Binding affinities for sulfonamide inhibitors with human thrombin using Monte Carlo simulations with a linear response method

Searching for potential acetylcholinesterase inhibitors: a combined approach of multi-step similarity search, machine learning and molecular dynamics simulations

Targeting acetylcholinesterase is one of the most important strategies for developing therapeutics against Alzheimer's disease. In this work, we have employed a new approach that combines machine learning models, a multi-step similarity search of the PubChem library and molecular dynamics simulations to investigate potential inhibitors for acetylcholinesterase. Our search strategy has been shown to significantly enrich the set of compounds with strong predicted binding affinity to acetylcholinesterase. Both machine learning prediction and binding free energy calculation, based on linear interaction energy, suggest that the compound CID54414454 would bind strongly to acetylcholinesterase and hence is a promising inhibitor.

Rapid, Accurate, Ranking of Protein-Ligand Binding Affinities with VM2, the Second-Generation Mining Minima Method

The structure-based technologies most widely used to rank the affinities of candidate small molecule drugs for proteins range from faster but less reliable docking methods to slower but more accurate explicit solvent free energy methods. In recent years, we have advanced another technology, which is called mining minima because it "mines" out the main contributions to the chemical potentials of the free and bound molecular species by identifying and characterizing their main local energy minima. The present study provides systematic benchmarks of the accuracy and computational speed of mining minima, as implemented in the VeraChem Mining Minima Generation 2 (VM2) code, across two well-regarded protein-ligand benchmark data sets, for which there are already benchmark data for docking, free energy, and other computational methods. A core result is that VM2's accuracy approaches that of explicit solvent free energy methods at a far lower computational cost. In finer-grained analyses, we also examine the influence of various run settings, such as the treatment of crystallographic water molecules, on the accuracy, and define the costs in time and dollars of representative runs on Amazon Web Services (AWS) compute instances with various CPU and GPU combinations. We also use the benchmark data to determine the importance of VM2's correction from generalized Born to finite-difference Poisson-Boltzmann results for each energy well and find that this correction affords a remarkably consistent improvement in accuracy at a modest computational cost. The present results establish VM2 as a distinctive technology for early-stage drug discovery, which provides a strong combination of efficiency and predictivity.

Calculation of binding affinity of JAK1 inhibitors via accurately computational estimation

Janus kinase 1 (JAK1) is a tyrosine kinase that is involved in the initiation of responses to a number of different cytokine receptor families. The JAK1-dependent pathway is a therapeutic target, and several JAK inhibitors have been developed thanks to intensive research. However, since the ATP binding sites of JAK family members are quite alike, JAK1 inhibitors can thus be less selective, resulting in unanticipated adverse effects. Despite this, minor variations in the ATP-binding site have been extensively used to find a variety of small compounds with different inhibitory properties. Stronger binding affinity of JAK1 inhibitors is believed to be able to reduce the negative effects, leading to better treatment results. Therefore, a thorough computational search that can effectively identify ligands with extremely high binding affinity for JAK1 to serve as promising inhibitors is required. Here, a method combining steered-molecular dynamic (SMD) simulations with a modified linear interaction energy (LIE) model has been developed to evaluate the binding affinities of known JAK1 inhibitors. The correlation coefficient between the estimated and experimental values was 0.72 and a root-mean-square error was 0.97 kcal•mol-1, revealing that the SMD/LIE method can precisely and quickly predict the binding free energies of JAK1 inhibitors. Furthermore, three marine fungus-derived compounds, namely hansforesters E, hansforesters G and tetroazolemycins B, were identified to be particularly promising JAK1 inhibitors, accordingly. These findings show that the SMD/LIE method has a lot of promise for in silico screening of possible JAK1 inhibitors from a vast number of compounds that are now accessible.Communicated by Ramaswamy H. Sarma.

Searching for potential inhibitors of SARS-COV-2 main protease using supervised learning and perturbation calculations

Inhibiting the biological activity of SARS-CoV-2 Mpro can prevent viral replication. In this context, a hybrid approach using knowledge- and physics-based methods was proposed to characterize potential inhibitors for SARS-CoV-2 Mpro. Initially, supervised machine learning (ML) models were trained to predict a ligand-binding affinity of ca. 2 million compounds with the correlation on a test set of R = 0.748 ± 0.044 . Atomistic simulations were then used to refine the outcome of the ML model. Using LIE/FEP calculations, nine compounds from the top 100 ML inhibitors were suggested to bind well to the protease with the domination of van der Waals interactions. Furthermore, the binding affinity of these compounds is also higher than that of nirmatrelvir, which was recently approved by the US FDA to treat COVID-19. In addition, the ligands altered the catalytic triad Cys145 - His41 - Asp187, possibly disturbing the biological activity of SARS-CoV-2.

