Improved Peptide and Protein Torsional Energetics with the OPLSAA Force Field

Competitive Effects of Anions on Protein Solvation by Aqueous Ionic Liquids

The present study utilizes molecular dynamics simulations to examine how different anions compete for protein solvation in aqueous solutions of ionic liquids (ILs). Ubiquitin is used as model protein and studied in IL mixtures sharing the same cation, 1-ethyl-3-methylimidazolium (EMIM), and two different anions in the same solution, from combinations of dicyanamide (DCA), chloride (Cl), nitrate (NO3), and tetrafluoroborate (BF4). Our findings reveal that specific interactions between anions and the protein are paramount in IL solvation, but that combinations of anions are not additive. For example, DCA exhibits a remarkable ability to form hydrogen bonds with the protein, resulting in a significantly stronger preferential binding to the protein than other anions. However, the combination of DCA with NO3, which also forms hydrogen bonds with the protein, results in a smaller preferential solvation of the protein than the combination of DCA with chloride ions, which are weaker binders. Thus, combining anions with varying affinities for the protein surface modulates the overall ion accumulation through nonadditive mechanisms, highlighting the importance of the understanding of competition for specific interaction sites, cooperative binding, bulk-solution affinity, and overall charge compensations, on the overall solvation capacity of the solution. Such knowledge may allow for the design of novel IL-based processes in biotechnology and material science, where fine-tuning protein solvation is crucial for optimizing performance and functionality.

Benchmarking Quantum Mechanical Levels of Theory for Valence Parametrization in Force Fields

A wide range of density functional methods and basis sets are available to derive the electronic structure and properties of molecules. Quantum mechanical calculations are too computationally intensive for routine simulation of molecules in the condensed phase, prompting the development of computationally efficient force fields based on quantum mechanical data. Parametrizing general force fields, which cover a vast chemical space, necessitates the generation of sizable quantum mechanical data sets with optimized geometries and torsion scans. To achieve this efficiently, choosing a quantum mechanical method that balances computational cost and accuracy is crucial. In this study, we seek to assess the accuracy of quantum mechanical theory for specific properties such as conformer energies and torsion energetics. To comprehensively evaluate various methods, we focus on a representative set of 59 diverse small molecules, comparing approximately 25 combinations of functional and basis sets against the reference level coupled cluster calculations at the complete basis set limit.

Molecular simulations guide immobilization of lipase on nest-like ZIFs with regulatable hydrophilic/hydrophobic surface

The catalytic performance of immobilized lipase is greatly influenced by functional support, which attracts growing interest for designing supports to achieve their promotive catalytic activity. Many lipases bind strongly to hydrophobic surfaces where they undergo interfacial activation. Herein, the behavioral differences of lipases with distinct lid structures on interfaces of varying hydrophobicity levels were firstly investigated by molecular simulations. It was found that a reasonable hydrophilic/hydrophobic surface could facilitate the lipase to undergo interfacial activation. Building on these findings, a novel "nest"-like superhydrophobic ZIFs (ZIFN) composed of hydrophobic ligands was prepared for the first time and used to immobilize lipase from Aspergillus oryzae (AOL@ZIFN). The AOL@ZIFN exhibited 2.0-folds higher activity than free lipase in the hydrolysis of p-Nitrophenyl palmitate (p-NPP). Especially, the modification of superhydrophobic ZIFN with an appropriate amount of hydrophilic tannic acid can significantly improve the activity of the immobilized lipase (AOL@ZIFN-TA). The AOL@ZIFN-TA exhibited 30-folds higher activity than free lipase, and still maintained 82% of its initial activity after 5 consecutive cycles, indicating good reusability. These results demonstrated that nanomaterials with rational arrangement of the hydrophilic/hydrophobic surface could facilitate the lipase to undergo interfacial activation and improve its activity, displaying the potential of the extensive application.

Tips and Tricks in the Modeling of Supramolecular Peptide Assemblies

Supramolecular peptide assemblies (SPAs) hold promise as materials for nanotechnology and biomedicine. Although their investigation often entails adapting experimental techniques from their protein counterparts, SPAs are fundamentally distinct from proteins, posing unique challenges for their study. Computational methods have emerged as indispensable tools for gaining deeper insights into SPA structures at the molecular level, surpassing the limitations of experimental techniques, and as screening tools to reduce the experimental search space. However, computational studies have grappled with issues stemming from the absence of standardized procedures and relevant crystal structures. Fundamental disparities between SPAs and protein simulations, such as the absence of experimentally validated initial structures and the importance of the simulation size, number of molecules, and concentration, have compounded these challenges. Understanding the roles of various parameters and the capabilities of different models and simulation setups remains an ongoing endeavor. In this review, we aim to provide readers with guidance on the parameters to consider when conducting SPA simulations, elucidating their potential impact on outcomes and validity.

HessFit: A Toolkit to Derive Automated Force Fields from Quantum Mechanical Information

In this study, we introduce a novel approach to enhance the accuracy of molecular dynamics simulations by refining the force fields (FFs) through a combination of transferable parameters and molecule-specific characteristics derived from quantum mechanical (QM) calculations. Traditional FFs often prioritize generality over precision, leading to limitations in the accuracy of accurately capturing intra- and intermolecular interactions. To address this, we present an open-source toolkit, called HessFit, designed to integrate QM-derived bonded parameters and atomic charges into existing FFs. In combination with bond, angle, torsional, and nonbonded parameters derivation, HessFit can easily extract multiple barrier terms of dihedrals from QM Hessian and gradient or return all terms through a fitting procedure scheme of QM potential energy surface. We showcase the effectiveness of HessFit through comprehensive evaluations of vibrational properties across a diverse set of small molecules and demonstrate that experimental results support its ability in predicting thermodynamic properties of organic molecules compared to previous state-of-the-art approaches. We further explore its application to Zn2+ metalloprotein models, which are hard systems to treat with automatic approaches. Our results demonstrate that HessFit parameters compete with GAFF2 and OPLS parameters to describing small organic molecules, and its feasibility is also comparable to current FFs used to modeling nonstandard residues in Zn proteins for molecular dynamics simulations. The effectiveness of the HessFit protocol makes it a valuable tool for deriving or extending force field parameters for novel compounds in several molecular modeling applications.

MiMiC: A high-performance framework for multiscale molecular dynamics simulations

MiMiC is a framework for performing multiscale simulations in which loosely coupled external programs describe individual subsystems at different resolutions and levels of theory. To make it highly efficient and flexible, we adopt an interoperable approach based on a multiple-program multiple-data (MPMD) paradigm, serving as an intermediary responsible for fast data exchange and interactions between the subsystems. The main goal of MiMiC is to avoid interfering with the underlying parallelization of the external programs, including the operability on hybrid architectures (e.g., CPU/GPU), and keep their setup and execution as close as possible to the original. At the moment, MiMiC offers an efficient implementation of electrostatic embedding quantum mechanics/molecular mechanics (QM/MM) that has demonstrated unprecedented parallel scaling in simulations of large biomolecules using CPMD and GROMACS as QM and MM engines, respectively. However, as it is designed for high flexibility with general multiscale models in mind, it can be straightforwardly extended beyond QM/MM. In this article, we illustrate the software design and the features of the framework, which make it a compelling choice for multiscale simulations in the upcoming era of exascale high-performance computing.

Navigating the landscape of enzyme design: from molecular simulations to machine learning

Global environmental issues and sustainable development call for new technologies for fine chemical synthesis and waste valorization. Biocatalysis has attracted great attention as the alternative to the traditional organic synthesis. However, it is challenging to navigate the vast sequence space to identify those proteins with admirable biocatalytic functions. The recent development of deep-learning based structure prediction methods such as AlphaFold2 reinforced by different computational simulations or multiscale calculations has largely expanded the 3D structure databases and enabled structure-based design. While structure-based approaches shed light on site-specific enzyme engineering, they are not suitable for large-scale screening of potential biocatalysts. Effective utilization of big data using machine learning techniques opens up a new era for accelerated predictions. Here, we review the approaches and applications of structure-based and machine-learning guided enzyme design. We also provide our view on the challenges and perspectives on effectively employing enzyme design approaches integrating traditional molecular simulations and machine learning, and the importance of database construction and algorithm development in attaining predictive ML models to explore the sequence fitness landscape for the design of admirable biocatalysts.

