Reparametrization of the COSMO Solvent Model for Semiempirical Methods PM6 and PM7

Nature-Inspired Gallinamides Are Potent Antischistosomal Agents: Inhibition of the Cathepsin B1 Protease Target and Binding Mode Analysis

Schistosomiasis, caused by a parasitic blood fluke of the genus Schistosoma, is a global health problem for which new chemotherapeutic options are needed. We explored the scaffold of gallinamide A, a natural peptidic metabolite of marine cyanobacteria that has previously been shown to inhibit cathepsin L-type proteases. We screened a library of 19 synthetic gallinamide A analogs and identified nanomolar inhibitors of the cathepsin B-type protease SmCB1, which is a drug target for the treatment of schistosomiasis mansoni. Against cultured S. mansoni schistosomula and adult worms, many of the gallinamides generated a range of deleterious phenotypic responses. Imaging with a fluorescent-activity-based probe derived from gallinamide A demonstrated that SmCB1 is the primary target for gallinamides in the parasite. Furthermore, we solved the high-resolution crystal structures of SmCB1 in complex with gallinamide A and its two analogs and describe the acrylamide covalent warhead and binding mode in the active site. Quantum chemical calculations evaluated the contribution of individual positions in the peptidomimetic scaffold to the inhibition of the target and demonstrated the importance of the P1' and P2 positions. Our study introduces gallinamides as a powerful chemotype that can be exploited for the development of novel antischistosomal chemotherapeutics.

Improving Quantum Chemical Solvation Models by Dynamic Radii Adjustment for Continuum Solvation (DRACO)

We present the Dynamic Radii Adjustment for COntinuum solvation (DRACO) approach, which employs precomputed atomic partial charges and coordination numbers of the solute atoms to improve the solute cavity. As such, DRACO is compatible with major solvation models, improving their performance significantly and robustly at virtually no extra cost, especially for charged solutes. Combined with the purely electrostatic CPCM and COSMO models, DRACO reduces the mean absolute deviation (MAD) of the solvation free energy by up to 4.5 kcal mol-1 (67%) for a large data set of polar and ionic solutes. Even in combination with the highly empirical universal solvation model (SMD), DRACO substantially reduces the MAD for charged solutes by up to 1.5 kcal mol-1 (39%), while neutral solutes are slightly improved (0.2 kcal mol-1 or 16%). We present an interface of DRACO with two computationally efficient atomic charge models that enables fully automated, out-of-the-box calculations with the widely used program packages Orca and TurboMole.

SQM2.20: Semiempirical quantum-mechanical scoring function yields DFT-quality protein-ligand binding affinity predictions in minutes

Accurate estimation of protein-ligand binding affinity is the cornerstone of computer-aided drug design. We present a universal physics-based scoring function, named SQM2.20, addressing key terms of binding free energy using semiempirical quantum-mechanical computational methods. SQM2.20 incorporates the latest methodological advances while remaining computationally efficient even for systems with thousands of atoms. To validate it rigorously, we have compiled and made available the PL-REX benchmark dataset consisting of high-resolution crystal structures and reliable experimental affinities for ten diverse protein targets. Comparative assessments demonstrate that SQM2.20 outperforms other scoring methods and reaches a level of accuracy similar to much more expensive DFT calculations. In the PL-REX dataset, it achieves excellent correlation with experimental data (average R2 = 0.69) and exhibits consistent performance across all targets. In contrast to DFT, SQM2.20 provides affinity predictions in minutes, making it suitable for practical applications in hit identification or lead optimization.

