Improved Peptide and Protein Torsional Energetics with the OPLSAA Force Field

Solubility Behavior of CO2 in Ionic Liquids Based on Ionic Polarity Index Analyses

Ionic liquids (ILs) can serve as effective CO₂ solvents with an appropriate selection of different anions and cations. However, due to the large library of potential IL compositions, rapid screening methods are needed for characterizing and ranking the expected properties. We have recently proposed the ionic polarity index (IPI) parameter, effectively connecting volume-based approaches and electrostatic potential analyses and providing a single metric that can potentially be used to rapidly screen for desirable IL properties. In this work, the corresponding anion and cation IPIs are used to generate correlations with respect to the CO₂ volumetric solubility in ILs. The relationships are generally applicable to groups of ILs within a homologous ion series, and this can be particularly valuable for prescreening different ion pairings for maximizing gas solvation performance.

Toward a Suite of Coarse-Grained Models for Molecular Simulation of Monoclonal Antibodies and Therapeutic Proteins

A series of coarse-grained models for molecular simulation of proteins are considered, with emphasis on the application of predicting protein-protein self-interactions for monoclonal antibodies (MAbs). As an illustrative example and for quantitative comparison, the models are used to predict osmotic virial coefficients over a broad range of attractive and repulsive self-interactions and solution conditions for a series of MAbs where the second osmotic virial coefficient has been experimentally determined in prior work. The models are compared based on how well they can predict experimental behavior, their computational burdens, and scalability. An intermediate-resolution model is also introduced that can capture specific electrostatic interactions with improved efficiency and similar or improved accuracy when compared to the previously published models. Guidance is included for the selection of coarse-grained models more generally for capturing a balance of electrostatic, steric, and short-ranged nonelectrostatic interactions for proteins from low to high concentrations.

Milk Peptides as Novel Multi-Targeted Therapeutic Candidates for SARS-CoV2

The COVID-19, an acute respiratory syndrome caused by SARS-CoV2 is a major catastrophic event of the twenty first century. Relentless efforts for the development of effective pharmaco-therapeutics are in progress but the respite is the development of effective vaccines. However, monotherapy might not always exhibit complete efficacy and may culminate in the rapid evolution of drug-resistant viral strains. Hence, simultaneous modulation of multiple druggable targets not only enhances therapeutic efficacy but also quell the prospects of mutant viruses. Currently, milk peptides have bloomed beyond just being a quintessential part of nutrition to prominent therapeutic implications in human health and diseases. Hence, we have focused on colostrum/milk peptides as they have already been acknowledged for their high potency, target specificity with significantly low or no side effects and bio-toleration. The results presented provide a conceptual strategy for the rational designing of prospective multitargeted peptide inhibitors for SARS-CoV2.

Identification of Potential Peptide Inhibitors of ACE-2 Target of SARS-CoV-2 from Buckwheat & Quinoa

It is well established fact that peptides from various foods offer human health benefits displaying diverse functionalities. Millets considered as super foods is a major alternative in recent days for traditional diet being rich in proteins and fibre along with trace minerals and vitamins. In this connection, proteins from Buckwheat and Quinoa were digested by in vitro simulation digestion for the generation of peptides, analyzed by nLC-MS/MS and the functional annotations of the identified proteins/peptides were carried out. The study led to the identification of 34 small peptides and their parent proteins clustered into 4 gene functional groups and their localization prediction indicated their involvement in energy metabolism, transport and storage. Interestingly, the identified peptides maximally displayed DPP-IV and ACE inhibitions. The present study was extended to unravel ACE-2 inhibition targeting COVID-19 by selecting ACE-2-Spike binding domain for molecular docking studies. The NWRTVKYG interacted with the ACE-2-Spike interface displaying the feasible binding energy (− 213.63) and docking score (− 12.43) and the MD simulation revealed the ability of the peptide in stabilizing the protein-peptide composite. The present investigation thus establishes newer vista for food derived peptides having ACE-2 inhibitory potential as tentative strategy for SARS-CoV-2 therapeutics.

Folding and self-assembly of short intrinsically disordered peptides and protein regions

Proteins and peptide fragments are highly relevant building blocks in self-assembly for nanostructures with plenty of applications. Intrinsically disordered proteins (IDPs) and protein regions (IDRs) are defined by the absence of a well-defined secondary structure, yet IDPs/IDRs show a significant biological activity. Experimental techniques and computational modelling procedures for the characterization of IDPs/IDRs are discussed. Directed self-assembly of IDPs/IDRs allows reaching a large variety of nanostructures. Hybrid materials based on the derivatives of IDPs/IDRs show a promising performance as alternative biocides and nanodrugs. Cell mimicking, in vivo compartmentalization, and bone regeneration are demonstrated for IDPs/IDRs in biotechnological applications. The exciting possibilities of IDPs/IDRs in nanotechnology with relevant biological applications are shown.

Mechanical behavior of actin and spectrin subjected to high strain rate: A molecular dynamics simulation study

Recent nanoscopy and super-resolution microscopy studies have substantiated the structural contribution of periodic actin-spectrin lattice to the axonal cytoskeleton of neuron. However, sufficient mechanical insight is not present for spectrin and actin-spectrin network, especially in high strain rate scenario. To quantify the mechanical behavior of actin-spectrin cytoskeleton in such conditions, this study determines individual stretching characteristics of actin and spectrin at high strain rate by molecular dynamics (MD) simulation. The actin-spectrin separation criteria are also determined. It is found that both actin and spectrin have high stiffness when susceptible to high strain rate and show strong dependence on applied strain rate. The stretching stiffness of actin and forced unfolding mechanism of spectrin are in harmony with the current literature. Actin-spectrin model provides novel insight into their interaction and separation stretch. It is shown that the region vulnerable to failure is the actin-spectrin interface at lower strain rate, while it is the inter-repeat region of spectrin at higher strain rate.

