Rapid and Reliable Binding Affinity Prediction of Bromodomain Inhibitors: A Computational Study

Accurate prediction of DNA-Intercalator binding energies: Ensemble of short or long molecular dynamics simulations?

Despite the wide use of molecular dynamics (MD) simulations for binding energy predictions in biomolecular systems, results from single MD simulations are non-reproducible and often deviate from experimental values, even when longer simulations are used. This study addresses these limitations using ensemble MD simulations for the formation of DNA-intercalator complexes. Twenty-five replicas of short (10 ns) and long (100 ns) MD simulations were performed on different intercalators binding into DNA. The MM/PBSA and MM/GBSA binding energies of the Doxorubicin intercalating into DNA, including entropy and deformation energy corrections, are -7.3 ± 2.0 kcal/mol and -8.9 ± 1.6 kcal/mol, using 25 replicas of 100 ns. These values were closely reproduced even with shorter simulations of 10 ns, where the energies, averaged over 25 replicas, are -7.6 ± 2.4 kcal/mol (MM/PBSA) and -8.3 ± 2.9 kcal/mol (MM/GBSA). In both cases, the energies align well with the experimental range of -7.7 ± 0.3 to -9.9 ± 0.1 kcal/mol. This shows that reproducibility and accuracy of the binding energies depend more on the number of replicas than on the simulation length. The study was repeated for the DNA-Proflavine system, where the corrected MM/PBSA and MM/GBSA binding energies, averaged over 25 replicas of 10 ns each, are -5.6 ± 1.4 and -5.3 ± 2.3 kcal/mol, respectively. These are congruent with the experimental range of -5.9 to -7.1 kcal/mol. Bootstrap analyses revealed that 6 replicas of 100 ns or 8 replicas of 10 ns provide a good balance between computational efficiency and accuracy within 1.0 kcal/mol from experimental values.

Impact of Varying Velocities and Solvation Boxes on Alchemical Free-Energy Simulations

Alchemical free-energy perturbation (FEP) is an accurate and thermodynamically stringent way to estimate relative energies for the binding of small ligands to biological macromolecules. It has repeatedly been pointed out that a single simulation normally stays near the starting point in phase space and therefore underestimates the uncertainty of the results. Therefore, it is better to run an ensemble of independent simulations. Traditionally, such an ensemble has been generated by using different starting velocities. We argue that it is better to use also other random choices made during the setup of the simulations, in particular the solvation of the solute. We show here that such solvent-induced independent simulations (SIS) sometimes give a larger standard deviation and slightly different results for the binding of 42 ligands to five different proteins, viz. human N-terminal bromodomain 4, the Leu99Ala mutant of T4 lysozyme, dihydrofolate reductase, blood-clotting factor Xa, and ferritin. SIS does not involve any increase in the time consumption. Therefore, we strongly recommend the use of SIS (in addition to different velocities) to start independent simulations. Other random or uncertain choices in the setup of the simulated systems, e.g., the selection of residues with alternative conformations or positions of added protons, may also be used to enhance the variation in independent simulations.

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.

Repurposing raltegravir for reducing inflammation and treating cancer: a bioinformatics analysis

Inflammation is a defensive mechanism that safeguards the human body against detrimental stimuli. Within this intricate process, ADAM17, a zinc-dependent metalloprotease, emerges as an indispensable element, fostering the activation of diverse inflammatory and growth factors within the organism. Nonetheless, ADAM17 malfunctions can augment the rate of growth, inflammatory factors, and subsequent damage. Thus, in this study, we examined and repurposed drugs to suppress the activity of ADAM17. To this end, we employed bioinformatics techniques such as molecular docking, molecular dynamics, and pharmacokinetic studies. Five FDA-approved drugs including Raltegravir, Conivaptan, Paclitaxel, Saquinavir, and Venetoclax with the ability to impede the activity of the ADAM17 metalloenzyme were identified. Moreover, these drugs did not include strong zinc-binding functional groups when verified by the ACE functional group finder. However, further in silico analysis has indicated that Raltegravir demonstrates a commendable interaction with the active site amino acids and exhibits the most favorable pharmacokinetic properties compared to others. Considering the results of bioinformatics tools, it can be concluded that Raltegravir as an antiviral drug could be repurposed to prevent severe inflammatory response and tumorigenesis resulting from ADAM17 malfunction.

Theoretical study on the selective binding of BH3-only protein BAD to anti-apoptotic protein BCL-xL instead of MCL-1

In this study, molecular dynamics simulations were used to systematically explore the reason why BH3-only protein BAD binds to anti-apoptotic protein BCL-xL but not to MCL-1 to give more theoretical hints for the design of BAD mimetic inhibitors for the dual-targeting of BCL-xL and MCL-1. Starting with the difference in residue-based binding energy contributions, a series of analyses were conducted to identify the hotspot residues in MCL-1 that significantly affect the interaction with BAD. Among them, the insertion of the T residue in the loop between α4 and α5 domains of MCL-1 is considered to be the main cause of BAD selective binding. The inserted T residue reduces the stability of the loop and weakens the hydrogen bond interactions that originally bound E19 of BAD in BCL-xL/BAD, and the freed E19 severely interferes with the salt bridge between D16 and Arg53 by electrostatic repulsion. This salt-bridge is believed to be critical for maintaining the binding between BCL-xL and BAD. By clarifying the reasons for differential binding, we can more specifically optimize the BAD sequence to target both BCL-xL and MCL-1.