Computational Analysis of Heat Capacity Effects in Protein-Ligand Binding

Heat capacity effects in protein-ligand binding as measured by calorimetric experiments have recently attracted considerable attention, particularly in the field of enzyme inhibitor design. A significant negative heat capacity change upon ligand binding implies a marked temperature dependence of the binding enthalpy, which is of high relevance for attempts to optimize protein-ligand interactions. In this work, we address the question of how well such heat capacity changes can be predicted by computer simulations. We examine a series of human thrombin inhibitors that all bind with ΔCp values of about -0.4 kcal/mol/K and calculate heat capacity changes from plain molecular dynamics simulations of the bound and free states of the enzyme and ligand. The results show that accurate ΔCp estimates within a few tenths of a kcal/mol/K of the experimental values can be obtained with this approach. This allows us to address the structural and energetic origin of the negative heat capacity changes for the thrombin inhibitors, and it is found that conformational equilibria of the free ligands in solution make a major contribution to the observed effect.

Developing a Differentiable Long-Range Force Field for Proteins with E(3) Neural Network-Predicted Asymptotic Parameters

Accurately describing long-range interactions is a significant challenge in molecular dynamics (MD) simulations of proteins. High-quality long-range potential is also an important component of the range-separated machine learning force field. This study introduces a comprehensive asymptotic parameter database encompassing atomic multipole moments, polarizabilities, and dispersion coefficients. Leveraging active learning, our database comprehensively represents protein fragments with up to 8 heavy atoms, capturing their conformational diversity with merely 78,000 data points. Additionally, the E(3) neural network (E3NN) is employed to predict the asymptotic parameters directly from the local geometry. The E3NN models demonstrate exceptional accuracy and transferability across all asymptotic parameters, achieving an R2 of 0.999 for both protein fragments and 20 amino acid dipeptide test sets. The long-range electrostatic and dispersion energies can be obtained using the E3NN-predicted parameters, with an error of 0.07 and 0.02 kcal/mol, respectively, when compared to symmetry-adapted perturbation theory (SAPT). Therefore, our force fields demonstrate the capability to accurately describe long-range interactions in proteins, paving the way for next-generation protein force fields.

Computational Investigation of BMAA and Its Carbamate Adducts as Potential GluR2 Modulators

Beta-N-methylamino-l-alanine (BMAA) is a potential neurotoxic nonprotein amino acid, which can reach the human body through the food chain. When BMAA interacts with bicarbonate in the human body, carbamate adducts are produced, which share a high structural similarity with the neurotransmitter glutamate. It is believed that BMAA and its l-carbamate adducts bind in the glutamate binding site of ionotropic glutamate receptor 2 (GluR2). Chronic exposure to BMAA and its adducts could cause neurological illness such as neurodegenerative diseases. However, the mechanism of BMAA action and its carbamate adducts bound to GluR2 has not yet been elucidated. Here, we investigate the binding modes and the affinity of BMAA and its carbamate adducts to GluR2 in comparison to the natural agonist, glutamate, to understand whether these can act as GluR2 modulators. Initially, we perform molecular dynamics simulations of BMAA and its carbamate adducts bound to GluR2 to examine the stability of the ligands in the S1/S2 ligand-binding core of the receptor. In addition, we utilize alchemical free energy calculations to compute the difference in the free energy of binding of the beta-carbamate adduct of BMAA to GluR2 compared to that of glutamate. Our findings indicate that carbamate adducts of BMAA and glutamate remain stable in the binding site of the GluR2 compared to BMAA. Additionally, alchemical free energy results reveal that glutamate and the beta-carbamate adduct of BMAA have comparable binding affinity to the GluR2. These results provide a rationale that BMAA carbamate adducts may be, in fact, the modulators of GluR2 and not BMAA itself.

Building polarization into protein-inhibitor binding dynamics in rational drug design for rheumatoid arthritis

Standard force field-based simulations to accomplish structure-based evaluations of lead molecules is a powerful tool. Combining protein fragmentation into tractable sub-systems with continuum solvation method is envisaged to enable quantum mechanics-based electronic structure calculations of macromolecules in their realistic environment. This along with incorporation of many-body polarization effect in molecular dynamics simulations may augment an accurate description of electrostatics of protein-inhibitor systems for effective drug design. Rheumatoid arthritis (RA) is a complex autoimmune disorder plagued by the ceiling effect of current targeted therapies, encouraging identification of new druggable targets and corresponding drug design to tackle the refractory form of disease. In this study, polarization-inclusive force field approach has been used to model protein solvation and ligand binding for 'Mitogen-activated protein kinase' (MAP3K8), a regulatory node of notable pharmacological relevance in RA synovial biology. For MAP3K8 inhibitors belonging to different scaffold series, the calculations illustrated differential electrostatic contribution to their relative binding affinities and successfully explained examples from available structure-activity relationship studies. Results from this study exemplified i) the advantage of this approach in reliably ranking inhibitors having close nanomolar range activities for the same target; and ii) its prospective application in lead molecule identification aiding drug discovery efforts in RA.Communicated by Ramaswamy H. Sarma.

Intrinsic Conformational Dynamics of Glycine and Alanine in Polarizable Molecular Dynamics Force Fields: Comparison to Spectroscopic Data

Molecular dynamics (MD) is a great tool for elucidating conformational dynamics of proteins and peptides in water at the atomistic level that often surpasses the level of detail available experimentally. Structure predictions, however, are limited by the accuracy of the underlying MD force field. This limitation is particularly stark in the case of intrinsically disordered peptides and proteins, which are characterized by solvent-accessible and disordered peptide regions and domains. Recent studies show that most additive MD force fields, including CHARMM36m, do not reproduce the intrinsic conformational distributions of guest amino acid residues x in cationic GxG peptides in water in line with experimental data. Positing that a lack of polarizability in additive MD force fields may be the culprit for the reported discrepancies, we here examine the conformational dynamics of guest glycine and alanine residues in cationic GxG peptides in water using two polarizable MD force fields, CHARMM Drude and AMOEBA. Our results indicate that while AMOEBA captures the experimental data better than CHARMM Drude, neither of the two polarizable force fields offers an improvement of the Ramachandran distributions of glycine and alanine residues in cationic GGG and GAG peptides, respectively, over CHARMM36m.

A Virtual Drug Discovery Screening Illuminates Campesterol as a Potent Estrogen Receptor Alpha Inhibitor in Breast Cancer

Breast cancer remains a global health challenge, and innovative strategies are required to target estrogen receptor α (ERα), a key player in its development. This study investigates the potential of campesterol, a natural phytosterol, as an ERα inhibitor for breast cancer. Our approach integrates in silico, in vitro, and ex vivo experiments to assess the therapeutic potential of campesterol. In silico analyses highlight campesterol as a promising ERα ligand with favorable binding affinities and dynamic properties. Structural analysis reveals conformational changes in ERα upon campesterol binding. In vitro studies confirm the selective growth inhibition of campesterol against ERα-positive breast cancer cells. This study extends to ER+ breast cancer patient-derived organoids (PDOs), showing the effectiveness of campesterol in ERα-positive breast cancer PDOs. Importantly, it emphasizes the receptor-specific nature of campesterol, providing insights into its context-dependent action. In conclusion, campesterol displays potential as an ERα inhibitor, offering new avenues for ER+ breast cancer treatment.

