Ab initio molecular dynamics-based assignment of the protonation state of pepstatin A/HIV-1 protease cleavage site

A review on description dynamics and conformational changes of proteins using combination of principal component analysis and molecular dynamics simulation

Understanding how proteins behave dynamically and undergo conformational changes is essential to comprehending their biological roles. This review article examines the potent tool of using Molecular Dynamics simulations in conjunction with Principal Component Analysis (PCA) to explore protein dynamics. Molecular dynamics data can be made easier to read by removing prominent patterns through the use of PCA, a sophisticated dimensionality reduction approach. Researchers can obtain critical insights into the fundamental principles governing protein function by using PCA on MD simulation data. We provide a systematic approach to PCA that includes data collection, input coordinate selection, and result interpretation. Protein collective movements and fundamental dynamics are made visible by PCA, which makes it possible to identify conformational substates that are crucial to function. By means of principal component analysis, scientists are able to observe and measure large-scale movements, like hinge bending and domain motions, as well as pinpoint areas of protein structural stiffness and flexibility. Moreover, PCA allows temporal separation, distinguishing slower global motions from faster local changes. A strong foundation for researching protein dynamics is provided by the combination of PCA and Molecular Dynamics simulations, which have applications in drug development and enhance our comprehension of intricate biological systems.

1H/17O Chemical Shift Waves in Carboxyl-Bridged Hydrogen Bond Networks in Organic Solids

We report solid-state 1H and 17O NMR results for four 17O-labeled organic compounds each containing an extensive carboxyl-bridged hydrogen bond (CBHB) network in the crystal lattice: tetrabutylammonium hydrogen di-[17O2]salicylate (1), [17O4]quinolinic acid (2), [17O4]dinicotinic acid (3), and [17O2]Gly/[17O2]Gly·HCl cocrystal (4). The 1H isotropic chemical shifts found for protons involved in different CBHB networks are between 8.2 and 20.5 ppm, which reflect very different hydrogen-bonding environments. Similarly, the 17O isotropic chemical shifts found for the carboxylate oxygen atoms in CBHB networks, spanning a large range between 166 and 341 ppm, are also remarkably sensitive to the hydrogen-bonding environments. We introduced a simple graphical representation in which 1H and 17O chemical shifts are displayed along the H and O atomic chains that form the CBHB network. In such a depiction, because wavy patterns are often observed, we refer to these wavy patterns as 1H/17O chemical shift waves. Typical patterns of 1H/17O chemical shift waves in CBHB networks are discussed. The reported 1H and 17O NMR parameters for the CBHB network models examined in this study can serve as benchmarks to aid in spectral interpretation for CBHB networks in proteins.

Computational insights for predicting the binding and selectivity of peptidomimetic plasmepsin IV inhibitors against cathepsin D

Plasmepsins (Plms) are aspartic proteases involved in the degradation of human hemoglobin by P. falciparum and are essential for the survival and growth of the parasite. Therefore, Plm enzymes are reported as an important antimalarial drug target. Herein, we have applied molecular docking, molecular dynamics (MD) simulations, and binding free energy with the Linear Interaction Energy (LIE) approach to investigate the binding of peptidomimetic PlmIV inhibitors with a particular focus on understanding their selectivity against the human Asp protease cathepsin D (CatD). The residual decomposition analysis results suggest that amino acid differences in the subsite S3 of PlmIV and CatD are responsible for the higher selectivity of the 5a inhibitor. These findings yield excellent agreement with experimental binding data and provide new details regarding van der Waals and electrostatic interactions of subsite residues as well as structural properties of the PlmIV and CatD systems.

