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Protein-protein interaction (PPI) is a crucial event for many biological functions. Studying the molecular details of PPI requires structure determination using X-ray crystallography, nuclear magnetic resistance (NMR), and single particle Cryo-EM. However, sometimes it is not easy to solve the complex structure for various reasons. For example, complex may be unstable, not enough protein expression for structural studies, etc. Further, PPI are intricate processes, and its molecular details cannot be fully explained by experimental observations. Here, we describe a quick and simple method to study the PPI using the combinatorial approach of molecular dynamics simulation and biophysical methods., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

In a recent work, Fuentes and Alejandre (2014) found that for TIP4P models there is a dipole moment of minimum density at 240 K and that the Lennard-Jones parameters can be adjusted to match the experimental dielectric constant at 300 K and the temperature of maximum density, respectively. The same procedure is used in this work to re-parameterize the simple point charge (SPC) model keeping the original geometry. The new model fails to reproduce the experimental self-diffusion coefficient and shear viscosity but improves the results at different temperatures and pressures of dielectric constant, isothermal compressibility, thermal expansion coefficient, surface tension, coexisting densities at the liquid–vapor interface, equation of state of ice Ih and equation of state of liquids at high pressures. A second model that reproduces the dielectric constant, self-diffusion coefficient and shear viscosity is proposed but the temperature of maximum density is 250 K, compared with the experimental value of 277 K. Both models improve the SPC/E results for almost all properties. The TIP3P model was also analyzed but the liquid density at 240 K always increases and a minimum in the dipole moment was not found. It is not possible to adjust for that model the charge distribution and short range interaction parameters to reproduce at the same time the target properties. [ABSTRACT FROM AUTHOR]

As intermolecular interactions such as the hydrogen bond are electrostatic in origin, rigorous treatment of this term within force field methodologies should be mandatory. We present a method able of accurately reproducing such interactions for seven van der Waals complexes. It uses atomic multipole moments up to hexadecupole moment mapped to the positions of the nuclear coordinates by the machine learning method kriging. Models were built at three levels of theory: HF/6-31G ** , B3LYP/aug-cc-pVDZ and M06-2X/aug-cc-pVDZ. The quality of the kriging models was measured by their ability to predict the electrostatic interaction energy between atoms in external test examples for which the true energies are known. At all levels of theory, >90% of test cases for small van der Waals complexes were predicted within 1 kJ mol −1 , decreasing to 60–70% of test cases for larger base pair complexes. Models built on moments obtained at B3LYP and M06-2X level generally outperformed those at HF level. For all systems the individual interactions were predicted with a mean unsigned error of less than 1 kJ mol −1 . [ABSTRACT FROM AUTHOR]

To elucidate how primary motor cortex (M1) neurons contribute to the performance of a broad range of different and even incompatible motor skills, we trained two monkeys to perform single-degree-of-freedom elbow flexion/extension movements that could be perturbed by a variety of externally generated force fields. Fields were presented in a pseudorandom sequence of trial blocks. Different computer monitor background colors signaled the nature of the force field throughout each block. There were five different force fields: null field without perturbing torque, assistive and resistive viscous fields proportional to velocity, a resistive elastic force field proportional to position and a resistive viscoelastic field that was the linear combination of the resistive viscous and elastic force fields. After the monkeys were extensively trained in the five field conditions, neural recordings were subsequently made in M1 contralateral to the trained arm. Many caudal M1 neurons altered their activity systematically across most or all of the force fields in a manner that was appropriate to contribute to the compensation for each of the fields. The net activity of the entire sample population likewise provided a predictive signal about the differences in the time course of the external forces encountered during the movements across all force conditions. The neurons showed a broad range of sensitivities to the different fields, and there was little evidence of a modular structure by which subsets of M1 neurons were preferentially activated during movements in specific fields or combinations of fields. [ABSTRACT FROM AUTHOR]

The generation of bond, angle, and torsion parameters for classical molecular dynamics force fields typically requires fitting parameters such that classical properties such as energies and gradients match precalculated quantum data for structures that scan the value of interest. We present a program, Paramfit, distributed as part of the AmberTools software package that automates and extends this fitting process, allowing for simplified parameter generation for applications ranging from single molecules to entire force fields. Paramfit implements a novel combination of a genetic and simplex algorithm to find the optimal set of parameters that replicate either quantum energy or force data. The program allows for the derivation of multiple parameters simultaneously using significantly fewer quantum calculations than previous methods, and can also fit parameters across multiple molecules with applications to force field development. Paramfit has been applied successfully to systems with a sparse number of structures, and has already proven crucial in the development of the Assisted Model Building with Energy Refinement Lipid14 force field. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

