Alchemical absolute protein-ligand binding free energies for drug design

Automated On-the-Fly Optimization of Resource Allocation for Efficient Free Energy Simulations

Computing the free energy of protein-ligand binding by employing molecular dynamics (MD) simulations is becoming a valuable tool in the early stages of drug discovery. However, the cost and complexity of such simulations are often prohibitive for high-throughput studies. We present an automated workflow for the thermodynamic integration scheme with the "on-the-fly" optimization of computational resource allocation for each λ-window of both relative and absolute binding free energy simulations. This iterative workflow utilizes automatic equilibration detection and convergence testing via the Jensen-Shannon distance to determine optimal simulation stopping points in an entirely data-driven manner. It is broadly applicable to multiple free energy calculations, such as ligand binding, amino acid mutations, and others, while utilizing different estimators, e.g., free energy perturbation, BAR, MBAR, etc. We benchmark our workflow on the well-characterized systems, namely, cyclin-dependent kinase 2 and T4 lysozyme L99A/M102Q mutant, and the more flexible SARS-CoV-2 papain-like protease. We demonstrate that this proposed protocol can achieve more than 85% reduction in computational expense while maintaining similar levels of accuracy compared to other benchmarking protocols. We examine the performance of this protocol on both small and large molecular transformations. The cost-accuracy tradeoff of repeated runs is also investigated.

Estimation of Absolute Binding Free Energies for Drugs That Bind Multiple Proteins

The Gibbs energy of binding (absolute binding free energy, ABFE) of a drug to proteins in the body determines the drug's affinity to its molecular target and its selectivity. ABFE is challenging to measure, and experimental values are not available for many proteins together with potential drugs and other molecules that bind them. Accurate means of calculating such values are, therefore, highly in demand. Realizing that toxicity and side effects are closely related to off-target binding, here we calculate the ABFE of two drugs, each to multiple proteins, in order to examine whether it is possible to carry out such calculations and achieve the required accuracy. The methods that were used were free energy perturbation with replica exchange molecular dynamics (FEP/REMD) and density functional theory (DFT) with a cluster approach and a simplified model. DFT calculations were supplemented with energy decomposition analysis (EDA). The accuracy of each method is discussed, and suggestions are made for the approach toward better ABFE calculations.

Equilibrium and Nonequilibrium Ensemble Methods for Accurate, Precise and Reproducible Absolute Binding Free Energy Calculations

Free energy calculations for protein-ligand complexes have become widespread in recent years owing to several conceptual, methodological and technological advances. Central among these is the use of ensemble methods which permits accurate, precise and reproducible predictions and is necessary for uncertainty quantification. Absolute binding free energies (ABFEs) are challenging to predict using alchemical methods and their routine application in drug discovery has remained out of reach until now. Here, we apply ensemble alchemical ABFE methods to a large data set comprising 219 ligand-protein complexes and obtain statistically robust results with high accuracy (<1 kcal/mol). We compare equilibrium and nonequilibrium methods for ABFE predictions at large scale and provide a systematic critical assessment of each method. The equilibrium method is more accurate, precise, faster, computationally more cost-effective and requires a much simpler protocol, making it preferable for large scale and blind applications. We find that the calculated free energy distributions are non-normal and discuss the consequences. We recommend a definitive protocol to perform ABFE calculations optimally. Using this protocol, it is possible to perform thousands of ABFE calculations within a few hours on modern exascale machines.

Leveraging the Thermodynamics of Protein Conformations in Drug Discovery

As the name implies, structure-based drug design requires confidence in the holo complex structure. The ability to clarify which protein conformation to use when ambiguity arises would be incredibly useful. We present a large scale validation of the computational method Protein Reorganization Free Energy Perturbation (PReorg-FEP) and demonstrate its quantitative accuracy in selecting the correct protein conformation among candidate models in apo or ligand induced states for 14 different systems. These candidate conformations are pulled from various drug discovery related campaigns: cryptic conformations induced by novel hits in lead identification, binding site rearrangement during lead optimization, and conflicting structural biology models. We also show an example of a pH-dependent conformational change, relevant to protein design.

RED-E-Function-Based Equilibrium Parameter Finder: Finding the Best Restraint Parameters in Absolute Binding Free Energy Calculations

Free energy perturbation (FEP)-based absolute binding free energy (ABFE) calculations have emerged as a powerful tool for the accurate prediction of ligand-protein binding affinities in drug discovery. The restraint addition is crucial in FEP-ABFE calculations; however, due to the non-orthogonal couplings between the restrained degrees of freedom, it typically requires numerous λ windows to ensure the phase-space overlap during restraint addition. This study introduces the RED-E-function-based equilibrium parameter finder (REPF), a novel method that relies on harmonic restraints to optimize the equilibrium values in restraints, enhancing phase-space overlap and improving the convergence of the restraint addition. REPF was applied to 44 protein-ligand complexes across 5 targets and compared to restraint schemes reported in the literature. We found that REPF-optimized restraints achieve an accuracy comparable to that of the 12λ approach while using only 2λ simulations, resulting in a significant reduction in computational costs. Extensive tests confirmed the improved convergence behavior and reduced energy fluctuations of REPF-optimized restraints.

Impact of protein conformations on binding free energy calculations in the beta-secretase 1 system

In binding free energy calculations, simulations must sample all relevant conformations of the system in order to obtain unbiased results. For instance, different ligands can bind to different metastable states of a protein, and if these protein conformational changes are not sampled in relative binding free energy calculations, the contribution of these states to binding is not accounted for and thus calculated binding free energies are inaccurate. In this work, we investigate the impact of different beta-sectretase 1 (BACE1) protein conformations obtained from x-ray crystallography on the binding of BACE1 inhibitors. We highlight how these conformational changes are not adequately sampled in typical molecular dynamics simulations. Furthermore, we show that insufficient sampling of relevant conformations induces substantial error in relative binding free energy calculations, as judged by a variation in calculated relative binding free energies up to 2 kcal/mol depending on the starting protein conformation. These results emphasize the importance of protein conformational sampling and pose this BACE1 system as a challenge case for further method development in the area of enhanced protein conformational sampling, either in combination with binding calculations or as an endpoint correction.