Hybrid QM/MM Free-Energy Evaluation of Drug-Resistant Mutational Effect on the Binding of an Inhibitor Indinavir to HIV-1 Protease

A human immunodeficiency virus-1 (HIV-1) protease is a homodimeric aspartic protease essential for the replication of HIV. The HIV-1 protease is a target protein in drug discovery for antiretroviral therapy, and various inhibitor molecules of transition state analogues have been developed. However, serious drug-resistant mutants have emerged. For understanding the molecular mechanism of the drug resistance, an accurate examination of the impacts of the mutations on ligand binding and enzymatic activity is necessary. Here, we present a molecular simulation study on the ligand binding of indinavir, a potent transition state analogue inhibitor, to the wild-type protein and a V82T/I84V drug-resistant mutant of the HIV-1 protease. We employed a hybrid ab initio quantum mechanical/molecular mechanical (QM/MM) free-energy optimization technique which combines a highly accurate QM description of the ligand molecule and its interaction with statistically ample conformational sampling of the MM protein environment by long-time molecular dynamics simulations. Through the free-energy calculations of protonation states of catalytic groups at the binding pocket and of the ligand-binding affinity changes upon the mutations, we successfully reproduced the experimentally observed significant reduction of the binding affinity upon the drug-resistant mutations and elucidated the underlying molecular mechanism. The present study opens the way for understanding the molecular mechanism of drug resistance through the direct quantitative comparison of ligand binding and enzymatic reaction with the same accuracy.

A Review on Combination of Ab Initio Molecular Dynamics and NMR Parameters Calculations

This review focuses on a combination of ab initio molecular dynamics (aiMD) and NMR parameters calculations using quantum mechanical methods. The advantages of such an approach in comparison to the commonly applied computations for the structures optimized at 0 K are presented. This article was designed as a convenient overview of the applied parameters such as the aiMD type, DFT functional, time step, or total simulation time, as well as examples of previously studied systems. From the analysis of the published works describing the applications of such combinations, it was concluded that including fast, small-amplitude motions through aiMD has a noticeable effect on the accuracy of NMR parameters calculations.

Deciphering Complex Mechanisms of Resistance and Loss of Potency through Coupled Molecular Dynamics and Machine Learning

Drug resistance threatens many critical therapeutics through mutations in the drug target. The molecular mechanisms by which combinations of mutations, especially those remote from the active site, alter drug binding to confer resistance are poorly understood and thus difficult to counteract. A machine learning strategy was developed that coupled parallel molecular dynamics simulations with experimental potency to identify specific conserved mechanisms underlying resistance. Physical features were extracted from the simulations, analyzed, and integrated into one consistent and interpretable elastic network model. To rigorously test this strategy, HIV-1 protease variants with diverse mutations were used, with potencies ranging from picomolar to micromolar to the drug darunavir. Feature reduction resulted in a model with four specific features that predicts for both the training and test sets inhibitor binding free energy within 1 kcal/mol of the experimental value over this entire range of potency. These predictive features are physically interpretable, as they vary specifically with affinity and diagonally transverse across the protease homodimer. This physics-based strategy of parallel molecular dynamics and machine learning captures mechanisms by which complex combinations of mutations confer resistance and identify critical features that serve as bellwethers of affinity, which will be critical in future drug design.

Machine learning-accelerated quantum mechanics-based atomistic simulations for industrial applications

Atomistic simulations have become an invaluable tool for industrial applications ranging from the optimization of protein-ligand interactions for drug discovery to the design of new materials for energy applications. Here we review recent advances in the use of machine learning (ML) methods for accelerated simulations based on a quantum mechanical (QM) description of the system. We show how recent progress in ML methods has dramatically extended the applicability range of conventional QM-based simulations, allowing to calculate industrially relevant properties with enhanced accuracy, at reduced computational cost, and for length and time scales that would have otherwise not been accessible. We illustrate the benefits of ML-accelerated atomistic simulations for industrial R&D processes by showcasing relevant applications from two very different areas, drug discovery (pharmaceuticals) and energy materials. Writing from the perspective of both a molecular and a materials modeling scientist, this review aims to provide a unified picture of the impact of ML-accelerated atomistic simulations on the pharmaceutical, chemical, and materials industries and gives an outlook on the exciting opportunities that could emerge in the future.