Interaction networks within disease-associated GαS variants characterized by an integrative biophysical approach

Activation of G proteins through nucleotide exchange initiates intracellular signaling cascades essential for life processes. Under normal conditions, nucleotide exchange is regulated by the formation of G protein-G protein-coupled receptor complexes. Single point mutations in the Gα subunit of G proteins bypass this interaction, leading to loss of function or constitutive gain of function, which is closely linked with the onset of multiple diseases. Despite the recognized significance of Gα mutations in disease pathology, structural information for most variants is lacking, potentially due to inherent protein dynamics that pose challenges for crystallography. To address this, we leveraged an integrative spectroscopic and computational approach to structurally characterize seven of the most frequently observed and clinically relevant mutations in the stimulatory Gα subunit, GαS. A previously proposed allosteric model of Gα activation linked structural changes in the nucleotide-binding pocket with functionally important changes in interactions between switch regions. We investigated this allosteric connection in GαS by integrating data from variable temperature CD spectroscopy, which measured changes in global protein structure and stability, and molecular dynamics simulations, which observed changes in interaction networks between GαS switch regions. Additionally, saturation-transfer difference NMR spectroscopy was applied to observe changes in nucleotide interactions with residues within the nucleotide binding site. These data have enabled testing of predictions regarding how mutations in GαS result in loss or gain of function and evaluation of proposed structural mechanisms. The integration of experimental and computational data allowed us to propose a more nuanced classification of mechanisms underlying GαS gain-of-function and loss-of-function mutations.

Atomistic Molecular Dynamics Simulations of ABA-Type Polymer Peptide Conjugates: Insights into Supramolecular Structures and their Circular Dichroism Spectra

A combination of atomistic molecular dynamics (aMD) simulations and circular dichroism (CD) analysis is used to explore supramolecular structures of amphiphilic ABA-type triblock polymer peptide conjugates (PPC). Using the example of a recently introduced PPC with pH- and temperature responsive self-assembling behavior [Otter et al., Macromolecular Rapid Communications 2018, 39, 1800459], this study shows how molecular dynamics simulations of simplified fragment molecules can add crucial information to CD data, which helps to correctly identify the self-assembled structures and monitor the folding/unfolding pathways of the molecules. The findings offer insights into the nature of structural transitions induced by external stimuli, thus contributing to the understanding of the connection of microscopic structures with macroscopic properties.

In-vitro and in-silico analyses of the thrombolytic potential of green kiwifruit

Cardiovascular diseases (CVDs), mainly caused by thrombosis complications, are the leading cause of mortality worldwide, making the development of alternative treatments highly desirable. In this study, the thrombolytic potential of green kiwifruit (Actinidia deliciosa cultivar Hayward) was assessed using in-vitro and in-silico approaches. The crude green kiwifruit extract demonstrated the ability to reduce blood clots significantly by 73.0 ± 1.12% (P < 0.01) within 6 h, with rapid degradation of Aα and Bβ fibrin chains followed by the γ chain in fibrinolytic assays. Molecular docking revealed six favorable conformations for the kiwifruit enzyme actinidin (ADHact) and fibrin chains, supported by spontaneous binding energies and distances. Moreover, molecular dynamics simulation confirmed the binding stability of the complexes of these conformations, as indicated by the stable binding affinity, high number of hydrogen bonds, and consistent distances between the catalytic residue Cys25 of ADHact and the peptide bond. The better overall binding affinity of ADHact to fibrin chains Aα and Bβ may contribute to their faster degradation, supporting the fibrinolytic results. In conclusion, this study demonstrated the thrombolytic potential of the green kiwifruit-derived enzyme and highlighted its potential role as a natural plant-based prophylactic and therapeutic agent for CVDs.

Assessing the Performance of Peptide Force Fields for Modeling the Solution Structural Ensembles of Cyclic Peptides

Molecular dynamics simulation is a powerful tool for characterizing the solution structural ensembles of cyclic peptides. However, the ability of simulation to recapitulate experimental results and make accurate predictions largely depends on the force fields used. In our work here, we evaluate the performance of seven state-of-the-art force fields in recapitulating the experimental NMR results in water of 12 benchmark cyclic peptides, consisting of 6 cyclic pentapeptides, 4 cyclic hexapeptides, and 2 cyclic heptapeptides. The results show that RSFF2+TIP3P, RSFF2C+TIP3P, and Amber14SB+TIP3P exhibit similar and the best performance, all recapitulating the NMR-derived structure information on 10 cyclic peptides. Amber19SB+OPC successfully recapitulates the NMR-derived structure information on 8 cyclic peptides. In contrast, OPLS-AA/M+TIP4P, Amber03+TIP3P, and Amber14SBonlysc+GB-neck2 could only recapitulate the NMR-derived structure information on 5 cyclic peptides, the majority of which are not well-structured.

Toward a Better Understanding of the Poly(glycerol sebacate)-Water Interface

Molecular simulations were conducted to provide a better description of the poly(glycerol sebacate) (PGS)-water interface. The density and the glass-transition temperature as well as their dependencies on the degree of esterification were examined in close connection with the available experimental data. The work of adhesion and water contact angle were calculated as a function of the degree of esterification. A direct correlation was established between the strength of the hydrogen bond network in the interfacial region and the change in the water contact angle with respect to the degree of esterification. The interfacial region was described by local density profiles and orientations of the water molecules.

Design, synthesis, molecular docking, and dynamic studies of novel thiazole derivatives incorporating benzimidazole moiety and assessment as antibacterial agents

A general and sustainable approach for the synthesis of benzimidazole-thiazole compounds via an efficient, one-pot, domino, pseudo-four-component reaction using 5-amino-2-mercaptobenzimidazole, aralkyl halides, ammonium thiocyanate, and substituted α-bromo-acetophenones in glacial acetic acid at ambient temperature to give final compounds (4a-p) in good yields in shorter time. The spectral data of synthesized compounds were evaluated by analytical and spectral techniques (IR, 1H-NMR, 13C-NMR, and ESI-HRMS). Further, some of the synthesized compounds were screened for their in-vitro antibacterial activity studies using the agar well diffusion method against Gram-positive Streptococcus pneumoniae (2451) bacteria and Gram-negative Proteous mirabilis (2081) bacteria. Based on the MIC results, it was observed that the most active compounds 4b, 4e, 4f, and 4k show promising antibacterial activity with the zone of inhibition values of 2.85 cm 2.75 cm, 3.6 cm, and 3.3 cm against both Gram-negative and positive bacteria cell lines, respectively. Further, we have also insight into the molecular simulation studies, based on the binding results, compound 4i showed stable binding interactions with streptomycin drug with the active site of the gyrase protein (PDB ID: 1KIJ). The structure-activity relationship (SAR) studies of all the title scaffolds were also established. The antibacterial activity, molecular docking studies, and molecular dynamic simulations of the title compounds suggested that these are promising antibacterial active skeletons.

Accelerating Computation of Acidity Constants and Redox Potentials for Aqueous Organic Redox Flow Batteries by Machine Learning Potential-Based Molecular Dynamics

Due to the increased concern about energy and environmental issues, significant attention has been paid to the development of large-scale energy storage devices to facilitate the utilization of clean energy sources. The redox flow battery (RFB) is one of the most promising systems. Recently, the high cost of transition-metal complex-based RFB has promoted the development of aqueous RFBs with redox-active organic molecules. To expand the working voltage, computational chemistry has been applied to search for organic molecules with lower or higher redox potentials. However, redox potential computation based on implicit solvation models would be challenging due to difficulty in parametrization when considering the complex solvation of supporting electrolytes. Besides, although ab initio molecular dynamics (AIMD) describes the supporting electrolytes with the same level of electronic structure theory as the redox couple, the application is impeded by the high computation costs. Recently, machine learning molecular dynamics (MLMD) has been illustrated to accelerate AIMD by several orders of magnitude without sacrificing the accuracy. It has been established that redox potentials can be computed by MLMD with two separated machine learning potentials (MLPs) for reactant and product states, which is redundant and inefficient. In this work, an automated workflow is developed to construct a universal MLP for both states, which can compute the redox potentials or acidity constants of redox-active organic molecules more efficiently. Furthermore, the predicted redox potentials can be evaluated at the hybrid functional level with much lower costs, which would facilitate the design of aqueous organic RFBs.

