scholarly journals Large scale relative protein ligand binding affinities using non-equilibrium alchemy

2020 ◽  
Vol 11 (4) ◽  
pp. 1140-1152 ◽  
Author(s):  
Vytautas Gapsys ◽  
Laura Pérez-Benito ◽  
Matteo Aldeghi ◽  
Daniel Seeliger ◽  
Herman van Vlijmen ◽  
...  

Relative ligand binding affinity calculations based on molecular dynamics (MD) simulations and non-physical (alchemical) thermodynamic cycles have shown great promise for structure-based drug design.

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Zbigniew Dutkiewicz

AbstractDrug design is an expensive and time-consuming process. Any method that allows reducing the time the costs of the drug development project can have great practical value for the pharmaceutical industry. In structure-based drug design, affinity prediction methods are of great importance. The majority of methods used to predict binding free energy in protein-ligand complexes use molecular mechanics methods. However, many limitations of these methods in describing interactions exist. An attempt to go beyond these limits is the application of quantum-mechanical description for all or only part of the analyzed system. However, the extensive use of quantum mechanical (QM) approaches in drug discovery is still a demanding challenge. This chapter briefly reviews selected methods used to calculate protein-ligand binding affinity applied in virtual screening (VS), rescoring of docked poses, and lead optimization stage, including QM methods based on molecular simulations.


2014 ◽  
Vol 20 (20) ◽  
pp. 3323-3337 ◽  
Author(s):  
M. Reddy ◽  
C. Reddy ◽  
R. Rathore ◽  
Mark Erion ◽  
P. Aparoy ◽  
...  

2021 ◽  
Author(s):  
Himanshu Goel ◽  
Anthony Hazel ◽  
Vincent D. Ustach ◽  
Sunhwan Jo ◽  
Wenbo Yu ◽  
...  

Predicting relative protein-ligand binding affinities is a central pillar of lead optimization efforts in structure-based drug design. The Site Identification by Ligand Competitive Saturation (SILCS) methodology is based on functional...


Author(s):  
Dheeraj Chitara ◽  
Sanjeev B. S.

Molecular Dynamics (MD) simulations model motion of molecules in atomistic detail and aid in drug design. While simulations on large systems may require several days to complete, analysis of terabytes of data generated in the process could also be time consuming. Recent studies captured exciting and dramatic drug-receptor interactions under cell-like complex conditions. Such advances make simulations of biomolecular interactions more realistic, insightful, and informative and have potential to make drug design more realistic. However, currently available resources and techniques do not provide, in reasonable time, a comprehensive understanding of events seen in simulations. We demonstrate that big data approach results in significant speedups, and provides rapid insights into simulations performed. Advancing this improvement, we propose a scalable, self-tuning, and responsive framework based on Cloud-infrastructure to accomplish the best possible MD studies with given priorities and within available resources.


2014 ◽  
Vol 1700 ◽  
pp. 61-66
Author(s):  
Guttormur Arnar Ingvason ◽  
Virginie Rollin

ABSTRACTAdding single walled carbon nanotubes (SWCNT) to a polymer matrix can improve the delamination properties of the composite. Due to the complexity of polymer molecules and the curing process, few 3-D Molecular Dynamics (MD) simulations of a polymer-SWCNT composite have been run. Our model runs on the Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS), with a COMPASS (Condensed phase Optimized Molecular Potential for Atomistic Simulations Studies) potential. This potential includes non-bonded interactions, as well as bonds, angles and dihedrals to create a MD model for a SWCNT and EPON 862/DETDA (Diethyltoluenediamine) polymer matrix. Two simulations were performed in order to test the implementation of the COMPASS parameters. The first one was a tensile test on a SWCNT, leading to a Young’s modulus of 1.4 TPa at 300K. The second one was a pull-out test of a SWCNT from an originally uncured EPON 862/DETDA matrix.


Soft Matter ◽  
2018 ◽  
Vol 14 (15) ◽  
pp. 2796-2807 ◽  
Author(s):  
Andrea Catte ◽  
Mark R. Wilson ◽  
Martin Walker ◽  
Vasily S. Oganesyan

Antimicrobial action of a cationic peptide is modelled by large scale MD simulations.


