Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations

Acetyl barlerin from Barleria trispinosa induces chemopreventive NQO1 and attenuates LPS-induced inflammation: in vitro and molecular dynamic studies

Extraction and fractionation of Barleria trispinosa growing in Saudi Arabia yielded four iridoid compounds identified by spectroscopic techniques as acetylbarlerin (1), barlerin (2), shanzhiside methyl ester (3) and 6-⍺-L-rhamnopyranosyl-8-O-acetylshanzihiside methyl ester (4). Preliminary experiments confirmed that compound 1 acts as an inducer of chemopreventive NAD(P)H:Quinone oxidoreductase 1 (NQO1) enzymatic activity in a murine hepatoma (Hepa1c1c7) chemoprevention model. It also demonstrated the ability to inhibit the lipopolysaccharides (LPS)-induced nitric oxide (NO) production in the RAW264.7 macrophage model. Western blotting revealed the ability of compound 1 to up-regulate the protein expression of the NQO1 marker. Furthermore, compound 1 elicited NO suppression in RAW264.7 macrophages by inhibiting iNOS protein expression. Molecular docking and molecular simulation studies of 1 supported its experimental results as an inhibitor of the nuclear factor erythroid 2-Kelch-like ECH-associated protein 1 (Nrf2-KEAP1) complex, resulting in Nrf2-mediated induction of chemopreventive NQO1.Communicated by Ramaswamy H. Sarma.

Strong and selective interactions of palmatine with G-rich sequences in TRF2 promoter; experimental and computational studies

G-rich sequences have the potential to fold into G-quadruplexes (GQs). G-quadruplexes, particularly those positioned in the regulatory regions of proto-oncogenes, have recently garnered attention in anti-cancer drug design. A thermal FRET assay was employed to conduct preliminary screening of various alkaloids, aiming to identify stronger interactions with a specific set of G-rich double-labeled oligonucleotides in both K + and Na + buffers. These oligonucleotides were derived from regions associated with Kit, Myc, Ceb, Bcl2, human telomeres, and potential G-quadruplex forming sequences found in the Nrf2 and Trf2 promoters. Palmatine generally increased the stability of different G-rich sequences into their folded GQ structures, more or less in a concentration dependent manner. The thermal stability and interaction of palmatine was further studied using transition FRET (t-FRET), CD and UV-visible spectroscopy and molecular dynamics simulation methods. Palmatine showed the strongest interaction with T RF2 in both K+ and Na+ buffers even at equimolar concentration ratio. T-FRET studies revealed that palmatine has the potential to disrupt double-strand formation by the T RF2 sequence in the presence of its complementary strand. Palmatine exhibits a stronger interaction with G-rich strand DNA, promoting its folding into G-quadruplex structures. It is noteworthy that palmatine exhibits the strongest interaction with T RF2, which is the shortest sequence among the G-rich oligonucleotides studied, featuring only one nucleotide for two of its loops. Palmatine represents a suitable structure for drug design to develop more specific ligands targeting G-quadruplexes. Whether palmatine can also affect the expression of the T RF2 gene requires further studies.Communicated by Ramaswamy H. Sarma.

Virtual Model Compound Approach for Calculating Redox Potentials of [Fe2S2]-Cys4 Centers in Proteins - Structure Quality Matters

The midpoint potential of the [Fe2S2]-Cys4-cluster in proteins is known to vary between -200 and -450 mV. This variation is caused by the different electrostatic environment of the cluster in the respective proteins. Continuum electrostatics can quantify the impact of the protein environment on the redox potential. Thus, if the redox potential of a [Fe2S2]-Cys4-cluster model compound in aqueous solution would be known, then redox potentials in various protein complexes could be calculated. However, [Fe2S2]-Cys4-cluster models are not water-soluble, and thus, their redox potential can not be measured in aqueous solution. To overcome this problem, we introduce a method that we call Virtual Model Compound Approach (VMCA) to extrapolate the model redox potential from known redox potentials of proteins. We carefully selected high-resolution structures for our analysis and divide them into a fit set, for fitting the model redox potential, and an independent test set, to check the validity of the model redox potential. However, from our analysis, we realized that the some structures can not be used as downloaded from the PDB but had to be re-refined in order to calculate reliable redox potentials. Because of the re-refinement, we were able to significantly reduce the standard deviation of our derived model redox potential for the [Fe2S2]-Cys4-cluster from 31 mV to 10 mV. As the model redox potential, we obtained -184 mV. This model redox potential can be used to analyze the redox behavior of [Fe2S2]-Cys4-clusters in larger protein complexes.

Synthesis, molecular modeling Insights, and anticancer assessment of novel polyfunctionalized Pyridine congeners

The present study describes synthesizing a novel series of polyfunctionalized pyridine congeners 1-18 and assessed for cytotoxic efficacies versus HCT-116, MCF-7, and HepG-2 among one non-cancerous BJ-1 human normal cell. Most compounds were precisely potent anticancer candidate drugs. The molecular impact of the most active compounds 9, 10, 11, 13, 15, and 17 was evaluated after MCF-7 treatment. The gene expression of pro- and ant-apoptosis markers P53, Bax, Caspase-3 and Bcl-2 as well as VEGFR-2 and HER2 were determined. Compounds 13 and 15 induced upregulation of pro-apoptosis of P53, Bax, Caspase-3 and downregulation of anti-apoptosis Bcl-2 gene. However, compound 15 showed higher effect compared to 13 and respective control. Moreover, a slight reduction in HER2 gene expression was detected due to compound 15 treatment, while VEGFR-2 gene was upregulated. In agreement, the immunoblotting analysis showed higher accumulation of P53, Bax, Caspase-3 proteins and of decrease the Bcl-2 protein levels. Furthermore, docking studies united with molecular dynamic simulation exposed compounds 13 and 15 fitting in the middle of the active site at the interface linking the ATP binding site and the allosteric hydrophobic binding pocket. Finally, we performed Petra/Osiris/ Molinspiration (POM) analysis for the newly synthesized compounds. The evaluation of primary in silico parameters revealed significant differences among individual polyfunctionalized pyridine compounds, highlighting the most promising candidates. These preliminary results may help in coordinating and initiating other research projects focused on polyfunctionalized pyridine compounds, especially those with predicted bioactivity, low toxicity, optimal ADME parameters, and promising perspectives.

Accelerating Alchemical Free Energy Prediction Using a Multistate Method: Application to Multiple Kinases

Alchemical free-energy methods based on molecular dynamics (MD) simulations have become important tools to identify modifications of small organic molecules that improve their protein binding affinity during lead optimization. The routine application of pairwise free-energy methods to rank potential binders from best to worst is impacted by the combinatorial increase in calculations to perform when the number of molecules to assess grows. To address this fundamental limitation, our group has developed replica-exchange enveloping distribution sampling (RE-EDS), a pathway-independent multistate method, enabling the calculation of alchemical free-energy differences between multiple ligands (N > 2) from a single MD simulation. In this work, we apply the method to a set of four kinases with diverse binding pockets and their corresponding inhibitors (42 in total), chosen to showcase the general applicability of RE-EDS in prospective drug design campaigns. We show that for the targets studied, RE-EDS is able to model up to 13 ligands simultaneously with high sampling efficiency, leading to a substantial decrease in computational cost when compared to pairwise methods.

Modern Alchemical Free Energy Methods for Drug Discovery Explained

This Perspective provides a contextual explanation of the current state-of-the-art alchemical free energy methods and their role in drug discovery as well as highlights select emerging technologies. The narrative attempts to answer basic questions about what goes on "under the hood" in free energy simulations and provide general guidelines for how to run simulations and analyze the results. It is the hope that this work will provide a valuable introduction to students and scientists in the field.