Computational Workflow for Refining AlphaFold Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive Human Adenosine A3 Receptor

A structure-based drug design pipeline that considers both thermodynamic and kinetic binding data of ligands against a receptor will enable the computational design of improved drug molecules. For unresolved GPCR-ligand complexes, a workflow that can apply both thermodynamic and kinetic binding data in combination with alpha-fold (AF)-derived or other homology models and experimentally resolved binding modes of relevant ligands in GPCR-homologs needs to be tested. Here, as test case, we studied a congeneric set of ligands that bind to a structurally unresolved G protein-coupled receptor (GPCR), the inactive human adenosine A3 receptor (hA3R). We tested three available homology models from which two have been generated from experimental structures of hA1R or hA2AR and one model was a multistate alphafold 2 (AF2)-derived model. We applied alchemical calculations with thermodynamic integration coupled with molecular dynamics (TI/MD) simulations to calculate the experimental relative binding free energies and residence time (τ)-random accelerated MD (τ-RAMD) simulations to calculate the relative residence times (RTs) for antagonists. While the TI/MD calculations produced, for the three homology models, good Pearson correlation coefficients, correspondingly, r = 0.74, 0.62, and 0.67 and mean unsigned error (mue) values of 0.94, 1.31, and 0.81 kcal mol-1, the τ-RAMD method showed r = 0.92 and 0.52 for the first two models but failed to produce accurate results for the multistate AF2-derived model. With subsequent optimization of the AF2-derived model by reorientation of the side chain of R1735.34 located in the extracellular loop 2 (EL2) that blocked ligand's unbinding, the computational model showed r = 0.84 for kinetic data and improved performance for thermodynamic data (r = 0.81, mue = 0.56 kcal mol-1). Overall, after refining the multistate AF2 model with physics-based tools, we were able to show a strong correlation between predicted and experimental ligand relative residence times and affinities, achieving a level of accuracy comparable to an experimental structure. The computational workflow used can be applied to other receptors, helping to rank candidate drugs in a congeneric series and enabling the prioritization of leads with stronger binding affinities and longer residence times.

Accelerating COVID-19 Drug Discovery with High-Performance Computing

The recent COVID-19 pandemic has served as a timely reminder that the existing drug discovery is a laborious, expensive, and slow process. Never has there been such global demand for a therapeutic treatment to be identified as a matter of such urgency. Unfortunately, this is a scenario likely to repeat itself in future, so it is of interest to explore ways in which to accelerate drug discovery at pandemic speed. Computational methods naturally lend themselves to this because they can be performed rapidly if sufficient computational resources are available. Recently, high-performance computing (HPC) technologies have led to remarkable achievements in computational drug discovery and yielded a series of new platforms, algorithms, and workflows. The application of artificial intelligence (AI) and machine learning (ML) approaches is also a promising and relatively new avenue to revolutionize the drug design process and therefore reduce costs. In this review, I describe how molecular dynamics simulations (MD) were successfully integrated with ML and adapted to HPC to form a powerful tool to study inhibitors for four of the COVID-19 target proteins. The emphasis of this review is on the strategy that was used with an explanation of each of the steps in the accelerated drug discovery workflow. For specific technical details, the reader is directed to the relevant research publications.

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.

Design, synthesis, computational study and cytotoxic evaluation of some new quinazoline derivatives containing pyrimidine moiety

Quinazoline derivatives, as an important category of heterocyclic compounds, have received much attention for the design and development of new drugs due to their various pharmacological properties. Besides, there is a great deal of evidence showing pyrimidine analogs as anticancer agents. Thus, in the present study, for the design of new target compounds with cytotoxic activity, we focused on various quinazolinone and pyrimidine hybrids. A new series of quinazoline-pyrimidine hybrid derivatives (6a-6n) have been designed and synthesized as novel antiproliferative agents. All the synthesized compounds characterized based on their IR, NMR and Mass spectroscopic data. Antiproliferative activities of the new compounds were evaluated against three human cancer cell lines (MCF-7, A549, SW-480). The compounds were found to have appropriate potential with IC50 values ranging from 2.3 ± 5.91 to 176.5 ± 0.7 μM against the tested cell lines. Compound 6n exerted the highest antiproliferative activity with IC50 values of 5.9 ± 1.69 μM, 2.3 ± 5.91 μM and 5.65 ± 2.33 μM against A549, SW-480 and MCF-7 respectively. The results indicated that 6n could induce apoptosis in A549 cell line in a dose dependent manner and arrest in the S phase of cell cycle. Docking studies were also done to investigate the detailed binding pattern of the synthesized compounds against EGFR. Furthermore, molecular dynamic simulation and binding free energy calculation have been done to rescore initial docking pose of the synthesized compounds using ensemble-based MMGB/PBSA free energy method. According to the results, free energy calculation confirmed biological activity of compounds and also, Arg 817 and Lys 721 residues had the pivotal role in the high potency of 6n. Finally, the drug likeness and in silico ADME study were also predicted.

The role of loop dynamics in the prediction of ligand-protein binding enthalpy

The enthalpic and entropic components of ligand-protein binding free energy reflect the interactions and dynamics between ligand and protein. Despite decades of study, our understanding and hence our ability to predict these individual components remains poor. In recent years, there has been substantial effort and success in the prediction of relative and absolute binding free energies, but the prediction of the enthalpic (and entropic) contributions in biomolecular systems remains challenging. Indeed, it is not even clear what kind of performance in terms of accuracy could currently be obtained for such systems. It is, however, relatively straight-forward to compute the enthalpy of binding. We thus evaluated the performance of absolute enthalpy of binding calculations using molecular dynamics simulation for ten inhibitors against a member of the bromodomain family, BRD4-1, against isothermal titration calorimetry data. Initial calculations, with the AMBER force-field showed good agreement with experiment (R² = 0.60) and surprisingly good accuracy with an average of root-mean-square error (RMSE) = 2.49 kcal mol^-1. Of the ten predictions, three were obvious outliers that were all over-predicted compared to experiment. Analysis of various simulation factors, including parameterization, buffer concentration and conformational dynamics, revealed that the behaviour of a loop (the ZA loop on the periphery of the binding site) strongly dictates the enthalpic prediction. Consistent with previous observations, the loop exists in two distinct conformational states and by considering one or the other or both states, the prediction for the three outliers can be improved dramatically to the point where the R² = 0.95 and the accuracy in terms of RMSE improves to 0.90 kcal mol^-1. However, performance across force-fields is not consistent: if OPLS and CHARMM are used, different outliers are observed and the correlation with the ZA loop behaviour is not recapitulated, likely reflecting parameterization as a confounding problem. The results provide a benchmark standard for future study and comparison.

