The application of quantum mechanics in structure-based drug design

A consensus reverse docking approach for identification of a competitive inhibitor of acetyltransferase enhanced intracellular survival protein from Mycobacterium tuberculosis

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (Mtb), which remains a significant global health challenge. The emergence of multidrug-resistant (MDR) Mtb strains imposes the development of new therapeutic strategies. This study focuses on the identification and evaluation of potential inhibitors against Mtb H37Ra through a comprehensive screening of an in-house chemolibrary. Subsequently, a promising pyrimidine derivative (LQM495) was identified as promising and then further investigated by experimental and in silico approaches. In this context, computational techniques were used to elucidate the potential molecular target underlying the inhibitory action of LQM495. Then, a consensus reverse docking (CRD) protocol was used to investigate the interactions between this compound and several Mtb targets. Out of 98 Mtb targets investigated, the enhanced intracellular survival (Eis) protein emerged as a target for LQM495. To gain insights into the stability of the LQM495-Eis complex, molecular dynamics (MD) simulations were conducted over a 400 ns trajectory. Further insights into its binding modes within the Eis binding site were obtained through a Quantum mechanics (QM) approach, using density functional theory (DFT), with B3LYP/D3 basis set. These calculations shed light on the electronic properties and reactivity of LQM495. Subsequently, inhibition assays and kinetic studies of the Eis activity were used to investigate the activity of LQM495. Then, an IC50 value of 11.0 ± 1.4 µM was found for LQM495 upon Eis protein. Additionally, its Vmax, Km, and Ki parameters indicated that it is a competitive inhibitor. Lastly, this study presents LQM495 as a promising inhibitor of Mtb Eis protein, which could be further explored for developing novel anti-TB drugs in the future.

A neural network potential based on pairwise resolved atomic forces and energies

Molecular simulations have become a key tool in molecular and materials design. Machine learning (ML)-based potential energy functions offer the prospect of simulating complex molecular systems efficiently at quantum chemical accuracy. In previous work, we have introduced the ML-based PairF-Net approach to neural network potentials, that adopts a pairwise interatomic scheme to predicting forces within a molecular system. Here, we further develop the PairF-Net model to intrinsically incorporate energy conservation and couple the model to a molecular mechanical (MM) environment within the OpenMM package. The updated PairF-Net model yields energy and force predictions and dynamical distributions in good agreement with the rMD17 dataset of ten small organic molecules in the gas-phase. We further show that these in vacuo ML models of small molecules can be applied to force predictions in aqueous solution via hybrid ML/MM simulations. We present a new benchmark dataset for these ten molecules in solution, obtained from QM/MM simulations, which we denote as rMD17-aq (https://zenodo.org/records/10048644); and assess the ability of PairF-Net to reproduce the molecular energy, atomic forces and dynamical distributions of these solution conformations via ML/MM simulations.

Quantum biochemistry description of PI3Kα enzyme bound to selective inhibitors

The PI3K class I is composed of four PI3K isoforms that serve as regulatory enzymes governing cellular metabolism, proliferation, and survival. The hyperactivation of PI3Kα is observed in various types of cancer and is linked to poor prognosis. Unfortunately, the development inhibitors selectively targeting one of the isoforms remains challenging, with only few agents in clinical use. The main difficulty arises from the high conservation among residues at the ATP-binding pocket across isoforms, which also serves as target pocket for inhibitors. In this work, molecular dynamics and quantum calculations were performed to investigate the molecular features guiding the binding of selective inhibitors, alpelisib and GDC-0326, into the ATP-binding pocket of PI3Kα. While molecular dynamics allowed crystallographic coordinates to relax, the interaction eergy between each amino acid residues and inhibitors was obtained by combining the Molecular Fractionation with Conjugated Caps scheme with Density Functional Theory calculations. In addition, the atomic charge of ligands in the bound and unbound (free) was calculated. Results indicated that the most relevant residues for the binding of alpelisib are Ile932, Glu859, Val851, Val850, Tyr836, Met922, Ile800, and Ile848, while the most important residues for the binding of GDC-0326 are Ile848, Ile800, Ile932, Gln859, Glu849, and Met922. In addition, residues Trp780, Ile800, Tyr836, Ile848, Gln859 Val850, Val851, Ile932 and Met922 are common hotspots for both inhibitors. Overall, the results from this work contribute to improving the understanding of the molecular mechanisms controlling selectivity and highlight important interactions to be considered during the rational design of new agents.Communicated by Ramaswamy H. Sarma.