Identifying Possible AChE Inhibitors from Drug-like Molecules via Machine Learning and Experimental Studies

Acetylcholinesterase (AChE) is one of the most important drug targets for Alzheimer's disease (AD) treatment. In this work, a machine learning model was trained to rapidly and accurately screen large chemical databases for the potential inhibitors of AChE. The obtained results were then validated via in vitro enzyme assay. Moreover, atomistic simulations including molecular docking and molecular dynamics simulations were then used to understand molecular insights into the binding process of ligands to AChE. In particular, two compounds including benzyl trifluoromethyl ketone and trifluoromethylstyryl ketone were indicated as highly potent inhibitors of AChE because they established IC₅₀ values of 0.51 and 0.33 μM, respectively. The obtained IC₅₀ of two compounds is significantly lower than that of galantamine (2.10 μM). The predicted log(BB) suggests that the compounds may be able to traverse the blood-brain barrier. A good agreement between computational and experimental studies was observed, indicating that the hybrid approach can enhance AD therapy.

Scoring Functions for Protein-Ligand Binding Affinity Prediction using Structure-Based Deep Learning: A Review

The rapid and accurate in silico prediction of protein-ligand binding free energies or binding affinities has the potential to transform drug discovery. In recent years, there has been a rapid growth of interest in deep learning methods for the prediction of protein-ligand binding affinities based on the structural information of protein-ligand complexes. These structure-based scoring functions often obtain better results than classical scoring functions when applied within their applicability domain. Here we review structure-based scoring functions for binding affinity prediction based on deep learning, focussing on different types of architectures, featurization strategies, data sets, methods for training and evaluation, and the role of explainable artificial intelligence in building useful models for real drug-discovery applications.

FFENCODER-PL: Pair Wise Energy Descriptors for Protein-Ligand Pose Selection

Scoring functions are the essential component in molecular docking methods. An accurate scoring function is expected to distinguish the native ligand pose from decoy poses. Our previous experience (Pei et al. J. Chem. Inf. Model. 2019, 59 (7), 3305-3315) proved that combining the random forest (RF) algorithm with knowledge-based potential functions can emphasize germane pair wise interactions and improve the performance of original knowledge-based potential functions on protein-ligand decoy detection. One of the most important potential function classes is the force field (FF) potential with one example being the Amber collection of FFs, which are widely available in the AMBER suite of simulation programs. However, for use in RF modeling studies, one needs pair wise energies that are hard to directly extract from Amber. To address this issue, FFENCODER-PL was constructed to calculate the pair wise energies based on the FF14SB and GAFF2 FFs in Amber. FFENCODER-PL was validated using 275 ligand and 21 protein-ligand structures. RF models were built by combining an RF classification algorithm with the pair wise energies calculated from FFENCODER-PL. CASF-2016 (Su et al. J. Chem. Inf. Model. 2019, 59, 895-913) was employed to test the performance of the resultant RF models, which outperformed 33 scoring functions on accuracy and native ranking tests. For the best decoy RMSD test, RF models give a best decoy with an RMSD of around 2 Å from the native pose after including the best decoy-decoy comparisons in the RF model. The relative importance of the RF algorithm and force field potentials was also tested with the results suggesting that both the RF algorithm and force field potentials are important and combining them is the only way to achieve high accuracy. Finally, FFENCODER-PL makes force field-based pair wise energies available for further development of machine learning-based scoring functions. The codes and data used in this paper can be found at https://github.com/JunPei000/Amber_protein_ligand_encoding.

Computational Graphics Software for Interactive Docking and Visualization of Ligand-Protein Complementarity

The Dockeye software is designed to complement automated docking protocols by allowing the user's chemical know-how and experience of what makes for good protein-ligand binding, knowledge that is not easily encoded into automated algorithms, to guide the docking. It allows the interactive manipulation of the ligand placement against a protein target. Real-time intuitively comprehensible feedback about the location, spatial density, and the extent of both favorable and unfavorable atomic interactions between ligand and protein is provided through a carefully designed graphical object. It is also a tool for the graphical analysis of the interactions of known protein-ligand complexes. Comparative docking of 58 protein-ligand complexes with Dockeye and Autodock Vina shows how this software can be used synergistically with automated docking programs to significantly improve the task of discovery of ligand placement.

