A Mixed QM/MM Scoring Function to Predict Protein-Ligand Binding Affinity

Leveraging Artificial Intelligence for Synergies in Drug Discovery: From Computers to Clinics

Over the period of the preceding decade, artificial intelligence (AI) has proved an outstanding performance in entire dimensions of science including pharmaceutical sciences. AI uses the concept of machine learning (ML), deep learning (DL), and neural networks (NNs) approaches for novel algorithm and hypothesis development by training the machines in multiple ways. AI-based drug development from molecule identification to clinical approval tremendously reduces the cost of development and the time over conventional methods. The COVID-19 vaccine development and approval by regulatory agencies within 1-2 years is the finest example of drug development. Hence, AI is fast becoming a boon for scientific researchers to streamline their advanced discoveries. AI-based FDA-approved nanomedicines perform well as target selective, synergistic therapies, recolonize the theragnostic pharmaceutical stream, and significantly improve drug research outcomes. This comprehensive review delves into the fundamental aspects of AI along with its applications in the realm of pharmaceutical life sciences. It explores AI's role in crucial areas such as drug designing, drug discovery and development, traditional Chinese medicine, integration of multi-omics data, as well as investigations into drug repurposing and polypharmacology studies.

In silico study of selective inhibition mechanism of S-adenosyl-L-methionine analogs for human DNA methyltransferase 3A

Epigenetic mechanisms leading to transcriptional regulation, including DNA methylation, are frequently dysregulated in diverse cancers. Interfering with aberrant DNA methylation performed by DNA cytosine methyltransferases (DNMTs) is a clinically validated approach. In particular, the selective inhibition of the de novo DNMT3A and DNMT3B enzymes, whose expression is limited to early embryogenesis, adult stem cells, and in cancers, is particularly attractive; such selectivity is likely to attenuate the dose limiting toxicity shown by current, non-selective DNMT inhibitors. We use molecular dynamics (MD) based computational analysis to study known small molecule binders of DNMT3A, then propose reversible, tight binding, and selective inhibitors that exploit the Asn¹¹⁹²/Arg⁶⁸⁸ difference between the maintenance DNMT1 and DNMT3A near the active site. A similar strategy exploiting the presence of a unique active site cysteine Cys⁶⁶⁶ is used to propose DNMT3A-selective irreversible inhibitors. We report our results of relative binding energies of the known and proposed compounds estimated using MM/GBSA and umbrella sampling (US) techniques, and our evaluation of other end-point binding free energy calculation methods for these receptors. These calculations offer insight into the potential for small molecules to selectively target the active site of DNMT3A.

How well do semiempirical QM methods describe the structure of proteins?

Semiempirical quantum-mechanical (QM) computational methods are an increasingly popular tool for the study of biomolecular systems. They were, however, developed and tested mostly on small model molecules. In this work, we explore one topic fundamental to these applications: the ability of the methods to describe the structure of proteins. In a set of 19 proteins for which a crystal structure with very high resolution is available, we analyze the properties of the protein geometries optimized using several semiempirical QM methods including PM6-D3H4, PM7, and GFN2-xTB. Some of the methods provide a very good description of the general structural features of the protein, yielding results better than or comparable to the AMBER ff03 force field. However, PM7 and PM6-D3H4 optimizations introduce artificial close contacts in the structure, which is partially remediated by reparameterization.

Computational methods for calculation of protein-ligand binding affinities in structure-based drug design

Drug design is an expensive and time-consuming process. Any method that allows reducing the time the costs of the drug development project can have great practical value for the pharmaceutical industry. In structure-based drug design, affinity prediction methods are of great importance. The majority of methods used to predict binding free energy in protein-ligand complexes use molecular mechanics methods. However, many limitations of these methods in describing interactions exist. An attempt to go beyond these limits is the application of quantum-mechanical description for all or only part of the analyzed system. However, the extensive use of quantum mechanical (QM) approaches in drug discovery is still a demanding challenge. This chapter briefly reviews selected methods used to calculate protein-ligand binding affinity applied in virtual screening (VS), rescoring of docked poses, and lead optimization stage, including QM methods based on molecular simulations.

