Ranking Power of the SQM/COSMO Scoring Function on Carbonic Anhydrase II-Inhibitor Complexes

Target-ligand binding affinity from single point enthalpy calculation and elemental composition

Reliable target-ligand binding thermodynamics data are essential for successful drug design and molecular engineering projects. Besides experimental methods, a number of theoretical approaches have been introduced for the generation of binding thermodynamics data. However, available approaches often neglect electronic effects or explicit water molecules influencing target-ligand interactions. To handle electronic effects within a reasonable time frame, we introduce a fast calculator QMH-L using a single target-ligand complex structure pre-optimized at the molecular mechanics level. QMH-L is composed of the semi-empirical quantum mechanics calculation of binding enthalpy with predicted explicit water molecules at the complex interface, and a simple descriptor based on the elemental composition of the ligand. QMH-L estimates the target-ligand binding free energy with a root mean square error (RMSE) of 0.94 kcal mol-1. The calculations also provide binding enthalpy values and they were compared with experimental binding thermodynamics data collected from the most reliable isothermal titration calorimetry studies of systems including various protein targets and challenging, large peptide ligands with a molecular weight of up to 2-3 thousand. The single point enthalpy calculations of QMH-L require modest computational resources and are based on short runs with open source and/or free software like Gromacs, Mopac, MobyWat, and Fragmenter. QMH-L can be applied for fast, automated scoring of drug candidates during a virtual screen, enthalpic engineering of new ligands or thermodynamic explanation of complex interactions.

Benchmarking biomolecular force field-based Zn2+ for mono- and bimetallic ligand binding sites

Zn²⁺ is one of the most versatile biologically available metal ions, but accurate modeling of Zn²⁺ -containing metalloproteins at the biomolecular force field level can be challenging. Since most Zn²⁺ models are parameterized in bulk solvent, in-depth knowledge about their performance in a protein environment is limited. Thus, we systematically investigate here the behavior of non-polarizable Zn²⁺ models for their ability to reproduce experimentally determined metal coordination and ligand binding in metalloproteins. The benchmarking is performed in challenging environments, including mono- (carbonic anhydrase II) and bimetallic (metallo-β-lactamase VIM-2) ligand binding sites. We identify key differences in the performance between the Zn²⁺ models with regard to the preferred ligating atoms (charged/non-charged), attraction of water molecules, and the preferred coordination geometry. Based on these results, we suggest suitable simulation conditions for varying Zn²⁺ site geometries that could guide the further development of biomolecular Zn²⁺ models.

Pyrimidines: Molecular docking and inhibition studies on carbonic anhydrase and cholinesterases

Alzheimer's disease (AD) is a neurodegenerative disorder. The disease is characterized by dementia, memory impairment, cognitive impairment, and speech impairment. Cholinesterases (ChEs; AChE, acetylcholinesterase and BChE, butyrylcholinesterase) inhibitors and their benefits of cholinergic replacement in the treatment of AD have been researched and documented by scientists in various ways to date. Recent studies prove that human carbonic anhydrases (hCAs) are also one of the important targets in the treatment of AD. Therefore, the development of new agents that can simultaneously modulate the various mechanisms or targets involved in the AD pathway may be a powerful strategy to treat AD, the current disease. Considering these data, the effects of the pyrimidines (1-7) were investigated in this study for the discovery and development of multitargeted ChEs and hCAs inhibitors associated with AD. In addition, the molecular docking analysis of the 4-amino-2-choloropyrimidine (2) was performed to understand the binding interactions on the active site of the enzyme. All compounds (1-7) showed satisfactory enzyme inhibitory potency in micromolar concentrations against AChE, BChE, hCAI, and hCAII with K_I values ranging from 0.099 to 0.241 μM, from 1.324 to 3.418 μM, from 0.201 to 0.884 μM, from 1.867 to 3.913 μM, respectively. Due to their ChEs and hCAs inhibition, these compounds (1-7) may be considered as leads for investigations in neurodegenerative diseases. All these results revealed that the 4-amino-5,6-dichloropyrimidine (7) (K_I value of 0.201 ± 0.041 μM for hCA I), the 4-amino-6-hydroxypyrimidine (4) (K_I value of 1.867 ± 0.296 μM for hCA II), the 4-amino-5,6-dichloropyrimidine (7) (K_I value of 0.099 ± 0.008 μM for AChE), and the 4-amino-2-chloropyrimidine (2) (K_I value of 1.324 ± 0.273 μM for BChE) from the pyrimidines in this series were the most promising derivatives, as they exhibited a good multifunctional inhibition at all experimental levels and in the in silico validation against these enzymes, for the treatment of AD.