Combinative effects of akarkara root-derived metabolites on anti-inflammatory and anti-alzheimer key enzymes: integrating bioassay-guided fractionation, GC-MS analysis, and in silico studies

BACKGROUND

Anacyclus pyrethrum L. (Akarkara root), a valuable Ayurvedic remedy, is reported to exhibit various pharmacological activities. Akarkara root was subjected to bioassay-guided fractionation, to isolate its active constituents and discover their potential bioactivities, followed by computational analysis.

METHODS

The methanol extract and its fractions, methylene chloride, and butanol, were assessed for their antioxidant, anti-inflammatory, and anticholinergic potentials. The antioxidant activity was determined using DPPH, ABTS, FRAP, and ORAC assays. The in vitro anticholinergic effect was evaluated via acetyl- and butyryl-cholinesterase inhibition, while anti-inflammatory effect weas determined using COX-2 and 5-LOX inhibitory assays. The methylene chloride fraction was subjected to GC/MS analysis and chromatographic fractionation to isolate its major compounds. The inhibitory effect on iNOS and various inflammatory mediators in LPS-activated RAW 264.7 macrophages was investigated. In silico computational analyses (molecular docking, ADME, BBB permeability prediction, and molecular dynamics) were performed.

RESULTS

Forty-one compounds were identified and quantified and the major compounds, namely, oleamide (A1), stigmasterol (A2), 2E,4E-deca-2,4-dienoic acid 2-phenylethyl amide (A3), and pellitorine (A4) were isolated from the methylene chloride fraction, the most active in all assays. All compounds showed significant in vitro antioxidant, anticholinergic and anti-inflammatory effects. They inhibited the secretion of pro-inflammatory cytokines (TNF-α, IL-1β, and IL-6) in activated RAW macrophages. The isolated compounds showed good fitting in the active sites of acetylcholinesterase and COX-2 with high docking scores. The ADME study revealed proper pharmacokinetics and drug likeness properties for the isolated compounds. The isolated compounds demonstrated high ability to cross the BBB and penetrate the CNS with values ranging from 1.596 to -1.651 in comparison with Donepezil (-1.464). Molecular dynamics simulation revealed stable conformations and binding patterns of the isolated compounds with the active sites of COX-2 and acetyl cholinesterase.

CONCLUSIONS

Ultimately, our results specify Akarkara compounds as promising candidates for the treatment of inflammatory and neurodegenerative diseases.

Alchemical Free Energy Calculations on Membrane-Associated Proteins

Membrane proteins have diverse functions within cells and are well-established drug targets. The advances in membrane protein structural biology have revealed drug and lipid binding sites on membrane proteins, while computational methods such as molecular simulations can resolve the thermodynamic basis of these interactions. Particularly, alchemical free energy calculations have shown promise in the calculation of reliable and reproducible binding free energies of protein-ligand and protein-lipid complexes in membrane-associated systems. In this review, we present an overview of representative alchemical free energy studies on G-protein-coupled receptors, ion channels, transporters as well as protein-lipid interactions, with emphasis on best practices and critical aspects of running these simulations. Additionally, we analyze challenges and successes when running alchemical free energy calculations on membrane-associated proteins. Finally, we highlight the value of alchemical free energy calculations calculations in drug discovery and their applicability in the pharmaceutical industry.

Free energies at QM accuracy from force fields via multimap targeted estimation

Accurate predictions of ligand binding affinities would greatly accelerate the first stages of drug discovery campaigns. However, using highly accurate interatomic potentials based on quantum mechanics (QM) in free energy methods has been so far largely unfeasible due to their prohibitive computational cost. Here, we present an efficient method to compute QM free energies from simulations using cheap reference potentials, such as force fields (FFs). This task has traditionally been out of reach due to the slow convergence of computing the correction from the FF to the QM potential. To overcome this bottleneck, we generalize targeted free energy methods to employ multiple maps-implemented with normalizing flow neural networks (NNs)-that maximize the overlap between the distributions. Critically, the method requires neither a separate expensive training phase for the NNs nor samples from the QM potential. We further propose a one-epoch learning policy to efficiently avoid overfitting, and we combine our approach with enhanced sampling strategies to overcome the pervasive problem of poor convergence due to slow degrees of freedom. On the drug-like molecules in the HiPen dataset, the method accelerates the calculation of the free energy difference of switching from an FF to a DFTB3 potential by three orders of magnitude compared to standard free energy perturbation and by a factor of eight compared to previously published nonequilibrium calculations. Our results suggest that our method, in combination with efficient QM/MM calculations, may be used in lead optimization campaigns in drug discovery and to study protein-ligand molecular recognition processes.

Alchemical Calculation of Relative Free Energies for Charge-Changing Mutations at Protein-Protein Interfaces Considering Fixed and Variable Protonation States

The calculation of relative free energies (ΔΔG) for charge-changing mutations at protein-protein interfaces through alchemical methods remains challenging due to variations in the system's net charge during charging steps, the possibility of mutated and contacting ionizable residues occurring in various protonation states, and undersampling issues. In this study, we present a set of strategies, collectively termed TIRST/TIRST-H+, to address some of these challenges. Our approaches combine thermodynamic integration (TI) with the prediction of pKa shifts to calculate ΔΔG values. Moreover, special sets of restraints are employed to keep the alchemically transformed molecules separated. The accuracy of the devised approaches was assessed on a large and diverse data set comprising 164 point mutations of charged residues (Asp, Glu, Lys, and Arg) to Ala at the protein-protein interfaces of complexes with known three-dimensional structures. Mean absolute and root-mean-square errors ranging from 1.38 to 1.66 and 1.89 to 2.44 kcal/mol, respectively, and Pearson correlation coefficients of ∼0.6 were obtained when testing the approaches on the selected data set using the GPU-TI module of Amber18 suite and the ff14SB force field. Furthermore, the inclusion of variable protonation states for the mutated acid residues improved the accuracy of the predicted ΔΔG values. Therefore, our results validate the use of TIRST/TIRST-H+ in prospective studies aimed at evaluating the impact of charge-changing mutations to Ala on the stability of protein-protein complexes.

Exploration of phytochemical compounds against Marburg virus using QSAR, molecular dynamics, and free energy landscape

Marburg virus disease (MVD) is caused by the Marburg virus, a one-of-a-kind zoonotic RNA virus from the genus Filovirus. Thus, this current study employed AI-based QSAR and molecular docking-based virtual screening for identifying potential binders against the target protein (nucleoprotein (NP)) of the Marburg virus. A total of 2727 phytochemicals were used for screening, out of which the top three compounds (74977521, 90470472, and 11953909) were identified based on their predicted bioactivity (pIC50) and binding score (< - 7.4 kcal/mol). Later, MD simulation in triplicates and trajectory analysis were performed which showed that 11953909 and 74977521 had the most stable and consistent complex formations and had the most significant interactions with the highest number of hydrogen bonds. PCA (principal component analysis) and FEL (free energy landscape) analysis indicated that these compounds had favourable energy states for most of the conformations. The total binding free energy of the compounds using the MM/GBSA technique showed that 11953909 (ΔGTOTAL = - 30.78 kcal/mol) and 74977521 (ΔGTOTAL = - 30 kcal/mol) had the highest binding affinity with the protein. Overall, this in silico pipeline proposed that the phytochemicals 11953909 and 74977521 could be the possible binders of NP. This study aimed to find phytochemicals inhibiting the protein's function and potentially treating MVD.