DLSSAffinity: protein-ligand binding affinity prediction via a deep learning model

Evaluating the protein-ligand binding affinity is a substantial part of the computer-aided drug discovery process. Most of the proposed computational methods predict protein-ligand binding affinity using either limited full-length protein 3D structures or simple full-length protein sequences as the input features. Thus, protein-ligand binding affinity prediction remains a fundamental challenge in drug discovery. In this study, we proposed a novel deep learning-based approach, DLSSAffinity, to accurately predict the protein-ligand binding affinity. Unlike the existing methods, DLSSAffinity uses the pocket-ligand structural pairs as the local information to predict short-range direct interactions. Besides, DLSSAffinity also uses the full-length protein sequence and ligand SMILES as the global information to predict long-range indirect interactions. We tested DLSSAffinity on the PDBbind benchmark. The results showed that DLSSAffinity achieves Pearson's R = 0.79, RMSE = 1.40, and SD = 1.35 on the test set. Comparing DLSSAffinity with the existing state-of-the-art deep learning-based binding affinity prediction methods, the DLSSAffinity model outperforms other models. These results demonstrate that combining global sequence and local structure information as the input features of a deep learning model can improve the accuracy of protein-ligand binding affinity prediction.

QM/MM Energy Decomposition Using the Interacting Quantum Atoms Approach

The interacting quantum atoms (IQA) method decomposes the quantum mechanical (QM) energy of a molecular system in terms of one- and two-center (atomic) contributions within the context of the quantum theory of atoms in molecules. Here, we demonstrate that IQA, enhanced with molecular mechanics (MM) and Poisson-Boltzmann surface-area (PBSA) solvation methods, is naturally extended to the realm of hybrid QM/MM methodologies, yielding intra- and inter-residue energy terms that characterize all kinds of covalent and noncovalent bonding interactions. To test the robustness of this approach, both metal-water interactions and QM/MM boundary artifacts are characterized in terms of the IQA descriptors derived from QM regions of varying size in Zn(II)- and Mg(II)-water clusters. In addition, we analyze a homologous series of inhibitors in complex with a matrix metalloproteinase (MMP-12) by carrying out QM/MM-PBSA calculations on their crystallographic structures followed by IQA energy decomposition. Overall, these applications not only show the advantages of the IQA QM/MM approach but also address some of the challenges lying ahead for expanding the QM/MM methodology.

Applications of machine learning in computer-aided drug discovery

Machine learning (ML) has revolutionised the field of structure-based drug design (SBDD) in recent years. During the training stage, ML techniques typically analyse large amounts of experimentally determined data to create predictive models in order to inform the drug discovery process. Deep learning (DL) is a subfield of ML, that relies on multiple layers of a neural network to extract significantly more complex patterns from experimental data, and has recently become a popular choice in SBDD. This review provides a thorough summary of the recent DL trends in SBDD with a particular focus on de novo drug design, binding site prediction, and binding affinity prediction of small molecules.

Computing Metal-Binding Proteins for Therapeutic Benefit

Over one third of biomolecules rely on metal ions to exert their cellular functions. Metal ions can play a structural role by stabilizing the structure of biomolecules, a functional role by promoting a wide variety of biochemical reactions, and a regulatory role by acting as messengers upon binding to proteins regulating cellular metal-homeostasis. These diverse roles in biology ascribe critical implications to metal-binding proteins in the onset of many diseases. Hence, it is of utmost importance to exhaustively unlock the different mechanistic facets of metal-binding proteins and to harness this knowledge to rationally devise novel therapeutic strategies to prevent or cure pathological states associated with metal-dependent cellular dysfunctions. In this compendium, we illustrate how the use of a computational arsenal based on docking, classical, and quantum-classical molecular dynamics simulations can contribute to extricate the minutiae of the catalytic, transport, and inhibition mechanisms of metal-binding proteins at the atomic level. This knowledge represents a fertile ground and an essential prerequisite for selectively targeting metal-binding proteins with small-molecule inhibitors aiming to (i) abrogate deregulated metal-dependent (mis)functions or (ii) leverage metal-dyshomeostasis to selectively trigger harmful cells death.

Integrated experimental/computational approaches to characterize the systems formed by vanadium with proteins and enzymes

An integrated instrumental/computational approach to characterize metallodrug–protein adducts at the molecular level is reviewed. A series of applications are described, focusing on potential vanadium drugs with a generalization to other metals.

