Free energy calculations offer insights into the influence of receptor flexibility on ligand–receptor binding affinities

Docking algorithms for computer-aided drug discovery and design often ignore or restrain the flexibility of the receptor, which may lead to a loss of accuracy of the relative free enthalpies of binding. In order to evaluate the contribution of receptor flexibility to relative binding free enthalpies, two host–guest systems have been examined: inclusion complexes of α-cyclodextrin (αCD) with 1-chlorobenzene (ClBn), 1-bromobenzene (BrBn) and toluene (MeBn), and complexes of DNA with the minor-groove binding ligands netropsin (Net) and distamycin (Dist). Molecular dynamics simulations and free energy calculations reveal that restraining of the flexibility of the receptor can have a significant influence on the estimated relative ligand–receptor binding affinities as well as on the predicted structures of the biomolecular complexes. The influence is particularly pronounced in the case of flexible receptors such as DNA, where a 50% contribution of DNA flexibility towards the relative ligand–DNA binding affinities is observed. The differences in the free enthalpy of binding do not arise only from the changes in ligand–DNA interactions but also from changes in ligand–solvent interactions as well as from the loss of DNA configurational entropy upon restraining.     

What induces pocket openings on protein surface patches involved in protein–protein interactions?

We previously showed for the proteins BCL-X_L, IL-2, and MDM2 that transient pockets at their protein–protein binding interfaces can be identified by applying the PASS algorithm to molecular dynamics (MD) snapshots. We now investigated which aspects of the natural conformational dynamics of proteins induce the formation of such pockets. The pocket detection protocol was applied to three different conformational ensembles for the same proteins that were extracted from MD simulations of the inhibitor bound crystal conformation in water and the free crystal/NMR structure in water and in methanol. Additional MD simulations studied the impact of backbone mobility. The more efficient CONCOORD or normal mode analysis (NMA) techniques gave significantly smaller pockets than MD simulations, whereas tCONCOORD generated pockets comparable to those observed in MD simulations for two of the three systems. Our findings emphasize the influence of solvent polarity and backbone rearrangements on the formation of pockets on protein surfaces and should be helpful in future generation of transient pockets as putative ligand binding sites at protein–protein interfaces. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Computationally driven discovery of SARS-CoV-2 Mpro inhibitors: from design to experimental validation

We report a fast-track computationally driven discovery of new SARS-CoV-2 main protease (M^pro) inhibitors whose potency ranges from mM for the initial non-covalent ligands to sub-μM for the final covalent compound (IC₅₀ = 830 ± 50 nM). The project extensively relied on high-resolution all-atom molecular dynamics simulations and absolute binding free energy calculations performed using the polarizable AMOEBA force field. The study is complemented by extensive adaptive sampling simulations that are used to rationalize the different ligand binding poses through the explicit reconstruction of the ligand–protein conformation space. Machine learning predictions are also performed to predict selected compound properties. While simulations extensively use high performance computing to strongly reduce the time-to-solution, they were systematically coupled to nuclear magnetic resonance experiments to drive synthesis and for in vitro characterization of compounds. Such a study highlights the power of in silico strategies that rely on structure-based approaches for drug design and allows the protein conformational multiplicity problem to be addressed. The proposed fluorinated tetrahydroquinolines open routes for further optimization of M^pro inhibitors towards low nM affinities.

The dominant binding mode of the QUB-00006-Int-07 main protease inhibitor during absolute binding free energy simulations.     