Identifying therapeutic antibacterial peptides against Vibrio cholerae to inhibit the function of Na(+)-translocating NADH-quinone reductase

Vibrio cholerae is the bacteria responsible for cholera, which is a significant threat to many nations. Curing and treating this infection requires identification of the critical protein and development of a drug to inhibit its function. In this context, Na(+)-translocating NADH-quinone reductase was considered a potential therapeutic target. A library of antibacterial peptides with residue lengths of 50 was screened using a docking method, and the five most potent peptides were selected on the basis of a weighted score derived from solvent accessible surface area and docking score. To investigate the stability of the protein-peptide complex, a 100-ns molecular dynamics simulation was performed. These peptides targeted the native dimeric binding interface of Na(+)-transporting NADH-quinone reductase. This study evaluated the binding affinity and conformational stability of these peptides with the protein using different post-simulation metrics. A peptide, CCL28, exhibited steady RMSD characteristics; nonetheless, it modified the docked conformation but stabilized in the new conformation. This peptide also demonstrated the best performance in addressing the protein's native binding interface. It demonstrated a binding free energy of -120 kcal/mol with the protein. Principal component analysis (PCA) revealed that the first PC had the lowest conformational variation and the greatest coverage. Eventually, these peptides were also evaluated using steered molecular dynamics, and it was discovered that CCL28 had a greater maximum force than the other five peptides, at 1139.08 kJ/mol/nm. Targeting the native binding interface, we present a CCL28 peptide with a strong potential to block the biological activity of Vibrio cholerae's Na(+)-translocating NADH-quinone reductase.Communicated by Ramaswamy H. Sarma.

Computing the relative binding affinity of ligands based on a pairwise binding comparison network

Structure-based lead optimization is an open challenge in drug discovery, which is still largely driven by hypotheses and depends on the experience of medicinal chemists. Here we propose a pairwise binding comparison network (PBCNet) based on a physics-informed graph attention mechanism, specifically tailored for ranking the relative binding affinity among congeneric ligands. Benchmarking on two held-out sets (provided by Schrödinger and Merck) containing over 460 ligands and 16 targets, PBCNet demonstrated substantial advantages in terms of both prediction accuracy and computational efficiency. Equipped with a fine-tuning operation, the performance of PBCNet reaches that of Schrödinger's FEP+, which is much more computationally intensive and requires substantial expert intervention. A further simulation-based experiment showed that active learning-optimized PBCNet may accelerate lead optimization campaigns by 473%. Finally, for the convenience of users, a web service for PBCNet is established to facilitate complex relative binding affinity prediction through an easy-to-operate graphical interface.

Taming multiple binding poses in alchemical binding free energy prediction: the β-cyclodextrin host-guest SAMPL9 blinded challenge

We apply the Alchemical Transfer Method (ATM) and a bespoke fixed partial charge force field to the SAMPL9 bCD host-guest binding free energy prediction challenge that comprises a combination of complexes formed between five phenothiazine guests and two cyclodextrin hosts. Multiple chemical forms, competing binding poses, and computational modeling challenges pose significant obstacles to obtaining reliable computational predictions for these systems. The phenothiazine guests exist in solution as racemic mixtures of enantiomers related by nitrogen inversions that bind the hosts in various binding poses, each requiring an individual free energy analysis. Due to the large size of the guests and the conformational reorganization of the hosts, which prevent a direct absolute binding free energy route, binding free energies are obtained by a series of absolute and relative binding alchemical steps for each chemical species in each binding pose. Metadynamics-accelerated conformational sampling was found to be necessary to address the poor convergence of some numerical estimates affected by conformational trapping. Despite these challenges, our blinded predictions quantitatively reproduced the experimental affinities for the β-cyclodextrin host and, to a lesser extent, those with a methylated derivative. The work illustrates the challenges of obtaining reliable free energy data in in silico drug design for even seemingly simple systems and introduces some of the technologies available to tackle them.

Ligand binding affinity prediction with fusion of graph neural networks and 3D structure-based complex graph

Accurate prediction of protein-ligand binding affinity is pivotal for drug design and discovery. Here, we proposed a novel deep fusion graph neural networks framework named FGNN to learn the protein-ligand interactions from the 3D structures of protein-ligand complexes. Unlike 1D sequences for proteins or 2D graphs for ligands, the 3D graph of protein-ligand complex enables the more accurate representations of the protein-ligand interactions. Benchmark studies have shown that our fusion models FGNN can achieve more accurate prediction of binding affinity than any individual algorithm. The advantages of fusion strategies have been demonstrated in terms of expressive power of data, learning efficiency and model interpretability. Our fusion models show satisfactory performances on diverse data sets, demonstrating their generalization ability. Given the good performances in both binding affinity prediction and virtual screening, our fusion models are expected to be practically applied for drug screening and design. Our work highlights the potential of the fusion graph neural network algorithm in solving complex prediction problems in computational biology and chemistry. The fusion graph neural networks (FGNN) model is freely available in https://github.com/LinaDongXMU/FGNN.

Calculations of Absolute Solvation Free Energies with Transformato─Application to the FreeSolv Database Using the CGenFF Force Field

We recently introduced transformato, an open-source Python package for the automated setup of large-scale calculations of relative solvation and binding free energy differences. Here, we extend the capabilities of transformato to the calculation of absolute solvation free energy differences. After careful validation against the literature results and reference calculations with the PERT module of CHARMM, we used transformato to compute absolute solvation free energies for most molecules in the FreeSolv database (621 out of 642). The force field parameters were obtained with the program cgenff (v2.5.1), which derives missing parameters from the CHARMM general force field (CGenFF v4.6). A long-range correction for the Lennard-Jones interactions was added to all computed solvation free energies. The mean absolute error compared to the experimental data is 1.12 kcal/mol. Our results allow a detailed comparison between the AMBER and CHARMM general force fields and provide a more in-depth understanding of the capabilities and limitations of the CGenFF small molecule parameters.

Kinetics from Metadynamics: Principles, Applications, and Outlook

Metadynamics is a popular enhanced sampling algorithm for computing the free energy landscape of rare events by using molecular dynamics simulation. Ten years ago, Tiwary and Parrinello introduced the infrequent metadynamics approach for calculating the kinetics of transitions across free energy barriers. Since then, metadynamics-based methods for obtaining rate constants have attracted significant attention in computational molecular science. Such methods have been applied to study a wide range of problems, including protein-ligand binding, protein folding, conformational transitions, chemical reactions, catalysis, and nucleation. Here, we review the principles of elucidating kinetics from metadynamics-like approaches, subsequent methodological developments in this area, and successful applications on chemical, biological, and material systems. We also highlight the challenges of reconstructing accurate kinetics from enhanced sampling simulations and the scope of future developments.

FEP Protocol Builder: Optimization of Free Energy Perturbation Protocols Using Active Learning

Significant improvements have been made in the past decade to methods that rapidly and accurately predict binding affinity through free energy perturbation (FEP) calculations. This has been driven by recent advances in small-molecule force fields and sampling algorithms combined with the availability of low-cost parallel computing. Predictive accuracies of ∼1 kcal mol-1 have been regularly achieved, which are sufficient to drive potency optimization in modern drug discovery campaigns. Despite the robustness of these FEP approaches across multiple target classes, there are invariably target systems that do not display expected performance with default FEP settings. Traditionally, these systems required labor-intensive manual protocol development to arrive at parameter settings that produce a predictive FEP model. Due to the (a) relatively large parameter space to be explored, (b) significant compute requirements, and (c) limited understanding of how combinations of parameters can affect FEP performance, manual FEP protocol optimization can take weeks to months to complete, and often does not involve rigorous train-test set splits, resulting in potential overfitting. These manual FEP protocol development timelines do not coincide with tight drug discovery project timelines, essentially preventing the use of FEP calculations for these target systems. Here, we describe an automated workflow termed FEP Protocol Builder (FEP-PB) to rapidly generate accurate FEP protocols for systems that do not perform well with default settings. FEP-PB uses an active-learning workflow to iteratively search the protocol parameter space to develop accurate FEP protocols. To validate this approach, we applied it to pharmaceutically relevant systems where default FEP settings could not produce predictive models. We demonstrate that FEP-PB can rapidly generate accurate FEP protocols for the previously challenging MCL1 system with limited human intervention. We also apply FEP-PB in a real-world drug discovery setting to generate an accurate FEP protocol for the p97 system. FEP-PB is able to generate a more accurate protocol than the expert user, rapidly validating p97 as amenable to free energy calculations. Additionally, through the active-learning workflow, we are able to gain insight into which parameters are most important for a given system. These results suggest that FEP-PB is a robust tool that can aid in rapidly developing accurate FEP protocols and increasing the number of targets that are amenable to the technology.

