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

Fluorinated Protein-Ligand Complexes: A Computational Perspective

Fluorine is an element renowned for its unique properties. Its powerful capability to modulate molecular properties makes it an attractive substituent for protein binding ligands; however, the rational design of fluorination can be challenging with effects on interactions and binding energies being difficult to predict. In this Perspective, we highlight how computational methods help us to understand the role of fluorine in protein-ligand binding with a focus on molecular simulation. We underline the importance of an accurate force field, present fluoride channels as a showcase for biomolecular interactions with fluorine, and discuss fluorine specific interactions like the ability to form hydrogen bonds and interactions with aryl groups. We put special emphasis on the disruption of water networks and entropic effects.

Adsorption of Flavonoids in a Transcriptional Regulator TtgR: Relative Binding Free Energies and Intermolecular Interactions

Antimicrobial resistance in bacteria often arises from their ability to actively identify and expel toxic compounds. The bacterium strain Pseudomonas putida DOT-T1E utilizes its TtgABC efflux pump to confer robust resistance against antibiotics, flavonoids, and organic solvents. This resistance mechanism is intricately regulated at the transcriptional level by the TtgR protein. Through molecular dynamics and alchemical free energy simulations, we systematically examine the binding of seven flavonoids and their derivatives with the TtgR transcriptional regulator. Our simulations reveal distinct binding geometries and free energies for the flavonoids in the active site of the protein, which are driven by a range of noncovalent forces encompassing van der Waals, electrostatic, and hydrogen bonding interactions. The interplay of molecular structures, substituent patterns, and intermolecular interactions effectively stabilizes the bound flavonoids, confining their movements within the TtgR binding pocket. These findings yield valuable insights into the molecular determinants that govern ligand recognition in TtgR and shed light on the mechanism of antimicrobial resistance in P. putida DOT-T1E.

Targeting the conserved active site of splicing machines with specific and selective small molecule modulators

The self-splicing group II introns are bacterial and organellar ancestors of the nuclear spliceosome and retro-transposable elements of pharmacological and biotechnological importance. Integrating enzymatic, crystallographic, and simulation studies, we demonstrate how these introns recognize small molecules through their conserved active site. These RNA-binding small molecules selectively inhibit the two steps of splicing by adopting distinctive poses at different stages of catalysis, and by preventing crucial active site conformational changes that are essential for splicing progression. Our data exemplify the enormous power of RNA binders to mechanistically probe vital cellular pathways. Most importantly, by proving that the evolutionarily-conserved RNA core of splicing machines can recognize small molecules specifically, our work provides a solid basis for the rational design of splicing modulators not only against bacterial and organellar introns, but also against the human spliceosome, which is a validated drug target for the treatment of congenital diseases and cancers.

Lambda-ABF: Simplified, Portable, Accurate, and Cost-Effective Alchemical Free-Energy Computation

We introduce the lambda-Adaptive Biasing Force (lambda-ABF) method for the computation of alchemical free-energy differences. We propose a software implementation and showcase it on biomolecular systems. The method arises from coupling multiple-walker adaptive biasing force with λ-dynamics. The sampling of the alchemical variable is continuous and converges toward a uniform distribution, making manual optimization of the λ schedule unnecessary. Contrary to most other approaches, alchemical free-energy estimates are obtained immediately without any postprocessing. Free diffusion of λ improves orthogonal relaxation compared to fixed-λ thermodynamic integration or free-energy perturbation. Furthermore, multiple walkers provide generic orthogonal space coverage with minimal user input and negligible computational overhead. We show that our high-performance implementations coupling the Colvars library with NAMD and Tinker-HP can address real-world cases including ligand-receptor binding with both fixed-charge and polarizable models, with a demonstrably richer sampling than fixed-λ methods. The implementation is fully open-source, publicly available, and readily usable by practitioners of current alchemical methods. Thanks to the portable Colvars library, lambda-ABF presents a unified user interface regardless of the back-end (NAMD, Tinker-HP, or any software to be interfaced in the future), sparing users the effort of learning multiple interfaces. Finally, the Colvars Dashboard extension of the visual molecular dynamics (VMD) software provides an interactive monitoring and diagnostic tool for lambda-ABF simulations.

Understanding the impact of binding free energy and kinetics calculations in modern drug discovery

INTRODUCTION

For rational drug design, it is crucial to understand the receptor-drug binding processes and mechanisms. A new era for the use of computer simulations in predicting drug-receptor interactions at an atomic level has begun with remarkable advances in supercomputing and methodological breakthroughs.

AREAS COVERED

End-point free energy calculation methods such as Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) or Molecular-Mechanics/Generalized Born Surface Area (MM/GBSA), free energy perturbation (FEP), and thermodynamic integration (TI) are commonly used for binding free energy calculations in drug discovery. In addition, kinetic dissociation and association rate constants (k off and k on ) play critical roles in the function of drugs. Nowadays, Molecular Dynamics (MD) and enhanced sampling simulations are increasingly being used in drug discovery. Here, the authors provide a review of the computational techniques used in drug binding free energy and kinetics calculations.

EXPERT OPINION

The applications of computational methods in drug discovery and design are expanding, thanks to improved predictions of the binding free energy and kinetic rates of drug molecules. Recent microsecond-timescale enhanced sampling simulations have made it possible to accurately capture repetitive ligand binding and dissociation, facilitating more efficient and accurate calculations of ligand binding free energy and kinetics.

