Efficient drug lead discovery and optimization

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

Alchemical free energy simulations have long been utilized to predict free energy changes for binding affinity and solubility of small molecules. However, while the theoretical foundation of these methods is well established, seamlessly handling many of the practical aspects regarding the preparation of the different thermodynamic end states of complex molecular systems and the numerous processing scripts often remains a burden for successful applications. In this work, we present CHARMM-GUI Free Energy Calculator (http://www.charmm-gui.org/input/fec) that provides various alchemical free energy perturbation molecular dynamics (FEP/MD) systems with input and post-processing scripts for NAMD and GENESIS. Four submodules are available: Absolute Ligand Binder (for absolute ligand binding FEP/MD), Relative Ligand Binder (for relative ligand binding FEP/MD), Absolute Ligand Solvator (for absolute ligand solvation FEP/MD), and Relative Ligand Solvator (for relative ligand solvation FEP/MD). Each module is designed to build multiple systems of a set of selected ligands at once for high-throughput FEP/MD simulations. The capability of Free Energy Calculator is illustrated by absolute and relative solvation FEP/MD of a set of ligands and absolute and relative binding FEP/MD of a set of ligands for T4-lysozyme in solution and the adenosine A_2A receptor in a membrane. The calculated free energy values are overall consistent with the experimental and published free energy results (within ∼1 kcal/mol). We hope that Free Energy Calculator is useful to carry out high-throughput FEP/MD simulations in the field of biomolecular sciences and drug discovery.

Explicit Representation of Cation-π Interactions in Force Fields with 1/r4 Nonbonded Terms

The binding energies for cation-π complexation are underestimated by traditional fixed-charge force fields owing to their lack of explicit treatment of ion-induced dipole interactions. To address this deficiency, an explicit treatment of cation-π interactions has been introduced into the OPLS-AA force field. Following prior work with atomic cations, it is found that cation-π interactions can be handled efficiently by augmenting the usual 12-6 Lennard-Jones potentials with 1/r⁴ terms. Results are provided for prototypical complexes as well as protein-ligand systems of relevance for drug design. Alkali cation, ammonium, guanidinium, and tetramethylammonium were chosen for the representative cations, while benzene and six heteroaromatic molecules were used as the π systems. The required nonbonded parameters were fit to reproduce structure and interaction energies for gas-phase complexes from density functional theory (DFT) calculations at the ωB97X-D/6-311++G(d,p) level. The impact of the solvent was then examined by computing potentials of mean force (pmfs) in both aqueous and tetrahydrofuran (THF) solutions using the free-energy perturbation (FEP) theory. Further testing was carried out for two cases of strong and one case of weak cation-π interactions between druglike molecules and their protein hosts, namely, the JH2 domain of JAK2 kinase and macrophage migration inhibitory factor. FEP results reveal greater binding by 1.5-4.4 kcal/mol from the addition of the explicit cation-π contributions. Thus, in the absence of such treatment of cation-π interactions, errors for computed binding or inhibition constants of 10¹-10³ are expected.

Flexi-pharma: a molecule-ranking strategy for virtual screening using pharmacophores from ligand-free conformational ensembles

Computer-aided strategies are useful for reducing the costs and increasing the success-rate in drug discovery. Among these strategies, methods based on pharmacophores (an ensemble of electronic and steric features representing the target active site) are efficient to implement over large compound libraries. However, traditional pharmacophore-based methods require knowledge of active compounds or ligand-receptor structures, and only few ones account for target flexibility. Here, we developed a pharmacophore-based virtual screening protocol, Flexi-pharma, that overcomes these limitations. The protocol uses molecular dynamics (MD) simulations to explore receptor flexibility, and performs a pharmacophore-based virtual screening over a set of MD conformations without requiring prior knowledge about known ligands or ligand-receptor structures for building the pharmacophores. The results from the different receptor conformations are combined using a "voting" approach, where a vote is given to each molecule that matches at least one pharmacophore from each MD conformation. Contrarily to other approaches that reduce the pharmacophore ensemble to some representative models and score according to the matching models or molecule conformers, the Flexi-pharma approach takes directly into account the receptor flexibility by scoring in regards to the receptor conformations. We tested the method over twenty systems, finding an enrichment of the dataset for 19 of them. Flexi-pharma is computationally efficient allowing for the screening of thousands of compounds in minutes on a single CPU core. Moreover, the ranking of molecules by vote is a general strategy that can be applied with any pharmacophore-filtering program.

A fast and high-quality charge model for the next generation general AMBER force field

The General AMBER Force Field (GAFF) has been broadly used by researchers all over the world to perform in silico simulations and modelings on diverse scientific topics, especially in the field of computer-aided drug design whose primary task is to accurately predict the affinity and selectivity of receptor-ligand binding. The atomic partial charges in GAFF and the second generation of GAFF (GAFF2) were originally developed with the quantum mechanics derived restrained electrostatic potential charge, but in practice, users usually adopt an efficient charge method, Austin Model 1-bond charge corrections (AM1-BCC), based on which, without expensive ab initio calculations, the atomic charges could be efficiently and conveniently obtained with the ANTECHAMBER module implemented in the AMBER software package. In this work, we developed a new set of BCC parameters specifically for GAFF2 using 442 neutral organic solutes covering diverse functional groups in aqueous solution. Compared to the original BCC parameter set, the new parameter set significantly reduced the mean unsigned error (MUE) of hydration free energies from 1.03 kcal/mol to 0.37 kcal/mol. More excitingly, this new AM1-BCC model also showed excellent performance in the solvation free energy (SFE) calculation on diverse solutes in various organic solvents across a range of different dielectric constants. In this large-scale test with totally 895 neutral organic solvent-solute systems, the new parameter set led to accurate SFE predictions with the MUE and the root-mean-square-error of 0.51 kcal/mol and 0.65 kcal/mol, respectively. This newly developed charge model, ABCG2, paved a promising path for the next generation GAFF development.

