Delineation of agonist binding to the human histamine H4 receptor using mutational analysis, homology modeling, and ab initio calculations

A three-dimensional homology model of the human histamine H 4 receptor was developed to investigate the binding mode of a series of structurally diverse H 4-agonists, i.e. histamine, clozapine, and the recently described selective, nonimidazole agonist VUF 8430. Mutagenesis studies and docking of these ligands in a rhodopsin-based homology model revealed two essential points of interactions in the binding pocket, i.e. Asp3.32 and Glu5.46 (Ballesteros-Weinstein numbering system). It is postulated that Asp3.32 interacts in its anionic state, whereas Glu5.46 interacts in its neutral form. The hypothesis was tested with the point mutations D3.32N and E5.46Q. For the D3.32N no binding affinity toward any of the ligands could be detected. This is in sharp contrast to the E5.46Q mutant, which discriminates between various ligands. The affinity of histamine-like ligands was decreased approximately a 1000-fold, whereas the affinity of all other ligands remained virtually unchanged. The proposed model for agonist binding as well as ab initio calculations for histamine and VUF 8430 explain the observed differences in binding to the H 4R mutants. These studies provide a molecular understanding for the action of a variety of H 4 receptor-ligands. The resulting H 4 receptor model will be the basis for the development of new H 4 receptor-ligands.     

Active site acidic residues and structural analysis of modelled human aromatase: A potential drug target for breast cancer

Summary This study sheds new light on the role of acidic residues present in the active site cavity of human aromatase. Eight acidic residues (E129, D222, E245, E302, D309, E379, D380 and D476) lining the cavity are identified and studied using comparative modeling, docking, molecular dynamics as well as statistical techniques. The structural environment of these acidic residues is studied to assess the stability of the corresponding carboxylate anions. Results indicate that the environment of the residues E245, E302 and D222 is most suitable for carboxylate ion formation in the uncomplexed form. However, the stability of D309, D222 and D476 anions is seen to increase on complexation to steroidal substrates. In particular, the interaction between D309 and T310, which assists proton transfer, is found to be formed following androgen/nor-androgen complexation. The residue D309 is found to be clamped in the presence of substrate which is not observed in the case of the other residues although they exhibit changes in properties following substrate binding. Information entropic analysis indicates that the residues D309, D222 and D476 have more conformational flexibility compared to E302 and E245 prior to substrate binding. Interaction similar to that between D476 and D309, which is expected to assist androgen aromatization, is proposed between E302 and E245. The inhibition of aromatase activity by 4-hydroxy androstenedione (formestane) is attributed to a critical hydrogen bond formation between the hydroxy moiety and T310/D309 as well as the large distance from D476. The results corroborate well with earlier site directed mutagenesis studies.     

人类蛋白激酶组的多靶点分子对接系统

蛋白激酶在信号转导、基因转录和蛋白翻译等生物过程起关键性作用,因而与大量人类疾病密切相关。所以,蛋白激酶的抑制剂筛选是抗肿瘤药物开发的热点,正在向基于全激酶组的高通量多靶点筛选模式发展。为了降低大规模实验筛选的成本,提高成功率,本文构建人类蛋白激酶组的多靶点分子对接系统,对抑制剂-激酶组的相互作用进行预测。我们首先利用同源模建方法,对人类激酶组约500个激酶变异体的催化域进行结构建模;接着以催化域结构模型为受体,用已知激酶抑制剂进行分子对接,对抑制剂与各激酶变异体的结合亲和力进行了定量计算。结果显示,本文所建立的多靶点分子对接系统可以准确预测抑制剂与激酶变异体的相互作用,结合自由能的计算值与实验值有很强的相关性。所以,该分子对接系统可用于多靶点激酶抑制剂的计算筛选,为激酶抑制剂开发与抗肿瘤药物设计提供理论依据。     

The Glu143 Residue Might Play a Significant Role in T20 Peptide Binding to HIV-1 Receptor gp41: An In Silico Study

Despite the enormous efforts made to develop other fusion inhibitors for HIV, the enfuvirtide (known as T20) peptide is the only approved HIV-1 inhibitory drug so far. Investigating the role of potential residues of the T20 peptide’s conformational dynamics could help us to understand the role of potential residues of the T20 peptide. We investigated T20 peptide conformation and binding interactions with the HIV-1 receptor (i.e., gp41) using MD simulations and docking techniques, respectively. Although the mutation of E143 into alanine decreased the flexibility of the E143A mutant, the conformational compactness of the mutant was increased. This suggests a potential role of E143 in the T20 peptide’s conformation. Interestingly, the free energy landscape showed a significant change in the wild-type T20 minimum, as the E143A mutant produced two observed minima. Finally, the docking results of T20 to the gp41 receptor showed a different binding interaction in comparison to the E143A mutant. This suggests that E143 residue can influence the binding interaction with the gp41 receptor. Overall, the E143 residue showed a significant role in conformation and binding to the HIV-1 receptor. These findings can be helpful in optimizing and developing HIV-1 inhibitor peptides.     