Replica Exchange of Expanded Ensembles: A Generalized Ensemble Approach with Enhanced Flexibility and Parallelizability

Generalized ensemble methods such as Hamiltonian replica exchange (HREX) and expanded ensemble (EE) have been shown effective in free energy calculations for various contexts, given their ability to circumvent free energy barriers via nonphysical pathways defined by states with different modified Hamiltonians. However, both HREX and EE methods come with drawbacks, such as limited flexibility in parameter specification or the lack of parallelizability for more complicated applications. To address this challenge, we present the method of replica exchange of expanded ensembles (REXEE), which integrates the principles of HREX and EE methods by periodically exchanging coordinates of EE replicas sampling different yet overlapping sets of alchemical states. With the solvation free energy calculation of anthracene and binding free energy calculation of the CB7-10 binding complex, we show that the REXEE method achieves the same level of accuracy in free energy calculations as the HREX and EE methods, while offering enhanced flexibility and parallelizability. Additionally, we examined REXEE simulations with various setups to understand how different exchange frequencies and replica configurations influence the sampling efficiency in the fixed-weight phase and the weight convergence in the weight-updating phase. The REXEE approach can be further extended to support asynchronous parallelization schemes, allowing looser communications between larger numbers of loosely coupled processors such as cloud computing and therefore promising much more scalable and adaptive executions of alchemical free energy calculations. All algorithms for the REXEE method are available in the Python package ensemble_md, which offers an interface for REXEE simulation management without modifying the source code in GROMACS.

Current State of Open Source Force Fields in Protein-Ligand Binding Affinity Predictions

In drug discovery, the in silico prediction of binding affinity is one of the major means to prioritize compounds for synthesis. Alchemical relative binding free energy (RBFE) calculations based on molecular dynamics (MD) simulations are nowadays a popular approach for the accurate affinity ranking of compounds. MD simulations rely on empirical force field parameters, which strongly influence the accuracy of the predicted affinities. Here, we evaluate the ability of six different small-molecule force fields to predict experimental protein-ligand binding affinities in RBFE calculations on a set of 598 ligands and 22 protein targets. The public force fields OpenFF Parsley and Sage, GAFF, and CGenFF show comparable accuracy, while OPLS3e is significantly more accurate. However, a consensus approach using Sage, GAFF, and CGenFF leads to accuracy comparable to OPLS3e. While Parsley and Sage are performing comparably based on aggregated statistics across the whole dataset, there are differences in terms of outliers. Analysis of the force field reveals that improved parameters lead to significant improvement in the accuracy of affinity predictions on subsets of the dataset involving those parameters. Lower accuracy can not only be attributed to the force field parameters but is also dependent on input preparation and sampling convergence of the calculations. Especially large perturbations and nonconverged simulations lead to less accurate predictions. The input structures, Gromacs force field files, as well as the analysis Python notebooks are available on GitHub.

Next-generation neuropeptide Y receptor small-molecule agonists inhibit mosquito-biting behavior

BACKGROUND

Female Aedes aegypti mosquitoes can spread disease-causing pathogens when they bite humans to obtain blood nutrients required for egg production. Following a complete blood meal, host-seeking is suppressed until eggs are laid. Neuropeptide Y-like receptor 7 (NPYLR7) plays a role in endogenous host-seeking suppression and previous work identified small-molecule NPYLR7 agonists that inhibit host-seeking and blood-feeding when fed to mosquitoes at high micromolar doses.

METHODS

Using structure-activity relationship analysis and structure-guided design we synthesized 128 compounds with similarity to known NPYLR7 agonists.

RESULTS

Although in vitro potency (EC50) was not strictly predictive of in vivo effect, we identified three compounds that reduced blood-feeding from a live host when fed to mosquitoes at a dose of 1 μM-a 100-fold improvement over the original reference compound.

CONCLUSIONS

Exogenous activation of NPYLR7 represents an innovative vector control strategy to block mosquito biting behavior and prevent mosquito-human host interactions that lead to pathogen transmission.

PLANET: A Multi-objective Graph Neural Network Model for Protein-Ligand Binding Affinity Prediction

Predicting protein-ligand binding affinity is a central issue in drug design. Various deep learning models have been published in recent years, where many of them rely on 3D protein-ligand complex structures as input and tend to focus on the single task of reproducing binding affinity. In this study, we have developed a graph neural network model called PLANET (Protein-Ligand Affinity prediction NETwork). This model takes the graph-represented 3D structure of the binding pocket on the target protein and the 2D chemical structure of the ligand molecule as input. It was trained through a multi-objective process with three related tasks, including deriving the protein-ligand binding affinity, protein-ligand contact map, and ligand distance matrix. Besides the protein-ligand complexes with known binding affinity data retrieved from the PDBbind database, a large number of non-binder decoys were also added to the training data for deriving the final model of PLANET. When tested on the CASF-2016 benchmark, PLANET exhibited a scoring power comparable to the best result yielded by other deep learning models as well as a reasonable ranking power and docking power. In virtual screening trials conducted on the DUD-E benchmark, PLANET's performance was notably better than several deep learning and machine learning models. As on the LIT-PCBA benchmark, PLANET achieved comparable accuracy as the conventional docking program Glide, but it only spent less than 1% of Glide's computation time to finish the same job because PLANET did not need exhaustive conformational sampling. Considering the decent accuracy and efficiency of PLANET in binding affinity prediction, it may become a useful tool for conducting large-scale virtual screening.