Computational Determination of Potential Inhibitors of SARS-CoV-2 Main Protease

The novel coronavirus (SARS-CoV-2) has infected several million people and caused thousands of deaths worldwide since December 2019. As the disease is spreading rapidly all over the world, it is urgent to find effective drugs to treat the virus. The main protease (Mpro) of SARS-CoV-2 is one of the potential drug targets. Therefore, in this context, we used rigorous computational methods, including molecular docking, fast pulling of ligand (FPL), and free energy perturbation (FEP), to investigate potential inhibitors of SARS-CoV-2 Mpro. We first tested our approach with three reported inhibitors of SARS-CoV-2 Mpro, and our computational results are in good agreement with the respective experimental data. Subsequently, we applied our approach on a database of ∼4600 natural compounds, as well as 8 available HIV-1 protease (PR) inhibitors and an aza-peptide epoxide. Molecular docking resulted in a short list of 35 natural compounds, which was subsequently refined using the FPL scheme. FPL simulations resulted in five potential inhibitors, including three natural compounds and two available HIV-1 PR inhibitors. Finally, FEP, the most accurate and precise method, was used to determine the absolute binding free energy of these five compounds. FEP results indicate that two natural compounds, cannabisin A and isoacteoside, and an HIV-1 PR inhibitor, darunavir, exhibit a large binding free energy to SARS-CoV-2 Mpro, which is larger than that of 13b, the most reliable SARS-CoV-2 Mpro inhibitor recently reported. The binding free energy largely arises from van der Waals interaction. We also found that Glu166 forms H-bonds to all of the inhibitors. Replacing Glu166 by an alanine residue leads to ∼2.0 kcal/mol decreases in the affinity of darunavir to SARS-CoV-2 Mpro. Our results could contribute to the development of potential drugs inhibiting SARS-CoV-2.

Effective estimation of the inhibitor affinity of HIV-1 protease via a modified LIE approach

The inhibition of the Human Immunodeficiency Virus Type 1 Protease (HIV-1 PR) can prevent the synthesis of new viruses. Computer-aided drug design (CADD) would enhance the discovery of new therapies, through which the estimation of ligand-binding affinity is critical to predict the most efficient inhibitor. A time-consuming binding free energy method would reduce the usefulness of CADD. The modified linear interaction energy (LIE) approach emerges as an appropriate protocol that performs this task. In particular, the polar interaction free energy, which is obtained via numerically resolving the linear Poisson-Boltzmann equation, plays as an important role in driving the binding mechanism of the HIV-1 PR + inhibitor complex. The electrostatic interaction energy contributes to the attraction between two molecules, but the vdW interaction acts as a repulsive factor between the ligand and the HIV-1 PR. Moreover, the ligands were found to adopt a very strong hydrophobic interaction with the HIV-1 PR. Furthermore, the results obtained corroborate the high accuracy and precision of computational studies with a large correlation coefficient value R = 0.83 and a small RMSE δ RMSE = 1.25 kcal mol-1. This method is less time-consuming than the other end-point methods, such as the molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) and free energy perturbation (FEP) approaches. Overall, the modified LIE approach would provide ligand-binding affinity with HIV-1 PR accurately, precisely, and rapidly, resulting in a more efficient design of new inhibitors.

Adequate prediction for inhibitor affinity of Aβ40 protofibril using the linear interaction energy method

The search for efficient inhibitors targeting Aβ oligomers and fibrils is an important issue in Alzheimer's disease treatment. As a consequence, an accurate and computationally cheap approach to estimate the binding affinity for many ligands interacting with Aβ peptides is very important. Here, the calculated binding free energies of 30 ligands interacting with 12Aβ11-40 peptides using the linear interaction energy (LIE) approach are found to be in good correlation with experimental data (R = 0.79). The binding affinities of these complexes are also calculated by using free energy perturbation (FEP) and molecular mechanic/Poisson-Boltzmann surface area (MM/PBSA) methods. The time-consuming FEP method provides results with similar correlation (R = 0.72), whereas MM/PBSA calculations show very low correlation with experimental data (R = 0.27). In all complexes, van der Waals interactions contribute much more than electrostatic interactions. The LIE model, which is much less time-consuming than both the FEP and MM/PBSA methods, opens the door to accurate and rapid affinity prediction of ligands with Aβ peptides and the design of new ligands.

