CHARMM-GUI Free Energy Calculator for Absolute and Relative Ligand Solvation and Binding Free Energy Simulations

Identification of novel LRRK2 inhibitors by structure-based virtual screening and alchemical free energy calculation

The Leucine-rich repeat kinase 2 (LRRK2) target has been identified as a promising drug target for Parkinson's disease (PD) treatment. This study focuses on optimizing the activity of LRRK2 inhibitors using alchemical relative binding free energy (RBFE) calculations. Initially, we assessed various free energy calculation methods across different LRRK2 kinase inhibitor scaffolds. The results indicate that alchemical free energy calculations are promising for prospective predictions on LRRK2 inhibitors, especially for the aminopyrimidine scaffold with an RMSE of 1.15 kcal mol-1 and Rp of 0.83. Following this, we optimized a potent LRRK2 kinase inhibitor identified from previous virtual screenings, featuring a novel scaffold. Guided by RBFE predictions using alchemical methods, this optimization led to the discovery of compound LY2023-001. This compound, with a [1,2,4]triazolo[5,6-b]indole scaffold, exhibited enhanced inhibitory activity against G2019S LRRK2 (IC50 = 12.9 nM). Molecular dynamics (MD) simulations revealed that LY2023-001 formed stable hydrogen bonds with Glu1948, and Ala1950 in the G2019S LRRK2 protein. Additionally, its phenyl substituents engage in strong electrostatic interactions with Lys1906 and van der Waals interactions with Leu1885, Phe1890, Val1893, Ile1933, Met1947, Leu1949, Leu2001, Ala2016, and Asp2017. Our findings underscore the potential of computational methods in the successful optimization of small molecules, offering important insights for the development of novel LRRK2 inhibitors.

Ligand Binding Affinity Prediction for Membrane Proteins with Alchemical Free Energy Calculation Methods

Alchemical relative binding free energy (ΔΔG) calculations have shown high accuracy in predicting ligand binding affinity and have been used as important tools in computer-aided drug discovery and design. However, there has been limited research on the application of ΔΔG methods to membrane proteins despite the fact that these proteins represent a significant proportion of drug targets, play crucial roles in biological processes, and are implicated in numerous diseases. In this study, to predict the binding affinity of ligands to G protein-coupled receptors (GPCRs), we employed two ΔΔG calculation methods: thermodynamic integration (TI) with AMBER and the alchemical transfer method (AToM) with OpenMM. We calculated ΔΔG values for 53 transformations involving four class A GPCRs and evaluated the performance of AMBER-TI and AToM-OpenMM. In addition, we conducted tests using different numbers of windows and varying simulation times to achieve reliable ΔΔG results and to optimize resource utilization. Overall, both AMBER-TI and AToM-OpenMM show good agreement with the experimental data. Our results validate the applicability of AMBER-TI and AToM-OpenMM for optimization of lead compounds targeting membrane proteins.

Caught between a ROCK and a hard place: current challenges in structure-based drug design

The discipline of structure-based drug design (SBDD) is several decades old and it is tempting to think that the proliferation of experimental structures for many drug targets might make computer-aided drug design (CADD) straightforward. However, this is far from true. In this review, we illustrate some of the challenges that CADD scientists face every day in their work, even now. We use Rho-associated protein kinase (ROCK), and public domain structures and data, as an example to illustrate some of the challenges we have experienced during our project targeting this protein. We hope that this will help to prevent unrealistic expectations of what CADD can accomplish and to educate non-CADD scientists regarding the challenges still facing their CADD colleagues.

Trans vs. cis: a computational study of enasidenib resistance due to IDH2 mutations

Isocitrate dehydrogenase 2 (IDH2) is a homodimeric enzyme that plays an important role in energy production. A mutation R140Q in one monomer makes the enzyme tumourigenic. Enasidenib is an effective inhibitor of IDH2/R140Q. A secondary mutation Q316E leads to enasidenib resistance. This mutation was hitherto only found in trans, i.e. where one monomer has the R140Q mutation and the other carries the Q316E mutation. It is not clear if the mutation only leads to resistance when in trans or if it has been discovered in trans only by chance, since it was only reported in two patients. Using molecular dynamics (MD) simulations we show that the binding of enasidenib to IDH2 is indeed much weaker when the Q316E mutation takes place in trans not in cis, which provides a molecular explanation for the clinical finding. This is corroborated by non-covalent interaction (NCI) analysis and DFT calculations. Whereas the MD simulations show a loss of one hydrogen bond upon the resistance mutation, NCI and energy decomposition analysis (EDA) reveal that a multitude of interactions are weakened.

GENESIS 2.1: High-Performance Molecular Dynamics Software for Enhanced Sampling and Free-Energy Calculations for Atomistic, Coarse-Grained, and Quantum Mechanics/Molecular Mechanics Models

GENeralized-Ensemble SImulation System (GENESIS) is a molecular dynamics (MD) software developed to simulate the conformational dynamics of a single biomolecule, as well as molecular interactions in large biomolecular assemblies and between multiple biomolecules in cellular environments. To achieve the latter purpose, the earlier versions of GENESIS emphasized high performance in atomistic MD simulations on massively parallel supercomputers, with or without graphics processing units (GPUs). Here, we implemented multiscale MD simulations that include atomistic, coarse-grained, and hybrid quantum mechanics/molecular mechanics (QM/MM) calculations. They demonstrate high performance and are integrated with enhanced conformational sampling algorithms and free-energy calculations without using external programs except for the QM programs. In this article, we review new functions, molecular models, and other essential features in GENESIS version 2.1 and discuss ongoing developments for future releases.

Studying the Collective Functional Response of a Receptor in Alchemical Ligand Binding Free Energy Simulations with Accelerated Solvation Layer Dynamics

Ligand binding free energy simulations (LB-FES) that involve sampling of protein functional conformations have been longstanding challenges in research on molecular recognition. Particularly, modeling of the conformational transition pathway and design of the heuristic biasing mechanism are severe bottlenecks for the existing enhanced configurational sampling (ECS) methods. Inspired by the key role of hydration in regulating conformational dynamics of macromolecules, this report proposes a novel ECS approach that facilitates binding-associated structural dynamics by accelerated hydration transitions in combination with the λ-exchange of free energy perturbation (FEP). Two challenging protein-ligand binding processes involving large configurational transitions of the receptor are studied, with hydration transitions at binding sites accelerated by Hamiltonian-simulated annealing of the hydration layer. Without the need for pathway analysis or ad hoc barrier flattening potential, LB-FES were performed with FEP/λ-exchange molecular dynamics simulation at a minor overhead for annealing of the hydration layer. The LB-FES studies showed that the accelerated rehydration significantly enhances the collective conformational transitions of the receptor, and convergence of binding affinity calculations is obtained at a sweet-spot simulation time scale. Alchemical LB-FES with the proposed ECS strategy is free from the effort of trial and error for the setup and realizes efficient on-the-fly sampling for the collective functional response of the receptor and bound water and therefore presents a practical approach to high-throughput screening in drug discovery.