Explaining and Fixing DFT Failures for Torsional Barriers

Most torsional barriers are predicted with high accuracies (about 1 kJ/mol) by standard semilocal functionals, but a small subset was found to have much larger errors. We created a database of almost 300 carbon-carbon torsional barriers, including 12 poorly behaved barriers, that stem from the Y═C-X group, where Y is O or S and X is a halide. Functionals with enhanced exchange mixing (about 50%) worked well for all barriers. We found that poor actors have delocalization errors caused by hyperconjugation. These problematic calculations are density-sensitive (i.e., DFT predictions change noticeably with the density), and using HF densities (HF-DFT) fixes these issues. For example, conventional B3LYP performs as accurately as exchange-enhanced functionals if the HF density is used. For long-chain conjugated molecules, HF-DFT can be much better than exchange-enhanced functionals. We suggest that HF-PBE0 has the best overall performance.

Potent Noncovalent Inhibitors of the Main Protease of SARS-CoV-2 from Molecular Sculpting of the Drug Perampanel Guided by Free Energy Perturbation Calculations

Starting from our previous finding of 14 known drugs as inhibitors of the main protease (Mpro) of SARS-CoV-2, the virus responsible for COVID-19, we have redesigned the weak hit perampanel to yield multiple noncovalent, nonpeptidic inhibitors with ca. 20 nM IC50 values in a kinetic assay. Free-energy perturbation (FEP) calculations for Mpro-ligand complexes provided valuable guidance on beneficial modifications that rapidly delivered the potent analogues. The design efforts were confirmed and augmented by determination of high-resolution X-ray crystal structures for five analogues bound to Mpro. Results of cell-based antiviral assays further demonstrated the potential of the compounds for treatment of COVID-19. In addition to the possible therapeutic significance, the work clearly demonstrates the power of computational chemistry for drug discovery, especially FEP-guided lead optimization.

Elucidating Solution Structures of Cyclic Peptides Using Molecular Dynamics Simulations

Protein-protein interactions are vital to biological processes, but the shape and size of their interfaces make them hard to target using small molecules. Cyclic peptides have shown promise as protein-protein interaction modulators, as they can bind protein surfaces with high affinity and specificity. Dozens of cyclic peptides are already FDA approved, and many more are in various stages of development as immunosuppressants, antibiotics, antivirals, or anticancer drugs. However, most cyclic peptide drugs so far have been natural products or derivatives thereof, with de novo design having proven challenging. A key obstacle is structural characterization: cyclic peptides frequently adopt multiple conformations in solution, which are difficult to resolve using techniques like NMR spectroscopy. The lack of solution structural information prevents a thorough understanding of cyclic peptides' sequence-structure-function relationship. Here we review recent development and application of molecular dynamics simulations with enhanced sampling to studying the solution structures of cyclic peptides. We describe novel computational methods capable of sampling cyclic peptides' conformational space and provide examples of computational studies that relate peptides' sequence and structure to biological activity. We demonstrate that molecular dynamics simulations have grown from an explanatory technique to a full-fledged tool for systematic studies at the forefront of cyclic peptide therapeutic design.

Atomistic insight on structure and dynamics of spinach acyl carrier protein with substrate length

The plant acyl-acyl carrier protein (ACP) desaturases are a family of soluble enzymes that convert saturated fatty acyl-ACPs into their cis-monounsaturated equivalents in an oxygen-dependent reaction. These enzymes play a key role in biosynthesis of monounsaturated fatty acids in plants. ACPs are central proteins in fatty acid biosynthesis that deliver acyl chains to desaturases. They have been reported to show a varying degree of local dynamics and structural variability depending on the acyl chain size. It has been suggested that substrate-specific changes in ACP structure and dynamics have a crucial impact on the desaturase enzymatic activity. Using molecular dynamics simulations, we investigated the intrinsic solution structure and dynamics of ACP from spinach with four different acyl chains: capric (C₁₀), myristic (C₁₄), palmitic (C₁₆), and stearic (C₁₈) acids. We found that the fatty acids can adopt two distinct structural binding motifs, which feature different binding free energies and influence the ACP dynamics in a different manner. Docking simulations of ACP to castor Δ9-desaturase and ivy Δ4-desaturase suggest that ACP desaturase interactions could lead to a preferential selection between the motifs.

Development and Validation of a DFT-Based Force Field for a Hydrated Homoalanine Polypeptide

A new force field has been created for simulating hydrated alanine polypeptides using the adaptive force matching (AFM) method. Only density functional theory calculations using the Perdew-Burke-Ernzerhof exchange-correlation functional and the D3 dispersion correction were used to fit the force field. The new force field, AFM2020, predicts NMR scalar coupling constants for hydrated homopolymeric alanine in better agreements with experimental data than several other models including those fitted directly to such data. For Ala₇, the new force field shows about 15% helical conformations, 20% conformation in the β basin, and 65% polyproline II. The predicted helical population of short hydrated alanine is higher than previous estimates based on the same experimental data. Gas-phase simulations indicate that the force field developed by AFM solution-phase data is likely to produce a reasonable conformation distribution when hydration water is no longer present, such as the interior of a protein.