Molecular Insights into the Impact of Mutations on the Binding Affinity of Targeted Covalent Inhibitors of BTK

Targeted covalent inhibitors (TCIs) have witnessed a significant resurgence in recent years, particularly in the kinase drug discovery field for treating diverse clinical indications. The inhibition of Bruton's tyrosine kinase (BTK) for treating B-cell cancers is a classic example where TCIs such as ibrutinib have had breakthroughs in targeted therapy. However, selectivity remains challenging, and the emergence of resistance mutations is a critical concern for clinical efficacy. Computational methods that can accurately predict the impact of mutations on inhibitor binding affinity could prove helpful in informing targeted approaches─providing insights into drug resistance mechanisms. In addition, such systems could help guide the systematic evaluation and impact of mutations in disease models for optimal experimental design. Here, we have employed in silico physics-based methods to understand the effects of mutations on the binding affinity and conformational dynamics of select TCIs of BTK. The TCIs studied include ibrutinib, acalabrutinib, and zanubrutinib─all of which are FDA-approved drugs for treating multiple forms of leukemia and lymphoma. Our results offer useful molecular insights into the structural determinants, thermodynamics, and conformational energies that impact ligand binding for this biological target of clinical relevance.

Next Generation Neuropeptide Y Receptor Small Molecule Agonists Inhibit Mosquito Biting Behavior

Female Aedes aegypti mosquitoes can spread disease-causing pathogens when they bite humans to obtain blood nutrients required for egg production. Following a complete blood meal, host-seeking is suppressed until eggs are laid. Neuropeptide Y-like Receptor 7 (NPYLR7) plays a role in endogenous host-seeking suppression and previous work identified small molecule NPYLR7 agonists that suppress host-seeking and blood feeding when fed to mosquitoes at high micromolar doses. Using structure activity relationship analysis and structure-guided design we synthesized 128 compounds with similarity to known NPYLR7 agonists. Although in vitro potency (EC50) was not strictly predictive of in vivo effect, we identified 3 compounds that suppressed blood feeding from a live host when fed to mosquitoes at a 1 μM dose, a 100-fold improvement over the original reference compound. Exogenous activation of NPYLR7 represents an innovative vector control strategy to block mosquito biting behavior and prevent mosquito/human host interactions that lead to pathogen transmission.

Addressing Suboptimal Poses in Nonequilibrium Alchemical Calculations

Alchemical transformations can be used to quantitatively estimate absolute binding free energies at a reasonable computational cost. However, most of the approaches currently in use require knowledge of the correct (crystallographic) pose. In this paper, we present a combined Hamiltonian replica exchange nonequilibrium alchemical method that allows us to reliably calculate absolute binding free energies, even when starting from suboptimal initial binding poses. Performing a preliminary Hamiltonian replica exchange enhances the sampling of slow degrees of freedom of the ligand and the target, allowing the system to populate the correct binding pose when starting from an approximate docking pose. We apply the method on 6 ligands of the first bromodomain of the BRD4 bromodomain-containing protein. For each ligand, we start nonequilibrium alchemical transformations from both the crystallographic pose and the top-scoring docked pose that are often significantly different. We show that the method produces statistically equivalent binding free energies, making it a useful tool for computational drug discovery pipelines.

Investigating Polypharmacology through Targeting Known Human Neutrophil Elastase Inhibitors to Proteinase 3

Using a combination of multisite λ-dynamics (MSλD) together with in vitro IC50 assays, we evaluated the polypharmacological potential of a scaffold currently in clinical trials for inhibition of human neutrophil elastase (HNE), targeting cardiopulmonary disease, for efficacious inhibition of Proteinase 3 (PR3), a related neutrophil serine proteinase. The affinities we observe suggest that the dihydropyrimidinone scaffold can serve as a suitable starting point for the establishment of polypharmacologically targeting both enzymes and enhancing the potential for treatments addressing diseases like chronic obstructive pulmonary disease.

Potent inhibitors targeting cyclin-dependent kinase 9 discovered via virtual high-throughput screening and absolute binding free energy calculations

Due to the crucial regulatory mechanism of cyclin-dependent kinase 9 (CDK9) in mRNA transcription, the development of kinase inhibitors targeting CDK9 holds promise as a potential treatment strategy for cancer. A structure-based virtual screening approach has been employed for the discovery of potential novel CDK9 inhibitors. First, compounds with kinase inhibitor characteristics were identified from the ZINC15 database via virtual high-throughput screening. Next, the predicted binding modes were optimized by molecular dynamics simulations, followed by precise estimation of binding affinities using absolute binding free energy calculations based on the free energy perturbation scheme. The binding mode of molecule 006 underwent an inward-to-outward flipping, and the new binding mode exhibited binding affinity comparable to the small molecule T6Q in the crystal structure (PDB ID: 4BCF), highlighting the essential role of molecular dynamics simulation in capturing a plausible binding pose bridging docking and absolute binding free energy calculations. Finally, structural modifications based on these findings further enhanced the binding affinity with CDK9. The results revealed that enhancing the molecule's rigidity through ring formation, while maintaining the major interactions, reduced the entropy loss during the binding process and, thus, enhanced binding affinities.

Uncovering New Conformational States of the Substrate Binding Pocket of LSD1 Potential for Inhibitor Design via Funnel Metadynamics

Lysine-specific demethylase 1 (LSD1) is a promising therapeutic target for cancer therapy. So far, over 80 crystal structures of LSD1 in different complex states have been deposited in the Protein Data Bank, which are valuable resources for performing structure-based drug design. However, among all of the crystal structures of LSD1, the substrate binding pocket, which is the most efficient druggable site for designing LSD1 inhibitors at present, is very similar no matter whether LSD1 is in the apo or any holo forms, which is inconsistent with its versatile demethylase functions. To investigate whether the substrate binding pocket is rigid or exhibits other representative conformations different from the crystal conformations that are feasible for designing new LSD1 inhibitors, we performed funnel metadynamics simulations to study the conformation dynamics of LSD1 in the binding process of two effective LSD1 inhibitors (CC-90011 and 6X0, CC-90011 undergoing clinical trials). Our results showed that the entrance of the substrate binding pocket is very flexible. Two representative entrance conformations of LSD1 counting against binding with the substrate of histone H3 were detected, which may be used for structure-based LSD1 inhibitor design. Besides, alternative optimal binding modes and prebinding modes for both inhibitors were also detected, which depicted that the key interactions changed along with the binding process. Our results should provide great help for LSD1 inhibitor design.