Exploring the Deprotonation Process during Incorporation of a Ligand Water Molecule at the Dangling Mn Site in Photosystem II

The Mn4CaO5 cluster, featuring four ligand water molecules (W1 to W4), serves as the water-splitting site in photosystem II (PSII). X-ray free electron laser (XFEL) structures exhibit an additional oxygen site (O6) adjacent to the O5 site in the fourth lowest oxidation state, S3, forming Mn4CaO6. Here, we investigate the mechanism of the second water ligand molecule at the dangling Mn (W2) as a potential incorporating species, using a quantum mechanical/molecular mechanical (QM/MM) approach. Previous QM/MM calculations demonstrated that W1 releases two protons through a low-barrier H-bond toward D1-Asp61 and subsequently releases an electron during the S2 to S3 transition, resulting in O•- at W1 and protonated D1-Asp61. During the process of Mn4CaO6 formation, O•-, rather than H2O or OH-, best reproduced the O5···O6 distance. Although the catalytic cluster with O•- at O6 is more stable than that with O•- at W1 in S3, it does not occur spontaneously due to the significantly uphill deprotonation process. Assuming O•- at W2 incorporates into the O6 site, an exergonic conversion from Mn1(III)Mn2(IV)Mn3(IV)Mn4(IV) (equivalent to the open-cubane S2 valence state) to Mn1(IV)Mn2(IV)Mn3(IV)Mn4(III) (equivalent to the closed-cubane S2 valence state) occurs. These findings provide energetic insights into the deprotonation and structural conversion events required for the Mn4CaO6 formation.

On the Validation of Protein Force Fields Based on Structural Criteria

Molecular dynamics simulations depend critically on the quality of the force field used to describe the interatomic interactions and the extent to which it has been validated for use in a specific application. Using a curated test set of 52 high-resolution structures, 39 derived from X-ray diffraction and 13 solved using NMR, we consider the extent to which different parameter sets of the GROMOS protein force field can be distinguished based on comparing a range of structural criteria, including the number of backbone hydrogen bonds, the number of native hydrogen bonds, polar and nonpolar solvent-accessible surface area, radius of gyration, the prevalence of secondary structure elements, J-coupling constants, nuclear Overhauser effect (NOE) intensities, positional root-mean-square deviations (RMSD), and the distribution of backbone ϕ and ψ dihedral angles. It is shown that while statistically significant differences between the average values of individual metrics could be detected, these were in general small. Furthermore, improvements in agreement in one metric were often offset by loss of agreement in another. The work establishes a framework and test set against which protein force fields can be validated. It also highlights the danger of inferring the relative quality of a given force field based on a small range of structural properties or small number of proteins.

Systematic Study of Different Types of Interactions in α-, β- and γ-Cyclodextrin: Quantum Chemical Investigation

In this work, comprehensive ab initio quantum chemical calculations using the DFT level of theory were performed to characterize the stabilization interactions (H-bonding and hyperconjugation effects) of two stable symmetrical conformations of α-, β-, and γ-cyclodextrins (CDs). For this purpose, we analyzed the electron density using "Atom in molecules" (AIM), "Natural Bond Orbital" (NBO), and energy decomposition method (CECA) in 3D and in Hilbert space. We also calculated the H-bond lengths and OH vibrational frequencies. In every investigated CD, the quantum chemical descriptors characterizing the strength of the interactions between the H-bonds of the primary OH (or hydroxymethyl) and secondary OH groups are examined by comparing the same quantity calculated for ethylene glycol, α-d-glucose (α-d-Glcp) and a water cluster as reference systems. By using these external standards, we can characterize more quantitatively the properties of these bonds (e.g., strength). We have demonstrated that bond critical points (BCP) of intra-unit H-bonds are absent in cyclodextrins, similar to α-d-Glcp and ethylene glycol. In contrast, the CECA analysis showed the existence of an exchange (bond-like) interaction between the interacting O…H atoms. Consequently, the exchange interaction refers to a chemical bond, namely the H-bond between two atoms, unlike BCP, which is not suitable for its detection.

Monomeric Esterase: Insights into Cooperative Behavior, Hysteresis/Allokairy

Herein, we present a novel esterase enzyme, Ade1, isolated from a metagenomic library of Amazonian dark earths soils, demonstrating its broad substrate promiscuity by hydrolyzing ester bonds linked to aliphatic groups. The three-dimensional structure of the enzyme was solved in the presence and absence of substrate (tributyrin), revealing its classification within the α/β-hydrolase superfamily. Despite being a monomeric enzyme, enzymatic assays reveal a cooperative behavior with a sigmoidal profile (initial velocities vs substrate concentrations). Our investigation brings to light the allokairy/hysteresis behavior of Ade1, as evidenced by a transient burst profile during the hydrolysis of substrates such as p-nitrophenyl butyrate and p-nitrophenyl octanoate. Crystal structures of Ade1, coupled with molecular dynamics simulations, unveil the existence of multiple conformational structures within a single molecular state (E̅1). Notably, substrate binding induces a loop closure that traps the substrate in the catalytic site. Upon product release, the cap domain opens simultaneously with structural changes, transitioning the enzyme to a new molecular state (E̅2). This study advances our understanding of hysteresis/allokairy mechanisms, a temporal regulation that appears more pervasive than previously acknowledged and extends its presence to metabolic enzymes. These findings also hold potential implications for addressing human diseases associated with metabolic dysregulation.

Influence of different pH milieu on the structure and function of human Aurora kinase B protein (AURK-B): Amalgamation of both spectroscopic and computational approach

Aurora kinase B (AURK-B) is a serine/threonine kinase protein that plays an essential role in chromosomal separation during the cell cycle event. AURK-B is highly expressed in various types of cancer such as human seminoma, thyroid carcinoma, non-small cell lung carcinoma (NSCLC), oral carcinoma, and gastric cancer. Hence, it is a potential therapeutic target in the treatment of various cancers. The structure of AURK-B in complex with one of its substrate inner centromeric protein (INCENP) is present, but the structural and functional characterization of native AURK-B at different pH environment is still unexplored.This study determines the effect of different pH milieu on the structure and function of AURK-B protein wherein the influence of pH on the protein conformation was probed using Circular dichroism (CD) and fluorescence spectroscopy. The structural studies were further combined with functional activity assay to observe the change in kinase activity at various pH milieu (2.0-11.0). CD and fluorescence spectroscopy experiments dictate that at high acidic conditions (pH 2.0 - 5.0), the secondary and tertiary structures of AURK-B become distorted, leading to diminished activity. The protein, however, was observed to stabilize towards pH 7.0 - 8.0 with minimal structure alteration over the basic pH range (pH 9.0 -11.0). The measured spectroscopic structural features were found to be in-line with obtained experimental kinase activity assays. Further, in-vitro experiments indicate that the enzyme is maximally active at pH 8.0. More ordered conformation and compact structure was observed at this pH (pH 8.0) as compared to other pH values through molecular dynamics simulation studies (MDS). As AURK-B localizes itself in the intracellular compartment, this study may provide a clue about the role of different pH environments in enhancing cancer growth, proliferation, and invasion.

Unveiling molecular insights: in silico exploration of TLR4 antagonist for management of dry eye syndrome

BACKGROUND

Dry eye disease is the most commonplace multifractional ocular complication, which has already affected millions of people in the world. It is identified by the excessive buildup of reactive oxygen species, leading to substantial corneal epithelial cell demise and ocular surface inflammation attributed to TLR4. In this study, we aimed to identify potential compounds to treat of dry eye syndrome by exploring in silico methods.

