Geometry Optimization Algorithms in Conjunction with the Machine Learning Potential ANI-2x Facilitate the Structure-Based Virtual Screening and Binding Mode Prediction.

Structure-based virtual screening utilizes molecular docking to explore and analyze ligand-macromolecule interactions, crucial for identifying and developing potential drug candidates. Although there is availability of several widely used docking programs, the accurate prediction of binding affinity and binding mode still presents challenges. In this study, we introduced a novel protocol that combines our in-house geometry optimization algorithm, the conjugate gradient with backtracking line search (CG-BS), which is capable of restraining and constraining rotatable torsional angles and other geometric parameters with a highly accurate machine learning potential, ANI-2x, renowned for its precise molecular energy predictions reassembling the wB97X/6-31G(d) model. By integrating this protocol with binding pose prediction using the Glide, we conducted additional structural optimization and potential energy prediction on 11 small molecule-macromolecule and 12 peptide-macromolecule systems. We observed that ANI-2x/CG-BS greatly improved the docking power, not only optimizing binding poses more effectively, particularly when the RMSD of the predicted binding pose by Glide exceeded around 5 Å, but also achieving a 26% higher success rate in identifying those native-like binding poses at the top rank compared to Glide docking. As for the scoring and ranking powers, ANI-2x/CG-BS demonstrated an enhanced performance in predicting and ranking hundreds or thousands of ligands over Glide docking. For example, Pearson's and Spearman's correlation coefficients remarkedly increased from 0.24 and 0.14 with Glide docking to 0.85 and 0.69, respectively, with the addition of ANI-2x/CG-BS for optimizing and ranking small molecules binding to the bacterial ribosomal aminoacyl-tRNA receptor. These results suggest that ANI-2x/CG-BS holds considerable potential for being integrated into virtual screening pipelines due to its enhanced docking performance.

Predicting anti-SARS-CoV-2 activities of chemical compounds using machine learning models.

To accelerate the discovery of novel drug candidates for Coronavirus Disease 2019 (COVID-19) therapeutics, we reported a series of machine learning (ML)-based models to accurately predict the anti-SARS-CoV-2 activities of screening compounds. We explored 6 popular ML algorithms in combination with 15 molecular descriptors for molecular structures from 9 screening assays in the COVID-19 OpenData Portal hosted by NCATS. As a result, the models constructed by k-nearest neighbors (KNN) using the molecular descriptor GAFF+RDKit achieved the best overall performance with the highest average accuracy of 0.68 and relatively high average area under the receiver operating characteristic curve of 0.74, better than other ML algorithms. Meanwhile, The KNN model for all assays using GAFF+RDKit descriptor outperformed using other descriptors. The overall performance of our developed models was better than REDIAL-2020 (R). A web server (https://clickff.org/amberweb/covid-19-cp) was developed to enable users to predict anti-SARS-CoV-2 activities of arbitrary compounds using the COVID-19-CP (P) models. Besides the descriptor-based machine learning models, we also developed graph-based Attentive FP (A) models for the 9 assays. We found that the Attentive FP models achieved a comparable performance to that of COVID-19-CP and outperformed the REDIAL-2020 models. The consensus prediction utilizing both COVID-19-CP and Attentive FP can significantly boost the prediction accuracy as assessed by comparing its performance with other three individual models (R, P, A) utilizing the Wilcoxon signed-rank test, thus can ultimately improve the success rate of COVID-19 drug discovery.

Development and test of highly accurate endpoint free energy methods. 3: partition coefficient prediction using a Poisson-Boltzmann method combined with a solvent accessible surface area model for SAMPL challenges.