Toward quantitative estimates of binding affinities for protein–ligand systems involving large inhibitor compounds: A steered molecular dynamics simulation route

Understanding binding mechanisms between enzymes and potential inhibitors and quantifying protein – ligand affinities in terms of binding free energy is of primary importance in drug design studies. In this respect, several approaches based on molecular dynamics simulations, often combined with docking techniques, have been exploited to investigate the physicochemical properties of complexes of pharmaceutical interest. Even if the geometric properties of a modeled protein – ligand complex can be well predicted by computational methods, it is still challenging to rank with chemical accuracy a series of ligand analogues in a consistent way. In this article, we face this issue calculating relative binding free energies of a focal adhesion kinase, an important target for the development of anticancer drugs, with pyrrolopyrimidine‐based ligands having different inhibitory power. To this aim, we employ steered molecular dynamics simulations combined with nonequilibrium work theorems for free energy calculations. This technique proves very powerful when a series of ligand analogues is considered, allowing one to tackle estimation of protein – ligand relative binding free energies in a reasonable time. In our cases, the calculated binding affinities are comparable with those recovered from experiments by exploiting the Michaelis – Menten mechanism with a competitive inhibitor.     

Role of bridging water molecules in GSK3β-inhibitor complexes: insights from QM/MM, MD, and molecular docking studies

The role of water molecules is increasingly gaining interest in drug design, and several studies have highlighted their paramount contributions to the specificity and the affinity of ligand binding. In this study, we employ the two-layer ONIOM-based quantum mechanics/molecular mechanics (QM/MM) calculations, molecular dynamics (MD) simulations, and molecular docking studies to investigate the effect of bridging water molecules at the GSK3β-inhibitors interfaces. The results obtained from the ONIOM geometry optimization and AIM analysis corroborated the presence of bridging water molecules that form hydrogen bonds with protein side chain of Thr138 and/or backbone of Gln185, and mediate interactions with inhibitors in the 10 selected GSK3β-inhibitor complexes. Subsequently, MD simulations carried out on a representative system of 1R0E demonstrated that the bridging water molecule is stable at the GSK3β-inhibitor interface and appears to contribute to the stability of the protein-inhibitor interactions. Furthermore, molecular docking studies of GSK3β-inhibitor complexes indicated that the inhibitors can increase binding affinities and the better docked conformation of inhibitors can be obtained by inclusion of the bridging water molecules, especially for the flexible inhibitors, in docking experiments into individual protein conformations. Our results elucidate the importance of bridging water molecules at the GSK3β-inhibitor interfaces and suggest that they might prove useful in rational drug design.     

A computational analysis of the binding model of MDM2 with inhibitors

Abstract  

It is a new and promising strategy for anticancer drug design to block the MDM2-p53 interaction using a non-peptide small-molecule inhibitor. We carry out molecular dynamics simulations to study the binding of a set of six non-peptide small-molecule inhibitors with the MDM2. The relative binding free energies calculated using molecular mechanics Poisson–Boltzmann surface area method produce a good correlation with experimentally determined results. The study shows that the van der Waals energies are the largest component of the binding free energy for each complex, which indicates that the affinities of these inhibitors for MDM2 are dominated by shape complementarity. The A-ligands and the B-ligands are the same except for the conformation of 2,2-dimethylbutane group. The quantum mechanics and the binding free energies calculation also show the B-ligands are the more possible conformation of ligands. Detailed binding free energies between inhibitors and individual protein residues are calculated to provide insights into the inhibitor-protein binding model through interpretation of the structural and energetic results from the simulations. The study shows that G1, G2 and G3 group mimic the Phe19, Trp23 and Leu26 residues in p53 and their interactions with MDM2, but the binding model of G4 group differs from the original design strategy to mimic Leu22 residue in p53.     

Binding of novel fullerene inhibitors to HIV-1 protease: insight through molecular dynamics and molecular mechanics Poisson–Boltzmann surface area calculations

The objectives of this study include the design of a series of novel fullerene-based inhibitors for HIV-1 protease (HIV-1 PR), by employing two strategies that can also be applied to the design of inhibitors for any other target. Additionally, the interactions which contribute to the observed exceptionally high binding free energies were analyzed. In particular, we investigated: (1) hydrogen bonding (H-bond) interactions between specific fullerene derivatives and the protease, (2) the regions of HIV-1 PR that play a significant role in binding, (3) protease changes upon binding and (4) various contributions to the binding free energy, in order to identify the most significant of them. This study has been performed by employing a docking technique, two 3D-QSAR models, molecular dynamics (MD) simulations and the molecular mechanics Poisson–Boltzmann surface area (MM–PBSA) method. Our computed binding free energies are in satisfactory agreement with the experimental results. The suitability of specific fullerene derivatives as drug candidates was further enhanced, after ADMET (absorption, distribution, metabolism, excretion and toxicity) properties have been estimated to be promising. The outcomes of this study revealed important protein–ligand interaction patterns that may lead towards the development of novel, potent HIV-1 PR inhibitors.     