MovableType Software for Fast Free Energy-Based Virtual Screening: Protocol Development, Deployment, Validation, and Assessment

For decades, the complicated energy surfaces found in macromolecular protein:ligand structures, which require large amounts of computational time and resources for energy state sampling, have been an inherent obstacle to fast, routine free energy estimation in industrial drug discovery efforts. Beginning in 2013, the Merz research group addressed this cost with the introduction of a novel sampling methodology termed “Movable Type” (MT). Using numerical integration methods, the MT method reduces the computational expense for energy state sampling by independently calculating each atomic partition function from an initial molecular conformation in order to estimate the molecular free energy using ensembles of the atomic partition functions. In this work, we report a software package, the DivCon Discovery Suite with the MovableType module from QuantumBio Inc., that performs this MT free energy estimation protocol in a fast, fully encapsulated manner. We discuss the computational procedures and improvements to the original work, and we detail the corresponding settings for this software package. Finally, we introduce two validation benchmarks to evaluate the overall robustness of the method against a broad range of protein:ligand structural cases. With these publicly available benchmarks, we show that the method can use a variety of input types and parameters and exhibits comparable predictability whether the method is presented with “expensive” X-ray structures or “inexpensively docked” theoretical models. We also explore some next steps for the method. The MovableType software is available at http://www.quantumbioinc.com/

Improved Alchemical Free Energy Calculations with Optimized Smoothstep Softcore Potentials

Progress in the development of GPU-accelerated free energy simulation software has enabled practical applications on complex biological systems and fueled efforts to develop more accurate and robust predictive methods. In particular, this work re-examines concerted (a.k.a., one-step or unified) alchemical transformations commonly used in the prediction of hydration and relative binding free energies (RBFEs). We first classify several known challenges in these calculations into three categories: endpoint catastrophes, particle collapse, and large gradient-jumps. While endpoint catastrophes have long been addressed using softcore potentials, the remaining two problems occur much more sporadically and can result in either numerical instability (i.e., complete failure of a simulation) or inconsistent estimation (i.e., stochastic convergence to an incorrect result). The particle collapse problem stems from an imbalance in short-range electrostatic and repulsive interactions and can, in principle, be solved by appropriately balancing the respective softcore parameters. However, the large gradient-jump problem itself arises from the sensitivity of the free energy to large values of the softcore parameters, as might be used in trying to solve the particle collapse issue. Often, no satisfactory compromise exists with the existing softcore potential form. As a framework for solving these problems, we developed a new family of smoothstep softcore (SSC) potentials motivated by an analysis of the derivatives along the alchemical path. The smoothstep polynomials generalize the monomial functions that are used in most implementations and provide an additional path-dependent smoothing parameter. The effectiveness of this approach is demonstrated on simple yet pathological cases that illustrate the three problems outlined. With appropriate parameter selection, we find that a second-order SSC(2) potential does at least as well as the conventional approach and provides vast improvement in terms of consistency across all cases. Last, we compare the concerted SSC(2) approach against the gold-standard stepwise (a.k.a., decoupled or multistep) scheme over a large set of RBFE calculations as might be encountered in drug discovery.

Structural investigation of 2-naphthyl phenyl ether inhibitors bound to WT and Y181C reverse transcriptase highlights key features of the NNRTI binding site

Human immunodeficiency virus (HIV)-1 remains as a global health issue that is primarily treated with highly active antiretroviral therapy, a combination of drugs that target the viral life cycle. One class of these drugs are non-nucleoside reverse transcriptase inhibitors (NNRTIs) that target the viral reverse transcriptase (RT). First generation NNRTIs were troubled with poor pharmacological properties and drug resistance, incentivizing the development of improved compounds. One class of developed compounds are the 2-naphthyl phenyl ethers, showing promising efficacy against the Y181C RT mutation. Further biochemical and structural work demonstrated differences in potency against the Y181C mutation and binding mode of the compounds. This work aims to understand the relationship between the binding mode and ability to overcome drug resistance using macromolecular x-ray crystallography. Comparison of 2-naphthyl phenyl ethers bound to Y181C RT reveal that compounds that interact with the invariant W229 are more capable of retaining efficacy against the resistance mutation. Additional modifications to these compounds at the 4-position, computationally designed to compensate for the Y181C mutation, do not demonstrate improved potency. Ultimately, we highlight important considerations for the development of future HIV-1 drugs that are able to combat drug resistance.

COVID-19: CADD to the rescue

The recent outbreak of the deadly COVID-19 disease, being caused by the novel coronavirus (SARS-CoV-2), has put the world on red alert as it keeps spreading and recording more fatalities. Research efforts are being carried out to curtail the disease from spreading as it has been declared as of global health emergency. Hence, there is an exigent need to identify and design drugs that are capable of curing the infection and hinder its continual spread across the globe. Herein, a computer-aided drug design tool known as the virtual screening method was used to screen a database of 44 million compounds to find compounds that have the potential to inhibit the surface glycoprotein responsible for virus entry and binding. The consensus scoring approach selected three compounds with promising physicochemical properties and favorable molecular interactions with the target protein. These selected compounds can undergo lead optimization to be further developed as drugs that can be used in treating the COVID-19 disease.

Prediction of Small Molecule Inhibitors Targeting the Severe Acute Respiratory Syndrome Coronavirus-2 RNA-dependent RNA Polymerase

The current COVID-19 outbreak warrants the design and development of novel anti-COVID therapeutics. Using a combination of bioinformatics and computational tools, we modelled the 3D structure of the RdRp (RNA-dependent RNA polymerase) of SARS-CoV2 (severe acute respiratory syndrome coronavirus-2) and predicted its probable GTP binding pocket in the active site. GTP is crucial for the formation of the initiation complex during RNA replication. This site was computationally targeted using a number of small molecule inhibitors of the hepatitis C RNA polymerase reported previously. Further optimizations suggested a lead molecule that may prove fruitful in the development of potent inhibitors against the RdRp of SARS-CoV2.

