free energy landscape (LFEL) approach, a new framework for computational molecular science based on partially transferable in resolution “caching” of local sampling. The first implementation of this new framework in protein structural refinement based on generalized solvation free energy (GSFE) theory [ 36 ] is briefly discussed; and Part 6 discusses connections among these three lines of algorithmic development, their specific advantages and prospective explorations.…”

Section: Introductionmentioning

confidence: 99%

“Dividing and Conquering” and “Caching” in Molecular Modeling

Cao

Tian

2021

IJMS

View full text Add to dashboard Cite

Molecular modeling is widely utilized in subjects including but not limited to physics, chemistry, biology, materials science and engineering. Impressive progress has been made in development of theories, algorithms and software packages. To divide and conquer, and to cache intermediate results have been long standing principles in development of algorithms. Not surprisingly, most important methodological advancements in more than half century of molecular modeling are various implementations of these two fundamental principles. In the mainstream classical computational molecular science, tremendous efforts have been invested on two lines of algorithm development. The first is coarse graining, which is to represent multiple basic particles in higher resolution modeling as a single larger and softer particle in lower resolution counterpart, with resulting force fields of partial transferability at the expense of some information loss. The second is enhanced sampling, which realizes “dividing and conquering” and/or “caching” in configurational space with focus either on reaction coordinates and collective variables as in metadynamics and related algorithms, or on the transition matrix and state discretization as in Markov state models. For this line of algorithms, spatial resolution is maintained but results are not transferable. Deep learning has been utilized to realize more efficient and accurate ways of “dividing and conquering” and “caching” along these two lines of algorithmic research. We proposed and demonstrated the local free energy landscape approach, a new framework for classical computational molecular science. This framework is based on a third class of algorithm that facilitates molecular modeling through partially transferable in resolution “caching” of distributions for local clusters of molecular degrees of freedom. Differences, connections and potential interactions among these three algorithmic directions are discussed, with the hope to stimulate development of more elegant, efficient and reliable formulations and algorithms for “dividing and conquering” and “caching” in complex molecular systems.

show abstract

Advances in enhanced sampling molecular dynamics simulations for biomolecules

Cited by 33 publications

References 157 publications

Advanced Sampling Methods for Multiscale Simulation of Disordered Proteins and Dynamic Interactions

Advanced Sampling Methods for Multiscale Simulation of Disordered Proteins and Dynamic Interactions

Fighting Against Bacterial Lipopolysaccharide-Caused Infections through Molecular Dynamics Simulations: A Review

“Dividing and Conquering” and “Caching” in Molecular Modeling

Contact Info

Product

Resources

About