Accelerating and Automating the Free Energy Perturbation Absolute Binding Free Energy Calculation with the RED-E Function

The accurate prediction of the binding affinities between small molecules and biological macromolecules plays a fundamental role in structure-based drug design, which is still challenging. The free energy perturbation-based absolute binding free energy (FEP-ABFE) approach has shown potential in its reliability. To correctly calculate the energy related to the ligand being restrained by the receptor, additional restraints between the ligand and the receptor are needed. However, determining the restraint parameters for individual ligands empirically is too trivial to be automated, and usually gives rise to numerical instabilities, which set back the applications of FEP-ABFE. To address these issues, we derived the analytical expression for the probability distribution of energy differences, P(ΔU), during the process of restraint addition, which is called the RED-E (restraint energy distribution at equilibrium position) function. Simulations indicated that the RED-E function can accurately describe P(ΔU) when restraints are added at the equilibrium position. Based on the RED-E function, an automatic restraint selection method was proposed to select the best restraint. With this method, there is a high phase-space overlap between the free and restrained states, such that using a 2-λ perturbation can accurately calculate the free energy of the restraint addition, which is a nearly 6 times acceleration compared with current widely used 12-λ perturbation method. The RED-E function gives insight into the non-Gaussian behavior of the sampled P(ΔU) in certain FEP processes in an analytical way. The highly automated and accelerated restraint selection also makes it possible for the large-scale application of FEP-ABFE in real drug discovery practices.

Shape-Based Virtual Screening of a Billion-Compound Library Identifies Mycobacterial Lipoamide Dehydrogenase Inhibitors

Lpd (lipoamide dehydrogenase) in Mycobacterium tuberculosis (Mtb) is required for virulence and is a genetically validated tuberculosis (TB) target. Numerous screens have been performed over the last decade, yet only two inhibitor series have been identified. Recent advances in large-scale virtual screening methods combined with make-on-demand compound libraries have shown the potential for finding novel hits. In this study, the Enamine REAL library consisting of ∼1.12 billion compounds was efficiently screened using the GPU Shape screen method against Mtb Lpd to find additional chemical matter that would expand on the known sulfonamide inhibitor series. We identified six new inhibitors with IC50 in the range of 5-100 μM. While these compounds remained chemically close to the already known sulfonamide series inhibitors, some diversity was found in the cores of the hits. The two most potent hits were further validated by one-step potency optimization to submicromolar levels. The co-crystal structure of optimized analogue TDI-13537 provided new insights into the potency determinants of the series.

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

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

Comparison of Equilibrium and Nonequilibrium Approaches for Relative Binding Free Energy Predictions

Alchemical relative binding free energy calculations have recently found important applications in drug optimization. A series of congeneric compounds are generated from a preidentified lead compound, and their relative binding affinities to a protein are assessed in order to optimize candidate drugs. While methods based on equilibrium thermodynamics have been extensively studied, an approach based on nonequilibrium methods has recently been reported together with claims of its superiority. However, these claims pay insufficient attention to the basis and reliability of both methods. Here we report a comparative study of the two approaches across a large data set, comprising more than 500 ligand transformations spanning in excess of 300 ligands binding to a set of 14 diverse protein targets. Ensemble methods are essential to quantify the uncertainty in these calculations, not only for the reasons already established in the equilibrium approach but also to ensure that the nonequilibrium calculations reside within their domain of validity. If and only if ensemble methods are applied, we find that the nonequilibrium method can achieve accuracy and precision comparable to those of the equilibrium approach. Compared to the equilibrium method, the nonequilibrium approach can reduce computational costs but introduces higher computational complexity and longer wall clock times. There are, however, cases where the standard length of a nonequilibrium transition is not sufficient, necessitating a complete rerun of the entire set of transitions. This significantly increases the computational cost and proves to be highly inconvenient during large-scale applications. Our findings provide a key set of recommendations that should be adopted for the reliable implementation of nonequilibrium approaches to relative binding free energy calculations in ligand-protein systems.

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.

Benzimidazoles Containing Piperazine Skeleton at C-2 Position as Promising Tubulin Modulators with Anthelmintic and Antineoplastic Activity

Benzimidazole anthelmintic drugs hold promise for repurposing as cancer treatments due to their interference with tubulin polymerization and depolymerization, manifesting anticancer properties. We explored the potential of benzimidazole compounds with a piperazine fragment at C-2 as tubulin-targeting agents. In particular, we assessed their anthelmintic activity against isolated Trichinella spiralis muscle larvae and their effects on glioblastoma (U-87 MG) and breast cancer (MDA-MB-231) cell lines. Compound 7c demonstrated exceptional anthelmintic efficacy, achieving a 92.7% reduction in parasite activity at 100 μg/mL after 48 hours. In vitro cytotoxicity analysis of MDA-MB 231 and U87 MG cell lines showed that derivatives 7b, 7d, and 7c displayed lower IC50 values compared to albendazole (ABZ), the control. These piperazine benzimidazoles effectively reduced cell migration in both cell lines, with compound 7c exhibiting the most significant reduction, making it a promising candidate for further study. The binding mode of the most promising compound 7c, was determined using the induced fit docking-molecular dynamics (IFD-MD) approach. Regular docking and IFD were also employed for comparison. The IFD-MD analysis revealed that 7c binds to tubulin in a unique binding cavity near that of ABZ, but the benzimidazole ring was fitted much deeper into the binding pocket. Finally, the absolute free energy of perturbation technique was applied to evaluate the 7c binding affinity, further confirming the observed binding mode.