Accurate Prediction of GPCR Ligand Binding Affinity with Free Energy Perturbation

The computational prediction of relative binding free energies is a crucial goal for drug discovery, and G protein-coupled receptors (GPCRs) are arguably the most important drug target class. However, they present increased complexity to model compared to soluble globular proteins. Despite breakthroughs, experimental X-ray crystal and cryo-EM structures are challenging to attain, meaning computational models of the receptor and ligand binding mode are sometimes necessary. This leads to uncertainty in understanding ligand-protein binding induced changes such as, water positioning and displacement, side chain positioning, hydrogen bond networks, and the overall structure of the hydration shell around the ligand and protein. In other words, the very elements that define structure activity relationships (SARs) and are crucial for accurate binding free energy calculations are typically more uncertain for GPCRs. In this work we use free energy perturbation (FEP) to predict the relative binding free energies for ligands of two different GPCRs. We pinpoint the key aspects for success such as the important role of key water molecules, amino acid ionization states, and the benefit of equilibration with specific ligands. Initial calculations following typical FEP setup and execution protocols delivered no correlation with experiment, but we show how results are improved in a logical and systematic way. This approach gave, in the best cases, a coefficient of determination (R²) compared with experiment in the range of 0.6-0.9 and mean unsigned errors compared to experiment of 0.6-0.7 kcal/mol. We anticipate that our findings will be applicable to other difficult-to-model protein ligand data sets and be of wide interest for the community to continue improving FE binding energy predictions.

SQM/COSMO Scoring Function: Reliable Quantum-Mechanical Tool for Sampling and Ranking in Structure-Based Drug Design

Quantum mechanical (QM) methods have been gaining importance in structure-based drug design where a reliable description of protein-ligand interactions is of utmost significance. However, strategies i. e. QM/MM, fragmentation or semiempirical (SQM) methods had to be pursued to overcome the unfavorable scaling of QM methods. Various SQM-based approaches have significantly contributed to the accuracy of docking and improvement of lead compounds. Parametrizations of SQM and implicit solvent methods in our laboratory have been instrumental to obtain a reliable SQM-based scoring function. The experience gained in its application for activity ranking of ligands binding to tens of protein targets resulted in setting up a faster SQM/COSMO scoring approach, which outperforms standard scoring methods in native pose identification for two dozen protein targets with ten thousand poses. Recently, SQM/COSMO was effectively applied in a proof-of-concept study of enrichment in virtual screening. Due to its superior performance, feasibility and chemical generality, we propose the SQM/COSMO approach as an efficient tool in structure-based drug design.

Computer-Assisted Design of Thiophene-Indole Hybrids as Leishmanial Agents

BACKGROUND

Chemoinformatics has several applications in the field of drug design, helping to identify new compounds against a range of ailments. Among these are Leishmaniasis, effective treatments for which are currently limited.

OBJECTIVE

To construct new indole 2-aminothiophene molecules using computational tools and to test their effectiveness against Leishmania amazonensis (sp.).

METHODS

Based on the chemical structure of thiophene-indol hybrids, we built regression models and performed molecular docking, and used these data as bases for design of 92 new molecules with predicted pIC50 and molecular docking. Among these, six compounds were selected for the synthesis and to perform biological assays (leishmanicidal activity and cytotoxicity).

RESULTS

The prediction models and docking allowed inference of characteristics that could have positive influences on the leishmanicidal activity of the planned compounds. Six compounds were synthesized, one-third of which showed promising antileishmanial activities, with IC50 ranging from 2.16 and 2.97 μM (against promastigote forms) and 0.9 and 1.71 μM (against amastigote forms), with selectivity indexes (SI) of 52 and 75.

CONCLUSION

These results demonstrate the ability of Quantitative Structure-Activity Relationship (QSAR)-based rational drug design to predict molecules with promising leishmanicidal potential, and confirming the potential of thiophene-indole hybrids as potential new leishmanial agents.

Combining Machine Learning and Enhanced Sampling Techniques for Efficient and Accurate Calculation of Absolute Binding Free Energies

Calculating absolute binding free energies is challenging and important. In this paper, we test some recently developed metadynamics-based methods and develop a new combination with a Hamiltonian replica-exchange approach. The methods were tested on 18 chemically diverse ligands with a wide range of different binding affinities to a complex target; namely, human soluble epoxide hydrolase. The results suggest that metadynamics with a funnel-shaped restraint can be used to calculate, in a computationally affordable and relatively accurate way, the absolute binding free energy for small fragments. When used in combination with an optimal pathlike variable obtained using machine learning or with the Hamiltonian replica-exchange algorithm SWISH, this method can achieve reasonably accurate results for increasingly complex ligands, with a good balance of computational cost and speed. An additional benefit of using the combination of metadynamics and SWISH is that it also provides useful information about the role of water in the binding mechanism.

Fragment-based quantum mechanical approach to biomolecules, molecular clusters, molecular crystals and liquids

To study large molecular systems beyond the system size that the current state-of-the-art ab initio electronic structure methods could handle, fragment-based quantum mechanical (QM) approaches have been developed over the past years, and proved to be efficient in dealing with large molecular systems at various ab initio levels. According to the fragmentation approach, a large molecular system can be divided into subsystems (fragments), and subsequently the property of the whole system can be approximately obtained by taking a proper combination of the corresponding terms of individual fragments. Therefore, the standard QM calculation of a large system could be circumvented by carrying out a series of calculations on small fragments, which significantly promotes computational efficiency. The electrostatically embedded generalized molecular fractionation with conjugate caps (EE-GMFCC) method is one of the fragment-based QM approaches which has been developed by our research group in recent years. This Perspective presents the theoretical framework of this fragmentation method and its applications in biomolecules, molecular clusters, molecular crystals and liquids, including total energy calculation, protein-ligand/protein binding affinity prediction, geometry optimization, vibrational spectrum simulation, ab initio molecular dynamics simulation, and prediction of excited-state properties.

Structure-Guided Identification of DNMT3B Inhibitors

Methyltransferase 3 beta (DNMT3B) inhibitors that interfere with cancer growth are emerging possibilities for treatment of melanoma. Herein we identify small molecule inhibitors of DNMT3B starting from a homology model based on a DNMT3A crystal structure. Virtual screening by docking led to purchase of 15 compounds, among which 5 were found to inhibit the activity of DNMT3B with IC₅₀ values of 13-72 μM in a fluorogenic assay. Eight analogues of 7, 10, and 12 were purchased to provide 2 more active compounds. Compound 11 is particularly notable as it shows good selectivity with no inhibition of DNMT1 and 22 μM potency toward DNMT3B. Following additional de novo design, exploratory synthesis of 17 analogues of 11 delivered 5 additional inhibitors of DNMT3B with the most potent being 33h with an IC₅₀ of 8.0 μM. This result was well confirmed in an ultrahigh-performance liquid chromatography (UHPLC)-based analytical assay, which yielded an IC₅₀ of 4.8 μM. Structure-activity data are rationalized based on computed structures for DNMT3B complexes.