A variety of topics are reviewed in the area of mathematical and computational modeling in biology, covering the range of scales from populations of organisms to electrons in atoms. The use of maximum entropy as an inference tool in the fields of biology and drug discovery is discussed. Mathematical and computational methods and models in the areas of epidemiology, cell physiology and cancer are surveyed. The technique of molecular dynamics is covered, with special attention to force fields for protein simulations and methods for the calculation of solvation free energies. The utility of quantum mechanical methods in biophysical and biochemical modeling is explored. The field of computational enzymology is examined. [ABSTRACT FROM AUTHOR]

Abstract: In most rotor design methods, the blade load is found by a blade element analysis in an iterative procedure with flow solvers like actuator disc and -line analyses as well as momentum balances. For the flow solvers the force field is the input. In most other aerodynamic analyses the force field is the output result instead of input. This is done by applying boundary conditions at the lifting surface with which the flow is solved and the pressure at the surface, so the load, is determined (only inviscid flows are considered here). Both approaches are consistent, but appear to differ with respect to the generation of vorticity. In the lifting surface approach, usually Helmoltz's laws are used to show that bound and free vorticity is conserved instead of being generated, while in the force field approach vorticity is generated instead of conserved. It is shown that both methods are consistent since sometimes Helmholtz's laws are incorrectly referred to. These laws have been derived in absence of non-conservative forces, while the surface pressure distribution is shown to be such a force field. Besides this, the question is discussed how a force field creates vorticity in an inviscid flow, since some papers consider viscosity to be necessary to generate vorticity. A literature study contradicts this, showing that in inviscid flows vorticity is generated by tangential pressure gradients or, equivalently, a non-uniform force field. This makes the Euler equation including the force field term well suited to express the generation of vorticity in characteristics of the force field. A comparison of the convection of vorticity in the wake of a disc, rotor blade and wing shows several differences. The azimuthal vorticity in the disc wake does not depend on vorticity conservation laws, in contrast to the axial and radial components. For a rotor and wing all components are governed by vorticity conservation. [Copyright &y& Elsevier]

A novel method is presented for fitting force-field dihedral angles using an ensemble of structures generated from an ab initio Monte Carlo simulation. Importance sampling is used to achieve an efficient algorithm using a low level of theory to minimize the system at each step with the dihedral angles constrained, followed by dihedral fitting using the single point energies at a higher level of theory. The resulting method is an order of magnitude more efficient than the traditional method of doing a constrained scan over each dihedral independently. Also as the sampling is more uniformly distributed, the full surface is approximated to a greater accuracy. The dihedral fitting is done with a nonlinear optimization method to vary the phase as well as the force constant. The utility of the method is demonstrated by fitting dihedrals of methyl L-lactate, diisopropyl fluorophosphate, isopentenyl phosphate, a leucine dipeptide, and two inhibitors of Signal Transducer and Activator of Transcription 5. The results show that the Monte Carlo scheme is more efficient than constrained scans and is particularly effective at approximating the underlying potential energy surface when the dihedral degrees are coupled. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Abstract: In this study we have systematically investigated the effects of surface configuration and molecular state for the adsorption of methionine (C5H11NO2S) on a graphite surface using two model force fields, AMBER 3 and MM+. Computational results were compared with experimental results. A commercial computational chemistry software (HyperChem™) was utilized in this study. Parameter sets for the force fields, AMBER 3 and MM+, were employed together with methionine in its non-ionic and zwitterionic state to investigate the molecular adsorption on a graphene sheet, a graphene sheet with hydrogen termination, as well as double layers of graphite with and without hydrogen termination. The results show that the model produces excellent qualitative results regarding inter-molecular configuration. Using the AMBER3 force field and methionine in its zwitterionic state adsorbed on a single graphite sheet leads to results which clearly resemble the experimental results and show hydrogen bonding between neighboring methionine molecules through the amino and carboxyl groups. Dimers are formed when two molecules are anti-parallel to each other, and parallel molecules are the building blocks of molecular rows. [Copyright &y& Elsevier]

Highlights: [•] Conformational analysis of repeating units in pneumococcal serotypes 19A and 19F. [•] CHARMM and GLYCAM parameter sets show broad agreement. [•] Force field differences are result of charges on aliphatic hydrogens. [•] Different conformational motifs revealed for 19A and 19F serotypes. [•] This may account for the lack of cross-protection of 19F vaccination against 19A. [Copyright &y& Elsevier]

The Nobel Prize in Chemistry, 2013 has been awarded to Martin Karplus, Michael Levitt and Arieh Warshel for their studies on development of multi-scale models for complex chemical systems. This article introduces the readers to the theoreticalmethods developed for the study of complex chemical and biological systems and connects the works of the awardees to this vast area of research. The development of quantum chemistry (QC or QM) to study molecules at electronic level is briefly mentioned. The growth of methods based on classical physics to study large systems at the molecular level (MM) is discussed. The origin of hybridQM/MM techniques to perform multi-scale modelling is then presented. Further, the parallel development of molecular simulations, which connects the macroscopic and the microscopic world elucidating the dynamical properties of molecules, is introduced. Finally, the impact of theoretical chemistry on the advancement of our understanding of complex chemical and biological systems is highlighted. [ABSTRACT FROM AUTHOR]