Targeted Protein Degradation: Advances, Challenges, and Prospects for Computational Methods

The therapeutic approach of targeted protein degradation (TPD) is gaining momentum due to its potentially superior effects compared with protein inhibition. Recent advancements in the biotech and pharmaceutical sectors have led to the development of compounds that are currently in human trials, with some showing promising clinical results. However, the use of computational tools in TPD is still limited, as it has distinct characteristics compared with traditional computational drug design methods. TPD involves creating a ternary structure (protein-degrader-ligase) responsible for the biological function, such as ubiquitination and subsequent proteasomal degradation, which depends on the spatial orientation of the protein of interest (POI) relative to E2-loaded ubiquitin. Modeling this structure necessitates a unique blend of tools initially developed for small molecules (e.g., docking) and biologics (e.g., protein-protein interaction modeling). Additionally, degrader molecules, particularly heterobifunctional degraders, are generally larger than conventional small molecule drugs, leading to challenges in determining drug-like properties like solubility and permeability. Furthermore, the catalytic nature of TPD makes occupancy-based modeling insufficient. TPD consists of multiple interconnected yet distinct steps, such as POI binding, E3 ligase binding, ternary structure interactions, ubiquitination, and degradation, along with traditional small molecule properties. A comprehensive set of tools is needed to address the dynamic nature of the induced proximity ternary complex and its implications for ubiquitination. In this Perspective, we discuss the current state of computational tools for TPD. We start by describing the series of steps involved in the degradation process and the experimental methods used to characterize them. Then, we delve into a detailed analysis of the computational tools employed in TPD. We also present an integrative approach that has proven successful for degrader design and its impact on project decisions. Finally, we examine the future prospects of computational methods in TPD and the areas with the greatest potential for impact.

Accurate and Efficient Multilevel Free Energy Simulations with Neural Network-Assisted Enhanced Sampling

Free energy differences (ΔF) are essential to quantitative characterization and understanding of chemical and biological processes. Their direct estimation with an accurate quantum mechanical potential is of great interest and yet impractical due to high computational cost and incompatibility with typical alchemical free energy protocols. One promising solution is the multilevel free energy simulation in which the estimate of ΔF at an inexpensive low level of theory is combined with the correction toward a higher level of theory. The poor configurational overlap generally expected between the two levels of theory, however, presents a major challenge. We overcome this challenge by using a deep neural network model and enhanced sampling simulations. An adversarial autoencoder is used to identify a low-dimensional (latent) space that compactly represents the degrees of freedom that encode the distinct distributions at the two levels of theory. Enhanced sampling in this latent space is then used to drive the sampling of configurations that predominantly contribute to the free energy correction. Results for both gas phase and condensed phase systems demonstrate that this data-driven approach offers high accuracy and efficiency with great potential for scalability to complex systems.

In silico prediction of protein binding affinities onto core-shell PEGylated noble metal nanoparticles for rational design of drug nanocarriers

Polymer-coated nanoparticles (NP) are commonly used as drug carriers or theranostic agents. Their uptake rates are modulated by the interactions with essential serum proteins such as transferrin and albumin. Understanding the control parameters of these interactions is crucial for improving the efficiency of these nanoscale devices. In this work, we perform a multiscale computational study of protein adsorption onto polyethylene glycol (PEG) coated gold and silver NPs, producing protein-NP adsorption rankings as a function of PEG grafting density, which are validated against previously reported experimental protein-NP binding constants. Furthermore, the applied nano-docking method provides information on the preferred orientation of proteins immobilised on the surface of NPs. We propose a method of construction of model core-shell NPs in silico. The presented protocol can provide molecular level insights for the experimental development of biosensors, nanocarriers, or other nanoplatforms where information on the preferred orientation of protein at the bio-nano interface is crucial, and enables fast in silico prescreening of assays of various nanocarriers, i.e., combinations of proteins, NPs, and coatings.

Can Free Energy Perturbation Simulations Coupled with Replica-Exchange Molecular Dynamics Study Ligands with Distributed Binding Sites?

Free energy perturbation coupled with replica exchange with solute tempering (FEP/REST) offers a rigorous approach to compute relative free energy changes for ligands. To determine the applicability of FEP/REST for the ligands with distributed binding poses, we considered two alchemical transformations involving three putative inhibitors I0, I1, and I2 of the Venezuelan equine encephalitis virus nuclear localization signal sequence binding to the importin-α (impα) transporter protein. I0 → I1 and I0 → I2 transformations, respectively, increase or decrease the polarity of the parent molecule. Our objective was three-fold─(i) to verify FEP/REST technical performance and convergence, (ii) to estimate changes in binding free energy ΔΔG, and (iii) to determine the utility of FEP/REST simulations for conformational binding analysis. Our results are as follows. First, our FEP/REST implementation properly follows FEP/REST formalism and produces converged ΔΔG estimates. Due to ligand inherent unbinding, the better FEP/REST strategy lies in performing multiple independent trajectories rather than extending their length. Second, I0 → I1 and I0 → I2 transformations result in overall minor changes in inhibitor binding free energy, slightly strengthening the affinity of I1 and weakening that of I2. Electrostatic interactions dominate binding interactions, determining the enthalpic changes. The two transformations cause opposite entropic changes, which ultimately govern binding affinities. Importantly, we confirm the validity of FEP/REST free energy estimates by comparing them with our previous REST simulations, directly probing binding of three ligands to impα. Third, we established that FEP/REST simulations can sample binding ensembles of ligands. Thus, FEP/REST can be applied (i) to study the energetics of the ligand binding without defined poses and showing minor differences in affinities |ΔΔG| ≲ 0.5 kcal/mol and (ii) to collect ligand binding conformational ensembles.

Assessing the Performance of Non-Equilibrium Thermodynamic Integration in Flavodoxin Redox Potential Estimation

Flavodoxins are enzymes that contain the redox-active flavin mononucleotide (FMN) cofactor and play a crucial role in numerous biological processes, including energy conversion and electron transfer. Since the redox characteristics of flavodoxins are significantly impacted by the molecular environment of the FMN cofactor, the evaluation of the interplay between the redox properties of the flavin cofactor and its molecular surroundings in flavoproteins is a critical area of investigation for both fundamental research and technological advancements, as the electrochemical tuning of flavoproteins is necessary for optimal interaction with redox acceptor or donor molecules. In order to facilitate the rational design of biomolecular devices, it is imperative to have access to computational tools that can accurately predict the redox potential of both natural and artificial flavoproteins. In this study, we have investigated the feasibility of using non-equilibrium thermodynamic integration protocols to reliably predict the redox potential of flavodoxins. Using as a test set the wild-type flavodoxin from Clostridium Beijerinckii and eight experimentally characterized single-point mutants, we have computed their redox potential. Our results show that 75% (6 out of 8) of the calculated reaction free energies are within 1 kcal/mol of the experimental values, and none exceed an error of 2 kcal/mol, confirming that non-equilibrium thermodynamic integration is a trustworthy tool for the quantitative estimation of the redox potential of this biologically and technologically significant class of enzymes.

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.