Accurately predicting solvation free energy is the key to predict protein-ligand binding free energy. In addition, the partition coefficient (log P), which is an important physicochemical property that determines the distribution of a drug in vivo, can be derived directly from transfer free energies, i.e., the difference between solvation free energies (SFEs) in different solvents. Within the Statistical Assessment of the Modeling of Proteins and Ligands (SAMPL) 9 challenge, we applied the Poisson-Boltzmann (PB) surface area (SA) approach to predict the toluene/water transfer free energy and partition coefficient (log Ptoluene/water) from SFEs. For each solute, only a single conformation automatically generated by the free software Open Babel was used. The PB calculation directly adopts our previously optimized boundary definition - a set of general AMBER force field 2 (GAFF2) atom-type based sphere radii for solute atoms. For the non-polar SA model, we newly developed the solvent-related molecular surface tension parameters γ and offset b for toluene and cyclohexane targeting experimental SFEs. This approach yielded the highest predictive accuracy in terms of root mean square error (RMSE) of 1.52 kcal mol-1 in transfer free energy for 16 small drug molecules among all 18 submissions in the SAMPL9 blind prediction challenge. The re-evaluation of the challenge set using multi-conformation strategies based on molecular dynamics (MD) simulations further reduces the prediction RMSE to 1.33 kcal mol-1. At the same time, an additional evaluation of our PBSA method on the SAMPL5 cyclohexane/water distribution coefficient (log Dcyclohexane/water) prediction revealed that our model outperformed COSMO-RS, the best submission model with RMSEPBSA = 1.88 versus RMSECOSMO-RS = 2.11 log units. Two external log Ptoluene/water and log Pcyclohexane/water datasets that contain 110 and 87 data points, respectively, are collected for extra validation and provide an in-depth insight into the error source of the PBSA method.

In Silico Screening of Natural Flavonoids against 3-Chymotrypsin-like Protease of SARS-CoV-2 Using Machine Learning and Molecular Modeling.

The "Long-COVID syndrome" has posed significant challenges due to a lack of validated therapeutic options. We developed a novel multi-step virtual screening strategy to reliably identify inhibitors against 3-chymotrypsin-like protease of SARS-CoV-2 from abundant flavonoids, which represents a promising source of antiviral and immune-boosting nutrients. We identified 57 interacting residues as contributors to the protein-ligand binding pocket. Their energy interaction profiles constituted the input features for Machine Learning (ML) models. The consensus of 25 classifiers trained using various ML algorithms attained 93.9% accuracy and a 6.4% false-positive-rate. The consensus of 10 regression models for binding energy prediction also achieved a low root-mean-square error of 1.18 kcal/mol. We screened out 120 flavonoid hits first and retained 50 drug-like hits after predefined ADMET filtering to ensure bioavailability and safety profiles. Furthermore, molecular dynamics simulations prioritized nine bioactive flavonoids as promising anti-SARS-CoV-2 agents exhibiting both high structural stability (root-mean-square deviation < 5 Å for 218 ns) and low MM/PBSA binding free energy (<-6 kcal/mol). Among them, KB-2 (PubChem-CID, 14630497) and 9-O-Methylglyceofuran (PubChem-CID, 44257401) displayed excellent binding affinity and desirable pharmacokinetic capabilities. These compounds have great potential to serve as oral nutraceuticals with therapeutic and prophylactic properties as care strategies for patients with long-COVID syndrome.

Distribution of Bound Conformations in Conformational Ensembles for X-ray Ligands Predicted by the ANI-2X Machine Learning Potential.

In this study, we systematically studied the energy distribution of bioactive conformations of small molecular ligands in their conformational ensembles using ANI-2X, a machine learning potential, in conjunction with one of our recently developed geometry optimization algorithms, known as a conjugate gradient with backtracking line search (CG-BS). We first evaluated the combination of these methods (ANI-2X/CG-BS) using two molecule sets. For the 231-molecule set, ab initio calculations were performed at both the ωB97X/6-31G(d) and B3LYP-D3BJ/DZVP levels for accuracy comparison, while for the 8,992-molecule set, ab initio calculations were carried out at the B3LYP-D3BJ/DZVP level. For each molecule in the two molecular sets, up to 10 conformations were generated, which diminish the influence of individual outliers on the performance evaluation. Encouraged by the performance of ANI-2x/CG-BS in these evaluations, we calculated the energy distributions using ANI-2x/CG-BS for more than 27,000 ligands in the protein data bank (PDB). Each ligand has at least one conformation bound to a biological molecule, and this ligand conformation is labeled as a bound conformation. Besides the bound conformations, up to 200 conformations were generated using OpenEye's Omega2 software (https://docs.eyesopen.com/applications/ omega/) for each conformation. We performed a statistical analysis of how the bound conformation energies are distributed in the ensembles for 17,197 PDB ligands that have their bound conformation energies within the energy ranges of the Omega2-generated conformation ensembles. We found that half of the ligands have their relative conformation energy lower than 2.91 kcal/mol for the bound conformations in comparison with the global conformations, and about 90% of the bound conformations are within 10 kcal/mol above the global conformation energies. This information is useful to guide the construction of libraries for shape-based virtual screening and to improve the docking algorithm to efficiently sample bound conformations.