Structure and dynamics of an archetypal DNA nanoarchitecture revealed via cryo-EM and molecular dynamics simulations

DNA can be folded into rationally designed, unique, and functional materials. To fully realise the potential of these DNA materials, a fundamental understanding of their structure and dynamics is necessary, both in simple solvents as well as more complex and diverse anisotropic environments. Here we analyse an archetypal six-duplex DNA nanoarchitecture with single-particle cryo-electron microscopy and molecular dynamics simulations in solvents of tunable ionic strength and within the anisotropic environment of biological membranes. Outside lipid bilayers, the six-duplex bundle lacks the designed symmetrical barrel-type architecture. Rather, duplexes are arranged in non-hexagonal fashion and are disorted to form a wider, less elongated structure. Insertion into lipid membranes, however, restores the anticipated barrel shape due to lateral duplex compression by the bilayer. The salt concentration has a drastic impact on the stability of the inserted barrel-shaped DNA nanopore given the tunable electrostatic repulsion between the negatively charged duplexes. By synergistically combining experiments and simulations, we increase fundamental understanding into the environment-dependent structural dynamics of a widely used nanoarchitecture. This insight will pave the way for future engineering and biosensing applications.

Dual A1/A3 Adenosine Receptor Antagonists: Binding Kinetics and Structure-Activity Relationship Studies Using Mutagenesis and Alchemical Binding Free Energy Calculations

Drugs targeting adenosine receptors (AR) can provide treatment for diseases. We report the identification of 7-(phenylamino)-pyrazolo[3,4-c]pyridines L2-L10, A15, and A17 as low-micromolar to low-nanomolar A₁R/A₃R dual antagonists, with 3-phenyl-5-cyano-7-(trimethoxyphenylamino)-pyrazolo[3,4-c]pyridine (A17) displaying the highest affinity at both receptors with a long residence time of binding, as determined using a NanoBRET-based assay. Two binding orientations of A17 produce stable complexes inside the orthosteric binding area of A₁R in molecular dynamics (MD) simulations, and we selected the most plausible orientation based on the agreement with alanine mutagenesis supported by affinity experiments. Interestingly, for drug design purposes, the mutation of L250^6.51 to alanine increased the binding affinity of A17 at A₁R. We explored the structure-activity relationships against A₁R using alchemical binding free energy calculations with the thermodynamic integration coupled with the MD simulation (TI/MD) method, applied on the whole G-protein-coupled receptor-membrane system, which showed a good agreement (r = 0.73) between calculated and experimental relative binding free energies.

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

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

Alchemical Free Energy Estimators and Molecular Dynamics Engines: Accuracy, Precision, and Reproducibility

The binding free energy between a ligand and its target protein is an essential quantity to know at all stages of the drug discovery pipeline. Assessing this value computationally can offer insight into where efforts should be focused in the pursuit of effective therapeutics to treat a myriad of diseases. In this work, we examine the computation of alchemical relative binding free energies with an eye for assessing reproducibility across popular molecular dynamics packages and free energy estimators. The focus of this work is on 54 ligand transformations from a diverse set of protein targets: MCL1, PTP1B, TYK2, CDK2, and thrombin. These targets are studied with three popular molecular dynamics packages: OpenMM, NAMD2, and NAMD3 alpha. Trajectories collected with these packages are used to compare relative binding free energies calculated with thermodynamic integration and free energy perturbation methods. The resulting binding free energies show good agreement between molecular dynamics packages with an average mean unsigned error between them of 0.50 kcal/mol. The correlation between packages is very good, with the lowest Spearman's, Pearson's and Kendall's tau correlation coefficients being 0.92, 0.91, and 0.76, respectively. Agreement between thermodynamic integration and free energy perturbation is shown to be very good when using ensemble averaging.

Ensemble Simulations and Experimental Free Energy Distributions: Evaluation and Characterization of Isoxazole Amides as SMYD3 Inhibitors

Optimization of binding affinities for ligands to their target protein is a primary objective in rational drug discovery. Herein, we report on a collaborative study that evaluates various compounds designed to bind to the SET and MYND domain-containing protein 3 (SMYD3). SMYD3 is a histone methyltransferase and plays an important role in transcriptional regulation in cell proliferation, cell cycle, and human carcinogenesis. Experimental measurements using the scintillation proximity assay show that the distributions of binding free energies from a large number of independent measurements exhibit non-normal properties. We use ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) and TIES (thermodynamic integration with enhanced sampling) protocols to predict the binding free energies and to provide a detailed chemical insight into the nature of ligand-protein binding. Our results show that the 1-trajectory ESMACS protocol works well for the set of ligands studied here. Although one unexplained outlier exists, we obtain excellent statistical ranking across the set of compounds from the ESMACS protocol and good agreement between calculations and experiments for the relative binding free energies from the TIES protocol. ESMACS and TIES are again found to be powerful protocols for the accurate comparison of the binding free energies.

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.

Alchemical Free Energy Methods Applied to Complexes of the First Bromodomain of BRD4

Accurate and rapid predictions of the binding affinity of a compound to a target are one of the ultimate goals of computer aided drug design. Alchemical approaches to free energy estimations follow the path from an initial state of the system to the final state through alchemical changes of the energy function during a molecular dynamics simulation. Herein, we explore the accuracy and efficiency of two such techniques: relative free energy perturbation (FEP) and multisite lambda dynamics (MSλD). These are applied to a series of inhibitors for the bromodomain-containing protein 4 (BRD4). We demonstrate a procedure for obtaining accurate relative binding free energies using MSλD when dealing with a change in the net charge of the ligand. This resulted in an impressive comparison with experiment, with an average difference of 0.4 ± 0.4 kcal mol-1. In a benchmarking study for the relative FEP calculations, we found that using 20 lambda windows with 0.5 ns of equilibration and 1 ns of data collection for each window gave the optimal compromise between accuracy and speed. Overall, relative FEP and MSλD predicted binding free energies with comparable accuracy, an average of 0.6 kcal mol-1 for each method. However, MSλD makes predictions for a larger molecular space over a much shorter time scale than relative FEP, with MSλD requiring a factor of 18 times less simulation time for the entire molecule space.