First-Principles Calculation of 1H NMR Chemical Shifts of Complex Metal Polyhydrides: The Essential Inclusion of Relativity and Dynamics

1H NMR spectroscopy has become an important technique for the characterization of transition-metal hydride complexes, whose metal-bound hydrides are often difficult to locate by X-ray diffraction. In this regard, the accurate prediction of 1H NMR chemical shifts provides a useful, but challenging, strategy to help in the interpretation of the experimental spectra. In this work, we establish a density-functional-theory protocol that includes relativistic, solvent, and dynamic effects at a high level of theory, allowing us to report an accurate and reliable interpretation of 1H NMR hydride chemical shifts of iridium polyhydride complexes. In particular, we have studied in detail the hydride chemical shifts of the [Ir6(IMe)8(CO)2H14]2+ complex in order to validate previous assignments. The computed 1H NMR chemical shifts are strongly dependent on the relativistic treatment, the choice of the DFT exchange-correlation functional, and the conformational dynamics. By combining a fully relativistic four-component electronic-structure treatment with ab initio molecular dynamics, we were able to reliably model both the terminal and bridging hydride chemical shifts and to show that two NMR hydride signals were inversely assigned in the experiment.

Incorporating the Coevolving Information of Substrates in Predicting HIV-1 Protease Cleavage Sites

Human immunodeficiency virus 1 (HIV-1) protease (PR) plays a crucial role in the maturation of the virus. The study of substrate specificity of HIV-1 PR as a new endeavor strives to increase our ability to understand how HIV-1 PR recognizes its various cleavage sites. To predict HIV-1 PR cleavage sites, most of the existing approaches have been developed solely based on the homogeneity of substrate sequence information with supervised classification techniques. Although efficient, these approaches are found to be restricted to the ability of explaining their results and probably provide few insights into the mechanisms by which HIV-1 PR cleaves the substrates in a site-specific manner. In this work, a coevolutionary pattern-based prediction model for HIV-1 PR cleavage sites, namely EvoCleave, is proposed by integrating the coevolving information obtained from substrate sequences with a linear SVM classifier. The experiment results showed that EvoCleave yielded a very promising performance in terms of ROC analysis and f-measure. We also prospectively assessed the biological significance of coevolutionary patterns by applying them to study three fundamental issues of HIV-1 PR cleavage site. The analysis results demonstrated that the coevolutionary patterns offered valuable insights into the understanding of substrate specificity of HIV-1 PR.

Exploring the concerted mechanistic pathway for HIV-1 PR-substrate revealed by umbrella sampling simulation

HIV-1 protease (HIV-1 PR) is an essential enzyme for the replication process of its virus, and therefore considered an important target for the development of drugs against the acquired immunodeficiency syndrome (AIDS). Our previous study shows that the catalytic mechanism of subtype B/C-SA HIV-1 PR follows a one-step concerted acyclic hydrolysis reaction process using a two-layered ONIOM B3LYP/6-31++G(d,p) method. This present work is aimed at exploring the proposed mechanism of the proteolysis catalyzed by HIV-1 PR and to ensure our proposed mechanism is not an artefact of a single theoretical technique. Hence, we present umbrella sampling method that is suitable for calculating potential mean force (PMF) for non-covalent ligand/substrate-enzyme association/dissociation interactions which provide thermodynamic details for molecular recognition. The free activation energy results were computed in terms of PMF analysis within the hybrid QM(DFTB)/MM approach. The theoretical findings suggest that the proposed mechanism corresponds in principle with experimental data. Given our observations, we suggest that the QM/MM MD method can be used as a reliable computational technique to rationalize lead compounds against specific targets such as the HIV-1 protease.

Isotope effects on chemical shifts in the study of hydrogen bonded biological systems

This review deals with biological systems and with deuterium isotope effects on chemical shifts caused by the replacement of OH, NH or SH protons by deuterons. Hydrogen bonding is clearly of central importance. Isotope effects on chemical shifts seems very suitable for use in studies of structures and reactions in the interior of proteins, as exchange of the label can be expected to be slow. One-bond deuterium isotope effects on ¹⁵N chemical shifts, and two-bond effects on ¹H chemical shifts for N(D)H_x systems can be used to gauge hydrogen bond strength in proteins as well as in salt bridges. Solvent isotope effects on ¹⁹F chemical shifts show promise in monitoring solvent access. Equilibrium isotope effects need in some cases to be taken into account. Schemes for calculation of deuterium isotope effects on chemical shifts are discussed and it is demonstrated how calculations may be used in the study of complex biological systems.