Docking and other computing tools in drug design against SARS-CoV-2

The use of computer simulation methods has become an indispensable component in identifying drugs against the SARS-CoV-2 coronavirus. There is a huge body of literature on application of molecular modelling to predict inhibitors against target proteins of SARS-CoV-2. To keep our review clear and readable, we limited ourselves primarily to works that use computational methods to find inhibitors and test the predicted compounds experimentally either in target protein assays or in cell culture with live SARS-CoV-2. Some works containing results of experimental discovery of corresponding inhibitors without using computer modelling are included as examples of a success. Also, some computational works without experimental confirmations are also included if they attract our attention either by simulation methods or by databases used. This review collects studies that use various molecular modelling methods: docking, molecular dynamics, quantum mechanics, machine learning, and others. Most of these studies are based on docking, and other methods are used mainly for post-processing to select the best compounds among those found through docking. Simulation methods are presented concisely, information is also provided on databases of organic compounds that can be useful for virtual screening, and the review itself is structured in accordance with coronavirus target proteins.

Binding Free Energy Calculation Based on the Fragment Molecular Orbital Method and Its Application in Designing Novel SHP-2 Allosteric Inhibitors

The accurate prediction of binding free energy is a major challenge in structure-based drug design. Quantum mechanics (QM)-based approaches show promising potential in predicting ligand-protein binding affinity by accurately describing the behavior and structure of electrons. However, traditional QM calculations face computational limitations, hindering their practical application in drug design. Nevertheless, the fragment molecular orbital (FMO) method has gained widespread application in drug design due to its ability to reduce computational costs and achieve efficient ab initio QM calculations. Although the FMO method has demonstrated its reliability in calculating the gas phase potential energy, the binding of proteins and ligands also involves other contributing energy terms, such as solvent effects, the 'deformation energy' of a ligand's bioactive conformations, and entropy. Particularly in cases involving ionized fragments, the calculation of solvation free energy becomes particularly crucial. We conducted an evaluation of some previously reported implicit solvent methods on the same data set to assess their potential for improving the performance of the FMO method. Herein, we develop a new QM-based binding free energy calculation method called FMOScore, which enhances the performance of the FMO method. The FMOScore method incorporates linear fitting of various terms, including gas-phase potential energy, deformation energy, and solvation free energy. Compared to other widely used traditional prediction methods such as FEP+, MM/PBSA, MM/GBSA, and Autodock vina, FMOScore showed good performance in prediction accuracies. By constructing a retrospective case study, it was observed that incorporating calculations for solvation free energy and deformation energy can further enhance the precision of FMO predictions for binding affinity. Furthermore, using FMOScore-guided lead optimization against Src homology-2-containing protein tyrosine phosphatase 2 (SHP-2), we discovered a novel and potent allosteric SHP-2 inhibitor (compound 8).

Improving protein-ligand docking results using the Semiempirical quantum mechanics: testing on the PDBbind 2016 core set

Molecular docking techniques are routinely employed for predicting ligand binding conformations and affinities in the in silico phase of the drug design and development process. In this study, a reliable semiempirical quantum mechanics (SQM) method, PM7, was employed for geometry optimization of top-ranked poses obtained from two widely used docking programs, AutoDock4 and AutoDock Vina. The PDBbind core set (version 2016), which contains high-quality crystal protein - ligand complexes with their corresponding experimental binding affinities, was used as an initial dataset in this research. It was shown that docking pose optimization improves the accuracy of pose predictions and is very useful for the refinement of docked complexes via removing clashes between ligands and proteins. It was also demonstrated that AutoDock Vina achieves a higher sampling power than AutoDock4 in generating accurate ligand poses (RMSD ≤ 2.0 Å), while AutoDock4 exhibits a better ranking power than AutoDock Vina. Finally, a new protocol based on a combination of the results obtained from the two docking programs was proposed for structure-based virtual screening studies, which benefits from the robust sampling abilities of AutoDock Vina and the reliable ranking performance of AutoDock4.Communicated by Ramaswamy H. Sarma.