Microchannel chips for the multiplexed analysis of human immunoglobulin G–antibody interactions by surface plasmon resonance imaging

We report the multiplexed, simultaneous analysis of antigen–antibody interactions that involve human immunoglobulin G (IgG) on a gold substrate by the surface plasmon resonance imaging method. A multichannel, microfluidic chip was fabricated from poly(dimethylsiloxane) (PDMS) to selectively functionalize the surface and deliver the analyte solutions. The sensing interface was constructed using avidin as a linker layer between the surface-bound biotinylated bovine serum albumin and biotinylated anti-human IgG antibodies. Four mouse anti-human IgG antibodies were selected for evaluation and the screening was achieved by simultaneously monitoring protein–protein interactions under identical conditions. Antibody–antigen binding affinities towards human immunoglobulin were quantitatively compared by employing Langmuir adsorption isotherms for the analysis of SPRi responses obtained under equilibrium conditions. We were able to identify two IgG samples with higher affinities towards the target, and the determined binding kinetics falls within the typical range of values reported in the literature. Direct measurement of proteins in serum samples by SPR imaging was achieved by developing methods to minimize nonspecific adsorption onto the avidin-functionalized surface, and a limit of detection (LOD) of 6.7 nM IgG was obtained for the treated serum samples. The combination of SPR imaging and multichannel PDMS chips offers convenience and flexibility for sensitive and label-free measurement of protein–protein interactions in complex conditions and enables high-throughput screening of pharmaceutically significant molecules. Figure Microchannel SPR imaging for protein–protein interactions     

Inhibition Mechanism of Hydroxyproline-like Small Inhibitors to Disorder HIF-VHL Interaction by Molecular Dynamic Simulations and Binding Free Energy Calculations

Protein-protein interactions are vital for a wide range of biological processes. The interactions between the hypoxia-inducible factor and von Hippel Lindau (VHL) are attractive drug targets for ischemic heart disease. In order to disrupt this interaction, the strategy to target VHL binding site using a hydroxyproline-like (pro-like) small molecule has been reported. In this study, we focused on the inhibition mechanism between the pro-like inhibitors and the VHL protein, which were investigated via molecular dynamics simulations and binding free energy calculations. It was found that pro-like inhibitors showed a strong binding affinity toward VHL. Binding free energy calculations and free energy decompositions suggested that the modification of various regions of pro-like inhibitors may provide useful information for future drug design.     

Studies of chirality effect of 4-(phenylamino)-pyrrolo[2,1-f][1,2,4]triazine on p38α by molecular dynamics simulations and free energy calculations

4-(Phenylamino)-pyrrolo[2,1-f][1,2,4]triazines have been discovered as inhibitors of p38α. Experimental assays have proven that the configuration of α-Me-benzyl connected with amide at C6 is essential for the binding affinity. The S-configured inhibitor (11j) displays 80 times more potency than the R-configured one (11k). Here we investigated the mechanism how different configurations influence the binding affinity using molecular dynamics simulations, free energy calculations and free energy decomposition analysis. We found that the van der Waals interactions play the most important role in differentiating the activities between 11j and 11k with p38α. The difference of the van der Waals interactions is primarily determined by two residues, LEU108 and LEU167. Consequently stabilization of pyrrolo[2,1-f][1,2,4]triazine ring is important for the activities of inhibitors. Meanwhile we observed that the different configuration of the α-Me-benzyl group leads to the difference of binding between 11j and 11k. In conclusion, our work shows that it is feasible to analyze the chirality effect of inhibitors with different configurations by molecular dynamics simulations and free energy calculations, and provides useful information for drug design.     

