Deep Learning-Based construction of a Drug-Like compound database and its application in virtual screening of HsDHODH inhibitors.

The process of virtual screening relies heavily on the databases, but it is disadvantageous to conduct virtual screening based on commercial databases with patent-protected compounds, high compound toxicity and side effects. Therefore, this paper utilizes generative recurrent neural networks (RNN) containing long short-term memory (LSTM) cells to learn the properties of drug compounds in the DrugBank, aiming to obtain a new and virtual screening compounds database with drug-like properties. Ultimately, a compounds database consisting of 26,316 compounds is obtained by this method. To evaluate the potential of this compounds database, a series of tests are performed, including chemical space, ADME properties, compound fragmentation, and synthesizability analysis. As a result, it is proved that the database is equipped with good drug-like properties and a relatively new backbone, its potential in virtual screening is further tested. Finally, a series of seedling compounds with completely new backbones are obtained through docking and binding free energy calculations.

Substituent effects on the selectivity of ambimodal [6+4]/[4+2] cycloaddition.

In this work, we report a density functional theory (DFT) study and a dynamical trajectory study of substituent effects on the ambimodal [6+4]/[4+2] cycloaddition proposed for 1,3,5,10,12-cycloheptadecapentaene, referred to as cycloheptadecapentaene. The proposed cycloaddition proceeds through an ambimodal transition state, which results in both a [6+4] adduct a [4+2] adduct directly. The [6+4] adduct can be readily converted to the [4+2] adduct via a Cope rearrangement. We study the selectivity of the reaction with regard to the position of substituents, steric effects of substituents, and electronic effects of substituents. In the dynamical trajectory study, we find that nitro-substituted reactants lead to a new product from the ambimodal transition state via the hetero Diels-Alder reaction, and this new product can then be converted to a [4+2] adduct by a hetero [3, 3]-sigmatropic rearrangement. These results may provide insights for designing more bridged heterocyclic compounds.

An Efficient Approach to the Accurate Prediction of Mutational Effects in Antigen Binding to the MHC1.

The major histocompatibility complex (MHC) can recognize and bind to external peptides to generate effective immune responses by presenting the peptides to T cells. Therefore, understanding the binding modes of peptide-MHC complexes (pMHC) and predicting the binding affinity of pMHCs play a crucial role in the rational design of peptide vaccines. In this study, we employed molecular dynamics (MD) simulations and free energy calculations with an Alanine Scanning with Generalized Born and Interaction Entropy (ASGBIE) method to investigate the protein-peptide interaction between HLA-A*02:01 and the G9209 peptide derived from the melanoma antigen gp100. The energy contribution of individual residue was calculated using alanine scanning, and hotspots on both the MHC and the peptides were identified. Our study shows that the pMHC binding is dominated by the van der Waals interactions. Furthermore, we optimized the ASGBIE method, achieving a Pearson correlation coefficient of 0.91 between predicted and experimental binding affinity for mutated antigens. This represents a significant improvement over the conventional MM/GBSA method, which yields a Pearson correlation coefficient of 0.22. The computational protocol developed in this study can be applied to the computational screening of antigens for the MHC1 as well as other protein-peptide binding systems.

Biosynthesis of eriodictyol in citrus waster by endowing P450BM3 activity of naringenin hydroxylation.

