A fast and high-quality charge model for the next generation general AMBER force field

An Imbalance in the Force: The Need for Standardized Benchmarks for Molecular Simulation

Force fields (FFs) for molecular simulation have been under development for more than half a century. As with any predictive model, rigorous testing and comparisons of models critically depends on the availability of standardized data sets and benchmarks. While such benchmarks are rather common in the fields of quantum chemistry, this is not the case for empirical FFs. That is, few benchmarks are reused to evaluate FFs, and development teams rather use their own training and test sets. Here we present an overview of currently available tests and benchmarks for computational chemistry, focusing on organic compounds, including halogens and common ions, as FFs for these are the most common ones. We argue that many of the benchmark data sets from quantum chemistry can in fact be reused for evaluating FFs, but new gas phase data is still needed for compounds containing phosphorus and sulfur in different valence states. In addition, more nonequilibrium interaction energies and forces, as well as molecular properties such as electrostatic potentials around compounds, would be beneficial. For the condensed phases there is a large body of experimental data available, and tools to utilize these data in an automated fashion are under development. If FF developers, as well as researchers in artificial intelligence, would adopt a number of these data sets, it would become easier to compare the relative strengths and weaknesses of different models and to, eventually, restore the balance in the force.

Solving an Old Puzzle: Elucidation and Evaluation of the Binding Mode of Salvinorin A at the Kappa Opioid Receptor

The natural product Salvinorin A (SalA) was the first nitrogen-lacking agonist discovered for the opioid receptors and exhibits high selectivity for the kappa opioid receptor (KOR) turning SalA into a promising analgesic to overcome the current opioid crisis. Since SalA's suffers from poor pharmacokinetic properties, particularly the absence of gastrointestinal bioavailability, fast metabolic inactivation, and subsequent short duration of action, the rational design of new tailored analogs with improved clinical usability is highly desired. Despite being known for decades, the binding mode of SalA within the KOR remains elusive as several conflicting binding modes of SalA were proposed hindering the rational design of new analgesics. In this study, we rationally determined the binding mode of SalA to the active state KOR by in silico experiments (docking, molecular dynamics simulations, dynophores) in the context of all available mutagenesis studies and structure-activity relationship (SAR) data. To the best of our knowledge, this is the first comprehensive evaluation of SalA's binding mode since the determination of the active state KOR crystal structure. SalA binds above the morphinan binding site with its furan pointing toward the intracellular core while the C2-acetoxy group is oriented toward the extracellular loop 2 (ECL2). SalA is solely stabilized within the binding pocket by hydrogen bonds (C210^ECL2, Y312^7.35, Y313^7.36) and hydrophobic contacts (V118^2.63, I139^3.33, I294^6.55, I316^7.39). With the disruption of this interaction pattern or the establishment of additional interactions within the binding site, we were able to rationalize the experimental data for selected analogs. We surmise the C2-substituent interactions as important for SalA and its analogs to be experimentally active, albeit with moderate frequency within MD simulations of SalA. We further identified the non-conserved residues 2.63, 7.35, and 7.36 responsible for the KOR subtype selectivity of SalA. We are confident that the elucidation of the SalA binding mode will promote the understanding of KOR activation and facilitate the development of novel analgesics that are urgently needed.

ACES: Optimized Alchemically Enhanced Sampling

We present an alchemical enhanced sampling (ACES) method implemented in the GPU-accelerated AMBER free energy MD engine. The methods hinges on the creation of an "enhanced sampling state" by reducing or eliminating selected potential energy terms and interactions that lead to kinetic traps and conformational barriers while maintaining those terms that curtail the need to otherwise sample large volumes of phase space. For example, the enhanced sampling state might involve transforming regions of a ligand and/or protein side chain into a noninteracting "dummy state" with internal electrostatics and torsion angle terms turned off. The enhanced sampling state is connected to a real-state end point through a Hamiltonian replica exchange (HREMD) framework that is facilitated by newly developed alchemical transformation pathways and smoothstep softcore potentials. This creates a counterdiffusion of real and enhanced-sampling states along the HREMD network. The effect of a differential response of the environment to the real and enhanced-sampling states is minimized by leveraging the dual topology framework in AMBER to construct a counterbalancing HREMD network in the opposite alchemical direction with the same (or similar) real and enhanced sampling states at inverted end points. The method has been demonstrated in a series of test cases of increasing complexity where traditional MD, and in several cases alternative REST2-like enhanced sampling methods, are shown to fail. The hydration free energy for acetic acid was shown to be independent of the starting conformation, and the values for four additional edge case molecules from the FreeSolv database were shown to have a significantly closer agreement with experiment using ACES. The method was further able to handle different rotamer states in a Cdk2 ligand identified as fractionally occupied in crystal structures. Finally, ACES was applied to T4-lysozyme and demonstrated that the side chain distribution of V111χ1 could be reliably reproduced for the apo state, bound to p-xylene, and in p-xylene→ benzene transformations. In these cases, the ACES method is shown to be highly robust and superior to a REST2-like enhanced sampling implementation alone.

AMBER Free Energy Tools: A New Framework for the Design of Optimized Alchemical Transformation Pathways

We develop a framework for the design of optimized alchemical transformation pathways in free energy simulations using nonlinear mixing and a new functional form for so-called "softcore" potentials. We describe the implementation and testing of this framework in the GPU-accelerated AMBER software suite. The new optimized alchemical transformation pathways integrate a number of important features, including (1) the use of smoothstep functions to stabilize behavior near the transformation end points, (2) consistent power scaling of Coulomb and Lennard-Jones (LJ) interactions with unitless control parameters to maintain balance of electrostatic attractions and exchange repulsions, (3) pairwise form based on the LJ contact radius for the effective interaction distance with separation-shifted scaling, and (4) rigorous smoothing of the potential at the nonbonded cutoff boundary. The new softcore potential form is combined with smoothly transforming nonlinear λ weights for mixing specific potential energy terms, along with flexible λ-scheduling features, to enable robust and stable alchemical transformation pathways. The resulting pathways are demonstrated and tested, and shown to be superior to the traditional methods in terms of numerical stability and minimal variance of the free energy estimates for all cases considered. The framework presented here can be used to design new alchemical enhanced sampling methods, and leveraged in robust free energy workflows for large ligand data sets.

Development and Characterization of Pharmaceutical Systems Containing Rifampicin

Rifampicin is a potent antimicrobial drug with some suboptimal properties, such as poor stability, low solubility, and variable bioavailability. Therefore, in the current study, a multicomponent complex between rifampicin, γ-cyclodextrin, and arginine was prepared with the aim of improving drug properties. Solubility was evaluated by phase-solubility studies. The mechanism of interaction was established through proton nuclear magnetic resonance spectroscopy and molecular modeling. Physicochemical characterization was investigated using Fourier transform-infrared spectroscopy, powder X-ray diffraction, and scanning electron microscopy. The dissolution properties, antimicrobial activity (antibacterial, antibiofilm, and antileishmanial), and stability of the different samples were studied. The results obtained in this investigation demonstrate that multicomponent complexes can improve the water solubility and dissolution rate of rifampicin, as well as its antibacterial and antileishmanial action, and present suitable stability. In conclusion, rifampicin complexed with γ-cyclodextrin and arginine is an attractive approach for developing pharmaceutical dosage forms of rifampicin with increased antimicrobial activities.