Predicting Solvation Free Energies Using Electronegativity-Equalization Atomic Charges and a Dense Neural Network: A Generalized-Born Approach

I propose a dense Neural Network, ESE-GB-DNN, for evaluation of solvation free energies ΔG°solv for molecules and ions in water and nonaqueous solvents. As input features, it employs generalized-Born monatomic and diatomic terms, as well as atomic surface areas and the molecular volume. The electrostatics calculation is based on a specially modified version of electronegativity-equalization atomic charges. ESE-GB-DNN evaluates ΔG°solv in a simple and highly efficient way, yet it offers a high accuracy, often challenging that of standard DFT-based methods. For neutral solutes, ESE-GB-DNN yields an RMSE between 0.7 and 1.3 kcal/mol, depending on the solvent class. ESE-GB-DNN performs particularly well for nonaqueous solutions of ions, with an RMSE of about 0.7 kcal/mol. For ions in water, the RMSE is larger (2.9 kcal/mol).

Dense Neural Network for Calculating Solvation Free Energies from Electronegativity-Equalization Atomic Charges

I propose a dense Neural Network for evaluation of solvation free energies ΔG°solv for molecules and ions in water and nonaqueous solvents, Easy Solvation Energy with Electronegativity Equalization charges and Dense Neural Network (ESE-EE-DNN). As input features, it uses the Conductor-like Screening Model (COSMO) electrostatic energy, atomic cavity surface areas, total cavity volume, and induced surface charges. For the COSMO calculation, electronegativity-equalization atomic charges are employed. ESE-EE-DNN exhibits fairly high accuracy, similar or even superior to that of mainstream density functional theory-based methods. For neutral solutes in water, polar protic, polar aprotic, and nonpolar solvents, ESE-EE-DNN yields a root-mean-square error (RMSE) of 1.25, 1.36, 0.70, and 0.71 kcal/mol, respectively. ESE-EE-DNN is particularly advantageous for ionic solutes, with an RMSE of 2.82 and 1.42 kcal/mol for aqueous and nonaqueous ion solutions, correspondingly. ESE-EE-DNN is very efficient due to a fast evaluation of the electronegativity-equalization charges.

AQDnet: Deep Neural Network for Protein-Ligand Docking Simulation

We have developed an innovative system, AI QM Docking Net (AQDnet), which utilizes the three-dimensional structure of protein-ligand complexes to predict binding affinity. This system is novel in two respects: first, it significantly expands the training dataset by generating thousands of diverse ligand configurations for each protein-ligand complex and subsequently determining the binding energy of each configuration through quantum computation. Second, we have devised a method that incorporates the atom-centered symmetry function (ACSF), highly effective in describing molecular energies, for the prediction of protein-ligand interactions. These advancements have enabled us to effectively train a neural network to learn the protein-ligand quantum energy landscape (P-L QEL). Consequently, we have achieved a 92.6% top 1 success rate in the CASF-2016 docking power, placing first among all models assessed in the CASF-2016, thus demonstrating the exceptional docking performance of our model.

A quick solvation energy estimator based on electronegativity equalization

ESE-EE (Easy Solvation Estimation with Electronegativity equalization) is a quick method for estimation of solvation-free energies ΔGº_solv , which uses a thoroughly fitted electronegativity equalization (EE) scheme to obtain atomic charges, which are further employed in a scaled noniterative COSMO-like calculation to evaluate the electrostatic component of ΔGº_solv . Nonelectrostatic corrections including adjustable parameters are also added. For neutral solutes, ESE-EE yields a mean absolute error (MAE) in ΔG_solv ° of 1.5 kcal/mol for aqueous solutions; 1.0 kcal/mol for nonaqueous polar protic solvents; 0.9 kcal/mol for polar aprotic solvents; and about 0.6 kcal/mol for nonpolar solvents. Since ESE-EE only requires a molecular geometry as input for a ΔGº_solv prediction, it can be utilized for a rapid screening of ΔGº_solv for large neutral molecules. However, for ionic solutes, ESE-EE yields larger errors (typically several kcal/mol) and is recommendable for preliminary estimations only. Upon a special refitting, ESE-EE is able to yield partition coefficients with a good accuracy.