The flavonoid naringenin is abundantly present in pomelo peels, and the unprocessed naringenin in wastes is not friendly for the environment once discarded directly. Fortunately, the hydroxylated product of eriodictyol from naringenin exhibits remarkable antioxidant and anticancer properties. The P450s was suggested promising for the bioconversion of the flavonoids, but less naturally existed P450s show hydroxylation activity to C3' of the naringenin. By well analyzing the catalytic mechanism and the conformations of the naringenin in P450, we proposed that the intermediate Cmpd I ((porphyrin)Fe = O) is more reasonable as key conformation for the hydrolyzation, and the distance between C3'/C5' of naringenin to the O atom of CmpdI determines the hydroxylating activity for the naringenin. Thus, the "flying kite model" that gradually drags the C-H bond of the substrate to the O atom of CmpdI was put forward for rational design. With ab initio design, we successfully endowed the self-sufficient P450-BM3 hydroxylic activity to naringenin and obtained mutant M5-5, with kcat, Km, and kcat/Km values of 230.45 min-1, 310.48 µM, and 0.742 min-1 µM-1, respectively. Furthermore, the mutant M4186 was screened with kcat/Km of 4.28-fold highly improved than the reported M13. The M4186 also exhibited 62.57% yield of eriodictyol, more suitable for the industrial application. This study provided a theoretical guide for the rational design of P450s to the nonnative compounds. KEY POINTS: •The compound I is proposed as the starting point for the rational design of the P450BM3 •"Flying kite model" is proposed based on the distance between O of Cmpd I and C3'/C5' of naringenin •Mutant M15-5 with 1.6-fold of activity than M13 was obtained by ab initio modification.

Screening Power of End-Point Free-Energy Calculations in Cucurbituril Host-Guest Systems.

End-point free-energy methods as an indispensable component in virtual screening are commonly recognized as a tool with a certain level of screening power in pharmaceutical research. While a huge number of records could be found for end-point applications in protein-ligand, protein-protein, and protein-DNA complexes from academic and industrial reports, up to now, there is no large-scale benchmark in host-guest complexes supporting the screening power of end-point free-energy techniques. A good benchmark requires a data set of sufficient coverage of pharmaceutically relevant chemical space, a long-time sampling length supporting the trajectory approximation of the ensemble average, and a sufficient sample size of receptor-acceptor pairs to stabilize the performance statistics. In this work, selecting a popular family of macrocyclic hosts named cucurbiturils, we construct a large data set containing 154 host-guest pairs, perform extensive end-point sampling of several hundred nanosecond lengths for each system, and extract the free-energy estimates with a variety of end-point free-energy techniques, including the advanced three-trajectory dielectric-constant-variable regime proposed in our recent work. The best-performing end-point protocol employs GAFF2 for solute descriptions, the three-trajectory end-point sampling regime, and the MM/GBSA Hamiltonian in free-energy extraction, achieving a high ranking metrics of Kendall τ > 0.6, a Pearlman predictive index of ∼0.8, and a high scoring power of Pearson r > 0.8. The current project as the first large-scale systematic benchmark of end-point methods in host-guest complexes in academic publications provides solid evidence of the applicability of end-point techniques and direct guidance of computational setups in practical host-guest systems.

Entropy driven cooperativity effect in multi-site drug optimization targeting SARS-CoV-2 papain-like protease.

Papain-like protease (PLpro), a non-structural protein encoded by SARS-CoV-2, is an important therapeutic target. Regions 1 and 5 of an existing drug, GRL0617, can be optimized to produce cooperativity with PLpro binding, resulting in stronger binding affinity. This work investigated the origin of the cooperativity using molecular dynamics simulations combined with the interaction entropy (IE) method. The regions' improvement exhibits cooperativity by calculating the binding free energies between the complex of PLpro-inhibitor. The thermodynamic integration method further verified the cooperativity generated in the drug improvement. To further determine the specific source of cooperativity, enthalpy and entropy in the complexes were calculated using molecular mechanics/generalized Born surface area and IE. The results show that the entropic change is an important contributor to the cooperativity. Our study also identified residues P248, Q269, and T301 that play a significant role in cooperativity. The optimization of the inhibitor stabilizes these residues and minimizes the entropic loss, and the cooperativity observed in the binding free energy can be attributed to the change in the entropic contribution of these residues. Based on our research, the application of cooperativity can facilitate drug optimization, and provide theoretical ideas for drug development that leverage cooperativity by reducing the contribution of entropy through multi-locus binding.