Unpolarized laser method for infrared spectrum calculation of amide I CO bonds in proteins using molecular dynamics simulation.

The investigation of the strong infrared (IR)-active amide I modes of peptides and proteins has received considerable attention because a wealth of detailed information on hydrogen bonding, dipole-dipole interactions, and the conformations of the peptide backbone can be derived from the amide I bands. The interpretation of experimental spectra typically requires substantial theoretical support, such as direct ab-initio molecular dynamics simulation or mixed quantum-classical description. However, considering the difficulties associated with these theoretical methods and their applications are limited in small peptides, it is highly desirable to develop a simple yet efficient approach for simulating the amide I modes of any large proteins in solution. In this work, we proposed a comprehensive computational method that extends the well-established molecular dynamics (MD) simulation method to include an unpolarized IR laser for exciting the CO bonds of proteins. We showed the amide I frequency corresponding to the frequency of the laser pulse which resonated with the CO bond vibration. At this frequency, the protein energy and the CO bond length fluctuation were maximized. Overall, the amide I bands of various single proteins and amyloids agreed well with experimental data. The method has been implemented into the AMBER simulation package, making it widely available to the scientific community. Additionally, the application of the method to simulate the transient amide I bands of amyloid fibrils during the IR laser-induced disassembly process was discussed in details.

Development and test of highly accurate endpoint free energy methods. 1: Evaluation of ABCG2 charge model on solvation free energy prediction and optimization of atom radii suitable for more accurate solvation free energy prediction by the PBSA method.

Accurate estimation of solvation free energy (SFE) lays the foundation for accurate prediction of binding free energy. The Poisson-Boltzmann (PB) or generalized Born (GB) combined with surface area (SA) continuum solvation method (PBSA and GBSA) have been widely used in SFE calculations because they can achieve good balance between accuracy and efficiency. However, the accuracy of these methods can be affected by several factors such as the charge models, polar and nonpolar SFE calculation methods and the atom radii used in the calculation. In this work, the performance of the ABCG2 (AM1-BCC-GAFF2) charge model as well as other two charge models, that is, RESP (Restrained Electrostatic Potential) and AM1-BCC (Austin Model 1-bond charge corrections), on the SFE prediction of 544 small molecules in water by PBSA/GBSA was evaluated. In order to improve the performance of the PBSA prediction based on the ABCG2 charge, we further explored the influence of atom radii on the prediction accuracy and yielded a set of atom radius parameters for more accurate SFE prediction using PBSA based on the ABCG2/GAFF2 by reproducing the thermodynamic integration (TI) calculation results. The PB radius parameters of carbon, oxygen, sulfur, phosphorus, chloride, bromide and iodine, were adjusted. New atom types, on, oi, hn1, hn2, hn3, were introduced to further improve the fitting performance. Then, we tuned the parameters in the nonpolar SFE model using the experimental SFE data and the PB calculation results. By adopting the new radius parameters and new nonpolar SFE model, the root mean square error (RMSE) of the SFE calculation for the 544 molecules decreased from 2.38 to 1.05 kcal/mol. Finally, the new radius parameters were applied in the prediction of protein-ligand binding free energies using the MM-PBSA method. For the eight systems tested, we could observe higher correlation between the experiment data and calculation results and smaller prediction errors for the absolute binding free energies, demonstrating that our new radius parameters can improve the free energy calculation using the MM-PBSA method.

Phosphorylation at Ser289 Enhances the Oligomerization of Tau Repeat R2.