GemSpot: A Pipeline for Robust Modeling of Ligands into Cryo-EM Maps

Producing an accurate atomic model of biomolecule-ligand interactions from maps generated by cryoelectron microscopy (cryo-EM) often presents challenges inherent to the methodology and the dynamic nature of ligand binding. Here, we present GemSpot, an automated pipeline of computational chemistry methods that take into account EM map potentials, quantum mechanics energy calculations, and water molecule site prediction to generate candidate poses and provide a measure of the degree of confidence. The pipeline is validated through several published cryo-EM structures of complexes in different resolution ranges and various types of ligands. In all cases, at least one identified pose produced both excellent interactions with the target and agreement with the map. GemSpot will be valuable for the robust identification of ligand poses and drug discovery efforts through cryo-EM.

Selective Janus Kinase 2 (JAK2) Pseudokinase Ligands with a Diaminotriazole Core

Janus kinases (JAKs) are non-receptor tyrosine kinases that are essential components of the JAK-STAT signaling pathway. Associated aberrant signaling is responsible for many forms of cancer and disorders of the immune system. The present focus is on the discovery of molecules that may regulate the activity of JAK2 by selective binding to the JAK2 pseudokinase domain, JH2. Specifically, the Val617Phe mutation in JH2 stimulates the activity of the adjacent kinase domain (JH1) resulting in myeloproliferative disorders. Starting from a non-selective screening hit, we have achieved the goal of discovering molecules that preferentially bind to the ATP binding site in JH2 instead of JH1. We report the design and synthesis of the compounds and binding results for the JH1, JH2, and JH2 V617F domains, as well as five crystal structures for JH2 complexes. Testing with a selective and non-selective JH2 binder on the autophosphorylation of wild-type and V617F JAK2 is also contrasted.

Augmenting Hit Identification by Virtual Screening Techniques in Small Molecule Drug Discovery

Two orthogonal approaches for hit identification in drug discovery are large-scale in vitro and in silico screening. In recent years, due to the emergence of new targets and a rapid increase in the size of the readily synthesizable chemical space, there is a growing emphasis on the integration of the two techniques to improve the hit finding efficiency. Here, we highlight three examples of drug discovery projects at Merck & Co., Inc., Kenilworth, NJ, USA in which different virtual screening (VS) techniques, each specifically tailored to leverage knowledge available for the target, were utilized to augment the selection of high-quality chemical matter for in vitro assays and to enhance the diversity and tractability of hits. Central to success is a fully integrated workflow combining in silico and experimental expertise at every stage of the hit identification process. We advocate that workflows encompassing VS as part of an integrated hit finding plan should be widely adopted to accelerate hit identification and foster cross-functional collaborations in modern drug discovery.

High-Throughput Docking Using Quantum Mechanical Scoring

Today high-throughput docking is one of the most commonly used computational tools in drug lead discovery. While there has been an impressive methodological improvement in docking accuracy, docking scoring still remains an open challenge. Most docking programs are rooted in classical molecular mechanics. However, to better characterize protein-ligand interactions, the use of a more accurate quantum mechanical (QM) description would be necessary. In this work, we introduce a QM-based docking scoring function for high-throughput docking and evaluate it on 10 protein systems belonging to diverse protein families, and with different binding site characteristics. Outstanding results were obtained, with our QM scoring function displaying much higher enrichment (screening power) than a traditional docking method. It is acknowledged that developments in quantum mechanics theory, algorithms and computer hardware throughout the upcoming years will allow semi-empirical (or low-cost) quantum mechanical methods to slowly replace force-field calculations. It is thus urgently needed to develop and validate novel quantum mechanical-based scoring functions for high-throughput docking toward more accurate methods for the identification and optimization of modulators of pharmaceutically relevant targets.

Going All In: A Strategic Investment in In Silico Toxicology

As vast numbers of new chemicals are introduced to market annually, we are faced with the grand challenge of protecting humans and the environment while minimizing economically and ethically costly animal testing. In silico models promise to be the solution we seek, but we find ourselves at crossroads of future development efforts that would ensure standalone applicability and reliability of these tools. A conscientious effort that prioritizes experimental testing to support the needs of in silico models (versus regulatory needs) is called for to achieve this goal. Using economic analogy in the title of this work, we argue that a prudent investment is to go all-in to support in silico model development, rather than gamble our future by keeping the status quo of a "balanced portfolio" of testing approaches. We discuss two paths to future in silico toxicology-one based on big-data statistics ("broadsword"), and the other based on direct modeling of molecular interactions ("scalpel")-and offer rationale that the latter approach is more transparent, is better aligned with our quest for fundamental knowledge, and has a greater potential to succeed if we are willing to transform our toxicity-testing paradigm.

Machine Learning to Identify Flexibility Signatures of Class A GPCR Inhibition

We show that machine learning can pinpoint features distinguishing inactive from active states in proteins, in particular identifying key ligand binding site flexibility transitions in GPCRs that are triggered by biologically active ligands. Our analysis was performed on the helical segments and loops in 18 inactive and 9 active class A G protein-coupled receptors (GPCRs). These three-dimensional (3D) structures were determined in complex with ligands. However, considering the flexible versus rigid state identified by graph-theoretic ProFlex rigidity analysis for each helix and loop segment with the ligand removed, followed by feature selection and k-nearest neighbor classification, was sufficient to identify four segments surrounding the ligand binding site whose flexibility/rigidity accurately predicts whether a GPCR is in an active or inactive state. GPCRs bound to inhibitors were similar in their pattern of flexible versus rigid regions, whereas agonist-bound GPCRs were more flexible and diverse. This new ligand-proximal flexibility signature of GPCR activity was identified without knowledge of the ligand binding mode or previously defined switch regions, while being adjacent to the known transmission switch. Following this proof of concept, the ProFlex flexibility analysis coupled with pattern recognition and activity classification may be useful for predicting whether newly designed ligands behave as activators or inhibitors in protein families in general, based on the pattern of flexibility they induce in the protein.