Subtractive proteomics-guided vaccine targets identification and designing of multi-epitopes vaccine for immune response instigation against Burkholderia pseudomallei

In this study, a methodical workflow using subtractive proteomics, vaccine designing, molecular simulation, and agent-based modeling approaches were used to annotate the whole proteome of Burkholderia pseudomallei (strain K96243) for vaccine designing. Among the total 5717 proteins in the whole proteome, 505 were observed to be essential for the pathogen's survival and pathogenesis predicted by the Database of Essential Genes. Among these, 23 vaccine targets were identified, of which fimbrial assembly chaperone (Q63UH5), Outer membrane protein (Q63UH1), and Hemolysin-like protein (Q63UE4) were selected for the subsequent analysis based on the systematic approaches. Using immunoinformatic approaches CTL (cytotoxic T lymphocytes), HTL (helper T lymphocytes), IFN-positive, and B cell epitopes were predicted for these targets. A total of 9 CTL epitopes were added using the GSS linker, 6 HTL epitopes using the GPGPG linker, and 6 B cell epitopes using the KK linker. An adjuvant was added for enhanced antigenicity, an HIV-TAT peptide for improved delivery, and a PADRE sequence was added to form a 466 amino acids long vaccine construct. The construct was classified as non-allergenic, highly antigenic, and experimentally feasible. Molecular docking results validated the robust interaction of MEVC with immune receptors such as TLR2/4. Furthermore, molecular simulation revealed stable dynamics and compact nature of the complexes. The binding free energy results further validated the robust binding. In silico cloning, results revealed GC contents of 50.73 % and a CIA value of 0.978 which shows proper downstream processing. Immune simulation results reported that after the three injections of the vaccine a robust secondary immune response, improved antigen clearance, and effective immune memory generation were observed highlighting its potential for effective and sustained immunity. Future directions should encompass experimental validations, animal model studies, and clinical trials to substantiate the vaccine's efficacy, safety, and immunogenicity.

NPEX: Never give up protein exploration with deep reinforcement learning

Elucidating unknown structures of proteins, such as metastable states, is critical in designing therapeutic agents. Protein structure exploration has been performed using advanced computational methods, especially molecular dynamics and Markov chain Monte Carlo simulations, which require untenably long calculation times and prior structural knowledge. Here, we developed an innovative method for protein structure determination called never give up protein exploration (NPEX) with deep reinforcement learning. The NPEX method leverages the soft actor-critic algorithm and the intrinsic reward system, effectively adding a bias potential without the need for prior knowledge. To demonstrate the method's effectiveness, we applied it to four models: a double well, a triple well, the alanine dipeptide, and the tryptophan cage. Compared with Markov chain Monte Carlo simulations, NPEX had markedly greater sampling efficiency. The significantly enhanced computational efficiency and lack of prior domain knowledge requirements of the NPEX method will revolutionize protein structure exploration.

Calculating Binding Free Energies in Model Host-Guest Systems with Unrestrained Advanced Sampling

Host-guest interactions are important to the design of pharmaceuticals and, more broadly, to soft materials as they can enable targeted, strong, and specific interactions between molecules. The binding process between the host and guest may be classified as a "rare event" when viewing the system at atomic scales, such as those explored in molecular dynamics simulations. To obtain equilibrium binding conformations and dissociation constants from these simulations, it is essential to resolve these rare events. Advanced sampling methods such as the adaptive biasing force (ABF) promote the occurrence of less probable configurations in a system, therefore facilitating the sampling of essential collective variables that characterize the host-guest interactions. Here, we present the application of ABF to a rod-cavitand coarse-grained model of host-guest systems to acquire the potential of mean force. We show that the employment of ABF enables the computation of the configurational and thermodynamic properties of bound and unbound states, including the free energy landscape. Moreover, we identify important dynamic bottlenecks that limit sampling and discuss how these may be addressed in more general systems.

Alchemical Enhanced Sampling with Optimized Phase Space Overlap

An alchemical enhanced sampling (ACES) method has recently been introduced to facilitate importance sampling in free energy simulations. The method achieves enhanced sampling from Hamiltonian replica exchange within a dual topology framework while utilizing new smoothstep softcore potentials. A common sampling problem encountered in lead optimization is the functionalization of aromatic rings that exhibit distinct conformational preferences when interacting with the protein. It is difficult to converge the distribution of ring conformations due to the long time scale of ring flipping events; however, the ACES method addresses this issue by modeling the syn and anti ring conformations within a dual topology. ACES thereby samples the conformer distributions by alchemically tunneling between states, as opposed to traversing a physical pathway with a high rotational barrier. We demonstrate the use of ACES to overcome conformational sampling issues involving ring flipping in ML300-derived noncovalent inhibitors of SARS-CoV-2 Main Protease (Mpro). The demonstrations explore how the use of replica exchange and the choice of softcore selection affects the convergence of the ring conformation distributions. Furthermore, we examine how the accuracy of the calculated free energies is affected by the degree of phase space overlap (PSO) between adjacent states (i.e., between neighboring λ-windows) and the Hamiltonian replica exchange acceptance ratios. Both of these factors are sensitive to the spacing between the intermediate states. We introduce a new method for choosing a schedule of λ values. The method analyzes short "burn-in" simulations to construct a 2D map of the nonlocal PSO. The schedule is obtained by optimizing an alchemical pathway on the 2D map that equalizes the PSO between the λ intervals. The optimized phase space overlap λ-spacing method (Opt-PSO) leads to more numerous end-to-end single passes and round trips due to the correlation between PSO and Hamiltonian replica exchange acceptance ratios. The improved exchange statistics enhance the efficiency of ACES method. The method has been implemented into the FE-ToolKit software package, which is freely available.

FEP Augmentation as a Means to Solve Data Paucity Problems for Machine Learning in Chemical Biology

In the realm of medicinal chemistry, the primary objective is to swiftly optimize a multitude of chemical properties of a set of compounds to yield a clinical candidate poised for clinical trials. In recent years, two computational techniques, machine learning (ML) and physics-based methods, have evolved substantially and are now frequently incorporated into the medicinal chemist's toolbox to enhance the efficiency of both hit optimization and candidate design. Both computational methods come with their own set of limitations, and they are often used independently of each other. ML's capability to screen extensive compound libraries expediently is tempered by its reliance on quality data, which can be scarce especially during early-stage optimization. Contrarily, physics-based approaches like free energy perturbation (FEP) are frequently constrained by low throughput and high cost by comparison; however, physics-based methods are capable of making highly accurate binding affinity predictions. In this study, we harnessed the strength of FEP to overcome data paucity in ML by generating virtual activity data sets which then inform the training of algorithms. Here, we show that ML algorithms trained with an FEP-augmented data set could achieve comparable predictive accuracy to data sets trained on experimental data from biological assays. Throughout the paper, we emphasize key mechanistic considerations that must be taken into account when aiming to augment data sets and lay the groundwork for successful implementation. Ultimately, the study advocates for the synergy of physics-based methods and ML to expedite the lead optimization process. We believe that the physics-based augmentation of ML will significantly benefit drug discovery, as these techniques continue to evolve.