In tauopathies such as Alzheimer's disease (AD), aberrant phosphorylation causes the dissociation of tau proteins from microtubules. The dissociated tau then aggregates into sequent forms from soluble oligomers to paired helical filaments and insoluble neurofibrillary tangles (NFTs). NFTs is a hallmark of AD, while oligomers are found to be the most toxic form of the tau aggregates. Therefore, understanding tau oligomerization with regard to abnormal phosphorylation is important for the therapeutic development of AD. In this study, we investigated the impact of phosphorylated Ser289, one of the 40 aberrant phosphorylation sites of full-length tau proteins, on monomeric and dimeric structures of tau repeat R2 peptides. We carried out intensive replica exchange molecular dynamics simulation with a total simulation time of up to 0.1 ms. Our result showed that the phosphorylation significantly affected the structures of both the monomer and the dimer. For the monomer, the phosphorylation enhanced ordered-disordered structural transition and intramolecular interaction, leading to more compactness of the phosphorylated R2 compared to the wild-type one. As to the dimer, the phosphorylation increased intermolecular interaction and ß-sheet formation, which can accelerate the oligomerization of R2 peptides. This result suggests that the phosphorylation at Ser289 is likely to promote tau aggregation. We also observed a phosphorylated Ser289-Na+-phosphorylated Ser289 bridge in the phosphorylated R2 dimer, suggesting an important role of cation ions in tau aggregation. Our findings suggest that phosphorylation at Ser289 should be taken into account in the inhibitor screening of tau oligomerization.

Development and test of highly accurate endpoint free energy methods. 2: Prediction of logarithm of n-octanol-water partition coefficient (logP) for druglike molecules using MM-PBSA method.

The logarithm of n-octanol-water partition coefficient (logP) is frequently used as an indicator of lipophilicity in drug discovery, which has substantial impacts on the absorption, distribution, metabolism, excretion, and toxicity of a drug candidate. Considering that the experimental measurement of the property is costly and time-consuming, it is of great importance to develop reliable prediction models for logP. In this study, we developed a transfer free energy-based logP prediction model-FElogP. FElogP is based on the simple principle that logP is determined by the free energy change of transferring a molecule from water to n-octanol. The underlying physical method to calculate transfer free energy is the molecular mechanics-Poisson Boltzmann surface area (MM-PBSA), thus this method is named as free energy-based logP (FElogP). The superiority of FElogP model was validated by a large set of 707 structurally diverse molecules in the ZINC database for which the measurement was of high quality. Encouragingly, FElogP outperformed several commonly-used QSPR or machine learning-based logP models, as well as some continuum solvation model-based methods. The root-mean-square error (RMSE) and Pearson correlation coefficient (R) between the predicted and measured values are 0.91 log units and 0.71, respectively, while the runner-up, the logP model implemented in OpenBabel had an RMSE of 1.13 log units and R of 0.67. Given the fact that FElogP was not parameterized against experimental logP directly, its excellent performance is likely to be expanded to arbitrary organic molecules covered by the general AMBER force fields.

Phosphorylation of Tau R2 Repeat Destabilizes Its Binding to Microtubules: A Molecular Dynamics Simulation Study.

Phosphorylation, the most popular post-translational modification of tau protein, plays an important role in regulating tau physiological functions. However, aberrant phosphorylation attenuates the binding affinity of tau to a microtubule (MT), resulting in MT destabilization followed by accumulation of neurofibrillary tangles in the brain. There are in total 85 potential phosphorylation sites in a full-length tau protein, and about half of them are abnormal as they occur in tau of Alzheimer's disease (AD) brain only. In this work, we investigated the impact of abnormal Ser289, Ser293, and Ser289/Ser293 phosphorylation on tau R2-MT binding and the conformation of tau R2 using molecular dynamics simulation. We found that the phosphorylation significantly affected R2-MT interaction and reduced the binding affinity of tau R2 peptides to MTs. Free energy decomposition analysis suggested that the post-translational modified residues themselves made a significant contribution to destabilize tau repeat R2-MT binding. Therefore, the phosphorylation may attenuate the binding affinity of tau to MTs. Additionally, the phosphorylation also enhanced helix-coil transition of monomeric R2 peptides, which may result in the acceleration of tau aggregation. Since these phosphorylated sites have not been examined in previous experimental studies, our finding through all-atom molecular dynamics simulations and free energy analysis can inspire experimental scientists to investigate the impact of the phosphorylation on MT binding and aggregation of full-length tau and the pathological roles of the phosphorylation at those sites in AD development through in vitro/in vivo assays.