phbuilder: A Tool for Efficiently Setting up Constant pH Molecular Dynamics Simulations in GROMACS

Constant pH molecular dynamics (MD) is a powerful technique that allows the protonation state of residues to change dynamically, thereby enabling the study of pH dependence in a manner that has not been possible before. Recently, a constant pH implementation was incorporated into the GROMACS MD package. Although this implementation provides good accuracy and performance, manual modification and the preparation of simulation input files are required, which can be complicated, tedious, and prone to errors. To simplify and automate the setup process, we present phbuilder, a tool that automatically prepares constant pH MD simulations for GROMACS by modifying the input structure and topology as well as generating the necessary parameter files. phbuilder can prepare constant pH simulations from both initial structures and existing simulation systems, and it also provides functionality for performing titrations and single-site parametrizations of new titratable group types. The tool is freely available at www.gitlab.com/gromacs-constantph. We anticipate that phbuilder will make constant pH simulations easier to set up, thereby making them more accessible to the GROMACS user community.

Ion Transport and Inhibitor Binding by Human NHE1: Insights from Molecular Dynamics Simulations and Free Energy Calculations

The human Na+/H+ exchanger (NHE1) plays a crucial role in maintaining intracellular pH by regulating the electroneutral exchange of a single intracellular H+ for one extracellular Na+ across the plasma membrane. Understanding the molecular mechanisms governing ion transport and the binding of inhibitors is of importance in the development of anticancer therapeutics targeting NHE1. In this context, we performed molecular dynamics (MD) simulations based on the recent cryo-electron microscopy (cryo-EM) structures of outward- and inward-facing conformations of NHE1. These simulations allowed us to explore the dynamics of the protein, examine the ion-translocation pore, and confirm that Asp267 is the ion-binding residue. Our free energy calculations did not show a significant difference between Na+ and K+ binding at the ion-binding site. Consequently, Na+ over K+ selectivity cannot be solely explained by differences in ion binding. Our MD simulations involving NHE1 inhibitors (cariporide and amiloride analogues) maintained stable interactions with Asp267 and Glu346. Our study highlights the importance of the salt bridge between the positively charged acylguanidine moiety and Asp267, which appears to play a role in the competitive inhibitory mechanism for this class of inhibitors. Our computational study provides a detailed mechanistic interpretation of experimental data and serves the basis of future structure-based inhibitor design.

Alchemical Free Energy Workflows for the Computation of Protein-Ligand Binding Affinities

Alchemical free energy methods can be used for the efficient computation of relative binding free energies during preclinical drug discovery stages. In recent years, this has been facilitated further by the implementation of workflows that enable non-experts to quickly and consistently set up the required simulations. Given the correct input structures, workflows handle the difficult aspects of setting up perturbations, including consistently defining the perturbable molecule, its atom mapping and topology generation, perturbation network generation, running of the simulations via different sampling methods, and analysis of the results. Different academic and commercial workflows are discussed, including FEW, FESetup, FEPrepare, CHARMM-GUI, Transformato, PMX, QLigFEP, TIES, ProFESSA, PyAutoFEP, BioSimSpace, FEP+, Flare, and Orion. These workflows differ in various aspects, such as mapping algorithms or enhanced sampling methods. Some workflows can accommodate more than one molecular dynamics (MD) engine and use external libraries for tasks. Differences between workflows can present advantages for different use cases, however a lack of interoperability of the workflows' components hinders systematic comparisons.

Standard Binding Free-Energy Calculations: How Far Are We from Automation?

Recent success stories suggest that in silico protein-ligand binding free-energy calculations are approaching chemical accuracy. However, their widespread application remains limited by the extensive human intervention required, posing challenges for the neophyte. As such, it is critical to develop automated workflows for estimating protein-ligand binding affinities with minimum personal involvement. Key human efforts include setting up and tuning enhanced-sampling or alchemical-transformation algorithms as a preamble to computational binding free-energy estimations. Additionally, preparing input files, bookkeeping, and postprocessing represent nontrivial tasks. In this Perspective, we discuss recent progress in automating standard binding free-energy calculations, featuring the development of adaptive or parameter-free algorithms, standardization of binding free-energy calculation workflows, and the implementation of user-friendly software. We also assess the current state of automated standard binding free-energy calculations and evaluate the limitations of existing methods. Last, we outline the requirements for future algorithms and workflows to facilitate automated free-energy calculations for diverse protein-ligand complexes.

Riding the wave of innovation: immunoinformatics in fish disease control

The spread of infectious illnesses has been a significant factor restricting aquaculture production. To maximise aquatic animal health, vaccination tactics are very successful and cost-efficient for protecting fish and aquaculture animals against many disease pathogens. However, due to the increasing number of immunological cases and their complexity, it is impossible to manage, analyse, visualise, and interpret such data without the assistance of advanced computational techniques. Hence, the use of immunoinformatics tools is crucial, as they not only facilitate the management of massive amounts of data but also greatly contribute to the creation of fresh hypotheses regarding immune responses. In recent years, advances in biotechnology and immunoinformatics have opened up new research avenues for generating novel vaccines and enhancing existing vaccinations against outbreaks of infectious illnesses, thereby reducing aquaculture losses. This review focuses on understanding in silico epitope-based vaccine design, the creation of multi-epitope vaccines, the molecular interaction of immunogenic vaccines, and the application of immunoinformatics in fish disease based on the frequency of their application and reliable results. It is believed that it can bridge the gap between experimental and computational approaches and reduce the need for experimental research, so that only wet laboratory testing integrated with in silico techniques may yield highly promising results and be useful for the development of vaccines for fish.

Development and Evaluation of a Self-Nanoemulsifying Drug Delivery System for Sinapic Acid with Improved Antiviral Efficacy against SARS-CoV-2

This study aimed to develop a self-nanoemulsifying drug delivery system (SNE) for sinapic acid (SA) to improve its solubility and antiviral activity. Optimal components for the SA-SNE formulation were selected, including Labrafil as the oil, Cremophor EL as the surfactant, and Transcutol as the co-surfactant. The formulation was optimized using surface response design, and the optimized SA-SNE formulation exhibited a small globule size of 83.6 nm, high solubility up to 127.1 ± 3.3, and a 100% transmittance. In vitro release studies demonstrated rapid and high SA release from the formulation. Pharmacokinetic analysis showed improved bioavailability by 2.43 times, and the optimized SA-SNE formulation exhibited potent antiviral activity against SARS-CoV-2. The developed SA-SNE formulation can enhance SA's therapeutic efficacy by improving its solubility, bioavailability, and antiviral activity. Further in silico, modeling, and Gaussian accelerated molecular dynamics (GaMD)-based studies revealed that SA could interact with and inhibit the viral main protease (Mpro). This research contributes to developing effective drug delivery systems for poorly soluble drugs like SA, opening new possibilities for their application via nebulization in SARS-CoV-2 therapy.