Broadening the Scope of Binding Free Energy Calculations Using a Separated Topologies Approach

Binding free energy calculations predict the potency of compounds to protein binding sites in a physically rigorous manner and see broad application in prioritizing the synthesis of novel drug candidates. Relative binding free energy (RBFE) calculations have emerged as an industry-standard approach to achieve highly accurate rank-order predictions of the potency of related compounds; however, this approach requires that the ligands share a common scaffold and a common binding mode, restricting the methods' domain of applicability. This is a critical limitation since complex modifications to the ligands, especially core hopping, are very common in drug design. Absolute binding free energy (ABFE) calculations are an alternate method that can be used for ligands that are not congeneric. However, ABFE suffers from a known problem of long convergence times due to the need to sample additional degrees of freedom within each system, such as sampling rearrangements necessary to open and close the binding site. Here, we report on an alternative method for RBFE, called Separated Topologies (SepTop), which overcomes the issues in both of the aforementioned methods by enabling large scaffold changes between ligands with a convergence time comparable to traditional RBFE. Instead of only mutating atoms that vary between two ligands, this approach performs two absolute free energy calculations at the same time in opposite directions, one for each ligand. Defining the two ligands independently allows the comparison of the binding of diverse ligands without the artificial constraints of identical poses or a suitable atom-atom mapping. This approach also avoids the need to sample the unbound state of the protein, making it more efficient than absolute binding free energy calculations. Here, we introduce an implementation of SepTop. We developed a general and efficient protocol for running SepTop, and we demonstrated the method on four diverse, pharmaceutically relevant systems. We report the performance of the method, as well as our practical insights into the strengths, weaknesses, and challenges of applying this method in an industrial drug design setting. We find that the accuracy of the approach is sufficiently high to rank order ligands with an accuracy comparable to traditional RBFE calculations while maintaining the additional flexibility of SepTop.

Identifying and Overcoming the Sampling Challenges in Relative Binding Free Energy Calculations of a Model Protein:Protein Complex

Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a graphics processing unit (GPU)-accelerated open-source relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches─alchemical replica exchange and alchemical replica exchange with solute tempering─for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and is available at https://github.com/choderalab/perses.

β-Sitosterol could serve as a dual inhibitor of Trypanosoma congolense sialidase and phospholipase A2: in vitro kinetic analyses and molecular dynamic simulations

The involvement of Trypanosoma congolense sialidase alongside phospholipase A2 has been widely accepted as the major contributing factor to anemia during African animal trypanosomiasis. The enzymes aid the parasite in scavenging sialic acid and fatty acids necessary for survival in the infected host, but there are no specific drug candidates against the two enzymes. This study investigated the inhibitory effects of β-sitosterol on the partially purified T. congolense sialidase and phospholipase A2. Purification of the enzymes using DEAE cellulose column led to fractions with highest specific activities of 8016.41 and 39.26 µmol/min/mg for sialidase and phospholipase A2, respectively. Inhibition kinetics studies showed that β-sitosterol is non-competitive and an uncompetitive inhibitor of sialidase and phospholipase A2 with inhibition binding constants of 0.368 and 0.549 µM, respectively. Molecular docking of the compound revealed binding energies of - 8.0 and - 8.6 kcal/mol against the sialidase and phospholipase A2, respectively. Furthermore, 100 ns molecular dynamics simulation using GROMACS revealed stable interaction of β-sitosterol with both enzymes. Hydrogen bond interactions between the ligand and Glu284 and Leu102 residues of the sialidase and phospholipase A2, respectively, were found to be the major stabilizing forces. In conclusion, β-sitosterol could serve as a dual inhibitor of T. congolense sialidase and phospholipase A2; hence, the compound could be exploited further in the search for newer trypanocides.

Application of Ligand- and Structure-Based Prediction Models for the Design of Alkylhydrazide-Based HDAC3 Inhibitors as Novel Anti-Cancer Compounds

Histone deacetylases (HDAC) represent promising epigenetic targets for several diseases including different cancer types. The HDAC inhibitors approved to date are pan-HDAC inhibitors and most show a poor selectivity profile, side effects, and in particular hydroxamic-acid-based inhibitors lack good pharmacokinetic profiles. Therefore, the development of isoform-selective non-hydroxamic acid HDAC inhibitors is a highly regarded field in medicinal chemistry. In this study, we analyzed different ligand-based and structure-based drug design techniques to predict the binding mode and inhibitory activity of recently developed alkylhydrazide HDAC inhibitors. Alkylhydrazides have recently attracted more attention as they have shown promising effects in various cancer cell lines. In this work, pharmacophore models and atom-based quantitative structure-activity relationship (QSAR) models were generated and evaluated. The binding mode of the studied compounds was determined using molecular docking as well as molecular dynamics simulations and compared with known crystal structures. Calculated free energies of binding were also considered to generate QSAR models. The created models show a good explanation of in vitro data and were used to develop novel HDAC3 inhibitors.

Assessing the performance of docking, FEP, and MM/GBSA methods on a series of KLK6 inhibitors

Kallikrein 6 (KLK6) is an attractive drug target for the treatment of neurological diseases and for various cancers. Herein, we explore the accuracy and efficiency of different computational methods and protocols to predict the free energy of binding (ΔG_bind) for a series of 49 inhibitors of KLK6. We found that the performance of the methods varied strongly with the tested system. For only one of the three KLK6 datasets, the docking scores obtained with rDock were in good agreement (R² ≥ 0.5) with experimental values of ΔG_bind. A similar result was obtained with MM/GBSA (using the ff14SB force field) calculations based on single minimized structures. Improved binding affinity predictions were obtained with the free energy perturbation (FEP) method, with an overall MUE and RMSE of 0.53 and 0.68 kcal/mol, respectively. Furthermore, in a simulation of a real-world drug discovery project, FEP was able to rank the most potent compounds at the top of the list. These results indicate that FEP can be a promising tool for the structure-based optimization of KLK6 inhibitors.

Comparison of Receptor-Ligand Restraint Schemes for Alchemical Absolute Binding Free Energy Calculations

Alchemical absolute binding free energy calculations are of increasing interest in drug discovery. These calculations require restraints between the receptor and ligand to restrict their relative positions and, optionally, orientations. Boresch restraints are commonly used, but they must be carefully selected in order to sufficiently restrain the ligand and to avoid inherent instabilities. Applying multiple distance restraints between anchor points in the receptor and ligand provides an alternative framework without inherent instabilities which may provide convergence benefits by more strongly restricting the relative movements of the receptor and ligand. However, there is no simple method to calculate the free energy of releasing these restraints due to the coupling of the internal and external degrees of freedom of the receptor and ligand. Here, a method to rigorously calculate free energies of binding with multiple distance restraints by imposing intramolecular restraints on the anchor points is proposed. Absolute binding free energies for the human macrophage migration inhibitory factor/MIF180, system obtained using a variety of Boresch restraints and rigorous and nonrigorous implementations of multiple distance restraints are compared. It is shown that several multiple distance restraint schemes produce estimates in good agreement with Boresch restraints. In contrast, calculations without orientational restraints produce erroneously favorable free energies of binding by up to approximately 4 kcal mol-1. These approaches offer new options for the deployment of alchemical absolute binding free energy calculations.

A Variational Ansatz for Taylorized Imaginary Time Evolution

Being able to predict molecular properties and interactions is of utmost interest for academia as well as industry. But the vast complexity of strongly correlated molecular systems limits the performance of classical algorithms. In contrast, quantum computation has the potential to be a game changer in the field of molecular simulations. Despite the hope in quantum computation, the capabilities of current quantum computers are still insufficient for handling molecular systems of interest. In this paper, we propose a variational ansatz for today's noisy quantum computers to calculate the ground state with the help of imaginary time evolution. Although the imaginary time evolution operator is not unitary, it can be implemented on a quantum computer by a linear decomposition and subsequent Taylor series expansion. This has the advantage that only a set of shallow circuits needs to be computed on a quantum computer. The parallel nature of this algorithm can be exploited to speed-up simulations even further, if a privileged access to quantum computers is granted.