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.

Structural and functional characterization of NEMO cleavage by SARS-CoV-2 3CLpro

In addition to its essential role in viral polyprotein processing, the SARS-CoV-2 3C-like protease (3CLpro) can cleave human immune signaling proteins, like NF-κB Essential Modulator (NEMO) and deregulate the host immune response. Here, in vitro assays show that SARS-CoV-2 3CLpro cleaves NEMO with fine-tuned efficiency. Analysis of the 2.50 Å resolution crystal structure of 3CLpro C145S bound to NEMO226-234 reveals subsites that tolerate a range of viral and host substrates through main chain hydrogen bonds while also enforcing specificity using side chain hydrogen bonds and hydrophobic contacts. Machine learning- and physics-based computational methods predict that variation in key binding residues of 3CLpro-NEMO helps explain the high fitness of SARS-CoV-2 in humans. We posit that cleavage of NEMO is an important piece of information to be accounted for, in the pathology of COVID-19.

Cytotoxic effect, enzyme inhibition, and in silico studies of some novel N-substituted sulfonyl amides incorporating 1,3,4-oxadiazol structural motif

Abstract

The acetylcholinesterase and carbonic anhydrase inhibitors (AChEIs and hCAIs) remain key therapeutic agents for many bioactivities such as anti-Alzheimer and antiobesity antiepileptic, anticancer, antiinfective, antiglaucoma, and diuretic effects. Here, it has been attempted to discover novel multi-target AChEIs and hCAIs that are highly potent, orally bioavailable, may be brain penetrant, and have higher effectiveness at lower doses than tacrine and acetazolamide. After detailed investigations both in vitro and in silico, novel N-substituted sulfonyl amide derivatives (6a–j) were determined to be highly potent inhibitors for AChE and hCAs (K_Is are in the range of 23.11–52.49 nM, 18.66–59.62 nM, and 9.33–120.80 nM for AChE, hCA I, and hCA II, respectively). Moreover, according to the cytotoxic effect studies, such as the ADME-Tox, cortex neuron cells, and neuroblastoma SH-SY5Y cell line, compounds 6a, 6d, and 6h, which are the most potent representative versus the target enzymes, were identified as orally bioavailable, highly selective, and brain preferentially distributed AChEIs and hCAIs. The docking studies revealed precise binding modes between 6a, 6d, and 6h and hCA II, hCA I, and AChE, respectively. The results presented here might provide a solid basis for further investigation into more potent AChEIs and hCAIs.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s11030-022-10422-8.

Reliable Prediction of the Protein-Ligand Binding Affinity Using a Charge Penetration Corrected AMOEBA Force Field: A Case Study of Drug Resistance Mutations in Abl Kinase

Protein mutations that directly impair drug binding are related to therapeutic resistance, and accurate prediction of their impact on drug binding would benefit drug design and clinical practice. Here, we have developed a scoring strategy that predicts the effect of the mutations on the protein-ligand binding affinity. In view of the critical importance of electrostatics in protein-ligand interactions, the charge penetration corrected AMOEBA force field (AMOEBA_CP model) was employed to improve the accuracy of the calculated electrostatic energy. We calculated the electrostatic energy using an energy decomposition analysis scheme based on the generalized Kohn-Sham (GKS-EDA). The AMOEBA_CP model was validated by a protein-fragment-ligand complex data set (Abl236) constructed from the co-crystal structures of the cancer target Abl kinase with six inhibitors. To predict ligand binding affinity changes upon protein mutation of Abl kinase, we used sampling protocol with multistep simulated annealing to search conformations of mutant proteins. The scoring strategy based on AMOEBA_CP model has achieved considerable performance in predicting resistance for 8 kinase inhibitors across 144 clinically identified point mutations. Overall, this study illustrates that the AMOEBA_CP model, which accurately treats electrostatics through penetration correction, enables the accurate prediction of the mutation-induced variation of protein-ligand binding affinity.