Enhanced Configurational Sampling Approaches to Alchemical Ligand Binding Free Energy Simulations: Current Status and Challenges

Ligand binding free energy simulations (LB-FES) have been routine tasks in modern drug discovery campaign. A long-standing challenge for LB-FES is the difficulty in adequately sampling nontrivial environmental reorganizations in response to ligand binding. Therefore, various enhanced configurational sampling (ECS) approaches were devised to speed up fluctuations of relevant slow degrees of freedom (SDOF) and ensure simulation convergence. However, in contrast to the achievements in parametrization, software performance, and workflow automation, efficient ECS methodology suitable for high throughput screening remains in an early stage of development. Here, a review of ECS developments with LB-FES is presented, revisiting current approaches and underlining the major technical pitfalls and challenges. This Perspective focuses on alchemical LB-FES on account of their predominant role in high throughput drug screening as well as the established partnership with ECS. The critical aspects of designing ECS approaches, from both theoretical and applied perspectives, are described. This work is intended to provide a contemporary review of the scientific, technical, and practical issues associated with the accelerating convergence of alchemical LB-FES.

pyCHARMM: Embedding CHARMM Functionality in a Python Framework

CHARMM is rich in methodology and functionality as one of the first programs addressing problems of molecular dynamics and modeling of biological macromolecules and their partners, e.g., small molecule ligands. When combined with the highly developed CHARMM parameters for proteins, nucleic acids, small molecules, lipids, sugars, and other biologically relevant building blocks, and the versatile CHARMM scripting language, CHARMM has been a trendsetting platform for modeling studies of biological macromolecules. To further enhance the utility of accessing and using CHARMM functionality in increasingly complex workflows associated with modeling biological systems, we introduce pyCHARMM, Python bindings, functions, and modules to complement and extend the extensive set of modeling tools and methods already available in CHARMM. These include access to CHARMM function-generated variables associated with the system (psf), coordinates, velocities and forces, atom selection variables, and force field related parameters. The ability to augment CHARMM forces and energies with energy terms or methods derived from machine learning or other sources, written in Python, CUDA, or OpenCL and expressed as Python callable routines is introduced together with analogous functions callable during dynamics calculations. Integration of Python-based graphical engines for visualization of simulation models and results is also accessible. Loosely coupled parallelism is available for workflows such as free energy calculations, using MBAR/TI approaches or high-throughput multisite λ-dynamics (MSλD) free energy methods, string path optimization calculations, replica exchange, and molecular docking with a new Python-based CDOCKER module. CHARMM accelerated platform kernels through the CHARMM/OpenMM API, CHARMM/DOMDEC, and CHARMM/BLaDE API are also readily integrated into this Python framework. We anticipate that pyCHARMM will be a robust platform for the development of comprehensive and complex workflows utilizing Python and its extensive functionality as well as an optimal platform for users to learn molecular modeling methods and practices within a Python-friendly environment such as Jupyter Notebooks.

CHARMM-GUI Membrane Builder: Past, Current, and Future Developments and Applications

Molecular dynamics simulations of membranes and membrane proteins serve as computational microscopes, revealing coordinated events at the membrane interface. As G protein-coupled receptors, ion channels, transporters, and membrane-bound enzymes are important drug targets, understanding their drug binding and action mechanisms in a realistic membrane becomes critical. Advances in materials science and physical chemistry further demand an atomistic understanding of lipid domains and interactions between materials and membranes. Despite a wide range of membrane simulation studies, generating a complex membrane assembly remains challenging. Here, we review the capability of CHARMM-GUI Membrane Builder in the context of emerging research demands, as well as the application examples from the CHARMM-GUI user community, including membrane biophysics, membrane protein drug-binding and dynamics, protein-lipid interactions, and nano-bio interface. We also provide our perspective on future Membrane Builder development.

Blackberry-Loaded AgNPs Attenuate Hepatic Ischemia/Reperfusion Injury via PI3K/Akt/mTOR Pathway

Liver ischemia-reperfusion injury (IRI) is a pathophysiological insult that often occurs during liver surgery. Blackberry leaves are known for their anti-inflammatory and antioxidant activities. Aims: To achieve site-specific delivery of blackberry leaves extract (BBE) loaded AgNPs to the hepatocyte in IRI and to verify possible molecular mechanisms. Methods: IRI was induced in male Wister rats. Liver injury, hepatic histology, oxidative stress markers, hepatic expression of apoptosis-related proteins were evaluated. Non-targeted metabolomics for chemical characterization of blackberry leaves extract was performed. Key findings: Pre-treatment with BBE protected against the deterioration caused by I/R, depicted by a significant improvement of liver functions and structure, as well as reduction of oxidative stress with a concomitant increase in antioxidants. Additionally, BBE promoted phosphorylation of antiapoptotic proteins; PI3K, Akt and mTOR, while apoptotic proteins; Bax, Casp-9 and cleaved Casp-3 expressions were decreased. LC-HRMS-based metabolomics identified a range of metabolites, mainly flavonoids and anthocyanins. Upon comprehensive virtual screening and molecular dynamics simulation, the major annotated anthocyanins, cyanidin and pelargonidin glucosides, were suggested to act as PLA2 inhibitors. Significance: BBE can ameliorate hepatic IRI augmented by BBE-AgNPs nano-formulation via suppressing, oxidative stress and apoptosis as well as stimulation of PI3K/Akt/mTOR signaling pathway.

Development of certain aminoquinazoline scaffolds as potential multitarget anticancer agents with apoptotic and anti-proliferative effects: Design, synthesis and biological evaluation