Effective Estimation of Ligand-Binding Affinity Using Biased Sampling Method

The binding between two biomolecules is one of the most critical factors controlling many bioprocesses. Therefore, it is of great interest to derive a reliable method to calculate the free binding energy between two biomolecules. In this work, we have demonstrated that the binding affinity of ligands to proteins can be determined through biased sampling simulations. The umbrella sampling (US) method was applied on 20 protein-ligand complexes, including the cathepsin K (CTSK), type II dehydroquinase (DHQase), heat shock protein 90 (HSP90), and factor Xa (FXa) systems. The ligand-binding affinity was evaluated as the difference between the largest and smallest values of the free-energy curve, which was obtained via a potential of mean force analysis. The calculated affinities differ sizably from the previously reported experimental values, with an average difference of ∼3.14 kcal/mol. However, the calculated results are in good correlation with the experimental data, with correlation coefficients of 0.76, 0.87, 0.96, and 0.97 for CTSK, DHQase, HSP90, and FXa, respectively. Thus, the binding free energy of a new ligand can be reliably estimated using our US approach. Furthermore, the root-mean-square errors (RMSEs) of binding affinity of these systems are 1.13, 0.90, 0.37, and 0.25 kcal/mol, for CTSK, DHQase, HSP90, and FXa, respectively. The small RMSE values indicate the good precision of the biased sampling method that can distinguish the ligands exhibiting similar binding affinities.

Ensemble- and Rigidity Theory-Based Perturbation Approach To Analyze Dynamic Allostery

Allostery describes the functional coupling between sites in biomolecules. Recently, the role of changes in protein dynamics for allosteric communication has been highlighted. A quantitative and predictive description of allostery is fundamental for understanding biological processes. Here, we integrate an ensemble-based perturbation approach with the analysis of biomolecular rigidity and flexibility to construct a model of dynamic allostery. Our model, by definition, excludes the possibility of conformational changes, evaluates static, not dynamic, properties of molecular systems, and describes allosteric effects due to ligand binding in terms of a novel free-energy measure. We validated our model on three distinct biomolecular systems: eglin c, protein tyrosine phosphatase 1B, and the lymphocyte function-associated antigen 1 domain. In all cases, it successfully identified key residues for signal transmission in very good agreement with the experiment. It correctly and quantitatively discriminated between positively or negatively cooperative effects for one of the systems. Our model should be a promising tool for the rational discovery of novel allosteric drugs.

Modeling the functional state of the reverse transcriptase of hepatitis B virus and its application to probing drug-protein interaction

BACKGROUND

Herein, the predicted atomic structures of five representative sequence variants of the reverse transcriptase protein (RT) of hepatitis B virus (HBV), sampled from patients with rapid or slow response to tenofovir disoproxil fumarate (TDF) treatment, have been examined to identify structural variations between them in order to assess structural and functional properties of HBV-RT variants associated with the differential responses to TDF treatment.

RESULTS

We utilized a hybrid computational approach to model the atomistic structures of HBV-RT/DNA-RNA/dATP and HBV-RT/DNA-RNA/TFV-DP (tenofovir diphosphate) complexes with the native hybrid DNA-RNA substrate in place. Multi-nanosecond molecular dynamics (MD) simulations of HBV-RT/DNA-RNA/dATP complexes revealed strong coupling of the natural nucleotide substrate, dATP, to the active site of the RT, and the differential involvement of the two putative magnesium cations (Mg(2+)) at the active site, whereby one Mg(2+) directly bridges the interaction between dATP and HBV-RT and the other serves as a coordinator to maintain an optimal configuration of the active site. Solvated interaction energy (SIE) calculated in MD simulations of HBV-RT/DNA-RNA/TFV-DP complexes indicate no differential binding affinity between TFV-DP and HBV-RT variants identified in patients with slow or rapid response to TDF treatment.

CONCLUSION

The predicted atomic structures accurately represent functional states of HBV-RT. The equivalent interaction between TFV-DP and each examined HBV-RT variants suggests that binding affinity of TFV-DP to HBV-RT is not associated with delayed viral clearance.

A large-scale test of free-energy simulation estimates of protein-ligand binding affinities

We have performed a large-scale test of alchemical perturbation calculations with the Bennett acceptance-ratio (BAR) approach to estimate relative affinities for the binding of 107 ligands to 10 different proteins. Employing 20-Å truncated spherical systems and only one intermediate state in the perturbations, we obtain an error of less than 4 kJ/mol for 54% of the studied relative affinities and a precision of 0.5 kJ/mol on average. However, only four of the proteins gave acceptable errors, correlations, and rankings. The results could be improved by using nine intermediate states in the simulations or including the entire protein in the simulations using periodic boundary conditions. However, 27 of the calculated affinities still gave errors of more than 4 kJ/mol, and for three of the proteins the results were not satisfactory. This shows that the performance of BAR calculations depends on the target protein and that several transformations gave poor results owing to limitations in the molecular-mechanics force field or the restricted sampling possible within a reasonable simulation time. Still, the BAR results are better than docking calculations for most of the proteins.