Drug Re-positioning Studies for Novel HIV-1 Inhibitors Using Binary QSAR Models and Multi-target-driven In Silico Studies

Current antiretroviral therapies against HIV involve the usage of at least two drugs that target different stages of HIV life cycle. However, potential drug interactions and side effects pose a problem. A promising concept for complex disease treatment is 'one molecule-multiple target' approach to overcome undesired effects of multiple drugs. Additionally, it is beneficial to consider drug re-purposing due to the cost of taking a drug into the market. Taking these into account, here potential anti-HIV compounds are suggested by virtually screening small approved drug molecules and clinical candidates. Initially, binary QSAR models are used to predict the therapeutic activity of around 7900 compounds against HIV and to predict the toxicity of molecules with high therapeutic activities. Selected compounds are considered for molecular docking studies against two targets, HIV-1 protease enzyme, and chemokine co-receptor CCR5. The top docking poses for all 549 molecules are then subjected to short (1 ns) individual molecular dynamics (MD) simulations and they are ranked based on their calculated relative binding free energies. Finally, 25 molecules are selected for long (200 ns) MD simulations, and 5 molecules are suggested as promising multi-target HIV agents. The results of this study may open new avenues for the designing of new dual HIV-1 inhibitor scaffolds.

Target-Specific Prediction of Ligand Affinity with Structure-Based Interaction Fingerprints

Discovery and optimization of small molecule inhibitors as therapeutic drugs have immensely benefited from rational structure-based drug design. With recent advances in high-resolution structure determination, computational power, and machine learning methodology, it is becoming more tractable to elucidate the structural basis of drug potency. However, the applicability of machine learning models to drug design is limited by the interpretability of the resulting models in terms of feature importance. Here, we take advantage of the large number of available inhibitor-bound HIV-1 protease structures and associated potencies to evaluate inhibitor diversity and machine learning models to predict ligand affinity. First, using a hierarchical clustering approach, we grouped HIV-1 protease inhibitors and identified distinct core structures. Explicit features including protein-ligand interactions were extracted from high-resolution cocrystal structures as 3D-based fingerprints. We found that a gradient boosting machine learning model with this explicit feature attribution can predict binding affinity with high accuracy. Finally, Shapley values were derived to explain local feature importance. We found specific van der Waals (vdW) interactions of key protein residues are pivotal for the predicted potency. Protein-specific and interpretable prediction models can guide the optimization of many small molecule drugs for improved potency.

An Overview of Molecular Modeling for Drug Discovery with Specific Illustrative Examples of Applications

In this paper we review the current status of high-performance computing applications in the general area of drug discovery. We provide an introduction to the methodologies applied at atomic and molecular scales, followed by three specific examples of implementation of these tools. The first example describes in silico modeling of the adsorption of small molecules to organic and inorganic surfaces, which may be applied to drug delivery issues. The second example involves DNA translocation through nanopores with major significance to DNA sequencing efforts. The final example offers an overview of computer-aided drug design, with some illustrative examples of its usefulness.

Four-component relativistic 31P NMR calculations for trans-platinum(ii) complexes: importance of the solvent and dynamics in spectral simulations

We report a combined experimental-theoretical study on the 31P NMR chemical shift for a number of trans-platinum(ii) complexes. Validity and reliability of the 31P NMR chemical shift calculations are examined by comparing with the experimental data. A successful computational protocol for the accurate prediction of the 31P NMR chemical shifts was established for trans-[PtCl2(dma)PPh3] (dma = dimethylamine) complexes. The reliability of the computed values is shown to be critically dependent on the level of relativistic effects (two-component vs. four component), choice of density functionals, dynamical averaging, and solvation effects. Snapshots obtained from ab initio molecular dynamics simulations were used to identify those solvent molecules which show the largest interactions with the platinum complex, through inspection by using the non-covalent interaction program. We observe satisfactory accuracy from the full four-component matrix Dirac-Kohn-Sham method (mDKS) based on the Dirac-Coulomb Hamiltonian, in conjunction with the KT2 density functional, and dynamical averaging with explicit solvent molecules.