Recent Force Field Strategies for Intrinsically Disordered Proteins

Intrinsically disordered proteins (IDPs) are widely distributed across eukaryotic cells, playing important roles in molecular recognition, molecular assembly, post-translational modification, and other biological processes. IDPs are also associated with many diseases such as cancers, cardiovascular diseases, and neurodegenerative diseases. Due to their structural flexibility, conventional experimental methods cannot reliably capture their heterogeneous structures. Molecular dynamics simulation becomes an important complementary tool to quantify IDP structures. This review covers recent force field strategies proposed for more accurate molecular dynamics simulations of IDPs. The strategies include adjusting dihedral parameters, adding grid-based energy correction map (CMAP) parameters, refining protein-water interactions, and others. Different force fields were found to perform well on specific observables of specific IDPs but also are limited in reproducing all available experimental observables consistently for all tested IDPs. We conclude the review with perspective areas for improvements for future force fields for IDPs.

Pushing and Pulling: A Dual pH Trigger Controlled by Varying the Alkyl Tail Length in Heme Coordinating Peptide Amphiphiles

Some organisms in nature that undergo anaerobic respiration utilize 1D nanoscale arrays of densely packed cytochromes containing the molecule heme. The assemblies can be mimicked with 1D nanoscale fibrils composed of peptide amphiphiles designed to coordinate heme in dense arrays. To create such materials and assemblies, it is critical to understand the assembly process and what controls the various aspects of hierarchical assembly. MD simulations suggest that shorter alkyl chains on the peptide lead to more dynamic structures than the peptides with longer chains that yield kinetically trapped states. The hydration parameters manifest themselves experimentally through the observation of a dual pH trigger, which controls the peptide assembly rate, the heme binding affinity, and heme organization kinetics. Great strides in understanding the relative complexity of the self-assembly process in relation to incorporating a functional moiety like heme opens up many possibilities in developing abiotic assemblies for bioelectronic devices and assemblies.

Recent advances in atomic molecular dynamics simulation of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) play important roles in cellular functions. The inherent structural heterogeneity of IDPs makes the high-resolution experimental characterization of IDPs extremely difficult. Molecular dynamics (MD) simulation could provide the atomic-level description of the structural and dynamic properties of IDPs. This perspective reviews the recent progress in atomic MD simulation studies of IDPs, including the development of force fields and sampling methods, as well as applications in IDP-involved protein-protein interactions. The employment of large-scale simulations and advanced sampling techniques allows more accurate estimation of the thermodynamics and kinetics of IDP-mediated protein interactions, and the holistic landscape of the binding process of IDPs is emerging.

Development of coarse-grained force field for alcohols: an efficient meta-multilinear interpolation parameterization algorithm

Coarse-grained (CG) molecular dynamics are powerful tools to access a mesoscopic phenomenon and simultaneously record microscopic details, but currently the CG force fields (FFs) are still limited by low parameterization efficiency and poor accuracy especially for polar molecules. In this work, we developed a Meta-Multilinear Interpolation Parameterization (Meta-MIP) algorithm to optimize the CG FFs for alcohols. This algorithm significantly boosts parameterization efficiency by constructing on-the-fly local databases to cover the global optimal parameterization path. In specific, an alcohol molecule is mapped to a heterologous model composed of an OH bead and a hydrocarbon portion which consists of alkane beads representing two to four carbon atoms. Non-bonded potentials are described by soft Morse functions that have no tail-corrections but can still retain good continuities at truncation distance. Nearly all of the properties in terms of density, heat of vaporization, surface tension, and solvation free energy for alcohols predicted by the current FFs deviate from experimental values by less than 7%. This Meta-MIP algorithm can be readily applied to force field development for a wide variety of molecules or functional groups, in many situations including but not limited to CG FFs.

Computational development and validation of a representative MDI-BDO-based polyurethane hard segment model

Segmented polyurethanes show extraordinary physicochemical properties, mainly owing to the nature and the chemistry of the hard segment domains. There are yet many inexplicable physiochemical properties of MDI-BDO-based hard polyurethane segments such as the geometry, cis-trans isomerism, electronic structure, chemical reactivity, the inter-hard-segment interactions, and the photo-response. In the present study, it was attempted to develop and validate a model system that would facilitate further research on the structural and chemical properties of the MDI-BDO hard segments. It was found that the trans isomer of urethane bond is more stable than the cis isomer, and it is argued here that thermal transformation from trans to cis not possible due to the high rotational energy barrier. The differences between the calculated IR spectra of the cis and trans isomers are proposed as a powerful differentiation tool. The calculated Fukui indices show that cis and trans isomers are different in their chemical reactivity. The findings of the present study suggest intermolecular and intramolecular pi-stacking and highly plausible two significant types of hydrogen bond types between hard segments. In the present study, a model system for MDI-BDO hard segment was developed and successfully validated via computational experiments. Further calculations done with the new model provided an indispensable understanding of the structure, cis-trans isomerism, reactivity, and intermolecular interactions of the MDI-BDO hard segments. The proposed model can be further improved in the future by incorporating suitable soft segments. In summary, the model system developed and validated in the present study has provided new opportunities to understand and further study the structural and chemical features of the hard segments of the MDI-BDO-based polyurethane.