Assessment of an Electrostatic Energy-Based Charge Model for Modeling the Electrostatic Interactions in Water Solvent.

The protein force field based on the restrained electrostatic potential (RESP) charges has limitations in accurately describing hydrogen bonding interactions in proteins. To address this issue, we propose an alternative approach called the electrostatic energy-based charges (EEC) model, which shows improved performance in describing electrostatic interactions (EIs) of hydrogen bonds in proteins. In this study, we further investigate the performance of the EEC model in modeling EIs in water solvent. Our findings demonstrate that the fixed EEC model can effectively reproduce the quantum mechanics/molecular mechanics (QM/MM)-calculated EIs between a water molecule and various water solvent environments. However, to achieve the same level of computational accuracy, the electrostatic potential (ESP) charge model needs to fluctuate according to the electrostatic environment. Our analysis indicates that the requirement for charge adjustments depends on the specific mathematical and physical representation of EIs as a function of the environment for deriving charges. By comparing with widely used empirical water models calibrated to reproduce experimental properties, we confirm that the performance of the EEC model in reproducing QM/MM EIs is similar to that of general purpose TIP4P-like water models such as TIP4P-Ew and TIP4P/2005. When comparing the computed 10,000 distinct EI values within the range of -40 to 0 kcal/mol with the QM/MM results calculated at the MP2/aug-cc-pVQZ/TIP3P level, we noticed that the mean unsigned error (MUE) for the EEC model is merely 0.487 kcal/mol, which is remarkably similar to the MUE values of the TIP4P-Ew (0.63 kcal/mol) and TIP4P/2005 (0.579 kcal/mol) models. However, both the RESP method and the TIP3P model exhibit a tendency to overestimate the EIs, as evidenced by their higher MUE values of 1.761 and 1.293 kcal/mol, respectively. EEC-based molecular dynamics simulations have demonstrated that, when combined with appropriate van der Waals parameters, the EEC model can closely reproduce oxygen-oxygen radial distribution function and density of water, showing a remarkable similarity to the well-established TIP4P-like empirical water models. Our results demonstrate that the EEC model has the potential to build force fields with comparable accuracy to more sophisticated empirical TIP4P-like water models.

A precise swaying map for how promiscuous cellobiose-2-epimerase operate bi-reaction.

Promiscuous enzymes play a crucial role in organism survival and new reaction mining. However, comprehensive mapping of the catalytic and regulatory mechanisms hasn't been well studied due to the characteristic complexity. The cellobiose 2-epimerase from Caldicellulosiruptor saccharolyticus (CsCE) with complex epimerization and isomerization was chosen to comprehensively investigate the promiscuous mechanisms. Here, the catalytic frame of ring-opening, cis-enediol mediated catalysis and ring-closing was firstly determined. To map the full view of promiscuous CE, the structure of CsCE complex with the isomerized product glucopyranosyl-ß1,4-fructose was determined. Combined with computational calculation, the promiscuity was proved a precise cooperation of the double subsites, loop rearrangement, and intermediate swaying. The flexible loop was like a gear, whose structural reshaping regulates the sway of the intermediates between the two subsites of H377-H188 and H377-H247, and thus regulates the catalytic directions. The different protonated states of cis-enediol intermediate catalyzed by H188 were the key point for the catalysis. The promiscuous enzyme tends to utilize all elements at hand to carry out the promiscuous functions.

Combined Antibodies Evusheld against the SARS-CoV-2 Omicron Variants BA.1.1 and BA.5: Immune Escape Mechanism from Molecular Simulation.