Thermodynamic and structural insights into the repurposing of drugs that bind to SARS-CoV-2 main protease

Although researchers have been working tirelessly since the COVID-19 outbreak, so far only three drugs - remdesivir, ronapreve and molnupiravir - have been approved for use in some countries which directly target the SARS-CoV-2 virus. Given the slow pace and substantial costs of new drug discovery and development, together with the urgency of the matter, repurposing of existing drugs for the ongoing disease is an attractive proposition. In a recent study, a high-throughput X-ray crystallographic screen was performed for a selection of drugs which have been approved or are in clinical trials. Thirty-seven compounds have been identified from drug libraries all of which bind to the SARS-CoV-2 main protease (3CL^pro). In the current study, we use molecular dynamics simulation and an ensemble-based free energy approach, namely, enhanced sampling of molecular dynamics with approximation of continuum solvent (ESMACS), to investigate a subset of the aforementioned compounds. The drugs studied here are highly diverse, interacting with different binding sites and/or subsites of 3CL^pro. The predicted free energies are compared with experimental results wherever they are available and they are found to be in excellent agreement. Our study also provides detailed energetic insights into the nature of the associated drug-protein binding, in turn shedding light on the design and discovery of potential drugs.

Reliable In Silico Ranking of Engineered Therapeutic TCR Binding Affinities with MMPB/GBSA

Accurate and efficient in silico ranking of protein–protein binding affinities is useful for protein design with applications in biological therapeutics. One popular approach to rank binding affinities is to apply the molecular mechanics Poisson–Boltzmann/generalized Born surface area (MMPB/GBSA) method to molecular dynamics (MD) trajectories. Here, we identify protocols that enable the reliable evaluation of T-cell receptor (TCR) variants binding to their target, peptide-human leukocyte antigens (pHLAs). We suggest different protocols for variant sets with a few (≤4) or many mutations, with entropy corrections important for the latter. We demonstrate how potential outliers could be identified in advance and that just 5–10 replicas of short (4 ns) MD simulations may be sufficient for the reproducible and accurate ranking of TCR variants. The protocols developed here can be applied toward in silico screening during the optimization of therapeutic TCRs, potentially reducing both the cost and time taken for biologic development.

Cu(II)(PhOMe-Salophen) Complex: Greener Pasture Biological Study, XRD/HAS Interactions, and MEP

PhOMe-salophen (1b) (salophen is N,N-bis(salycilidene)-1,2-phenylenediamine with two tert-butyl on each ring) and Cu(II) complex with PhOMe-salophen (1c) have been synthesized and characterized using various tools, including X-ray diffraction for the Cu(II)-complex (1c, C₄₃H₅₂CuN₂O₃)). The copper complex has been obtained by Cu²⁺ templated approach using 1b. PhOMe-salophen (1b) has been obtained in reasonably high yield using a mixture of the Schiff-base, 1a, Pd(OAc)₂, PPh₃, Na₂CO₃, 4-methoxyphenylboronic acid in benzene. We focus in this research work on the electronic and structural properties of the Cu–Schiff base complex. The tetra-coordinate τ₄ index was calculated, indicating almost a perfect square planner in agreement with X-ray diffraction results. MEP reveals the maximum positive regions in 1/-associated with the azomethine and methoxyphenyl C–H bonds with an average value of 0.03 a.u. Hirshfeld surface analysis (HSA) was also studied to highlight the significant inter-atomic contacts and their percentage contribution through 2D Fingerprint plot. In a fair comparative molecular docking study, 1b and 1c were docked together with N-[{(5-methylisoxazol-3-yl)-carbonyl}alanyl}-l-valyl]-N1-((1R,2Z)-4-(benzyloxy)-4-oxo-1-[{(3R)-2-oxopyrrolidin-3-yl}methyl]but-2-enyl)-l-leucinamide, N3 against main protease M^pro, (PDB code 7BQY) using the same parameters and conditions. Interesting here to use the free energy, in silico, molecular docking approach, which aims to rank our molecules with respect to the well-known inhibitor, N3. The binding scores of 1b, 1c, N3 are –7.8, –9.0, and –8.4 kcal/mol, respectively. These preliminary results propose that ligands deserve additional study in the context of possible remedial agents for COVID-19.

Pandemic drugs at pandemic speed: infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- and physics-based simulations on high-performance computers

The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case, developed for linear accelerators, and physics-based methods. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here, we present an innovative infrastructural development that combines both approaches to accelerate drug discovery. The scale of the potential resulting workflow is such that it is dependent on supercomputing to achieve extremely high throughput. We have demonstrated the viability of this workflow for the study of inhibitors for four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead antiviral compounds through repurposing on a variety of supercomputers.

Understanding the avidin-biotin binding based on polarized protein-specific charge

In this study, charge updating schemes based on the local polarized protein-specific charge (LPPC) were introduced to vary the atomic charges of the biotin molecule and the residues in close contact during the simulation of the avidin-biotin complexes. The need of the charge variation of the ligand in response to changes in its surroundings was thoroughly studied. The results show that the calculated binding energy difference between biotin (BTN1) and 2'-iminobiotin (BTN2) and avidin is in excellent agreement with the experimental value, thus verifying the feasibility of updating the atomic charges of ligands during the simulation.

Selectively targeting individual bromodomain: Drug discovery and molecular mechanisms

Bromodomain-containing proteins include bromodomain and extra-terminal (BET) and non-BET families. Due to the conserved bromodomain (BD) module between BD-containing proteins, and especially BETs with each member having two BDs (BD1 and BD2), the high degree of structural similarity makes BD-selective inhibitors much difficult to be designed. However, increasing evidences emphasized that individual BDs had distinct functions and different cellular phenotypes after pharmacological inhibition, and selectively targeting one of the BDs could result in a different efficacy and tolerability profile. This review is to summarize the pioneering progress of BD-selective inhibitors targeting BET and non-BET proteins, focusing on their structural features, biological activity, therapeutic application and experimental/theoretical mechanisms. The present proteolysis targeting chimeras (PROTAC) degraders targeting BDs, and clinical status of BD-selective inhibitors were also analyzed, providing a new insight into future direction of bromodomain-selective drug discovery.