Mapping a Noncovalent Protein–Peptide Interface by Top-Down FTICR Mass Spectrometry Using Electron Capture Dissociation

Noncovalent protein–ligand and protein–protein complexes are readily detected using electrospray ionization mass spectrometry (ESI MS). Furthermore, recent reports have demonstrated that careful use of electron capture dissociation (ECD) fragmentation allows covalent backbone bonds of protein complexes to be dissociated without disruption of noncovalent protein–ligand interactions. In this way the site of protein–ligand interfaces can be identified. To date, protein–ligand complexes, which have proven tractable to this technique, have been mediated by ionic electrostatic interactions, i.e., ion pair interactions or salt bridging. Here we extend this methodology by applying ECD to study a protein–peptide complex that contains no electrostatics interactions. We analyzed the complex between the 21 kDa p53-inhibitor protein anterior gradient-2 and its hexapeptide binding ligand (PTTIYY). ECD fragmentation of the 1:1 complex occurs with retention of protein–peptide binding and analysis of the resulting fragments allows the binding interface to be localized to a C-terminal region between residues 109 and 175. These finding are supported by a solution-phase competition assay, which implicates the region between residues 108 and 122 within AGR2 as the PTTIYY binding interface. Our study expands previous findings by demonstrating that top-down ECD mass spectrometry can be used to determine directly the sites of peptide–protein interfaces. This highlights the growing potential of using ECD and related top-down fragmentation techniques for interrogation of protein–protein interfaces.     

新型二氟甲基磷酸类酪氨酸蛋白磷酸酯酶1B抑制剂的分子动力学模拟和结合自由能计算

通过分子对接建立了一系列含二氟甲基磷酸基团(DFMP)或二氟甲基硫酸基团(DFMS)的抑制剂与酪氨酸蛋白磷酸酯酶1B(PTP1B)的相互作用模式, 并通过1 ns的分子动力学模拟和molecular mechanics/generalized Born surface area (MM/GBSA)方法计算了其结合自由能. 计算获得的结合自由能排序和抑制剂与靶酶间结合能力排序一致; 通过基于主方程的自由能计算方法, 获得了抑制剂与靶酶残基间相互作用的信息, 这些信息显示DFMP/DFMS基团的负电荷中心与PTP1B的221位精氨酸正电荷中心之间的静电相互作用强弱决定了此类抑制剂的活性, 进一步的分析还显示位于DFMP/DFMS基团中的氟原子或其他具有适当原子半径的氢键供体原子会增进此类抑制剂与PTP1B活性位点的结合能力.     

Calculation of ligand binding free energies from molecular dynamics simulations

A recently developed method for predicting binding affinities in ligand–receptor complexes, based on interaction energy averaging and conformational sampling by molecular dynamics simulation, is presented. Polar and nonpolar contributions to the binding free energy are approximated by a linear scaling of the corresponding terms in the average intermolecular interaction energy for the bound and free states of the ligand. While the method originally assumed the validity of electrostatic linear response, we show that incorporation of systematic deviations from linear response derived from free energy perturbation calculations enhances the accuracy of the approach. The method is applied to complexes of wild-type and mutant human dihydrofolate reductases with 2,4-diaminopteridine and 2,4-diaminoquinazoline inhibitors. It is shown that a binding energy accuracy of about 1 kcal/mol is attainable even for multiply ionized compounds, such as methotrexate, for which electrostatic interactions energies are very large. © 1998 John Wiley & Sons, Inc. Int J Quant Chem 69: 77–88, 1998     

Empirical estimation of the energetic contribution of individual interface residues in structures of protein–protein complexes