The Omicron lineage of SARS-CoV-2, which was first reported in November 2021, has spread globally and become dominant, splitting into several sublineages. Experiments have shown that Omicron lineage has escaped or reduced the activity of existing monoclonal antibodies, but the origin of escape mechanism caused by mutation is still unknown. This work uses molecular dynamics and umbrella sampling methods to reveal the escape mechanism of BA.1.1 to monoclonal antibody (mAb) Tixagevimab (AZD1061) and BA.5 to mAb Cilgavimab (AZD8895), both mAbs were combined to form antibody cocktail, Evusheld (AZD7442). The binding free energy of BA.1.1-AZD1061 and BA.5-AZD8895 has been severely reduced due to multiple-site mutated Omicron variants. Our results show that the two Omicron variants, which introduce a substantial number of positively charged residues, can weaken the electrostatic attraction between the receptor binding domain (RBD) and AZD7442, thus leading to a decrease in affinity. Additionally, using umbrella sampling along dissociation pathway, we found that the two Omicron variants severely impaired the interaction between the RBD of SARS-CoV-2's spike glycoprotein (S protein) and complementary determining regions (CDRs) of mAbs, especially in CDR3H. Although mAbs AZD8895 and AZD1061 are knocked out by BA.5 and BA.1.1, respectively, our results confirm that the antibody cocktail AZD7442 retains activity against BA.1.1 and BA.5 because another antibody is still on guard. The study provides theoretical insights for mAbs interacting with BA.1.1 and BA.5 from both energetic and dynamic perspectives, and we hope this will help in developing new monoclonals and combinations to protect those unable to mount adequate vaccine responses.

DeepBindGCN: Integrating Molecular Vector Representation with Graph Convolutional Neural Networks for Protein-Ligand Interaction Prediction.

The core of large-scale drug virtual screening is to select the binders accurately and efficiently with high affinity from large libraries of small molecules in which non-binders are usually dominant. The binding affinity is significantly influenced by the protein pocket, ligand spatial information, and residue types/atom types. Here, we used the pocket residues or ligand atoms as the nodes and constructed edges with the neighboring information to comprehensively represent the protein pocket or ligand information. Moreover, the model with pre-trained molecular vectors performed better than the one-hot representation. The main advantage of DeepBindGCN is that it is independent of docking conformation, and concisely keeps the spatial information and physical-chemical features. Using TIPE3 and PD-L1 dimer as proof-of-concept examples, we proposed a screening pipeline integrating DeepBindGCN and other methods to identify strong-binding-affinity compounds. It is the first time a non-complex-dependent model has achieved a root mean square error (RMSE) value of 1.4190 and Pearson r value of 0.7584 in the PDBbind v.2016 core set, respectively, thereby showing a comparable prediction power with the state-of-the-art affinity prediction models that rely upon the 3D complex. DeepBindGCN provides a powerful tool to predict the protein-ligand interaction and can be used in many important large-scale virtual screening application scenarios.

Revealing the Sequence Characteristics and Molecular Mechanisms of ACE Inhibitory Peptides by Comprehensive Characterization of 160,000 Tetrapeptides.

Chronic diseases, such as hypertension, cause great harm to human health. Conventional drugs have promising therapeutic effects, but also cause significant side effects. Food-sourced angiotensin-converting enzyme (ACE) inhibitory peptides are an excellent therapeutic alternative to pharmaceuticals, as they have fewer side effects. However, there is no systematic and effective screening method for ACE inhibitory peptides, and the lack of understanding of the sequence characteristics and molecular mechanism of these inhibitory peptides poses a major obstacle to the development of ACE inhibitory peptides. Through systematically calculating the binding effects of 160,000 tetrapeptides with ACE by molecular docking, we found that peptides with Tyr, Phe, His, Arg, and especially Trp were the characteristic amino acids of ACE inhibitory peptides. The tetrapeptides of WWNW, WRQF, WFRV, YYWK, WWDW, and WWTY rank in the top 10 peptides exhibiting significantly high ACE inhibiting behaviors, with IC50 values between 19.98 ± 8.19 µM and 36.76 ± 1.32 µM. Salt bridges, π-π stacking, π-cations, and hydrogen bonds contributed to the high binding characteristics of the inhibitors and ACE. Introducing eight Trp into rabbit skeletal muscle protein (no Trp in wide sequence) endowed the protein with a more than 90% ACE inhibition rate, further suggesting that meat with a high content of Trp could have potential utility in hypertension regulation. This study provides a clear direction for the development and screening of ACE inhibitory peptides.