Recent Advances in Molecular Docking for the Research and Discovery of Potential Marine Drugs

Marine drugs have long been used and exhibit unique advantages in clinical practices. Among the marine drugs that have been approved by the Food and Drug Administration (FDA), the protein–ligand interactions, such as cytarabine–DNA polymerase, vidarabine–adenylyl cyclase, and eribulin–tubulin complexes, are the important mechanisms of action for their efficacy. However, the complex and multi-targeted components in marine medicinal resources, their bio-active chemical basis, and mechanisms of action have posed huge challenges in the discovery and development of marine drugs so far, which need to be systematically investigated in-depth. Molecular docking could effectively predict the binding mode and binding energy of the protein–ligand complexes and has become a major method of computer-aided drug design (CADD), hence this powerful tool has been widely used in many aspects of the research on marine drugs. This review introduces the basic principles and software of the molecular docking and further summarizes the applications of this method in marine drug discovery and design, including the early virtual screening in the drug discovery stage, drug target discovery, potential mechanisms of action, and the prediction of drug metabolism. In addition, this review would also discuss and prospect the problems of molecular docking, in order to provide more theoretical basis for clinical practices and new marine drug research and development.

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.

What Next for Quantum Mechanics in Structure-Based Drug Discovery?

There is significant potential for electronic structure methods to improve the quality of the predictions furnished by the tools of computer-aided drug design, which typically rely on empirically derived functions. In this perspective, we consider some recent examples of how quantum mechanics has been applied in predicting protein-ligand geometries, protein-ligand binding affinities and ligand strain on binding. We then outline several significant developments in quantum mechanics methodology likely to influence these approaches: in particular, we note the advent of more computationally expedient ab initio quantum mechanical methods that can provide chemical accuracy for larger molecular systems than hitherto possible. We highlight the emergence of increasingly accurate semiempirical quantum mechanical methods and the associated role of machine learning and molecular databases in their development. Indeed, the convergence of improved algorithms for solving and analyzing electronic structure, modern machine learning methods, and increasingly comprehensive benchmark data sets of molecular geometries and energies provides a context in which the potential of quantum mechanics will be increasingly realized in driving future developments and applications in structure-based drug discovery.

A Fragmenting Protocol with Explicit Hydration for Calculation of Binding Enthalpies of Target-Ligand Complexes at a Quantum Mechanical Level

Optimization of the enthalpy component of binding thermodynamics of drug candidates is a successful pathway of rational molecular design. However, the large size and missing hydration structure of target-ligand complexes often hinder such optimizations with quantum mechanical (QM) methods. At the same time, QM calculations are often necessitated for proper handling of electronic effects. To overcome the above problems, and help the QM design of new drugs, a protocol is introduced for atomic level determination of hydration structure and extraction of structures of target-ligand complex interfaces. The protocol is a combination of a previously published program MobyWat, an engine for assigning explicit water positions, and Fragmenter, a new tool for optimal fragmentation of protein targets. The protocol fostered a series of fast calculations of ligand binding enthalpies at the semi-empirical QM level. Ligands of diverse chemistry ranging from small aromatic compounds up to a large peptide helix of a molecular weight of 3000 targeting a leukemia protein were selected for systematic investigations. Comparison of various combinations of implicit and explicit water models demonstrated that the presence of accurately predicted explicit water molecules in the complex interface considerably improved the agreement with experimental results. A single scaling factor was derived for conversion of QM reaction heats into binding enthalpy values. The factor links molecular structure with binding thermodynamics via QM calculations. The new protocol and scaling factor will help automated optimization of binding enthalpy in future molecular design projects.

Computer-aided design, synthesis and biological characterization of novel inhibitors for PKMYT1

In the current work, we applied computational methods to analyze the membrane-associated inhibitory kinase PKMYT1 and small molecule inhibitors. PKMYT1 regulates the cell cycle at G2/M transition and phosphorylates Thr14 and Tyr15 in the Cdk1-cyclin B complex. A combination of in silico and in vitro screening was applied to identify novel PKMYT1 inhibitors. The computational approach combined structural analysis, molecular docking, binding free energy calculations, and quantitative structure-activity relationship (QSAR) models. In addition, a computational fragment growing approach was applied to a set of previously identified diaminopyrimidines. Based on the derived computational models, several derivatives were synthesized and tested in vitro on PKMYT1. Novel inhibitors active in the sub-micromolar range were identified which provide the basis for further characterization of PKMYT1 as putative target for cancer therapy.