Hydration Structure and Dynamics of Inhibitor-Bound HIV-1 Protease

Water is essential in many biological processes, and the hydration structure plays a critical role in facilitating protein folding, dynamics, and ligand binding. A variety of biophysical spectroscopic techniques have been used to probe the water solvating proteins, often complemented with molecular dynamics (MD) simulations to resolve the spatial and dynamic features of the hydration shell, but comparing relative water structure is challenging. In this study 1 μs MD simulations were performed to identify and characterize hydration sites around HIV-1 protease bound to an inhibitor, darunavir (DRV). The water density, hydration site occupancy, extent and anisotropy of fluctuations, coordinated water molecules, and hydrogen bonds were characterized and compared to the properties of bulk water. The water density of the principal hydration shell was found to be higher than bulk, dependent on the topology and physiochemical identity of the biomolecular surface. The dynamics of water molecules occupying principal hydration sites was highly dependent on the number of water-water interactions and inversely correlated with hydrogen bonds to the protein-inhibitor complex. While many waters were conserved following the symmetry of homodimeric HIV protease, the asymmetry induced by DRV resulted in asymmetric lower-occupancy hydration sites at the concave surface of the active site. Key interactions between water molecules and the protease, that stabilize the protein in the inhibited form, were altered in a drug resistant variant of the protease indicating that modulation of solvent-solute interactions might play a key role in conveying drug resistance. Our analysis provides insights into the interplay between an enzyme inhibitor complex and the hydration shell and has implications in elucidating water structure in a variety of biological processes and applications including ligand binding, inhibitor design, and resistance.

Polar solvent fluctuations drive proton transfer in hydrogen bonded complexes of carboxylic acid with pyridines: NMR, IR and ab initio MD study

We study a series of intermolecular hydrogen-bonded 1 : 1 complexes formed by chloroacetic acid with 19 substituted pyridines and one aliphatic amine dissolved in CD₂Cl₂ at low temperature by ¹H and ¹³C NMR and FTIR spectroscopy. The hydrogen bond geometries in these complexes vary from molecular (O-HN) to zwitterionic (O^-H-N⁺) ones, while NMR spectra show the formation of short strong hydrogen bonds in intermediate cases. Analysis of C[double bond, length as m-dash]O stretching and asymmetric CO₂^- stretching bands in FTIR spectra reveal the presence of proton tautomerism. On the basis of these data, we construct the overall proton transfer pathway. In addition to that, we also study by use of ab initio molecular dynamics the complex formed by chloroacetic acid with 2-methylpyridine, surrounded by 71 CD₂Cl₂ molecules, revealing a dual-maximum distribution of hydrogen bond geometries in solution. The analysis of the calculated trajectory shows that the proton jumps between molecular and zwitterionic forms are indeed driven by dipole-dipole solvent-solute interactions, but the primary cause of the jumps is the formation/breaking of weak CHO bonds from solvent molecules to oxygen atoms of the carboxylate group.

Effect of polarization on HIV-1protease and fluoro-substituted inhibitors binding energies by large scale molecular dynamics simulations

Molecular dynamics simulations in explicit water are carried out to study the binding of six inhibitors to HIV-1 protease (PR) for up to 700 ns using the standard AMBER force field and polarized protein-specific charge (PPC). PPC is derived from quantum mechanical calculation for protein in solution and therefore it includes electronic polarization effect. Our results show that in all six systems, the bridging water W301 drifts away from the binding pocket in AMBER simulation. However, it is very stable in all six complexes systems using PPC. Especially, intra-protease, protease-inhibitor hydrogen bonds are dynamic stabilized in MD simulation. The computed binding free energies of six complexes have a significantly linear correlation with those experiment values and the correlation coefficient is found to be 0.91 in PPC simulation. However, the result from AMBER simulation shows a weaker correlation with the correlation coefficient of −0.51 due to the lack of polarization effect. Detailed binding interactions of W301, inhibitors with PR are further analyzed and discussed. The present study provides important information to quantitative understanding the interaction mechanism of PR-inhibitor and PR-W301 and these data also emphasizes the importance of both the electronic polarization and the bridging water molecule in predicting precisely binding affinities.