A conformational factorisation approach for estimating the binding free energies of macromolecules

We present a conformational factorization approach. The theory is based on a superposition partition function, decomposed as a sum over contributions from local minima. The factorisation greatly reduces the number of minima that need to be considered, by employing the same local configurations for groups that are sufficiently distant from the binding site. The theory formalises the conditions required to analyse how our definition of the binding site region affects the free energy difference between the apo and holo states. We employ basin-hopping parallel tempering to sample minima that contribute significantly to the partition function, and calculate the binding free energies within the harmonic normal mode approximation. A further significant gain in efficiency is achieved using a recently developed local rigid body framework in both the sampling and the normal mode analysis, which reduces the number of degrees of freedom. We benchmark this approach for human aldose reductase (PDB code 2INE). When varying the size of the rigid region, the free energy difference converges for factorisation of groups at a distance of 14 Å from the binding site, which corresponds to 80% of the protein being locally rigidified.

Implicit solvent methods for free energy estimation

Solvation is a fundamental contribution in many biological processes and especially in molecular binding. Its estimation can be performed by means of several computational approaches. The aim of this review is to give an overview of existing theories and methods to estimate solvent effects giving a specific focus on the category of implicit solvent models and their use in Molecular Dynamics. In many of these models, the solvent is considered as a continuum homogenous medium, while the solute can be represented at the atomic detail and at different levels of theory. Despite their degree of approximation, implicit methods are still widely employed due to their trade-off between accuracy and efficiency. Their derivation is rooted in the statistical mechanics and integral equations disciplines, some of the related details being provided here. Finally, methods that combine implicit solvent models and molecular dynamics simulation, are briefly described.

Ligand-receptor affinities computed by an adapted linear interaction model for continuum electrostatics and by protein conformational averaging

Accurate calculations of free energies involved in small-molecule binding to a receptor are challenging. Interactions between ligand, receptor, and solvent molecules have to be described precisely, and a large number of conformational microstates has to be sampled, particularly for ligand binding to a flexible protein. Linear interaction energy models are computationally efficient methods that have found considerable success in the prediction of binding free energies. Here, we parametrize a linear interaction model for implicit solvation with coefficients adapted by ligand and binding site relative polarities in order to predict ligand binding free energies. Results obtained for a diverse series of ligands suggest that the model has good predictive power and transferability. We also apply implicit ligand theory and propose approximations to average contributions of multiple ligand-receptor poses built from a protein conformational ensemble and find that exponential averages require proper energy discrimination between plausible binding poses and false-positives (i.e., decoys). The linear interaction model and the averaging procedures presented can be applied independently of each other and of the method used to obtain the receptor structural representation.

Molecular recognition of metabotropic glutamate receptor type 1 (mGluR1): synergistic understanding with free energy perturbation and linear response modeling

Metabotropic glutamate receptors (mGluRs) constitute an important family of the G-protein coupled receptors. Due to their widespread distribution in the central nervous system (CNS), these receptors are attractive candidates for understanding the molecular basis of various cognitive processes as well as for designing inhibitors for relevant psychiatric and neurological disorders. Despite many studies on drugs targeting the mGluR receptors to date, the molecular level details on the ligand binding dynamics still remain unclear. In this study, we performed in silico experiments for mGluR1 with 29 different ligands including known synthetic agonists and antagonists as well as natural amino acids. The ligand-receptor binding affinities were estimated by the use of atomistic simulations combined with the mathematically rigorous, Free Energy Perturbation (FEP) method, which successfully recognized the native agonist l-glutamate among the highly favorable binders, and also accurately distinguished antagonists from agonists. Comparative contact analysis also revealed the binding mode differences between natural and non-natural amino acid-based ligands. Several factors potentially affecting the ligand binding affinity and specificity were identified including net charges, dipole moments, and the presence of aromatic rings. On the basis of these findings, linear response models (LRMs) were built for different sets of ligands that showed high correlations (R(2) > 0.95) to the corresponding FEP binding affinities. These results identify some key factors that determine ligand-mGluR1 binding and could be used for future inhibitor designs and support a role for in silico modeling for understanding receptor ligand interactions.

Accessing the applicability of polarized protein-specific charge in linear interaction energy analysis

The reliability of the linear interaction energy (LIE) depends on the atomic charge model used to delineate the Coulomb interaction between the ligand and its environment. In this work, the polarized protein-specific charge (PPC) implementing a recently proposed fitting scheme has been examined in the LIE calculations of the binding affinities for avidin and β-secretase binding complexes. This charge fitting scheme, termed delta restrained electrostatic potential, bypasses the prevalent numerical difficulty of rank deficiency in electrostatic-potential-based charge fitting methods via a dual-step fitting strategy. A remarkable consistency between the predicted binding affinities and the experimental measurement has been observed. This work serves as a direct evidence of PPC's applicability in rational drug design.