Newly designed 4 - aminoquinazoline derivatives (5a-f, 6a, b, 7, 8, 9, 10a-c, 11a, b, 12a, b and 13a, b) have been synthesized and evaluated for their potential multitarget anticancer activities, apoptotic and anti-proliferative effects. Thereupon, in vitro cytotoxic activities of all the synthesized compounds were screened against NCI 60 human cancer cell lines (nine subpanels) at NCI, USA. Successfully, 2-morpholino-N-(quinazolin-4-yl) acetohydrazide 5e was granted an NSC code, owing to its significant potency and broad spectrum of activity against various cancer cell lines; leukemia K-562, non-small cell lung cancer NCI-H522 cells, colon cancer SW-620, melanoma LOX IMVI, MALME-3M, renal cancer RXF 393, ACHN and breast cancer MDA-MB231/ATCC (GI% = 99.6, 161, 126.03, 90.22, 174.47, 139.7, 191 and 97, respectively). Compound 5e showed the best inhibitory activity (GI₅₀ = 1.3 µM) against melanoma LOX IMVI, when tested at five doses against NCI 60 cell lines. Furthermore, compound 5e showed comparable EGFR and CDK2 inhibitory activity results (IC₅₀ = 0.093 ± 0.006 μM and 0.143 ± 0.008 μM, respectively) to those of lapatinib and ribociclib (IC₅₀ = 0.03 ± 0.002 μM and 0.067 ± 0.004 μM, respectively). Western blotting analysis of compound 5e against melanoma LOX IMVI marked out significant reduced EGFR and CDK2 protein expression percentages, up to 32.97% and 34.09%, respectively, if compared to lapatinib (31.18%) and ribociclib (29.66%). Moreover, compound 5e caused clear cell cycle arrests at S phase of renal UO-31 cells and at G1 phase of both breast cancer MCF7 and ovarian cancer IGROV1, associated with remarkable increase of DNA content of the controls. In accordance, it demonstrated promising anti- proliferative and apoptotic activities, showing a significant increase in total apoptotic percentages of renal cancer UO-31, breast cancer MCF7 and ovarian IGROV1 cancer cell lines, if compared to the control untreated cells (from 1.79% to 46.72%, 2.19% to 39.02% and 1.66 to 42.51%, respectively). Molecular modelling and dynamic simulation study results supported the main objectives of the present work.

Aloin A inhibits SARS CoV-2 replication by targeting its binding with ACE2 - Evidence from modeling-supported molecular dynamics simulation

The current study aimed to expand on the recently published results and assess the inhibitory efficacy of aloin A against SARS CoV-2. In vitro testing of aloin A against SARS CoV-2 proteases (i.e., MPro and PLPro) showed weak to moderate activity (IC50 = 68.56 ± 1.13 µM and 24.77 ± 1.57 µM, respectively). However, aloin A was able to inhibit the replication of SARS CoV-2 in Vero E6 cells efficiently with an IC50 of 0.095 ± 0.022 µM. Depending on the reported poor permeability of aloin A alongside its insignificant protease inhibitory activities presented in this study, we ran a number of extensive virtual screenings and physics-based simulations to determine the compound's potential mode of action. As a result, RBD-ACE2 was identified as a key target for aloin A. Results from 600 ns-long molecular dynamics (MD) simulation experiments pointed to aloin A's role as an RBD-ACE2 destabilizer. Therefore, the results of this work may pave the way for further development of this scaffold and the eventual production of innovative anti-SARS CoV-2 medicines with several mechanisms of action.Communicated by Ramaswamy H. Sarma.

Three-Dimensional-QSAR and Relative Binding Affinity Estimation of Focal Adhesion Kinase Inhibitors

Precise binding affinity predictions are essential for structure-based drug discovery (SBDD). Focal adhesion kinase (FAK) is a member of the tyrosine kinase protein family and is overexpressed in a variety of human malignancies. Inhibition of FAK using small molecules is a promising therapeutic option for several types of cancer. Here, we conducted computational modeling of FAK-targeting inhibitors using three-dimensional structure-activity relationship (3D-QSAR), molecular dynamics (MD), and hybrid topology-based free energy perturbation (FEP) methods. The structure-activity relationship (SAR) studies between the physicochemical descriptors and inhibitory activities of the chemical compounds were performed with reasonable statistical accuracy using CoMFA and CoMSIA. These are two well-known 3D-QSAR methods based on the principle of supervised machine learning (ML). Essential information regarding residue-specific binding interactions was determined using MD and MM-PB/GBSA methods. Finally, physics-based relative binding free energy (ΔΔGRBFEA→B) terms of analogous ligands were estimated using alchemical FEP simulation. An acceptable agreement was observed between the experimental and computed relative binding free energies. Overall, the results suggested that using ML and physics-based hybrid approaches could be useful in synergy for the rational optimization of accessible lead compounds with similar scaffolds targeting the FAK receptor.

TIES 2.0: A Dual-Topology Open Source Relative Binding Free Energy Builder with Web Portal

Relative binding free energy (RBFE) calculations are widely used to aid the process of drug discovery. TIES, Thermodynamic Integration with Enhanced Sampling, is a dual-topology approach to RBFE calculations with support for NAMD and OpenMM molecular dynamics engines. The software has been thoroughly validated on publicly available datasets. Here we describe the open source software along with a web portal (https://ccs-ties.org) that enables users to perform such calculations correctly and rapidly.

A Guide to In Silico Drug Design

The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.

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.

A Comprehensive In Silico Study of New Metabolites from Heteroxenia fuscescens with SARS-CoV-2 Inhibitory Activity

Chemical investigation of the total extract of the Egyptian soft coral Heteroxenia fuscescens, led to the isolation of eight compounds, including two new metabolites, sesquiterpene fusceterpene A (1) and a sterol fuscesterol A (4), along with six known compounds. The structures of 1–8 were elucidated via intensive studies of their 1D, 2D-NMR, and HR-MS analyses, as well as a comparison of their spectral data with those mentioned in the literature. Subsequent comprehensive in-silico-based investigations against almost all viral proteins, including those of the new variants, e.g., Omicron, revealed the most probable target for these isolated compounds, which was found to be M^pro. Additionally, the dynamic modes of interaction of the putatively active compounds were highlighted, depending on 50-ns-long MDS. In conclusion, the structural information provided in the current investigation highlights the antiviral potential of H. fuscescens metabolites with 3β,5α,6β-trihydroxy steroids with different nuclei against SARS-CoV-2, including newly widespread variants.

Anti-Inflammatory and Antioxidant Properties of Malapterurus electricus Skin Fish Methanolic Extract in Arthritic Rats: Therapeutic and Protective Effects

The protective and therapeutic anti-inflammatory and antioxidant potency of Malapterurus electricus (F. Malapteruridae) skin fish methanolic extract (FE) (300 mg/kg.b.wt/day for 7 days, orally) was tested in monosodium urate(MSU)-induced arthritic Wistar albino male rats' joints. Serum uric acid, TNF-α, IL-1β, NF-𝜅B, MDA, GSH, catalase, SOD, and glutathione reductase levels were all measured. According to the findings, FE significantly reduced uric acid levels and ankle swelling in both protective and therapeutic groups. Furthermore, it has anti-inflammatory effects by downregulating inflammatory cytokines, primarily through decreased oxidative stress and increased antioxidant status. All the aforementioned lesions were significantly improved in protected and treated rats with FE, according to histopathological findings. iNOS immunostaining revealed that protected and treated arthritic rats with FE had weak positive immune-reactive cells. Phytochemical analysis revealed that FE was high in fatty and amino acids. The most abundant compounds were vaccenic (24.52%), 9-octadecenoic (11.66%), palmitic (34.66%), stearic acids (14.63%), glycine (0.813 mg/100 mg), and alanine (1.645 mg/100 mg). Extensive molecular modelling and dynamics simulation experiments revealed that compound 4 has the potential to target and inhibit COX isoforms with a higher affinity for COX-2. As a result, we contend that FE could be a promising protective and therapeutic option for arthritis, aiding in the prevention and progression of this chronic inflammatory disease.