Molecular Dynamics Simulations of Matrix Metalloproteinase 13 and the Analysis of the Specificity Loop and the S1'-Site

The specificity loop of Matrix Metalloproteinases (MMPs) is known to regulate recognition of their substrates, and the S1'-site surrounded by the loop is a unique place to address the selectivity of ligands toward each MMP. Molecular dynamics (MD) simulations of apo-MMP-13 and its complex forms with various ligands were conducted to identify the role of the specificity loop for the ligand binding to MMP-13. The MD simulations showed the dual role of T247 as a hydrogen bond donor to the ligand, as well as a contributor to the formation of the van der Waal surface area, with T245 and K249 on the S1'-site. The hydrophobic surface area mediated by T247 blocks the access of water molecules to the S1'-site of MMP-13 and stabilizes the ligand in the site. The F252 residue is flexible in order to search for the optimum location in the S1'-site of the apo-MMP-13, but once a ligand binds to the S1'-site, it can form offset π-π or edge-to-π stacking interactions with the ligand. Lastly, H222 and Y244 provide the offset π-π and π-CH(Cβ) interactions on each side of the phenyl ring of the ligand, and this sandwiched interaction could be critical for the ligand binding to MMP-13.

Development and Benchmarking of Open Force Field 2.0.0: The Sage Small Molecule Force Field

We introduce the Open Force Field (OpenFF) 2.0.0 small molecule force field for drug-like molecules, code-named Sage, which builds upon our previous iteration, Parsley. OpenFF force fields are based on direct chemical perception, which generalizes easily to highly diverse sets of chemistries based on substructure queries. Like the previous OpenFF iterations, the Sage generation of OpenFF force fields was validated in protein-ligand simulations to be compatible with AMBER biopolymer force fields. In this work, we detail the methodology used to develop this force field, as well as the innovations and improvements introduced since the release of Parsley 1.0.0. One particularly significant feature of Sage is a set of improved Lennard-Jones (LJ) parameters retrained against condensed phase mixture data, the first refit of LJ parameters in the OpenFF small molecule force field line. Sage also includes valence parameters refit to a larger database of quantum chemical calculations than previous versions, as well as improvements in how this fitting is performed. Force field benchmarks show improvements in general metrics of performance against quantum chemistry reference data such as root-mean-square deviations (RMSD) of optimized conformer geometries, torsion fingerprint deviations (TFD), and improved relative conformer energetics (ΔΔE). We present a variety of benchmarks for these metrics against our previous force fields as well as in some cases other small molecule force fields. Sage also demonstrates improved performance in estimating physical properties, including comparison against experimental data from various thermodynamic databases for small molecule properties such as ΔHmix, ρ(x), ΔGsolv, and ΔGtrans. Additionally, we benchmarked against protein-ligand binding free energies (ΔGbind), where Sage yields results statistically similar to previous force fields. All the data is made publicly available along with complete details on how to reproduce the training results at https://github.com/openforcefield/openff-sage.

Screening and Biological Evaluation of Soluble Epoxide Hydrolase Inhibitors: Assessing the Role of Hydrophobicity in the Pharmacophore-Guided Search of Novel Hits

The human soluble epoxide hydrolase (sEH) is a bifunctional enzyme that modulates the levels of regulatory epoxy lipids. The hydrolase activity is carried out by a catalytic triad located at the center of a wide L-shaped binding site, which contains two hydrophobic subpockets at both sides. On the basis of these structural features, it can be assumed that desolvation is a major factor in determining the maximal achievable affinity that can be attained for this pocket. Accordingly, hydrophobic descriptors may be better suited to the search of novel hits targeting this enzyme. This study examines the suitability of quantum mechanically derived hydrophobic descriptors in the discovery of novel sEH inhibitors. To this end, three-dimensional quantitative structure-activity relationship (3D-QSAR) pharmacophores were generated by combining electrostatic and steric or alternatively hydrophobic and hydrogen-bond parameters in conjunction with a tailored list of 76 known sEH inhibitors. The pharmacophore models were then validated by using two external sets chosen (i) to rank the potency of four distinct series of compounds and (ii) to discriminate actives from decoys, using in both cases datasets taken from the literature. Finally, a prospective study was performed including a virtual screening of two chemical libraries to identify new potential hits, which were subsequently experimentally tested for their inhibitory activity on human, rat, and mouse sEH. The use of hydrophobic-based descriptors led to the identification of six compounds as inhibitors of the human enzyme with IC50 < 20 nM, including two with IC50 values of 0.4 and 0.7 nM. The results support the use of hydrophobic descriptors as a valuable tool in the search of novel scaffolds that encode a proper hydrophilic/hydrophobic distribution complementary to the target's binding site.

3D rhombohedral microcrystals metal-organic frameworks for electrochemical and fluorescence sensing of tetracycline

Zirconium-based metal-organic frameworks (MOF) exhibiting 3D rhombohedral microcrystals were synthesized by the solvothermal method. The structure, morphology, composition, and optical properties of the synthesized MOF were carried out using different spectroscopic, microscopic, and diffraction techniques. Synthesized MOF was rhombohedral in shape and the cage structure of these crystalline molecules was the active binding site of the analyte, tetracycline (TET). The electronic property and size of the cages are chosen such that a specific interaction with TET was observed. Sensing of the analyte was demonstrated by both the electrochemical and fluorescent techniques. The MOF had significant luminescent properties and exhibited excellent electro-catalytic activity due to embedded zirconium metal ions. An electrochemical and fluorescence sensor was fabricated towards TET where TET binds via hydrogen bond to MOF, and causes fluorescence quenching due to the transfer of electrons. Both approaches exhibited high selectivity and good stability in the presence of interfering molecules such as antibiotics, biomolecules, and ions; and showed excellent reliability in tap water and wastewater sample analysis.

Accelerated Enveloping Distribution Sampling to Probe the Presence of Water Molecules

Determining the presence of water molecules at protein-ligand interfaces is still a challenging task in free-energy calculations. The inappropriate placement of water molecules results in the stabilization of wrong conformational orientations of the ligand. With classical alchemical perturbation methods, such as thermodynamic integration (TI), it is essential to know the amount of water molecules in the active site of the respective ligands. However, the resolution of the crystal structure and the correct assignment of the electron density do not always lead to a clear placement of water molecules. In this work, we apply the one-step perturbation method named accelerated enveloping distribution sampling (AEDS) to determine the presence of water molecules in the active site by probing them in a fast and straightforward way. Based on these results, we combined the AEDS method with standard TI to calculate accurate binding free energies in the presence of buried water molecules. The main idea is to perturb the water molecules with AEDS such that they are allowed to alternate between regular water molecules and non-interacting dummy particles while treating the ligand with TI over an alchemical pathway. We demonstrate the use of AEDS to probe the presence of water molecules for six different test systems. For one of these, previous calculations showed difficulties to reproduce the experimental binding free energies, and here, we use the combined TI-AEDS approach to tackle these issues.

Enhancing Hit Discovery in Virtual Screening through Absolute Protein-Ligand Binding Free-Energy Calculations

In the hit identification stage of drug discovery, a diverse chemical space needs to be explored to identify initial hits. Contrary to empirical scoring functions, absolute protein-ligand binding free-energy perturbation (ABFEP) provides a theoretically more rigorous and accurate description of protein-ligand binding thermodynamics and could, in principle, greatly improve the hit rates in virtual screening. In this work, we describe an implementation of an accurate and reliable ABFEP method in FEP+. We validated the ABFEP method on eight congeneric compound series binding to eight protein receptors including both neutral and charged ligands. For ligands with net charges, the alchemical ion approach is adopted to avoid artifacts in electrostatic potential energy calculations. The calculated binding free energies correlate with experimental results with a weighted average of R² = 0.55 for the entire dataset. We also observe an overall root-mean-square error (RMSE) of 1.1 kcal/mol after shifting the zero-point of the simulation data to match the average experimental values. Through ABFEP calculations using apo versus holo protein structures, we demonstrated that the protein conformational and protonation state changes between the apo and holo proteins are the main physical factors contributing to the protein reorganization free energy manifested by the overestimation of raw ABFEP calculated binding free energies using the holo structures of the proteins. Furthermore, we performed ABFEP calculations in three virtual screening applications for hit enrichment. ABFEP greatly improves the hit rates as compared to docking scores or other methods like metadynamics. The good performance of ABFEP in rank ordering compounds demonstrated in this work confirms it as a useful tool to improve the hit rates in virtual screening, thus facilitating hit discovery.