Molecular Modeling Study of a Receptor-Orthosteric Ligand-Allosteric Modulator Signaling Complex.

Allosteric modulators (AMs) are considered as a perpetual hotspot in research for their higher selectivity and various effects on orthosteric ligands (OL). They are classified in terms of their functionalities as positive, negative, or silent allosteric modulators (PAM, NAM, or SAM, respectively). In the present work, 11 pairs of three-dimensional (3D) structures of receptor-orthosteric ligand and receptor-orthosteric ligand-allosteric modulator complexes have been collected for the studies, including three different systems: GPCR, enzyme, and ion channel. Molecular dynamics (MD) simulations are applied to quantify the dynamic interactions in both the orthosteric and allosteric binding pockets and the structural fluctuation of the involved proteins. Our results showed that MD simulations of moderately large molecules or peptides undergo insignificant changes compared to crystal structure results. Furthermore, we also studied the conformational changes of receptors that bound with PAM and NAM, as well as the different allosteric binding sites in a receptor. There should be no preference for the position of the allosteric binding pocket after comparing the allosteric binding pockets of these three systems. Finally, we aligned four distinct ß2 adrenoceptor structures and three N-methyl-d-aspartate receptor (NMDAR) structures to investigate conformational changes. In the ß2 adrenoceptor systems, the aligned results revealed that transmembrane (TM) helices 1, 5, and 6 gradually increased outward movement from an enhanced inactive state to an improved active state. TM6 endured the most significant conformational changes (around 11 Å). For NMDAR, the bottom section of NMDAR's ligand-binding domain (LBD) experienced an upward and outward shift during the gradually activating process. In conclusion, our research provides insight into receptor-orthosteric ligand-allosteric modulator studies and the design and development of allosteric modulator drugs using MD simulation.

Investigation of the Structure of Full-Length Tau Proteins with Coarse-Grained and All-Atom Molecular Dynamics Simulations.

Tau proteins not only have many important biological functions but also are associated with several neurodegenerative diseases, such as Parkinson's disease and Alzheimer's disease (AD). However, it is still a challenge to identify the atomic structure of full-length tau proteins due to their lengthy and disordered characteristics and the factor that there are no crystal structures of full-length tau proteins available. We performed multi- and large-scale molecular dynamics simulations of the full-length tau monomer (the 2N4R isoform and 441 residues) in aqueous solution under biological conditions with coarse-grained and all-atom force fields. The obtained atomic structures produced radii of gyration and chemical shifts that are in excellent agreement with those of experiment. The generated monomer structure ensemble would be very useful for further studying the oligomerization mechanism and discovering tau oligomerization inhibitors, which are important events in AD drug development.

Stable Cavitation Interferes with Aß16-22 Oligomerization.

Ultrasound and microbubbles are used for many medical applications nowadays. Scanning ultrasound can remove amyloid-ß (Aß) aggregates in the mouse brain and restores memory in an Alzheimer's disease mouse model. In vitro studies showed that amyloid fibrils are fragmented due to the ultrasound-induced bubble inertial cavitation, and ultrasonic pulses accelerate the depolymerization of Aß fibrils into monomers at 1 µM of concentration. Under applied ultrasound, microbubbles can be in a stable oscillating state or unstable inertial cavitation state. The latter occurs when ultrasound causes a dramatic change of bubble sizes above a certain acoustic pressure. We have developed and implemented a nonequilibrium molecular dynamics simulation algorithm to the AMBER package, to facilitate the investigation of the molecular mechanism of Aß oligomerization under stable cavitation. Our results indicated that stable cavitation not only inhibited oligomeric formation, but also prevented the formation of ß-rich oligomers. The network analysis of state transitions revealed that stable cavitation altered the oligomerization pathways of Aß16-22 peptides. Our simulation tool may be applied to optimize the experimental conditions to achieve the best therapeutical effect.