The Movable Type Method Applied to Protein-Ligand Binding

Accurately computing the free energy for biological processes like protein folding or protein-ligand association remains a challenging problem. Both describing the complex intermolecular forces involved and sampling the requisite configuration space make understanding these processes innately difficult. Herein, we address the sampling problem using a novel methodology we term "movable type". Conceptually it can be understood by analogy with the evolution of printing and, hence, the name movable type. For example, a common approach to the study of protein-ligand complexation involves taking a database of intact drug-like molecules and exhaustively docking them into a binding pocket. This is reminiscent of early woodblock printing where each page had to be laboriously created prior to printing a book. However, printing evolved to an approach where a database of symbols (letters, numerals, etc.) was created and then assembled using a movable type system, which allowed for the creation of all possible combinations of symbols on a given page, thereby, revolutionizing the dissemination of knowledge. Our movable type (MT) method involves the identification of all atom pairs seen in protein-ligand complexes and then creating two databases: one with their associated pairwise distant dependent energies and another associated with the probability of how these pairs can combine in terms of bonds, angles, dihedrals and non-bonded interactions. Combining these two databases coupled with the principles of statistical mechanics allows us to accurately estimate binding free energies as well as the pose of a ligand in a receptor. This method, by its mathematical construction, samples all of configuration space of a selected region (the protein active site here) in one shot without resorting to brute force sampling schemes involving Monte Carlo, genetic algorithms or molecular dynamics simulations making the methodology extremely efficient. Importantly, this method explores the free energy surface eliminating the need to estimate the enthalpy and entropy components individually. Finally, low free energy structures can be obtained via a free energy minimization procedure yielding all low free energy poses on a given free energy surface. Besides revolutionizing the protein-ligand docking and scoring problem this approach can be utilized in a wide range of applications in computational biology which involve the computation of free energies for systems with extensive phase spaces including protein folding, protein-protein docking and protein design.

Flooding enzymes: quantifying the contributions of interstitial water and cavity shape to ligand binding using extended linear response free energy calculations

Glutamate racemase (GR) is a cofactor independent amino acid racemase that has recently garnered increasing attention as an antimicrobial drug target. There are numerous high resolution crystal structures of GR, yet these are invariably bound to either d-glutamate or very weakly bound oxygen-based salts. Recent in silico screens have identified a number of new competitive inhibitor scaffolds, which are not based on d-Glu, but exploit many of the same hydrogen bond donor positions. In silico studies on 1-H-benzimidazole-2-sulfonic acid (BISA) show that the sulfonic acid points to the back of the GR active site, in the most buried region, analogous to the C2-carboxylate binding position in the GR-d-glutamate complex. Furthermore, BISA has been shown to be the strongest nonamino acid competitive inhibitor. Previously published computational studies have suggested that a portion of this binding strength is derived from complexation with a more closed active site, relative to weaker ligands, and in which the internal water network is more isolated from the bulk solvent. In order to validate key contacts between the buried sulfonate moiety of BISA and moieties in the back of the enzyme active site, as well as to probe the energetic importance of the potentially large number of interstitial waters contacted by the BISA scaffold, we have designed several mutants of Asn75. GR-N75A removes a key hydrogen bond donor to the sulfonate of BISA, but also serves to introduce an additional interstitial water, due to the newly created space of the mutation. GR- N75L should also show the loss of a hydrogen bond donor to the sulfonate of BISA, but does not (a priori) seem to permit an additional interstitial water contact. In order to investigate the dynamics, structure, and energies of this water-mediated complexation, we have employed the extended linear response (ELR) approach for the calculation of binding free energies to GR, using the YASARA2 knowledge based force field on a set of ten GR complexes, and yielding an R-squared value of 0.85 and a RMSE of 2.0 kJ/mol. Surprisingly, the inhibitor set produces a uniformly large interstitial water contribution to the electrostatic interaction energy (⟨V^el⟩), ranging from 30 to >50%, except for the natural substrate (d-glutamate), which has only a 7% contribution of ⟨V^el⟩ from water. The broader implications for predicting and exploiting significant interstitial water contacts in ligand–enzyme complexation are discussed.