Meta-Analysis Reveals That Absolute Binding Free-Energy Calculations Approach Chemical Accuracy

Systematic and quantitative analysis of the reliability of formally exact methods that in silico calculate absolute protein-ligand binding free energies remains lacking. Here, we provide, for the first time, evidence-based information on the reliability of these methods by statistically studying 853 cases from 34 different research groups through meta-analysis. The results show that formally exact methods approach chemical accuracy (error = 1.58 kcal/mol), even if people are challenging difficult tasks such as blind drug screening in recent years. The geometrical-pathway-based methods prove to possess a better convergence ability than the alchemical ones, while the latter have a larger application range. We also reveal the importance of always using the latest force fields to guarantee reliability and discuss the pros and cons of turning to an implicit solvent model in absolute binding free-energy calculations. Moreover, based on the meta-analysis, an evidence-based guideline for in silico binding free-energy calculations is provided.

CHARMM-GUI Implicit Solvent Modeler for Various Generalized Born Models in Different Simulation Programs

Implicit solvent models are widely used because they are advantageous to speed up simulations by drastically decreasing the number of solvent degrees of freedom, which allows one to achieve long simulation time scales for large system sizes. CHARMM-GUI, a web-based platform, has been developed to support the setup of complex multicomponent molecular systems and prepare input files. This study describes an Implicit Solvent Modeler (ISM) in CHARMM-GUI for various generalized Born (GB) implicit solvent simulations in different molecular dynamics programs such as AMBER, CHARMM, GENESIS, NAMD, OpenMM, and Tinker. The GB models available in ISM include GB-HCT, GB-OBC, GB-neck, GBMV, and GBSW with the CHARMM and Amber force fields for protein, DNA, RNA, glycan, and ligand systems. Using the system and input files generated by ISM, implicit solvent simulations of protein, DNA, and RNA systems produce similar results for different simulation packages with the same input information. Protein-ligand systems are also considered to further validate the systems and input files generated by ISM. Simple ligand root-mean-square deviation (RMSD) and molecular mechanics generalized Born surface area (MM/GBSA) calculations show that the performance of implicit simulations is better than docking and can be used for early stage ligand screening. These reasonable results indicate that ISM is a useful and reliable tool to provide various implicit solvent simulation applications.

¹H-NMR Metabolic Profiling, Antioxidant Activity, and Docking Study of Common Medicinal Plant-Derived Honey

The purpose of this investigation was to determine ¹H-NMR profiling and antioxidant activity of the most common types of honey, namely, citrus honey (HC1) (Morcott tangerine L. and Jaffa orange L.), marjoram honey (HM1) (Origanum majorana L.), and clover honey (HT1) (Trifolium alexandrinum L.), compared to their secondary metabolites (HC2, HM2, HT2, respectively). By using a ¹H-NMR-based metabolomic technique, PCA, and PLS-DA multivariate analysis, we found that HC2, HM2, HC1, and HM1 were clustered together. However, HT1 and HT2 were quite far from these and each other. This indicated that HC1, HM1, HC2, and HM2 have similar chemical compositions, while HT1 and HT2 were unique in their chemical profiles. Antioxidation potentials were determined colorimetrically for scavenging activities against DPPH, ABTS, ORAC, 5-LOX, and metal chelating activity in all honey extract samples and their secondary metabolites. Our results revealed that HC2 and HM2 possessed more antioxidant activities than HT2 in vitro. HC2 demonstrated the highest antioxidant effect in all assays, followed by HM2 (DPPH assay: IC₅₀ 2.91, 10.7 μg/mL; ABTS assay: 431.2, 210.24 at 50 ug/mL Trolox equivalent; ORAC assay: 259.5, 234.8 at 50 ug/mL Trolox equivalent; 5-LOX screening assay/IC₅₀: 2.293, 6.136 ug/mL; and metal chelating activity at 50 ug/mL: 73.34526%, 63.75881% inhibition). We suggest that the presence of some secondary metabolites in HC and HM, such as hesperetin, linalool, and caffeic acid, increased the antioxidant activity in citrus and marjoram compared to clover honey.

Elucidating the enhanced binding affinity of a double mutant SP-D with trimannose on the influenza A virus using molecular dynamics

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The Asp325Ala mutation in SP-D promotes a trimannose conformational change to a more stable state.

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The Arg343Val mutation in SP-D reduces its interaction with Glu333 to increase the binding affinity with trimannose.

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The Arg343Val mutation contributes more to the increase of SP-D’s binding affinity with trimannose than Asp325Ala.

Surfactant protein D (SP-D) is an essential component of the human pulmonary surfactant system, which is crucial in the innate immune response against glycan-containing pathogens, including Influenza A viruses (IAV) and SARS-CoV-2. Previous studies have shown that wild-type (WT) SP-D can bind IAV but exhibits poor antiviral activities. However, a double mutant (DM) SP-D consisting of two point mutations (Asp325Ala and Arg343Val) inhibits IAV more potently. Presently, the structural mechanisms behind the point mutations’ effects on SP-D’s binding affinity with viral surface glycans are not fully understood. Here we use microsecond-scale, full-atomistic molecular dynamics (MD) simulations to understand the molecular mechanism of mutation-induced SP-D’s higher antiviral activity. We find that the Asp325Ala mutation promotes a trimannose conformational change to a more stable state. Arg343Val increases the binding with trimannose by increasing the hydrogen bonding interaction with Glu333. Free energy perturbation (FEP) binding free energy calculations indicate that the Arg343Val mutation contributes more to the increase of SP-D’s binding affinity with trimannose than Asp325Ala. This study provides a molecular-level exploration of how the two mutations increase SP-D binding affinity with trimannose, which is vital for further developing preventative strategies for related diseases.

