How Accurately Do Current Force Fields Predict Experimental Peptide Conformations? An Adiabatic Free Energy Dynamics Study

Conformational energies of reference organic molecules: benchmarking of common efficient computational methods against coupled cluster theory

We selected 145 reference organic molecules that include model fragments used in computer-aided drug design. We calculated 158 conformational energies and barriers using force fields, with wide applicability in commercial and free softwares and extensive application on the calculation of conformational energies of organic molecules, e.g. the UFF and DREIDING force fields, the Allinger's force fields MM3-96, MM3-00, MM4-8, the MM2-91 clones MMX and MM+, the MMFF94 force field, MM4, ab initio Hartree-Fock (HF) theory with different basis sets, the standard density functional theory B3LYP, the second-order post-HF MP2 theory and the Domain-based Local Pair Natural Orbital Coupled Cluster DLPNO-CCSD(T) theory, with the latter used for accurate reference values. The data set of the organic molecules includes hydrocarbons, haloalkanes, conjugated compounds, and oxygen-, nitrogen-, phosphorus- and sulphur-containing compounds. We reviewed in detail the conformational aspects of these model organic molecules providing the current understanding of the steric and electronic factors that determine the stability of low energy conformers and the literature including previous experimental observations and calculated findings. While progress on the computer hardware allows the calculations of thousands of conformations for later use in drug design projects, this study is an update from previous classical studies that used, as reference values, experimental ones using a variety of methods and different environments. The lowest mean error against the DLPNO-CCSD(T) reference was calculated for MP2 (0.35 kcal mol-1), followed by B3LYP (0.69 kcal mol-1) and the HF theories (0.81-1.0 kcal mol-1). As regards the force fields, the lowest errors were observed for the Allinger's force fields MM3-00 (1.28 kcal mol-1), ΜΜ3-96 (1.40 kcal mol-1) and the Halgren's MMFF94 force field (1.30 kcal mol-1) and then for the MM2-91 clones MMX (1.77 kcal mol-1) and MM+ (2.01 kcal mol-1) and MM4 (2.05 kcal mol-1). The DREIDING (3.63 kcal mol-1) and UFF (3.77 kcal mol-1) force fields have the lowest performance. These model organic molecules we used are often present as fragments in drug-like molecules. The values calculated using DLPNO-CCSD(T) make up a valuable data set for further comparisons and for improved force field parameterization.

Molecular modeling, molecular dynamics simulation, and essential dynamics analysis of grancalcin: An upregulated biomarker in experimental autoimmune encephalomyelitis mice

The experimental autoimmune encephalomyelitis mouse model is the most commonly used animal model, and it best represents multiple sclerosis. Grancalcin (GCA) was discovered to be upregulated in EAE mice. GCA comprises 220 amino acids that have been assigned the UniprotKB ID Q8VC88. It is a calcium-binding protein that helps neutrophils adhere to fibronectin and the formation of focal adhesions. However, the protein data bank does not contain the crystal structure of mouse GCA. The current study aims to analyze the structural and physicochemical properties of GCA. Mouse GCA showed a high percentage identity (87%) with the crystal structure of des (1–52) grancalcin with bound calcium (chain A) from Homo sapiens identified by its PDB id 1k94_A. Using the SWISS-MODEL server, we used 1k94_A as a template protein to model the mouse GCA protein. Compared to the template structure 1K94, three potential binding sites for calcium-binding have been proposed, ranging from 13 to 20, 80 to 91, and 109 to 120 amino acids. On an i5 personal computer with 8GB of RAM, GROMACS 2020.1 was utilized to run a 100 ns molecular dynamics (MD) simulation. RMSD, Rg, and RMSF analysis of an MD simulation trajectory indicate a stable and compact state throughout the simulation period of modeled proteins. We found that GCA is primarily alpha helical (Class 1), with eight alpha helices. The essential dynamics analysis captures PCA and SASA, culminating in the biological motions that correspond to the last 1000 frames. These findings will aid the development of potential inhibitors as well as the determination of binding pockets and residues for drug-like molecules.