Multi-shelled ECIF: improved extended connectivity interaction features for accurate binding affinity prediction

Motivation

Extended connectivity interaction features (ECIF) is a method developed to predict protein-ligand binding affinity, allowing for detailed atomic representation. It performed very well in terms of Comparative Assessment of Scoring Functions 2016 (CASF-2016) scoring power. However, ECIF has the limitation of not being able to adequately account for interatomic distances.

Results

To investigate what kind of distance representation is effective for P-L binding affinity prediction, we have developed two algorithms that improved ECIF's feature extraction method to take distance into account. One is multi-shelled ECIF, which takes into account the distance between atoms by dividing the distance between atoms into multiple layers. The other is weighted ECIF, which weights the importance of interactions according to the distance between atoms. A comparison of these two methods shows that multi-shelled ECIF outperforms weighted ECIF and the original ECIF, achieving a CASF-2016 scoring power Pearson correlation coefficient of 0.877.

Availability and implementation

All the codes and data are available on GitHub (https://github.com/koji11235/MSECIFv2).

The maximal and current accuracy of rigorous protein-ligand binding free energy calculations

Computational techniques can speed up the identification of hits and accelerate the development of candidate molecules for drug discovery. Among techniques for predicting relative binding affinities, the most consistently accurate is free energy perturbation (FEP), a class of rigorous physics-based methods. However, uncertainty remains about how accurate FEP is and can ever be. Here, we present what we believe to be the largest publicly available dataset of proteins and congeneric series of small molecules, and assess the accuracy of the leading FEP workflow. To ascertain the limit of achievable accuracy, we also survey the reproducibility of experimental relative affinity measurements. We find a wide variability in experimental accuracy and a correspondence between binding and functional assays. When careful preparation of protein and ligand structures is undertaken, FEP can achieve accuracy comparable to experimental reproducibility. Throughout, we highlight reliable protocols that can help maximize the accuracy of FEP in prospective studies.

Investigating the role of Sterol C24-Methyl transferase mutation on drug resistance in leishmaniasis and identifying potential inhibitors

Leishmaniasis is a fatal disease caused by the leishmania parasite. For the survival of the leishmania parasite, Sterol C24-Methyl Transferase (SMT) is essential which is an enzyme of the ergosterol pathway. SMT protein mutation is responsible for Amphotericin-B drug resistance in Leishmania, which is the main treatment for visceral leishmaniasis. Amphotericin-B resistance is caused by three mutated residues V131I, V321I and F72C. The underlying mechanisms and structural changes in SMT enzymes responsible for resistance due to mutation are still not well understood. In the current study, the potential mechanism of resistance due to these mutations and the structure variation of wild and mutant SMT proteins were investigated through molecular dynamics simulations and molecular docking analysis. The results showed that AmB established strong bonding interaction with wild SMT as compare to mutants SMT. The binding energy calculation showed that binding energy of AmB with mutants SMT increases as compare to the wild SMT. Further structural based virtual screening was carried out to design potential inhibitors for the mutant SMT. On the basis of structural-based virtual screening four inhibitors (SANC01057, SANC00882, SANC00414, SANC01047) were computationally identified as potential mutant SMT (F72C) inhibitors. This work provides valuable information for improved management of drug resistant Leishmaniasis.Communicated by Ramaswamy H. Sarma.

Computation of Absolute Binding Free Energies for Noncovalent Inhibitors with SARS-CoV-2 Main Protease

Accurate, routine calculation of absolute binding free energies (ABFEs) for protein-ligand complexes remains a key goal of computer-aided drug design since it can enable screening and optimization of drug candidates. For development and testing of related methods, it is important to have high-quality datasets. To this end, from our own experimental studies, we have selected a set of 16 inhibitors of the SARS-CoV-2 main protease (Mpro) with structural diversity and well-distributed BFEs covering a 5 kcal/mol range. There is also minimal structural uncertainty since X-ray crystal structures have been deposited for 12 of the compounds. For methods testing, we report ABFE results from 2 μs molecular dynamics (MD) simulations using free energy perturbation (FEP) theory. The correlation of experimental and computed results is encouraging, with a Pearson's r2 of 0.58 and a Kendall τ of 0.24. The results indicate that current FEP-based ABFE calculations can be used for identification of active compounds (hits). While their accuracy for lead optimization is not yet sufficient, this activity remains addressable in separate lead series by relative BFE calculations.

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.

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.

Revisiting MMPBSA by Adoption of MC-Based Surface Area/Volume, ANI-ML Potentials, and Two-Valued Interior Dielectric Constant

Here, we report the accuracy improvements of molecular mechanics Poisson-Boltzmann surface area (MMPBSA) calculations by adoption of ANI-ML potentials in replacement of MM terms, the use of solvent-accessible surface area (SASA) and volume (SAV) values from the Monte Carlo sampling of the probe, and introducing two different interior dielectric constants for electrostatic interactions of protein-ligand (P-L) and polar solvation term in the MMPBSA calculations. Our results show that the Pearson correlation coefficients of MMPBSA-calculated values with respect to experimental binding free energies can be drastically improved from 0.48 to 0.90 by adoption of ANI-ML potentials in replacement of MM energy terms in the equation, referred to as ANI-PBSA. Moreover, we show that the SASA/SAV-combined equation in the scaled particle theory (SPT) can be a better choice to model nonpolar solvation term, reaching nearly the same accuracy by ANI-PBSA calculations. Finally, we introduce two different values of interior dielectric constants, which could be an alternative strategy between the single and variable constant definitions.

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.