Use of the Multilayer Fragment Molecular Orbital Method to Predict the Rank Order of Protein-Ligand Binding Affinities: A Case Study Using Tankyrase 2 Inhibitors

In computational drug discovery, ranking a series of compound analogues in the order that is consistent with the experimental binding affinities remains a challenge. Many of the computational methods available for evaluating binding affinities have adopted molecular mechanics (MM)-based force fields, although they cannot completely describe protein-ligand interactions. By contrast, quantum mechanics (QM) calculations play an important role in understanding the protein-ligand interactions; however, their huge computational costs hinder their application in drug discovery. In this study, we have evaluated the ability to rank the binding affinities of tankyrase 2 ligands by combining both MM and QM calculations. Our computational approach uses the protein-ligand binding energies obtained from a cost-effective multilayer fragment molecular orbital (MFMO) method combined with the solvation energy obtained from the MM-Poisson-Boltzmann/surface area (MM-PB/SA) method to predict the binding affinity. This approach enabled us to rank tankyrase 2 inhibitor analogues, outperforming several MM-based methods, including rescoring by molecular docking and the MM-PB/SA method alone. Our results show that this computational approach using the MFMO method is a promising tool for predicting the rank order of the binding affinities of inhibitor analogues.

Fragment-based quantum mechanical calculation of protein-protein binding affinities

The electrostatically embedded generalized molecular fractionation with conjugate caps (EE-GMFCC) method has been successfully utilized for efficient linear-scaling quantum mechanical (QM) calculation of protein energies. In this work, we applied the EE-GMFCC method for calculation of binding affinity of Endonuclease colicin-immunity protein complex. The binding free energy changes between the wild-type and mutants of the complex calculated by EE-GMFCC are in good agreement with experimental results. The correlation coefficient (R) between the predicted binding energy changes and experimental values is 0.906 at the B3LYP/6-31G*-D level, based on the snapshot whose binding affinity is closest to the average result from the molecular mechanics/Poisson-Boltzmann surface area (MM/PBSA) calculation. The inclusion of the QM effects is important for accurate prediction of protein-protein binding affinities. Moreover, the self-consistent calculation of PB solvation energy is required for accurate calculations of protein-protein binding free energies. This study demonstrates that the EE-GMFCC method is capable of providing reliable prediction of relative binding affinities for protein-protein complexes. © 2018 Wiley Periodicals, Inc.

Structural Basis and Mechanism of Chiral Benzedrine Molecules Interacting With Third Dopamine Receptor

In order to investigate the chiral benzedrine molecules corresponding to their different characteristics in biochemical systems, we studied their interaction with D3 R using the docking method, molecular dynamic simulation, and quantum chemistry. The obtained results indicate that the active residues for R-benzedrine (RAT) bound with D3 R are Ala132, Asp133, and Tyr55, while Asn57, Asp133, Asp168, Cys172, Gly54, Trp24, and Vall136 act as the active residues for S-benzedrine (SAT). The different active pockets are observed for ART or SAT because they possess different active residues. The binding energies between RAT and SAT with D3 R were determined to be -44.0 kJ.mol(-1) and -71.2 kJ.mol(-1) , respectively. These results demonstrate that SAT within the studied pocket of D3 R has a stronger capability of binding with D3 R, while it is more feasible for RAT to leave from the interior positions of D3 R. In addition, the results suggest that the D3 R protein can recognize chiral benzedrine molecules and influence their different addictive and pharmacological effects in biochemical systems. Chirality 28:674-685, 2016. © 2016 Wiley Periodicals, Inc.