ACE2-derived peptides interact with the RBD domain of SARS-CoV-2 spike glycoprotein, disrupting the interaction with the human ACE2 receptor

Vaccines could be the solution to the current SARS-CoV-2 outbreak. However, some studies have shown that the immunological memory only lasts three months. Thus, it is imperative to develop pharmacological treatments to cope with COVID-19. Here, the in silico approach by molecular docking, dynamic simulations and quantum biochemistry revealed that ACE2-derived peptides strongly interact with the SARS-CoV-2 RBD domain of spike glycoprotein (S-RBD). ACE2-Dev-PepI, ACE2-Dev-PepII, ACE2-Dev-PepIII and ACE2-Dev-PepIV complexed with S-RBD provoked alterations in the 3D structure of S-RBD, leading to disruption of the correct interaction with the ACE2 receptor, a pivotal step for SARS-CoV-2 infection. This wrong interaction between S-RBD and ACE2 could inhibit the entry of SARS-CoV-2 in cells, and thus virus replication and the establishment of COVID-19 disease. Therefore, we suggest that ACE2-derived peptides can interfere with recognition of ACE2 in human cells by SARS-CoV-2 in vivo. Bioinformatic prediction showed that these peptides have no toxicity or allergenic potential. By using ACE2-derived peptides against SARS-CoV-2, this study points to opportunities for further in vivo research on these peptides, seeking to discover new drugs and entirely new perspectives to treat COVID-19.Communicated by Ramaswamy H. Sarma.

Target SARS-CoV-2: computation of binding energies with drugs of dexamethasone/umifenovir by molecular dynamics using OPLS-AA force field

Introduction

In recent times, myriads of public have been infected with a novel SARS-CoV-2, and the fatality toll has reached thousands and been mounting step by step, which is a major crisis in the world. The challenge for this burning issue pertinent to repurposed medicines which prevent novel coronavirus is of immense concern for all scientists around the globe until the arrival of the vaccine.

Methods

Because of the global high priority rating on the search for the repurposed drugs which outfits clinical suitability to SARS-CoV-2, a unique theoretical methodology is proposed. The approach is based on explorations of biothermodynamics computed via molecular dynamics, root-mean-square deviation (RMSD), radius of gyration (Rg) and interactions. This unique methodology is tested for umifenovir/dexamethasone drugs on (SARS-CoV-2) main protease.

Results

This theoretical exploration not only suggested the presence of strong interactions between (SARS-CoV-2 + umifenovir/dexamethasone) but also emphasized the clinical suitability of dexamethasone over umifenovir to treat SARS-CoV-2. This supremacy of dexamethasone is well supported by the results of global clinical trials and COVID-19 therapeutic approved management guidelines of countries.

Conclusions

Thus, this work will pave a way for incremental advancement towards future design and development of more specific inhibitors for the treatment of SARS-CoV-2 infection in humans.

Studying the mechanism of phase separation in aqueous solutions of globular proteins via molecular dynamics computer simulations

Proteins are the most abundant biomacromolecules in living cells, where they perform vital roles in virtually every biological process. To maintain their function, proteins need to remain in a stable (native) state. Inter- and intramolecular interactions in aqueous protein solutions govern the fate of proteins, as they can provoke their unfolding or association into aggregates. The initial steps of protein aggregation are difficult to capture experimentally, therefore we used molecular dynamics simulations in this study. We investigated the initial phase of aggregation of two different lysozymes, hen egg-white (HEWL) and T4 WT* lysozyme and also human lens γ-D crystallin by using atomistic simulations. We monitored the phase stability of their aqueous solutions by calculating time-dependent density fluctuations. We found that all proteins remained in their compact form despite aggregation. With an extensive analysis of intermolecular residue-residue interactions we discovered that arginine is of paramount importance in the initial stage of aggregation of HEWL and γ-D crystallin, meanwhile lysine was found to be the most involved amino acid in forming initial contacts between T4 WT* molecules.

Computational and theoretical exploration for clinical suitability of Remdesivir drug to SARS-CoV-2

A methodology for the exploration of clinical suitability of Remdesivir drug to SARS-CoV-2 main protease based on the computational, theoretical analysis pertinent to Gibb's free energy computed from the Molecular Dynamic simulations with OPLS-AA force field at 300 K/atmospheric pressure and the variation of thermodynamic potentials over the entire simulation run of 100 ns. This study emphasized the suitability of Remdesivir drug to SARS-CoV-2 protein and the same is emphasized by the results of global clinical trials. This methodology can be applied for future design, development of more specific repurposed inhibitors for the treatment of SARS-CoV-2 infection.

Development and Testing of Force Field Parameters for Phenylalanine and Tyrosine Derivatives

Theoretical analyses are valuable for the exploration of the effects of unnatural amino acids on enzyme functions; however, many necessary parameters for unnatural amino acids remain lacking. In this study, we developed and tested force field parameters compatible with Amber ff14SB for 18 phenylalanine and tyrosine derivatives. The charge parameters were derived from ab initio calculations using the RESP fitting approach and then adjusted to reproduce the benchmark relative energies (at the MP2/TZ level) of the α- and β-backbones for each unnatural amino acid dipeptide. The structures optimized under the proposed force field parameters for the 18 unnatural amino acid dipeptides in both the α- and β-backbone forms were in good agreement with their QM structures, as the average RMSD was as small as 0.1 Å. The force field parameters were then tested in their application to seven proteins containing unnatural amino acids. The RMSDs of the simulated configurations of these unnatural amino acids were approximately 1.0 Å compared with those of the crystal structures. The vital interactions between proteins and unnatural amino acids in five protein–ligand complexes were also predicted using MM/PBSA analysis, and they were largely consistent with experimental observations. This work will provide theoretical aid for drug design involving unnatural amino acids.