Understanding the Structure-Activity Relationship through Density Functional Theory: A Simple Method Predicts Relative Binding Free Energies of Metalloenzyme Fragment-like Inhibitors

Despite being involved in several human diseases, metalloenzymes are targeted by a small percentage of FDA-approved drugs. Development of novel and efficient inhibitors is required, as the chemical space of metal binding groups (MBGs) is currently limited to four main classes. The use of computational chemistry methods in drug discovery has gained momentum thanks to accurate estimates of binding modes and binding free energies of ligands to receptors. However, exact predictions of binding free energies in metalloenzymes are challenging due to the occurrence of nonclassical phenomena and interactions that common force field-based methods are unable to correctly describe. In this regard, we applied density functional theory (DFT) to predict the binding free energies and to understand the structure-activity relationship of metalloenzyme fragment-like inhibitors. We tested this method on a set of small-molecule inhibitors with different electronic properties and coordinating two Mn²⁺ ions in the binding site of the influenza RNA polymerase PA_N endonuclease. We modeled the binding site using only atoms from the first coordination shell, hence reducing the computational cost. Thanks to the explicit treatment of electrons by DFT, we highlighted the main contributions to the binding free energies and the electronic features differentiating strong and weak inhibitors, achieving good qualitative correlation with the experimentally determined affinities. By introducing automated docking, we explored alternative ways to coordinate the metal centers and we identified 70% of the highest affinity inhibitors. This methodology provides a fast and predictive tool for the identification of key features of metalloenzyme MBGs, which can be useful for the design of new and efficient drugs targeting these ubiquitous proteins.

A computational study of the reaction mechanism and stereospecificity of dihydropyrimidinase

Dihydropyrimidinase (DHPase) is a key enzyme in the pyrimidine pathway, the catabolic route for synthesis of β-amino acids. It catalyses the reversible conversion of 5,6-dihydrouracil (DHU) or 5,6-dihydrothymine (DHT) to the corresponding N-carbamoyl-β-amino acids. This enzyme has the potential to be used as a tool in the production of β-amino acids. Here, the reaction mechanism and origin of stereospecificity of DHPases from Saccharomyces kluyveri and Sinorhizobium meliloti CECT4114 were investigated and compared using a quantum mechanical cluster approach based on density functional theory. Two models of the enzyme active site were designed from the X-ray crystal structure of the native enzyme: a small cluster to characterize the mechanism and the stationary points and a large model to probe the stereospecificity and the role of stereo-gate-loop (SGL) residues. It is shown that a hydroxide ion first performs a nucleophilic attack on the substrate, followed by the abstraction of a proton by Asp358, which occurs concertedly with protonation of the ring nitrogen by the same residue. For the DHT substrate, the enzyme displays a preference for the L-configuration, in good agreement with experimental observation. Comparison of the reaction energetics of the two models reveals the importance of SGL residues in the stereospecificity of catalysis. The role of the conserved Tyr172 residue in transition-state stabilization is confirmed as the Tyr172Phe mutation increases the activation barrier of the reaction by ∼8 kcal mol^-1. A detailed understanding of the catalytic mechanism of the enzyme could offer insight for engineering in order to enhance its activity and substrate scope.

Application of Molecular Simulation Methods in Food Science: Status and Prospects

Molecular simulation methods, such as molecular docking, molecular dynamic (MD) simulation, and quantum chemical (QC) calculation, have become popular as characterization and/or virtual screening tools because they can visually display interaction details that in vitro experiments can not capture and quickly screen bioactive compounds from large databases with millions of molecules. Currently, interdisciplinary research has expanded molecular simulation technology from computer aided drug design (CADD) to food science. More food scientists are supporting their hypotheses/results with this technology. To understand better the use of molecular simulation methods, it is necessary to systematically summarize the latest applications and usage trends of molecular simulation methods in the research field of food science. However, this type of review article is rare. To bridge this gap, we have comprehensively summarized the principle, combination usage, and application of molecular simulation methods in food science. We also analyzed the limitations and future trends and offered valuable strategies with the latest technologies to help food scientists use molecular simulation methods.

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.

Designing drugs when there is low data availability: one-shot learning and other approaches to face the issues of a long-term concern

INTRODUCTION

Modern drug discovery generally is accessed by useful information from previous large databases or uncovering novel data. The lack of biological and/or chemical data tends to slow the development of scientific research and innovation. Here, approaches that may help provide solutions to generate or obtain enough relevant data or improve/accelerate existing methods within the last five years were reviewed.