Binding modes of GDP, GTP and GNP to NRAS deciphered by using Gaussian accelerated molecular dynamics simulations

Probing binding modes of GDP, GTP and GNP to NRAS are of significance for understanding the regulation mechanism on the activity of RAS proteins. Four separate Gaussian accelerated molecular dynamics (GaMD) simulations were performed on the apo, GDP-, GTP- and GNP-bound NRAS. Dynamics analyses suggest that binding of three ligands highly affects conformational states of the switch domains from NRAS, which disturbs binding of NRAS to its effectors. The analyses of free energy landscapes (FELs) indicate that binding of GDP, GTP and GNP induces more energetic states of NRAS compared to the apo NRAS but the presence of GNP makes the switch domains more ordered than binding of GDP and GNP. The information of interaction networks of ligands with NRAS reveals that the π-π interaction of residue F28 and the salt bridge interactions of K16 and D119 with ligands stabilize binding of GDP, GTP and GNP to NRAS. Meanwhile magnesium ion plays a bridge role in interactions of ligands with NRAS, which is favourable for associations of GDP, GTP and GNP with NRAS. This work is expected to provide useful information for deeply understanding the function and activity of NRAS.

Practical Guidance for Consensus Scoring and Force Field Selection in Protein-Ligand Binding Free Energy Simulations

The advances in ligand binding affinity prediction have been fostered by system generation tools and improved force fields (FFs). CHARMM-GUI Free Energy Calculator provides input and postprocessing scripts for AMBER-TI free energy calculations with various FFs. In this study, we used 12 different FF combinations (ff14SB and ff19SB for protein, GAFF2.2 and OpenFF for ligand, and TIP3P, TIP4PEW, and OPC for water) to calculate relative binding free energies (ΔΔG_bind) for 80 alchemical transformations (among the JACS benchmark set) with different numbers of λ windows (12 or 21) and simulation times (1, 5, or 10 ns). Our results show that 12 λ windows and 5 ns simulation time for each window are sufficient to obtain reliable ΔΔG_bind with 4 independent runs for the current benchmark set. While there is no statistically noticeable performance difference among 12 different FF combinations compared to the experimental values, a combination of ff14SB + GAFF2.2 + TIP3P FFs appears to be best with a mean unsigned error of 0.87 [0.69, 1.07] kcal/mol, a root-mean-square error of 1.22 [0.94, 1.50] kcal/mol, a Pearson's correlation of 0.64 [0.52, 0.76], a Spearman's correlation of 0.73 [0.58, 0.83], and a Kendell's correlation of 0.54 [0.42, 0.64]. This large-scale ΔΔG_bind calculation study provides useful information about ΔΔG_bind prediction with different AMBER FF combinations and presents valuable suggestions for FF selection in AMBER-TI ΔΔG_bind calculations.

AMBER Drug Discovery Boost Tools: Automated Workflow for Production Free-Energy Simulation Setup and Analysis (ProFESSA)

We report an automated workflow for production free-energy simulation setup and analysis (ProFESSA) using the GPU-accelerated AMBER free-energy engine with enhanced sampling features and analysis tools, part of the AMBER Drug Discovery Boost package that has been integrated into the AMBER22 release. The workflow establishes a flexible, end-to-end pipeline for performing alchemical free-energy simulations that brings to bear technologies, including new enhanced sampling features and analysis tools, to practical drug discovery problems. ProFESSA provides the user with top-level control of large sets of free-energy calculations and offers access to the following key functionalities: (1) automated setup of file infrastructure; (2) enhanced conformational and alchemical sampling with the ACES method; and (3) network-wide free-energy analysis with the optional imposition of cycle closure and experimental constraints. The workflow is applied to perform absolute and relative solvation free-energy and relative ligand-protein binding free-energy calculations using different atom-mapping procedures. Results demonstrate that the workflow is internally consistent and highly robust. Further, the application of a new network-wide Lagrange multiplier constraint analysis that imposes key experimental constraints substantially improves binding free-energy predictions.

Molecular recognition of morphine and fentanyl by the human μ-opioid receptor

Morphine and fentanyl are among the most used opioid drugs that confer analgesia and unwanted side effects through both G protein and arrestin signaling pathways of μ-opioid receptor (μOR). Here, we report structures of the human μOR-G protein complexes bound to morphine and fentanyl, which uncover key differences in how they bind the receptor. We also report structures of μOR bound to TRV130, PZM21, and SR17018, which reveal preferential interactions of these agonists with TM3 side of the ligand-binding pocket rather than TM6/7 side. In contrast, morphine and fentanyl form dual interactions with both TM3 and TM6/7 regions. Mutations at the TM6/7 interface abolish arrestin recruitment of μOR promoted by morphine and fentanyl. Ligands designed to reduce TM6/7 interactions display preferential G protein signaling. Our results provide crucial insights into fentanyl recognition and signaling of μOR, which may facilitate rational design of next-generation analgesics.

Repositioning Mifepristone as a Leukaemia Inhibitory Factor Receptor Antagonist for the Treatment of Pancreatic Adenocarcinoma

Pancreatic cancer is a leading cause of cancer mortality and is projected to become the second-most common cause of cancer mortality in the next decade. While gene-wide association studies and next generation sequencing analyses have identified molecular patterns and transcriptome profiles with prognostic relevance, therapeutic opportunities remain limited. Among the genes that are upregulated in pancreatic ductal adenocarcinomas (PDAC), the leukaemia inhibitory factor (LIF), a cytokine belonging to IL-6 family, has emerged as potential therapeutic candidate. LIF is aberrantly secreted by tumour cells and promotes tumour progression in pancreatic and other solid tumours through aberrant activation of the LIF receptor (LIFR) and downstream signalling that involves the JAK1/STAT3 pathway. Since there are no LIFR antagonists available for clinical use, we developed an in silico strategy to identify potential LIFR antagonists and drug repositioning with regard to LIFR antagonists. The results of these studies allowed the identification of mifepristone, a progesterone/glucocorticoid antagonist, clinically used in medical abortion, as a potent LIFR antagonist. Computational studies revealed that mifepristone binding partially overlapped the LIFR binding site. LIF and LIFR are expressed by human PDAC tissues and PDAC cell lines, including MIA-PaCa-2 and PANC-1 cells. Exposure of these cell lines to mifepristone reverses cell proliferation, migration and epithelial mesenchymal transition induced by LIF in a concentration-dependent manner. Mifepristone inhibits LIFR signalling and reverses STAT3 phosphorylation induced by LIF. Together, these data support the repositioning of mifepristone as a potential therapeutic agent in the treatment of PDAC.