Design and Synthesis of Novel 1,3,4-Oxadiazole and 1,2,4-Triazole Derivatives as Cyclooxygenase-2 Inhibitors with Anti-inflammatory and Antioxidant activity in LPS-stimulated RAW264.7 Macrophages

In an attempt to obtain new candidates with potential anti-inflammatory activity, two series of 1,3,4-oxadiazole based derivatives (8a-g) and 1,2,4-triazole based derivatives (10a,b and 11a-g) were synthesized and evaluated for their COX-1/COX-2 inhibitory activity. In vitro assays showed potent COX-2 inhibitory activity and selectivity of the novel designed compounds (IC50 = 0.04 - 0.16 μM, SI = 60.71 - 337.5) compared to celecoxib (IC50 = 0.045 μM, SI = 326.67). The anti-inflammatory and antioxidant activity of the synthesized compounds was investigated via testing their ability to inhibit pro-inflammatory [tumour necrosis factor (TNF-α) and interleukin-6 (IL-6)] and oxidative stress [nitric oxide (NO) and reactive oxygen species (ROS)] markers production in lipopolysaccharide (LPS)-activated RAW 264.7 macrophages. Most of the novel compounds exhibited potent anti-inflammatory and antioxidant activity. In particular, the novel compounds showed excellent IL-6 inhibitory activity (IC50 = 0.96 - 11.14 μM) when compared to celecoxib (IC50 = 13.04 μM) and diclofenac sodium (IC50 = 22.97 μM). Moreover, the most potent and selective COX-2 inhibitor 11c (IC50 = 0.04 μM, SI = 337.5) displayed significantly higher activity against NO and ROS production compared to celecoxib (IC50 = 2.60 and 3.01 μM vs. 16.47 and 14.30 μM, respectively). Molecular modelling studies of the novel designed molecules into COX-2 active sites analysed their binding affinity. In-silico simulation studies indicated their acceptable physicochemical properties and pharmacokinetic profiles. This study suggests that the novel synthesized COX-2 inhibitors exert potent anti-inflammatory and antioxidant activity, highlighting their potential as promising therapeutic agents for the treatment of inflammation and oxidative stress-related diseases.

Possible neuroprotective effects of amide alkaloids from Bassia indica and Agathophora alopecuroides: in vitro and in silico investigations

In Alzheimer's disease (AD), the accumulation of amyloid-β plaques, overactivity of MAO-B, and phosphorylated tau protein in the central nervous system result in neuroinflammation and cognitive impairments. Therefore, the multi-targeting of these therapeutic targets has emerged as a promising strategy for the development of AD treatments. The current study reports the isolation and identification of seven amide alkaloids, namely, N-trans-feruloyl-3-methoxytyramine (1), N-trans-feruloyltyramine (2), S-(-)-N-trans-feruloylnormetanephrine (3), S-(-)-N-trans-feruloyloctopamine (4), R-(+)-N-trans-feruloyloctopamine (5), N-trans-caffeoyltyramine (6), and S-(-)-3-(4-hydroxy-3-methoxyphenyl)-N-[2-(4-hydroxyphenyl)-methoxyethyl]acrylamide (7), from B. indica and A. alopecuroides, which are halophytic plants that have been reported to contain diverse phytochemicals. Additionally, the study explores the potential inhibition effects of the isolates on β-secretase, monoamine oxidase enzymes, and phosphorylated tau protein, and their anti-aggregation effects on amyloid-β fibrils. Compounds 1, 2, and 7 showed potent inhibitory activity against BACE1, MAO-B, and phosphorylated tau protein, as well as anti-aggregation activity against Aβ-peptides. Additionally, compound 6 displayed promising inhibition activity against MAO-B enzyme. Further in-depth in silico and modeling analyses (i.e., docking, absolute binding free energy calculations, and molecular dynamics simulations) were carried out to reveal the binding mode of each active compound inside the corresponding enzyme (i.e., MAO-B and BACE1). The results indicate that B. indica, A. alopecuroides, and the isolated amide alkaloids might be useful in the development of lead compounds for the prevention of neurodegenerative diseases, especially AD.

The performance of ensemble-based free energy protocols in computing binding affinities to ROS1 kinase

Optimization of binding affinities for compounds to their target protein is a primary objective in drug discovery. Herein we report on a collaborative study that evaluates a set of compounds binding to ROS1 kinase. We use ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) and TIES (thermodynamic integration with enhanced sampling) protocols to rank the binding free energies. The predicted binding free energies from ESMACS simulations show good correlations with experimental data for subsets of the compounds. Consistent binding free energy differences are generated for TIES and ESMACS. Although an unexplained overestimation exists, we obtain excellent statistical rankings across the set of compounds from the TIES protocol, with a Pearson correlation coefficient of 0.90 between calculated and experimental activities.

Modified Hamiltonian in FEP Calculations for Reducing the Computational Cost of Electrostatic Interactions

The free-energy perturbation (FEP) method predicts relative and absolute free-energy changes of biomolecules in solvation and binding with other molecules. FEP is, therefore, one of the most essential tools in in silico drug design. In conventional FEP, to smoothly connect two thermodynamic states, the potential energy is modified as a linear combination of the end-state potential energies by introducing scaling factors. When the particle mesh Ewald is used for electrostatic calculations, conventional FEP requires two reciprocal-space calculations per time step, which largely decreases the computational performance. To overcome this problem, we propose a new FEP scheme by introducing a modified Hamiltonian instead of interpolation of the end-state potential energies. The scheme introduces nonuniform scaling into the electrostatic potential as used in Replica Exchange with Solute Tempering 2 (REST2) and does not require additional reciprocal-space calculations. We tested this modified Hamiltonian in FEP calculations in several biomolecular systems. In all cases, the calculated free-energy changes with the current scheme are in good agreement with those from conventional FEP. The modified Hamiltonian in FEP greatly improves the computational performance, which is particularly marked for large biomolecular systems whose reciprocal-space calculations are the major bottleneck of total computational time.

Bioactive Phytochemicals of Citrus reticulata Seeds—An Example of Waste Product Rich in Healthy Skin Promoting Agents

Phytochemical investigation of Egyptian mandarin orange (Citrus reticulata Blanco, F. Rutaceae) seeds afforded thirteen known compounds, 1–13. The structures of isolated compounds were assigned using 1D and 2D NMR and HRESIMS analyses. To characterize the pharmacological activity of these compounds, several integrated virtual screening-based and molecular dynamics simulation-based experiments were applied. As a result, compounds 2, 3 and 5 were putatively identified as hyaluronidase, xanthine oxidase and tyrosinase inhibitors. The subsequent in vitro testing was done to validate the in silico-based experiments to highlight the potential of these flavonoids as promising hyaluronidase, xanthine oxidase and tyrosinase inhibitors with IC50 values ranging from 6.39 ± 0.36 to 73.7 ± 2.33 µM. The present study shed light on the potential of Egyptian mandarin orange’s waste product (i.e., its seeds) as a skin health-promoting natural agent. Additionally, it revealed the applicability of integrated inverse docking-based virtual screening and MDS-based experiments in efficiently predicting the biological potential of natural products.

Comparing the Performance of Different AMBER Protein Forcefields, Partial Charge Assignments, and Water Models for Absolute Binding Free Energy Calculations

Identifying chemical starting points is a vital first step in small molecule drug discovery and can take significant time and money. For this reason, computational approaches to virtual screening are of great interest as they can lower the cost and shorten timeframes. However, simple approaches such as molecular docking and pharmacophore screening are of limited accuracy and provide a low probability of success. Alchemical binding free energies represent a promising approach for virtual screening as they naturally incorporate the key effects of water molecules, protein flexibility, and binding entropy. However, the calculations are technically very challenging, with performance depending on the specific forcefield used. For this reason, it is important that the community has access to benchmark test sets to assess prediction accuracy. In this paper, we present an approach to alchemical binding free energies using OpenMM. We identify effective simulation parameters using an existing BRD4(1) test set and present two new benchmark sets (cMET and PDE2A) that can be used in the community for validation purposes. Our findings also highlight the effectiveness of some AMBER forcefields, in particular, AMBER ff15ipq.