A neoceptor approach to unraveling microscopic interactions between the human A2A adenosine receptor and its agonists

Strategically mutated neoceptors, e.g., with anionic residues in TMs 3 and 7 intended for pairing with positively charged amine-modified nucleosides, were derived from the antiinflammatory A(2A) adenosine receptor (AR). Adenosine derivatives functionalized at the 5', 2, and N(6) positions were synthesized. The T88D mutation selectively enhanced the binding of the chain-length-optimized 5'-(2-aminoethyl)uronamide but not 5'-(2-hydroxyethyl)uronamide, suggesting a critical role of the positively charged amine. Combination of this modification with the N(6)-(2-methylbenzyl) group enhanced affinity at the Q89D- and N181D- but not the T88D-A(2A)AR. Amino groups placed near the 2- or N(6)-position only slightly affected the binding to mutant receptors. The 5'-hydrazide MRS3412 was 670- and 161-fold enhanced, in binding and functionally, respectively, at the Q89D-A(2A)AR compared to the wild-type. Thus, we identified and modeled pairs of A(2A)AR-derived neoceptor-neoligand, which are pharmacologically orthogonal with respect to the native species.     

Different binding modes of structurally diverse ligands for human D3DAR

Five different dopamine D3 receptors (D3DARs) models were created considering some suggested binding modes for D3DAR antagonists reported in earlier computational studies. Different hypotheses are justified because of the lack of experimental information about the putative site of interaction and are also due to the variability in scaffolds and size of D3DAR ligands. In this study 114 potent and selective D3DAR antagonists or partial agonists are used as key experimental information to discriminate the most reliable receptor model and to build a docking based 3D quantitative structure-activity relationship model able to indicate the ligand properties and the residues important for activity. The ability of this D3DAR model to discriminate the binding mode of different classes of ligands, showing a good quantitative correlation with their activity, encourages us to use it for screening novel lead compounds.     

Interaction with specific HSP90 residues as a scoring function: validation in the D3R Grand Challenge 2015

Here is reported the development of a novel scoring function that performs remarkably well at identifying the native binding pose of a subset of HSP90 inhibitors containing aminopyrimidine or resorcinol based scaffolds. This scoring function is called PocketScore, and consists of the interaction energy between a ligand and three residues in the binding pocket: Asp93, Thr184 and a water molecule. We integrated PocketScore into a molecular docking workflow, and used it to participate in the Drug Design Data Resource (D3R) Grand Challenge 2015 (GC2015). PocketScore was able to rank 180 molecules of the GC2015 according to their binding affinity with satisfactory performance. These results indicate that the specific residues considered by PocketScore are determinant to properly model the interaction between HSP90 and its subset of inhibitors containing aminopyrimidine or resorcinol based scaffolds. Moreover, the development of PocketScore aimed at improving docking power while neglecting the prediction of binding affinities, suggesting that accurate identification of native binding poses is a determinant factor for the performance of virtual screens.     

An ab initio benchmark study of hydrogen bonded formamide dimers

The five singly and doubly hydrogen bonded dimers of formamide are calculated at the correlated level by using resolution of identity M?ller-Plesset second-order perturbation theory (RIMP2) and the coupled cluster with singles, doubles, and perturbative triples [CCSD(T)] method. All structures are optimized with the Dunning aug-cc-pVTZ and aug-cc-pVQZ basis sets. The binding energies are extrapolated to the complete basis set (CBS) limit by using the aug-cc-pVXZ (X = D, T, Q) basis set series. The effect of extending the basis set to aug-cc-pV5Z on the geometries and binding energies is studied for the centrosymmetric doubly N-H...O bonded dimer FA1 and the doubly C-H...O bonded dimer FA5. The MP2 CBS limits range from -5.19 kcal/mol for FA5 to -14.80 kcal/mol for the FA1 dimer. The DeltaCCSD(T) corrections to the MP2 CBS limit binding energies calculated with the 6-31+G(d,p), aug-cc-pVDZ, and aug-cc-pVTZ basis sets are mutually consistent to within < or =0.03 kcal/mol. The DeltaCCSD(T) correction increases the binding energy of the C-H...O bonded FA5 dimer by 0.4 kcal/mol or approximately 9% over the distance range +/-0.5 Angstrom relative to the potential minimum. This implies that the ubiquitous long-range C-H...O interactions in proteins are stronger than hitherto calculated.     