Antagonization of OX1 Receptor Potentiates CB2 Receptor Function in Microglia from APPSw/Ind Mice Model

Microdialysis assays demonstrated a possible role of orexin in the regulation of amyloid beta peptide (Aß) levels in the hippocampal interstitial fluid in the APP transgenic model. CB₂R is overexpressed in activated microglia, showing a neuroprotective effect. These two receptors may interact, forming CB₂-OX₁-Hets and becoming a new target to combat Alzheimer's disease. Aims: Demonstrate the potential role of CB₂-OX₁-Hets expression and function in microglia from animal models of Alzheimer's disease. Receptor heteromer expression was detected by immunocytochemistry, bioluminescence resonance energy transfer (BRET) and proximity ligation assay (PLA) in transfected HEK-293T cells and microglia primary cultures. Quantitation of signal transduction events in a heterologous system and in microglia cells was performed using the AlphaScreen^® SureFire^® kit, western blot, the GCaMP6 calcium sensor and the Lance Ultra cAMP kit (PerkinElmer). The formation of CB₂-OX₁ receptor complexes in transfected HEK-293T cells has been demonstrated. The tetrameric complex is constituted by one CB₂R homodimer, one OX₁R homodimer and two G proteins, a G_i and a G_q. The use of TAT interfering peptides showed that the CB₂-OX₁ receptor complex interface is TM4-TM5. At the functional level it has been observed that the OX₁R antagonist, SB334867, potentiates the action induced by CB₂R agonist JWH133. This effect is observed in transfected HEK-293T cells and microglia, and it is stronger in the Alzheimer's disease (AD) animal model APPSw/Ind where the expression of the complex assessed by the proximity ligation assay indicates an increase in the number of complexes compared to resting microglia. The CB₂-OX₁ receptor complex is overexpressed in microglia from AD animal models where OX₁R antagonists potentiate the neuroprotective actions of CB₂R activation. Taken together, these results point to OX₁R antagonists as drugs with therapeutic potential to combat AD. Data access statement: Raw data will be provided by the corresponding author upon reasonable requirement.

Methotrexate recognition by the human reduced folate carrier SLC19A1

Folates are essential nutrients with important roles as cofactors in one-carbon transfer reactions, being heavily utilized in the synthesis of nucleic acids and the metabolism of amino acids during cell division^1,2. Mammals lack de novo folate synthesis pathways and thus rely on folate uptake from the extracellular milieu³. The human reduced folate carrier (hRFC, also known as SLC19A1) is the major importer of folates into the cell^1,3, as well as chemotherapeutic agents such as methotrexate^4-6. As an anion exchanger, RFC couples the import of folates and antifolates to anion export across the cell membrane and it is a major determinant in methotrexate (antifolate) sensitivity, as genetic variants and its depletion result in drug resistance^4-8. Despite its importance, the molecular basis of substrate specificity by hRFC remains unclear. Here we present cryo-electron microscopy structures of hRFC in the apo state and captured in complex with methotrexate. Combined with molecular dynamics simulations and functional experiments, our study uncovers key determinants of hRFC transport selectivity among folates and antifolate drugs while shedding light on important features of anion recognition by hRFC.

Exploring the deactivation mechanism of human β2 adrenergic receptor by accelerated molecular dynamic simulations

The β2 adrenergic receptor (β2AR), one of important members of the G protein coupled receptors (GPCRs), has been suggested as an important target for cardiac and asthma drugs. Two replicas of Gaussian accelerated molecular dynamics (GaMD) simulations are performed to explore the deactivation mechanism of the active β2AR bound by three different substrates, including the agonist (P0G), antagonist (JTZ) and inverse agonist (JRZ). The simulation results indicate that the Gs protein is needed to stabilize the active state of the β2AR. Without the Gs protein, the receptor could transit from the active state toward the inactive state. During the transition process, helix TM6 moves toward TM3 and TM5 in geometric space and TM5 shrinks upwards. The intermediate state is captured during the transition process of the active β2AR toward the inactive one, moreover the changes in hydrophobic interaction networks between helixes TM3, TM5, and TM6 and the formation of a salt bridge between residues Arg3.50 and Glu6.30 drive the transition process. We expect that this finding can provide energetic basis and molecular mechanism for further understanding the function and target roles of the β2AR.

Preserving the Integrity of Empirical Force Fields

Generalized force fields (FFs) act as extensions to biomolecular FFs to provide a wide coverage of organic molecules. However, their precise application to an arbitrary molecule presents a separate challenge. We show that MATCH assigns different atom types and bonded and nonbonded parameters than CGenFF, and the AM1-BCC charge model, commonly used with GAFF/GAFF2, does not exactly reproduce the performance of the RESP charge model. The results indicate the need for caution when employing FFs to ensure their integrity with respect to their implementation and validation.

Identification of Potential Cytochrome P450 3A5 Inhibitors: An Extensive Virtual Screening through Molecular Docking, Negative Image-Based Screening, Machine Learning and Molecular Dynamics Simulation Studies

Cytochrome P450 3A5 (CYP3A5) is one of the crucial CYP family members and has already proven to be an important drug target for cardiovascular diseases. In the current study, the PubChem database was screened through molecular docking and high-affinity molecules were adopted for further assessment. A negative image-based (NIB) model was used for a similarity search by considering the complementary shape and electrostatics of the target and small molecules. Further, the molecules were segregated into active and inactive groups through six machine learning (ML) matrices. The active molecules found in each ML model were used for in silico pharmacokinetics and toxicity assessments. A total of five molecules followed the acceptable pharmacokinetics and toxicity profiles. Several potential binding interactions between the proposed molecules and CYP3A5 were observed. The dynamic behavior of the selected molecules in the CYP3A5 was explored through a molecular dynamics (MD) simulation study. Several parameters obtained from the MD simulation trajectory explained the stability of the protein–ligand complexes in dynamic states. The high binding affinity of each molecule was revealed by the binding free energy calculation through the MM-GBSA methods. Therefore, it can be concluded that the proposed molecules might be potential CYP3A5 molecules for therapeutic application in cardiovascular diseases subjected to in vitro/in vivo validations.

Gaussian Accelerated Molecular Dynamics in OpenMM

Gaussian accelerated molecular dynamics (GaMD) is a computational technique that provides both unconstrained enhanced sampling and free energy calculations of biomolecules. Here, we present the implementation of GaMD in the OpenMM simulation package and validate it on model systems of alanine dipeptide and RNA folding. For alanine dipeptide, 30 ns GaMD production simulations reproduced free energy profiles of 1000 ns conventional molecular dynamics (cMD) simulations. In addition, GaMD simulations captured the folding pathways of three hyperstable RNA tetraloops (UUCG, GCAA, and CUUG) and binding of the rbt203 ligand to the HIV-1 Tar RNA, both of which involved critical electrostatic interactions such as hydrogen bonding and base stacking. Together with previous implementations, GaMD in OpenMM will allow for wider applications in simulations of proteins, RNA, and other biomolecules.

In silico testing of flavonoids as potential inhibitors of protease and helicase domains of dengue and Zika viruses

Background

Dengue and Zika are two major vector-borne diseases. Dengue causes up to 25,000 deaths and nearly a 100 million cases worldwide per year, while the incidence of Zika has increased in recent years. Although Zika has been associated to fetal microcephaly and Guillain-Barré syndrome both it and dengue have common clinical symptoms such as severe headache, retroocular pain, muscle and join pain, nausea, vomiting, and rash. Currently, vaccines have been designed and antivirals have been identified for these diseases but there still need for more options for treatment. Our group previously obtained some fractions from medicinal plants that blocked dengue virus (DENV) infection in vitro. In the present work, we explored the possible targets by molecular docking a group of molecules contained in the plant fractions against DENV and Zika virus (ZIKV) NS3-helicase (NS3-hel) and NS3-protease (NS3-pro) structures. Finally, the best ligands were evaluated by molecular dynamic simulations.