Aurasperone A Inhibits SARS CoV-2 In Vitro: An Integrated In Vitro and In Silico Study

Several natural products recovered from a marine-derived Aspergillus niger were tested for their inhibitory activity against SARS CoV-2 in vitro. Aurasperone A (3) was found to inhibit SARS CoV-2 efficiently (IC₅₀ = 12.25 µM) with comparable activity with the positive control remdesivir (IC₅₀ = 10.11 µM). Aurasperone A exerted minimal cytotoxicity on Vero E6 cells (CC₅₀ = 32.36 mM, SI = 2641.5) and it was found to be much safer than remdesivir (CC₅₀ = 415.22 µM, SI = 41.07). To putatively highlight its molecular target, aurasperone A was subjected to molecular docking against several key-viral protein targets followed by a series of molecular dynamics-based in silico experiments that suggested M^pro to be its primary viral protein target. More potent anti-SARS CoV-2 M^pro inhibitors can be developed according to our findings presented in the present investigation.

Neoechinulin A as a Promising SARS-CoV-2 Mpro Inhibitor: In Vitro and In Silico Study Showing the Ability of Simulations in Discerning Active from Inactive Enzyme Inhibitors

The COVID-19 pandemic and its continuing emerging variants emphasize the need to discover appropriate treatment, where vaccines alone have failed to show complete protection against the new variants of the virus. Therefore, treatment of the infected cases is critical. This paper discusses the bio-guided isolation of three indole diketopiperazine alkaloids, neoechinulin A (1), echinulin (2), and eurocristatine (3), from the Red Sea-derived Aspergillus fumigatus MR2012. Neoechinulin A (1) exhibited a potent inhibitory effect against SARS-CoV-2 Mpro with IC50 value of 0.47 μM, which is comparable to the reference standard GC376. Despite the structural similarity between the three compounds, only 1 showed a promising effect. The mechanism of inhibition is discussed in light of a series of extensive molecular docking, classical and steered molecular dynamics simulation experiments. This paper sheds light on indole diketopiperazine alkaloids as a potential structural motif against SARS-CoV-2 Mpro. Additionally, it highlights the potential of different molecular docking and molecular dynamics simulation approaches in the discrimination between active and inactive structurally related Mpro inhibitors.

Relative Binding Free Energy Calculations for Ligands with Diverse Scaffolds with the Alchemical Transfer Method

We present an extension of the alchemical transfer method (ATM) for the estimation of relative binding free energies of molecular complexes applicable to conventional, as well as scaffold-hopping, alchemical transformations. Named ATM-RBFE, the method is implemented in the free and open-source OpenMM molecular simulation package and aims to provide a simpler and more generally applicable route to the calculation of relative binding free energies than what is currently available. ATM-RBFE is based on sound statistical mechanics theory and a novel coordinate perturbation scheme designed to swap the positions of a pair of ligands such that one is transferred from the bulk solvent to the receptor binding site while the other moves simultaneously in the opposite direction. The calculation is conducted directly in a single solvent box with a system prepared with conventional setup tools, without splitting of electrostatic and nonelectrostatic transformations, and without pairwise soft-core potentials. ATM-RBFE is validated here against the absolute binding free energies of the SAMPL8 GDCC host-guest benchmark set and against protein-ligand benchmark sets that include complexes of the estrogen receptor ERα and those of the methyltransferase EZH2. In each case the method yields self-consistent and converged relative binding free energy estimates in agreement with absolute binding free energies and reference literature values, as well as experimental measurements.

CHARMM-GUI Ligand Designer for Template-Based Virtual Ligand Design in a Binding Site

Rational drug design involves a task of finding ligands that would bind to a specific target protein. This work presents CHARMM-GUI Ligand Designer that is an intuitive and interactive web-based tool to design virtual ligands that match the shape and chemical features of a given protein binding site. Ligand Designer provides ligand modification capabilities with 3D visualization that allow researchers to modify and redesign virtual ligands while viewing how the protein-ligand interactions are affected. Virtual ligands can also be parameterized for further molecular dynamics (MD) simulations and free energy calculations. Using 8 targets from 8 different protein classes in the directory of useful decoys, enhanced (DUD-E) data set, we show that Ligand Designer can produce similar ligands to the known active ligands in the crystal structures. Ligand Designer also produces stable protein-ligand complex structures when tested using short MD simulations. We expect that Ligand Designer can be a useful and user-friendly tool to design small molecules in any given potential ligand binding site on a protein of interest.

Cryo-EM structure of the sodium-driven chloride/bicarbonate exchanger NDCBE

SLC4 transporters play significant roles in pH regulation and cellular sodium transport. The previously solved structures of the outward facing (OF) conformation for AE1 (SLC4A1) and NBCe1 (SLC4A4) transporters revealed an identical overall fold despite their different transport modes (chloride/bicarbonate exchange versus sodium-carbonate cotransport). However, the exact mechanism determining the different transport modes in the SLC4 family remains unknown. In this work, we report the cryo-EM 3.4 Å structure of the OF conformation of NDCBE (SLC4A8), which shares transport properties with both AE1 and NBCe1 by mediating the electroneutral exchange of sodium-carbonate with chloride. This structure features a fully resolved extracellular loop 3 and well-defined densities corresponding to sodium and carbonate ions in the tentative substrate binding pocket. Further, we combine computational modeling with functional studies to unravel the molecular determinants involved in NDCBE and SLC4 transport.

FEPrepare: A Web-Based Tool for Automating the Setup of Relative Binding Free Energy Calculations

Relative binding free energy calculations in drug design are becoming a useful tool in facilitating lead binding affinity optimization in a cost- and time-efficient manner. However, they have been limited by technical challenges such as the manual creation of large numbers of input files to set up, run, and analyze free energy simulations. In this Application Note, we describe FEPrepare, a novel web-based tool, which automates the setup procedure for relative binding FEP calculations for the dual-topology scheme of NAMD, one of the major MD engines, using OPLS-AA force field topology and parameter files. FEPrepare provides the user with all necessary files needed to run a FEP/MD simulation with NAMD. FEPrepare can be accessed and used at https://feprepare.vi-seem.eu/.

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.