METHODS

In this research, molecular docking and dynamics simulation tests were used to examine the effects of selected compounds on TLR4 receptor. Compounds were extracted from different databases and were prepared and docked against TLR4 receptor via Autodock Vina. Celastrol, lumacaftor and nilotinib were selected for further molecular dynamics studies for a deeper understanding of molecular systems consisting of protein and ligands by using the Desmond module of the Schrodinger Suite.

RESULTS

The docking results revealed that the compounds are having binding affinity in the range of -5.1 to -8.78 based on the binding affinity and three-dimensional interactions celastrol, lumacaftor and nilotinib were further studied for their activity by molecular dynamics. Among the three compounds, celastrol was the most stable based on molecular dynamics trajectory analysis from 100 ns in the catalytic pockets of 2Z63.pdb.pdb. Root mean square deviation of celastrol/2Z63 was in the range of 1.8-4.8 Å.

CONCLUSION

In particular, Glu376 of TLR4 receptor is crucial for the identification and binding of lipopolysaccharides (LPS), which are part of Gram-negative bacteria's outer membrane. In our investigation, celastrol binds to Glu376, suggesting that celastrol may prevent the dry eye syndrome by inhibiting LPS's binding to TLR4.

Simulation and Computational Study of RING Domain Mutants of BRCA1 and Ube2k in AD/PD Pathophysiology

Lysine-based post-translational modification (PTM) such as acylation, acetylation, deamination, methylation, SUMOylation, and ubiquitination has proven to be a major regulator of gene expression, chromatin structure, protein stability, protein-protein interaction, protein degradation, and cellular localization. However, besides all the PTMs, ubiquitination stands as the second most common PTM after phosphorylation that is involved in the etiology of neurodegenerative diseases (NDDs) namely, Alzheimer's disease (AD) and Parkinson's disease (PD). NDDs are characterized by the accumulation of misfolded protein aggregates in the brain that lead to disease-related gene mutation and irregular protein homeostasis. The ubiquitin-proteasome system (UPS) is in charge of degrading these misfolded proteins, which involve an interplay of E1, E2, E3, and deubiquitinase enzymes. Impaired UPS has been commonly observed in NDDs and E3 ligases are the key members of the UPS, thus, dysfunction of the same can accelerate the neurodegeneration process. Therefore, the aim of this study is firstly, to find E3 ligases that are common in both AD and PD through data mining. Secondly, to study the impact of mutation on its structure and function. The study deciphered 74 E3 ligases that were common in both AD and PD. Later, 10 hub genes were calculated of which protein-protein interaction, pathway enrichment, lysine site prediction, domain, and motif analysis were performed. The results predicted BRCA1, PML, and TRIM33 as the top three putative lysine-modified E3 ligases involved in AD and PD pathogenesis. However, based on structural characterization, BRCA1 was taken further to study RING domain mutation that inferred K32Y, K32L, K32C, K45V, K45Y, and K45G as potential mutants that alter the structural and functional ability of BRCA1 to interact with Ube2k, E2-conjugating enzyme. The most probable mutant observed after molecular dynamics simulation of 50 ns is K32L. Therefore, our study concludes BRCA1, a potential E3 ligase common in AD and PD, and RING domain mutation at sites K32 and K45 possibly disturbs its interaction with its E2, Ube2k.

The search for novel proline analogs for viscosity reduction and stabilization of highly concentrated monoclonal antibody solutions

Administration of monoclonal antibodies (mAbs) is currently focused on subcutaneous injection associated with increased patient adherence and reduced treatment cost, leading to sustainable healthcare. The main bottleneck is low volume that can be injected, requiring highly concentrated mAb solutions. The latter results in increased solution viscosity with pronounced mAb aggregation propensity because of intensive protein-protein interactions. Small molecule excipients have been proposed to restrict the protein-protein interactions, contributing to reduced viscosity. The aim of the study was to discover novel compounds that reduce the viscosity of highly concentrated mAb solution. First, the chemical space of proline analogs was explored and 35 compounds were determined. Viscosity measurements revealed that 18 proline analogs reduced the mAb solution viscosity similar to or more than proline. The compounds forming both electrostatic and hydrophobic interactions with mAb reduced the viscosity of the formulation more efficiently without detrimentally effecting mAb physical stability. A correlation between the level of interaction and viscosity-reducing effect was confirmed with molecular dynamic simulations. Structure rigidity of the compounds and aromaticity contributed to their viscosity-reducing effect, dependent on molecule size. The study results highlight the novel proline analogs as an effective approach in viscosity reduction in development of biopharmaceuticals for subcutaneous administration.

Solvation Shifts the Band-Edge Position of Colloidal Quantum Dots by Nearly 1 eV

The optoelectronic properties of colloidal quantum dots (cQDs) depend critically on the absolute energy of the conduction and valence band edges. It is well known these band-edge energies are sensitive to the ligands on the cQD surface, but it is much less clear how they depend on other experimental conditions, like solvation. Here, we experimentally determine the band-edge positions of thin films of PbS and ZnO cQDs via spectroelectrochemical measurements. To achieve this, we first carefully evaluate and optimize the electrochemical injection of electrons and holes into PbS cQDs. This results in electrochemically fully reversible electron injection with >8 electrons per PbS cQDs, allowing the quantitative determination of the conduction band energy for PbS cQDs with various diameters and surface compositions. Surprisingly, we find that the band-edge energies shift by nearly 1 eV in the presence of different solvents, a result that also holds true for ZnO cQDs. We argue that complexation and partial charge transfer between solvent and surface ions are responsible for this large effect of the solvent on the band-edge energy. The trend in the energy shift matches the results of density functional theory (DFT) calculations in explicit solvents and scales with the energy of complexation between surface cations and solvents. As a first approximation, the solvent Lewis basicity can be used as a good descriptor to predict the shift of the conduction and valence band edges of solvated cQDs.

Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments

Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.

Multiscale Computational Framework for the Liquid-Liquid Phase Separation of Intrinsically Disordered Proteins

The reversible assembly of intrinsically disordered proteins (IDPs) to form membraneless organelles (MLOs) is a fundamental process involved in the spatiotemporal regulation in living cells. MLOs formed via liquid-liquid phase separation (LLPS) serve as molecule-enhancing hubs to regulate cell functions. Owing to the complexity and dynamic nature of the protein assembly via a network of weak inter- and intra-molecular interactions, it is challenging to describe and predict the LLPS behavior. We have developed a multiscale computational model for IDPs, using the fused in sarcoma (FUS) protein and its variants as illustrative examples. To simplify the description of protein, FUS is represented as a linear chain of stickers interspaced by spacers, as inspired by the associative polymer theory. Low-complexity aromatic-rich kinked segments (LARKS) available in FUS were identified using LARKSdb and represented as "stickers". The pairwise potential energies of each pair of stickers and their β-sheet-forming propensity were estimated via molecular docking and all atomistic molecular dynamics (AA-MD) simulations. Subsequently, FUS chains were randomly positioned in a cubic lattice as coarse-grained (CG) beads, with the bead assignment based on the Kuhn length estimation of stickers and spacers. Stochastic FUS movements were modeled by Monte Carlo (MC) simulations. In addition to the Metropolis algorithm, discretized pair potential distributions between stickers were considered in the move acceptance criteria. The chosen pair potential represents one of the possible binding energy states, with its probability determined by the frequency of the binding energy distribution histogram. The fluctuations of averaged radial distribution functions (RDFs) in successive MC trial move intervals of equilibrated lattice MC simulations were used to indicate the dynamic nature of assembly/disassembly of the protein chains. This multiscale computational framework provides an economical and efficient way of predicting and describing the LLPS behavior of IDPs.