Machine Learning of First-Principles Force-Fields for Alkane and Polyene Hydrocarbons

Machine learning (ML) interatomic potentials (ML-IAPs) are generated for alkane and polyene hydrocarbons using on-the-fly adaptive sampling and a sparse Gaussian process regression (SGPR) algorithm. The ML model is generated based on the PBE+D3 level of density functional theory (DFT) with molecular dynamics (MD) for small alkane and polyene molecules. Intermolecular interactions are also trained with clusters and condensed phases of small molecules. It shows excellent transferability to long alkanes and closely describes the ab inito potential energy surface for polyenes. Simulation of liquid ethane also shows reasonable agreement with experimental reports. This is a promising initiative toward a universal ab initio quality force-field for hydrocarbons and organic molecules.

A New Lipid Force Field (FUJI)

To explore inhomogeneous and anisotropic systems such as lipid bilayers, the Lennard-Jones particle mesh Ewald (LJ-PME) method has been applied without a conventional isotropic dispersion correction. As the popular AMBER and CHARMM lipid force fields were developed using a cutoff scheme, their lipid bilayers unacceptably shrink when using the LJ-PME method. In this study, a new all-atom lipid force field (FUJI) was developed on the basis of the AMBER force-field scheme including the Lipid14 van der Waals parameters. Point charges were calculated using the restrained electrostatic potentials of many lipid conformers. Further, torsion energy profiles were calculated using high-level ab initio molecular orbitals (LCCSD(T)/Aug-cc-pVTZ//LMP2/Aug-cc-pVTZ), following which the molecular mechanical dihedral parameters were derived through a fast Fourier transform. By incorporation of these parameters into a new lipid force field without fitting experimental data, the desired lipid characteristics such as the area per lipid and lateral diffusion coefficients were obtained through GROMACS molecular dynamics simulations using the LJ-PME method and virtual hydrogen sites. The calculated area per lipid and lateral diffusion coefficients showed satisfactory agreement with experimental data. Furthermore, the electron-density profiles along the membrane normal were calculated for pure lipid bilayers, and the resulting membrane thicknesses agreed well with the experimental values. As the new lipid force field is compatible with FUJI for protein and small molecules, the new FUJI force field will offer accurate modeling for complex systems consisting of various membrane proteins and lipids.

Characterization Of Blood-Brain Barrier Crossing And Tumor Homing Peptides By Molecular Dynamics Simulations

Introduction

The new frontier of tumor diagnosis and treatment relies on the development of delivery strategies capable of allowing the specific targeting of the diagnostic agents/chemotherapeutics, avoiding side effects. In the case of brain tumors, achieving this goal is made more difficult by the presence of the blood–brain barrier (BBB). Peptides have been revealed as excellent candidates for both BBB crossing and specific cancer homing. Nanoparticles (NPs), functionalized with BBB crossing and tumor homing (TH) peptides, are emerging as smart theranostic systems. However, there is still poor knowledge concerning the molecular structure and dynamical properties of these peptides, essential requirements for a suitable functionalization of the delivery systems themselves.

Methods

In this work, by means of molecular dynamics (MD) simulations, we have extensively characterized the structural and dynamical behavior of several peptides, known to be endowed of BBB crossing and TH properties.

Results

The simulations point out that, on the basis of their conformational dynamics, the peptides can be classified in two main groups: 1) peptides assuming a specific structural conformation, a feature that could be important for interacting with the molecular target but that may limit their use as functionalizing molecules and 2) highly flexible peptides whose interaction with the target may be independent of a particular structural conformation and that may represent good candidates for the functionalization of theranostic NP-based platforms.

Discussion

Such findings may be useful for the de novo designing of NP-based delivery systems.

Point your SmartPhone at the code above. If you have a QR code reader the video abstract will appear. Or use:

https://youtu.be/wp8npbWb754

Reaction-field electrostatics in molecular dynamics simulations: development of a conservative scheme compatible with an atomic cutoff

Shifting and switching schemes are developed to enable strict energy conservation in molecular dynamics simulations relying on reaction-field electrostatic (as well as Lennard-Jones) interactions with an atom-based cutoff truncation.