Binding of berberine derivates to G-quadruplex: insight from a computational study.

Human telomerase exhibits significant activity in cancer cells relative to normal cells, which contributes to the immortal proliferation of cancer cells. To counter this, the stabilization of G-quadruplexes formed in the guanine-rich sequence of the cancer cell chromosome has emerged as a promising avenue for anti-cancer therapy. Berberine (BER), an alkaloid that is derived from traditional Chinese medicines, has shown potential for stabilizing G-quadruplexes. To investigate the atomic interactions between G-quadruplexes and BER and its derivatives, molecular dynamics simulations were conducted. Modeling the interactions between G-quadruplexes and ligands accurately is challenging due to the strong negative charge of nucleic acids. Thus, various force fields and charge models for the G-quadruplex and ligands were tested to obtain precise simulation results. The binding energies were calculated by a combination of molecular mechanics/generalized Born surface area and interaction entropy methods, and the calculated results correlated well with experimental results. B-factor and hydrogen bond analyses demonstrated that the G-quadruplex was more stable in the presence of ligands than in the absence of ligands. Calculation of the binding free energy showed that the BER derivatives bind to a G-quadruplex with higher affinity than that of BER. The breakdown of the binding free energy to per-nucleotide energies suggested that the first G-tetrad played a primary role in binding. Additionally, energy and geometric properties analyses indicated that van der Waals interactions were the most favorable interactions between the derivatives and the G-quadruplexes. Overall, these findings provide crucial atomic-level insights into the binding of G-quadruplexes and their inhibitors.

MolTaut: A Tool for the Rapid Generation of Favorable Tautomer in Aqueous Solution.

Fast and proper treatment of the tautomeric states for drug-like molecules is critical in computer-aided drug discovery since the major tautomer of a molecule determines its pharmacophore features and physical properties. We present MolTaut, a tool for the rapid generation of favorable states of drug-like molecules in water. MolTaut works by enumerating possible tautomeric states with tautomeric transformation rules, ranking tautomers with their relative internal energies and solvation energies calculated by AI-based models, and generating preferred ionization states according to predicted microscopic pKa. Our test shows that the ranking ability of the AI-based tautomer scoring approach is comparable to the DFT method (wB97X/6-31G*//M062X/6-31G*/SMD) from which the AI models try to learn. We find that the substitution effect on tautomeric equilibrium is well predicted by MolTaut, which is helpful in computer-aided ligand design. The source code of MolTaut is freely available to researchers and can be accessed at https://github.com/xundrug/moltaut. To facilitate the usage of MolTaut by medicinal chemists, we made a free web server, which is available at http://moltaut.xundrug.cn. MolTaut is a handy tool for investigating the tautomerization issue in drug discovery.

Comprehensive Evaluation of End-Point Free Energy Techniques in Carboxylated-Pillar[6]arene Host-Guest Binding: III. Force-Field Comparison, Three-Trajectory Realization and Further Dielectric Augmentation.