Adaptive lambda schemes for efficient relative binding free energy calculation

The relative free energy perturbation (RFEP) calculation is one of the most theoretically sound computational chemistry approaches for the binding affinity prediction. However, its application is often hindered by the complexity of the calculation choices and the requirement of expertise in the field. Improper lambda scheme of RFEP may result in deviations from an accurate description of the perturbation process and is prone to erroneous affinity predictions. To address such challenges, an automated adaptive lambda method is proposed where the adaptive lambda schemes are obtained through a split-and-merge algorithm based on the pilot runs. The newly established workflow along with a series of improvements to the perturbation settings increases the consistency of the RFEP calculation results. Comparing the pilot and adaptive lambda schemes, the latter demonstrated improvements in convergence and reproducibility and lowered the mean unsigned error and the root-mean-square error. Overall, the adaptive lambda method is a reliable and robust choice to predict small molecule relative binding free energy and can be capitalized to benefit routine RFEP calculations for drug discovery projects.

Extracting binding energies and binding modes from biomolecular simulations of fragment binding to endothiapepsin

Fragment-based drug discovery (FBDD) aims to discover a set of small binding fragments that may be subsequently linked together. Therefore, in-depth knowledge of the individual fragments' structural and energetic binding properties is essential. In addition to experimental techniques, the direct simulation of fragment binding by molecular dynamics (MD) simulations became popular to characterize fragment binding. However, former studies showed that long simulation times and high computational demands per fragment are needed, which limits applicability in FBDD. Here, we performed short, unbiased MD simulations of direct fragment binding to endothiapepsin, a well-characterized model system of pepsin-like aspartic proteases. To evaluate the strengths and limitations of short MD simulations for the structural and energetic characterization of fragment binding, we predicted the fragments' absolute free energies and binding poses based on the direct simulations of fragment binding and compared the predictions to experimental data. The predicted absolute free energies are in fair agreement with the experiment. Combining the MD data with binding mode predictions from molecular docking approaches helped to correctly identify the most promising fragments for further chemical optimization. Importantly, all computations and predictions were done within 5 days, suggesting that MD simulations may become a viable tool in FBDD projects.

BioProtIS: Streamlining protein-ligand interaction pipeline for analysis in genomic and transcriptomic exploration

The identification of protein-ligand interactions plays a pivotal role in elucidating biological processes and discovering potential bioproducts. Harnessing the capabilities of computational methods in drug discovery, we introduce an innovative Inverted Virtual Screening (IVS) pipeline. This pipeline Integrated molecular dynamics and docking analyses to ensure that protein structures are not only energetically favorable but also representative of stable conformations. The primary objective of this pipeline is to automate and streamline the analysis of protein-ligand interactions at both genomic and transcriptomic scales. In the contemporary post-genomic era, high-throughput computational screening for bioproducts, biological systems, and therapeutic drugs has become a cornerstone practice. This approach offers the promise of cost-effectiveness, time efficiency, and optimization of laboratory work. Nevertheless, a notable deficiency persists in the availability of efficient pipelines capable of automating the virtual screening process, seamlessly integrating input and output, and leveraging the full potential of open-source tools. To bridge this critical gap, we have developed a versatile pipeline known as BioProtIS. This tool seamlessly integrates a suite of state-of-the-art tools, including Modeller, AlphaFold, Gromacs, FPOCKET, and AutoDock Vina, thus facilitating the streamlined docking of ligands with an expansive repertoire of proteins sourced from genomes and transcriptomes, and substrates. To assess the pipeline's performance, we employed the transcriptomes of Cereus jamacaru (a cactus species) and Aspisoma lineatum (firefly), along with the genome of Homo sapiens. This integration not only improves the accuracy of ligand-protein interactions by minimizing replicability deviations but also optimizes the discovery process by enabling the simultaneous evaluation of multiple substrates. Furthermore, our pipeline accommodates distinct testing scenarios, such as blind docking or site-specific targeting, which are invaluable in applications ranging from drug repositioning to the exploration of new allosteric binding sites and toxicity assessments. BioProtIS has been designed with modularity at its core. This inherent flexibility empowers users to make custom modifications directly within the source code, tailoring the pipeline to their specific research needs. Moreover, it lays the foundation for seamless integration of diverse docking algorithms in future iterations, promising ongoing advancements in the field of computational biology. This pipeline is available for free distribution and can be download at: https://github.com/BBMDO/BioProtIS.

Elucidating the Mechanism of Metabolism of Cannabichromene by Human Cytochrome P450s

Cannabichromene (CBC) is a nonpsychoactive phytocannabinoid well-known for its wide-ranging health advantages. However, there is limited knowledge regarding its human metabolism following CBC consumption. This research aimed to explore the metabolic pathways of CBC by various human liver cytochrome P450 (CYP) enzymes and support the outcomes using in vivo data from mice. The results unveiled two principal CBC metabolites generated by CYPs: 8'-hydroxy-CBC and 6',7'-epoxy-CBC, along with a minor quantity of 1″-hydroxy-CBC. Notably, among the examined CYPs, CYP2C9 demonstrated the highest efficiency in producing these metabolites. Moreover, through a molecular dynamics simulation spanning 1 μs, it was observed that CBC attains stability at the active site of CYP2J2 by forming hydrogen bonds with I487 and N379, facilitated by water molecules, which specifically promotes the hydroxy metabolite's formation. Additionally, the presence of cytochrome P450 reductase (CPR) amplified CBC's binding affinity to CYPs, particularly with CYP2C8 and CYP3A4. Furthermore, the metabolites derived from CBC reduced cytokine levels, such as IL6 and NO, by approximately 50% in microglia cells. This investigation offers valuable insights into the biotransformation of CBC, underscoring the physiological importance and the potential significance of these metabolites.

LC-MS/MS profiling of Tipuana tipu flower, HPLC-DAD quantification of its bioactive components, and interrelationships with antioxidant, and anti-inflammatory activity: in vitro and in silico approaches

BACKGROUND

Fabaceae plays a crucial role in African traditional medicine as a source of large number of important folk medication, agriculture and food plants. In a search of potential antioxidant and anti-inflammatory candidates derived from locally cultivated plants, the flowers of Tipuana tipu (Benth.) Lillo growing in Egypt were subjected to extensive biological and phytochemical studies. The impact of the extraction technique on the estimated biological activities was investigated.