Automated relative binding free energy calculations from SMILES to ΔΔG

In drug discovery, computational methods are a key part of making informed design decisions and prioritising experiments. In particular, optimizing compound affinity is a central concern during the early stages of development. In the last 10 years, alchemical free energy (FE) calculations have transformed our ability to incorporate accurate in silico potency predictions in design decisions, and represent the 'gold standard' for augmenting experiment-driven drug discovery. However, relative FE calculations are complex to set up, require significant expert intervention to prepare the calculation and analyse the results or are provided only as closed-source software, not allowing for fine-grained control over the underlying settings. In this work, we introduce an end-to-end relative FE workflow based on the non-equilibrium switching approach that facilitates calculation of binding free energies starting from SMILES strings. The workflow is implemented using fully modular steps, allowing various components to be exchanged depending on licence availability. We further investigate the dependence of the calculated free energy accuracy on the initial ligand pose generated by various docking algorithms. We show that both commercial and open-source docking engines can be used to generate poses that lead to good correlation of free energies with experimental reference data.

Computational approaches streamlining drug discovery

Computer-aided drug discovery has been around for decades, although the past few years have seen a tectonic shift towards embracing computational technologies in both academia and pharma. This shift is largely defined by the flood of data on ligand properties and binding to therapeutic targets and their 3D structures, abundant computing capacities and the advent of on-demand virtual libraries of drug-like small molecules in their billions. Taking full advantage of these resources requires fast computational methods for effective ligand screening. This includes structure-based virtual screening of gigascale chemical spaces, further facilitated by fast iterative screening approaches. Highly synergistic are developments in deep learning predictions of ligand properties and target activities in lieu of receptor structure. Here we review recent advances in ligand discovery technologies, their potential for reshaping the whole process of drug discovery and development, as well as the challenges they encounter. We also discuss how the rapid identification of highly diverse, potent, target-selective and drug-like ligands to protein targets can democratize the drug discovery process, presenting new opportunities for the cost-effective development of safer and more effective small-molecule treatments.

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.

Quantum Mechanical-Cluster Approach to Solve the Bioisosteric Replacement Problem in Drug Design

Bioisosteres are molecules that differ in substituents but still have very similar shapes. Bioisosteric replacements are ubiquitous in modern drug design, where they are used to alter metabolism, change bioavailability, or modify activity of the lead compound. Prediction of relative affinities of bioisosteres with computational methods is a long-standing task; however, the very shape closeness makes bioisosteric substitutions almost intractable for computational methods, which use standard force fields. Here, we design a quantum mechanical (QM)-cluster approach based on the GFN2-xTB semi-empirical quantum-chemical method and apply it to a set of H → F bioisosteric replacements. The proposed methodology enables advanced prediction of biological activity change upon bioisosteric substitution of -H with -F, with the standard deviation of 0.60 kcal/mol, surpassing the ChemPLP scoring function (0.83 kcal/mol), and making QM-based ΔΔG estimation comparable to ∼0.42 kcal/mol standard deviation of in vitro experiment. The speed of the method and lack of tunable parameters makes it affordable in current drug research.

Development of Pharmacophore Models for the Important Off-Target 5-HT2B Receptor

Toxicity is a major cause of attrition in the development of pharmaceuticals, and the off-target effects are a frequent contributor. The 5-HT_2B receptor agonism is known to be responsible for a variety of safety concerns including valvular heart disease which was the cause for the withdrawal of several compounds from the market. An early detection of potential binding to this receptor is thus desirable. Herein, we present the identification of key amino acid residues in the active site of 5-HT_2B by molecular dynamics simulations, the development of pharmacophore models and their performance on in-house data, and a structurally highly diverse subset of Enamine REAL labeled for 5-HT_2B activity by a machine learning model. These models may be used as filters employed on screening compound sets for the early filtration of compounds with potential 5-HT_2B off-target liabilities.

Investigating the Solvent Effects on Binding Affinity of PAHs-ExBox4+ Complexes: An Alchemical Approach

Polycyclic aromatic hydrocarbons (PAHs) are polluting agents, produced naturally or artificially, widely dispersed in the environment and potentially carcinogenic and immunotoxic to humans and animals, mainly for marine life. Recently, a tetracationic box-shaped cyclophane (ExBox⁴⁺) was synthesized, fully characterized, and revealed to form host-guest complexes with PAHs in acetonitrile, demonstrating the potential ability for it to act as a PAHs scavenger. This work investigates, through Molecular Dynamics (MD) simulations, the binding affinity between different PAHs and ExBox⁴⁺ in different solvents: chloroform (nonpolar), acetonitrile (polar protic), and water (polar protic). An alchemical method of simultaneous decoupling-recoupling (SDR) was used and implemented in a newly developed Python program called GHOAT, which fully automates the calculation of binding free energies and invokes the AMBER 2020 simulation package. The results showed that the affinity between ExBox⁴⁺ and PAHs in water is much larger than in organic media, with free energies between -5 and -20 kcal/mol, being able to act as a PHAs scavenger with great potential for applications in environmental chemistry such as soil washing. The results also reveal a significant correlation with the experimental available ΔG values. The methodology employed presents itself as an important tool for the in silico determination of binding affinities, not only available for charged cyclophanes but also extensible to several other HG supramolecular systems in condensed media, aiding in the rational design of host-guest systems in a significant way.

Evolutionary divergence in the conformational landscapes of tyrosine vs serine/threonine kinases

Inactive conformations of protein kinase catalytic domains where the DFG motif has a "DFG-out" orientation and the activation loop is folded present a druggable binding pocket that is targeted by FDA-approved 'type-II inhibitors' in the treatment of cancers. Tyrosine kinases (TKs) typically show strong binding affinity with a wide spectrum of type-II inhibitors while serine/threonine kinases (STKs) usually bind more weakly which we suggest here is due to differences in the folded to extended conformational equilibrium of the activation loop between TKs vs. STKs. To investigate this, we use sequence covariation analysis with a Potts Hamiltonian statistical energy model to guide absolute binding free-energy molecular dynamics simulations of 74 protein-ligand complexes. Using the calculated binding free energies together with experimental values, we estimated free-energy costs for the large-scale (~17-20 Å) conformational change of the activation loop by an indirect approach, circumventing the very challenging problem of simulating the conformational change directly. We also used the Potts statistical potential to thread large sequence ensembles over active and inactive kinase states. The structure-based and sequence-based analyses are consistent; together they suggest TKs evolved to have free-energy penalties for the classical 'folded activation loop' DFG-out conformation relative to the active conformation, that is, on average, 4-6 kcal/mol smaller than the corresponding values for STKs. Potts statistical energy analysis suggests a molecular basis for this observation, wherein the activation loops of TKs are more weakly 'anchored' against the catalytic loop motif in the active conformation and form more stable substrate-mimicking interactions in the inactive conformation. These results provide insights into the molecular basis for the divergent functional properties of TKs and STKs, and have pharmacological implications for the target selectivity of type-II inhibitors.