Regulation of different protonated states of two intimate histidine residues on the reductive half-reaction of glucose oxidase

Glucose oxidase (GOx) can catalyze the oxidation of β-D-glucose under mild conditions to directly convert biological energy into electrical energy, which has great potential for applications in the fields of enzyme biofuel cells and glucose biosensors. In enzymatic biofuel cells, GOx is often used as an anodic catalyst to improve the performance. The important role of two intimate histidine residues, His505 and His548 (PDB code 4YNU), in the GOx active center has been highlighted in the catalytic oxidation of β-D-glucose, but there is still a lack of systematic examination on the influence of different protonated states of His505 and His548 on the catalytic oxidation of β-D-glucose in GOx. Therefore, in the present work, the GOx active center under the possible protonated states of His548 and His505 is systematically examined by using ONIOM calculations, as well as the influence of remote Arg210 is considered. The calculations reveal that the intimate His505 and His548 can modulate the interaction of the β-D-glucose substrate with isoalloxazine and then control the deprotonization of the hydroxyl group bound to the anomeric carbon of β-D-glucose like controllers. The remote Arg210 provides the driving force for the transfer of two electrons from β-D-glucose to isoalloxazine of FAD via the long-range electrostatic attraction like a horse. Specially, the protonated His505 can serve as a good helper of Arg210 to promote the occurring of the two-proton-coupled two-electron transfer from β-D-glucose to isoalloxazine and His548 in the active center of GOx. These findings provide much insight into the catalytic reactions of GOx in a low pH environment, which may be beneficial to expand the applications of GOx.

SMA-BmobaSNO: an intelligent photoresponsive nitric oxide releasing polymer for drug nanoencapsulation and targeted delivery

Nitric oxide (NO) is an important biological signalling molecule that acts to vasodilate blood vessels and change the permeability of the blood vessel wall. Due to these cardiovascular actions, co-administering NO with a therapeutic could enhance drug uptake. However current NO donors are not suitable for targeted drug delivery as they systemically release NO. To overcome this limitation we report the development of a smart polymer, SMA-BmobaSNO, designed to release NO in response to a photostimulus. The polymer's NO releasing functionality is an S-nitrosothiol group that, at 10 mg ml^-1, is highly resistant to both thermal (t_1/216 d) and metabolic (t_1/232 h) decomposition, but rapidly brakes down under photoactivation (2700 W m^-2, halogen source) to release NO (t_1/225 min). Photoresponsive NO release from SMA-BmobaSNO was confirmed in a cardiovascular preparation, where irradiation resulted in a 12-fold decrease in vasorelaxation EC₅₀(from 5.2μM to 420 nM). To demonstrate the polymer's utility for drug delivery we then used SMA-BmobaSNO to fabricate a nanoparticle containing the probe Nile Red (NR). The resulting SMA-BmobaSNO-NR nanoparticle exhibited spherical morphology (180 nm diameter) and sustained NR release (≈20% over 5 d). Targeted delivery was characterised in an abdominal preparation, where photoactivation (450 W m^-2) caused localized increases in vasodilation and blood vessel permeability, resulting in a 3-fold increase in NR uptake into photoactivated tissue. Nanoparticles fabricated from SMA-BmobaSNO therefore display highly photoresponsive NO release and can apply the Trojan Horse paradigm by using endogenous NO signalling pathways to smuggle a therapeutic cargo into target tissue.