Free Energy Calculations for Protein-Ligand Binding Prediction

Computational prediction of protein-ligand binding involves initial determination of the binding mode and subsequent evaluation of the strength of the protein-ligand interactions, which directly correlates with ligand binding affinities. As a consequence of increasing computer power, rigorous approaches to calculate protein-ligand binding affinities, such as free energy perturbation (FEP) methods, are becoming an essential part of the toolbox of computer-aided drug design. In this chapter, we provide a general overview of these methods and introduce the QFEP modules, which are open-source API workflows based on our molecular dynamics (MD) package Q. The module QligFEP allows estimation of relative binding affinities along ligand series, while QresFEP is a module to estimate binding affinity shifts caused by single-point mutations of the protein. We herein provide guidelines for the use of each of these modules based on data extracted from ligand-design projects. While these modules are stand-alone, the combined use of the two workflows in a drug-design project yields complementary perspectives of the ligand binding problem, providing two sides of the same coin. The selected case studies illustrate how to use QFEP to approach the two key questions associated with ligand binding prediction: identifying the most favorable binding mode from different alternatives and establishing structure-affinity relationships that allow the rational optimization of hit compounds.

Molecular simulations of IDPs: From ensemble generation to IDP interactions leading to disorder-to-order transitions

Intrinsically disordered proteins (IDPs) lack a well-defined three-dimensional structure but do exhibit some dynamical and structural ordering. The structural plasticity of IDPs indicates that entropy-driven motions are crucial for their function. Many IDPs undergo function-related disorder-to-order transitions upon by their interaction with specific binding partners. Approaches that are based on both experimental and theoretical tools enable the biophysical characterization of IDPs. Molecular simulations provide insights into IDP structural ensembles and disorder-to-order transition mechanisms. However, such studies depend strongly on the chosen force field parameters and simulation techniques. In this chapter, we provide an overview of IDP characteristics, review all-atom force fields recently developed for IDPs, and present molecular dynamics-based simulation methods that allow IDP ensemble generation as well as the characterization of disorder-to-order transitions. In particular, we introduce metadynamics, replica exchange molecular dynamics simulations, and also kinetic models resulting from Markov State modeling, and provide various examples for the successful application of these simulation methods to IDPs.

ProMod3-A versatile homology modelling toolbox

Computational methods for protein structure modelling are routinely used to complement experimental structure determination, thus they help to address a broad spectrum of scientific questions in biomedical research. The most accurate methods today are based on homology modelling, i.e. detecting a homologue to the desired target sequence that can be used as a template for modelling. Here we present a versatile open source homology modelling toolbox as foundation for flexible and computationally efficient modelling workflows. ProMod3 is a fully scriptable software platform that can perform all steps required to generate a protein model by homology. Its modular design aims at fast prototyping of novel algorithms and implementing flexible modelling pipelines. Common modelling tasks, such as loop modelling, sidechain modelling or generating a full protein model by homology, are provided as production ready pipelines, forming the starting point for own developments and enhancements. ProMod3 is the central software component of the widely used SWISS-MODEL web-server.

AMOEBA binding free energies for the SAMPL7 TrimerTrip host-guest challenge

As part of the SAMPL7 host-guest binding challenge, the AMOEBA force field was applied to calculate the absolute binding free energy for 16 charged organic ammonium guests to the TrimerTrip host, a recently reported acyclic cucurbituril-derived clip host structure with triptycene moieties at its termini. Here we report binding free energy calculations for this system using the AMOEBA polarizable atomic multipole force field and double annihilation free energy methodology. Conformational analysis of the host suggests three families of conformations that do not interconvert in solution on a time scale available to nanosecond molecular dynamics (MD) simulations. Two of these host conformers, referred to as the "indent" and "overlap" structures, are capable of binding guest molecules. As a result, the free energies of all 16 guests binding to both conformations were computed separately, and combined to produce values for comparison with experiment. Initial ranked results submitted as part of the SAMPL7 exercise had a mean unsigned error (MUE) from experimental binding data of 2.14 kcal/mol. Subsequently, a rigorous umbrella sampling reference calculation was used to better determine the free energy difference between unligated "indent" and "overlap" host conformations. Revised binding values for the 16 guests pegged to this umbrella sampling reference reduced the MUE to 1.41 kcal/mol, with a correlation coefficient (Pearson R) between calculated and experimental binding values of 0.832 and a rank correlation (Kendall τ) of 0.65. Overall, the AMOEBA results demonstrate no significant systematic error, suggesting the force field provides an accurate energetic description of the TrimerTrip host, and an appropriate balance of solvation and desolvation effects associated with guest binding.

3,4-Dihydropyrimidin-2(1H)-ones as Antagonists of the Human A2B Adenosine Receptor: Optimization, Structure-Activity Relationship Studies, and Enantiospecific Recognition

We present and thoroughly characterize a large collection of 3,4-dihydropyrimidin-2(1H)-ones as A_2BAR antagonists, an emerging strategy in cancer (immuno) therapy. Most compounds selectively bind A_2BAR, with a number of potent and selective antagonists further confirmed by functional cyclic adenosine monophosphate experiments. The series was analyzed with one of the most exhaustive free energy perturbation studies on a GPCR, obtaining an accurate model of the structure-activity relationship of this chemotype. The stereospecific binding modeled for this scaffold was confirmed by resolving the two most potent ligands [(±)-47, and (±)-38 K_i = 10.20 and 23.6 nM, respectively] into their two enantiomers, isolating the affinity on the corresponding (S)-eutomers (K_i = 6.30 and 11.10 nM, respectively). The assessment of the effect in representative cytochromes (CYP3A4 and CYP2D6) demonstrated insignificant inhibitory activity, while in vitro experiments in three prostate cancer cells demonstrated that this pair of compounds exhibits a pronounced antimetastatic effect.