Machine Learning Integrating Protein Structure, Sequence, and Dynamics to Predict the Enzyme Activity of Bovine Enterokinase Variants

Despite recent advances in computational protein science, the dynamic behavior of proteins, which directly governs their biological activity, cannot be gleaned from sequence information alone. To overcome this challenge, we propose a framework that integrates the peptide sequence, protein structure, and protein dynamics descriptors into machine learning algorithms to enhance their predictive capabilities and achieve improved prediction of the protein variant function. The resulting machine learning pipeline integrates traditional sequence and structure information with molecular dynamics simulation data to predict the effects of multiple point mutations on the fold improvement of the activity of bovine enterokinase variants. This study highlights how the combination of structural and dynamic data can provide predictive insights into protein functionality and address protein engineering challenges in industrial contexts.

Type Label Framework for Bonded Force Fields in LAMMPS

New functionality is added to the LAMMPS molecular simulation package, which increases the versatility with which LAMMPS can interface with supporting software and manipulate information associated with bonded force fields. We introduce the "type label" framework that allows atom types and their higher-order interactions (bonds, angles, dihedrals, and impropers) to be represented in terms of the standard atom type strings of a bonded force field. Type labels increase the human readability of input files, enable bonded force fields to be supported by the OpenKIM repository, simplify the creation of reaction templates for the REACTER protocol, and increase compatibility with external visualization tools, such as VMD and OVITO. An introductory primer on the forms and use of bonded force fields is provided to motivate this new functionality and serve as an entry point for LAMMPS and OpenKIM users unfamiliar with bonded force fields. The type label framework has the potential to streamline modeling workflows that use LAMMPS by increasing the portability of software, files, and scripts for preprocessing, running, and postprocessing a molecular simulation.

Exploring Biginelli-based scaffolds as A2B adenosine receptor antagonists: Unveiling novel structure-activity relationship trends, lead compounds, and potent colorectal anticancer agents

Antagonists of the A2B adenosine receptor have recently emerged as targeted anticancer agents and immune checkpoint inhibitors within the realm of cancer immunotherapy. This study presents a comprehensive evaluation of novel Biginelli-assembled pyrimidine chemotypes, including mono-, bi-, and tricyclic derivatives, as A2BAR antagonists. We conducted a comprehensive examination of the adenosinergic profile (both binding and functional) of a large compound library consisting of 168 compounds. This approach unveiled original lead compounds and enabled the identification of novel structure-activity relationship (SAR) trends, which were supported by extensive computational studies, including quantum mechanical calculations and free energy perturbation (FEP) analysis. In total, 25 molecules showed attractive affinity (Ki < 100 nM) and outstanding selectivity for A2BAR. From these, five molecules corresponding to the new benzothiazole scaffold were below the Ki < 10 nM threshold, in addition to a novel dual A2A/A2B antagonist. The most potent compounds, and the dual antagonist, showed enantiospecific recognition in the A2BAR. Two A2BAR selective antagonists and the dual A2AAR/A2BAR antagonist reported in this study were assessed for their impact on colorectal cancer cell lines. The results revealed a significant and dose-dependent reduction in cell proliferation. Notably, the A2BAR antagonists exhibited remarkable specificity, as they did not impede the proliferation of non-tumoral cell lines. These findings support the efficacy and potential that A2BAR antagonists as valuable candidates for cancer therapy, but also that they can effectively complement strategies involving A2AAR antagonism in the context of immune checkpoint inhibition.

Toward transferable empirical valence bonds: Making classical force fields reactive

The empirical valence bond technique allows classical force fields to model reactive processes. However, parametrization from experimental data or quantum mechanical calculations is required for each reaction present in the simulation. We show that the parameters present in the empirical valence bond method can be predicted using a neural network model and the SMILES strings describing a reaction. This removes the need for quantum calculations in the parametrization of the empirical valence bond technique. In doing so, we have taken the first steps toward defining a new procedure for enabling reactive atomistic simulations. This procedure would allow researchers to use existing classical force fields for reactive simulations, without performing additional quantum mechanical calculations.

Why Do Empirical Valence Bond Simulations Yield Accurate Arrhenius Plots?

Computer simulations of the temperature dependence of enzyme reactions using the empirical valence bond (EVB) method have proven to give very accurate results in terms of the thermodynamic activation parameters. Here, we analyze the reasons for why such simulations are able to correctly capture activation enthalpies and entropies and how sensitive these quantities are to parametrization of the reactive potential energy function. We examine first the solution reference reaction for the enzyme ketosteroid isomerase, which corresponds to the acetate catalyzed deprotonation of the steroid in water. The experimentally determined activation parameters for this reaction turn out to be remarkably well reproduced by the calculations. By modifying the EVB potential so that the activation and reaction free energies become significantly shifted, we show that the activation entropy is basically invariant to such changes and that ΔS⧧ is instead determined by the specific mixture of the underlying force fields in the transition state region. The coefficients of this mixture do not change appreciably when the EVB potential is modified within reasonable limits, and hence, the estimate of ΔS⧧ becomes very robust. This is further verified by examining a more complex concerted hydride and proton transfer reaction in the enzyme hydroxybutyrate dehydrogenase.

From salt water to bioceramics: Mimic nature through pressure-controlled hydration and crystallization

Modern synthetic technology generally invokes high temperatures to control the hydration level of ceramics, but even the state-of-the-art technology can still only control the overall hydration content. Magically, natural organisms can produce bioceramics with tailorable hydration profiles and crystallization traits solely from amorphous precursors under physiological conditions. To mimic the biomineralization tactic, here, we report pressure-controlled hydration and crystallization in fabricated ceramics, solely from the amorphous precursors of purely inorganic gels (PIGs) synthesized from biocompatible aqueous solutions with most common ions in organisms (Ca2+, Mg2+, CO32-, and PO43-). Transparent ceramic tablets are directly produced by compressing the PIGs under mild pressure, while the pressure regulates the hydration characteristics and the subsequent crystallization behaviors of the synthesized ceramics. Among the various hydration species, the moderately bound and ordered water appears to be a key in regulating the crystallization rate. This nature-inspired study offers deeper insights into the magic behind biomineralization.

The accuracy limit of chemical shift predictions for species in aqueous solution

Interpreting NMR experiments benefits from first-principles predictions of chemical shifts. Reaching the accuracy limit of theory is relevant for unambiguous structural analysis and dissecting theoretical approximations. Since accurate chemical shift measurements are based on using internal reference compounds such as trimethylsilylpropanesulfonate (DSS), a detailed comparison of experimental with theoretical data requires simultaneous consideration of both target and reference species ensembles in the same solvent environment. Here we show that ab initio molecular dynamics simulations to generate liquid-state ensembles of target and reference compounds, including explicitly their short-range solvation environments and combined with quantum-mechanical solvation models, allows for predicting highly accurate 1H (∼0.1-0.5 ppm) and aliphatic 13C (∼1.5 ppm) chemical shifts for aqueous solutions of the model compounds trimethylamine N-oxide (TMAO) and N-methylacetamide (NMA), referenced to DSS without any system-specific adjustments. This encompasses the two peptide bond conformations of NMA identified by NMR. The results are used to derive a general-purpose guideline set for predictive NMR chemical shift calculations of NMA in the liquid state and to identify artifacts of force field models. Accurate predictions are only obtained if a sufficient number of explicit water molecules is included in the quantum-mechanical calculations, disproving a purely electrostatic model of the solvent effect on chemical shifts.