Methods

To establish if these molecules could act as wide spectrum inhibitors, we used structures from four DENV serotypes and from ZIKV. ADFR 1.2 rc1 software was used for docking analysis; subsequently molecular dynamics analysis was carried out using AMBER20.

Results

Docking suggested that 3,5-dicaffeoylquinic acid (DCA01), quercetin 3-rutinoside (QNR05) and quercetin 3,7-diglucoside (QND10) can tightly bind to both NS3-hel and NS3-pro. However, after a molecular dynamics analysis, tight binding was not maintained for NS3-hel. In contrast, NS3-pro from two dengue serotypes, DENV3 and DENV4, retained both QNR05 and QND10 which converged near the catalytic site. After the molecular dynamics analysis, both ligands presented a stable trajectory over time, in contrast to DCA01. These findings allowed us to work on the design of a molecule called MOD10, using the QND10 skeleton to improve the interaction in the active site of the NS3-pro domain, which was verified through molecular dynamics simulation, turning out to be better than QNR05 and QND10, both in interaction and in the trajectory.

Discussion

Our results suggests that NS3-hel RNA empty binding site is not a good target for drug design as the binding site located through docking is too big. However, our results indicate that QNR05 and QND10 could block NS3-pro activity in DENV and ZIKV. In the interaction with these molecules, the sub-pocket-2 remained unoccupied in NS3-pro, leaving opportunity for improvement and drug design using the quercetin scaffold. The analysis of the NS3-pro in complex with MOD10 show a molecule that exerts contact with sub-pockets S1, S1', S2 and S3, increasing its affinity and apparent stability on NS3-pro.

Targeting Human CD22/Siglec-2 with Dimeric Sialosides as Novel Oligosaccharide Mimetics

Significant interest in the development of high-affinity ligands for Siglecs exists due to the various therapeutically relevant functions of these proteins. Here, we report a new strategy to develop and design Siglec ligands as disialyl-oligosaccharide mimetics exemplified on Siglec-2 (CD22). We report insights into development of dimeric ligands with high affinity and avidity to cell surface-expressed CD22, assay development, tool compounds, structure activity relationships, and biological data on calcium flux regulation in B-cells. The binding modes of selected ligands have been modeled based on state-of-the-art molecular dynamics simulations on the microsecond timescale, providing detailed views on ligand binding and opening a new perspective on drug design efforts for Siglecs. High-avidity dimeric ligands containing a linker opening the way towards bispecifics are presented as well.

Exploring the potential mechanism of emetine against coronavirus disease 2019 combined with lung adenocarcinoma: bioinformatics and molecular simulation analyses

BACKGROUND

Patients with lung adenocarcinoma (LUAD) may be more predisposed to coronavirus disease 2019 (COVID-19) and have a poorer prognosis. Currently, there is still a lack of effective anti-LUAD/COVID-19 drugs. Thus, this study aimed to screen for an effective anti-LUAD/COVID-19 drug and explore the potential mechanisms.

METHODS

Firstly, we performed differentially expressed gene (DEG) analysis on LUAD transcriptome profiling data in The Cancer Genome Atlas (TCGA), where intersections with COVID-19-related genes were screened out. Then, we conducted Cox proportional hazards analyses on these LUAD/COVID-19 DEGs to construct a risk score. Next, LUAD/COVID-19 DEGs were uploaded on Connectivity Map to obtain drugs for anti-LUAD/COVID-19. Finally, we used network pharmacology, molecular docking, and molecular dynamics (MD) simulation to explore the drug's therapeutic targets and potential mechanisms for anti-LUAD/COVID-19.

RESULTS

We identified 230 LUAD/COVID-19 DEGs and constructed a risk score containing 7 genes (BTK, CCL20, FURIN, LDHA, TRPA1, ZIC5, and SDK1) that could classify LUAD patients into two risk groups. Then, we screened emetine as an effective drug for anti-LUAD/COVID-19. Network pharmacology analyses identified 6 potential targets (IL6, DPP4, MIF, PRF1, SERPING1, and SLC6A4) for emetine in anti-LUAD/COVID-19. Molecular docking and MD simulation analyses showed that emetine exhibited excellent binding capacities to DDP4 and the main protease (Mpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

CONCLUSIONS

This study found that emetine may inhibit the entry and replication of SARS-CoV-2 and enhance tumor immunity by bounding to DDP4 and Mpro.

Enhanced-Sampling Simulations for the Estimation of Ligand Binding Kinetics: Current Status and Perspective

The dissociation rate (k off) associated with ligand unbinding events from proteins is a parameter of fundamental importance in drug design. Here we review recent major advancements in molecular simulation methodologies for the prediction of k off. Next, we discuss the impact of the potential energy function models on the accuracy of calculated k off values. Finally, we provide a perspective from high-performance computing and machine learning which might help improve such predictions.

Delineating the conformational landscape and intrinsic properties of the angiotensin II type 2 receptor using a computational study

As a key regulator for the renin-angiotensin system, a class A G protein-coupled receptor (GPCR), AngII type 2 receptor (AT₂R), plays a pivotal role in the homeostasis of the cardiovascular system. Compared with other GPCRs, AT₂R has a unique antagonist-bound conformation and its mechanism is still an enigma. Here, we applied combined dynamic and evolutional approaches to investigate the conformational space and intrinsic properties of AT₂R. With molecular dynamic simulations, Markov State Models, and statistics coupled analysis, we captured the conformational landscape of AT₂R and identified its uniquity from both dynamical and evolutional viewpoints. A cryptic pocket was also discovered in the intermediate state during conformation transitions. These findings offer a deeper understanding of the AT₂R mechanism at an atomic level and provide hints for the design of novel AT₂R modulators.

Development of disulfide-functionalized peptides covalently binding G protein-coupled receptors

A broadly applicable synthesis of peptides incorporating mixed disulfides between cysteine and homocysteine and cysteamine was developed. The method was established using pharmacologically relevant G protein-coupled receptor (GPCR) ligands including the μ-receptor agonist Dmt-DALDA and extended to the orexin derivative Oxa(17-33) and NT(8-13), the C-terminal hexapeptide of neurotensin. The newly developed NT(8-13) analog 6b incorporating an S-functionalized homocysteine revealed covalent binding of the neurotensin receptor 1 (NTSR1) in a radioligand depletion study.