Perturbation Free-Energy Toolkit: An Automated Alchemical Topology Builder

Free-energy calculations play an important role in the application of computational chemistry to a range of fields, including protein biochemistry, rational drug design, or materials science. Importantly, the free-energy difference is directly related to experimentally measurable quantities such as partition and adsorption coefficients, water activity, and binding affinities. Among several techniques aimed at predicting free-energy differences, perturbation approaches, involving the alchemical transformation of one molecule into another through intermediate states, stand out as rigorous methods based on statistical mechanics. However, despite the importance of free-energy calculations, the applicability of the perturbation approaches is still largely impeded by a number of challenges, including the definition of the perturbation path, i.e., alchemical changes leading to the transformation of one molecule to the other. To address this, an automatic perturbation topology builder based on a graph-matching algorithm is developed, which can identify the maximum common substructure (MCS) of two or multiple molecules and provide the perturbation topologies suitable for free-energy calculations using the GROMOS and the GROMACS simulation packages. Various MCS search options are presented leading to alternative definitions of the perturbation pathway. Moreover, perturbation topologies generated using the default multistate MCS search are used to calculate the changes in free energy between lysine and its two post-translational modifications, 3-methyllysine and acetyllysine. The pairwise free-energy calculations performed on this test system led to a cycle closure of 0.5 ± 0.3 and 0.2 ± 0.2 kJ mol-1, with GROMOS and GROMACS simulation packages, respectively. The same relative free energies between the three states are obtained by employing the enveloping distribution sampling (EDS) approach when compared to the pairwise perturbations. Importantly, this toolkit is made available online as an open-source Python package (https://github.com/drazen-petrov/SMArt).

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.

Incorporation efficiency and inhibition mechanism of 2'-substituted nucleotide analogs against SARS-CoV-2 RNA-dependent RNA polymerase

The ongoing pandemic caused by SARS-CoV-2 emphasizes the need for effective therapeutics. Inhibition of SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) by nucleotide analogs provides a promising antiviral strategy. One common group of RdRp inhibitors, 2'-modified nucleotides, are reported to exhibit different behaviors in the SARS-CoV-2 RdRp transcription assay. Three of these analogs, 2'-O-methyl UTP, Sofosbuvir, and 2'-methyl CTP, act as effective inhibitors in previous biochemical experiments, while Gemcitabine and ara-UTP show no inhibitory activity. To understand the impact of the 2'-modification on their inhibitory effects, we conducted extensive molecular dynamics simulations and relative binding free energy calculations using the free energy perturbation method on SARS-CoV-2 replication-transcription complex (RTC) with these five nucleotide analogs. Our results reveal that the five nucleotide analogs display comparable binding affinities to SARS-CoV-2 RdRp and they can all be added to the nascent RNA chain. Moreover, we examine how the incorporation of these nucleotide triphosphate (NTP) analogs will impact the addition of the next nucleotide. Our results indicate that 2'-O-methyl UTP can weaken the binding of the subsequent NTP and consequently lead to partial chain termination. Additionally, Sofosbuvir and 2'-methyl CTP can cause immediate termination due to the strong steric hindrance introduced by their bulky 2'-methyl groups. In contrast, nucleotide analogs with smaller substitutions, such as the fluorine atoms and the ara-hydroxyl group in Gemcitabine and ara-UTP, have a marginal impact on the polymerization process. Our findings are consistent with experimental observations, and more importantly, shed light on the detailed molecular mechanism of SARS-CoV-2 RdRp inhibition by 2'-substituted nucleotide analogs, and may facilitate the rational design of antiviral agents to inhibit SARS-CoV-2 RdRp.

Recent Developments in Free Energy Calculations for Drug Discovery

The grand challenge in structure-based drug design is achieving accurate prediction of binding free energies. Molecular dynamics (MD) simulations enable modeling of conformational changes critical to the binding process, leading to calculation of thermodynamic quantities involved in estimation of binding affinities. With recent advancements in computing capability and predictive accuracy, MD based virtual screening has progressed from the domain of theoretical attempts to real application in drug development. Approaches including the Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA), Linear Interaction Energy (LIE), and alchemical methods have been broadly applied to model molecular recognition for drug discovery and lead optimization. Here we review the varied methodology of these approaches, developments enhancing simulation efficiency and reliability, remaining challenges hindering predictive performance, and applications to problems in the fields of medicine and biochemistry.

CHARMM-GUI LBS Finder & Refiner for Ligand Binding Site Prediction and Refinement

A protein performs its task by binding a variety of ligands in its local region that is also known as the ligand-binding-site (LBS). Therefore, accurate prediction, characterization, and refinement of LBS can facilitate protein functional annotations and structure-based drug design. In this work, we present CHARMM-GUI LBS Finder & Refiner (https://www.charmm-gui.org/input/lbsfinder) that predicts potential LBS, offers interactive features for local LBS structure analysis, and prepares various molecular dynamics (MD) systems and inputs by setting up distance restraint potentials for LBS structure refinement. LBS Finder & Refiner supports 5 different commonly used simulation programs, such as NAMD, AMBER, GROMACS, GENESIS, and OpenMM, for LBS structure refinement together with hydrogen mass repartitioning. The capability of LBS Finder & Refiner is illustrated through LBS structure predictions and refinements of 48 modeled and 20 apo benchmark target proteins. Overall, successful LBS structure predictions and refinements are seen in our benchmark tests. We hope that LBS Finder & Refiner is useful to predict, characterize, and refine potential LBS on any given protein of interest.

Marine Sulfated Polysaccharides as Promising Antiviral Agents: A Comprehensive Report and Modeling Study Focusing on SARS CoV-2

SARS-CoV-2 (severe acute respiratory syndrome coronavirus-2) is a novel coronavirus strain that emerged at the end of 2019, causing millions of deaths so far. Despite enormous efforts being made through various drug discovery campaigns, there is still a desperate need for treatments with high efficacy and selectivity. Recently, marine sulfated polysaccharides (MSPs) have earned significant attention and are widely examined against many viral infections. This article attempted to produce a comprehensive report about MSPs from different marine sources alongside their antiviral effects against various viral species covering the last 25 years of research articles. Additionally, these reported MSPs were subjected to molecular docking and dynamic simulation experiments to ascertain potential interactions with both the receptor-binding domain (RBD) of SARS CoV-2's spike protein (S-protein) and human angiotensin-converting enzyme-2 (ACE2). The possible binding sites on both S-protein's RBD and ACE2 were determined based on how they bind to heparin, which has been reported to exhibit significant antiviral activity against SARS CoV-2 through binding to RBD, preventing the virus from affecting ACE2. Moreover, our modeling results illustrate that heparin can also bind to and block ACE2, acting as a competitor and protective agent against SARS CoV-2 infection. Nine of the investigated MSPs candidates exhibited promising results, taking into consideration the newly emerged SARS CoV-2 variants, of which five were not previously reported to exert antiviral activity against SARS CoV-2, including sulfated galactofucan (1), sulfated polymannuroguluronate (SPMG) (2), sulfated mannan (3), sulfated heterorhamnan (8), and chondroitin sulfate E (CS-E) (9). These results shed light on the importance of sulfated polysaccharides as potential SARS-CoV-2 inhibitors.