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

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

Absolute Binding Free Energy Calculations for Buried Water Molecules

Water often plays a key role in mediating protein-ligand interactions. Understanding contributions from active-site water molecules to binding thermodynamics of a ligand is important in predicting binding free energies for ligand optimization. In this work, we tested a non-equilibrium switching method for absolute binding free energy calculations on water molecules in binding sites of 13 systems. We discuss the lessons we learned about identified issues that affected our calculations and ways to address them. This work fits with our larger focus on how to do accurate ligand binding free energy calculations when water rearrangements are very slow, such as rearrangements due to ligand modification (as in relative free energy calculations) or ligand binding (as in absolute free energy calculations). The method studied in this work can potentially be used to account for limited water sampling via providing endpoint corrections to free energy calculations using our calculated binding free energy of water.

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

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

Probing Factor Xa Protein-Ligand Interactions: Accurate Free Energy Calculations and Experimental Validations of Two Series of High-Affinity Ligands

The accurate prediction of protein-ligand binding affinity belongs to one of the central goals in computer-based drug design. Molecular dynamics (MD)-based free energy calculations have become increasingly popular in this respect due to their accuracy and solid theoretical basis. Here, we present a combined study which encompasses experimental and computational studies on two series of factor Xa ligands, which enclose a broad chemical space including large modifications of the central scaffold. Using this integrated approach, we identified several new ligands with different heterocyclic scaffolds different from the previously identified indole-2-carboxamides that show superior or similar affinity. Furthermore, the so far underexplored terminal alkyne moiety proved to be a suitable non-classical bioisosteric replacement for the higher halogen-π aryl interactions. With this challenging example, we demonstrated the ability of the MD-based non-equilibrium free energy calculation approach for guiding crucial modifications in the lead optimization process, such as scaffold replacement and single-site modifications at molecular interaction hot spots.

Assessing the Activation of Tyrosine Kinase KIT through Free Energy Calculations

KIT is a type 3 receptor tyrosine kinase that plays a crucial role in cellular growth and proliferation. Mutations in KIT can dysregulate its active-inactive equilibrium. Activating mutations drive cancer growth, while deactivating mutations result in the loss of skin and hair pigmentation in a disease known as piebaldism. Here, we propose a method based on molecular dynamics and free energy calculations to predict the functional effect of KIT mutations. Our calculations may have important clinical implications by defining the functional significance of previously uncharacterized KIT mutations and guiding targeted therapy.

Evaluating the use of absolute binding free energy in the fragment optimisation process

Key to the fragment optimisation process within drug design is the need to accurately capture the changes in affinity that are associated with a given set of chemical modifications. Due to the weakly binding nature of fragments, this has proven to be a challenging task, despite recent advancements in leveraging experimental and computational methods. In this work, we evaluate the use of Absolute Binding Free Energy (ABFE) calculations in guiding fragment optimisation decisions, retrospectively calculating binding free energies for 59 ligands across 4 fragment elaboration campaigns. We first demonstrate that ABFEs can be used to accurately rank fragment-sized binders with an overall Spearman's r of 0.89 and a Kendall τ of 0.67, although often deviating from experiment in absolute free energy values with an RMSE of 2.75 kcal/mol. We then also show that in several cases, retrospective fragment optimisation decisions can be supported by the ABFE calculations. Comparing against cheaper endpoint methods, namely Nwat-MM/GBSA, we find that ABFEs offer better ranking power and correlation metrics. Our results indicate that ABFE calculations can usefully guide fragment elaborations to maximise affinity.

Comparative Interaction Studies of Quercetin with 2-Hydroxyl-propyl-β-cyclodextrin and 2,6-Methylated-β-cyclodextrin

Quercetin (QUE) is a well-known natural product that can exert beneficial properties on human health. However, due to its low solubility its bioavailability is limited. In the present study, we examine whether its formulation with two cyclodextrins (CDs) may enhance its pharmacological profile. Comparative interaction studies of quercetin with 2-hydroxyl-propyl-β-cyclodextrin (2HP-β-CD) and 2,6-methylated cyclodextrin (2,6Me-β-CD) were performed using NMR spectroscopy, DFT calculations, and in silico molecular dynamics (MD) simulations. Using T1 relaxation experiments and 2D DOSY it was illustrated that both cyclodextrin vehicles can host quercetin. Quantum mechanical calculations showed the formation of hydrogen bonds between QUE with 2HP-β-CD and 2,6Μe-β-CD. Six hydrogen bonds are formed ranging between 2 to 2.8 Å with 2HP-β-CD and four hydrogen bonds within 2.8 Å with 2,6Μe-β-CD. Calculations of absolute binding free energies show that quercetin binds favorably to both 2,6Me-β-CD and 2HP-β-CD. MM/GBSA results show equally favorable binding of quercetin in the two CDs. Fluorescence spectroscopy shows moderate binding of quercetin in 2HP-β-CD (520 M-1) and 2,6Me-β-CD (770 M-1). Thus, we propose that both formulations (2HP-β-CD:quercetin, 2,6Me-β-CD:quercetin) could be further explored and exploited as small molecule carriers in biological studies.