Fast, Accurate, and Reliable Protocols for Routine Calculations of Protein-Ligand Binding Affinities in Drug Design Projects Using AMBER GPU-TI with ff14SB/GAFF

Accurate prediction of the absolute or relative protein-ligand binding affinity is one of the major tasks in computer-aided drug design projects, especially in the stage of lead optimization. In principle, the alchemical free energy (AFE) methods such as thermodynamic integration (TI) or free-energy perturbation (FEP) can fulfill this task, but in practice, a lot of hurdles prevent them from being routinely applied in daily drug design projects, such as the demanding computing resources, slow computing processes, unavailable or inaccurate force field parameters, and difficult and unfriendly setting up and post-analysis procedures. In this study, we have exploited practical protocols of applying the CPU (central processing unit)-TI and newly developed GPU (graphic processing unit)-TI modules and other tools in the AMBER software package, combined with ff14SB/GAFF1.8 force fields, to conduct efficient and accurate AFE calculations on protein-ligand binding free energies. We have tested 134 protein-ligand complexes in total for four target proteins (BACE, CDK2, MCL1, and PTP1B) and obtained overall comparable performance with the commercial Schrodinger FEP+ program (WangJ. Am. Chem. Soc.2015, 137, 2695-2703). The achieved accuracy fits within the requirements for computations to generate effective guidance for experimental work in drug lead optimization, and the needed wall time is short enough for practical application. Our verified protocol provides a practical solution for routine AFE calculations in real drug design projects.

FragPELE: Dynamic Ligand Growing within a Binding Site. A Novel Tool for Hit-To-Lead Drug Design

The early stages of drug discovery rely on hit-to-lead programs, where initial hits undergo partial optimization to improve binding affinities for their biological target. This is an expensive and time-consuming process, requiring multiple iterations of trial and error designs, an ideal scenario for applying computer simulation. However, most state-of-the-art modeling techniques fail to provide a fast and reliable answer to the Induced-Fit protein-ligand problem. To aid in this matter, we present FragPELE, a new tool for in silico hit-to-lead drug design, capable of growing a fragment from a bound core while exploring the protein-ligand conformational space. We tested the ability of FragPELE to predict crystallographic data, even in cases where cryptic sub-pockets open because of the presence of particular R-groups. Additionally, we evaluated the potential of the software on growing and scoring five congeneric series from the 2015 FEP+ dataset, comparing them to FEP+, SP and Induced-Fit Glide, and MMGBSA simulations. Results show that FragPELE could be useful not only for finding new cavities and novel binding modes in cases where standard docking tools cannot but also to rank ligand activities in a reasonable amount of time and with acceptable precision.

QM Implementation in Drug Design: Does It Really Help?

Computational chemistry allows one to characterize the structure, dynamics, and energetics of protein-ligand interactions, which makes it a valuable tool in drug discovery in both academic research and pharmaceutical industry. Molecular mechanics (MM)-based approaches are widely utilized to assist the discovery of new drug candidates. However, the complexity of protein-ligand interactions challenges the accuracy and efficiency of the commonly used empirical methods. Aiming to provide better accuracy in the description of protein-ligand interactions, quantum mechanics (QM)-based approaches are becoming increasingly explored. In principle, QM calculation includes all contributions to the energy, accounting for terms usually missing in empirical force fields, and provides a greater degree of transferability. The usefulness of QM in drug design cannot be overemphasized. In this chapter, we present recent developments and applications of fragment-based QM method in studying the protein-ligand and protein-protein interactions. We critically discuss the performance of the fragment-based QM method at different ab initio levels while trying to answer a critical question: do QM-based methods really help in drug design?

Can machine learning consistently improve the scoring power of classical scoring functions? Insights into the role of machine learning in scoring functions

How to accurately estimate protein-ligand binding affinity remains a key challenge in computer-aided drug design (CADD). In many cases, it has been shown that the binding affinities predicted by classical scoring functions (SFs) cannot correlate well with experimentally measured biological activities. In the past few years, machine learning (ML)-based SFs have gradually emerged as potential alternatives and outperformed classical SFs in a series of studies. In this study, to better recognize the potential of classical SFs, we have conducted a comparative assessment of 25 commonly used SFs. Accordingly, the scoring power was systematically estimated by using the state-of-the-art ML methods that replaced the original multiple linear regression method to refit individual energy terms. The results show that the newly-developed ML-based SFs consistently performed better than classical ones. In particular, gradient boosting decision tree (GBDT) and random forest (RF) achieved the best predictions in most cases. The newly-developed ML-based SFs were also tested on another benchmark modified from PDBbind v2007, and the impacts of structural and sequence similarities were evaluated. The results indicated that the superiority of the ML-based SFs could be fully guaranteed when sufficient similar targets were contained in the training set. Moreover, the effect of the combinations of features from multiple SFs was explored, and the results indicated that combining NNscore2.0 with one to four other classical SFs could yield the best scoring power. However, it was not applicable to derive a generic target-specific SF or SF combination.

Design and SAR Analysis of Covalent Inhibitors Driven by Hybrid QM/MM Simulations

Quantum mechanics/molecular mechanics (QM/MM) hybrid technique is emerging as a reliable computational method to investigate and characterize chemical reactions occurring in enzymes. From a drug discovery perspective, a thorough understanding of enzyme catalysis appears pivotal to assist the design of inhibitors able to covalently bind one of the residues belonging to the enzyme catalytic machinery. Thanks to the current advances in computer power, and the availability of more efficient algorithms for QM-based simulations, the use of QM/MM methodology is becoming a viable option in the field of covalent inhibitor design. In the present review, we summarized our experience in the field of QM/MM simulations applied to drug design problems which involved the optimization of agents working on two well-known drug targets, namely fatty acid amide hydrolase (FAAH) and epidermal growth factor receptor (EGFR). In this context, QM/MM simulations gave valuable information in terms of geometry (i.e., of transition states and metastable intermediates) and reaction energetics that allowed to correctly predict inhibitor binding orientation and substituent effect on enzyme inhibition. What is more, enzyme reaction modelling with QM/MM provided insights that were translated into the synthesis of new covalent inhibitor featured by a unique combination of intrinsic reactivity, on-target activity, and selectivity.