METHODS

The flowers were extracted using different solvents (aqueous, methanol, water/methanol (1:1), methanol/methylene chloride (1:1), and methylene chloride). The different extracts were subjected to antioxidant (DPPH, ABTS, and FRAP) and anti-inflammatory (COX-2 and 5-LOX) assays. The methanol extract was assessed for its inhibitory activity against iNOS, NO production, and pro-inflammatory cytokines (NF-KB, TNF-R2, TNF-α, IL-1β, and IL-6) in LPS-activated RAW 264.7 macrophages. The composition-activity relationship of the active methanol extract was further investigated using a comprehensive LC-QTOF-MS/MS analysis. The major identified phenolic compounds were further quantified using HPLC-DAD technique. The affinity of representative compounds to iNOS, COX-2, and 5-LOX target active sites was investigated using molecular docking and molecular dynamics simulations.

RESULTS

The methanol extract exhibited the highest radical scavenging capacity and enzyme inhibitory activities against COX-2 and 5-LOX enzymes with IC50 values of 10.6 ± 0.4 and 14.4 ± 1.0 µg/mL, respectively. It also inhibited iNOS enzyme activity, suppressed NO production, and decreased the secretion of pro-inflammatory cytokines. In total, 62 compounds were identified in the extract including flavonoids, coumarins, organic, phenolic, and fatty acids. Among them 18 phenolic compounds were quantified by HPLC-DAD. The highest docking scores were achieved by kaempferol-3-glucoside and orientin. Additionally, molecular dynamics simulations supported the docking findings.

CONCLUSION

The flower could be considered a potentially valuable component in herbal medicines owing to its unique composition and promising bioactivities. These findings encourage increased propagation of T. tipu or even tissue culturing of its flowers for bioprospecting of novel anti-inflammatory drugs. Such applications could be adopted as future approaches that benefit the biomedical field.

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.

Binding Sites of Bicarbonate in Phosphoenolpyruvate Carboxylase

Phosphoenolpyruvate carboxylase (PEPC) is used in plant metabolism for fruit maturation or seed development as well as in the C4 and crassulacean acid metabolism (CAM) mechanisms in photosynthesis, where it is used for the capture of hydrated CO2 (bicarbonate). To find the yet unknown binding site of bicarbonate in this enzyme, we have first identified putative binding sites with nonequilibrium molecular dynamics simulations and then ranked these sites with alchemical free energy calculations with corrections of computational artifacts. Fourteen pockets where bicarbonate could bind were identified, with three having realistic binding free energies with differences with the experimental value below 1 kcal/mol. One of these pockets is found far from the active site at 14 Å and predicted to be an allosteric binding site. In the two other binding sites, bicarbonate is in direct interaction with the magnesium ion; neither sequence alignment nor the study of mutant K606N allowed to discriminate between these two pockets, and both are good candidates as the binding site of bicarbonate in phosphoenolpyruvate carboxylase.

Water Network-Augmented Two-State Model for Protein-Ligand Binding Affinity Prediction

Water network rearrangement from the ligand-unbound state to the ligand-bound state is known to have significant effects on the protein-ligand binding interactions, but most of the current machine learning-based scoring functions overlook these effects. In this study, we endeavor to construct a comprehensive and realistic deep learning model by incorporating water network information into both ligand-unbound and -bound states. In particular, extended connectivity interaction features were integrated into graph representation, and graph transformer operator was employed to extract features of the ligand-unbound and -bound states. Through these efforts, we developed a water network-augmented two-state model called ECIFGraph::HM-Holo-Apo. Our new model exhibits satisfactory performance in terms of scoring, ranking, docking, screening, and reverse screening power tests on the CASF-2016 benchmark. In addition, it can achieve superior performance in large-scale docking-based virtual screening tests on the DEKOIS2.0 data set. Our study highlights that the use of a water network-augmented two-state model can be an effective strategy to bolster the robustness and applicability of machine learning-based scoring functions, particularly for targets with hydrophilic or solvent-exposed binding pockets.

Enhancing Generalizability in Protein-Ligand Binding Affinity Prediction with Multimodal Contrastive Learning

Improving the generalization ability of scoring functions remains a major challenge in protein-ligand binding affinity prediction. Many machine learning methods are limited by their reliance on single-modal representations, hindering a comprehensive understanding of protein-ligand interactions. We introduce a graph-neural-network-based scoring function that utilizes a triplet contrastive learning loss to improve protein-ligand representations. In this model, three-dimensional complex representations and the fusion of two-dimensional ligand and coarse-grained pocket representations converge while distancing from decoy representations in latent space. After rigorous validation on multiple external data sets, our model exhibits commendable generalization capabilities compared to those of other deep learning-based scoring functions, marking it as a promising tool in the realm of drug discovery. In the future, our training framework can be extended to other biophysical- and biochemical-related problems such as protein-protein interaction and protein mutation prediction.

Directional ΔG Neural Network (DrΔG-Net): A Modular Neural Network Approach to Binding Free Energy Prediction

The protein-ligand binding free energy is a central quantity in structure-based computational drug discovery efforts. Although popular alchemical methods provide sound statistical means of computing the binding free energy of a large breadth of systems, they are generally too costly to be applied at the same frequency as end point or ligand-based methods. By contrast, these data-driven approaches are typically fast enough to address thousands of systems but with reduced transferability to unseen systems. We introduce DrΔG-Net (or simply Dragnet), an equivariant graph neural network that can blend ligand-based and protein-ligand data-driven approaches. It is based on a 3D fingerprint representation of the ligand alone and in complex with the protein target. Dragnet is a global scoring function to predict the binding affinity of arbitrary protein-ligand complexes, but can be easily tuned via transfer learning to specific systems or end points, performing similarly to common 2D ligand-based approaches in these tasks. Dragnet is evaluated on a total of 28 validation proteins with a set of congeneric ligands derived from the Binding DB and one custom set extracted from the ChEMBL Database. In general, a handful of experimental binding affinities are sufficient to optimize the scoring function for a particular protein and ligand scaffold. When not available, predictions from physics-based methods such as absolute free energy perturbation can be used for the transfer learning tuning of Dragnet. Furthermore, we use our data to illustrate the present limitations of data-driven modeling of binding free energy predictions.