Exploring Routes to Enhance the Calculation of Free Energy Differences via Non-Equilibrium Work SQM/MM Switching Simulations Using Hybrid Charge Intermediates between MM and SQM Levels of Theory or Non-Linear Switching Schemes

Non-equilibrium work switching simulations and Jarzynski's equation are a reliable method for computing free energy differences, ΔAlow→high, between two levels of theory, such as a pure molecular mechanical (MM) and a quantum mechanical/molecular mechanical (QM/MM) description of a system of interest. Despite the inherent parallelism, the computational cost of this approach can quickly become very high. This is particularly true for systems where the core region, the part of the system to be described at different levels of theory, is embedded in an environment such as explicit solvent water. We find that even for relatively simple solute-water systems, switching lengths of at least 5 ps are necessary to compute ΔAlow→high reliably. In this study, we investigate two approaches towards an affordable protocol, with an emphasis on keeping the switching length well below 5 ps. Inserting a hybrid charge intermediate state with modified partial charges, which resembles the charge distribution of the desired high level, makes it possible to obtain reliable calculations with 2 ps switches. Attempts using step-wise linear switching paths, on the other hand, did not lead to improvement, i.e., a faster convergence for all systems. To understand these findings, we analyzed the solutes' properties as a function of the partial charges used and the number of water molecules in direct contact with the solute, and studied the time needed for water molecules to reorient themselves upon a change in the solute's charge distribution.

Rational Design of Daunorubicin C-14 Hydroxylase Based on the Understanding of Its Substrate-Binding Mechanism

Doxorubicin is one of the most widely used antitumor drugs and is currently produced via the chemical conversion method, which suffers from high production costs, complex product separation processes, and serious environmental pollution. Biocatalysis is considered a more efficient and environment-friendly method for drug production. The cytochrome daunorubicin C-14 hydroxylase (DoxA) is the essential enzyme catalyzing the conversion of daunorubicin to doxorubicin. Herein, the DoxA from Streptomyces peucetius subsp. caesius ATCC 27952 was expressed in Escherichia coli, and the rational design strategy was further applied to improve the enzyme activity. Eight amino acid residues were identified as the key sites via molecular docking. Using a constructed screening library, we obtained the mutant DoxA(P88Y) with a more rational protein conformation, and a 56% increase in bioconversion efficiency was achieved by the mutant compared to the wild-type DoxA. Molecular dynamics simulation was applied to understand the relationship between the enzyme's structural property and its substrate-binding efficiency. It was demonstrated that the mutant DoxA(P88Y) formed a new hydrophobic interaction with the substrate daunorubicin, which might have enhanced the binding stability and thus improved the catalytic activity. Our work lays a foundation for further exploration of DoxA and facilitates the industrial process of bio-production of doxorubicin.

Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies

The application of computational approaches in drug discovery has been consolidated in the last decades. These families of techniques are usually grouped under the common name of "computer-aided drug design" (CADD), and they now constitute one of the pillars in the pharmaceutical discovery pipelines in many academic and industrial environments. Their implementation has been demonstrated to tremendously improve the speed of the early discovery steps, allowing for the proficient and rational choice of proper compounds for a desired therapeutic need among the extreme vastness of the drug-like chemical space. Moreover, the application of CADD approaches allows the rationalization of biochemical and interactive processes of pharmaceutical interest at the molecular level. Because of this, computational tools are now extensively used also in the field of rational 3D design and optimization of chemical entities starting from the structural information of the targets, which can be experimentally resolved or can also be obtained with other computer-based techniques. In this work, we revised the state-of-the-art computer-aided drug design methods, focusing on their application in different scenarios of pharmaceutical and biological interest, not only highlighting their great potential and their benefits, but also discussing their actual limitations and eventual weaknesses. This work can be considered a brief overview of computational methods for drug discovery.

Study of SQ109 analogs binding to mycobacterium MmpL3 transporter using MD simulations and alchemical relative binding free energy calculations

N-geranyl-N΄-(2-adamantyl)ethane-1,2-diamine (SQ109) is a tuberculosis drug that has high potency against Mycobacterium tuberculosis (Mtb) and may function by blocking cell wall biosynthesis. After the crystal structure of MmpL3 from Mycobacterium smegmatis in complex with SQ109 became available, it was suggested that SQ109 inhibits Mmpl3 mycolic acid transporter. Here, we showed using molecular dynamics (MD) simulations that the binding profile of nine SQ109 analogs with inhibitory potency against Mtb and alkyl or aryl adducts at C-2 or C-1 adamantyl carbon to MmpL3 was consistent with the X-ray structure of MmpL3 - SQ109 complex. We showed that rotation of SQ109 around carbon-carbon bond in the monoprotonated ethylenediamine unit favors two gauche conformations as minima in water and lipophilic solvent using DFT calculations as well as inside the transporter's binding area using MD simulations. The binding assays in micelles suggested that the binding affinity of the SQ109 analogs was increased for the larger, more hydrophobic adducts, which was consistent with our results from MD simulations of the SQ109 analogues suggesting that sizeable C-2 adamantyl adducts of SQ109 can fill a lipophilic region between Y257, Y646, F260 and F649 in MmpL3. This was confirmed quantitatively by our calculations of the relative binding free energies using the thermodynamic integration coupled with MD simulations method with a mean assigned error of 0.74 kcal mol^-1 compared to the experimental values.

New Indazol-Pyrimidine-Based Derivatives as Selective Anticancer Agents: Design, Synthesis, and In Silico Studies

In this research study, the authors successfully synthesized potent new anticancer agents derived from indazol-pyrimidine. All the prepared compounds were tested for in vitro cell line inhibitory activity against three different cancerous cell lines. Results demonstrated that five of the novel compounds-4f, 4i, 4a, 4g, and 4d-possessed significant cytotoxic inhibitory activity against the MCF-7 cell line, with IC₅₀ values of 1.629, 1.841, 2.958, 4.680, and 4.798 μM, respectively, compared to the reference drug with an IC₅₀ value of 8.029 μM, thus demonstrating promising suppression power. Compounds 4i, 4g, 4e, 4d, and 4a showed effective cytotoxic activity stronger than the standard against Caco2 cells. Moreover, compounds 4a and 4i exhibited potent antiproliferative activity against the A549 cell line that was stronger than the reference drug. The most active products, 4f and 4i, werr e further examined for their mechanism of action. It turns out that they were capable of activating caspase-3/7 and, therefore, inducing apoptosis. However, produced a higher safety profile than the reference drug, towards the normal cells (MCF10a). Furthermore, the dynamic nature, binding interaction, and protein-ligand stability were explored through a Molecular Dynamics (MD) simulation study. Various analysis parameters (RMSD, RMSF, RoG, and SASA) from the MD simulation trajectory have suggested the stability of the compounds during the 20 ns MD simulation study. In silico ADMET results revealed that the synthesized compounds had low toxicity, good solubility, and an absorption profile since they met Lipinski's rule of five and Veber's rule. The present research highlights the potential of derivatives with indazole scaffolds bearing pyrimidine as a lead compound for designing anticancer agents.

Tadalafil Rescues the p.M325T Mutant of Best1 Chloride Channel

Bestrophin 1 (Best1) is a chloride channel that localises to the plasma membrane of retinal pigment epithelium (RPE) cells. Mutations in the BEST1 gene are associated with a group of untreatable inherited retinal dystrophies (IRDs) called bestrophinopathies, caused by protein instability and loss-of-function of the Best1 protein. 4PBA and 2-NOAA have been shown to rescue the function, expression, and localisation of Best1 mutants; however, it is of interest to find more potent analogues as the concentration of the drugs required is too high (2.5 mM) to be given therapeutically. A virtual docking model of the COPII Sec24a site, where 4PBA has been shown to bind, was generated and a library of 1416 FDA-approved compounds was screened at the site. The top binding compounds were tested in vitro in whole-cell patch-clamp experiments of HEK293T cells expressing mutant Best1. The application of 25 μM tadalafil resulted in full rescue of Cl^- conductance, comparable to wild type Best1 levels, for p.M325T mutant Best1 but not for p.R141H or p.L234V mutants.