Accurate Binding Free Energy Method from End-State MD Simulations

Herein, we introduce a new strategy to estimate binding free energies using end-state molecular dynamics simulation trajectories. The method is adopted from linear interaction energy (LIE) and ANI-2x neural network potentials (machine learning) for the atomic simulation environment (ASE). It predicts the single-point interaction energies between ligand–protein and ligand–solvent pairs at the accuracy of the wb97x/6-31G* level for the conformational space that is sampled by molecular dynamics (MD) simulations. Our results on 54 protein–ligand complexes show that the method can be accurate and have a correlation of R = 0.87–0.88 to the experimental binding free energies, outperforming current end-state methods with reduced computational cost. The method also allows us to compare BFEs of ligands with different scaffolds. The code is available free of charge (documentation and test files) at https://github.com/otayfuroglu/deepQM.

Absolute binding free energy calculations improve enrichment of actives in virtual compound screening

We determined the effectiveness of absolute binding free energy (ABFE) calculations to refine the selection of active compounds in virtual compound screening, a setting where the more commonly used relative binding free energy approach is not readily applicable. To do this, we conducted baseline docking calculations of structurally diverse compounds in the DUD-E database for three targets, BACE1, CDK2 and thrombin, followed by ABFE calculations for compounds with high docking scores. The docking calculations alone achieved solid enrichment of active compounds over decoys. Encouragingly, the ABFE calculations then improved on this baseline. Analysis of the results emphasizes the importance of establishing high quality ligand poses as starting points for ABFE calculations, a nontrivial goal when processing a library of diverse compounds without informative co-crystal structures. Overall, our results suggest that ABFE calculations can play a valuable role in the drug discovery process.

Drug Discovery by Automated Adaptation of Chemical Structure and Identity

Computer-aided drug design offers the potential to dramatically reduce the cost and effort required for drug discovery. While screening-based methods are valuable in the early stages of hit identification, they are frequently succeeded by iterative, hypothesis-driven computations that require recurrent investment of human time and intuition. To increase automation, we introduce a computational method for lead refinement that combines concerted dynamics of the ligand/protein complex via molecular dynamics simulations with integrated Monte Carlo-based changes in the chemical formula of the ligand. This approach, which we refer to as ligand-exchange Monte Carlo molecular dynamics, accounts for solvent- and entropy-based contributions to competitive binding free energies by coupling the energetics of bound and unbound states during the ligand-exchange attempt. Quantitative comparison of relative binding free energies to reference values from free energy perturbation, conducted in vacuum, indicates that ligand-exchange Monte Carlo molecular dynamics simulations sample relevant conformational ensembles and are capable of identifying strongly binding compounds. Additional simulations demonstrate the use of an implicit solvent model. We speculate that the use of chemical graphs in which exchanges are only permitted between ligands with sufficient similarity may enable an automated search to capture some of the benefits provided by human intuition during hypothesis-guided lead refinement.

Large Scale Study of Ligand-Protein Relative Binding Free Energy Calculations: Actionable Predictions from Statistically Robust Protocols

The accurate and reliable prediction of protein-ligand binding affinities can play a central role in the drug discovery process as well as in personalized medicine. Of considerable importance during lead optimization are the alchemical free energy methods that furnish an estimation of relative binding free energies (RBFE) of similar molecules. Recent advances in these methods have increased their speed, accuracy, and precision. This is evident from the increasing number of retrospective as well as prospective studies employing them. However, such methods still have limited applicability in real-world scenarios due to a number of important yet unresolved issues. Here, we report the findings from a large data set comprising over 500 ligand transformations spanning over 300 ligands binding to a diverse set of 14 different protein targets which furnish statistically robust results on the accuracy, precision, and reproducibility of RBFE calculations. We use ensemble-based methods which are the only way to provide reliable uncertainty quantification given that the underlying molecular dynamics is chaotic. These are implemented using TIES (Thermodynamic Integration with Enhanced Sampling). Results achieve chemical accuracy in all cases. Ensemble simulations also furnish information on the statistical distributions of the free energy calculations which exhibit non-normal behavior. We find that the "enhanced sampling" method known as replica exchange with solute tempering degrades RBFE predictions. We also report definitively on numerous associated alchemical factors including the choice of ligand charge method, flexibility in ligand structure, and the size of the alchemical region including the number of atoms involved in transforming one ligand into another. Our findings provide a key set of recommendations that should be adopted for the reliable application of RBFE methods.

GROMACS in the Cloud: A Global Supercomputer to Speed Up Alchemical Drug Design

We assess costs and efficiency of state-of-the-art high-performance cloud computing and compare the results to traditional on-premises compute clusters. Our use case is atomistic simulations carried out with the GROMACS molecular dynamics (MD) toolkit with a particular focus on alchemical protein-ligand binding free energy calculations. We set up a compute cluster in the Amazon Web Services (AWS) cloud that incorporates various different instances with Intel, AMD, and ARM CPUs, some with GPU acceleration. Using representative biomolecular simulation systems, we benchmark how GROMACS performs on individual instances and across multiple instances. Thereby we assess which instances deliver the highest performance and which are the most cost-efficient ones for our use case. We find that, in terms of total costs, including hardware, personnel, room, energy, and cooling, producing MD trajectories in the cloud can be about as cost-efficient as an on-premises cluster given that optimal cloud instances are chosen. Further, we find that high-throughput ligand-screening can be accelerated dramatically by using global cloud resources. For a ligand screening study consisting of 19 872 independent simulations or ∼200 μs of combined simulation trajectory, we made use of diverse hardware available in the cloud at the time of the study. The computations scaled-up to reach peak performance using more than 4 000 instances, 140 000 cores, and 3 000 GPUs simultaneously. Our simulation ensemble finished in about 2 days in the cloud, while weeks would be required to complete the task on a typical on-premises cluster consisting of several hundred nodes.