A Binary Bivalent Supramolecular Assembly Platform Based on Cucurbit[8]uril and Dimeric Adapter Protein 14‐3‐3

Interactions between proteins frequently involve recognition sequences based on multivalent binding events. Dimeric 14‐3‐3 adapter proteins are a prominent example and typically bind partner proteins in a phosphorylation‐dependent mono‐ or bivalent manner. Herein we describe the development of a cucurbit[8]uril (Q8)‐based supramolecular system, which in conjunction with the 14‐3‐3 protein dimer acts as a binary and bivalent protein assembly platform. We fused the phenylalanine–glycine–glycine (FGG) tripeptide motif to the N‐terminus of the 14‐3‐3‐binding epitope of the estrogen receptor α (ERα) for selective binding to Q8. Q8‐induced dimerization of the ERα epitope augmented its affinity towards 14‐3‐3 through a binary bivalent binding mode. The crystal structure of the Q8‐induced ternary complex revealed molecular insight into the multiple supramolecular interactions between the protein, the peptide, and Q8.     

A molecular docking study of potential inhibitors and repurposed drugs against SARS-CoV-2 main protease enzyme

COVID-19 has affected millions of people. Although many drugs are in use to combat disease, there is not any sufficient treatment yet. Having critical role in propagation of the novel coronavirus (SARS-CoV-2) works Main Protease up into a significant drug target. We have performed a molecular docking study to define possible inhibitor candidates against SARS-CoV-2 Main Protease enzyme. Besides docking Remdesivir, Ribavirin, Chloroquine and 28 other antiviral inhibitors (totally 31 inhibitors) to Main Protease enzyme, we have also performed a molecular docking study of 2177 ligands, which are used against Main Protease for the first time by using molecular docking program Autodock4. All ligands were successfully docked into Main Protease enzyme binding site. Among all ligands, EY16 coded ligand which previously used as EBNA1-DNA binding blocker candidate showed the best score for Main Protease with a binding free energy of −10.83 ​kcal/mol which was also lower than re-docking score of N3 ligand (−10.72 ​kcal/mol) contained in crystal structure of Main Protease. After analyzing the docking modes and docking scores we have found that our ligands have better binding free energy values than the inhibitors in use of treatment. We believe that further studies such as molecular dynamics or Molecular Mechanic Poisson Boltzmann Surface Area studies can make contribution that is more exhaustive to the docking results.     

含磷嘧啶类CDK9抑制剂的分子对接、3D-QSAR和分子动力学模拟

选取64个具有潜力的含磷嘧啶类细胞周期依赖性蛋白激酶(CDK9)小分子抑制剂,采用分子对接方法研究了该类小分子与CDK9的结合作用,结果表明,分子构象、氢键形成、疏水性和氨基酸残基Cys106在此类抑制剂与CDK9的结合过程中具有重要作用.在配体叠合的基础上,运用比较分子力场分析(Co MFA)、比较分子相似性指数分析(Co MSIA)和Topomer Co MFA(T-COMFA)研究了分子结构与抑制活性的关系,发现由训练集立体场、静电场和疏水场组合的Co MSIA模型为最优模型,其内部交叉验证相关系数(Q2=0.557)、非交叉验证相关系数(R2=0.959)和外部预测相关系数(r2=0.863)具有统计学意义,该模型的三维等值线图直观显示了化合物的活性与其三维结构的关系.根据这些结果设计了10个具有新结构的含磷嘧啶类化合物,分子对接和分子动力学模拟结果表明,新化合物和CDK9的结合模式与原化合物64相同,自由能分析从理论上证明了新化合物64d的CDK9抑制活性优于化合物64,并且显示含磷基团与残基Asp109的静电场能在化合物与CDK9作用过程中有重要作用.     

GPCR structure-based virtual screening approach for CB2 antagonist search

The potential for therapeutic specificity in regulating diseases has made cannabinoid (CB) receptors one of the most important G-protein-coupled receptor (GPCR) targets in search for new drugs. Considering the lack of related 3D experimental structures, we have established a structure-based virtual screening protocol to search for CB2 bioactive antagonists based on the 3D CB2 homology structure model. However, the existing homology-predicted 3D models often deviate from the native structure and therefore may incorrectly bias the in silico design. To overcome this problem, we have developed a 3D testing database query algorithm to examine the constructed 3D CB2 receptor structure model as well as the predicted binding pocket. In the present study, an antagonist-bound CB2 receptor complex model was initially generated using flexible docking simulation and then further optimized by molecular dynamic and mechanical (MD/MM) calculations. The refined 3D structural model of the CB2-ligand complex was then inspected by exploring the interactions between the receptor and ligands in order to predict the potential CB2 binding pocket for its antagonist. The ligand-receptor complex model and the predicted antagonist binding pockets were further processed and validated by FlexX-Pharm docking against a testing compound database that contains known antagonists. Furthermore, a consensus scoring (CScore) function algorithm was established to rank the binding interaction modes of a ligand on the CB2 receptor. Our results indicated that the known antagonists seeded in the testing database can be distinguished from a significant amount of randomly chosen molecules. Our studies demonstrated that the established GPCR structure-based virtual screening approach provided a new strategy with a high potential for in silico identifying novel CB2 antagonist leads based on the homology-generated 3D CB2 structure model.     