Toward rational antibody design: recent advancements in molecular dynamics simulations

Because antibodies have become an important therapeutic tool, rational antibody design is a challenging issue involving various science and technology fields. From the computational aspect, many types of design-assist methods have been developed, but their accuracy is not fully satisfactory. Because of recent advancements in computational power, molecular dynamics (MD) simulation has become a helpful tool to trace the motion of proteins and to characterize their properties. Thus, MD simulation has been applied to various systems involving antigen-antibody complexes and has been shown to provide accurate insight into antigen-antibody interactions and dynamics at an atomic resolution. Therefore, it is highly possible that MD simulation will play several roles complementing the conventional antibody design. In this review, we address several important features of MD simulation in the context of rational antibody design.

Stochastic Neural Network Approach for Learning High-Dimensional Free Energy Surfaces

The generation of free energy landscapes corresponding to conformational equilibria in complex molecular systems remains a significant computational challenge. Adding to this challenge is the need to represent, store, and manipulate the often high-dimensional surfaces that result from rare-event sampling approaches employed to compute them. In this Letter, we propose the use of artificial neural networks as a solution to these issues. Using specific examples, we discuss network training using enhanced-sampling methods and the use of the networks in the calculation of ensemble averages.

Effect of the solvent on the conformational behavior of the alanine dipeptide deduced from MD simulations

In general, peptides do not exhibit a well-defined conformational profile in solution. However, despite the experimental blurred picture associated with their structure, compelling spectroscopic evidence shows that peptides exhibit local order. The conformational profile of a peptide is the result of a balance between intramolecular interactions between different atoms of the molecule and intermolecular interactions between atoms of the molecule and the solvent. Accordingly, the conformational profile of a peptide will change upon the properties of the solvent it is soaked. To get insight into the balance between intra- and intermolecular interactions on the conformational preferences of the peptide backbone we have studied the conformational profile of the alanine dipeptide in diverse solvents using molecular dynamics as sampling technique. Solvents studied include chloroform, methanol, dimethyl sulfoxide, water and N-methylacetamide. Different treatments of the solvent have been studied in the present work including explicit solvent molecules, a generalized Born model and using the bulk dielectric constant of the solvent. The diverse calculations identify four major conformations with different populations in the diverse solvents: the C₇^eq only sampled in chloroform; the C₅ or extended conformation; the polyproline (PII) conformation and the right-handed α-helix conformation (α_R). The results of present calculations permit to analyze how the balance between intra- and intermolecular interactions explains the populations of the diverse conformations observed.

Dynamics at a Peptide-TiO2 Anatase (101) Interface

The interface between biological matter and inorganic materials is a widely investigated research topic due to possible applications in biomedicine and nanotechnology. In this context, the molecular level adsorption mechanism that drives specific recognition between small peptide sequences and inorganic surfaces represents an important topic likely to provide much information useful for designing bioderived materials. Here, we investigate the dynamics at the interface between a Ti-binding peptide sequence (AMRKLPDAPGMHC) and a TiO₂ anatase surface by using molecular dynamics (MD) simulations. In the simulations the adsorption mechanism is characterized by diffusion of the peptide from the bulk water phase toward the TiO₂ surface, followed by the anchoring of the peptide to the surface. The anchoring is mediated by the interfacial water layers by means of the charged groups of the side chains of the peptide. The peptide samples anchored and dissociated states from the surface and its conformation is not affected by the surface when anchored.

The Allostery Landscape: Quantifying Thermodynamic Couplings in Biomolecular Systems

Allostery plays a fundamental role in most biological processes. However, little theory is available to describe it outside of two-state models. Here we use a statistical mechanical approach to show that the allosteric coupling between two collective variables is not a single number, but instead a two-dimensional thermodynamic coupling function that is directly related to the mutual information from information theory and the copula density function from probability theory. On this basis, we demonstrate how to quantify the contribution of specific energy terms to this thermodynamic coupling function, enabling an approximate decomposition that reveals the mechanism of allostery. We illustrate the thermodynamic coupling function and its use by showing how allosteric coupling in the alanine dipeptide molecule contributes to the overall shape of the Φ/Ψ free energy surface, and by identifying the interactions that are necessary for this coupling.