Binding Free Energy Calculation Using Quantum Mechanics Aimed for Drug Lead Optimization

The routine use of in silico tools is already established in drug lead design. Besides the use of molecular docking methods to screen large chemical libraries and thus prioritize compounds for purchase or synthesis, more accurate calculations of protein-ligand binding free energy has shown the potential to guide lead optimization, thus saving time and resources. Theoretical developments and advances in computing power have allowed quantum mechanical-based methods applied to calculations on biomacromolecules to be increasingly explored and used, with the purpose of providing a more accurate description of protein-ligand interactions and an enhanced level of accuracy in the calculation of binding affinities. It should be noted that the quantum mechanical formulation includes, in principle, all contributions to the energy, considering terms usually neglected in molecular mechanics force fields, such as electronic polarization, metal coordination, and covalent binding; moreover, quantum mechanical approaches are systematically improvable. By treating all elements and interactions on equal footing, and avoiding the need of system-dependent parameterizations, they provide a greater degree of transferability. In this review, we illustrate the increasing relevance of quantum mechanical methods for binding free energy calculation in the context of structure-based drug lead optimization, showing representative applications of the different approaches available.

Molecular Docking Using Quantum Mechanical-Based Methods

Computational methods are a powerful and consolidated tool in the early stage of the drug lead discovery process. Among these techniques, high-throughput molecular docking has proved to be extremely useful in identifying novel bioactive compounds within large chemical libraries. In the docking procedure, the predominant binding mode of each small molecule within a target binding site is assessed, and a docking score reflective of the likelihood of binding is assigned to them. These methods also shed light on how a given hit could be modified in order to improve protein-ligand interactions and are thus able to guide lead optimization. The possibility of reducing time and cost compared to experimental approaches made this technology highly appealing. Due to methodological developments and the increase of computational power, the application of quantum mechanical methods to study macromolecular systems has gained substantial attention in the last decade. A quantum mechanical description of the interactions involved in molecular association of biomolecules may lead to better accuracy compared to molecular mechanics, since there are many physical phenomena that cannot be correctly described within a classical framework, such as covalent bond formation, polarization effects, charge transfer, bond rearrangements, halogen bonding, and others, that require electrons to be explicitly accounted for. Considering the fact that quantum mechanics-based approaches in biomolecular simulation constitute an active and important field of research, we highlight in this work the recent developments of quantum mechanical-based molecular docking and high-throughput docking.

Modelling flexible protein–ligand binding in p38α MAP kinase using the QUBE force field

The accuracy of quantum mechanical bespoke (QUBE) force fields for protein–ligand binding free energy calculations are benchmarked against experiment.

Benzodioxane-Benzamides as Antibacterial Agents: Computational and SAR Studies to Evaluate the Influence of the 7-Substitution in FtsZ Interaction

FtsZ is a crucial prokaryotic protein involved in bacterial cell replication. It recently arose as a promising target in the search for antimicrobial agents able to fight antimicrobial resistance. In this work, going on with our structure-activity relationship (SAR) study, we developed variously 7-substituted 1,4-benzodioxane compounds, linked to the 2,6-difluorobenzamide by a methylenoxy bridge. Compounds exhibit promising antibacterial activities not only against multidrug-resistant Staphylococcus aureus, but also on mutated Escherichia coli strains, thus enlarging their spectrum of action toward Gram-negative bacteria as well. Computational studies elucidated, through a validated FtsZ binding protocol, the structural features of new promising derivatives as FtsZ inhibitors.

Ligand Binding Thermodynamic Cycles: Hysteresis, the Locally Weighted Histogram Analysis Method, and the Overlapping States Matrix

Free energy perturbation (FEP) simulations have been widely applied to obtain predictions of the relative binding free energy for a series of congeneric ligands binding to the same receptor, which is an essential component for the lead optimization process in computer-aided drug discovery. In the case of several congeneric ligands forming a perturbation map involving a closed thermodynamic cycle, the summation of the estimated free energy change along each edge in the cycle using Bennett acceptance ratio (BAR) usually will deviate from zero due to systematic and random errors, which is the hysteresis of cycle closure. In this work, the advanced reweighting techniques binless weighted histogram analysis method (UWHAM) and locally weighted histogram analysis method (LWHAM) are applied to provide statistical estimators of the free energy change along each edge in order to eliminate the hysteresis effect. As an example, we analyze a closed thermodynamic cycle involving four congeneric ligands which bind to HIV-1 integrase, a promising target which has emerged for antiviral therapy. We demonstrate that, compared with FEP and BAR, more accurate and hysteresis-free estimates of free energy differences can be achieved by using UWHAM to find a single estimate of the density of states based on all of the data in the cycle. Furthermore, by comparison of LWHAM results obtained from the inclusion of different numbers of neighboring states with UWHAM estimation involving all the states, we show how to determine the optimal neighborhood size in the LWHAM analysis to balance the trade-offs between computational cost and accuracy of the free energy prediction. Even with the smallest neighborhood, LWHAM can improve the BAR free energy estimates using the same input data as BAR. We introduce an overlapping states matrix that is constructed by using the global jump formula of LWHAM and plot its heat map. The heat map provides a quantitative measure of the overlap between pairs of alchemical/thermodynamic states. We explain how to identify and improve the FEP calculations along the edges that most likely cause large systematic errors by using the heat map of the overlapping states matrix and by comparing the BAR and UWHAM estimates of the free energy change.

Large scale relative protein ligand binding affinities using non-equilibrium alchemy

Ligand binding affinity calculations based on molecular dynamics (MD) simulations and non-physical (alchemical) thermodynamic cycles have shown great promise for structure-based drug design. However, their broad uptake and impact is held back by the notoriously complex setup of the calculations. Only a few tools other than the free energy perturbation approach by Schrödinger Inc. (referred to as FEP+) currently enable end-to-end application. Here, we present for the first time an approach based on the open-source software pmx that allows to easily set up and run alchemical calculations for diverse sets of small molecules using the GROMACS MD engine. The method relies on theoretically rigorous non-equilibrium thermodynamic integration (TI) foundations, and its flexibility allows calculations with multiple force fields. In this study, results from the Amber and Charmm force fields were combined to yield a consensus outcome performing on par with the commercial FEP+ approach. A large dataset of 482 perturbations from 13 different protein-ligand datasets led to an average unsigned error (AUE) of 3.64 ± 0.14 kJ mol-1, equivalent to Schrödinger's FEP+ AUE of 3.66 ± 0.14 kJ mol-1. For the first time, a setup is presented for overall high precision and high accuracy relative protein-ligand alchemical free energy calculations based on open-source software.