Dual inhibition of SARS-CoV-2 spike and main protease through a repurposed drug, rutin

The global health emergency caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to alarming numbers of fatalities across the world. So far the researchers worldwide have not been able to discover a breakthrough in the form of a potent drug or an effective vaccine. Therefore, it is imperative to discover drugs to curb the ongoing menace. In silico approaches using FDA approved drugs can expedite the drug discovery process by providing leads that can be pursued. In this report, two drug targets, namely the spike protein and main protease, belonging to structural and non-structural class of proteins respectively, were utilized to carry out drug repurposing based screening. The exposed nature of the spike protein on the viral surface along with its instrumental role in host infection and the involvement of main protease in processing of polyproteins along with no human homologue make these proteins attractive drug targets. Interestingly, the screening identified a common high efficiency binding molecule named rutin. Further, molecular dynamics simulations in explicit solvent affirmed the stable and sturdy binding of rutin with these proteins. The decreased R_g value (4 nm for spike-rutin and 2.23 nm for main protease-rutin) and stagnant SASA analysis (485 nm/S²/N in spike-rutin and 152 nm/S2/N in main protease-rutin) for protein surface and its orientation in the exposed and buried regions suggests a strong binding interaction of the drug. Further, cluster analysis and secondary structure analysis of complex trajectories validated the conformational changes due to binding of rutin.

Intrinsic Conformational Dynamics of Alanine in Water/Ethanol Mixtures: An Experiment-Driven Molecular Dynamics Study

In vitro, cationic glycylalanylglycine (GAG) forms a hydrogel in binary mixtures of water and ethanol. In water, alanine residue is known for its high polyproline II (pPII) content. Spectroscopic data, including three J-coupling constants and amide I' profiles, indicate that addition of 42% ethanol to water significantly reduces the pPII content of alanine residue in GAG. Here, experiment-based Gaussian Ramachandran distributions of alanine in GAG at different ethanol fractions are examined and three MD force fields are evaluated with respect to their ability to capture these ethanol-induced conformational changes. MD simulations on monomeric GAG in eight different water/ethanol mixtures within Amber ff14SB, OPLS-AA/M, and CHARMM36m reveal that only Amber ff14SB partially captures the ethanol-induced conformational changes of alanine residue in monomeric GAG when 42% ethanol is added to water. MD simulations of 200 mM GAG ensembles in pure water and in the aqueous solution with 42% ethanol showcase the ability of CHARMM36m to capture the effect of ethanol on the average pPII content of alanine in GAG and provide a plausible explanation for this effect, which may stem from an increased propensity of GAG to form oligomers in the presence of ethanol.

Ionic Liquid Clusters Generated from Electrospray Thrusters: Cold Ion Spectroscopic Signatures of Size-Dependent Acid-Base Interactions

We determine the intramolecular distortions at play in the 2-hydroxyethylhydrazinium nitrate (HEHN) ionic liquid (IL) propellant, which presents the interesting case that the HEH⁺ cation has multiple sites (i.e., hydroxy, primary amine, and secondary ammonium groups) available for H-bonding with the nitrate anion. These interactions are quantified by analyzing the vibrational band patterns displayed by cold cationic clusters, (HEH⁺)_n(NO₃^-)_n-1, n = 2-6, which are obtained using IR photodissociation of the cryogenically cooled, mass-selected ions. The strong interaction involving partial proton transfer of the acidic N-H proton in HEH⁺ cation to the nitrate anion is strongly enhanced in the ternary n = 2 cluster but is suppressed with increasing cluster size. The cluster spectra recover the bands displayed by the bulk liquid by n = 5, thus establishing the minimum domain required to capture this aspect of macroscopic behavior.

Identification of 14 Known Drugs as Inhibitors of the Main Protease of SARS-CoV-2

A consensus virtual screening protocol has been applied to ca. 2000 approved drugs to seek inhibitors of the main protease (M^pro) of SARS-CoV-2, the virus responsible for COVID-19. 42 drugs emerged as top candidates, and after visual analyses of the predicted structures of their complexes with M^pro, 17 were chosen for evaluation in a kinetic assay for M^pro inhibition. Remarkably 14 of the compounds at 100-μM concentration were found to reduce the enzymatic activity and 5 provided IC₅₀ values below 40 μM: manidipine (4.8 μM), boceprevir (5.4 μM), lercanidipine (16.2 μM), bedaquiline (18.7 μM), and efonidipine (38.5 μM). Structural analyses reveal a common cloverleaf pattern for the binding of the active compounds to the P1, P1′, and P2 pockets of M^pro. Further study of the most active compounds in the context of COVID-19 therapy is warranted, while all of the active compounds may provide a foundation for lead optimization to deliver valuable chemotherapeutics to combat the pandemic.

Including residual contact information into replica-exchange MD simulations significantly enriches native-like conformations

Proteins are complex biomolecules which perform critical tasks in living organisms. Knowledge of a protein's structure is essential for understanding its physiological function in detail. Despite the incredible progress in experimental techniques, protein structure determination is still expensive, time-consuming, and arduous. That is why computer simulations are often used to complement or interpret experimental data. Here, we explore how in silico protein structure determination based on replica-exchange molecular dynamics (REMD) can benefit from including contact information derived from theoretical and experimental sources, such as direct coupling analysis or NMR spectroscopy. To reflect the influence from erroneous and noisy data we probe how false-positive contacts influence the simulated ensemble. Specifically, we integrate varying numbers of randomly selected native and non-native contacts and explore how such a bias can guide simulations towards the native state. We investigate the number of contacts needed for a significant enrichment of native-like conformations and show the capabilities and limitations of this method. Adhering to a threshold of approximately 75% true-positive contacts within a simulation, we obtain an ensemble with native-like conformations of high quality. We find that contact-guided REMD is capable of delivering physically reasonable models of a protein's structure.