Kartograf: A Geometrically Accurate Atom Mapper for Hybrid-Topology Relative Free Energy Calculations

Relative binding free energy (RBFE) calculations have emerged as a powerful tool that supports ligand optimization in drug discovery. Despite many successes, the use of RBFEs can often be limited by automation problems, in particular, the setup of such calculations. Atom mapping algorithms are an essential component in setting up automatic large-scale hybrid-topology RBFE calculation campaigns. Traditional algorithms typically employ a 2D subgraph isomorphism solver (SIS) in order to estimate the maximum common substructure. SIS-based approaches can be limited by time-intensive operations and issues with capturing geometry-linked chemical properties, potentially leading to suboptimal solutions. To overcome these limitations, we have developed Kartograf, a geometric-graph-based algorithm that uses primarily the 3D coordinates of atoms to find a mapping between two ligands. In free energy approaches, the ligand conformations are usually derived from docking or other previous modeling approaches, giving the coordinates a certain importance. By considering the spatial relationships between atoms related to the molecule coordinates, our algorithm bypasses the computationally complex subgraph matching of SIS-based approaches and reduces the problem to a much simpler bipartite graph matching problem. Moreover, Kartograf effectively circumvents typical mapping issues induced by molecule symmetry and stereoisomerism, making it a more robust approach for atom mapping from a geometric perspective. To validate our method, we calculated mappings with our novel approach using a diverse set of small molecules and used the mappings in relative hydration and binding free energy calculations. The comparison with two SIS-based algorithms showed that Kartograf offers a fast alternative approach. The code for Kartograf is freely available on GitHub (https://github.com/OpenFreeEnergy/kartograf). While developed for the OpenFE ecosystem, Kartograf can also be utilized as a standalone Python package.

Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives

Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning- and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.

Enthalpies and entropies of hydration from Monte Carlo simulations

The changes in free energy, enthalpy, and entropy for transfer of a solute from the gas phase into solution are the fundamental thermodynamic quantities that characterize the solvation process. Owing to the development of methods based on free-energy perturbation theory, computation of free energies of solvation has become routine in conjunction with Monte Carlo (MC) statistical mechanics and molecular dynamics (MD) simulations. Computation of the enthalpy change and by inference the entropy change is more challenging. Two methods are considered in this work corresponding to direct averaging for the solvent and solution and to computing the temperature derivative of the free energy in the van't Hoff approach. The application is for neutral organic solutes in TIP4P water using long MC simulations to improve precision. Definitive results are also provided for pure TIP4P water. While the uncertainty in computed free energies of hydration is ca. 0.05 kcal mol-1, it is ca. 0.4 kcal mol-1 for the enthalpy changes from either van't Hoff plots or the direct method with sampling for 5 billion MC configurations. Partial molar volumes of hydration are also computed by the direct method; they agree well with experimental data with an average deviation of 3 cm3 mol-1. In addition, the results permit breakdown of the errors in the free energy changes from the OPLS-AA force field into their enthalpic and entropic components. The excess hydrophobicity of organic solutes is enthalpic in origin.

Toward physics-based precision medicine: Exploiting protein dynamics to design new therapeutics and interpret variants

The goal of precision medicine is to utilize our knowledge of the molecular causes of disease to better diagnose and treat patients. However, there is a substantial mismatch between the small number of food and drug administration (FDA)-approved drugs and annotated coding variants compared to the needs of precision medicine. This review introduces the concept of physics-based precision medicine, a scalable framework that promises to improve our understanding of sequence-function relationships and accelerate drug discovery. We show that accounting for the ensemble of structures a protein adopts in solution with computer simulations overcomes many of the limitations imposed by assuming a single protein structure. We highlight studies of protein dynamics and recent methods for the analysis of structural ensembles. These studies demonstrate that differences in conformational distributions predict functional differences within protein families and between variants. Thanks to new computational tools that are providing unprecedented access to protein structural ensembles, this insight may enable accurate predictions of variant pathogenicity for entire libraries of variants. We further show that explicitly accounting for protein ensembles, with methods like alchemical free energy calculations or docking to Markov state models, can uncover novel lead compounds. To conclude, we demonstrate that cryptic pockets, or cavities absent in experimental structures, provide an avenue to target proteins that are currently considered undruggable. Taken together, our review provides a roadmap for the field of protein science to accelerate precision medicine.

Virtual display of targets: A new level to rise the current understanding of ochratoxin A toxicity from a molecular standpoint

Ochratoxin A (OTA) is a mycotoxin spread worldwide contaminating several food and feed commodities and rising concerns for humans and animals. OTA toxicity has been thoroughly assessed over the last 60 years revealing a variety of adverse effects, including nephrotoxicity, hepatotoxicity and possible carcinogenicity. However, the underpinning mechanisms of action have yet to be completely displayed and understood. In this framework, we applied a virtual pipeline based on molecular docking, dynamics and umbrella simulations to display new OTA potential targets. The results collected consistently identified OGFOD1, a key player in protein translation, as possibly inhibited by OTA and its 2'R diastereomer. This is consistent with the current knowledge of OTA's molecular toxicology and may fill some gaps from a mechanistic standpoint. This could pave the way for further dedicated analysis focusing their attention on the OTA-OGFOD1 interaction, expanding the current understanding of OTA toxicity at a molecular level.

Dissecting the Interaction Fingerprints and Binding Affinity of BYL719 Analogs Targeting PI3Kα

Phosphatidylinositol-3-kinase Alpha (PI3Kα) is a lipid kinase which regulates signaling pathways involved in cell proliferation. Dysregulation of these pathways promotes several human cancers, pushing for the development of anticancer drugs to target PI3Kα. One such medicinal chemistry campaign at Novartis led to the discovery of BYL719 (Piqray, Alpelicib), a PI3Kα inhibitor approved by the FDA in 2019 for treatment of HR+/HER2-advanced breast cancer with a PIK3CA mutation. Structure-based drug design played a key role in compound design and optimization throughout the discovery process. However, further characterization of potency drivers via structural dynamics and energetic analyses can be advantageous for ensuing PI3Kα programs. Here, our goal is to employ various in-silico techniques, including molecular simulations and machine learning, to characterize 14 ligands from the BYL719 analogs and predict their binding affinities. The structural insights from molecular simulations suggest that although the ligand-hinge interaction is the primary driver of ligand stability at the pocket, the R group positioning at C2 or C6 of pyridine/pyrimidine also plays a major role. Binding affinities predicted via thermodynamic integration (TI) are in good agreement with previously reported IC50s. Yet, computationally demanding techniques such as TI might not always be the most efficient approach for affinity prediction, as in our case study, fast high-throughput techniques were capable of classifying compounds as active or inactive, and one docking approach showed accuracy comparable to TI.