Assessment of Kaistella jeonii esterase conformational dynamics in response to poly(ethylene terephthalate) binding

The pervasive presence of plastic in the environment has reached a concerning scale, being identified in many ecosystems. Bioremediation is the cheapest and most eco-friendly alternative to remove this polymer from affected areas. Recent work described that a novel cold-active esterase enzyme extracted from the bacteria Kaistella jeonii could promiscuously degrade PET. Compared to the well-known PETase from Ideonella sakaiensis, this novel esterase presents a low sequence identity yet has a remarkably similar folding. However, enzymatic assays demonstrated a lower catalytic efficiency. In this work, we employed a strict computational approach to investigate the binding mechanism between the esterase and PET. Understanding the underlying mechanism of binding can shed light on the evolutive mechanism of how enzymes have been evolving to degrade these artificial molecules and help develop rational engineering approaches to improve PETase-like enzymes. Our results indicate that this esterase misses a disulfide bridge, keeping the catalytic residues closer and possibly influencing its catalytic efficiency. Moreover, we describe the structural response to the interaction between enzyme and PET, indicating local and global effects. Our results aid in deepening the knowledge behind the mechanism of biological catalysis of PET degradation and as a base for the engineering of novel PETases.

Investigating Biomolecules in Deep Eutectic Solvents with Molecular Dynamics Simulations: Current State, Challenges and Future Perspectives

Deep eutectic solvents (DESs) have recently gained increased attention for their potential in biotechnological applications. DESs are binary mixtures often consisting of a hydrogen bond acceptor and a hydrogen bond donor, which allows for tailoring their properties for particular applications. If produced from sustainable resources, they can provide a greener alternative to many traditional organic solvents for usage in various applications (e.g., as reaction environment, crystallization agent, or storage medium). To navigate this large design space, it is crucial to comprehend the behavior of biomolecules (e.g., enzymes, proteins, cofactors, and DNA) in DESs and the impact of their individual components. Molecular dynamics (MD) simulations offer a powerful tool for understanding thermodynamic and transport processes at the atomic level and offer insights into their fundamental phenomena, which may not be accessible through experiments. While the experimental investigation of DESs for various biotechnological applications is well progressed, a thorough investigation of biomolecules in DESs via MD simulations has only gained popularity in recent years. Within this work, we aim to provide an overview of the current state of modeling biomolecules with MD simulations in DESs and discuss future directions with a focus for optimizing the molecular simulations and increasing our fundamental knowledge.

Visualizing the impact of disease-associated mutations on G protein-nucleotide interactions

Activation of G proteins stimulates ubiquitous intracellular signaling cascades essential for life processes. Under normal physiological conditions, nucleotide exchange is initiated upon the formation of complexes between a G protein and G protein-coupled receptor (GPCR), which facilitates exchange of bound GDP for GTP, subsequently dissociating the trimeric G protein into its Gα and Gβγ subunits. However, single point mutations in Gα circumvent nucleotide exchange regulated by GPCR-G protein interactions, leading to either loss-of-function or constitutive gain-of-function. Mutations in several Gα subtypes are closely linked to the development of multiple diseases, including several intractable cancers. We leveraged an integrative spectroscopic and computational approach to investigate the mechanisms by which seven of the most frequently observed clinically-relevant mutations in the α subunit of the stimulatory G protein result in functional changes. Variable temperature circular dichroism (CD) spectroscopy showed a bimodal distribution of thermal melting temperatures across all GαS variants. Modeling from molecular dynamics (MD) simulations established a correlation between observed thermal melting temperatures and structural changes caused by the mutations. Concurrently, saturation-transfer difference NMR (STD-NMR) highlighted variations in the interactions of GαS variants with bound nucleotides. MD simulations indicated that changes in local interactions within the nucleotide-binding pocket did not consistently align with global structural changes. This collective evidence suggests a multifaceted energy landscape, wherein each mutation may introduce distinct perturbations to the nucleotide-binding site and protein-protein interaction sites. Consequently, it underscores the importance of tailoring therapeutic strategies to address the unique challenges posed by individual mutations.

Development and Comprehensive Benchmark of a High-Quality AMBER-Consistent Small Molecule Force Field with Broad Chemical Space Coverage for Molecular Modeling and Free Energy Calculation

Biomolecular simulations have become an essential tool in contemporary drug discovery, and molecular mechanics force fields (FFs) constitute its cornerstone. Developing a high quality and broad coverage general FF is a significant undertaking that requires substantial expert knowledge and computing resources, which is beyond the scope of general practitioners. Existing FFs originate from only a limited number of groups and organizations, and they either suffer from limited numbers of training sets, lower than desired quality because of oversimplified representations, or are costly for the molecular modeling community to access. To address these issues, in this work, we developed an AMBER-consistent small molecule FF with extensive chemical space coverage, and we provide Open Access parameters for the entire modeling community. To validate our FF, we carried out benchmarks of quantum mechanics (QM)/molecular mechanics conformer comparison and free energy perturbation calculations on several benchmark data sets. Our FF achieves a higher level of performance at reproducing QM energies and geometries than two popular open-source FFs, OpenFF2 and GAFF2. In relative binding free energy calculations for 31 protein-ligand data sets, comprising 1079 pairs of ligands, the new FF achieves an overall root-mean-square error of 1.19 kcal/mol for ΔΔG and 0.92 kcal/mol for ΔG on a subset of 463 ligands without bespoke fitting to the data sets. The results are on par with those of the leading commercial series of OPLS FFs.

Probing Electronic Band Structures of Dielectric Polymers via Pre-Breakdown Conduction

The electronic band structure, especially the defect states at the conduction band tail, dominates electron transport and electrical degradation of a dielectric material under an extremely high electric field. However, the electronic band structure in a dielectric is barely well studied due to experimental challenges in detecting the electrical conduction to an extremely high electric field, i.e., prebreakdown. In this work, the electronic band structure of polymer dielectric films is probed through an in situ prebreakdown conduction measurement method in conjunction with a space-charge-limited-current spectroscopic analysis. An exponential distribution of defect states at the conduction band tail with varying trap levels is observed in accordance with the specific morphological disorder in the polymer dielectric, and the experimental defect states show also a favorable agreement with the calculated density of states from the density functional theory. The methodology demonstrated in this work bridges the molecule-structure-determined electronic band structure and the macro electrical conduction behavior with a highly improved understanding of material properties that control the electrical breakdown, and paves a way for guiding the modification of existing material and the exploration of novel materials for high electric field applications.

OPLS/2020 Force Field for Unsaturated Hydrocarbons, Alcohols, and Ethers

The OPLS all-atom force field was updated and applied to modeling unsaturated hydrocarbons, alcohols, and ethers. Testing has included gas-phase conformational energetics, properties of pure liquids, and free energies of hydration. Monte Carlo statistical mechanics (MC) calculations were used to model 60 liquids. In addition, a robust, automated procedure was devised to compute the free energies of hydration with high precision via free-energy perturbation (FEP) calculations using double annihilation. Testing has included larger molecules than in the past, and parameters are reported for the first time for some less common groups including alkynes, allenes, dienes, and acetals. The average errors in comparison with experimental data for the computed properties of the pure liquids were improved with the modified force field (OPLS/2020). For liquid densities and heats of vaporization, the average unsigned errors are 0.01 g/cm3 and 0.2 kcal/mol. The average error and signed error for free energies of hydration are both 1.2 kcal/mol. As noted before, this reflects a systematic overestimate of the hydrophobicity of organic molecules when the parametrization is done to minimize the errors for properties of pure liquids. Implications for the modeling of biomolecular systems with standard force fields are considered.

Linear and ring polypeptides complexed with oppositely charged surfactants: the cohesion of the complexes as revealed in atomistic simulations

The use of linear supercharged unfolded polypeptides (SUPs) and oppositely charged surfactants in aqueous solution has demonstrated impressive adhesive properties. These substances possess biocompatibility, biodegradability and other necessary properties for practical application as a biomedical glue in wound repair. The success of these substances, coupled with limited knowledge about such systems, provides hope for enhancing the performance of the final product. One potential approach involves altering the topology of the polypeptide chain. In this article, we conduct a comparative analysis to examine the behavior of the ring and linear chains of a polypeptide in aqueous solution. This analysis utilizes full-atomic computer modeling to monitor the properties of the chains. We investigate the temperature dependence of the shape and size of individual polypeptides in the solution, as well as the formation of complexes via mixing the polypeptide chains with oppositely charged sodium dodecylbenzene sulfonate (SDBS) surfactant molecules in a stoichiometric ratio. Additionally, we explore the cohesive properties of the resulting complex through power experiments involving the extraction of single polypeptide chains out of the SUP-SDBS complexes.