Funnel metadynamics as accurate binding free-energy method

A detailed description of the events ruling ligand/protein interaction and an accurate estimation of the drug affinity to its target is of great help in speeding drug discovery strategies. We have developed a metadynamics-based approach, named funnel metadynamics, that allows the ligand to enhance the sampling of the target binding sites and its solvated states. This method leads to an efficient characterization of the binding free-energy surface and an accurate calculation of the absolute protein-ligand binding free energy. We illustrate our protocol in two systems, benzamidine/trypsin and SC-558/cyclooxygenase 2. In both cases, the X-ray conformation has been found as the lowest free-energy pose, and the computed protein-ligand binding free energy in good agreement with experiments. Furthermore, funnel metadynamics unveils important information about the binding process, such as the presence of alternative binding modes and the role of waters. The results achieved at an affordable computational cost make funnel metadynamics a valuable method for drug discovery and for dealing with a variety of problems in chemistry, physics, and material science.

Molecular docking methodologies

Molecular docking represents an important technology for structure-based drug design. Docking is a computational technique aimed at the prediction of the most favorable ligand-target spatial configuration and an estimate of the corresponding complex free energy, although as stated at the beginning accurate scoring methods remain still elusive. In this chapter, the state of art of molecular docking methodologies and their applications in drug discovery is summarized.

Computational design of a Diels-Alderase from a thermophilic esterase: the importance of dynamics

A novel computational Diels-Alderase design, based on a relatively rare form of carboxylesterase from Geobacillus stearothermophilus, is presented and theoretically evaluated. The structure was found by mining the PDB for a suitable oxyanion hole-containing structure, followed by a combinatorial approach to find suitable substrates and rational mutations. Four lead designs were selected and thoroughly modeled to obtain realistic estimates of substrate binding and prearrangement. Molecular dynamics simulations and DFT calculations were used to optimize and estimate binding affinity and activation energies. A large quantum chemical model was used to capture the salient interactions in the crucial transition state (TS). Our quantitative estimation of kinetic parameters was validated against four experimentally characterized Diels-Alderases with good results. The final designs in this work are predicted to have rate enhancements of ≈ 10(3)-10(6) and high predicted proficiencies. This work emphasizes the importance of considering protein dynamics in the design approach, and provides a quantitative estimate of the how the TS stabilization observed in most de novo and redesigned enzymes is decreased compared to a minimal, 'ideal' model. The presented design is highly interesting for further optimization and applications since it is based on a thermophilic enzyme (T (opt) = 70 °C).

Binding affinity prediction for ligands and receptors forming tautomers and ionization species: inhibition of mitogen-activated protein kinase-activated protein kinase 2 (MK2)

Treatment of ionization and tautomerism of ligands and receptors is one of the unresolved issues in structure-based prediction of binding affinities. Our solution utilizes the thermodynamic master equation, expressing the experimentally observed association constant as the sum of products, each valid for a specific ligand–receptor species pair, consisting of the association microconstant and the fractions of the involved ligand and receptor species. The microconstants are characterized by structure-based simulations, which are run for individual species pairs. Here we incorporated the multispecies approach into the QM/MM linear response method and used it for structural correlation of published inhibition data on mitogen-activated protein kinase (MAPK)-activated protein kinase (MK2) by 66 benzothiophene and pyrrolopyridine analogues, forming up to five tautomers and seven ionization species under experimental conditions. Extensive cross-validation showed that the resulting models were stable and predictive. Inclusion of all tautomers and ionization ligand species was essential: the explained variance increased to 90% from 66% for the single-species model.

DESIGN OF HYBRID INHIBITORS TO HIV-1 PROTEASE

A series of HIV-1 protease (PR) inhibitors are designed to increase the binding affinity with PR subsites based on the quantum analysis of the contributions of molecular fragments in six FDA-approved PR drugs to the total binding interaction. The binding free energies were estimated by modified linear interaction energy approach [Zoete H, Michielin O, Karplus M, J Comput Aided Mol Des17:861, 2003], in which the binding free energy is written as a linear combination of the electrostatic interaction energy between PR and the ligand, Eelec, the van der Waals interaction energy between PR and the ligand, E vdW , and the difference of the solvation free energies of the complex, the receptor and the isolated ligand, ΔG solv . The parameters of these energy terms were fitted for a training set of 14 HIV-1 protease–inhibitor complexes of known 3D structure with a correlation coefficient of 0.91 and an unsigned mean error of 0.83 kcal/mol.