Semiempirical Quantum Mechanical Methods for Noncovalent Interactions for Chemical and Biochemical Applications

Semiempirical (SE) methods can be derived from either Hartree-Fock or density functional theory by applying systematic approximations, leading to efficient computational schemes that are several orders of magnitude faster than ab initio calculations. Such numerical efficiency, in combination with modern computational facilities and linear scaling algorithms, allows application of SE methods to very large molecular systems with extensive conformational sampling. To reliably model the structure, dynamics, and reactivity of biological and other soft matter systems, however, good accuracy for the description of noncovalent interactions is required. In this review, we analyze popular SE approaches in terms of their ability to model noncovalent interactions, especially in the context of describing biomolecules, water solution, and organic materials. We discuss the most significant errors and proposed correction schemes, and we review their performance using standard test sets of molecular systems for quantum chemical methods and several recent applications. The general goal is to highlight both the value and limitations of SE methods and stimulate further developments that allow them to effectively complement ab initio methods in the analysis of complex molecular systems.

The SQM/COSMO filter: reliable native pose identification based on the quantum-mechanical description of protein–ligand interactions and implicit COSMO solvation

Strictly uphill – in cognate docking experiments we show that a quantum mechanical description of interaction and solvation outperforms established scoring functions in sharply distinguishing the native state from decoy poses.

TcCYPR04, a Cacao Papain-Like Cysteine-Protease Detected in Senescent and Necrotic Tissues Interacts with a Cystatin TcCYS4

The interaction amongst papain-like cysteine-proteases (PLCP) and their substrates and inhibitors, such as cystatins, can be perceived as part of the molecular battlefield in plant-pathogen interaction. In cacao, four cystatins were identified and characterized by our group. We identified 448 proteases in cacao genome, whereof 134 were cysteine-proteases. We expressed in Escherichia coli a PLCP from cacao, named TcCYSPR04. Immunoblottings with anti-TcCYSPR04 exhibited protein increases during leaf development. Additional isoforms of TcCYSPR04 appeared in senescent leaves and cacao tissues infected by Moniliophthora perniciosa during the transition from the biotrophic to the saprophytic phase. TcCYSPR04 was induced in the apoplastic fluid of Catongo and TSH1188 cacao genotypes, susceptible and resistant to M. perniciosa, respectively, but greater intensity and additional isoforms were observed in TSH1188. The fungal protein MpNEP induced PLCP isoform expression in tobacco leaves, according to the cross reaction with anti-TcCYSPR04. Several protein isoforms were detected at 72 hours after treatment with MpNEP. We captured an active PLCP from cacao tissues, using a recombinant cacao cystatin immobilized in CNBr-Sepharose. Mass spectrometry showed that this protein corresponds to TcCYSPR04. A homology modeling was obtained for both proteins. In order to become active, TcCYSPR04 needs to lose its inhibitory domain. Molecular docking showed the physical-chemical complementarities of the interaction between the cacao enzyme and its inhibitor. We propose that TcCYSPR04 and its interactions with cacao cystatins are involved in the senescence and necrosis events related to witches' broom symptoms. This molecular interaction may be the target for future interventions to control witches' broom disease.

A "Stepping Stone" Approach for Obtaining Quantum Free Energies of Hydration

We present a method which uses DFT (quantum, QM) calculations to improve free energies of binding computed with classical force fields (classical, MM). To overcome the incomplete overlap of configurational spaces between MM and QM, we use a hybrid Monte Carlo approach to generate quickly correct ensembles of structures of intermediate states between a MM and a QM/MM description, hence taking into account a great fraction of the electronic polarization of the quantum system, while being able to use thermodynamic integration to compute the free energy of transition between the MM and QM/MM. Then, we perform a final transition from QM/MM to full QM using a one-step free energy perturbation approach. By using QM/MM as a stepping stone toward the full QM description, we find very small convergence errors (<1 kJ/mol) in the transition to full QM. We apply this method to compute hydration free energies, and we obtain consistent improvements over the MM values for all molecules we used in this study. This approach requires large-scale DFT calculations as the full QM systems involved the ligands and all waters in their simulation cells, so the linear-scaling DFT code ONETEP was used for these calculations.