The role of histone tails in the nucleosome: a computational study

Histone tails play an important role in gene transcription and expression. We present here a systematic computational study of the role of histone tails in the nucleosome, using replica exchange molecular dynamics simulations with an implicit solvent model and different well-established force fields. We performed simulations for all four histone tails, H4, H3, H2A, and H2B, isolated and with inclusion of the nucleosome. The results confirm predictions of previous theoretical studies for the secondary structure of the isolated tails but show a strong dependence on the force field used. In the presence of the entire nucleosome for all force fields, the secondary structure of the histone tails is destabilized. Specific contacts are found between charged lysine and arginine residues and DNA phosphate groups and other binding sites in the minor and major DNA grooves. Using cluster analysis, we found a single dominant configuration of binding to DNA for the H4 and H2A histone tails, whereas H3 and H2B show multiple binding configurations with an equal probability. The leading stabilizing contribution for those binding configurations is the attractive interaction between the positively charged lysine and arginine residues and the negatively charged phosphate groups, and thus the resulting charge neutralization. Finally, we present results of molecular dynamics simulations in explicit solvent to confirm our conclusions. Results from both implicit and explicit solvent models show that large portions of the histone tails are not bound to DNA, supporting the complex role of these tails in gene transcription and expression and making them possible candidates for binding sites of transcription factors, enzymes, and other proteins.

Going clean: structure and dynamics of peptides in the gas phase and paths to solvation

The gas phase is an artificial environment for biomolecules that has gained much attention both experimentally and theoretically due to its unique characteristic of providing a clean room environment for the comparison between theory and experiment. In this review we give an overview mainly on first-principles simulations of isolated peptides and the initial steps of their interactions with ions and solvent molecules: a bottom up approach to the complexity of biological environments. We focus on the accuracy of different methods to explore the conformational space, the connections between theory and experiment regarding collision cross section evaluations and (anharmonic) vibrational spectra, and the challenges faced in this field.

Understanding the conformational behaviour of Ac-Ala-NHMe in different media. A joint NMR and DFT study

The conformational behaviour of Ac-Ala-NHMe was studied in the gas-phase and in solution by theoretical calculations (B3LYP-D3/aug-cc-pVDZ level) and experimental (1)H NMR. The conformational preferences of this compound were shown to result from a complex interplay between the strengths of possible intramolecular hydrogen bonds, steric interactions, hyperconjugation, entropy effects and the overall dipole moments. The Ac-Ala-N(Me)2 derivative was studied in addition, to design a system akin to Ac-Ala-NHMe, but with disrupted intramolecular hydrogen bonds involving the -NHMe group, mimicking the effect of polar protic solvents.

Improved Peptide and Protein Torsional Energetics with the OPLSAA Force Field

The development and validation of new peptide dihedral parameters are reported for the OPLS-AA force field. High accuracy quantum chemical methods were used to scan φ, ψ, χ1, and χ2 potential energy surfaces for blocked dipeptides. New Fourier coefficients for the dihedral angle terms of the OPLS-AA force field were fit to these surfaces, utilizing a Boltzmann-weighted error function and systematically examining the effects of weighting temperature. To prevent overfitting to the available data, a minimal number of new residue-specific and peptide-specific torsion terms were developed. Extensive experimental solution-phase and quantum chemical gas-phase benchmarks were used to assess the quality of the new parameters, named OPLS-AA/M, demonstrating significant improvement over previous OPLS-AA force fields. A Boltzmann weighting temperature of 2000 K was determined to be optimal for fitting the new Fourier coefficients for dihedral angle parameters. Conclusions are drawn from the results for best practices for developing new torsion parameters for protein force fields.