Complementary experimental and computational analysis of the effects of non-ionic detergents and phospholipids on insulin amyloid aggregation

Amphiphilic compounds, both detergents and lipids, are important tools for in vitro analysis of water-soluble and integral membrane proteins. A key question is whether these two groups of amphiphilic molecules use the same pathway to affect structural and functional integrity of proteins. In the present study, we tested the effect of non-ionic detergent dodecyl maltoside (DDM), two phospholipids, 1,2-dimyristoyl-sn-glycero-3- phosphocholine (DMPC), 1,2-dihexanoyl-sn-glycero-3-phosphocholine (DHPC), and the detergent-phospholipid mixtures on insulin amyloidogenesis in vitro. Amyloidogenesis of insulin is significantly affected by DDM in a time-and dose-dependent manner, but only slightly affected by either of phospholipids. Addition of DHPC or DMPC to detergent does not alter the inhibiting pattern, suggesting that DDM preferably binds to insulin. The molecular modeling revealed that DDM and the phospholipids occupy equivalent binding sites. DDM, due to the presence of maltose with several oxygen atoms (hydroxylic, glycosidic and ring) is involved in more hydrogen bonds than DHPC or DMPC. Hydrophobic interactions are important factors to stabilize both, DDM and phospholipids in their binding sites. Our results indicate that certain detergents (applying DDM as an example) and selected phospholipids are not always interchangeable in their use to investigate the effect of amphiphilic compounds on the behavior of amyloid-prone proteins.

Application of a Distance-Dependent Sigmoidal Dielectric Constant to the REMC/SAAP3D Simulations of Chignolin, Trp-Cage, and the G10q Mutant

The replica-exchange Monte Carlo method based on the single amino acid potential (SAAP) force field, i.e., REMC/SAAP3D, was recently developed by our group for the molecular simulation of short peptides. In this study, the method has been improved by applying a distance-dependent dielectric (DDD) constant and extended to the peptides containing D-amino acid (AA) residues. For chignolin (10 AAs), a sigmoidal DDD model reasonably allocated the native-like β-hairpin structure with all-atom root mean square deviation (RMSD) = 2.0 Å as a global energy minimum. The optimal DDD condition was subsequently applied for Trp-cage (20 AAs) and its G10q mutant. The native-like α-rich folded structures with main-chain RMSD = 3.7 and 3.8 Å were obtained as global energy minima for Trp-cage and G10q, respectively. The results suggested that the REMC/SAAP3D method with the sigmoidal DDD model is useful for structural prediction for the short peptides comprised of up to 20 AAs. In addition, the relative contributions of SAAP to the total energy (%SAAP) were evaluated by energetic component analysis. The ratios of %SAAP were about 40 and 20% for chignolin and Trp-cage (or G10q), respectively. It was proposed that SAAP is more important for the secondary structure formation than for the assembly to a higher-order folded structure, in which the attractive van der Waals interaction may play a more important role.

Transferable Molecular Model of Woven Covalent Organic Framework Materials

Two woven covalent organic framework materials (COF-505 and COF-506) have been synthesized since 2016, and the latter demonstrated the ability to take up dyes and other small molecules. This opens the door to applications such as separations, sensing, and catalysis. However, accelerating the design of future woven materials by changing the chemistry of the "threads" will require a computational model for these materials. Since no such atomic-scale model exists, we have developed a protocol for optimizing a force field for woven materials which can be used as the input to molecular dynamics simulations. Their high density and elasticity made these COFs challenging to model at a semiempirical level. Our modeling approach required simultaneous optimization of lattice parameters and elasticity using density functional theory-derived energy barriers and available experimental results. We used this force field, parameterized to fit COF-505, without change, to predict the structure of COF-506. This model allowed us to predict an anisotropy in 505's elasticity and preferred directions for diffusion which cannot be seen experimentally. The pore size distribution for 506 is dominated by small pores (80% <10 Å dia.), though 5% of the pores are up to 20 Å in diameter. We confirmed the experimental result that gases (barring helium) do not diffuse appreciably in COF-505. We validated our (unaltered) force field model to accurately predict experimental uptake data for tetrahydrofuran and methyl orange dye in COF-506. We proposed an atomic-scale mechanism by which COF-505 becomes metallated and demetallated. In addition, in advance of experimental studies, we determined the ability of 505 to incorporate other metals, such as Zn and Fe, which might be considered artificial photosynthesis agents. These predictions validate that Cu was a particularly appropriate choice of metal center for the synthesis, showcasing the ability of this model to play a role in designing woven materials a priori.

Explicit Representation of Cation-π Interactions in Force Fields with 1/r4 Nonbonded Terms

The binding energies for cation-π complexation are underestimated by traditional fixed-charge force fields owing to their lack of explicit treatment of ion-induced dipole interactions. To address this deficiency, an explicit treatment of cation-π interactions has been introduced into the OPLS-AA force field. Following prior work with atomic cations, it is found that cation-π interactions can be handled efficiently by augmenting the usual 12-6 Lennard-Jones potentials with 1/r⁴ terms. Results are provided for prototypical complexes as well as protein-ligand systems of relevance for drug design. Alkali cation, ammonium, guanidinium, and tetramethylammonium were chosen for the representative cations, while benzene and six heteroaromatic molecules were used as the π systems. The required nonbonded parameters were fit to reproduce structure and interaction energies for gas-phase complexes from density functional theory (DFT) calculations at the ωB97X-D/6-311++G(d,p) level. The impact of the solvent was then examined by computing potentials of mean force (pmfs) in both aqueous and tetrahydrofuran (THF) solutions using the free-energy perturbation (FEP) theory. Further testing was carried out for two cases of strong and one case of weak cation-π interactions between druglike molecules and their protein hosts, namely, the JH2 domain of JAK2 kinase and macrophage migration inhibitory factor. FEP results reveal greater binding by 1.5-4.4 kcal/mol from the addition of the explicit cation-π contributions. Thus, in the absence of such treatment of cation-π interactions, errors for computed binding or inhibition constants of 10¹-10³ are expected.