Author(s):  
Juan J Galano-Frutos ◽  
Helena García-Cebollada ◽  
Javier Sancho

Abstract The increasing ease with which massive genetic information can be obtained from patients or healthy individuals has stimulated the development of interpretive bioinformatics tools as aids in clinical practice. Most such tools analyze evolutionary information and simple physical–chemical properties to predict whether replacement of one amino acid residue with another will be tolerated or cause disease. Those approaches achieve up to 80–85% accuracy as binary classifiers (neutral/pathogenic). As such accuracy is insufficient for medical decision to be based on, and it does not appear to be increasing, more precise methods, such as full-atom molecular dynamics (MD) simulations in explicit solvent, are also discussed. Then, to describe the goal of interpreting human genetic variations at large scale through MD simulations, we restrictively refer to all possible protein variants carrying single-amino-acid substitutions arising from single-nucleotide variations as the human variome. We calculate its size and develop a simple model that allows calculating the simulation time needed to have a 0.99 probability of observing unfolding events of any unstable variant. The knowledge of that time enables performing a binary classification of the variants (stable-potentially neutral/unstable-pathogenic). Our model indicates that the human variome cannot be simulated with present computing capabilities. However, if they continue to increase as per Moore’s law, it could be simulated (at 65°C) spending only 3 years in the task if we started in 2031. The simulation of individual protein variomes is achievable in short times starting at present. International coordination seems appropriate to embark upon massive MD simulations of protein variants.


2020 ◽  
Vol 14 ◽  
Author(s):  
Thao N. T. Ho ◽  
Nikita Abraham ◽  
Richard J. Lewis

Neuronal nicotinic acetylcholine receptors (nAChRs) are prototypical cation-selective, ligand-gated ion channels that mediate fast neurotransmission in the central and peripheral nervous systems. nAChRs are involved in a range of physiological and pathological functions and hence are important therapeutic targets. Their subunit homology and diverse pentameric assembly contribute to their challenging pharmacology and limit their drug development potential. Toxins produced by an extensive range of algae, plants and animals target nAChRs, with many proving pivotal in elucidating receptor pharmacology and biochemistry, as well as providing templates for structure-based drug design. The crystal structures of these toxins with diverse chemical profiles in complex with acetylcholine binding protein (AChBP), a soluble homolog of the extracellular ligand-binding domain of the nAChRs and more recently the extracellular domain of human α9 nAChRs, have been reported. These studies have shed light on the diverse molecular mechanisms of ligand-binding at neuronal nAChR subtypes and uncovered critical insights useful for rational drug design. This review provides a comprehensive overview and perspectives obtained from structure and function studies of diverse plant and animal toxins and their associated inhibitory mechanisms at neuronal nAChRs.


MRS Advances ◽  
2017 ◽  
Vol 2 (29) ◽  
pp. 1571-1576
Author(s):  
Vinicius Splugues ◽  
Pedro Alves da Silva Autreto ◽  
Douglas S. Galvao

ABSTRACTThe advent of graphene created a revolution in materials science. Because of this there is a renewed interest in other carbon-based structures. Graphene is the ultimate (just one atom thick) membrane. It has been proposed that graphene can work as impermeable membrane to standard gases, such argon and helium. Graphene-like porous membranes, but presenting larger porosity and potential selectivity would have many technological applications. Biphenylene carbon (BPC), sometimes called graphenylene, is one of these structures. BPC is a porous two-dimensional (planar) allotrope carbon, with its pores resembling typical sieve cavities and/or some kind of zeolites. In this work, we have investigated the hydrogenation dynamics of BPC membranes under different conditions (hydrogenation plasma density, temperature, etc.). We have carried out an extensive study through fully atomistic molecular dynamics (MD) simulations using the reactive force field ReaxFF, as implemented in the well-known Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS) code. Our results show that the BPC hydrogenation processes exhibit very complex patterns and the formation of correlated domains (hydrogenated islands) observed in the case of graphene hydrogenation was also observed here. MD results also show that under hydrogenation BPC structure undergoes a change in its topology, the pores undergoing structural transformations and extensive hydrogenation can produce significant structural damages, with the formation of large defective areas and large structural holes, leading to structural collapse.


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