Structure activity relationship towards design of cryptosporidium specific thymidylate synthase inhibitors

Cryptosporidiosis is a human gastrointestinal disease caused by protozoans of the genus Cryptosporidium, which can be fatal in immunocompromised individuals. The essential enzyme, thymidylate synthase (TS), is responsible for de novo synthesis of deoxythymidine monophosphate. The TS active site is relatively conserved between Cryptosporidium and human enzymes. In previous work, we identified compound 1, (2-amino-4-oxo-4,7-dihydro-pyrrolo[2,3-d]pyrimidin-methyl-phenyl-l-glutamic acid), as a promising selective Cryptosporidium hominis TS (ChTS) inhibitor. In the present study, we explore the structure-activity relationship around 1 glutamate moiety by synthesizing and biochemically evaluating the inhibitory activity of analogues against ChTS and human TS (hTS). X-Ray crystal structures were obtained for compounds bound to both ChTS and hTS. We establish the importance of the 2-phenylacetic acid moiety methylene linker in optimally positioning compounds 23, 24, and 25 within the active site. Moreover, through the comparison of structural data for 5, 14, 15, and 23 bound in both ChTS and hTS identified that active site rigidity is a driving force in determining inhibitor selectivity.

Insights Into the Explication of Potent Tyrosinase Inhibitors with Reference to Computational Studies

Background:

Pigment melanin has primarily a photo defensive role in human skin, its unnecessary production and irregular distribution can cause uneven skin tone ultimately results in hyper pigmentation. Melanin biosynthesis is initiated by tyrosine oxidation through tyrosinase, the key enzyme for melanogenesis. Not only in humans, tyrosinase is also widely distributed in plants and liable for browning of vegetables and fruits. Search for the inhibitors of tyrosinase have been an important target to facilitate development of therapies for the prevention of hyperpigmentary disorders and an undesired browning of vegetables and fruits.

Methods:

Different natural and synthetic chemical compounds have been tested as potential tyrosinase inhibitors, but the mechanism of inhibition is not known, and the quest for information regarding interaction between tyrosinase and its inhibitors is one of the recent areas of research. Computer based methods hence are useful to overcome such issues. Successful utilization of in silico tools like molecular docking simulations make it possible to interpret the tyrosinase and its inhibitor’s intermolecular interactions and helps in identification and development of new and potent tyrosinase inhibitors.

Results:

The present review has pointed out the prominent role of computer aided approaches for the explication of promising tyrosinase inhibitors with a focus on molecular docking approach. Highlighting certain examples of natural compounds whose antityrosinase effects has been evaluated using computational simulations.

Conclusion:

The investigation of new and potent inhibitors of tyrosinase using computational chemistry and bioinformatics will ultimately help millions of peoples to get rid of hyperpigmentary disorders as well as browning of fruits and vegetables.

Rigorous sampling of docking poses unveils binding hypothesis for the halogenated ligands of L-type Amino acid Transporter 1 (LAT1)

L-type Amino acid Transporter 1 (LAT1) plays a significant role in the growth and propagation of cancer cells by facilitating the cross-membrane transport of essential nutrients, and is an attractive drug target. Several halogen-containing L-phenylalanine-based ligands display high affinity and high selectivity for LAT1; nonetheless, their molecular mechanism of binding remains unclear. In this study, a combined in silico strategy consisting of homology modeling, molecular docking, and Quantum Mechanics-Molecular Mechanics (QM-MM) simulation was applied to elucidate the molecular basis of ligand binding in LAT1. First, a homology model of LAT1 based on the atomic structure of a prokaryotic homolog was constructed. Docking studies using a set of halogenated ligands allowed for deriving a binding hypothesis. Selected docking poses were subjected to QM-MM calculations to investigate the halogen interactions. Collectively, the results highlight the dual nature of the ligand-protein binding mode characterized by backbone hydrogen bond interactions of the amino acid moiety of the ligands and residues I63, S66, G67, F252, G255, as well as hydrophobic interactions of the ligand’s side chains with residues I139, I140, F252, G255, F402, W405. QM-MM optimizations indicated that the electrostatic interactions involving halogens contribute to the binding free energy. Importantly, our results are in good agreement with the recently unraveled cryo-Electron Microscopy structures of LAT1.

In silico study revealed major conserve architectures and novel features of pyrabactin binding to Oryza sativa ABA receptors compare to the Arabidopsis thaliana

Enhancing water use efficiency (WUE) of crops in irrigated agriculture and drought tolerance in rain-fed agriculture is the major goal for sustaining and enhancing agricultural productivity in the future. The phytohormone abscisic acid (ABA) signaling pathway is a major target for the agronomic management of WUE and genetic improvement of drought tolerance in crops. The START domain proteins PYRABACTIN RESISTANCE1 (PYR1)/PYR1-like (PYL)/Regulatory Components of ABA Receptors (RCARs) of the model plant Arabidopsis thaliana have been characterized as bona fide ABA receptors (ABARs). ABA signaling pathway can be activated or repressed by using specific agonist and antagonist against ABAR and therefore, can be used to control ABA-mediated physiological changes in plants. In the present work, we have reported the 3 D structure models of three ABARs (OsPYL1-3) from drought-tolerant Indica rice N22 (Oryza sativa L. sp. Indica cv N22) in apo- and ligand-bound conformations developed using comparative modeling techniques. Subsequently, these models were used in docking study to investigate the binding mode of known ABAR agonists and antagonists. Further, molecular dynamics studies on the selected systems verified the residues involved in protein-ligand interactions. The study identified the important ligand-binding features for the future development of specific agonists/antagonists to modulate the ABA activity in O. sativa and provides in silico models for designing and virtual screening to identify potent ABA receptor ligands.Communicated by Ramaswamy H. Sarma.

