Binding Thermodynamics and Interaction Patterns of Inhibitor-Major Urinary Protein-I Binding from Extensive Free-Energy Calculations: Benchmarking AMBER Force Fields

2021

IJMS

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.

Section: Challenges In Molecular Modelingmentioning

confidence: 99%

Section: Accurate Description Of Molecular Interactionsmentioning

confidence: 99%

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

Cao

2021

IJMS

“…This is in strong contrast to decades of trade-off in molecular modeling that improved efficiency being always accompanied more or less by reduced accuracy, and increased efficiency being always accompanied by more or less reduction of accuracy! When compared with conventional molecular mechanical force fields [30][31][32][33] or knowledge based potentials, [34][35][36] the ability of accounting for many-body correlations is another advantage of LFEL that is likely to contribute to improved accuracy. It is important to note that many neural network based force fields (NNFF) methodologies have been developed up to date.…”

Section: Repetitive Local Samplingmentioning

confidence: 99%

The repetitive local sampling and the local distribution theory

2021

Preprint

Previously, the ubiquitous issue regarding severe wasting of computational resources in all forms of molecular simulations due to repetitive local sampling was raised, and the local free energy landscape approach was proposed to address it. This approach is derived from a simple idea of first learning local distributions, and followed by dynamic assembly of which to infer global joint distribution of a target molecular system. When compared with conventional explicit solvent molecular dynamics simulations, a simple and approximate implementation of this theory in protein structural refinement harvested acceleration of about six orders of magnitude without loss of accuracy. While this initial test revealed tremendous benefits for addressing repetitive local sampling, there are some implicit assumptions need to be articulated.Here, I present a more thorough discussion of repetitive local sampling; potential options for learning local distributions; a more general formulation with potential extension to simulation of near equilibrium molecular systems; generalization of repetitive local sampling to repetitive local computation and potential application in accelerating numerical solving of complex equations; the prospect of developing computation driven molecular science; and the connection to mainstream residue pair distance distribution based protein structure prediction/refinement. 1This more general development is termed the local distribution theory to release the limitation of strict thermodynamic equilibrium in its potential wide application in both soft condensed molecular systems and complex equations.

“…This is in strong contrast to decades of trade-off in molecular simulation that improved efficiency being always accompanied more or less by reduced accuracy, and increased efficiency being always accompanied by more or less reduction of accuracy! When compared with conventional molecular mechanical force fields [30][31][32][33] or knowledge based potentials, [34][35][36] the ability of accounting for many-body correlations is another advantage of LFEL that is likely to contribute to improved accuracy. It is important to note that many neural network based force fields (NNFF) methodologies have been developed up to date.…”

Section: Repetitive Local Samplingmentioning

confidence: 99%

The repetitive local sampling and the local distribution theory

2021

Preprint

Molecular simulation is a mature and versatile tool set widely utilized in many subjects with more than 30,000 publications each year. However, its methodology development has been struggling with a tradeoff between accuracy/resolution and speed, significant improvement of both beyond present state of the art is necessary to reliably substitute many expensive and laborious experiments in molecular biology, materials science and nanotechnology. Previously, the ubiquitous issue regarding severe wasting of computational resources in all forms of molecular simulations due to repetitive local sampling was raised, and the local free energy landscape approach was proposed to address it. This approach is derived from a simple idea of first learning local distributions, and followed by dynamic assembly of which to infer global joint distribution of a target molecular system. When compared with conventional explicit solvent molecular dynamics simulations, a simple and approximate implementation of this theory in protein structural refinement harvested acceleration of about six orders of magnitude without loss of accuracy. While this initial test revealed tremendous benefits for addressing repetitive local sampling, there are some implicit assumptions need to be articulated. Here, I present a more thorough discussion of repetitive local sampling; potential options for learning 1 local distributions; a more general formulation with potential extension to simulation of near equilibrium molecular systems; the prospect of developing computation driven molecular science; the connection to mainstream residue pair distance distribution based protein structure prediction/refinement; and the fundamental difference of utilizing averaging from conventional molecular simulation framework based on potential of mean force. This more general development is termed the local distribution theory to release the limitation of strict thermodynamic equilibrium in its potential wide application in general soft condensed molecular systems.