FFParam: Standalone package for CHARMM additive and Drude polarizable force field parametrization of small molecules

Kumar, Anmol; Yoluk, Özge; MacKerell, Alexander D.

doi:10.1002/jcc.26138

Cited by 70 publications

(69 citation statements)

References 40 publications

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“…The LibDock module used a grid placed in the binding site and used polar and non-polar probes to calculate protein hot spots, then further used hot spots to arrange the ligands to form favorable interactions. Moreover, the study also used the Smart Minimiser algorithm and the CHARMm force field (Cambridge, MA, USA) to minimize the ligands ( 27 ). Then, all ligands’ positions were adjusted and ranked according to the calculated ligand scores.…”

Section: Methodsmentioning

confidence: 99%

Novel Natural Inhibitors Targeting Enhancer of Zeste Homolog 2: A Comprehensive Structural Biology Research

Ding

Zhao

et al. 2021

Front. Oncol.

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The enhancer of zeste homolog 2 (EZH2) is a methylated modification enzyme of Histone H3-Lys 27. The high expression of EZH2 in cells is closely related to the progression, invasion, and metastasis of neoplasm. Therefore, this target is gradually becoming one of the research hot spots of tumor pathogenesis, and the inhibitors of the EZH2 enzyme are expected to become new antitumor drugs. This study used a series of virtual screening technologies to calculate the affinity between the compounds obtained from the ZINC15 database and the target protein EZH2, the stability of the ligand–receptor complex. This experiment also predicted the toxicity and absorption, distribution, metabolism, and excretion (ADME) properties of the candidate drugs in order to obtain compounds with excellent pharmacological properties. Finally, the ligand–receptor complex under in vivo situation was estimated by molecular dynamics simulation to observe whether the complex could exist steadily in the body. The experimental results showed that the two natural compounds ZINC000004217536 and ZINC000003938642 could bind tightly to EZH2, and the ligand–receptor complex could exist stably in vivo. Moreover, these two compounds were calculated to be nontoxic. They also had a high degree of intestinal absorption and high bioavailability. In vitro experiments confirmed that drug ZINC000003938642 could inhibit the proliferation and migration of osteosarcoma, which could serve as potential lead compounds. Therefore, the discovery of these two natural products had broad prospects in the development of EZH2 inhibitors, providing new clues for the treatment or adjuvant treatment of tumors.

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Section: Methodsmentioning

confidence: 99%

Novel Natural Inhibitors Targeting Enhancer of Zeste Homolog 2: A Comprehensive Structural Biology Research

Ding

Zhao

et al. 2021

Front. Oncol.

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“…However, such quantum mechanically detailed computation is prohibitively expensive for any realistic complex molecular systems. Molecular interactions are traditionally represented by explicit functions and pairwise approximations as exemplified by typical physics based atomistic molecular mechanical (MM) force fields (FFs) [ 44 , 45 , 46 , 47 ]:

or knowledge based potential functions [ 48 , 49 , 50 ]: these simple functions, while being amenable to rapid computation and are physically sound grounded near local energy minima (e.g., harmonic behavior of bonding, bending near equilibrium bond lengths and bend angles), are potentially problematic for anharmonic interactions, which are very common in some molecular systems [ 51 ]. It is well understood that properly parameterized Lennard–Jones potentials are accurate only near the bottom of its potential well.…”

Section: Challenges In Molecular Modelingmentioning

confidence: 99%

Section: Accurate Description Of Molecular Interactionsmentioning

confidence: 99%

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

Cao

Tian

2021

IJMS

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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.

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“…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

Tian

2021

Preprint

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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.

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FFParam: Standalone package for CHARMM additive and Drude polarizable force field parametrization of small molecules

Cited by 70 publications

References 40 publications

Novel Natural Inhibitors Targeting Enhancer of Zeste Homolog 2: A Comprehensive Structural Biology Research

Novel Natural Inhibitors Targeting Enhancer of Zeste Homolog 2: A Comprehensive Structural Biology Research

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

The repetitive local sampling and the local distribution theory

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