Ensembles Are Required to Handle Aleatoric and Parametric Uncertainty in Molecular Dynamics Simulation

Classical molecular dynamics is a computer simulation technique that is in widespread use across many areas of science, from physics and chemistry to materials, biology, and medicine. The method continues to attract criticism due its oft-reported lack of reproducibility which is in part due to a failure to submit it to reliable uncertainty quantification (UQ). Here we show that the uncertainty arises from a combination of (i) the input parameters and (ii) the intrinsic stochasticity of the method controlled by the random seeds. To illustrate the situation, we make a systematic UQ analysis of a widely used molecular dynamics code (NAMD), applied to estimate binding free energy of a ligand-bound to a protein. In particular, we replace the usually fixed input parameters with random variables, systematically distributed about their mean values, and study the resulting distribution of the simulation output. We also perform a sensitivity analysis, which reveals that, out of a total of 175 parameters, just six dominate the variance in the code output. Furthermore, we show that binding energy calculations dampen the input uncertainty, in the sense that the variation around the mean output free energy is less than the variation around the mean of the assumed input distributions, if the output is ensemble-averaged over the random seeds. Without such ensemble averaging, the predicted free energy is five times more uncertain. The distribution of the predicted properties is thus strongly dependent upon the random seed. Owing to this substantial uncertainty, robust statistical measures of uncertainty in molecular dynamics simulation require the use of ensembles in all contexts.

The effect of protein mutations on drug binding suggests ensuing personalised drug selection

The advent of personalised medicine promises a deeper understanding of mechanisms and therefore therapies. However, the connection between genomic sequences and clinical treatments is often unclear. We studied 50 breast cancer patients belonging to a population-cohort in the state of Qatar. From Sanger sequencing, we identified several new deleterious mutations in the estrogen receptor 1 gene (ESR1). The effect of these mutations on drug treatment in the protein target encoded by ESR1, namely the estrogen receptor, was achieved via rapid and accurate protein-ligand binding affinity interaction studies which were performed for the selected drugs and the natural ligand estrogen. Four nonsynonymous mutations in the ligand-binding domain were subjected to molecular dynamics simulation using absolute and relative binding free energy methods, leading to the ranking of the efficacy of six selected drugs for patients with the mutations. Our study shows that a personalised clinical decision system can be created by integrating an individual patient's genomic data at the molecular level within a computational pipeline which ranks the efficacy of binding of particular drugs to variant proteins.

Artificial intelligence in drug design: algorithms, applications, challenges and ethics

The discovery paradigm of drugs is rapidly growing due to advances in machine learning (ML) and artificial intelligence (AI). This review covers myriad faces of AI and ML in drug design. There is a plethora of AI algorithms, the most common of which are summarized in this review. In addition, AI is fraught with challenges that are highlighted along with plausible solutions to them. Examples are provided to illustrate the use of AI and ML in drug discovery and in predicting drug properties such as binding affinities and interactions, solubility, toxicology, blood–brain barrier permeability and chemical properties. The review also includes examples depicting the implementation of AI and ML in tackling intractable diseases such as COVID-19, cancer and Alzheimer’s disease. Ethical considerations and future perspectives of AI are also covered in this review.

Uncertainty quantification in classical molecular dynamics

Molecular dynamics simulation is now a widespread approach for understanding complex systems on the atomistic scale. It finds applications from physics and chemistry to engineering, life and medical science. In the last decade, the approach has begun to advance from being a computer-based means of rationalizing experimental observations to producing apparently credible predictions for a number of real-world applications within industrial sectors such as advanced materials and drug discovery. However, key aspects concerning the reproducibility of the method have not kept pace with the speed of its uptake in the scientific community. Here, we present a discussion of uncertainty quantification for molecular dynamics simulation designed to endow the method with better error estimates that will enable it to be used to report actionable results. The approach adopted is a standard one in the field of uncertainty quantification, namely using ensemble methods, in which a sufficiently large number of replicas are run concurrently, from which reliable statistics can be extracted. Indeed, because molecular dynamics is intrinsically chaotic, the need to use ensemble methods is fundamental and holds regardless of the duration of the simulations performed. We discuss the approach and illustrate it in a range of applications from materials science to ligand-protein binding free energy estimation. This article is part of the theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico'.

TIES 20: Relative Binding Free Energy with a Flexible Superimposition Algorithm and Partial Ring Morphing

The TIES (Thermodynamic Integration with Enhanced Sampling) protocol is a formally exact alchemical approach in computational chemistry to the calculation of relative binding free energies. The validity of TIES relies on the correctness of matching atoms across compared pairs of ligands, laying the foundation for the transformation along an alchemical pathway. We implement a flexible topology superimposition algorithm which uses an exhaustive joint-traversal for computing the largest common component(s). The algorithm is employed to enable matching and morphing of partial rings in the TIES protocol along with a validation study using 55 transformations and five different proteins from our previous work. We find that TIES 20 with the RESP charge system, using the new superimposition algorithm, reproduces the previous results with mean unsigned error of 0.75 kcal/mol with respect to the experimental data. Enabling the morphing of partial rings decreases the size of the alchemical region in the dual-topology transformations resulting in a significant improvement in the prediction precision. We find that increasing the ensemble size from 5 to 20 replicas per λ window only has a minimal impact on the accuracy. However, the non-normal nature of the relative free energy distributions underscores the importance of ensemble simulation. We further compare the results with the AM1-BCC charge system and show that it improves agreement with the experimental data by slightly over 10%. This improvement is partly due to AM1-BCC affecting only the charges of the atoms local to the mutation, which translates to even fewer morphed atoms, consequently reducing issues with sampling and therefore ensemble averaging. TIES 20, in conjunction with the enablement of ring morphing, reduces the size of the alchemical region and significantly improves the precision of the predicted free energies.