Highly potent inhibitors of cathepsin K with a differently positioned cyanohydrazide warhead: structural analysis of binding mode to mature and zymogen-like enzymes

Cathepsin K (CatK) is a target for the treatment of osteoporosis, arthritis, and bone metastasis. Peptidomimetics with a cyanohydrazide warhead represent a new class of highly potent CatK inhibitors; however, their binding mechanism is unknown. We investigated two model cyanohydrazide inhibitors with differently positioned warheads: an azadipeptide nitrile Gü1303 and a 3-cyano-3-aza-β-amino acid Gü2602. Crystal structures of their covalent complexes were determined with mature CatK as well as a zymogen-like activation intermediate of CatK. Binding mode analysis, together with quantum chemical calculations, revealed that the extraordinary picomolar potency of Gü2602 is entropically favoured by its conformational flexibility at the nonprimed-primed subsites boundary. Furthermore, we demonstrated by live cell imaging that cyanohydrazides effectively target mature CatK in osteosarcoma cells. Cyanohydrazides also suppressed the maturation of CatK by inhibiting the autoactivation of the CatK zymogen. Our results provide structural insights for the rational design of cyanohydrazide inhibitors of CatK as potential drugs.

Quantum-Chemical Quasi-Docking for Molecular Dynamics Calculations

The quantum quasi-docking procedure is used to compare the docking accuracies of two quantum-chemical semiempirical methods, namely, PM6-D3H4X and PM7. Quantum quasi-docking is an approximation to quantum docking. In quantum docking, it is necessary to search directly for the global minimum of the energy of the protein-ligand complex calculated by the quantum-chemical method. In quantum quasi-docking, firstly, we look for a wide spectrum of low-energy minima, calculated using the MMFF94 force field, and secondly, we recalculate the energies of all these minima using the quantum-chemical method, and among these recalculated energies we determine the lowest energy and the corresponding ligand position. Both PM6-D3H4X and PM7 are novel methods that describe well-dispersion interactions, hydrogen and halogen bonds. The PM6-D3H4X and PM7 methods are used with the COSMO implicit solvent model as it is implemented in the MOPAC program. The comparison is made for 25 high quality protein-ligand complexes. Firstly, the docking positioning accuracies have been compared, and we demonstrated that PM7+COSMO provides better positioning accuracy than PM6-D3H4X. Secondly, we found that PM7+COSMO demonstrates a much higher correlation between the calculated and measured protein-ligand binding enthalpies than PM6-D3H4X. For future quantum docking PM7+COSMO is preferable, but the COSMO model must be improved.

Predicting Solvent-Dependent Nucleophilicity Parameter with a Causal Structure Property Relationship

Solvent-dependent reactivity is a key aspect of synthetic science, which controls reaction selectivity. The contemporary focus on new, sustainable solvents highlights a need for reactivity predictions in different solvents. Herein, we report the excellent machine learning prediction of the nucleophilicity parameter N in the four most-common solvents for nucleophiles in the Mayr's reactivity parameter database (R² = 0.93 and 81.6% of predictions within ±2.0 of the experimental values with Extra Trees algorithm). A Causal Structure Property Relationship (CSPR) approach was utilized, with focus on the physicochemical relationships between the descriptors and the predicted parameters, and on rational improvements of the prediction models. The nucleophiles were represented with a series of electronic and steric descriptors and the solvents were represented with principal component analysis (PCA) descriptors based on the ACS Solvent Tool. The models indicated that steric factors do not contribute significantly, because of bias in the experimental database. The most important descriptors are solvent-dependent HOMO energy and Hirshfeld charge of the nucleophilic atom. Replacing DFT descriptors with Parameterization Method 6 (PM6) descriptors for the nucleophiles led to an 8.7-fold decrease in computational time, and an ∼10% decrease in the percentage of predictions within ±2.0 and ±1.0 of the experimental values.

Solvation Free Energies for Aqueous and Nonaqueous Solutions Computed Using PM7 Atomic Charges

We describe a simple and accurate method, ESE-PM7, for calculating solvation free energies ΔG_solv^° in aqueous and nonaqueous solutions. The method is based on a noniterative COSMO algorithm. Molecular geometries and atomic charges calculated using the semiempirical method PM7 are used to calculate ΔG_solv^°. The method has been tested on 92 different solvents and 988 solutes. The mean absolute errors (MAEs) in ΔG_solv^° in aqueous solutions estimated by the ESE-PM7 approach are found to be 1.62 kcal/mol for 389 neutral solutes and 3.06 kcal/mol for 139 ions. The MAEs for neutral molecules in organic solvents are 0.97, 0.74, and 0.51 kcal/mol in polar protic, polar aprotic, and nonpolar solvents, respectively. The developed method can be employed to quickly screen ΔG_solv^° values of extended molecular systems including pharmaceutical and biological molecules.