Computational Analysis on the Allostery of Tryptophan Synthase: Relationship between α/β-Ligand Binding and Distal Domain Closure

Tryptophan synthase (TRPS) is a bifunctional enzyme consisting of α and β-subunits and catalyzes the last two steps of l-tryptophan (L-Trp) biosynthesis, namely, cleavage of 3-indole-d-glycerol-3'-phosphate (IGP) into indole and glyceraldehyde-3-phosphate (G3P) in the α-subunit, and a pyridoxal phosphate (PLP)-dependent reaction of indole and l-serine (L-Ser) to produce L-Trp in the β-subunit. Importantly, the IGP binding at the α-subunit affects the β-subunit conformation and its ligand-binding affinity, which, in turn, enhances the enzymatic reaction at the α-subunit. The intersubunit communications in TRPS have been investigated extensively for decades because of the fundamental and pharmaceutical importance, while it is still difficult to answer how TRPS allostery is regulated at the atomic detail. Here, we investigate the allosteric regulation of TRPS by all-atom classical molecular dynamics (MD) simulations and analyze the potential of mean-force (PMF) along conformational changes of the α- and β-subunits. The present simulation has revealed a widely opened conformation of the β-subunit, which provides a pathway for L-Ser to enter into the β-active site. The IGP binding closes the α-subunit and induces a wide opening of the β-subunit, thereby enhancing the binding affinity of L-Ser to the β-subunit. Structural analyses have identified critical hydrogen bonds (HBs) at the interface of the two subunits (αG181-βS178, αP57-βR175, etc.) and HBs between the β-subunit (βT110 - βH115) and a complex of PLP and L-Ser (an α-aminoacrylate intermediate). The former HBs regulate the allosteric, β-subunit opening, whereas the latter HBs are essential for closing the β-subunit in a later step. The proposed mechanism for how the interdomain communication in TRPS is realized with ligand bindings is consistent with the previous experimental data, giving a general idea to interpret the allosteric regulations in multidomain proteins.

Development and Validation of Fluorinated, Aromatic Amino Acid Parameters for Use with the AMBER ff15ipq Protein Force Field

We developed force field parameters for fluorinated, aromatic amino acids enabling molecular dynamics (MD) simulations of fluorinated proteins. These parameters are tailored to the AMBER ff15ipq protein force field and enable the modeling of 4, 5, 6, and 7F-tryptophan, 3F- and 3,5F-tyrosine, and 4F- or 4-CF3-phenylalanine. The parameters include 181 unique atomic charges derived using the implicitly polarized charge (IPolQ) scheme in the presence of SPC/Eb explicit water molecules and 9 unique bond, angle, or torsion terms. Our simulations of benchmark peptides and proteins maintain expected conformational propensities on the μs time scale. In addition, we have developed an open-source Python program to calculate fluorine relaxation rates from MD simulations. The extracted relaxation rates from protein simulations are in good agreement with experimental values determined by 19F NMR. Collectively, our results illustrate the power and robustness of the IPolQ lineage of force fields for modeling the structure and dynamics of fluorine-containing proteins at the atomic level.

Integration of Experimental Data and Use of Automated Fitting Methods in Developing Protein Force Fields

The development of accurate protein force fields has been the cornerstone of molecular simulations for the past 50 years. During this period, many lessons have been learned regarding the use of experimental target data and parameter fitting procedures. Here, we review recent advances in protein force field development. We discuss the recent emergence of polarizable force fields and the role of electronic polarization and areas in which additive force fields fall short. The use of automated fitting methods and the inclusion of additional experimental solution data during parametrization is discussed as a means to highlight possible routes to improve the accuracy of force fields even further.

The effect of off-center σ -hole on the atom-centered partial charges in halogenated molecules

Partial atomic charges belong to key concepts of computational chemistry. In some cases, however, they fail in describing the electrostatics of molecules. One such example is the σ -hole, a region of positive electrostatic potential located on halogens and other atoms. In molecular mechanics, the σ -hole is often modeled as a pseudo-atom with a positive partial charge located off the halogen nucleus. Here we address a question, to what extent the pseudo-atom affects partial charges of other atoms in the molecule. To this aim, we have thoroughly analyzed partial charges of over 2300 halogenated molecules from the ZINC database calculated by the restricted electrostatic potential (RESP) method and compared them with the charges fitted by RESP including the pseudo-atom. We show that the pseudo-atom improves charge fitting for a vast majority of molecules. The σ -hole, modeled as the off-center charge, affects the atoms within three covalent bonds from the halogen.

Exploring structural dynamics and optical properties of UnaG fluorescent protein upon N57 mutations

UnaG is a new class of fluorescence protein in which an endogenous ligand, namely bilirubin (BLR), plays the role of chromophore. Upon photoexcitation, holoUnaG emits green light. A single mutation at residue 57 induces a decrease in the fluorescence quantum yield. To our knowledge, no atomic simulation at the atomic level has been carried out to date to explain this fluorescence decay in N57A and N57Q mutants. Herein molecular dynamics simulations were carried out on wild-type (WT) UnaG and both mutants to investigate the structural impact of the mutation on its global structure, on BLR and the absorption spectra. Our study reveals significant global changes upon mutation at the protein entrance (L3, H2, and, H3) governing a BLR modification. BLR in WT UnaG is rather rigid while when embedded into N57A or N57Q, dihedral angles between endo and exo vinyl moieties and between A and B rings at the entrance of UnaG are strongly modified along with the number of inter-/intramolecular interactions. The water molecules play an important role in the modification of the shape of the binding cavity. For the first time, we show that the structural modifications upon ligand mutations are tightly related to the key structural changes in the protein such as Loop3 (L3), β sheet 2 (B2), and β sheet 3 (B3) dynamics. The present work suggests that the quenching of the fluorescence properties of UnaG mutants is mainly a non-radiative process closely related to the BLR flexibility induced by global structural changes.

Property Map Collective Variable as a Useful Tool for a Force Field Correction

The accuracy of biomolecular simulations depends on the accuracy of an empirical molecular mechanics potential known as a force field: a set of parameters and expressions to estimate the potential from atomic coordinates. Accurate parametrization of force fields for small organic molecules is a challenge due to their high diversity. One of the possible approaches is to apply a correction to the existing force fields. Here, we propose an approach to estimate the density functional theory (DFT)-derived force field correction which is calculated during the run of molecular dynamics without significantly affecting its speed. Using the formula known as a property map collective variable, we approximate the force field correction by a weighted average of this force field correction calculated only for a small series of reference structures. To validate this method, we used seven AMBER force fields, and we show how it is possible to convert one force field to behave like the other one. We also present the force field correction for the important anticancer drug Imatinib as a use case example. Our method appears to be suitable for adjusting the force field for general drug-like molecules. We provide a pipeline that generates the correction; this pipeline is available at https://pmcvff-correction.cerit-sc.cz/.

Mapping the binding sites of UDP and prostaglandin E2 glyceryl ester in the nucleotide receptor P2Y6

Cyclooxygenase‐2 catalyzes the biosynthesis of prostaglandins from arachidonic acid and the biosynthesis of prostaglandin glycerol esters (PG‐Gs) from 2‐arachidonoylglycerol. PG‐Gs are mediators of several biological actions such as macrophage activation, hyperalgesia, synaptic plasticity, and intraocular pressure. Recently, the human UDP receptor P2Y₆ was identified as a target for the prostaglandin E2 glycerol ester (PGE₂‐G). Here, we show that UDP and PGE₂‐G are evolutionary conserved endogenous agonists at vertebrate P2Y₆ orthologs. Using sequence comparison of P2Y₆ orthologs, homology modeling, and ligand docking studies, we proposed several receptor positions participating in agonist binding. Site‐directed mutagenesis and functional analysis of these P2Y₆ mutants revealed that both UDP and PGE₂‐G share in parts one ligand‐binding site. Thus, the convergent signaling of these two chemically very different agonists has already been manifested in the evolutionary design of the ligand‐binding pocket.