In silico assessment of a natural small molecule as an inhibitor of programmed death ligand 1 for cancer immunotherapy: a computational approach

Programmed cell death ligand 1 (PD-L1) is a crucial target for cancer therapy. Here, an in silico study investigates PD-L1 to inhibit its interaction with PD1, thereby promoting an immune response to eliminate cancer cells. The study employed machine learning (ML) -based QSAR to detect PDL1 inhibitors. Morgan's fingerprint with docking score showed a 0.83 correlation with the experimental IC50, enabling the screening of 3200 natural compounds. The top three compounds, considered 2819, 2821 and 3188, were selected from the ML-based QSAR and subjected to molecular docking and simulation. The binding scores for 2819, 2821 and 3188 were -7.0, -9.0 and -8.9 kcal/mol, respectively. The stability of the ligands during a 100 ns simulation was assessed using RMSD, showing that 2819 and 2821 maintained stable patterns comparable to the control inhibitor. Notably, 2819 exhibited a consistent stable pattern throughout the simulation, while 2821 showed stability in the last 40 ns. The control compound showed the highest number of hydrogen bonds with proteins, whereas compounds 2819 and 2821 formed continuous H-bonds. 3188 was separated from the protein in later phases and is not regarded as a potential PD-L1-binding molecule. MMGBSA binding free energy for complexes was computed. Control had the lowest binding free energy, while 2819 and 2821 also had lower binding energies. In contrast, 3188 showed poor binding free energy, causing protein separation. Principal component analysis showed a loss of entropy and reduced protein conformational variation. Overall, 2819 and 2821 are potential binders for PD-L1 inhibition and immune response triggering.Communicated by Ramaswamy H. Sarma.

Mechanism of RGD-conjugated nanodevice binding to its target protein integrin αVβ3 by atomistic molecular dynamics and machine learning

Active targeting strategies have been proposed to enhance the selective uptake of nanoparticles (NPs) by diseased cells, and recent experimental findings have proven the effectiveness of this approach. However, no mechanistic studies have yet revealed the atomistic details of the interactions between ligand-activated NPs and integrins. As a case study, here we investigate, by means of advanced molecular dynamics simulations (MD) and machine learning methods (namely equilibrium MD, binding free energy calculations and training of self-organized maps), the interaction of a cyclic-RGD-conjugated PEGylated TiO2 NP (the nanodevice) with the extracellular segment of integrin αVβ3 (the target), the latter experimentally well-known to be over-expressed in several solid tumors. Firstly, we proved that the cyclic-RGD ligand binding to the integrin pocket is established and kept stable even in the presence of the cumbersome realistic model of the nanodevice. In this respect, the unsupervised machine learning analysis allowed a detailed comparison of the ligand/integrin binding in the presence and in the absence of the nanodevice, which unveiled differences in the chemical features. Then, we discovered that unbound cyclic RGDs conjugated to the NP largely contribute to the interactions between the nanodevice and the integrin. Finally, by increasing the density of cyclic RGDs on the PEGylated TiO2 NP, we observed a proportional enhancement of the nanodevice/target binding. All these findings can be exploited to achieve an improved targeting selectivity and cellular uptake, and thus a more successful clinical outcome.

Machine Learning Methods as a Cost-Effective Alternative to Physics-Based Binding Free Energy Calculations

The rank ordering of ligands remains one of the most attractive challenges in drug discovery. While physics-based in silico binding affinity methods dominate the field, they still have problems, which largely revolve around forcefield accuracy and sampling. Recent advances in machine learning have gained traction for protein-ligand binding affinity predictions in early drug discovery programs. In this article, we perform retrospective binding free energy evaluations for 172 compounds from our internal collection spread over four different protein targets and five congeneric ligand series. We compared multiple state-of-the-art free energy methods ranging from physics-based methods with different levels of complexity and conformational sampling to state-of-the-art machine-learning-based methods that were available to us. Overall, we found that physics-based methods behaved particularly well when the ligand perturbations were made in the solvation region, and they did not perform as well when accounting for large conformational changes in protein active sites. On the other end, machine-learning-based methods offer a good cost-effective alternative for binding free energy calculations, but the accuracy of their predictions is highly dependent on the experimental data available for training the model.

What to Make of Zero: Resolving the Statistical Noise from Conformational Reorganization in Alchemical Binding Free Energy Estimates with Metadynamics Sampling

We introduce the self-relative binding free energy (self-RBFE) approach to evaluate the intrinsic statistical variance of dual-topology alchemical binding free energy estimators. The self-RBFE is the relative binding free energy between a ligand and a copy of the same ligand, and its true value is zero. Nevertheless, because the two copies of the ligand move independently, the self-RBFE value produced by a finite-length simulation fluctuates and can be used to measure the variance of the model. The results of this validation provide evidence that a significant fraction of the errors observed in benchmark studies reflect the statistical fluctuations of unconverged estimates rather than the models' accuracy. Furthermore, we find that ligand reorganization is a significant contributing factor to the statistical variance of binding free energy estimates and that metadynamics-accelerated conformational sampling of the torsional degrees of freedom of the ligand can drastically reduce the time to convergence.

Comprehensive, Open-Source, and Automated Workflow for Multisite λ-Dynamics in Lead Optimization