Using docking and alchemical free energy approach to determine the binding mechanism of eEF2K inhibitors and prioritizing the compound synthesis

A-484954 is a known eEF2K inhibitor with submicromolar IC₅₀ potency. However, the binding mechanism and the crystal structure of the kinase remains unknown. Here, we employ a homology eEF2K model, docking and alchemical free energy simulations to probe the binding mechanism of eEF2K, and in turn, guide the optimization of potential lead compounds. The inhibitor was docked into the ATP-binding site of a homology model first. Three different binding poses, hypothesis 1, 2, and 3, were obtained and subsequently applied to molecular dynamics (MD) based alchemical free energy simulations. The calculated relative binding free energy of the analogs of A-484954 using the binding pose of hypothesis 1 show a good correlation with the experimental IC₅₀ values, yielding an r² coefficient of 0.96 after removing an outlier (compound 5). Calculations using another two poses show little correlation with experimental data, (r² of less than 0.5 with or without removing any outliers). Based on hypothesis 1, the calculated relative free energy suggests that bigger cyclic groups, at R1 e.g., cyclobutyl and cyclopentyl promote more favorable binding than smaller groups, such as cyclopropyl and hydrogen. Moreover, this study also demonstrates the ability of the alchemical free energy approach in combination with docking and homology modeling to prioritize compound synthesis. This can be an effective means of facilitating structure-based drug design when crystal structures are not available.

Docking and binding free energy calculations of sirtuin inhibitors

Sirtuins form a unique and highly conserved class of NAD(+)-dependent lysine deacylases. Among these the human subtypes Sirt1-3 has been implicated in the pathogenesis of numerous diseases such as cancer, metabolic syndromes, viral diseases and neurological disorders. Most of the sirtuin inhibitors that have been identified so far show limited potency and/or isoform selectivity. Here, we introduce a promising method to generate protein-inhibitor complexes of human Sirt1, Sirt2 and Sirt3 by means of ligand docking and molecular dynamics simulations. This method highly reduces the complexity of such applications and can be applied to other protein targets beside sirtuins. To the best of our knowledge, we present the first binding free energy method developed by using a validated data set of sirtuin inhibitors, where both a fair number of compounds (33 thieno[3,2-d]pyrimidine-6-carboxamide derivatives) was developed and tested in the same laboratory and also crystal structures in complex with the enzyme have been reported. A significant correlation between binding free energies derived from MM-GBSA calculations and in vitro data was found for all three sirtuin subtypes. The developed MM-GBSA protocol is computationally inexpensive and can be applied as a post-docking filter in virtual screening to find novel Sirt1-3 inhibitors as well as to prioritize compounds with similar chemical structures for further biological characterization.

Theoretical insight into the structural mechanism for the binding of vinblastine with tubulin

Vinblastine (VLB) is one of vinca alkaloids with high cytotoxicity toward cancer cells approved for clinical use. However, because of drug resistance, toxicity, and other side effects caused from the use of VLB, new vinca alkaloids with higher cytotoxicity toward cancer cells and other good qualities need to develop. One strategy is to further study and better understand the essence why VLB possesses the high cytotoxicity toward cancer cells. In present work, by using molecular simulation, molecular docking, density functional calculation, and the crystal structure of α,β-tubulin complex, we find two modes labeled in catharanthine moiety (CM) and vindoline moiety (VM) modes of VLB bound with the interface of α,β-tubulin to probe the essence why VLB has the high cytotoxicity toward cancer cells. In the CM mode, nine key residues B-Ser178, B-Asp179, B-Glu183, B-Tyr210, B-Asp226, C-Lys326, C-Asp327, C-Lys336, and C-Lys352 from the α,β-tubulin complex are determined as the active sites for the interaction of VLB with α,β-tubulin. Some of them such as B-Ser178, B-Glu183, B-Tyr210, B-Asp226, C-Lys326, C-Asp327, and C-Lys336 are newly identified as the active sites in present work. The affinity between VLB and the active pocket within the interface of α,β-tubulin is -60.8 kJ mol(-1) in the CM mode. In the VM mode, that is a new mode established in present paper, nine similar key residues B-Lys176, B-Ser178, B-Asp179, B-Glu183, B-Tyr210, B-Asp226, C-Lys326, C-Asp327, and C-Lys336 from the α,β-tubulin complex are found as the active sites for the interaction with VLB. The difference is from one key residue C-Lys352 in the CM mode changed to the key residue B-Lys176 in the VM mode. The affinity between VLB and the active pocket within the interface of α,β-tubulin is -96.3 kJ mol(-1) in the VM mode. Based on the results obtained in present work, and because VLB looks like two faces, composed of CM and VM both to have similar polar active groups, to interact with the active sites, we suggest double-faces sticking mechanism for the binding of VLB to the interface of α,β-tubulin. The double-faces sticking mechanism can be used to qualitatively explain high cytotoxicity toward cancer cells of vinca alkaloids including vinblastine, vincristine, vindestine, and vinorelbine approved for clinical use and vinflunine still in a phase III clinical trial. Furthermore, this mechanism will be applied to develop novel vinca alkaloids with much higher cytotoxicity toward cancer cells.