Discovery of Bile Acid Derivatives as Potent ACE2 Activators by Virtual Screening and Essential Dynamics

The angiotensin-converting enzyme II (ACE2) is a key molecular player in the regulation of vessel contraction, inflammation, and reduction of oxidative stress. In addition, ACE2 has assumed a prominent role in the fight against the COVID-19 pandemic-causing virus SARS-CoV-2, as it is the very first receptor in the host of the viral spike protein. The binding of the spike protein to ACE2 triggers a cascade of events that eventually leads the virus to enter the host cell and initiate its life cycle. At the same time, SARS-CoV-2 infection downregulates ACE2 expression especially in the lung, altering the biochemical signals regulated by the enzyme and contributing to the poor clinical prognosis characterizing the late stage of the COVID-19 disease. Despite its important biological role, a very limited number of ACE2 activators are known. Here, using a combined in silico and experimental approach, we show that ursodeoxycholic acid (UDCA) derivatives work as ACE2 activators. In detail, we have identified two potent ACE2 ligands, BAR107 and BAR708, through a docking virtual screening campaign and elucidated their mechanism of action from essential dynamics of the enzyme observed during microsecond molecular dynamics calculations. The in silico results were confirmed by in vitro pharmacological assays with the newly identified compounds showing ACE2 activity comparable to that of DIZE, the most potent ACE2 activator known so far. Our work provides structural insight into ACE2/ligand-binding interaction useful for the design of compounds with therapeutic potential against SARS-CoV-2 infection, inflammation, and other ACE2-related diseases.

Binding free energies for the SAMPL8 CB8 "Drugs of Abuse" challenge from umbrella sampling combined with Hamiltonian replica exchange

Umbrella sampling along a one-dimensional order parameter in combination with Hamiltonian replica exchange was employed to calculate the binding free energy of five guest molecules with known affinity to cucurbit[8]uril. A simple empirical approach correcting for the overestimation of the affinity by the GAFF force field was proposed and subsequently applied to the seven guest molecules of the “Drugs of Abuse” SAMPL8 challenge. Compared to the uncorrected binding free energies, the systematic error decreased but quantitative agreement with experiment was only reached for a few compounds. From a retrospective analysis a weak point of the correction term was identified.

A General Picture of Cucurbit[8]uril Host-Guest Binding

Describing, understanding, and designing complex interaction networks within macromolecular systems remain challenging in modern chemical research. Host-guest systems, despite their relative simplicity in both the structural feature and interaction patterns, still pose problems in theoretical modeling. The barrel-shaped supramolecular container cucurbit[8]uril (CB8) shows promising functionalities in various areas, e.g., catalysis and molecular recognition. It can stably coordinate a series of structurally diverse guests with high affinities. In this work, we examine the binding of seven commonly abused drugs to the CB8 host, aiming at providing a general picture of CB8-guest binding. Extensive sampling of the configurational space of these host-guest systems is performed, and the binding pathway and interaction patterns of CB8-guest complexes are investigated. A thorough comparison of widely used fixed-charge models for drug-like molecules is presented. Iterative refitting of the atomic charges suggests significant conformation dependence of charge generation. The initial model generated at the original conformation could be inaccurate for new conformations explored during conformational search, and the newly fitted charge set improves the prediction-experiment correlation significantly. Our investigations of the configurational space of CB8-drug complexes suggest that the host-guest interactions are more complex than expected. Despite the structural simplicities of these molecules, the conformational fluctuations of the host and the guest molecules and orientations of functional groups lead to the existence of an ensemble of binding modes. The insights of the binding thermodynamics, performance of fixed-charge models, and binding patterns of the CB8-guest systems are useful for studying and elucidating the binding mechanism of other host-guest complexes.

Design and Synthesis of Hsp90 Inhibitors with B-Raf and PDHK1 Multi-Target Activity

The design of multi-target ligands has become an innovative approach for the identification of effective therapeutic treatments against complex diseases, such as cancer. Recent studies have demonstrated that the combined inhibition of Hsp90 and B-Raf provides synergistic effects against several types of cancers. Moreover, it has been reported that PDHK1, which presents an ATP-binding pocket similar to that of Hsp90, plays an important role in tumor initiation, maintenance and progression, participating also to the senescence process induced by B-Raf oncogenic proteins. Based on these premises, the simultaneous inhibition of these targets may provide several benefits for the treatment of cancer. In this work, we set up a design strategy including the assembly and integration of molecular fragments known to be important for binding to the Hsp90, PDHK1 and B-Raf targets, aided by molecular docking for the selection of a set of compounds potentially able to exert Hsp90-B-Raf-PDHK1 multi-target activities. The designed compounds were synthesized and experimentally validated in vitro. According to the in vitro assays, compounds 4 a, 4 d and 4 e potently inhibited Hsp90 and moderately inhibited the PDHK1 kinase. Finally, molecular dynamics simulations were performed to provide further insights into the structural basis of their multi-target activity.

Screening of β1- and β2-Adrenergic Receptor Modulators through Advanced Pharmacoinformatics and Machine Learning Approaches

Cardiovascular diseases (CDs) are a major concern in the human race and one of the leading causes of death worldwide. β-Adrenergic receptors (β1-AR and β2-AR) play a crucial role in the overall regulation of cardiac function. In the present study, structure-based virtual screening, machine learning (ML), and a ligand-based similarity search were conducted for the PubChem database against both β1- and β2-AR. Initially, all docked molecules were screened using the threshold binding energy value. Molecules with a better binding affinity were further used for segregation as active and inactive through ML. The pharmacokinetic assessment was carried out on molecules retained in the above step. Further, similarity searching of the ChEMBL and DrugBank databases was performed. From detailed analysis of the above data, four compounds for each of β1- and β2-AR were found to be promising in nature. A number of critical ligand-binding amino acids formed potential hydrogen bonds and hydrophobic interactions. Finally, a molecular dynamics (MD) simulation study of each molecule bound with the respective target was performed. A number of parameters obtained from the MD simulation trajectories were calculated and substantiated the stability between the protein-ligand complex. Hence, it can be postulated that the final molecules might be crucial for CDs subjected to experimental validation.