In silico binding affinity prediction for metabotropic glutamate receptors using both endpoint free energy methods and a machine learning-based scoring function.

Metabotropic glutamate receptors (mGluRs) play an important role in regulating glutamate signal pathways, which are involved in neuropathy and periphery homeostasis. mGluR4, which belongs to Group III mGluRs, is most widely distributed in the periphery among all the mGluRs. It has been proved that the regulation of this receptor is involved in diabetes, colorectal carcinoma and many other diseases. However, the application of structure-based drug design to identify small molecules to regulate the mGluR4 receptor is limited due to the absence of a resolved mGluR4 protein structure. In this work, we first built a homology model of mGluR4 based on a crystal structure of mGluR8, and then conducted hierarchical virtual screening (HVS) to identify possible active ligands for mGluR4. The HVS protocol consists of three hierarchical filters including Glide docking, molecular dynamic (MD) simulation and binding free energy calculation. We successfully prioritized active ligands of mGluR4 from a set of screening compounds using HVS. The predicted active ligands based on binding affinities can almost cover all the experiment-determined active ligands, with only one ligand missed. The correlation between the measured and predicted binding affinities is significantly improved for the MM-PB/GBSA-WSAS methods compared to the Glide docking method. More importantly, we have identified hotspots for ligand binding, and we found that SER157 and GLY158 tend to contribute to the selectivity of mGluR4 ligands, while ALA154 and ALA155 could account for the ligand selectivity to mGluR8. We also recognized other 5 key residues that are critical for ligand potency. The difference of the binding profiles between mGluR4 and mGluR8 can guide us to develop more potent and selective modulators. Moreover, we evaluated the performance of IPSF, a novel type of scoring function trained by a machine learning algorithm on residue-ligand interaction profiles, in guiding drug lead optimization. The cross-validation root-mean-square errors (RMSEs) are much smaller than those by the endpoint methods, and the correlation coefficients are comparable to the best endpoint methods for both mGluRs. Thus, machine learning-based IPSF can be applied to guide lead optimization, albeit the total number of actives/inactives are not big, a typical scenario in drug discovery projects.

Joint Computational/Cell-Based Approach for Screening Inhibitors of Tau Oligomerization: A Proof-of-Concept Study.

BACKGROUND: Tau assembly produces soluble oligomers and insoluble neurofibrillary tangles, which are neurotoxic to the brain and associated with Alzheimer's and Parkinson's diseases. Therefore, preventing tau aggregation is a promising therapy for those neurodegenerative disorders. OBJECTIVE: The aim of this study was to develop a joint computational/cell-based oligomerization protocol for screening inhibitors of tau assembly. METHODS: Virtual oligomerization inhibition (VOI) experiment using molecular dynamics simulation was performed to screen potential oligomerization inhibitors of PHF6 hexapeptide. Tau seeding assay, which is directly related to the outcome of therapeutic intervention, was carried out to confirm a ligand's ability in inhibiting tau assembly formation. RESULTS: Our protocol was tested on two known compounds, EGCG and Blarcamesine. EGCG inhibited both the aggregation of PHF6 peptide in VOI and tau assembly in tau seeding assay, while Blarcamesine was not a good inhibitor at the two tasks. We also pointed out that good binding affinity to tau aggregates is needed, but not sufficient for a ligand to become a good inhibitor of tau oligomerization. CONCLUSION: VOI goes beyond traditional computational inhibitor screening of amyloid aggregation by directly examining the inhibitory ability of a ligand to tau oligomerization. Comparing with the traditional biochemical assays, tau seeding activities in cells is a better indicator for the outcome of a therapeutic intervention. Our hybrid protocol has been successfully validated. It can effectively and efficiently identify the inhibitors of amyloid oligomerization/aggregation processes, thus, facilitate to the drug development of tau-related neurodegenerative diseases.

A multiple-step in silico screening protocol to identify allosteric inhibitors of Spike-hACE2 binding.