Modification of a Selective NTRK2 Agonist and Confirmation of Activity in a Glaucoma-on-a-Chip Model

Purpose

RNYK is a selective agonist of the neurotrophic tyrosine kinase receptor type 2 (NTRK2) which has been screened from a phage-displayed peptide library. Its sequence is SGVYKVAYDWQH, similar to a native NTRK2 ligand, that is, brain-derived neurotrophic factor (BDNF). The current study was performed to recognize and confirm critical residues for RNYK activity in a glaucoma-on-a-chip model.

Methods

We designed a modified RNYK (mRNYK) peptide based on hotspots of the RNYK sequence identified by alanine scanning. The critical residues consisted of tyrosine, valine, aspartic acid, and tryptophan (YVDW); however, lysine and glutamine were also maintained in the final sequence (YKVDWQ) for forming amide bonds and peptide dimerization. The affinity of mRNYK binding was confirmed by testing against NTRK2 receptors on the surface of ATRA-treated SH-SY5Y cells. The neuroprotective effect of mRNYK was also evaluated in cell culture after elevated pressure insult in a glaucoma-on-a-chip model.

Results

The primary amine on the lysine side-chain from one sequence (YKVDWQ) reacted with a γ-carboxamide group of glutamine from the other sequence, forming dimeric mRNYK. In silico, molecular dynamic simulations of the mRNYK-NTRK2 complex showed more stable and stronger interactions as compared to the RNYK-NTRK2 complex. In vitro, mRNYK demonstrated a neuroprotective effect on SH-SY5Y cells under normal and elevated pressure comparable to RNYK. The 50% effective concentration (logEC50) for mRNYK was 0.7009, which was better than RNYK with a logEC50 of 0.8318.

Conclusion

The modified peptide studied herein showed improved stability over the original peptide (RNYK) and demonstrated potential for use as a BDNF agonist with neuroprotective properties for treatment of neurodegenerative disorders such as glaucoma.

Transferable Implicit Solvation via Contrastive Learning of Graph Neural Networks

Implicit solvent models are essential for molecular dynamics simulations of biomolecules, striking a balance between computational efficiency and biological realism. Efforts are underway to develop accurate and transferable implicit solvent models and coarse-grained (CG) force fields in general, guided by a bottom-up approach that matches the CG energy function with the potential of mean force (PMF) defined by the finer system. However, practical challenges arise due to the lack of analytical expressions for the PMF and algorithmic limitations in parameterizing CG force fields. To address these challenges, a machine learning-based approach is proposed, utilizing graph neural networks (GNNs) to represent the solvation free energy and potential contrasting for parameter optimization. We demonstrate the effectiveness of the approach by deriving a transferable GNN implicit solvent model using 600,000 atomistic configurations of six proteins obtained from explicit solvent simulations. The GNN model provides solvation free energy estimations much more accurately than state-of-the-art implicit solvent models, reproducing configurational distributions of explicit solvent simulations. We also demonstrate the reasonable transferability of the GNN model outside of the training data. Our study offers valuable insights for deriving systematically improvable implicit solvent models and CG force fields from a bottom-up perspective.

Improved Protein Model in SPICA Force Field

The previous version of the SPICA coarse-grained (CG) force field (FF) protein model focused primarily on membrane proteins and successfully reproduced the dimerization free energies of several transmembrane helices and the stable structures of various membrane protein assemblies. However, that model had limited accuracy when applied to other proteins, such as intrinsically disordered proteins (IDPs) and peripheral proteins, because the dimensions of the IDPs in an aqueous solution were too compact, and protein binding on the lipid membrane surface was overstabilized. To improve the accuracy of the SPICA FF model for the simulation of such systems, in this study, we introduce protein secondary structure-dependent nonbonded interaction parameters to the backbone segments and reoptimize almost all nonbonded parameters for amino acids. The improved FF proposed here successfully reproduces the radii of gyration of various IDPs, the binding sensitivity of several peripheral membrane proteins, and the dimerization free energies of several transmembrane helices. The new model also shows improved agreement with experiments on the free energy of peptide association in water. In addition, an extensive library of nonbonded interactions between proteins and lipids, including various glycerophospholipids, sphingolipids, and cholesterol, allows the study of specific interactions between lipids and peripheral and transmembrane proteins. Hence, the new SPICA FF (version 2) proposed herein is applicable with high accuracy for simulating a wide range of protein systems.

Staphylococcal superantigen-like protein 10 enhances the amyloidogenic biofilm formation in Staphylococcus aureus

Staphylococcus aureus is a highly infectious pathogen that represents a significant burden on the current healthcare system. Bacterial attachment to medical implants and host tissue, and the establishment of a mature biofilm, play an important role in chronic diseases such as endocarditis, osteomyelitis and wound infections. These biofilms decrease bacterial susceptibility to antibiotics and immune defences, making the infections challenging to treatment. S. aureus produces numerous exotoxins that contribute to the pathogenesis of the bacteria. In this study, we have identified a novel function of staphylococcal superantigen-like protein 10 (SSL10) in enhancing the formation of staphylococcal biofilms. Biofilm biomass is significantly increased when SSL10 is added exogenously to bacterial cultures, whereas SSL2 and SSL12 are found to be less active. Exogenously added SSL10 mask the surface charge of the bacterial cells and lowers their zeta potential, leading to the aggregation of the cells. Moreover, the biofilm formation by SSL10 is governed by amyloid aggregation, as evident from spectroscopic and microscopic studies. These findings thereby give the first overview of the SSL-mediated amyloid-based biofilm formation and further drive the future research in identifying potential molecules for developing new antibacterial therapies against Staphylococcus aureus.

A comprehensive computer simulation insight into inhibitory mechanisms of EGCG and NQTrp ligands on amyloid-beta assemblies as the Alzheimer's disease insignia

Amyloid-β peptide with predominant presence in the senile plaques is the most common agent for Alzheimer's disease (AD) incidence. Assembly of the amyloid-β(1-42) (Aβ1-42) isoform is known as the main reason for the AD appearance. Epigallocatechin gallate (EGCG) and 1,4-naphthoquinone-2-yl-L-tryptophan (NQTrp) are two small molecules that inhibit the formation of the Aβ1-42 fibrils. The present study provides molecular insight to clarify the inhibitory mechanisms of the EGCG and NQTrp ligands on the Aβ1-42 assemblies by using molecular dynamics (MD) simulation. Hence, nine different Aβ1-42-containing systems including the monomer, dimer, and hexamer of Aβ1-42 considering each of them in a media with no ligands, in the presence of one EGCG ligand, and in the presence of one EGCG ligand were studied with a simulation time of 1 µs for each system. The precise investigation of the peptide-ligand distance, conformational factor (Pi), solvent accessible surface area (SASA), dictionary of secondary structure (DSSP), and Lys28-Ala42 salt bridge analyses confirmed that the hydroxyl-rich structure of the EGCG ligand applied its inhibitory effect on the aggregation of the peptides indirectly by involving water molecules. While the hydroxyl-free structure of the NQTrp ligand exposed its inhibitory effect through a direct interaction with the Aβ1-42 peptides. Besides, reduced density gradient (RDG) analysis clarified the hydrogen bonding interactions as the dominant ones for the peptide-EGCG systems, and also, steric and van der Waals interactions for the peptide-NQTrp systems.Communicated by Ramaswamy H. Sarma.