Rapid, accurate, precise and reproducible ligand-protein binding free energy prediction

A central quantity of interest in molecular biology and medicine is the free energy of binding of a molecule to a target biomacromolecule. Until recently, the accurate prediction of binding affinity had been widely regarded as out of reach of theoretical methods owing to the lack of reproducibility of the available methods, not to mention their complexity, computational cost and time-consuming procedures. The lack of reproducibility stems primarily from the chaotic nature of classical molecular dynamics (MD) and the associated extreme sensitivity of trajectories to their initial conditions. Here, we review computational approaches for both relative and absolute binding free energy calculations, and illustrate their application to a diverse set of ligands bound to a range of proteins with immediate relevance in a number of medical domains. We focus on ensemble-based methods which are essential in order to compute statistically robust results, including two we have recently developed, namely thermodynamic integration with enhanced sampling and enhanced sampling of MD with an approximation of continuum solvent. Together, these form a set of rapid, accurate, precise and reproducible free energy methods. They can be used in real-world problems such as hit-to-lead and lead optimization stages in drug discovery, and in personalized medicine. These applications show that individual binding affinities equipped with uncertainty quantification may be computed in a few hours on a massive scale given access to suitable high-end computing resources and workflow automation. A high level of accuracy can be achieved using these approaches.

Hit-to-lead and lead optimization binding free energy calculations for G protein-coupled receptors

We apply the hit-to-lead ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) and lead-optimization TIES (thermodynamic integration with enhanced sampling) methods to compute the binding free energies of a series of ligands at the A1 and A2A adenosine receptors, members of a subclass of the GPCR (G protein-coupled receptor) superfamily. Our predicted binding free energies, calculated using ESMACS, show a good correlation with previously reported experimental values of the ligands studied. Relative binding free energies, calculated using TIES, accurately predict experimentally determined values within a mean absolute error of approximately 1 kcal mol-1. Our methodology may be applied widely within the GPCR superfamily and to other small molecule-receptor protein systems.

Large-scale binding affinity calculations on commodity compute clouds

In recent years, it has become possible to calculate binding affinities of compounds bound to proteins via rapid, accurate, precise and reproducible free energy calculations. This is imperative in drug discovery as well as personalized medicine. This approach is based on molecular dynamics (MD) simulations and draws on sequence and structural information of the protein and compound concerned. Free energies are determined by ensemble averages of many MD replicas, each of which requires hundreds of cores and/or GPU accelerators, which are now available on commodity cloud computing platforms; there are also requirements for initial model building and subsequent data analysis stages. To automate the process, we have developed a workflow known as the binding affinity calculator. In this paper, we focus on the software infrastructure and interfaces that we have developed to automate the overall workflow and execute it on commodity cloud platforms, in order to reliably predict their binding affinities on time scales relevant to the domains of application, and illustrate its application to two free energy methods.

Identification of Optimal Ligand Growth Vectors Using an Alchemical Free-Energy Method

In this work, a novel method to rationally design inhibitors with improved steric contacts and enhanced binding free energies is presented. This new method uses alchemical single step perturbation calculations to rapidly optimize the van der Waals interactions of a small molecule in a protein-ligand complex in order to maximize its binding affinity. The results of the optimizer are used to predict beneficial growth vectors on the ligand, and good agreement is found between the predictions from the optimizer and a more rigorous free energy calculation, with a Spearman's rank order correlation of 0.59. The advantage of the method presented here is the significant speed up of over 10-fold compared to traditional free energy calculations and sublinear scaling with the number of growth vectors assessed. Where experimental data were available, mutations from hydrogen to a methyl group at sites highlighted by the optimizer were calculated with MBAR, and the mean unsigned error between experimental and calculated values of the binding free energy was 0.83 kcal/mol.

Diameter Is a Key 3D Characteristic for Assessments of Efficient Inhibitors of Protein-Protein Interactions

Three-dimensional (3D) molecular descriptors, including physicochemical and shape properties, for protein-protein interaction (PPI) interface inhibitors have become a topic of discussion. However, the relationships between such properties and binding free energy have not been adequately investigated. In this study, we focused on identifying key 3D molecular descriptors related to the binding free energy and/or the ligand efficiency (LE) of PPI interface inhibitors. A positive correlation was found between the binding free energy and the diameter (D) of cylindrical 3D molecules, in addition to a correlation between LE and D/heavy atom count (HAC). In addition, we showed a correlation between LE and D/HAC for macrocyclic compounds, suggesting that the present findings could be applied during assessments of the potential of macrocyclic PPI interface inhibitors in drug discovery processes.

On Restraints in End-Point Protein-Ligand Binding Free Energy Calculations

The impact of harmonic restraints on protein heavy atoms and ligand atoms on end-point free energy calculations is systematically characterized for 54 protein-ligand complexes. We observe that stronger restraints reduce the equilibration time and statistical inefficiency, suppress conformational sampling, influence correlation with experiment, and monotonically decrease the estimated loss of entropy upon binding, leading to stronger estimated binding free energies in most systems. A statistical estimator that reweights for the biasing potential and includes data prior to the estimated equilibration time has the highest correlation with experiment. A spring constant of 20 cal mol-1 Å-2 maintains a near-native energy landscape and suppresses artifactual energy minima while minimally limiting thermal fluctuations about the crystal structure. © 2019 Wiley Periodicals, Inc.

Accuracy and Precision of Alchemical Relative Free Energy Predictions with and without Replica-Exchange

A systematic and statistically robust protocol is applied for the evaluation of free energy calculations with and without replica-exchange. The protocol is based on ensemble averaging to generate accurate assessments of the uncertainties in the predictions. Comparison is made between FEP+ and TIES-free energy perturbation and thermodynamic integration with enhanced sampling-the latter with and without the so-called "enhanced sampling" based on replica-exchange protocols. Standard TIES performs best for a reference set of targets and compounds; no benefits accrue from replica-exchange methods. Evaluation of FEP+ and TIES with REST-replica-exchange with solute tempering-reveals a systematic and significant underestimation of free energy differences in FEP+, which becomes increasingly large for long duration simulations, is confirmed by extensive analysis of previous publications, and raises a number of questions pertaining to the accuracy of the predictions with the REST technique not hitherto discussed.