Quality over quantity: Sampling high probability rare events with the weighted ensemble algorithm

The prediction of (un)binding rates and free energies is of great significance to the drug design process. Although many enhanced sampling algorithms and approaches have been developed, there is not yet a reliable workflow to predict these quantities. Previously we have shown that free energies and transition rates can be calculated by directly simulating the binding and unbinding processes with our variant of the WE algorithm "Resampling of Ensembles by Variation Optimization", or "REVO". Here, we calculate binding free energies retrospectively for three SAMPL6 host-guest systems and prospectively for a SAMPL9 system to test a modification of REVO that restricts its cloning behavior in quasi-unbound states. Specifically, trajectories cannot clone if they meet a physical requirement that represents a high likelihood of unbinding, which in the case of this work is a center-of-mass to center-of-mass distance. The overall effect of this change was difficult to predict, as it results in fewer unbinding events each of which with a much higher statistical weight. For all four systems tested, this new strategy produced either more accurate unbinding free energies or more consistent results between simulations than the standard REVO algorithm. This approach is highly flexible, and any feature of interest for a system can be used to determine cloning eligibility. These findings thus constitute an important improvement in the calculation of transition rates and binding free energies with the weighted ensemble method.

Identification of Potential Lead Compounds Targeting Novel Druggable Cavity of SARS-CoV-2 Spike Trimer by Molecular Dynamics Simulations

The global pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become an urgent public health problem. Spike (S) protein mediates the fusion between the virus and the host cell membranes, consequently emerging as an important target of drug design. The lack of comparisons of in situ full-length S homotrimer structures in different states hinders understanding the structures and revealing the function, thereby limiting the discovery and development of therapeutic agents. Here, the steady-state structures of the in situ full-length S trimer in closed and open states (Sclosed and Sopen) were modeled with the constraints of density maps, associated with the analysis of the dynamic structural differences. Subsequently, we identified various regions with structure and property differences as potential binding pockets for ligands that promote the formation of inactive trimeric protein complexes. By using virtual screening strategy and a newly defined druggable cavity, five ligands were screened with potential bioactivities. Then molecular dynamic (MD) simulations were performed on apo protein structures and ligand bound complexes to reveal the conformational changes upon ligand binding. Our simulation results revealed that sulforaphane (SFN), which has the best binding affinity, could inhibit the conformational changes of S homotrimer that would occur during the viral membrane fusion. Our results could aid in the understanding of the regulation mechanism of S trimer aggregation and the structure-activity relationship, facilitating the development of potential antiviral agents.

Synthesis, molecular docking, and binding Gibbs free energy calculation of β-nitrostyrene derivatives: Potential inhibitors of SARS-CoV-2 3CL protease

The outbreak of novel coronavirus disease 2019 (COVID-19), caused by the novel coronavirus (SARS-CoV-2), has had a significant impact on human health and the economic development. SARS-CoV-2 3CL protease (3CLpro) is highly conserved and plays a key role in mediating the transcription of virus replication. It is an ideal target for the design and screening of anti-coronavirus drugs. In this work, seven β-nitrostyrene derivatives were synthesized by Henry reaction and β-dehydration reaction, and their inhibitory effects on SARS-CoV-2 3CL protease were identified by enzyme activity inhibition assay in vitro. Among them, 4-nitro-β-nitrostyrene (compound a) showed the lowest IC₅₀ values of 0.7297 μM. To investigate the key groups that determine the activity of β-nitrostyrene derivatives and their interaction mode with the receptor, the molecular docking using the CDOCKER protocol in Discovery Studio 2016 was performed. The results showed that the hydrogen bonds between β-NO₂ and receptor GLY-143 and the π-π stacking between the aryl ring of the ligand and the imidazole ring of receptor HIS-41 significantly contributed to the ligand activity. Furthermore, the ligand-receptor absolute binding Gibbs free energies were calculated using the Binding Affinity Tool (BAT.py) to verify its correlation with the activity of β-nitrostyrene 3CLpro inhibitors as a scoring function. The higher correlation(r²=0.6) indicates that the absolute binding Gibbs free energy based on molecular dynamics can be used to predict the activity of new β-nitrostyrene 3CLpro inhibitors. These results provide valuable insights for the functional group-based design, structure optimization and the discovery of high accuracy activity prediction means of anti-COVID-19 lead compounds.

Recent Advances in Alchemical Binding Free Energy Calculations for Drug Discovery

Rigorous physics-based methods to calculate binding free energies of protein-ligand complexes have become a valued component of structure-based drug design. Relative and absolute binding free energy calculations have been deployed prospectively in support of solving diverse drug discovery challenges. Here we review recent applications of binding free energy calculations to fragment growing and linking, scaffold hopping, binding pose validation, virtual screening, covalent enzyme inhibition, and positional analogue scanning. Furthermore, we discuss the merits of using protein models and highlight recent efforts to replace costly binding free energy calculations with predictions from machine learning models trained on a limited number of free energy perturbation or thermodynamic integration calculations thereby allowing for extended chemical space exploration.

Identifying and overcoming the sampling challenges in relative binding free energy calculations of a model protein:protein complex

Relative alchemical binding free energy calculations are routinely used in drug discovery projects to optimize the affinity of small molecules for their drug targets. Alchemical methods can also be used to estimate the impact of amino acid mutations on protein:protein binding affinities, but these calculations can involve sampling challenges due to the complex networks of protein and water interactions frequently present in protein:protein interfaces. We investigate these challenges by extending a GPU-accelerated opensource relative free energy calculation package (Perses) to predict the impact of amino acid mutations on protein:protein binding. Using the well-characterized model system barnase:barstar, we describe analyses for identifying and characterizing sampling problems in protein:protein relative free energy calculations. We find that mutations with sampling problems often involve charge-changes, and inadequate sampling can be attributed to slow degrees of freedom that are mutation-specific. We also explore the accuracy and efficiency of current state-of-the-art approaches-alchemical replica exchange and alchemical replica exchange with solute tempering-for overcoming relevant sampling problems. By employing sufficiently long simulations, we achieve accurate predictions (RMSE 1.61, 95% CI: [1.12, 2.11] kcal/mol), with 86% of estimates within 1 kcal/mol of the experimentally-determined relative binding free energies and 100% of predictions correctly classifying the sign of the changes in binding free energies. Ultimately, we provide a model workflow for applying protein mutation free energy calculations to protein:protein complexes, and importantly, catalog the sampling challenges associated with these types of alchemical transformations. Our free open-source package (Perses) is based on OpenMM and available at https://github.com/choderalab/perses .

Advances in the discovery of new chemotypes through ultra-large library docking

INTRODUCTION

The size and complexity of virtual screening libraries in drug discovery have skyrocketed in recent years, reaching up to multiple billions of accessible compounds. However, virtual screening of such ultra-large libraries poses several challenges associated with preparing the libraries, sampling, and pre-selection of suitable compounds. The utilization of artificial intelligence (AI)-assisted screening approaches, such as deep learning, poses a promising countermeasure to deal with this rapidly expanding chemical space. For example, various AI-driven methods were recently successfully used to identify novel small molecule inhibitors of the SARS-CoV-2 main protease (M^pro).

AREAS COVERED

This review focuses on presenting various kinds of virtual screening methods suitable for dealing with ultra-large libraries. Challenges associated with these computational methodologies are discussed, and recent advances are highlighted in the example of the discovery of novel M^pro inhibitors targeting the SARS-CoV-2 virus.

EXPERT OPINION

With the rapid expansion of the virtual chemical space, the methodologies for docking and screening such quantities of molecules need to keep pace. Employment of AI-driven screening compounds has already been shown to be effective in a range from a few thousand to multiple billion compounds, furthered by de novo generation of drug-like molecules without human interference.