We report a simple algorithm to scan interfaces in protein–protein complexes for identifying binding ‘hot spots’. The change in side-chain solvent accessible area (ΔASA) of interface residues has been related to change in binding energy due to mutating interface residues to Ala (ΔΔG _X → ALA) based on two criteria—hydrogen bonding across the interface and location in the interface core—both of which are major determinants in specific, high-affinity binding. These relationships are used to predict the energetic contribution of individual interface residues. The predictions are tested against 462 experimental X → ALA mutations from 28 interfaces with an average unsigned error of 1.04 kcal/mol. More than 80% of interface hot spots (with experimental ΔΔG ≥ 2 kcal/mol) could be identified as being energetically important. From the experimental values, Asp, Lys, Tyr and Trp are found to contribute most of the binding energy, burying >45 Å² on average. The method described here would be useful to understand and interfere with protein interactions by assessing the energetic importance of individual interface residues. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Computational study of ligand binding in lipid transfer proteins: Structures,interfaces, and free energies of protein‐lipid complexes

Plant nonspecific lipid transfer proteins (nsLTPs) bind a wide variety of lipids, which allows them to perform disparate functions. Recent reports on their multifunctionality in plant growth processes have posed new questions on the versatile binding abilities of these proteins. The lack of binding specificity has been customarily explained in qualitative terms on the basis of a supposed structural flexibility and nonspecificity of hydrophobic protein‐ligand interactions. We present here a computational study of protein‐ligand complexes formed between five nsLTPs and seven lipids bound in two different ways in every receptor protein. After optimizing geometries in molecular dynamics calculations, we computed Poisson‐Boltzmann electrostatic potentials, solvation energies, properties of the protein‐ligand interfaces, and estimates of binding free energies of the resulting complexes. Our results provide the first quantitative information on the ligand abilities of nsLTPs, shed new light into protein‐lipid interactions, and reveal new features which supplement commonly held assumptions on their lack of binding specificity. © 2012 Wiley Periodicals, Inc.     

Probing molecular mechanism of inhibitor bindings to bromodomain-containing protein 4 based on molecular dynamics simulations and principal component analysis

ABSTRACT

It is well known that bromodomain-containing protein 4 (BRD4) has been thought as a promising target utilized for treating various human diseases, such as inflammatory disorders, malignant tumours, acute myelogenous leukaemia (AML), bone diseases, etc. For this study, molecular dynamics (MD) simulations, binding free energy calculations, and principal component analysis (PCA) were integrated together to uncover binding modes of inhibitors 8P9, 8PU, and 8PX to BRD4(1). The results obtained from binding free energy calculations show that van der Waals interactions act as the main regulator in bindings of inhibitors to BRD4(1). The information stemming from PCA reveals that inhibitor associations extremely affect conformational changes, internal dynamics, and movement patterns of BRD4(1). Residue-based free energy decomposition method was wielded to unveil contributions of independent residues to inhibitor bindings and the data signify that hydrogen bonding interactions and hydrophobic interactions are decisive factors affecting bindings of inhibitors to BRD4(1). Meanwhile, eight residues Trp81, Pro82, Val87, Leu92, Leu94, Cys136, Asn140, and Ile146 are recognized as the common hot interaction spots of three inhibitors with BRD4(1). The results from this work are expected to provide a meaningfully theoretical guidance for design and development of effective inhibitors inhibiting of the activity of BRD4.     

Structure–activity relationships of diphenyl-ether as protoporphyrinogen oxidase inhibitors: insights from computational simulations

Protoporphyrinogen oxidase (PPO, EC 1.3.3.4), which has been identified as a significant target for a great family of herbicides with diverse chemical structures, is the last common enzyme responsible for the seventh step in the biosynthetic pathway to heme and chlorophyll. Among the existing PPO inhibitors, diphenyl-ether is the first commercial family of PPO inhibitors and used as agriculture herbicides for decades. Most importantly, diphenyl-ether inhibitors have been found recently to possess the potential in Photodynamic therapy (PDT) to treat cancer. Herein, molecular dynamics simulations, approximate free energy calculations and hydrogen bond energy calculations were integrated together to uncover the structure–activity relationships of this type of PPO inhibitors. The calculated binding free energies are correlated very well with the values derived from the experimental k _i data. According to the established computational models and the results of approximate free energy calculation, the substitution effects at different position were rationalized from the view of binding free energy. Some outlier (e.g. LS) in traditional QSAR study can also be explained reasonably. In addition, the hydrogen bond energy calculation and interaction analysis results indicated that the carbonyl oxygen on position-9 and the NO₂ group at position-8 are both vital for the electrostatic interaction with Arg98, which made a great contribution to the binding free energy. These insights from computational simulations are not only helpful for understanding the molecular mechanism of PPO-inhibitor interactions, but also beneficial to the future rational design of novel promising PPO inhibitors.     