Effects of All-Atom Molecular Mechanics Force Fields on Amyloid Peptide Assembly: The Case of PHF6 Peptide of Tau Protein

Molecular dynamics (MD) simulations play a vital role in revealing the mechanism of amyloid aggregation that is crucial to the therapeutic agent development for Alzheimer's Disease. However, the accuracy of MD simulation results strongly depends on the force field employed. In our previous benchmark for 17 all-atom force fields on modeling of amyloid aggregation using the Aβ_16-22 dimer, we showed that AMBER14SB and CHARMM36m are suitable force fields for amyloid aggregation simulation, while GROMOS54a7 and OPLSAA are not good for the task. In this work, we continue assessing the applicability of atomistic force fields on amyloid aggregation using the VQIVYK (PHF6) peptide which is essential for tau-protein aggregation. Although, both Aβ_16-22 and PHF6 peptides formed fibrils in vitro, the PHF6 fibrils are parallel β-sheets, while the Aβ_16-22 fibrils are antiparallel β-sheets. We performed an all-atom large-scale MD simulation in explicit water on the PHF6 dimer and octa-peptides systems using five mainstream force fields, including AMBER99SB-disp, AMBER14SB, CHARMM36m, GROMOS54a7, and OPLSAA. The accumulated simulation time is 0.2 ms. Our result showed that the β-sheet structures of PHF6 peptides sampled by AMBER99SB-disp, AMBER14SB, GROMOS54a7, and OPLSAA are in favor of the antiparallel β-sheets, while the dominant type of β-sheet structures is parallel β-sheet by using CHARMM36m. Among the five force fields, CHARMM36m provides the strongest CH-π interaction that was observed in an NMR study. The comparison between our results and experimental observation indicates that CHARMM36m achieved the best performance on modeling the aggregation of PHF6 peptides. In summary, CHARMM36m is currently the most suitable force field for studying the aggregation of both amyloid-β and Tau through MD simulations.

CHARMM-GUI Free Energy Calculator for Practical Ligand Binding Free Energy Simulations with AMBER

Alchemical free energy methods, such as free energy perturbation (FEP) and thermodynamic integration (TI), become increasingly popular and crucial for drug design and discovery. However, the system preparation of alchemical free energy simulation is an error-prone, time-consuming, and tedious process for a large number of ligands. To address this issue, we have recently presented CHARMM-GUI Free Energy Calculator that can provide input and postprocessing scripts for NAMD and GENESIS FEP molecular dynamics systems. In this work, we extended three submodules of Free Energy Calculator to work with the full suite of GPU-accelerated alchemical free energy methods and tools in AMBER, including input and postprocessing scripts. The BACE1 (β-secretase 1) benchmark set was used to validate the AMBER-TI simulation systems and scripts generated by Free Energy Calculator. The overall results of relatively large and diverse systems are almost equivalent with different protocols (unified and split) and with different timesteps (1, 2, and 4 fs), with R² > 0.9. More importantly, the average free energy differences between two protocols are small and reliable with four independent runs, with a mean unsigned error (MUE) below 0.4 kcal/mol. Running at least four independent runs for each pair with AMBER20 (and FF19SB/GAFF2.1/OPC force fields), we obtained a MUE of 0.99 kcal/mol and root-mean-square error of 1.31 kcal/mol for 58 alchemical transformations in comparison with experimental data. In addition, a set of ligands for T4-lysozyme was used to further validate our free energy calculation protocol whose results are close to experimental data (within 1 kcal/mol). In summary, Free Energy Calculator provides a user-friendly web-based tool to generate the AMBER-TI system and input files for high-throughput binding free energy calculations with access to the full set of GPU-accelerated alchemical free energy, enhanced sampling, and analysis methods in AMBER.

Molecular dynamics: a powerful tool for studying the medicinal chemistry of ion channel modulators

Molecular dynamics (MD) simulations allow researchers to investigate the behavior of desired biological targets at ever-decreasing costs with ever-increasing precision. Among the biological macromolecules, ion channels are remarkable transmembrane proteins, capable of performing special biological processes and revealing a complex regulatory matrix, including modulation by small molecules, either endogenous or exogenous. Recently, given the developments in ion channel structure determination and accessibility of bio-computational techniques, MD and related tools are becoming increasingly popular in the intense research area regarding ligand-channel interactions. This review synthesizes and presents the most important fields of MD involvement in investigating channel-molecule interactions, including, but not limited to, deciphering the binding modes of ligands to their ion channel targets and the mechanisms through which chemical compounds exert their effect on channel function. Special attention is devoted to the importance of more elaborate methods, such as free energy calculations, while principles regarding drug design and discovery are highlighted. Several technical aspects involving the creation and simulation of channel-molecule MD systems (ligand parameterization, proper membrane setup, system building, etc.) are also presented.

A Computer-Driven Scaffold-Hopping Approach Generating New PTP1B Inhibitors from the Pyrrolo[1,2-a]quinoxaline Core

Protein tyrosine phosphatase 1B (PTP1B) is a very promising target for the treatment of metabolic disorders such as type II diabetes mellitus. Although it was validated as a promising target for this disease more than 30 years ago, as yet there is no drug in advanced clinical trials, and its biochemical mechanism and functions are still being studied. In the present study, based on our experience generating PTP1B inhibitors, we have developed and implemented a scaffold-hopping approach to vary the pyrrole ring of the pyrrolo[1,2-a]quinoxaline core, supported by extensive computational techniques aimed to explain the molecular interaction with PTP1B. Using a combination of docking, molecular dynamics and end-point free-energy calculations, we have rationally designed a hypothesis for new PTP1B inhibitors, supporting their recognition mechanism at a molecular level. After the design phase, we were able to easily synthesize proposed candidates and their evaluation against PTP1B was found to be in good concordance with our predictions. Moreover, the best candidates exhibited glucose uptake increments in cellulo model, thus confirming their utility for PTP1B inhibition and validating this approach for inhibitors design and molecules thus obtained.

Generalizing the Discrete Gibbs Sampler-Based λ-Dynamics Approach for Multisite Sampling of Many Ligands

In this work, the discrete λ variant of the Gibbs sampler-based λ-dynamics (d-GSλD) method is developed to enable multiple functional group perturbations to be investigated at one or more sites of substitution off a common ligand core. The theoretical framework and special considerations for constructing discrete λ states for multisite d-GSλD are presented. The precision and accuracy of the d-GSλD method is evaluated with three test cases of increasing complexity. Specifically, methyl → methyl symmetric perturbations in water, 1,4-benzene hydration free energies and protein-ligand binding affinities for an example HIV-1 reverse transcriptase inhibitor series are computed with d-GSλD. Complementary MSλD calculations were also performed to compare with d-GSλD's performance. Excellent agreement between d-GSλD and MSλD is observed, with mean unsigned errors of 0.12 and 0.22 kcal/mol for computed hydration and binding free energy test cases, respectively. Good agreement with experiment is also observed, with errors of 0.5-0.7 kcal/mol. These findings support the applicability of the d-GSλD free energy method for a variety of molecular design problems, including structure-based drug design. Finally, a discussion of d-GSλD versus MSλD approaches is presented to compare and contrast features of both methods.

Molecularly Informed Field Theories from Bottom-up Coarse-Graining

Polymer formulations possessing mesostructures or phase coexistence are challenging to simulate using atomistic particle-explicit approaches due to the disparate time and length scales, while the predictive capability of field-based simulations is hampered by the need to specify interactions at a coarser scale (e.g., χ-parameters). To overcome the weaknesses of both, we introduce a bottom-up coarse-graining methodology that leverages all-atom molecular dynamics to molecularly inform coarser field-theoretic models. Specifically, we use relative-entropy coarse-graining to parametrize particle models that are directly and analytically transformable into statistical field theories. We demonstrate the predictive capability of this approach by reproducing experimental aqueous poly(ethylene oxide) (PEO) cloud-point curves with no parameters fit to experimental data. This synergistic approach to multiscale polymer simulations opens the door to de novo exploration of phase behavior across a wide variety of polymer solutions and melt formulations.