AREAS COVERED

One-shot learning (OSL) approaches, structural modeling, molecular docking, scoring function space (SFS), molecular dynamics (MD), and quantum mechanics (QM) may be used to amplify the amount of available data to drug design and discovery campaigns, presenting methods, their perspectives, and discussions to be employed in the near future.

EXPERT OPINION

Recent works have successfully used these techniques to solve a range of issues in the face of data scarcity, including complex problems such as the challenging scenario of drug design aimed at intrinsically disordered proteins and the evaluation of potential adverse effects in a clinical scenario. These examples show that it is possible to improve and kickstart research from scarce available data to design and discover new potential drugs.

Comparative assessment of QM-based and MM-based models for prediction of protein-ligand binding affinity trends

Methods which accurately predict protein-ligand binding strengths are critical for drug discovery. In the last two decades, advances in chemical modelling have enabled steadily accelerating progress in the discovery and optimization of structure-based drug design. Most computational methods currently used in this context are based on molecular mechanics force fields that often have deficiencies in describing the quantum mechanical (QM) aspects of molecular binding. In this study, we show the competitiveness of our QM-based Molecules-in-Molecules (MIM) fragmentation method for characterizing binding energy trends for seven different datasets of protein-ligand complexes. By using molecular fragmentation, the MIM method allows for accelerated QM calculations. We demonstrate that for classes of structurally similar ligands bound to a common receptor, MIM provides excellent correlation to experiment, surpassing the more popular Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) and Molecular Mechanics Generalized Born Surface Area (MM/GBSA) methods. The MIM method offers a relatively simple, well-defined protocol by which binding trends can be ascertained at the QM level and is suggested as a promising option for lead optimization in structure-based drug design.

Preliminary modelling as the first stage of targeted organic synthesis

The review aims to present a classification and applicability analysis of methods for preliminary molecular modelling for targeted organic, catalytic and biocatalytic synthesis. The following three main approaches are considered as a primary classification of the methods: modelling of the target – ligand coordination without structural information on both the target and the resulting complex; calculations based on experimentally obtained structural information about the target; and dynamic simulation of the target – ligand complex and the reaction mechanism with calculation of the free energy of the reaction. The review is meant for synthetic chemists to be used as a guide for building an algorithm for preliminary modelling and synthesis of structures with specified properties.

The bibliography includes 353 references.

Unraveling individual host-guest interactions in molecular recognition from first principles quantum mechanics: Insights into the nature of nicotinic acetylcholine receptor agonist binding

Drug binding to a protein target is governed by a complex pattern of noncovalent interactions between the ligand and the residues in the protein's binding pocket. Here we introduce a generally applicable, parameter-free, computational method that allows for the identification, quantification, and analysis of the key ligand-residue interactions responsible for molecular recognition. Our strategy relies on Local Energy Decomposition analysis at the "gold-standard" coupled cluster DLPNO-CCSD(T) level. In the study case shown in this paper, nicotine and imidacloprid binding to the nicotinic acetylcholine receptor, our approach provides new insights into how individual amino acids in the active site determine sensitivity and selectivity of the ligands, extending and refining classical pharmacophore hypotheses. By inference, the method is applicable to any kind of host/guest interactions with potential applications in industrial biocatalysis and protein engineering.

Quantum binding energy features of the drug olmesartan bound to angiotensin type-1 receptors in the therapeutics of stroke

We investigated the binding energies of 105 residues within a 10 Å pocket radius, predicted the energetic relevance of olmesartan regions, and the influence of individual protein segments on OLM -AT1 binding.

Computerized Molecular Modeling of Carbohydrates

Computerized molecular modeling continues to increase in capability and applicability to carbohydrates. This chapter covers nomenclature and conformational aspects of carbohydrates, perhaps of greater use to computational chemists who do not have a strong background in carbohydrates, and its comments on various methods and studies might be of more use to carbohydrate chemists who are inexperienced with computation. Work on the intrinsic variability of glucose, an overall theme, is described. Other areas of the authors' emphasis, including evaluation of hydrogen bonding by the atoms-in-molecules approach, and validation of modeling methods with crystallographic results are also presented.

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.