QUANTUM CALCULATION OF PROTEIN SOLVATION AND PROTEIN–LIGAND BINDING FREE ENERGY FOR HIV-1 PROTEASE/WATER COMPLEX

HIV-1 protease (PR) is a primary target for anti-HIV therapeutics. A well conserved water molecule, denoted as W301, is found in almost all the crystallographic structures of PR/inhibitor complexes and it plays an important role in PR/inhibitor binding. As the PR/inhibitor interaction depends on the ionization state of the cleavage site which contains an aspartyl dyad (Asp25/Asp25′), the determination of the protonation states of aspartyl dyad in PR may be essential for drug design. In this study, a linear scaling quantum mechanical method, molecular fragmentation with conjugate caps (MFCC), is used for interaction study of PR/ABT-538 and W301 at four different monoprotonation states of the Asp25/Asp25′. Combined method of MFCC and conductor-like polarizable continuum model (CPCM) is applied in binding affinity calculation for four minimum energy structures which are extracted from four different molecular dynamics trajectories corresponding to four different monoprotonation states of Asp25/Asp25′. Our result is in good agreement with previous result obtained by FEP/TI method, showing that the conserved W301 contributes significantly to the binding free energy of PR/ABT-538 complex and different protonation states of Asp25/Asp25′ have significant impact on the binding free energy contribution from W301.

COMPUTATIONAL STUDY FOR BINDING OF OSCILLARIN TO HUMAN α-THROMBIN

Quantum mechanical calculation and molecular dynamics simulation have been carried out to study binding of Oscillarin (OSC), an antithrombotic marine natural product to human α-thrombin. The binding interaction energies between the inhibitor and individual protein fragments are calculated using a combination of HF and DFT methods. Study shows that the strong binding of OSC to Asp189, Ser214, Trp215, Gly216, and Gly219 is the primary mechanism of drug binding to thrombin. The individual residue–ligand interaction energies provide detailed quantitative information about specific residue interaction with the ligand that should be extremely useful to our understanding of the molecular nature of protein–ligand binding.

Probing the structural and energetic basis of kinesin-microtubule binding using computational alanine-scanning mutagenesis

Kinesin-microtubule (MT) binding plays a critical role in facilitating and regulating the motor function of kinesins. To obtain a detailed structural and energetic picture of kinesin-MT binding, we performed large-scale computational alanine-scanning mutagenesis based on long-time molecular dynamics (MD) simulations of the kinesin-MT complex in both ADP and ATP states. First, we built three all-atom kinesin-MT models: human conventional kinesin bound to ADP and mouse KIF1A bound to ADP and ATP. Then, we performed 30 ns MD simulations followed by kinesin-MT binding free energy calculations for both the wild type and mutants obtained after substitution of each charged residue of kinesin with alanine. We found that the kinesin-MT binding free energy is dominated by van der Waals interactions and further enhanced by electrostatic interactions. The calculated mutational changes in kinesin-MT binding free energy are in excellent agreement with results of an experimental alanine-scanning study with a root-mean-square error of ~0.32 kcal/mol [Woehlke, G., et al. (1997) Cell 90, 207-216]. We identified a set of important charged residues involved in the tuning of kinesin-MT binding, which are clustered on several secondary structural elements of kinesin (including well-studied loops L7, L8, L11, and L12, helices α4, α5, and α6, and less-explored loop L2). In particular, we found several key residues that make different contributions to kinesin-MT binding in ADP and ATP states. The mutations of these residues are predicted to fine-tune the motility of kinesin by modulating the conformational transition between the ADP state and the ATP state of kinesin.

Physics-based scoring of protein–ligand interactions: explicit polarizability, quantum mechanics and free energies

The ability to accurately predict the interaction of a ligand with its receptor is a key limitation in computer-aided drug design approaches such as virtual screening and de novo design. In this article, we examine current strategies for a physics-based approach to scoring of protein–ligand affinity, as well as outlining recent developments in force fields and quantum chemical techniques. We also consider advances in the development and application of simulation-based free energy methods to study protein–ligand interactions. Fuelled by recent advances in computational algorithms and hardware, there is the opportunity for increased integration of physics-based scoring approaches at earlier stages in computationally guided drug discovery. Specifically, we envisage increased use of implicit solvent models and simulation-based scoring methods as tools for computing the affinities of large virtual ligand libraries. Approaches based on end point simulations and reference potentials allow the application of more advanced potential energy functions to prediction of protein–ligand binding affinities. Comprehensive evaluation of polarizable force fields and quantum mechanical (QM)/molecular mechanical and QM methods in scoring of protein–ligand interactions is required, particularly in their ability to address challenging targets such as metalloproteins and other proteins that make highly polar interactions. Finally, we anticipate increasingly quantitative free energy perturbation and thermodynamic integration methods that are practical for optimization of hits obtained from screened ligand libraries