Toward <Emphasis Type="Italic">Mycobacterium tuberculosis</Emphasis> DXR inhibitor design: homology modeling and molecular dynamics simulations

Mycobacterium tuberculosis 1-deoxy-d-xylulose-5-phosphate reductoisomerase (MtDXR) is a potential target for antitubercular chemotherapy. In the absence of its crystallographic structure, our aim was to develop a structural model of MtDXR. This will allow us to gain early insight into the structure and function of the enzyme and its likely binding to ligands and cofactors and thus, facilitate structure-based inhibitor design. To achieve this goal, initial models of MtDXR were generated using MODELER. The best quality model was refined using a series of minimizations and molecular dynamics simulations. A protein–ligand complex was also developed from the initial homology model of the target protein by including information about the known ligand as spatial restraints and optimizing the mutual interactions between the ligand and the binding site. The final model was evaluated on the basis of its ability to explain several site-directed mutagenesis data. Furthermore, a comparison of the homology model with the X-ray structure published in the final stages of the project shows excellent agreement and validates the approach. The knowledge gained from the current study should prove useful in the design and development of inhibitors as potential novel therapeutic agents against tuberculosis by either de novo drug design or virtual screening of large chemical databases. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Recognition determinants in a T4 ← G4 mutant derived from a 5′-GCGTGGGCGT-3′ oligomer in a zinc finger 268–DNA complex.

The 5′-GCGTGGGCGT-3′ (T4) oligomer found in the zinc finger 268–DNA complex was mutated into the sequence 5′-GCGGGGGCGT-3′ (G4). A 3D model was constructed from the T4 sequence using an X-ray structure as a template. Molecular dynamics simulations were used to test the thermal stability of the model. A 500-ps trajectory was obtained for the fully charged complex in water using GROMOS87. The complex and the G4 sequence are found to have dynamically stationary behavior. Comparisons made with a previous T4 sequence molecular dynamics simulation show both systems have similar thermal stability. The structure of DNA appears to be maintained by its global interactions with the protein although the mutated site does not contribute with its full potential for binding. The protein structure shows some small differences compared to the T4 simulation. The simulation provided evidence for the role of a chloride ion interacting with the protein and helping in the recognition process. Received: 21 June 1999 / Accepted: 19 October 1999 / Published online: 14 March 2000     

A computational workflow for the design of irreversible inhibitors of protein kinases

Design of irreversible inhibitors is an emerging and relatively less explored strategy for the design of protein kinase inhibitors. In this paper, we present a computational workflow that was specifically conceived to assist such design. The workflow takes the form of a multi-step procedure that includes: the creation of a database of already known reversible inhibitors of protein kinases, the selection of the most promising scaffolds that bind one or more desired kinase templates, the modification of the scaffolds by introduction of chemically reactive groups (suitable cysteine traps) and the final evaluation of the reversible and irreversible protein–ligand complexes with molecular dynamics simulations and binding free energy predictions. Most of these steps were automated. In order to prove that this is viable, the workflow was tested on a database of known inhibitors of ERK2, a protein kinase possessing a cysteine in the ATP site. The modeled ERK2-ligand complexes and the values of the estimated binding free energies of the putative ligands provide useful indicators of their aptitude to bind reversibly and irreversibly to the protein kinase. Moreover, the computational data are used to rank the ligands according to their computed binding free energies and their ability to bind specific protein residues in the reversible and irreversible complexes, thereby providing a useful decision-making tool for each step of the design. In this work we present the overall procedure and the first proof of concept results.