The Tinker software, currently released as version 8, is a modular molecular mechanics and dynamics package written primarily in a standard, easily portable dialect of Fortran 95 with OpenMP extensions It supports a wide variety of force fields, including polarizable models such as the Atomic Multipole Optimized Energetics for Biomolecular Applications (AMOEBA) force field The package runs on Linux, macOS, and Windows systems In addition to canonical Tinker, there are branches, Tinker-HP and Tinker-OpenMM, designed for use on message passing interface (MPI) parallel distributed memory supercomputers and state-of-the-art graphical processing units (GPUs), respectively The Tinker suite also includes a tightly integrated Java-based graphical user interface called Force Field Explorer (FFE), which provides molecular visualization capabilities as well as the ability to launch and control Tinker calculations

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Tinker 8: Software Tools for Molecular Design.

Molecular dynamics (MD) and related methods are close to becoming routine computational tools for drug discovery. Their main advantage is in explicitly treating structural flexibility and entropic effects. This allows a more accurate estimate of the thermodynamics and kinetics associated with drug-target recognition and binding, as better algorithms and hardware architectures increase their use. Here, we review the theoretical background of MD and enhanced sampling methods, focusing on free-energy perturbation, metadynamics, steered MD, and other methods most consistently used to study drug-target binding. We discuss unbiased MD simulations that nowadays allow the observation of unsupervised ligand-target binding, assessing how these approaches help optimizing target affinity and drug residence time toward improved drug efficacy. Further issues discussed include allosteric modulation and the role of water molecules in ligand binding and optimization. We conclude by calling for more prospective studies to attest to these methods' utility in discovering novel drug candidates.

Role of Molecular Dynamics and Related Methods in Drug Discovery.

The location of energy minima on the conformational energy surface of molecules by computational methods (conformational searching) continues to play a key role in computer-assisted molecular modeling. Although a number of conformational search procedures have been devised over the past several years, new more efficient methods are urgently needed if molecules with increased complexity are to be treated in a quantitative manner. In this paper we describe a method, termed low-mode search (LMOD), which is based on eigenvector following (or mode following), for the exhaustive exploration of the potential energy hypersurface of molecules. It is particularly efficient at searching the conformational space of both cyclic and acyclic molecules, and we describe its effectiveness for a number of conformational search problems including acyclic, monocyclic, and bicyclic hydrocarbons and cyclic pentapeptides. No special treatment of rings in cyclic molecules is necessary, nor is it necessary to define rotatable bond...

Electrochemical oxidation and structural changes of 5,6-dihydrobenzo[c]cinnolines

We report the application of Langevin dynamics to the physics-based united-residue (UNRES) force field developed in our laboratory. Ten trajectories were run on seven proteins [PDB ID codes 1BDD (α; 46 residues), 1GAB (α; 47 residues), 1LQ7 (α; 67 residues), 1CLB (α; 75 residues), 1E0L (β; 28 residues), and 1E0G (α+β; 48 residues), and 1IGD (α+β; 61 residues)] with the UNRES force field parameterized by using our recently developed method for obtaining a hierarchical structure of the energy landscape. All α-helical proteins and 1E0G folded to the native-like structures, whereas 1IGD and 1E0L yielded mostly nonnative α-helical folds although the native-like structures are lowest in energy for these two proteins, which can be attributed to neglecting the entropy factor in the current parameterization of UNRES. Average folding times for successful folding simulations were of the order of nanoseconds, whereas even the ultrafast-folding proteins fold only in microseconds, which implies that the UNRES time scale is approximately three orders of magnitude larger than the experimental time scale because the fast motions of the secondary degrees of freedom are averaged out. Folding with Langevin dynamics required 2–10 h of CPU time on average with a single AMD Athlon MP 2800+ processor depending on the size of the protein. With the advantage of parallel processing, this process leads to the possibility to explore thousands of folding pathways and to predict not only the native structure but also the folding scenario of a protein together with its quantitative kinetic and thermodynamic characteristics.

https://www.pnas.org/content/pnas/102/7/2362.full.pdf

Ab initio simulations of protein-folding pathways by molecular dynamics with the united-residue model of polypeptide chains.

Global energy optimization of a molecular system is difficult due to the well-known “multiple minimum” problem. The rugged potential energy surface (PES) characteristic of multidimensional systems can be transformed reversibly using potential smoothing to generate a new surface that is easier to search for favorable configurations. The diffusion equation method (DEM) is one example of a potential smoothing algorithm. Potential smoothing as implemented in DEM is intuitively appealing and has certain appropriate statistical mechanical properties, but often fails to identify the global minimum even for relatively small problems. In the present paper, extensions to DEM capable of correcting its empirical behavior are systematically investigated. Two types of local search (LS) procedures are applied during the reversing schedule from the smooth deformed PES to the undeformed surface. Changes needed to generate smoothable versions of standard molecular mechanics force fields such as AMBER/OPLS and MM2 are also described. The resulting methods are applied in an attempt to determine the global energy minimum for a variety of systems in different coordinate representations. The problems studied include argon clusters, cycloheptadecane, capped polyalanine, and the docking of R-helices. Depending on the specific problem, potential smoothing and search (PSS) is performed in Cartesian, torsional, or rigid body space. For example, PSS finds a very low energy structure for cycloheptadecane with much greater efficiency than a search restricted to the undeformed potential surface. It is shown that potential smoothing is characterized by three salient features. As the level of smoothing is increased, unique minima merge into a common basin, crossings can occur in the relative energies of a pair of minima, and the spatial locations of minima are shifted due to the averaging effects of smoothing. Local search procedures improve the ability of smoothing methods to locate global minima because they facilitate the post facto correction of errors due to energy crossings that may have occurred at higher levels of smoothing. PSS methods should serve as useful tools for global energy optimization on a variety of difficult problems of practical interest.

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Analysis and Application of Potential Energy Smoothing and Search Methods for Global Optimization

Although Monte Carlo and molecular dynamics are the primary methods used for free energy simulations of molecular systems, their application to molecules that have multiple conformations separated by energy barriers of ≥ 3 kcal/mol is problematic because of slow rates of convergence. In this article we introduce a hybrid simulation method termed MC-SD which mixes Monte Carlo (MC) and stochastic dynamics (SD). This new method generates a canonical ensemble via alternating MC and SD steps and combines the local exploration strengths of dynamics with the barrier-crossing ability of large-step Monte Carlo. Using calculations on double-well potentials and long simulations (108 steps of MC and 1 μs of SD) of the simple, conformationally flexible molecule n-pentane, we find that MC-SD simulations converage faster than either MC or SD alone and generate ensembles which are equivalent to those created by classical MC or SD. Using pure SD at 300 K, the conformational populations of n-pentane are shown to be poorly converged even after a full microsecond of simulation. © 1994 by John Wiley & Sons, Inc.

Frank Guarnieri

Papers

A rapidly convergent simulation method: mixed Monte Carlo/stochastic dynamics