Host-guest binding, despite the relatively simple structural and chemical features of individual components, still poses a challenge in computational modelling. The extreme underperformance of standard end-point methods in host-guest binding makes them practically useless. In the current work, we explore a potentially promising modification of the three-trajectory realization. The alteration couples the binding-induced structural reorganization into free energy estimation and suffers from dramatic fluctuations in internal energies in protein-ligand situations. Fortunately, the relatively small size of host-guest systems minimizes the magnitude of internal fluctuations and makes the three-trajectory realization practically suitable. Due to the incorporation of intra-molecular interactions in free energy estimation, a strong dependence on the force field parameters could be incurred. Thus, a term-specific investigation of transferable GAFF derivatives is presented, and noticeable differences in many aspects are identified between commonly applied GAFF and GAFF2. These force-field differences lead to different dynamic behaviors of the macrocyclic host, which ultimately would influence the end-point sampling and binding thermodynamics. Therefore, the three-trajectory end-point free energy calculations are performed with both GAFF versions. Additionally, due to the noticeable differences between host dynamics under GAFF and GAFF2, we add additional benchmarks of the single-trajectory end-point calculations. When only the ranks of binding affinities are pursued, the three-trajectory realization performs very well, comparable to and even better than the regressed PBSA_E scoring function and the dielectric constant-variable regime. With the GAFF parameter set, the TIP3P water in explicit solvent sampling and either PB or GB implicit solvent model in free energy estimation, the predictive power of the three-trajectory realization in ranking calculations surpasses all existing end-point methods on this dataset. We further combine the three-trajectory realization with another promising modified end-point regime of varying the interior dielectric constant. The combined regime does not incur sizable improvements for ranks and deviations from experiment exhibit non-monotonic variations.

Deep Learning-Based Bioactive Therapeutic Peptide Generation and Screening.

Many bioactive peptides demonstrated therapeutic effects over complicated diseases, such as antiviral, antibacterial, anticancer, etc. It is possible to generate a large number of potentially bioactive peptides using deep learning in a manner analogous to the generation of de novo chemical compounds using the acquired bioactive peptides as a training set. Such generative techniques would be significant for drug development since peptides are much easier and cheaper to synthesize than compounds. Despite the limited availability of deep learning-based peptide-generating models, we have built an LSTM model (called LSTM_Pep) to generate de novo peptides and fine-tuned the model to generate de novo peptides with specific prospective therapeutic benefits. Remarkably, the Antimicrobial Peptide Database has been effectively utilized to generate various kinds of potential active de novo peptides. We proposed a pipeline for screening those generated peptides for a given target and used the main protease of SARS-COV-2 as a proof-of-concept. Moreover, we have developed a deep learning-based protein-peptide prediction model (DeepPep) for rapid screening of the generated peptides for the given targets. Together with the generating model, we have demonstrated that iteratively fine-tuning training, generating, and screening peptides for higher-predicted binding affinity peptides can be achieved. Our work sheds light on developing deep learning-based methods and pipelines to effectively generate and obtain bioactive peptides with a specific therapeutic effect and showcases how artificial intelligence can help discover de novo bioactive peptides that can bind to a particular target.

Rational Design of a Highly Specific Prolyl Endopeptidase To Activate the Antihypertensive Effect of Peptides.

Enzymatic hydrolysis of food-derived proteins to produce bioactive peptides could activate food functions such as antihypertension. However, the diversity of enzymatic hydrolysis products can reduce bioactive peptides' efficacy. Highly specific proteases can homogenize the hydrolysis products to reduce the production of impotent peptides. In this study, we successfully obtained M. xanthus prolyl endopeptidase mutant Y451M by constraint/free molecular dynamics simulations and binding energy calculations. The specificity of Y451M for proline was increased by 286 % compared to WT, while its activity was almost unchanged. Milk-derived substrates processed with Y451M showed an antihypertensive effect that was 567 % higher than without enzymes. The ability to activate food antihypertension increased 152 % and the use of enzyme by 192 % compared with WT. Specific proteases are thus valuable tools in the processing of complex substrates to obtain bioactive peptides.

An efficient method to predict protein thermostability in alanine mutation.

The relationship between protein sequence and its thermodynamic stability is a critical aspect of computational protein design. In this work, we present a new theoretical method to calculate the free energy change (ΔΔG) resulting from a single-point amino acid mutation to alanine in a protein sequence. The method is derived based on physical interactions and is very efficient in estimating the free energy changes caused by a series of alanine mutations from just a single molecular dynamics (MD) trajectory. Numerical calculations are carried out on a total of 547 alanine mutations in 19 diverse proteins whose experimental results are available. The comparison between the experimental ΔΔGexp and the calculated values shows a generally good correlation with a correlation coefficient of 0.67. Both the advantages and limitations of this method are discussed. This method provides an efficient and valuable tool for protein design and engineering.