Hidden Conformational States and Strange Temperature Optima in Enzyme Catalysis

The existence of temperature optima in enzyme catalysis that occur before protein melting sets in can be described by different types of kinetic models. Such optima cause distinctly curved Arrhenius plots and have, for example, been observed in several cold-adapted enzymes from psychrophilic species. The two main explanations proposed for this behavior either invoke conformational equilibria with inactive substrate-bound states or postulate differences in heat capacity between the reactant and transition states. Herein, we analyze the implications of the different types of kinetic models in terms of apparent activation enthalpies, entropies, and heat capacities, using the catalytic reaction of a cold-adapted α-amylase as a prototypic example. We show that the behavior of these thermodynamic activation parameters is fundamentally different between equilibrium and heat capacity models, and in the α-amylase case, computer simulations have shown the former model to be correct. A few other enzyme-catalyzed reactions are also discussed in this context.

Simple corrections for the static dielectric constant of liquid mixtures from model force fields

Pair-wise additive force fields provide fairly accurate predictions, through classical molecular simulations, for a wide range of structural, thermodynamic, and dynamical properties of many materials. However, one key property that has not been well captured is the static dielectric constant, which characterizes the response of a system to an applied electric field and is important in determining the screening of electrostatic interactions through a system. A simple correction has been found to provide a relatively robust method to improve the estimate of the static dielectric constant from molecular simulations for a broad range of compounds. This approach accounts for the electronic contribution to molecular polarizability and assumes that the charges that couple a molecule to an applied electric field are proportional to the effective force field charges. In this work, we examine how this correction performs for systems at different temperatures and for binary mixtures. Using a value for the electronic polarizability, based on the experimental index of refraction, and a charge scaling factor, determined at a single temperature, we find that the static dielectric constant can be predicted remarkably well, in comparison to the experimentally measured values. This provides good evidence that the effective charges that appear in pair-wise additive force fields developed to reproduce the potential energy surface of a system are not the same as those that determine the static dielectric constant; however, they can be captured in a relatively simple manner, which is dependent on the particular force field.

In silico and in vitro investigations on the protein-protein interactions of glutathione S-transferases with mitogen-activated protein kinase 8 and apoptosis signal-regulating kinase 1

Cytosolic glutathione S-transferase (GST) enzymes participate in several cellular processes in addition to facilitating glutathione conjugation reactions that eliminate endogenous and exogenous toxic compounds, especially electrophiles. GSTs are thought to interact with various kinases, resulting in the modulation of apoptotic processes and cellular proliferation. The present research used a combination of in silico and in vitro studies to investigate protein-protein interactions between the seven most abundant cytosolic GSTs-GST alpha-1 (GST-A1), GST alpha-2 (GST-A2), GST mu-1 (GST-M1), GST mu-2 (GST-M2), GST mu-5 (GST-M5), GST theta-1 (GST-T1) and GST pi-1 (GST-P1)-and Mitogen-activated protein kinase 8 (MAPK8) and Apoptosis signal-regulating kinase 1 (ASK1). MAPK8 and ASK1 were chosen as this study's protein interaction partners because of their predominant role in electrophile or cytokine-induced stress-mediated apoptosis, inflammation and fibrosis. The highest degree of sequence homology or sequence similarity was observed in two GST subgroups: the GST-A1, GST-A2 and GST-P1 isoforms constituted subgroup1; the GST-M1, GST-M2 and GST-M5 isoforms constituted subgroup 2. The GST-T1 isoform diverged from these isoforms. In silico investigations revealed that GST-M1 showed a significantly higher binding affinity to MAPK8, and its complex was more structurally stable than the other isoforms, in the order GST-M1 > GST-M5 > GST-P1 > GST-A2 > GST-A1 > GST-M2 > GST-T1. Similarly, GST-A1, GST-P1 and GST-T1 actively interacted with ASK1, and their structural stability was also better, in the order GST-T1 > GST-A1 > GST-P1 > GST-A2 > GST-M5 > GST-M1 > GST-M2. To validate in silico results, we performed in vitro crosslinking and mass spectroscopy experiments. Results indicated that GST-M1 interacted with GST-T1 to form heterodimers and confirmed the predicted interaction between GST-M1 and MAPK8.Communicated by Ramaswamy H. Sarma.

Metadynamics as a Postprocessing Method for Virtual Screening with Application to the Pseudokinase Domain of JAK2

With standard scoring methods, top-ranked compounds from virtual screening by docking often turn out to be inactive. For this reason, metadynamics, a method used to sample rare events, was studied to further evaluate docking poses with the aim of reducing false positives. Specifically, virtual screening was performed with Glide SP to seek potential molecules to bind to the ATP site in the pseudokinase domain of JAK2 kinase, and promising compounds were selected from the top-ranked 1000 based on visualization. Rescoring with Glide XP, GOLD, and MM/GBSA was unable to differentiate well between active and inactive compounds. Metadynamics was then used to gauge the relative binding affinity from the required time or the potential of mean force needed to dissociate the ligand from the bound complex. With consideration of previously known binders of varying affinities, metadynamics was able to differentiate between the most active compounds and inactive or weakly active ones, and it could identify correctly most of the selected virtual screening compounds as false positives. Thus, metadynamics has the potential to be a viable postprocessing method for virtual screening, minimizing the expense of buying or synthesizing inactive compounds.