Xanthine oxidoreductase inhibition – A review of computational aspect

Xanthine Oxidoreductase (XOR) exists in a variety of organisms from bacteria to humans and catalyzes the oxidation of hypoxanthine to xanthine and from xanthine to uric acid. Excessive uric acid could lead to gout and hyperuricemia. In this paper, we have reviewed the recent computational studies on xanthine oxidase inhibition. Computational methods, such as molecular dynamics (molecular mechanics), quantum mechanics, and quantum mechanics/molecular mechanics (QM/MM), have been employed to investigate the binding affinity of xanthine oxidase with synthesized and isolated nature inhibitors. The limitations of different computational methods for xanthine oxidase inhibition studies were also discussed. Implications of the computational approach could be used to help to understand the existing arguments on substrate/product orientation in xanthine oxidase inhibition, which allows designing new inhibitors with higher efficacy.

Quantum Mechanical Investigation of Three-Dimensional Activity Cliffs Using the Molecules-in-Molecules Fragmentation-Based Method

The concept of activity cliff (AC) (i.e., a small structural modification resulting in a substantial bioactivity change) is widely encountered in medicinal chemistry during compound design. Whereas the study of ACs is of high interest as it provides a wealth of opportunities for effective drug design, its practical application in the actual drug development process has been difficult because of significant computational challenges. To provide some understanding of the ACs, we have carried out a rigorous quantum-mechanical investigation of the electronic interactions of a wide range of ACs (205 cliffs formed by 261 protein-ligand complexes covering 37 different receptor types) using multilayer molecules-in-molecules (MIM) fragmentation-based methodology. The MIM methodology enables performing accurate high-level quantum mechanical (QM) calculations at a substantially lower computational cost, while allowing for a quantitative decomposition of the protein-ligand binding energy into the contributions from individual residues, solvation, and entropy. Our investigation in this study is mainly focused on whether the QM binding energy calculation can correctly identify the higher potency cliff partner for a given ligand pair having a sufficiently high activity difference. We have also analyzed the effect of including crystal water molecules as a part of the receptor as well as the impact of ligand desolvation energy on the correct identification of the more potent ligand in a cliff pair. Our analysis reveals that, in the majority of the cases, the AC prediction could be significantly improved by carefully identifying the critical crystal water molecules, whereas the contribution from the ligand desolvation also remains essential. Additionally, we have exploited the residue-specific interaction energies provided by MIM to identify the key residues and interaction hot-spots that are responsible for the experimentally observed drastic activity changes. The results show that our MIM fragmentation-based protocol provides comprehensive interaction energy profiles that can be employed to understand the distinctiveness of ligand modifications, for potential applications in structure-based drug design.

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.

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.

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.

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.

Intermolecular interactions of cn-716 and acyl-KR-aldehyde dipeptide inhibitors against Zika virus

Structural representation and graphic panel showing the most relevant residues that contribute to the ZIKV NS2B–NS3–ligand complexes.

Observation of quantum signature in rivastigmine chemical bond break-up and quantum energetics, spectral studies of anti-Alzheimer inhibitors

Semi-empirical calculations on the torsion potential for dihedral angle of rivastigmine linking NAP and Carbamyle moieties consistently show regions of several discontinuities and a cusp indicating molecular instability and eventual break-up of rivastigmine observed in the X-ray structure. The phenomena can be explained both by definition of large classical force or quantum nature of chemical bond break-up. Also, to better understand the molecular properties and quantum energetics of the inhibitor molecules, we have performed several ab initio based calculations on all four inhibitors at equilibrium geometry, in ground state and gas phase using the density functional theory level wB97X/6-31G* and HF/6-31G*. A number of properties like computational vibrational (IR), Raman and nuclear magnetic resonance (NMR) spectra as well as HOMO and LUMO orbital energies at optimized geometries have been computed by SPARTAN16 and Gaussian16 utilities. Also, the thermodynamic and QSAR properties of the inhibitors have been assessed and compared by a number of different semi-quantum, Hartree-Fock and density functional methods. The theoretical NMR and IR spectra have been benchmarked against experimental spectrum to compare and assess suitability of the computational methodologies and basis set levels for the calculations.Communicated by Ramaswamy H. Sarma.

GPU Accelerated Quantum Virtual Screening: Application for the Natural Inhibitors of New Dehli Metalloprotein (NDM-1)

Quantum mechanical approaches for the massive computation on large biological system such as virtual screening in drug design and development have presented a challenge to computational chemists for many years. In this study, we demonstrated that by taking advantage of rapid growth of GPU-based hardware and software (i.e., teraChem), it is feasible to perform virtual screening of a refined chemical library at quantum mechanical level in order to identify the lead compounds with improved accuracy, especially for the drug targets such as metalloproteins in which significant charge transfer and polarization occur amongst the metal ions and their coordinated amino acids. Our calculations predicted four nature compounds (i.e., Curcumin, Catechin, menthol, and Ferulic acid) as the suitable inhibitors for antibiotics resistance against New Delhi Metallo-β-lactamase-1 (NDM-1). Molecular orbitals (MOs) of the QM region of metal ions and their coordinated residues indicate that the bridged hydroxide ion delocalized the electron over the Zn-OH-Zn group at HOMO, different from MOs when the OH^- is not presented in NDM-1. This indicates that the bridged hydroxide ion plays an important role in the design of antibiotics and other inhibitors targeting the metalloproteins.