Comprehensive evaluation of end-point free energy techniques in carboxylated-pillar[6]arene host-guest binding: II. regression and dielectric constant.

End-point free energy calculations as a powerful tool have been widely applied in protein-ligand and protein-protein interactions. It is often recognized that these end-point techniques serve as an option of intermediate accuracy and computational cost compared with more rigorous statistical mechanic models (e.g., alchemical transformation) and coarser molecular docking. However, it is observed that this intermediate level of accuracy does not hold in relatively simple and prototypical host-guest systems. Specifically, in our previous work investigating a set of carboxylated-pillar[6]arene host-guest complexes, end-point methods provide free energy estimates deviating significantly from the experimental reference, and the rank of binding affinities is also incorrectly computed. These observations suggest the unsuitability and inapplicability of standard end-point free energy techniques in host-guest systems, and alteration and development are required to make them practically usable. In this work, we consider two ways to improve the performance of end-point techniques. The first one is the PBSA_E regression that varies the weights of different free energy terms in the end-point calculation procedure, while the second one is considering the interior dielectric constant as an additional variable in the end-point equation. By detailed investigation of the calculation procedure and the simulation outcome, we prove that these two treatments (i.e., regression and dielectric constant) are manipulating the end-point equation in a somehow similar way, i.e., weakening the electrostatic contribution and strengthening the non-polar terms, although there are still many detailed differences between these two methods. With the trained end-point scheme, the RMSE of the computed affinities is improved from the standard ~ 12 kcal/mol to ~ 2.4 kcal/mol, which is comparable to another altered end-point method (ELIE) trained with system-specific data. By tuning PBSA_E weighting factors with the host-specific data, it is possible to further decrease the prediction error to ~ 2.1 kcal/mol. These observations along with the extremely efficient optimized-structure computation procedure suggest the regression (i.e., PBSA_E as well as its GBSA_E extension) as a practically applicable solution that brings end-point methods back into the library of usable tools for host-guest binding. However, the dielectric-constant-variable scheme cannot effectively minimize the experiment-calculation discrepancy for absolute binding affinities, but is able to improve the calculation of affinity ranks. This phenomenon is somehow different from the protein-ligand case and suggests the difference between host-guest and biomacromolecular (protein-ligand and protein-protein) systems. Therefore, the spectrum of tools usable for protein-ligand complexes could be unsuitable for host-guest binding, and numerical validations are necessary to screen out really workable solutions in these 'prototypical' situations.

Comprehensive evaluation of end-point free energy techniques in carboxylated-pillar[6]arene host-guest binding: I. Standard procedure.

Despite the massive application of end-point free energy methods in protein-ligand and protein-protein interactions, computational understandings about their performance in relatively simple and prototypical host-guest systems are limited. In this work, we present a comprehensive benchmark calculation with standard end-point free energy techniques in a recent host-guest dataset containing 13 host-guest pairs involving the carboxylated-pillar[6]arene host. We first assess the charge schemes for solutes by comparing the charge-produced electrostatics with many ab initio references, in order to obtain a preliminary albeit detailed view of the charge quality. Then, we focus on four modelling details of end-point free energy calculations, including the docking procedure for the generation of initial condition, the charge scheme for host and guest molecules, the water model used in explicit-solvent sampling, and the end-point methods for free energy estimation. The binding thermodynamics obtained with different modelling schemes are compared with experimental references, and some practical guidelines on maximizing the performance of end-point methods in practical host-guest systems are summarized. Further, we compare our simulation outcome with predictions in the grand challenge and discuss further developments to improve the prediction quality of end-point free energy methods. Overall, unlike the widely acknowledged applicability in protein-ligand binding, the standard end-point calculations cannot produce useful outcomes in host-guest binding and thus are not recommended unless alterations are performed.