Embedding Quantum Statistical Excitations in a Classical Force Field

Quantum-mechanically driven charge polarization and charge transfer are ubiquitous in biomolecular systems, controlling reaction rates, allosteric interactions, ligand-protein binding, membrane transport, and dynamically driven structural transformations. Molecular dynamics (MD) simulations of these processes require quantum mechanical (QM) information in order to accurately describe their reactive dynamics. However, current techniques-empirical force fields, subsystem approaches, ab initio MD, and machine learning-vary in their ability to achieve a consistent chemical description across multiple atom types, and at scale. Here we present a physics-based, atomistic force field, the ensemble DFT charge-transfer embedded-atom method, in which QM forces are described at a uniform level of theory across all atoms, avoiding the need for explicit solution of the Schrödinger equation or large, precomputed training data sets. Coupling between the electronic and atomistic length scales is effected through an ensemble density functional theory formulation of the embedded-atom method originally developed for elemental materials. Charge transfer is expressed in terms of ensembles of ionic state basis densities of individual atoms, and charge polarization, in terms of atomic excited-state basis densities. This provides a highly compact yet general representation of the force field, encompassing both local and system-wide effects. Charge rearrangement is realized through the evolution of ensemble weights, adjusted at each dynamical time step via chemical potential equalization.

Pathways and Mechanism of Caffeine Binding to Human Adenosine A2A Receptor

Caffeine (CFF) is a common antagonist to the four subtypes of adenosine G-protein-coupled receptors (GPCRs), which are critical drug targets for treating heart failure, cancer, and neurological diseases. However, the pathways and mechanism of CFF binding to the target receptors remain unclear. In this study, we have performed all-atom-enhanced sampling simulations using a robust Gaussian-accelerated molecular dynamics (GaMD) method to elucidate the binding mechanism of CFF to human adenosine A_2A receptor (A_2AAR). Multiple 500–1,000 ns GaMD simulations captured both binding and dissociation of CFF in the A_2AAR. The GaMD-predicted binding poses of CFF were highly consistent with the x-ray crystal conformations with a characteristic hydrogen bond formed between CFF and residue N6.55 in the receptor. In addition, a low-energy intermediate binding conformation was revealed for CFF at the receptor extracellular mouth between ECL2 and TM1. While the ligand-binding pathways of the A_2AAR were found similar to those of other class A GPCRs identified from previous studies, the ECL2 with high sequence divergence serves as an attractive target site for designing allosteric modulators as selective drugs of the A_2AAR.

Mechanistic analysis of light-driven overcrowded alkene-based molecular motors by multiscale molecular simulations

We analyze light-driven overcrowded alkene-based molecular motors, an intriguing class of small molecules that have the potential to generate MHz-scale rotation rates. The full rotation process is simulated at multiple scales by combining quantum surface-hopping molecular dynamics (MD) simulations for the photoisomerization step with classical MD simulations for the thermal helix inversion step. A Markov state analysis resolves conformational substates, their interconversion kinetics, and their roles in the motor's rotation process. Furthermore, motor performance metrics, including rotation rate and maximal power output, are computed to validate computations against experimental measurements and to inform future designs. Lastly, we find that to correctly model these motors, the force field must be optimized by fitting selected parameters to reference quantum mechanical energy surfaces. Overall, our simulations yield encouraging agreement with experimental observables such as rotation rates, and provide mechanistic insights that may help future designs.

An electrostatic energy-based charge model for molecular dynamics simulation

The interactions of the polar chemical bonds such as C=O and N-H with an external electric field were investigated, and a linear relationship between the QM/MM interaction energies and the electric field along the chemical bond is established in the range of weak to intermediate electrical fields. The linear relationship indicates that the electrostatic interactions of a polar group with its surroundings can be described by a simple model of a dipole with constant moment under the action of an electric field. This relationship is employed to develop a general approach to generating an electrostatic energy-based charge (EEC) model for molecules containing single or multiple polar chemical bonds. Benchmark test studies of this model were carried out for (CH₃)₂-CO and N-methyl acetamide in explicit water, and the result shows that the EEC model gives more accurate electrostatic energies than those given by the widely used charge model based on fitting to the electrostatic potential (ESP) in direct comparison to the energies computed by the QM/MM method. The MD simulations of the electric field at the active site of ketosteroid isomerase based on EEC demonstrated that EEC gave a better representation of the electrostatic interaction in the hydrogen-bonding environment than the Amber14SB force field by comparison with experiment. The current study suggests that EEC should be better suited for molecular dynamics study of molecular systems with polar chemical bonds such as biomolecules than the widely used ESP or RESP (restrained ESP) charge models.

Molecular mechanisms of inhibitor bindings to A-FABP deciphered by using molecular dynamics simulations and calculations of MM-GBSA

Adipocyte fatty-acid binding protein (A-FABP) plays a central role in many aspects of metabolic diseases. It is an important target in drug design for treatment of FABP-related diseases. In this study, molecular dynamics (MD) simulations followed by calculations of molecular mechanics generalized Born surface area (MM-GBSA) and principal components analysis (PCA) were implemented to decipher molecular mechanism correlating with binding of inhibitors 57Q, 57P and L96 to A-FABP. The results show that van der Waals interactions are the leading factors to control associations of 57Q, 57P, and L96 with A-FABP, which reveals an energetic basis for designing of clinically available inhibitors towards A-FABP. The information from PCA and cross-correlation analysis rationally unveils that inhibitor bindings affect conformational changes of A-FABP and change relative movements between residues. Decomposition of binding affinity into contributions of individual residues not only detects hot spots of inhibitor/A-FABP binding but also shows that polar interactions of the positively charged residue Arg126 with three inhibitors provide a significant contribution for stabilization of the inhibitor/A-FABP bindings. Furthermore, the binding strength of L96 to residues Ser55, Phe57 and Lys58 are stronger than that of inhibitors 57Q and 57P to these residues.

Binding thermodynamics and interaction patterns of human purine nucleoside phosphorylase-inhibitor complexes from extensive free energy calculations

Human purine nucleoside phosphorylase (hPNP) plays a significant role in the catabolism of deoxyguanosine. The trimeric protein is an important target in the treatment of T-cell cancers and autoimmune disorders. Experimental studies on the inhibition of the hPNP observe that the first ligand bound to one of three subunits effectively inhibits the protein, while the binding of more ligands to the subsequent sites shows negative cooperativities. In this work, we performed extensive end-point and alchemical free energy calculations to determine the binding thermodynamics of the trimeric protein-ligand system. 13 Immucillin inhibitors with experimental results are under calculation. Two widely accepted charge schemes for small molecules including AM1-BCC and RESP are adopted for ligands. The results of RESP are in better agreement with the experimental reference. Further investigations of the interaction networks in the protein-ligand complexes reveal that several residues play significant roles in stabilizing the complex structure. The most commonly observed ones include PHE200, GLU201, MET219, and ASN243. The conformations of the protein in different protein-ligand complexes are observed to be similar. We expect these insights to aid the development of potent drugs targeting hPNP.