Quantum Chemical Approaches in Structure-Based Virtual Screening and Lead Optimization

Today computational chemistry is a consolidated tool in drug lead discovery endeavors. Due to methodological developments and to the enormous advance in computer hardware, methods based on quantum mechanics (QM) have gained great attention in the last 10 years, and calculations on biomacromolecules are becoming increasingly explored, aiming to provide better accuracy in the description of protein-ligand interactions and the prediction of binding affinities. In principle, the QM formulation includes all contributions to the energy, accounting for terms usually missing in molecular mechanics force-fields, such as electronic polarization effects, metal coordination, and covalent binding; moreover, QM methods are systematically improvable, and provide a greater degree of transferability. In this mini-review we present recent applications of explicit QM-based methods in small-molecule docking and scoring, and in the calculation of binding free-energy in protein-ligand systems. Although the routine use of QM-based approaches in an industrial drug lead discovery setting remains a formidable challenging task, it is likely they will increasingly become active players within the drug discovery pipeline.

Structures of the prefusion form of measles virus fusion protein in complex with inhibitors

Measles virus (MeV), a major cause of childhood morbidity and mortality, is highly immunotropic and one of the most contagious pathogens. MeV may establish, albeit rarely, persistent infection in the central nervous system, causing fatal and intractable neurodegenerative diseases such as subacute sclerosing panencephalitis and measles inclusion body encephalitis. Recent studies have suggested that particular substitutions in the MeV fusion (F) protein are involved in the pathogenesis by destabilizing the F protein and endowing it with hyperfusogenicity. Here we show the crystal structures of the prefusion MeV-F alone and in complex with the small compound AS-48 or a fusion inhibitor peptide. Notably, these independently developed inhibitors bind the same hydrophobic pocket located at the region connecting the head and stalk of MeV-F, where a number of substitutions in MeV isolates from neurodegenerative diseases are also localized. Since these inhibitors could suppress membrane fusion mediated by most of the hyperfusogenic MeV-F mutants, the development of more effective inhibitors based on the structures may be warranted to treat MeV-induced neurodegenerative diseases.

Correct Modeling of Cisplatin: a Paradigmatic Case

Quantum chemistry is a useful tool in modern approaches to drug and material design, but only when the adopted model reflects a correct physical picture. Paradigmatic is the case of cis‐diaminodichloroplatinum(II), cis‐[Pt(NH₃)₂Cl₂], for which the correct simulation of the structural and vibrational properties measured experimentally still remains an open question. By using this molecule as a proof of concept, it is shown that state‐of‐the‐art quantum chemical calculations and a simple model, capturing the basic physical flavors, a cis‐[Pt(NH₃)₂Cl₂] dimer, can provide the accuracy required for interpretative purposes. The present outcomes have fundamental implications for benchmark studies aiming at assessing the accuracy of a given computational protocol.

Implementing QM in docking calculations: is it a waste of computational time?

The greatest challenge in molecular docking (MD) is the deficiency of scoring functions (SFs), which limits their reliability. SFs are too simplified to represent the true features of the complex free energy of protein-ligand interactions. Investigations of docking functions have traded accuracy for speed through the use of approximations and simplifications. Consequently, there has been an increase in the popularity of quantum-mechanical (QM)-based methods as reference points for the development of fast, reliable, valuable, yet inexpensive, tools. As we discuss here, one significant QM-based parameter used to predict docking is the accuracy of atomic partial charges, which is strongly related to the accuracy of the SF prediction.

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.