Robust and Efficient Implicit Solvation Model for Fast Semiempirical Methods

We present a robust and efficient method to implicitly account for solvation effects in modern semiempirical quantum mechanics and force fields. A computationally efficient yet accurate solvation model based on the analytical linearized Poisson-Boltzmann (ALPB) model is parameterized for the extended tight binding (xTB) and density functional tight binding (DFTB) methods as well as for the recently proposed GFN-FF general force field. The proposed methods perform well over a broad range of systems and applications, from conformational energies over transition-metal complexes to large supramolecular association reactions of charged species. For hydration free energies of small molecules, GFN1-xTB(ALPB) is reaching the accuracy of sophisticated explicitly solvated approaches, with a mean absolute deviation of only 1.4 kcal/mol compared to the experiment. Logarithmic octanol-water partition coefficients (log K_ow) are computed with a mean absolute deviation of about 0.65 using GFN2-xTB(ALPB) compared to experimental values indicating a consistent description of differential solvent effects. Overall, more than twenty solvents for each of the six semiempirical methods are parameterized and tested. They are readily available in the xtb and dftb+ programs for diverse computational applications.

Docking Paradigm in Drug Design

Docking is in demand for the rational computer aided structure based drug design. A review of docking methods and programs is presented. Different types of docking programs are described. They include docking of non-covalent small ligands, protein-protein docking, supercomputer docking, quantum docking, the new generation of docking programs and the application of docking for covalent inhibitors discovery. Taking into account the threat of COVID-19, we present here a short review of docking applications to the discovery of inhibitors of SARS-CoV and SARS-CoV-2 target proteins, including our own result of the search for inhibitors of SARS-CoV-2 main protease using docking and quantum chemical post-processing. The conclusion is made that docking is extremely important in the fight against COVID-19 during the process of development of antivirus drugs having a direct action on SARS-CoV-2 target proteins.

Non-Covalent Interactions Atlas Benchmark Data Sets 3: Repulsive Contacts

The new R739×5 data set from the Non-Covalent Interactions Atlas series (www.nciatlas.org) focuses on repulsive contacts in molecular complexes, covering organic molecules, sulfur, phosphorus, halogens, and noble gases. Information on the repulsive parts of the potential energy surface is crucial for the development of robust empirically parametrized computational methods. We use the new data set of highly accurate CCSD(T)/CBS interaction energies to test selected density functional theory (DFT) and semiempirical quantum-mechanical methods. The double-hybrid functionals were the best performing, with the revDSD-PBEP86-D3 being the most accurate DFT method, followed by the range-separated ωB97X functionals. Out of semiempirical methods, GFN2-xTB yielded the best results. On the example of the PM6 method, we analyze the source of error and its relation to the difficulties in the description of conformational energies, and we also devise an immediately applicable correction that fixes the most serious uncorrected issues previously encountered in practical calculations.

Structural and Thermodynamic Analysis of the Resistance Development to Pimodivir (VX-787), the Clinical Inhibitor of Cap Binding to PB2 Subunit of Influenza A Polymerase