Multisite λ-dynamics (MSLD) is a highly efficient binding free energy calculation method that samples multiple ligands in a single round by assigning different λ values to the alchemical part of each ligand. This method holds great promise for lead optimization (LO) in drug discovery. However, the complex data preparation and simulation process limits its widespread application in diverse protein-ligand systems. To address this challenge, we developed a comprehensive, open-source, and automated workflow for MSLD calculations based on the BLaDE dynamics engine. This workflow incorporates the Ligand Internal and Cartesian coordinate reconstruction-based alignment algorithm (LIC-align) and an optimized maximum common substructure (MCS) search algorithm to accurately generate MSLD multiple topologies with ideal perturbation patterns. Furthermore, our workflow is highly modularized, allowing straightforward integration and extension of various simulation techniques, and is highly accessible to nonexperts. This workflow was validated by calculating the relative binding free energies of large-scale congeneric ligands, many of which have large perturbing groups. The agreement between the calculations and experiments was excellent, with an average unsigned error of 1.08 ± 0.47 kcal/mol. More than 57.1% of the ligands had an error of less than 1.0 kcal/mol, and the perturbations of 6 targets were fully connected via the calculations, while those of 2 targets were connected via both calculations and experimental data. The Pearson correlation coefficient reached 0.88, indicating that the MSLD workflow provides accurate predictions that can guide lead optimization in drug discovery. We also examined the impact of single-site versus multisite perturbations, ligand grouping by perturbing group size, and the position of the anchor atom on the MSLD performance. By integrating our proposed LIC-align and optimized MCS search algorithm along with the coping strategies to handle challenging molecular substructures, our workflow can handle many realistic scenarios more reasonably than all previously published methods. Moreover, we observed that our MSLD workflow achieved similar accuracy to free energy perturbation (FEP) while improving computational efficiency by over 1 order of magnitude in speedup. These findings provide valuable insights and strategies for further MSLD development, making MSLD a competitive tool for lead optimization.

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.

A discovery of potent kaempferol derivatives as multi-target medicines against diabetes as well as bacterial infections: an in silico approach

Flavonoids demonstrate beneficial effects on human health because flavonoids contain important biological properties. Kaempferol is a flavonol, type of flavonoid found in eatable plants and in plants usually employed in ancient drugs (Moringa oleifera, Tilia spp., fern genus spp. and gingko etc.). Some medicinal studies have shown that the use of foods full of kaempferol decreases the risk of many (cancer, vascular) diseases. All the data of 50 kaempferol derivatives were collected from PubChem database. Through Schrödinger software, 3D-QSAR study was performed for 50 compounds by using method of field base. Conformer of kaempferol derivatives was docked against anti-diabetic, anti-microbial co-crystal structures and protein. To monitor the best anti-diabetic and antibacterial agent, particular kaempferol derivatives were downloaded from PubChem database. Virtual screening by molecular docking provided four lead compounds with four different proteins. These hit compounds were found to be potent inhibitor for diabetic enzymes alpha-amylase and DPP IV and had the potential to suppress DNA gyrase and dihydrofolate reductase synthesis. Molecular dynamic simulation of docked complexes evaluates the value of root mean square fluctuation by iMOD server. Kaempferol 3-O-alpha-L-(2, 3-di-Z-p-coumaroyl) rhamnoside (42) compound used as anti-diabetic and kaempferol 3-O-gentiobioside (3) as antibacterial with good results can be used for drug discovery.Communicated by Ramaswamy H. Sarma.

Accurate calculation of affinity changes to the close state of influenza A M2 transmembrane domain in response to subtle structural changes of adamantyl amines using free energy perturbation methods in different lipid bilayers

Experimental binding free energies of 27 adamantyl amines against the influenza M2(22-46) WT tetramer, in its closed form at pH 8, were measured by ITC in DPC micelles. The measured Kd's range is ~44 while the antiviral potencies (IC50) range is ~750 with a good correlation between binding free energies computed with Kd and IC50 values (r = 0.76). We explored with MD simulations (ff19sb, CHARMM36m) the binding profile of complexes with strong, moderate and weak binders embedded in DMPC, DPPC, POPC or a viral mimetic membrane and using different experimental starting structures of M2. To predict accurately differences in binding free energy in response to subtle changes in the structure of the ligands, we performed 18 alchemical perturbative single topology FEP/MD NPT simulations (OPLS2005) using the BAR estimator (Desmond software) and 20 dual topology calculations TI/MD NVT simulations (ff19sb) using the MBAR estimator (Amber software) for adamantyl amines in complex with M2(22-46) WT in DMPC, DPPC, POPC. We observed that both methods with all lipids show a very good correlation between the experimental and calculated relative binding free energies (r = 0.77-0.87, mue = 0.36-0.92 kcal mol-1) with the highest performance achieved with TI/MBAR and lowest performance with FEP/BAR in DMPC bilayers. When antiviral potencies are used instead of the Kd values for computing the experimental binding free energies we obtained also good performance with both FEP/BAR (r = 0.83, mue = 0.75 kcal mol-1) and TI/MBAR (r = 0.69, mue = 0.77 kcal mol-1).

Biomolecular dynamics in the 21st century

The relevance of motions in biological macromolecules has been clear since the early structural analyses of proteins by X-ray crystallography. Computer simulations have been applied to provide a deeper understanding of the dynamics of biological macromolecules since 1976, and are now a standard tool in many labs working on the structure and function of biomolecules. In this mini-review we highlight some areas of current interest and active development for simulations, in particular all-atom molecular dynamics simulations.

Application of temperature-dependent and steered molecular dynamics simulation to screen anti-dengue compounds against Marburg virus

Marburg virus infections are extremely fatal with a fatality range of 23% to 90%, therefore there is an urgent requirement to design and develop efficient therapeutic molecules. Here, a comprehensive temperature-dependent molecular dynamics (MD) simulation method was implemented to identify the potential molecule from the anti-dengue compound library that can inhibit the function of the VP24 protein of Marburg. Virtual high throughput screening identified five effective binders of VP24 after screening 484 anti-dengue compounds. These compounds were treated in MD simulation at four different temperatures: 300, 340, 380, and 420 K. Higher temperatures showed dissociation of hit compounds from the protein. Further, triplicates of 100 ns MD simulation were conducted which showed that compounds ID = 118717693, and ID = 5361 showed strong stability with the protein molecule. These compounds were further validated using Δ G binding free energies and they showed: -30.38 kcal/mol, and -67.83 kcal/mol binding free energies, respectively. Later, these two compounds were used in steered MD simulation to detect its dissociation. Compound ID = 5361 showed the maximum pulling force of 199.02 kcal/mol/nm to dissociate the protein-ligand complex while ID = 118717693 had a pulling force of 101.11 kcal/mol/nm, respectively. This ligand highest number of hydrogen bonds with varying occupancies at 89.93%, 69.80%, 57.93%, 52.33%, and 50.63%. This study showed that ID = 5361 can bind with the VP24 strongly and has the potential to inhibit its function which can be validated in the in-vitro experiment.Communicated by Ramaswamy H. Sarma.