Currently, the best existing molecular dynamics (MD) force fields cannot accurately reproduce the global free-energy minimum which realizes the experimental protein structure. As a result, long MD trajectories tend to drift away from the starting coordinates (e.g., crystallographic structures). To address this problem, we have devised a new simulation strategy aimed at protein crystals. An MD simulation of protein crystal is essentially an ensemble simulation involving multiple protein molecules in a crystal unit cell (or a block of unit cells). To ensure that average protein coordinates remain correct during the simulation, we introduced crystallography-based restraints into the MD protocol. Because these restraints are aimed at the ensemble-average structure, they have only minimal impact on conformational dynamics of the individual protein molecules. So long as the average structure remains reasonable, the proteins move in a native-like fashion as dictated by the original force field. To validate this approach, we have used the data from solid-state NMR spectroscopy, which is the orthogonal experimental technique uniquely sensitive to protein local dynamics. The new method has been tested on the well-established model protein, ubiquitin. The ensemble-restrained MD simulations produced lower crystallographic R factors than conventional simulations; they also led to more accurate predictions for crystallographic temperature factors, solid-state chemical shifts, and backbone order parameters. The predictions for 15N [ABSTRACT FROM AUTHOR]

*Scientific research supported by the Ministry of Science and Higher Education, Poland in years 2008-2010. Grant No. N N516 265835: Protein Structure Similarity Searching in Distributed Multi Agent System †To whom the entire correspondence should be directed: Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland, e-mail:Dariusz.Mrozek@polsl.pl

An all-atom force field is developed for metal organic frameworks ZnO(RCO2) by fitting to quantum mechanics data. Molecular simulations are conducted to validate the force field by calculating thermal expansion coefficients, crystal bulk and Young's moduli, power spectra, self-diffusion coefficients, and activation energies of self-diffusions for benzene and n-hexane. The calculated results are in good agreement with available experimental data. The proposed force field is suitable for simulations of adsorption or diffusion of organic molecules with flexible frameworks. [ABSTRACT FROM AUTHOR]

We report the performance of a classical density functional theory (CDFT) in the competition for the solvation free-energy category of the SAMPL4 blind prediction event. The theoretical calculations were carried out with the TIP3P water model and different combinations of solute configurations and molecular force fields. In comparison with the experimental data, the blind test yields an average unsigned error of 2.38 kcal/mol and the root mean square deviation of 2.99 kcal/mol. Whereas these numbers are significantly larger than the best results from explicit-solvent MD simulations, we find that the theoretical performance is sensitive to both the molecular force fields and solute configurations and that a comparable level of accuracy can be achieved by a judicious selection of the solute configurations and the force-field parameters. Most importantly, CDFT reduces the computational cost of MD simulation by almost 3 orders of magnitude, making it very attractive for large-scale hydration free-energy calculations (e.g., screening the aqueous solubility of drug-like molecules). [ABSTRACT FROM AUTHOR]

Abstract: A nonempirical, physics-based modeling and simulation (M&S) capability for energetic materials can only be obtained within a multiscale M&S framework that has a quantum mechanical foundation. In this chapter, quantum mechanical methods and applications to investigate these challenging materials will be given, along with descriptions of how these can be used to enable a quantum-informed multiscale M&S capability for energetic materials. Current capability gaps will be presented, along with suggestions for research advances needed to address these deficiencies. [Copyright &y& Elsevier]

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Asphaltenes are heavy crude oil compounds, defined as soluble in toluene and precipitating in alkanes. To understand the relation between asphaltene structure and aggregation, we perform equilibrium molecular dynamics with Large-scale Atomic and Molecular Massively Parallel Software (LAMMPS), using the atomistic force field PCFF+ in the MedeA® environment. The following three molecular models are considered: the continental model (1350 g/mol) that has a large polyaromatic core and long alkyl chains, the island model (780 g/mol) that has a smaller polyaromatic unit and shorter chains and the archipelago model (1350 g/mol) that has three polyaromatic nuclei bridged with alkyl chains. The aggregation in a given solvent is monitored by visualising solvent-free configurations over 15 ns trajectories at 350 K. Nanoaggregates are characterised by stacked polyaromatic units separated by 0.33–0.4 nm. Irreversible aggregation is found with the continental model in both solvents. Aggregation of the island model is significant inn-heptane and low in toluene. The archipelago model does not aggregate significantly. Our results confirm that the island model is a reasonable average model of asphaltenes [Headen TF, Boek ES, Skipper NT. Energy Fuels 2009;23:1220–1229]. The open structure of nanoaggregates and the limited number of stacked molecules are also in agreement with previous interpretations of experimental data [Fenistein D. et al. Langmuir 1998;14:1013–1020]. [ABSTRACT FROM PUBLISHER]