Dynamic and Electrostatic Effects on the Reaction Catalyzed by HIV-1 Protease

HIV-1 Protease (HIV-1 PR) is one of the three enzymes essential for the replication process of HIV-1 virus, which explains why it has been the main target for design of drugs against acquired immunodeficiency syndrome (AIDS). This work is focused on exploring the proteolysis reaction catalyzed by HIV-1 PR, with special attention to the dynamic and electrostatic effects governing its catalytic power. Free energy surfaces for all possible mechanisms have been computed in terms of potentials of mean force (PMFs) within hybrid QM/MM potentials, with the QM subset of atoms described at semiempirical (AM1) and DFT (M06-2X) level. The results suggest that the most favorable reaction mechanism involves formation of a gem-diol intermediate, whose decomposition into the product complex would correspond to the rate-limiting step. The agreement between the activation free energy of this step with experimental data, as well as kinetic isotope effects (KIEs), supports this prediction. The role of the protein dynamic was studied by protein isotope labeling in the framework of the Variational Transition State Theory. The predicted enzyme KIEs, also very close to the values measured experimentally, reveal a measurable but small dynamic effect. Our calculations show how the contribution of dynamic effects to the effective activation free energy appears to be below 1 kcal·mol^-1. On the contrary, the electric field created by the protein in the active site of the enzyme emerges as being critical for the electronic reorganization required during the reaction. These electrostatic properties of the active site could be used as a mold for future drug design.

Allosteric-Activation Mechanism of Bovine Chymosin Revealed by Bias-Exchange Metadynamics and Molecular Dynamics Simulations

The aspartic protease, bovine chymosin, catalyzes the proteolysis of κ-casein proteins in milk. The bovine chymosin-κ-casein complex is of industrial interest as the enzyme is used extensively in the manufacturing of processed dairy products. The apo form of the enzyme adopts a self-inhibited conformation in which the side chain of Tyr77 occludes the binding site. On the basis of kinetic, mutagenesis, and crystallographic data, it has been widely reported that a HPHPH sequence in the P8-P4 residues of the natural substrate κ-casein acts as the allosteric activator, but the mechanism by which this occurs has not previously been elucidated due to the challenges associated with studying this process by experimental methods. Here we have employed two computational techniques, molecular dynamics and bias-exchange metadynamics simulations, to study the mechanism of allosteric activation and to compute the free energy surface for the process. The simulations reveal that allosteric activation is initiated by interactions between the HPHPH sequence of κ-casein and a small α-helical region of chymosin (residues 112-116). A small conformational change in the α-helix causes the side chain of Phe114 to vacate a pocket that may then be occupied by the side chain of Tyr77. The free energy surface for the self-inhibited to open transition is significantly altered by the presence of the HPHPH sequence of κ-casein.

Unraveling HIV protease flaps dynamics by Constant pH Molecular Dynamics simulations

The active site of HIV protease (HIV-PR) is covered by two flaps. These flaps are known to be essential for the catalytic activity of the HIV-PR, but their exact conformations at the different stages of the enzymatic pathway remain subject to debate. Understanding the correct functional dynamics of the flaps might aid the development of new HIV-PR inhibitors. It is known that, the HIV-PR catalytic efficiency is pH-dependent, likely due to the influence of processes such as charge transfer and protonation/deprotonation of ionizable residues. Several Molecular Dynamics (MD) simulations have reported information about the HIV-PR flaps. However, in MD simulations the protonation of a residue is fixed and thus it is not possible to study the correlation between conformation and protonation state. To address this shortcoming, this work attempts to capture, through Constant pH Molecular Dynamics (CpHMD), the conformations of the apo, substrate-bound and inhibitor-bound HIV-PR, which differ drastically in their flap arrangements. The results show that the HIV-PR flaps conformations are defined by the protonation of the catalytic residues Asp25/Asp25' and that these residues are sensitive to pH changes. This study suggests that the catalytic aspartates can modulate the opening of the active site and substrate binding.