Performance and Analysis of the Alchemical Transfer Method for Binding-Free-Energy Predictions of Diverse Ligands

The Alchemical Transfer Method (ATM) is herein validated against the relative binding-free energies (RBFEs) of a diverse set of protein-ligand complexes. We employed a streamlined setup workflow, a bespoke force field, and AToM-OpenMM software to compute the RBFEs of the benchmark set prepared by Schindler and collaborators at Merck KGaA. This benchmark set includes examples of standard small R-group ligand modifications as well as more challenging scenarios, such as large R-group changes, scaffold hopping, formal charge changes, and charge-shifting transformations. The novel coordinate perturbation scheme and a dual-topology approach of ATM address some of the challenges of single-topology alchemical RBFE methods. Specifically, ATM eliminates the need for splitting electrostatic and Lennard-Jones interactions, atom mapping, defining ligand regions, and postcorrections for charge-changing perturbations. Thus, ATM is simpler and more broadly applicable than conventional alchemical methods, especially for scaffold-hopping and charge-changing transformations. Here, we performed well over 500 RBFE calculations for eight protein targets and found that ATM achieves accuracy comparable to that of existing state-of-the-art methods, albeit with larger statistical fluctuations. We discuss insights into the specific strengths and weaknesses of the ATM method that will inform future deployments. This study confirms that ATM can be applied as a production tool for RBFE predictions across a wide range of perturbation types within a unified, open-source framework.

Binding of Cholesterol to the N-Terminal Domain of the NPC1L1 Transporter: Analysis of the Epimerization-Related Binding Selectivity and Loop Mutations

Cholesterol is a fat-like substance with a pivotal physiological relevance in humans, and its homeostasis is tightly regulated by various cellular processes, including the import in the small intestine and the reabsorption in the biliary ducts by the Niemann-Pick C1 Like 1 (NPC1L1) importer. NPC1L1 can mediate the absorption of a variety of sterols but strikingly exhibits a large sensitivity to cholesterol epimerization. This study examines the molecular basis of the epimerization-related selective binding of cholesterol by combining extended unbiased molecular dynamics simulations of the apo and holo species of the N-terminal domain of wild-type NPC1L1, in conjunction with relative binding free energy, umbrella sampling, and well-tempered metadynamics calculations. The analysis of the results discloses the existence of two distinct binding modes for cholesterol and epi-cholesterol. The former binds deeper in the cavity, forming key hydrogen-bond interactions with Q95, S56, and a water molecule. In contrast, epi-cholesterol is shifted ca. 3 Å to the mouth of the cavity and the transition to the Q95 site is prevented by an energetic barrier of 4.1 kcal·mol-1. Thus, the configuration of the hydroxyl group of cholesterol, together with the presence of a structural water molecule, is a key feature for effective absorption. Finally, whereas these findings may seemingly be challenged by single-point mutations that impair cholesterol transport but have a mild impact on the binding of cholesterol to the Q95 binding site, our results reveal that they have a drastic influence on the conformational landscape of the α8/β7 loop in the apo species compared to the wild-type protein. Overall, the results give support to the functional role played by the α8/β7 loop in regulating the access of ligands to NPC1L1, and hence to interpreting the impact of these mutations on diseases related to disruption of sterol absorption, paving the way to understanding certain physiological dysfunctions.

Optimizing the Extraction of Polyphenols from the Bark of Terminalia arjuna and an In-silico Investigation on its Activity in Colorectal Cancer

BACKGROUND

The interconnection between different fields of research has gained interest due to its cutting-edge perspectives in solving scientific problems. Terminalia arjuna is indigenously used in India for curing several diseases, and its pharmacological activities are being revisited in recent drug-repurposing research.

OBJECTIVES

Efficient ultrasound-assisted extraction of phytochemicals from the bark of Terminalia arjuna is highlighted in this study. Following the optimization of the extraction process, the crude hydroethanolic extract is subjected to phytochemical profiling and an in-silico investigation of its anti-cancer properties.

MATERIALS AND METHODS

A three-level four-factor Box-Behnken design is exploited to optimize four operational parameters, namely extraction time, ultrasonic power, ethanol concentration (as the extracting solvent) and solute (in g): solvent (in mL) ratio. At the optimum parametric condition, the crude extract is obtained, and its GC-MS analysis is carried out. An analysis of network pharmacology (by constructing and visualizing biological networks using Cytoscape) combined with molecular docking reveals the potential antineoplastic targets of the crude extract.

RESULTS

The ANOVA table exhibits the significance, adequacy and reliability of the proposed second-order polynomial model with the R² value of 0.917 and adjusted R² of 0.865. Experimental results portray the significant antioxidant potential of the prepared extract in its crude form. The GC-MS analysis of the crude extract predicts the extracted phytochemicals, while the constructed biological networks highlight its multi-targeted activity in colorectal cancer.

CONCLUSION

The study identifies three phytochemicals viz. luteolin, β-sitosterol and arjunic acid as potent anti-cancer agents and can be extended with in-vitro and in-vivo experiments to validate the in-silico results, thus establishing lead phytochemicals in multi-targeted colorectal cancer therapies.

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.

Computer-Aided Drug Design and Drug Discovery: A Prospective Analysis

In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADDs historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADDs predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open-Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. This paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery.

Fast free energy estimates from λ-dynamics with bias-updated Gibbs sampling

Relative binding free energy calculations have become an integral computational tool for lead optimization in structure-based drug design. Classical alchemical methods, including free energy perturbation or thermodynamic integration, compute relative free energy differences by transforming one molecule into another. However, these methods have high operational costs due to the need to perform many pairwise perturbations independently. To reduce costs and accelerate molecular design workflows, we present a method called λ-dynamics with bias-updated Gibbs sampling. This method uses dynamic biases to continuously sample between multiple ligand analogues collectively within a single simulation. We show that many relative binding free energies can be determined quickly with this approach without compromising accuracy. For five benchmark systems, agreement to experiment is high, with root mean square errors near or below 1.0 kcal mol-1. Free energy results are consistent with other computational approaches and within statistical noise of both methods (0.4 kcal mol-1 or less). Notably, large efficiency gains over thermodynamic integration of 18-66-fold for small perturbations and 100-200-fold for whole aromatic ring substitutions are observed. The rapid determination of relative binding free energies will enable larger chemical spaces to be more readily explored and structure-based drug design to be accelerated.

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.