While the COVID-19 pandemic continues to worsen, effective medicines that target the life cycle of SARS-CoV-2 are still under development. As more highly infective and dangerous variants of the coronavirus emerge, the protective power of vaccines will decrease or vanish. Thus, the development of drugs, which are free of drug resistance is direly needed. The aim of this study is to identify allosteric binding modulators from a large compound library to inhibit the binding between the Spike protein of the SARS-CoV-2 virus and human angiotensin-converting enzyme 2 (hACE2). The binding of the Spike protein to hACE2 is the first step of the infection of host cells by the coronavirus. We first built a compound library containing 77 448 antiviral compounds. Molecular docking was then conducted to preliminarily screen compounds which can potently bind to the Spike protein at two allosteric binding sites. Next, molecular dynamics simulations were performed to accurately calculate the binding affinity between the spike protein and an identified compound from docking screening and to investigate whether the compound can interfere with the binding between the Spike protein and hACE2. We successfully identified two possible drug binding sites on the Spike protein and discovered a series of antiviral compounds which can weaken the interaction between the Spike protein and hACE2 receptor through conformational changes of the key Spike residues at the Spike-hACE2 binding interface induced by the binding of the ligand at the allosteric binding site. We also applied our screening protocol to another compound library which consists of 3407 compounds for which the inhibitory activities of Spike/hACE2 binding were measured. Encouragingly, in vitro data supports that the identified compounds can inhibit the Spike-ACE2 binding. Thus, we developed a promising computational protocol to discover allosteric inhibitors of the binding of the Spike protein of SARS-CoV-2 to the hACE2 receptor, and several promising allosteric modulators were discovered.

Development and Evaluation of Geometry Optimization Algorithms in Conjunction with ANI Potentials.

An efficient yet accurate method for producing a large amount of energy data for molecular mechanical force field (MMFF) parameterization is on demand, especially for torsional angle parameters which are typically derived to reproduce ab initio rotational profiles or torsional potential energy surfaces (PESs). Recently, an active learning potential (ANI-1x) for organic molecules which can produce smooth and physically meaningful PESs has been developed. The high efficiency and accuracy make ANI-1x especially attractive for geometry optimization at low cost. To apply the ANI-1x potential in MMFF parameterization, one needs to perform constrained geometry optimization. In this work, we first developed a computational protocol to constrain rotatable torsional angles and other geometric parameters for a molecule whose geometry is described by Cartesian coordinates. The constraint is successfully achieved by force projection for the two conjugated gradient (CG) algorithms. We then conducted large-scale assessments on ANI-1x along with four different optimization algorithms in reproducing DFT energies and geometries for two CG algorithms, CG backtracking line search (CG-BS) and CG Wolfe line search (CG-WS), and two quasi-Newton algorithms, Broyden-Fletcher-Goldfarb-Shanno (BFGS) and low-memory BFGS (L-BFGS). Note that CG-BS is a new algorithm we developed in this work. All four algorithms take the ANI energies and forces to optimize a molecule geometry. Last, we conducted a large-scale assessment of applying ANI-1x in MMFF development in three aspects. First, we performed full optimizations for 100 drug molecules, each consisting of five distinct conformations. The average root-mean-square error (RMSE) between ANI-1x and DFT is about 1.3 kcal/mol, and the root-mean-square displacement (RMSD) of heavy atoms is about 0.35 Å. Second, we generated torsional PESs for 160 organic molecules, and constrained optimizations were performed for up to 18 conformations for each PES. We found that the RMSE of all the conformers is 1.23 kcal/mol. Last, we carried out constrained optimizations for alanine dipeptide with both ϕ and φ angles being frozen. The Ramachandran plots indicate that the two CG algorithms in conjunction with the ANI-1x potential could well reproduce the DFT-optimized geometries and torsional PESs. We concluded that CG-BS and CG-WS are good choices for generating PESs, while CG-WS or BFGS is ideal for performing full geometry optimization. With the continuously increased quality of ANI, it is expected that the computational algorithms and protocols presented in this work will have great applications in improving the quality of an existing small-molecule MMFF.