Investigations on the mechanisms of interactions between matrix metalloproteinase 9 and its flavonoid inhibitors using a combination of molecular docking, hybrid quantum mechanical/molecular mechanical calculations, and molecular dynamics simulations

Many experimental studies have found that flavonoids can inhibit the activities of matrix metalloproteinases (MMPs), but the relevant mechanisms are still unclear. In this paper, the interaction mechanisms of MMP-9 with its five flavonoid inhibitors are investigated using a combination of molecular docking, hybrid quantum mechanical and molecular mechanical (QM/MM) calculations, and molecular dynamics simulations. The molecular dynamics simulation results show a good linear correlation between the calculated binding free energies of QM/MM−Poisson–Boltzmann surface area (PBSA) and the experimental −log(EC50) regarding the studied five flavonoids on MMP-9 inhibition in explicit solvent. It is found that compared with the MM−PBSA method, the QM/MM−PBSA method can obviously improve the accuracy for the calculated binding free energies. The predicted binding modes of the five flavonoid−MMP-9 complexes reveal that the different hydrogen bond networks can form besides producing the Zn−O coordination bonds, which can reasonably explain previous experimental results. The agreement between our calculated results and the previous experimental facts indicates that the force field parameters used here are effective and reliable for investigating the systems of flavonoid−MMP-9 interactions, and thus, these simulations and analyses could be reproduced for the other related systems involving protein−ligand interactions. This paper may be helpful for designing the new MMP-9 inhibitors having higher biological activities by carrying out the structural modifications of flavonoid molecules.

QM/MM molecular dynamics studies of metal binding proteins

Mixed quantum-classical (quantum mechanical/molecular mechanical (QM/MM)) simulations have strongly contributed to providing insights into the understanding of several structural and mechanistic aspects of biological molecules. They played a particularly important role in metal binding proteins, where the electronic effects of transition metals have to be explicitly taken into account for the correct representation of the underlying biochemical process. In this review, after a brief description of the basic concepts of the QM/MM method, we provide an overview of its capabilities using selected examples taken from our work. Specifically, we will focus on heme peroxidases, metallo-β-lactamases, α-synuclein and ligase ribozymes to show how this approach is capable of describing the catalytic and/or structural role played by transition (Fe, Zn or Cu) and main group (Mg) metals. Applications will reveal how metal ions influence the formation and reduction of high redox intermediates in catalytic cycles and enhance drug metabolism, amyloidogenic aggregate formation and nucleic acid synthesis. In turn, it will become manifest that the protein frame directs and modulates the properties and reactivity of the metal ions.

Induced fit docking, and the use of QM/MM methods in docking

Docking methods are popular computational techniques in drug discovery to identify new active molecules that bind to a given biological target. Although widely used, the predictive reliability of docking methods is often limited by the inability to accurately and efficiently model protein flexibility and quantify binding strength. We highlight several emerging concepts that address those methodological issues including a discussion on the incorporation of QM/MM methodologies in the scoring process.

Does a more precise chemical description of protein-ligand complexes lead to more accurate prediction of binding affinity?