Characterizing the protonation states of the catalytic residues in apo and substrate-bound human T-cell leukemia virus type 1 protease

Human T-cell leukemia virus type 1 (HTLV-1) protease is an attractive target when developing inhibitors to treat HTLV-1 associated diseases. To study the catalytic mechanism and design novel HTLV-1 protease inhibitors, the protonation states of the two catalytic aspartic acid residues must be determined. Free energy simulations have been conducted to study the proton transfer reaction between the catalytic residues of HTLV-1 protease using a combined quantum mechanical and molecular mechanical (QM/MM) molecular dynamics simulation. The free energy profiles for the reaction in the apo-enzyme and in an enzyme - substrate complex have been obtained. In the apo-enzyme, the two catalytic residues are chemically equivalent and are expected to be both unprotonated. Upon substrate binding, the catalytic residues of HTLV-1 protease evolve to a singly protonated state, in which the OD1 of Asp32 is protonated and forms a hydrogen bond with the OD1 of Asp32', which is unprotonated. The HTLV-1 protease-substrate complex structure obtained from this simulation can serve as the Michaelis complex structure for further mechanistic studies of HTLV-1 protease while providing a receptor structure with the correct protonation states for the active site residues toward the design of novel HTLV-1 protease inhibitors through virtual screening.

Strong, low-barrier hydrogen bonds may be available to enzymes

The debate over the possible role of strong, low-barrier hydrogen bonds in stabilizing reaction intermediates at enzyme active sites has taken place in the absence of an awareness of the upper limits to the strengths of low-barrier hydrogen bonds involving amino acid side chains. Hydrogen bonds exhibit their maximal strengths in isolation, i.e., in the gas phase. In this work, we measured the ionic hydrogen bond strengths of three enzymatically relevant model systems in the gas phase using anion photoelectron spectroscopy; we calibrated these against the hydrogen bond strength of HF2(-), measured using the same technique, and we compared our results with other gas-phase experimental data. The model systems studied here, the formate-formic acid, acetate-acetic acid, and imidazolide-imidazole anionic complexes, all exhibit very strong hydrogen bonds, whose strengths compare favorably with that of the hydrogen bifluoride anion, the strongest known hydrogen bond. The hydrogen bond strengths of these gas-phase complexes are stronger than those typically estimated as being required to stabilize enzymatic intermediates. If there were to be enzyme active site environments that can facilitate the retention of a significant fraction of the strengths of these isolated (gas-phase), hydrogen bonded couples, then low-barrier hydrogen bonding interactions might well play important roles in enzymatic catalysis.

Capturing the reaction pathway in near-atomic-resolution crystal structures of HIV-1 protease

Snapshots of three consecutive steps in the proteolytic reaction of HIV-1 protease (PR) were obtained in crystal structures at resolutions of 1.2-1.4 Å. Structures of wild-type protease and two mutants (PR(V32I) and PR(I47V)) with V32I and I47V substitutions, which are common in drug resistance, reveal the gem-diol tetrahedral intermediate, the separating N- and C-terminal products, and the C-terminal product of an autoproteolytic peptide. These structures represent three stages in the reaction pathway and shed light on the reaction mechanism. The near-atomic-resolution geometric details include a short hydrogen bond between the intermediate and the outer carboxylate oxygen of one catalytic Asp25 that is conserved in all three structures. The two products in the complex with mutant PR(I47V) have a 2.2 Å separation of the amide and carboxyl carbon of the adjacent ends, suggesting partial cleavage prior to product release. The complex of mutant PR(V32I) with a single C-terminal product shows density for water molecules in the other half of the binding site, including a partial occupancy water molecule interacting with the product carboxylate end and the carbonyl oxygen of one conformation of Gly27, which suggests a potential role of Gly27 in recycling from the product complex to the ligand-free enzyme. These structural details at near-atomic resolution enhance our understanding of the reaction pathway and will assist in the design of mechanism-based inhibitors as antiviral agents.

On the reliability of the corrected semiempirical quantum chemical method (PM6-DH2) for assigning the protonation states in HIV-1 protease/inhibitor complexes

A novel computational protocol for determining the most probable protonation states in protein/ligand complexes is presented. The method consists in treating large parts of the enzyme using the corrected semiempirical quantum chemical (QM) method – PM6-D2 for optimization and PM6-DH2 for single-point energies – while the rest is calculated using molecular mechanics (MM) within a hybrid QM/MM fashion. The surrounding solvent is approximated by an implicit model. This approach is applied to two model systems, two different carboxylate pairs in one general and one unique HIV-1 protease/inhibitor complex. The effect of the size of the movable QM part is investigated in a series of several sizes, 3-, 6-, 8- and 10-Å regions surrounding the inhibitor. For the smallest region (< 450 atoms) the computationally more costly DFT QM/MM optimizations are performed as a check of the correctness. Proton transfer (PT) phenomena occur at both the PM6-D2 and DFT levels, which underlines the requirement for a QM approach. The barriers of PT are checked in model carboxylic acid pairs using the highly accurate MP2 and CCSD(T) values. An important result of this study is the fine-tuning of the protocol which can be used in further applications; its limitations are also shown, pointing to future developments. The calculations reveal which protonation variants of the active site are the most stable. In conclusion, the presented protocol can also be utilized for defining probable isomers in biomolecular systems. It can also serve as a preparatory step for further interaction-energy and binding-score calculations.