Application of the ESMACS Binding Free Energy Protocol to a Multi-Binding Site Lactate Dehydogenase A Ligand Dataset

Over the past two decades, the use of fragment-based lead generation has become a common, mature approach to identify tractable starting points in chemical space for the drug discovery process. This approach naturally involves the study of the binding properties of highly heterogeneous ligands. Such datasets challenge computational techniques to provide comparable binding free energy estimates from different binding modes. The performance of a range of statistically robust ensemble-based binding free energy calculation protocols, called ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent), is evaluated. Ligands designed to target two binding pockets in the lactate dehydogenase, a target protein, which vary in size, charge, and binding mode, are studied. When compared to experimental results, excellent statistical rankings are obtained across this highly diverse set of ligands. In addition, three approaches to account for entropic contributions are investigated: 1) normal mode analysis, 2) weighted solvent accessible surface area (WSAS), and 3) variational entropy. Normal mode analysis and WSAS correlate strongly with each other-although the latter is computationally far cheaper-but do not improve rankings. Variational entropy corrects exaggerated discrimination of ligands bound in different pockets but creates three outliers which reduce the quality of the overall ranking.

Ensemble-Based Steered Molecular Dynamics Predicts Relative Residence Time of A2A Receptor Binders

Drug-target residence time, the length of time for which a small molecule stays bound to its receptor target, has increasingly become a key property for optimization in drug discovery programs. However, its in silico prediction has proven difficult. Here we describe a method, using atomistic ensemble-based steered molecular dynamics (SMD), to observe the dissociation of ligands from their target G protein-coupled receptor in a time scale suitable for drug discovery. These dissociation simulations accurately, precisely, and reproducibly identify ligand-residue interactions and quantify the change in ligand energy values for both protein and water. The method has been applied to 17 ligands of the A_2A adenosine receptor, all with published experimental kinetic binding data. The residues that interact with the ligand as it dissociates are known experimentally to have an effect on binding affinities and residence times. There is a good correlation ( R² = 0.79) between the computationally calculated change in water-ligand interaction energy and experimentally determined residence time. Our results indicate that ensemble-based SMD is a rapid, novel, and accurate semi-empirical method for the determination of drug-target relative residence time.

Application of ESMACS binding free energy protocols to diverse datasets: Bromodomain-containing protein 4

As the application of computational methods in drug discovery pipelines becomes more widespread it is increasingly important to understand how reproducible their results are and how sensitive they are to choices made in simulation setup and analysis. Here we use ensemble simulation protocols, termed ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent), to investigate the sensitivity of the popular molecular mechanics Poisson-Boltzmann surface area (MMPBSA) methodology. Using the bromodomain-containing protein 4 (BRD4) system bound to a diverse set of ligands as our target, we show that robust rankings can be produced only through combining ensemble sampling with multiple trajectories and enhanced solvation via an explicit ligand hydration shell.

Ensemble-Based Replica Exchange Alchemical Free Energy Methods: The Effect of Protein Mutations on Inhibitor Binding

The accurate prediction of the binding affinity changes of drugs caused by protein mutations is a major goal in clinical personalized medicine. We have developed an ensemble-based free energy approach called thermodynamic integration with enhanced sampling (TIES), which yields accurate, precise, and reproducible binding affinities. TIES has been shown to perform well for predictions of free energy differences of congeneric ligands to a wide range of target proteins. We have recently introduced variants of TIES, which incorporate the enhanced sampling technique REST2 (replica exchange with solute tempering) and the free energy estimator MBAR (Bennett acceptance ratio). Here we further extend the TIES methodology to study relative binding affinities caused by protein mutations when bound to a ligand, a variant which we call TIES-PM. We apply TIES-PM to fibroblast growth factor receptor 3 (FGFR3) to investigate binding free energy changes upon protein mutations. The results show that TIES-PM with REST2 successfully captures a large conformational change and generates correct free energy differences caused by a gatekeeper mutation located in the binding pocket. Simulations without REST2 fail to overcome the energy barrier between the conformations, and hence the results are highly sensitive to the initial structures. We also discuss situations where REST2 does not improve the accuracy of predictions.

High-throughput binding affinity calculations at extreme scales

Background

Resistance to chemotherapy and molecularly targeted therapies is a major factor in limiting the effectiveness of cancer treatment. In many cases, resistance can be linked to genetic changes in target proteins, either pre-existing or evolutionarily selected during treatment. Key to overcoming this challenge is an understanding of the molecular determinants of drug binding. Using multi-stage pipelines of molecular simulations we can gain insights into the binding free energy and the residence time of a ligand, which can inform both stratified and personal treatment regimes and drug development. To support the scalable, adaptive and automated calculation of the binding free energy on high-performance computing resources, we introduce the High-throughput Binding Affinity Calculator (HTBAC). HTBAC uses a building block approach in order to attain both workflow flexibility and performance.

Results

We demonstrate close to perfect weak scaling to hundreds of concurrent multi-stage binding affinity calculation pipelines. This permits a rapid time-to-solution that is essentially invariant of the calculation protocol, size of candidate ligands and number of ensemble simulations.

Conclusions

As such, HTBAC advances the state of the art of binding affinity calculations and protocols. HTBAC provides the platform to enable scientists to study a wide range of cancer drugs and candidate ligands in order to support personalized clinical decision making based on genome sequencing and drug discovery.

From bench to bedside, via desktop. Recent advances in the application of cutting-edge in silico tools in the research of drugs targeting bromodomain modules

The discipline of drug discovery has greatly benefited by computational tools and in silico algorithms aiming at rationalization of many related processes, from the stage of early hit identification to the preclinical phases of drug candidate validation. The various methodologies referred to as molecular modeling tools span a broad spectrum of applications, from straightforward approaches such as virtual screening of compound libraries to more advanced techniques involving the precise estimation of free energy upon binding of the candidate drug to its macromolecular target. To this end, we report an overview of specific studies where implementation of such sophisticated modeling algorithms has shown to be indispensable for addressing challenging systems and biological questions otherwise difficult to answer. We focus our attention on the emerging field of bromodomain inhibitors. Bromodomains are small modules involved in epigenetic signaling and currently comprise high-priority targets for developing both drug candidates and chemical probes for basic biomedical research. We attempt a critical presentation of selected cases utilizing cutting-edge in silico methodologies, with our main emphasis being on absolute or relative free energy simulations, on implementation of quantum-mechanics level calculations and on characterization of solvent thermodynamics. We discuss the advantages and strengths as well as the drawbacks and weaknesses of computational tools utilized in those works and we attempt to comment on specific issues related to their integration into the regular medicinal chemistry practice. Our conclusion is that while such methods indeed represent highly promising resources for further advancing the discipline, their application is not always trivial.