Influenza A virus (IAV) encodes a polymerase composed of three subunits: PA, with endonuclease activity, PB1 with polymerase activity and PB2 with host RNA five-prime cap binding site. Their cooperation and stepwise activation include a process called cap-snatching, which is a crucial step in the IAV life cycle. Reproduction of IAV can be blocked by disrupting the interaction between the PB2 domain and the five-prime cap. An inhibitor of this interaction called pimodivir (VX-787) recently entered the third phase of clinical trial; however, several mutations in PB2 that cause resistance to pimodivir were observed. First major mutation, F404Y, causing resistance was identified during preclinical testing, next the mutation M431I was identified in patients during the second phase of clinical trials. The mutation H357N was identified during testing of IAV strains at Centers for Disease Control and Prevention. We set out to provide a structural and thermodynamic analysis of the interactions between cap-binding domain of PB2 wild-type and PB2 variants bearing these mutations and pimodivir. Here we present four crystal structures of PB2-WT, PB2-F404Y, PB2-M431I and PB2-H357N in complex with pimodivir. We have thermodynamically analysed all PB2 variants and proposed the effect of these mutations on thermodynamic parameters of these interactions and pimodivir resistance development. These data will contribute to understanding the effect of these missense mutations to the resistance development and help to design next generation inhibitors.

Molecular level insights into the direct health impacts of some organic aerosol components

Quantum chemistry and biomodeling indicate that the studied organic aerosol components cannot directly cause oxidative stress or mutagenicity/carcinogenicity.

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.

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.

Femtosecond-to-nanosecond dynamics of flavin mononucleotide monitored by stimulated Raman spectroscopy and simulations

Flavin mononucleotide (FMN) belongs to the large family of flavins, ubiquitous yellow-coloured biological chromophores that contain an isoalloxazine ring system. As a cofactor in flavoproteins, it is found in various enzymes and photosensory receptors, like those featuring the light-oxygen-voltage (LOV) domain. The photocycle of FMN is triggered by blue light and proceeds via a cascade of intermediate states. In this work, we have studied isolated FMN in an aqueous solution in order to elucidate the intrinsic electronic and vibrational changes of the chromophore upon excitation. The ultrafast transitions of excited FMN were monitored through the joint use of femtosecond stimulated Raman spectroscopy (FSRS) and transient absorption spectroscopy encompassing a time window between 0 ps and 6 ns with 50 fs time resolution. Global analysis of the obtained transient visible absorption and transient Raman spectra in combination with extensive quantum chemistry calculations identified unambiguously the singlet and triplet FMN populations and addressed solvent dynamics effects. The good agreement between the experimental and theoretical spectra facilitated the assignment of electronic transitions and vibrations. Our results represent the first steps towards more complex experiments aimed at tracking structural changes of FMN embedded in light-inducible proteins upon photoexcitation.

User-Friendly Quantum Mechanics: Applications for Drug Discovery

Quantum mechanics (QM) methods provide a fine description of receptor-ligand interactions and of chemical reactions. Their use in drug design and drug discovery is increasing, especially for complex systems including metal ions in the binding sites, for the design of highly selective inhibitors, for the optimization of bi-specific compounds, to understand enzymatic reactions, and for the study of covalent ligands and prodrugs. They are also used for generating molecular descriptors for predictive QSAR/QSPR models and for the parameterization of force fields. Thanks to the continuous increase of computational power offered by GPUs and to the development of sophisticated algorithms, QM methods are becoming part of the standard tools used in computer-aided drug design (CADD). We present the most used QM methods and software packages, and we discuss recent representative applications in drug design and drug discovery.

Performance of 3D-RISM-KH in Predicting Hydration Free Energy: Effect of Solute Parameters

The three-dimensional reference interaction site model molecular solvation theory with the Kovalenko-Hirata closure relation has been shown to produce excellent solvation characteristics for a large class of (bio)chemical systems in solution. Correct calculation of hydration free energy is central to successful application of any solvation model. In order to find out the best possible force-field parameters to be used for hydration free energy calculation with the aforementioned theory, we have developed an extended database containing a large number of experimental solvation free energies available in the current literature and used a plethora of theoretical models for assessment. The general Amber force field was found to perform satisfactorily, whereas special care should be taken in solute charge assignment with the universal force field.

A simple COSMO-based method for calculation of hydration energies of neutral molecules

A simple yet accurate method to estimate hydration free energy of neutral molecules is developed.