Substrate binding mechanism of HIV-1 protease from explicit-solvent atomistic simulations

The binding mechanism of a peptide substrate (Thr-Ile-Met-Met-Gln-Arg, cleavage site p2-NC of the viral polyprotein) to wild-type HIV-1 protease has been investigated by 1.6 micros biased all-atom molecular dynamics simulations in explicit water. The configuration space has been explored biasing seven reaction coordinates by the bias-exchange metadynamics technique. The structure of the Michaelis complex is obtained starting from the substrate outside the enzyme within a backbone rmsd of 0.9 A. The calculated free energy of binding is -6 kcal/mol, and the kinetic constants for association and dissociation are 1.3 x 10(6) M(-1) s(-1) and 57 s(-1), respectively, consistent with experiments. In the main binding pathway, the flaps of the protease do not open sizably. The substrate slides inside the enzyme cavity from the tight lateral channel. This may contrast with the natural polyprotein substrate which is expected to bind by opening the flaps. Thus, mutations might influence differently the binding kinetics of peptidomimetic ligands and of the natural substrate.

Prediction and analysis of binding affinities for chemically diverse HIV-1 PR inhibitors by the modified SAFE_p approach

One of the biggest challenges in the "in silico" screening of enzyme ligands is to have a protocol that could predict the ligand binding free energies. In our group we have developed a very simple screening function (referred to as solvent accessibility free energy of binding predictor, SAFE_p) which we have applied previously to the study of peptidic HIV-1 protease (HIV-1 PR) inhibitors and later to cyclic urea type HIV-1 PR inhibitors. In this work, we have extended the SAFE_p protocol to a chemically diverse set of HIV-1 PR inhibitors with binding constants that differ by several orders of magnitude. The resulting function is able to reproduce the ranking and in many cases the value of the inhibitor binding affinities for the HIV-1 PR, with accuracy comparable with that of costlier protocols. We also demonstrate that the binding pocket SAFE_p analysis can contribute to the understanding of the physical forces that participate in ligand binding. The analysis tools afforded by our protocol have allowed us to identify an induced fit phenomena mediated by the inhibitor and have demonstrated that larger fragments do not necessarily contribute the most to the binding free energy, an outcome partially brought about by the substantial role the desolvation penalty plays in the energetics of binding. Finally, we have revisited the effect of the Asp dyad protonation state on the predicted binding affinities.

Assessment of QM/MM scoring functions for molecular docking to HIV-1 protease

We explore the ability of four quantum mechanical (QM)/molecular mechanical (MM) models to accurately identify the native pose of six HIV-1 protease inhibitors and compare them with the AMBER force field and ChemScore and GoldScore scoring functions. Three QM/MM scoring functions treated the ligand at the HF/6-31G*, AM1d, and PM3 levels; the fourth QM/MM function modeled the ligand and active site at the PM3-D level. For the discrimination of native from non-native poses, solvent-corrected HF/6-31G*:AMBER and AMBER functions exhibited the best overall performance. While the electrostatic component of the MM and QM/MM functions appears important for discriminating the native pose of the ligand, the polarization contribution in the QM/MM functions was relatively insensitive to a ligand's binding mode and, for one ligand, actually hindered discrimination. The inclusion of a desolvation penalty, here using a generalized Born solvent model, improved discrimination for the MM and QM/MM methods. There appeared to be no advantage to binding mode prediction by incorporating active site polarization at the PM3-D level. Finally, we found that choice of the protonation state of the aspartyl dyad in the HIV-1 protease active site influenced the ability of scoring methods to determine the native binding pose.