Reproducibility of Free Energy Calculations across Different Molecular Simulation Software Packages

Alchemical free energy calculations are an increasingly important modern simulation technique to calculate free energy changes on binding or solvation. Contemporary molecular simulation software such as AMBER, CHARMM, GROMACS, and SOMD include support for the method. Implementation details vary among those codes, but users expect reliability and reproducibility, i.e., for a given molecular model and set of force field parameters, comparable free energy differences should be obtained within statistical bounds regardless of the code used. Relative alchemical free energy (RAFE) simulation is increasingly used to support molecule discovery projects, yet the reproducibility of the methodology has been less well tested than its absolute counterpart. Here we present RAFE calculations of hydration free energies for a set of small organic molecules and demonstrate that free energies can be reproduced to within about 0.2 kcal/mol with the aforementioned codes. Absolute alchemical free energy simulations have been carried out as a reference. Achieving this level of reproducibility requires considerable attention to detail and package-specific simulation protocols, and no universally applicable protocol emerges. The benchmarks and protocols reported here should be useful for the community to validate new and future versions of software for free energy calculations.

Simple Entropy Terms for End-Point Binding Free Energy Calculations

We introduce a number of computationally inexpensive modifications to the MM/PBSA and MM/GBSA estimators for binding free energies, which are based on average receptor-ligand interaction energies in simulations of a noncovalent complex, to improve the treatment of entropy: second- and higher-order terms in a cumulant expansion and a confining potential on ligand external degrees of freedom. We also consider a filter for snapshots where ligands have drifted from the initial binding pose. The variations were tested on six sets of systems for which binding modes and free energies have previously been experimentally determined. For some data sets, none of the tested estimators led to results significantly correlated with measured free energies. In data sets with nontrivial correlation, a ligand RMSD cutoff of 3 Å and a second-order truncation of the cumulant expansion was found to be comparable or better than the average interaction energy by several statistical metrics.

Modeling the value of predictive affinity scoring in preclinical drug discovery

Drug discovery is widely recognized to be a difficult and costly activity in large part due to the challenge of identifying chemical matter which simultaneously optimizes multiple properties, one of which is affinity for the primary biological target. Further, many of these properties are difficult to predict ahead of expensive and time-consuming compound synthesis and experimental testing. Here we highlight recent work to develop compound affinity prediction models, and extensively investigate the value such models may provide to preclinical drug discovery. We demonstrate that the ability of these models to improve the overall probability of success is crucially dependent on the shape of the error distribution, not just the root-mean-square error. In particular, while scoring more molecule ideas generally improves the probability of project success when the error distribution is Gaussian, fat-tail distributions such as a Cauchy distribution, can lead to a situation where scoring more ideas actually decreases the overall probability of success.

Uncertainty Quantification in Alchemical Free Energy Methods

Alchemical free energy methods have gained much importance recently from several reports of improved ligand–protein binding affinity predictions based on their implementation using molecular dynamics simulations. A large number of variants of such methods implementing different accelerated sampling techniques and free energy estimators are available, each claimed to be better than the others in its own way. However, the key features of reproducibility and quantification of associated uncertainties in such methods have barely been discussed. Here, we apply a systematic protocol for uncertainty quantification to a number of popular alchemical free energy methods, covering both absolute and relative free energy predictions. We show that a reliable measure of error estimation is provided by ensemble simulation—an ensemble of independent MD simulations—which applies irrespective of the free energy method. The need to use ensemble methods is fundamental and holds regardless of the duration of time of the molecular dynamics simulations performed.

Using the fast fourier transform in binding free energy calculations

According to implicit ligand theory, the standard binding free energy is an exponential average of the binding potential of mean force (BPMF), an exponential average of the interaction energy between the unbound ligand ensemble and a rigid receptor. Here, we use the fast Fourier transform (FFT) to efficiently evaluate BPMFs by calculating interaction energies when rigid ligand configurations from the unbound ensemble are discretely translated across rigid receptor conformations. Results for standard binding free energies between T4 lysozyme and 141 small organic molecules are in good agreement with previous alchemical calculations based on (1) a flexible complex ( R≈0.9 for 24 systems) and (2) flexible ligand with multiple rigid receptor configurations ( R≈0.8 for 141 systems). While the FFT is routinely used for molecular docking, to our knowledge this is the first time that the algorithm has been used for rigorous binding free energy calculations. © 2017 Wiley Periodicals, Inc.

Predicting Binding Free Energies of PDE2 Inhibitors. The Difficulties of Protein Conformation

A congeneric series of 21 phosphodiesterase 2 (PDE2) inhibitors are reported. Crystal structures show how the molecules can occupy a 'top-pocket' of the active site. Molecules with small substituents do not enter the pocket, a critical leucine (Leu770) is closed and water molecules are present. Large substituents enter the pocket, opening the Leu770 conformation and displacing the waters. We also report an X-ray structure revealing a new conformation of the PDE2 active site domain. The relative binding affinities of these compounds were studied with free energy perturbation (FEP) methods and it represents an attractive real-world test case. In general, the calculations could predict the energy of small-to-small, or large-to-large molecule perturbations. However, accurately capturing the transition from small-to-large proved challenging. Only when using alternative protein conformations did results improve. The new X-ray structure, along with a modelled dimer, conferred stability to the catalytic domain during the FEP molecular dynamics (MD) simulations, increasing the convergence and thereby improving the prediction of ΔΔG of binding for some small-to-large transitions. In summary, we found the most significant improvement in results when using different protein structures, and this data set is useful for future free energy validation studies.