Recent progress in general force fields of small molecules.

Recent advances in computational hardware and free energy algorithms enable a broader application of molecular simulation of binding interactions between receptors and small-molecule ligands. The underlying molecular mechanics force fields (FFs) for small molecules have also achieved advancements in accuracy, user-friendliness, and speed during the past several years (2018-2020). Besides the expansion of chemical space coverage of ligand-like molecules among major popular classical additive FFs and polarizable FFs, new charge models have been proposed for better accuracy and transferability, new chemical perception of avoiding predefined atom types have been applied, and new automated parameterization toolkits, including machine learning approaches, have been developed for users' convenience.

Effects of All-Atom Molecular Mechanics Force Fields on Amyloid Peptide Assembly: The Case of PHF6 Peptide of Tau Protein.

Molecular dynamics (MD) simulations play a vital role in revealing the mechanism of amyloid aggregation that is crucial to the therapeutic agent development for Alzheimer's Disease. However, the accuracy of MD simulation results strongly depends on the force field employed. In our previous benchmark for 17 all-atom force fields on modeling of amyloid aggregation using the Aß16-22 dimer, we showed that AMBER14SB and CHARMM36m are suitable force fields for amyloid aggregation simulation, while GROMOS54a7 and OPLSAA are not good for the task. In this work, we continue assessing the applicability of atomistic force fields on amyloid aggregation using the VQIVYK (PHF6) peptide which is essential for tau-protein aggregation. Although, both Aß16-22 and PHF6 peptides formed fibrils in vitro, the PHF6 fibrils are parallel ß-sheets, while the Aß16-22 fibrils are antiparallel ß-sheets. We performed an all-atom large-scale MD simulation in explicit water on the PHF6 dimer and octa-peptides systems using five mainstream force fields, including AMBER99SB-disp, AMBER14SB, CHARMM36m, GROMOS54a7, and OPLSAA. The accumulated simulation time is 0.2 ms. Our result showed that the ß-sheet structures of PHF6 peptides sampled by AMBER99SB-disp, AMBER14SB, GROMOS54a7, and OPLSAA are in favor of the antiparallel ß-sheets, while the dominant type of ß-sheet structures is parallel ß-sheet by using CHARMM36m. Among the five force fields, CHARMM36m provides the strongest CH-π interaction that was observed in an NMR study. The comparison between our results and experimental observation indicates that CHARMM36m achieved the best performance on modeling the aggregation of PHF6 peptides. In summary, CHARMM36m is currently the most suitable force field for studying the aggregation of both amyloid-ß and Tau through MD simulations.

In silico binding profile characterization of SARS-CoV-2 spike protein and its mutants bound to human ACE2 receptor.

Severe acute respiratory syndrome coronavirus (SARS-CoV-2), a novel coronavirus, has brought an unprecedented pandemic to the world and affected over 64 million people. The virus infects human using its spike glycoprotein mediated by a crucial area, receptor-binding domain (RBD), to bind to the human ACE2 (hACE2) receptor. Mutations on RBD have been observed in different countries and classified into nine types: A435S, D364Y, G476S, N354D/D364Y, R408I, V341I, V367F, V483A and W436R. Employing molecular dynamics (MD) simulation, we investigated dynamics and structures of the complexes of the prototype and mutant types of SARS-CoV-2 spike RBDs and hACE2. We then probed binding free energies of the prototype and mutant types of RBD with hACE2 protein by using an end-point molecular mechanics Poisson Boltzmann surface area (MM-PBSA) method. According to the result of MM-PBSA binding free energy calculations, we found that V367F and N354D/D364Y mutant types showed enhanced binding affinities with hACE2 compared to the prototype. Our computational protocols were validated by the successful prediction of relative binding free energies between prototype and three mutants: N354D/D364Y, V367F and W436R. Thus, this study provides a reliable computational protocol to fast assess the existing and emerging RBD mutations. More importantly, the binding hotspots identified by using the molecular mechanics generalized Born surface area (MM-GBSA) free energy decomposition approach can guide the rational design of small molecule drugs or vaccines free of drug resistance, to interfere with or eradicate spike-hACE2 binding.