Computing Relative Binding Affinity of Ligands to Receptor: An Effective Hybrid Single-Dual-Topology Free-Energy Perturbation Approach in NAMD

An effective hybrid single-dual-topology protocol is designed for the calculation of relative binding affinities of small ligands to a receptor. The protocol was developed as an extension of the NAMD molecular dynamics program, which exclusively supports a dual-topology framework for relative alchemical free-energy perturbation (FEP) calculations. In this protocol, the alchemical end states are represented as two separate molecules sharing a common substructure identified through maximum structural mapping. Within the substructure, an atom-to-atom correspondence is established, and each pair of corresponding atoms is holonomically constrained to share identical coordinates at all time throughout the simulation. The forces are projected and combined at each step for propagation. Following this formulation, a set of illustrative calculations of reliable experiment/simulation data, including relative solvation free energies of small molecules and relative binding affinities of drug compounds to proteins, are presented. To enhance sampling of the dual-topology region, the FEP calculations were carried out within a replica-exchange MD scheme supported by the multiple-copy algorithm module of NAMD, with periodically attempted swapping of the thermodynamic coupling parameter λ between neighboring states. The results are consistent with experiments and benchmarks reported in the literature, lending support to the validity of the current protocol. In summary, this hybrid single-dual-topology approach combines the conceptual simplicity of the dual-topology paradigm with the advantageous sampling efficiency of the single-topology approach, making it an ideal strategy for high-throughput in silico drug design.

De novo design approaches targeting an envelope protein pocket to identify small molecules against dengue virus

Dengue fever is a mosquito-borne viral disease that has become a major public health concern worldwide. This disease presents with a wide range of clinical manifestations, from a mild cold-like illness to the more serious hemorrhagic dengue fever and dengue shock syndrome. Currently, neither an approved drug nor an effective vaccine for the treatment are available to fight the disease. The envelope protein (E) is a major component of the virion surface. This protein plays a key role during the viral entry process, constituting an attractive target for the development of antiviral drugs. The crystal structure of the E protein reveals the existence of a hydrophobic pocket occupied by the detergent n-octyl-β-d-glucoside (β-OG). This pocket lies at the hinge region between domains I and II and is important for the low pH-triggered conformational rearrangement required for the fusion of the virion with the host's cell. Aiming at the design of novel molecules which bind to E and act as virus entry inhibitors, we undertook a de novo design approach by "growing" molecules inside the hydrophobic site (β-OG). From more than 240000 small-molecules generated, the 2,4 pyrimidine scaffold was selected as the best candidate, from which one synthesized compound displayed micromolar activity. Molecular dynamics-based optimization was performed on this hit, and thirty derivatives were designed in silico, synthesized and evaluated on their capacity to inhibit dengue virus entry into the host cell. Four compounds were found to be potent antiviral compounds in the low-micromolar range. The assessment of drug-like physicochemical and in vitro pharmacokinetic properties revealed that compounds 3e and 3h presented acceptable solubility values and were stable in mouse plasma, simulated gastric fluid, simulated intestinal fluid, and phosphate buffered saline solution.

Computational and Experimental Studies of Inhibitor Design for Aldolase A

Glycolytic enzyme fructose-bisphosphate aldolase A is an emerging therapeutic target in cancer. Recently, we have solved the crystal structure of murine aldolase in complex with naphthalene-2,6-diyl bisphosphate (ND1) that served as a template of the design of bisphosphate-based inhibitors. In this work, a series of ND1 analogues containing difluoromethylene (-CF₂), methylene (-CH₂), or aldehyde substitutions were designed. All designed compounds were studied using molecular dynamics (MD) simulations with the AMOEBA force field. Both energetics and structural analyses have been done to understand the calculated binding free energies. The average distances between ligand and protein atoms for ND1 were very similar to those for the ND1 crystal structure, which indicates that our MD simulation is sampling the correct conformation well. CF₂ insertion lowers the binding free energy by 10-15 kcal/mol, while CF₂ substitution slightly increases the binding free energy, which matches the experimental measurement. In addition, we found that NDB with two CF₂ insertions, the strongest binder, is entropically driven, while others including NDA with one CF₂ insertion are all enthalpically driven. This work provides insights into the mechanisms underlying protein-phosphate binding and enhances the capability of applying computational and theoretical frameworks to model, predict, and design diagnostic strategies targeting cancer.

Structural and pharmacological evaluation of a novel non-nucleoside reverse transcriptase inhibitor as a promising long acting nanoformulation for treating HIV

Combination antiretroviral therapy (cART) has been proven effective in inhibiting human immunodeficiency virus type 1 (HIV-1) infection and has significantly improved the health outcomes in acquired immune deficiency syndrome (AIDS) patients. The therapeutic benefits of cART have been challenged because of the toxicity and emergence of drug-resistant HIV-1 strains along with lifelong patient compliance resulting in non-adherence. These issues also hinder the clinical benefits of non-nucleoside reverse transcriptase inhibitors (NNRTIs), which are one of the vital components of cART for the treatment of HIV-1 infection. In this study, using a computational and structural based drug design approach, we have discovered an effective HIV -1 NNRTI, compound I (Cmpd I) that is very potent in biochemical assays and which targets key residues in the allosteric binding pocket of wild-type (WT)-RT as revealed by structural studies. Furthermore, Cmpd I exhibited very potent antiviral activity in HIV-1 infected T cells, lacked cytotoxicity (therapeutic index >100,000), and no significant off-target effects were noted in pharmacological assays. To address the issue of non-adherence, we developed a long-acting nanoformulation of Cmpd I (Cmpd I-NP) using poly (lactide-coglycolide) (PLGA) particles. The pharmacokinetic studies of free and nanoformulated Cmpd I were carried out in BALB/c mice. Intraperitoneal administration of Cmpd I and Cmpd I-NP in BALB/c mice revealed prolonged serum residence time of 48 h and 30 days, respectively. The observed serum concentrations of Cmpd I in both cases were sufficient to provide >97% inhibition in HIV-1 infected T-cells. The significant antiviral activity along with favorable pharmacological and pharmacokinetic profile of Cmpd I, provide compelling and critical support for its further development as an anti-HIV therapeutic agent.