Highlights: [•] The choice of force field, or interatomic potential, can affect simulation results. [•] Examples are given to show how results can vary with choice of force field. [•] Suggestions are given for users to select and appropriately use force fields. [•] Particular attention is paid to the importance of accurate force field representations. [ABSTRACT FROM AUTHOR]

Highlights: [•] Ideas for creating and maintaining a community code are discussed. [•] Lessons learned by the authors for the LAMMPS and GULP codes are described. [•] Software design attributes for making a community code extensible are detailed. [ABSTRACT FROM AUTHOR]

Anthropological accounts struggle to incorporate both subjectivity and supra-personal structures. Focus on the shape and type of relations neglects giving attention to the transformations that emerge as a result of relationships. This article proposes a toolbox of concepts that re-specify the notion of relations in a way that takes into account the transformative effect of relationships. The concepts are force fields, vectors, direction-of-attention and unprotected backs. These notions incorporate directionality and transformation into the notion of relations and provide three key understandings of i) shifts in personal effectiveness; ii) the limitations of personal power without undermining the subject; and iii) intentionality without undermining the effectiveness of structures. Each of these understandings is illustrated by means of the strivings of environmental activists members of a transnational environmentalist federation. Vectors make it possible to take seriously the subject and inter-subjective sociality and, simultaneously, the effects of history and impersonal structures. [ABSTRACT FROM AUTHOR]

ABSTRACT We present a brief, and largely personal, history of computer simulations of DNA and RNA oligonucleotides, with an emphasis on duplex structures and the Amber force fields. Both explicit and implicit solvent models are described, and methods for estimating structures, thermodynamics and mechanical properties of duplexes are illustrated. This overview, covering about two decades of work, provides a perspective for a discussion of prospects and obstacles for future simulations of RNA and DNA. © 2013 Wiley Periodicals, Inc. Biopolymers 99: 969-977, 2013. [ABSTRACT FROM AUTHOR]

Animal models are widely used to explore the mechanisms underlying sensorimotor control and learning. However, current experimental paradigms allow only limited control over task difficulty and cannot provide detailed information on forelimb kinematics and dynamics. Here we propose a novel robotic device for use in motor learning investigations with rats. The compact, highly transparent, three degree-of-freedom manipulandum is capable of rendering nominal forces of 2 N to guide or perturb rat forelimb movements, while providing objective and quantitative assessments of endpoint motor performance in a 50\times 30~\mm^2 planar workspace. Preliminary experiments with six healthy rats show that the animals can be familiarized with the experimental setup and are able to grasp and manipulate the end-effector of the robot. Further, dynamic perturbations and guiding force fields (i.e., haptic tunnels) rendered by the device had significant influence on rat motor behavior (ANOVA, p < 0.001). This approach opens up new research avenues for future characterizations of motor learning stages, both in healthy and in stroke models. [ABSTRACT FROM PUBLISHER]

We investigate adaptation under a reaching task with an acceleration-based force field perturbation designed to alter the nominal straight hand trajectory in a potentially benign manner: pushing the hand off course in one direction before subsequently restoring towards the target. In this particular task, an explicit strategy to reduce motor effort requires a distinct deviation from the nominal rectilinear hand trajectory. Rather, our results display a clear directional preference during learning, as subjects adapted perturbed curved trajectories towards their initial baselines. We model this behavior using the framework of stochastic optimal control theory and an objective function that trades off the discordant requirements of 1) target accuracy, 2) motor effort, and 3) kinematic invariance. Our work addresses the underlying objective of a reaching movement, and we suggest that robustness, particularly against internal model uncertainly, is as essential to the reaching task as terminal accuracy and energy efficiency. [ABSTRACT FROM AUTHOR]

In the last decade many advances have been made on molecular dynamics simulations and different force fields were developed from the combination of differentiable functions of the atomic coordinates to represent the system energy and of parameters that describe the geometric and energetic properties of inter-particle interactions. However, it has been shown that very subtle modifications to commonly used molecular mechanical potentials can significantly alter the behavior of those potentials inducing stabilizing or destabilizing effects in the patterns of peptides or proteins. In this article we describe the behavior of polyalanine peptides under the influence of various 'force fields'. The polyalanines were chosen as study model since their structural features were already studied experimentally and thus our computational results were easily comparable with the experimental ones. In particular, three peptides composed of 8, 10 and 12 alanine residues were subjected to molecular dynamics simulations using 12 different force fields to understand what is the most appropriate force field to properly simulate their folding. Our results showed that Amber99ϕ is the best force field able to generate helical conformations in agreement with experimental data. [ABSTRACT FROM AUTHOR]