Predicting the binding affinities of large sets of diverse molecules against a range of macromolecular targets is an extremely challenging task. The scoring functions that attempt such computational prediction are essential for exploiting and analyzing the outputs of docking, which is in turn an important tool in problems such as structure-based drug design. Classical scoring functions assume a predetermined theory-inspired functional form for the relationship between the variables that describe an experimentally determined or modeled structure of a protein–ligand complex and its binding affinity. The inherent problem of this approach is in the difficulty of explicitly modeling the various contributions of intermolecular interactions to binding affinity. New scoring functions based on machine-learning regression models, which are able to exploit effectively much larger amounts of experimental data and circumvent the need for a predetermined functional form, have already been shown to outperform a broad range of state-of-the-art scoring functions in a widely used benchmark. Here, we investigate the impact of the chemical description of the complex on the predictive power of the resulting scoring function using a systematic battery of numerical experiments. The latter resulted in the most accurate scoring function to date on the benchmark. Strikingly, we also found that a more precise chemical description of the protein–ligand complex does not generally lead to a more accurate prediction of binding affinity. We discuss four factors that may contribute to this result: modeling assumptions, codependence of representation and regression, data restricted to the bound state, and conformational heterogeneity in data.

Virtual screening of PRK1 inhibitors: ensemble docking, rescoring using binding free energy calculation and QSAR model development

Protein kinase C Related Kinase 1 (PRK1) has been shown to be involved in the regulation of androgen receptor signaling and has been identified as a novel potential drug target for prostate cancer therapy. Since there is no PRK1 crystal structure available to date, multiple PRK1 homology models were generated in order to address the protein flexibility. An in-house library of compounds tested on PRK1 was docked into the ATP binding site of the generated models. In most cases a correct pose of the inhibitors could be identified by ensemble docking, while there is still a challenge of finding a reasonable scoring function that is able to rank compounds according to their biological activity. We estimated the binding free energy for our data set of structurally diverse PRK1 inhibitors using the MM-PB(GB)SA and QM/MM-GBSA methods. The obtained results demonstrate that a correlation between calculated binding free energies and experimental IC50 values was found to be usually higher than using docking scores. Furthermore, the developed approach was tested on a set of diverse PRK1 inhibitors taken from literature, which resulted in a significant correlation. The developed method is computationally inexpensive and can be applied as a postdocking filter in virtual screening as well as for optimization of PRK1 inhibitors in order to prioritize compounds for further biological characterization.

Quantum mechanics-based scoring rationalizes the irreversible inactivation of parasitic Schistosoma mansoni cysteine peptidase by vinyl sulfone inhibitors

The quantum mechanics (QM)-based scoring function that we previously developed for the description of noncovalent binding in protein-ligand complexes has been modified and extended to treat covalent binding of inhibitory ligands. The enhancements are (i) the description of the covalent bond breakage and formation using hybrid QM/semiempirical QM (QM/SQM) restrained optimizations and (ii) the addition of the new ΔG(cov)' term to the noncovalent score, describing the "free" energy difference between the covalent and noncovalent complexes. This enhanced QM-based scoring function is applied to a series of 20 vinyl sulfone-based inhibitory compounds inactivating the cysteine peptidase cathepsin B1 of the Schistosoma mansoni parasite (SmCB1). The available X-ray structure of the SmCB1 in complex with a potent vinyl sulfone inhibitor K11017 is used as a template to build the other covalently bound complexes and to model the derived noncovalent complexes. We present the correlation of the covalent score and its constituents with the experimental binding data. Four outliers are identified. They contain bulky R1' substituents structurally divergent from the template, which might induce larger protein rearrangements than could be accurately modeled. In summary, we propose a new computational approach and an optimal protocol for the rapid evaluation and prospective design of covalent inhibitors with a conserved binding mode.

Correlating structure and energetics in protein-ligand interactions: paradigms and paradoxes

Predicting protein-binding affinities of small molecules, even closely related ones, is a formidable challenge in biomolecular recognition and medicinal chemistry. A thermodynamic approach to optimizing affinity in protein-ligand interactions requires knowledge and understanding of how altering the structure of a small molecule will be manifested in protein-binding enthalpy and entropy changes; however, there is a relative paucity of such detailed information. In this review, we examine two strategies commonly used to increase ligand potency. The first of these involves introducing a cyclic constraint to preorganize a small molecule in its biologically active conformation, and the second entails adding nonpolar groups to a molecule to increase the amount of hydrophobic surface that is buried upon binding. Both of these approaches are motivated by paradigms suggesting that protein-binding entropy changes should become more favorable, but paradoxes can emerge that defy conventional wisdom.