A Comparative Study of Molecular Structure, pKa, Lipophilicity, Solubility, Absorption and Polar Surface Area of Some Antiplatelet Drugs

Theoretical chemistry methods have been used to study the molecular properties of antiplatelet agents (ticlopidine, clopidogrel, prasugrel, elinogrel, ticagrelor and cangrelor) and several thiol-containing active metabolites. The geometries and energies of most stable conformers of these drugs have been computed at the Becke3LYP/6-311++G(d,p) level of density functional theory. Computed dissociation constants show that the active metabolites of prodrugs (ticlopidine, clopidogrel and prasugrel) and drugs elinogrel and cangrelor are completely ionized at pH 7.4. Both ticagrelor and its active metabolite are present at pH = 7.4 in neutral undissociated form. The thienopyridine prodrugs ticlopidine, clopidogrel and prasugrel are lipophilic and insoluble in water. Their lipophilicity is very high (about 2.5–3.5 logP values). The polar surface area, with regard to the structurally-heterogeneous character of these antiplatelet drugs, is from very large interval of values of 3–255 Ų. Thienopyridine prodrugs, like ticlopidine, clopidogrel and prasugrel, with the lowest polar surface area (PSA) values, exhibit the largest absorption. A high value of polar surface area (PSA) of cangrelor (255 Ų) results in substantial worsening of the absorption in comparison with thienopyridine drugs.

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.

Predicting the relative binding affinity of mineralocorticoid receptor antagonists by density functional methods

In drug discovery, prediction of binding affinity ahead of synthesis to aid compound prioritization is still hampered by the low throughput of the more accurate methods and the lack of general pertinence of one method that fits all systems. Here we show the applicability of a method based on density functional theory using core fragments and a protein model with only the first shell residues surrounding the core, to predict relative binding affinity of a matched series of mineralocorticoid receptor (MR) antagonists. Antagonists of MR are used for treatment of chronic heart failure and hypertension. Marketed MR antagonists, spironolactone and eplerenone, are also believed to be highly efficacious in treatment of chronic kidney disease in diabetes patients, but is contra-indicated due to the increased risk for hyperkalemia. These findings and a significant unmet medical need among patients with chronic kidney disease continues to stimulate efforts in the discovery of new MR antagonist with maintained efficacy but low or no risk for hyperkalemia. Applied on a matched series of MR antagonists the quantum mechanical based method gave an R(2) = 0.76 for the experimental lipophilic ligand efficiency versus relative predicted binding affinity calculated with the M06-2X functional in gas phase and an R(2) = 0.64 for experimental binding affinity versus relative predicted binding affinity calculated with the M06-2X functional including an implicit solvation model. The quantum mechanical approach using core fragments was compared to free energy perturbation calculations using the full sized compound structures.

Structural basis for Marburg virus neutralization by a cross-reactive human antibody

The filoviruses, including Marburg and Ebola, express a single glycoprotein on their surface, termed GP, which is responsible for attachment and entry of target cells. Filovirus GPs differ by up to 70% in protein sequence, and no antibodies are yet described that cross-react among them. Here, we present the 3.6 Å crystal structure of Marburg virus GP in complex with a cross-reactive antibody from a human survivor, and a lower resolution structure of the antibody bound to Ebola virus GP. The antibody, MR78, recognizes a GP1 epitope conserved across the filovirus family, which likely represents the binding site of their NPC1 receptor. Indeed, MR78 blocks binding of the essential NPC1 domain C. These structures and additional small-angle X-ray scattering of mucin-containing MARV and EBOV GPs suggest why such antibodies were not previously elicited in studies of Ebola virus, and provide critical templates for development of immunotherapeutics and inhibitors of entry.

Improvements, trends, and new ideas in molecular docking: 2012-2013 in review

Molecular docking is a computational method for predicting the placement of ligands in the binding sites of their receptor(s). In this review, we discuss the methodological developments that occurred in the docking field in 2012 and 2013, with a particular focus on the more difficult aspects of this computational discipline. The main challenges and therefore focal points for developments in docking, covered in this review, are receptor flexibility, solvation, scoring, and virtual screening. We specifically deal with such aspects of molecular docking and its applications as selection criteria for constructing receptor ensembles, target dependence of scoring functions, integration of higher-level theory into scoring, implicit and explicit handling of solvation in the binding process, and comparison and evaluation of docking and scoring methods.

Mathematical and computational modeling in biology at multiple scales

A variety of topics are reviewed in the area of mathematical and computational modeling in biology, covering the range of scales from populations of organisms to electrons in atoms. The use of maximum entropy as an inference tool in the fields of biology and drug discovery is discussed. Mathematical and computational methods and models in the areas of epidemiology, cell physiology and cancer are surveyed. The technique of molecular dynamics is covered, with special attention to force fields for protein simulations and methods for the calculation of solvation free energies. The utility of quantum mechanical methods in biophysical and biochemical modeling is explored. The field of computational enzymology is examined.