Ligand Strain and Its Conformational Complexity Is a Major Factor in the Binding of Cyclic Dinucleotides to STING Protein

STING (stimulator of interferon genes) is a key regulator of innate immunity that has recently been recognized as a promising drug target. STING is activated by cyclic dinucleotides (CDNs) which eventually leads to expression of type I interferons and other cytokines. Factors underlying the affinity of various CDN analogues are poorly understood. Herein, we correlate structural biology, isothermal calorimetry (ITC) and computational modeling to elucidate factors contributing to binding of six CDNs-three pairs of natural (ribo) and fluorinated (2'-fluororibo) 3',3'-CDNs. X-ray structural analyses of six {STING:CDN} complexes did not offer any explanation for the different affinities of the studied ligands. ITC showed entropy/enthalpy compensation up to 25 kcal mol-1 for this set of similar ligands. The higher affinities of fluorinated analogues are explained with help of computational methods by smaller loss of entropy upon binding and by smaller strain (free) energy.

Thermochemical and Quantum Descriptor Calculations for Gaining Insight into Ricin Toxin A (RTA) Inhibitors

In this work, we performed a study to assess the interactions between the ricin toxin A (RTA) subunit of ricin and some of its inhibitors using modern semiempirical quantum chemistry and ONIOM quantum mechanics/molecular mechanics (QM/MM) methods. Two approaches were followed (calculation of binding enthalpies, ΔH _bind, and reactivity quantum chemical descriptors) and compared with the respective half-maximal inhibitory concentration (IC₅₀) experimental data, to gain insight into RTA inhibitors and verify which quantum chemical method would better describe RTA-ligand interactions. The geometries for all RTA-ligand complexes were obtained after running classical molecular dynamics simulations in aqueous media. We found that single-point energy calculations of ΔH _bind with the PM6-DH+, PM6-D3H4, and PM7 semiempirical methods and ONIOM QM/MM presented a good correlation with the IC₅₀ data. We also observed, however, that the correlation decreased significantly when we calculated ΔH _bind after full-atom geometry optimization with all semiempirical methods. Based on the results from reactivity descriptors calculations for the cases studied, we noted that both types of interactions, molecular overlap and electrostatic interactions, play significant roles in the overall affinity of these ligands for the RTA binding pocket.

The crystal structures of 2-(4-benzhydrylpiperazin-1-yl)-N-(4-sulfamoylphenyl)acetamide in complex with human carbonic anhydrase II and VII provide insights into selective CA inhibitor development

Our studies suggest that the acetamide linker and long tails are suitable structural features to design selective CA inhibitors.

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.

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.

Benchmarking of Semiempirical Quantum-Mechanical Methods on Systems Relevant to Computer-Aided Drug Design

The semiempirical quantum mechanical (SQM) methods used in drug design are commonly parametrized and tested on data sets of systems that may not be representative models for drug-biomolecule interactions in terms of both size and chemical composition. This is addressed here with a new benchmark data set, PLF547, derived from protein-ligand complexes, consisting of complexes of ligands with protein fragments (such as amino-acid side chains), with interaction energies based on MP2-F12 and DLPNO-CCSD(T) calculations. From these, composite benchmark interaction energies are also built for complexes of the ligand with the complete active site of the protein (PLA15 data set). These data sets are used to test multiple SQM methods with corrections for noncovalent interactions; the role of the solvation model in the calculations is tested as well.

The 12th AFMC International Medicinal Chemistry Symposium (AIMECS 2019) in Istanbul, Turkey

AFMC-AIMECS meetings are internationally organized biannually by the Asian Federation for Medicinal Chemistry (AFMC) and are focused on recent studies in drug discovery and development both in academia and industry. Member organizations of the AFMC are the Pharmaceutical Society of Japan, the Chinese Pharmaceutical Association, the Royal Australian Chemical Institute, the Pharmaceutical Society of Korea, the Korean Chemical Society, the Chemical Society Located in Taipei, the Indonesian Society of Medicinal Chemistry, the Medicinal Chemistry Section of the Israel Chemical Society, and the Computer-Aided Drug Design & Development Society in Turkey. Each time, the symposium is organized within these member countries. The AIMECS 2019 symposium was held in Turkey this year, as Prof. Dr. Esin Aki-Yalcin is the current president of the AFMC (2018-2020); the next AIMECS meeting will be organized in 2021 in Tokyo, Japan. In this report, we discuss key topics at the 12^th AFMC International Medicinal Chemistry Symposium - New Avenues for Design and Development of Translational Medicine (AIMECS 2019) held in Istanbul, September 8-11, 2019.

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.

Impressive Enrichment of Semiempirical Quantum Mechanics-Based Scoring Function: HSP90 Protein with 4541 Inhibitors and Decoys

This paper describes the excellent performance of a newly developed scoring function (SF), based on the semiempirical QM (SQM) PM6-D3H4X method combined with the conductor-like screening implicit solvent model (COSMO). The SQM/COSMO, Amber/GB and nine widely used SFs have been evaluated in terms of ranking power on the HSP90 protein with 72 biologically active compounds and 4469 structurally similar decoys. Among conventional SFs, the highest early and overall enrichment measured by EF₁ and AUC% obtained using single-scoring-function ranking has been found for Glide SP and Gold-ASP SFs, respectively (7, 75 % and 3, 76 %). The performance of other standard SFs has not been satisfactory, mostly even decreasing below random values. The SQM/COSMO SF, where P-L structures were optimised at the advanced Amber level, has resulted in a dramatic enrichment increase (47, 98 %), almost reaching the best possible receiver operator characteristic (ROC) curve. The best SQM frame thus inserts about seven times more active compounds into the selected dataset than the best standard SF.

Description of halogen bonding in semiempirical quantum-mechanical and self-consistent charge density-functional tight-binding methods

This article analyzes the ability of semiempirical quantum-mechanical methods (PM6 and PM7) and self-consistent charge density-functional tight-binding (SCC-DFTB) method DFTB3 to describe halogen bonds. Calculations of the electrostatic potential on the surface of molecules containing halogens show that the σ-hole could be described well in modified neglect of diatomic overlap-based methods. The situation is more complex in the case of DFTB3 where a simpler model is used for the electrostatics, but short-ranged effects are covered in the Hamiltonian. All these methods can thus capture the effects that, for example, define the geometry of halogen bonds. The interaction energies are, however, affected by generally underestimated repulsion, which has been addressed earlier by standalone empirical corrections. Another approach to correcting this issue in DFTB3 is presented here-a modification of the energies of d-orbitals on halogens yields better results than the empirical correction in DFTB3-D3X, although it remains difficult to describe halogen and hydrogen bonds simultaneously. © 2019 Wiley Periodicals, Inc.

Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges

Structure-based virtual screening (VS) is a widely used approach that employs the knowledge of the three-dimensional structure of the target of interest in the design of new lead compounds from large-scale molecular docking experiments. Through the prediction of the binding mode and affinity of a small molecule within the binding site of the target of interest, it is possible to understand important properties related to the binding process. Empirical scoring functions are widely used for pose and affinity prediction. Although pose prediction is performed with satisfactory accuracy, the correct prediction of binding affinity is still a challenging task and crucial for the success of structure-based VS experiments. There are several efforts in distinct fronts to develop even more sophisticated and accurate models for filtering and ranking large libraries of compounds. This paper will cover some recent successful applications and methodological advances, including strategies to explore the ligand entropy and solvent effects, training with sophisticated machine-learning techniques, and the use of quantum mechanics. Particular emphasis will be given to the discussion of critical aspects and further directions for the development of more accurate empirical scoring functions.

Inhibition of carbonic anhydrases by a substrate analog: benzyl carbamate directly coordinates the catalytic zinc ion mimicking bicarbonate binding

N-Unsubstituted carbamates can be used as lead compounds for the development of carbonic anhydrase inhibitors possessing a binding mode similar to bicarbonate.