Interpretation of scoring functions using 3D molecular fields. Mapping the diacyl-hydrazine-binding pocket of an insect ecdysone receptor

A method is presented for the interpretation of receptor docking score values (rough measures of binding affinities) of ligands in terms of 3D molecular field interaction contributions. The FlexX and FlexX-Pharm methods were used to dock the structures of designed sets of ligands into the ligand-binding pocket of a selected receptor. In the next step the relationship was investigated between the FlexX and CScore scores and 3D molecular fields obtained for the docked conformations of the ligands, using the CoMFA (Comparative Molecular Field Analysis) and CoMSIA (Comparative Molecular Similarity Indices Analysis) methods. The approach yielded highly significant CoMFA and CoMSIA models demonstrating that a high portion of the variance in the docking score values of the ligands can be explained by steric, electrostatic, hydrophobic, and hydrogen bond donor and acceptor molecular field interaction contributions. The approach was exemplified by using the crystal structure of the ligand-binding domain of the ecdysone receptor (EcR) of the moth Heliotis virescens as well as virtual molecule libraries of analogues of known diacyl-hydrazine (DAH) type ecdysteroid agonists. By docking appropriately designed virtual compound libraries into the DAH binding pocket of EcR followed by CoMFA and CoMSIA of the docked conformations, hitherto unexplored regions of the receptor cavity could be mapped. By mapping the significant molecular field interaction contributions onto the model of the receptor-ligand complex, important receptor-ligand interactions could be highlighted that may help the design of novel highly scored receptor ligands. An advantage of the method is that no experimental biological activity data are required to exhaustively map the receptor-binding site.     

Highly Accurate CCSD(T) and DFT–SAPT Stabilization Energies of H‐Bonded and Stacked Structures of the Uracil Dimer

The CCSD(T) interaction energies for the H‐bonded and stacked structures of the uracil dimer are determined at the aug‐cc‐pVDZ and aug‐cc‐pVTZ levels. On the basis of these calculations we can construct the CCSD(T) interaction energies at the complete basis set (CBS) limit. The most accurate energies, based either on direct extrapolation of the CCSD(T) correlation energies obtained with the aug‐cc‐pVDZ and aug‐cc‐pVTZ basis sets or on the sum of extrapolated MP2 interaction energies (from aug‐cc‐pVTZ and aug‐cc‐pVQZ basis sets) and extrapolated ΔCCSD(T) correction terms [difference between CCSD(T) and MP2 interaction energies] differ only slightly, which demonstrates the reliability and robustness of both techniques. The latter values, which represent new standards for the H‐bonding and stacking structures of the uracil dimer, differ from the previously published data for the S22 set by a small amount. This suggests that interaction energies of the S22 set are generated with chemical accuracy. The most accurate CCSD(T)/CBS interaction energies are compared with interaction energies obtained from various computational procedures, namely the SCS–MP2 (SCS: spin‐component‐scaled), SCS(MI)–MP2 (MI: molecular interaction), MP3, dispersion‐augmented DFT (DFT–D), M06–2X, and DFT–SAPT (SAPT: symmetry‐adapted perturbation theory) methods. Among these techniques, the best results are obtained with the SCS(MI)–MP2 method. Remarkably good binding energies are also obtained with the DFT–SAPT method. Both DFT techniques tested yield similarly good interaction energies. The large magnitude of the stacking energy for the uracil dimer, compared to that of the benzene dimer, is explained by attractive electrostatic interactions present in the stacked uracil dimer. These interactions force both subsystems to approach each other and the dispersion energy benefits from a shorter intersystem separation.     

Binding of human ACE2 and RBD of Omicron enhanced by unique interaction patterns among SARS-CoV-2 variants of concern

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus causing COVID-19, has continued to mutate and spread worldwide despite global vaccination efforts. In particular, the Omicron variant, first identified in South Africa in late November 2021, has become the dominant strain worldwide. Compared to the original strain identified in Wuhan, Omicron features 50 genetic mutations, with 15 mutations in the receptor-binding domain (RBD) of the spike protein, which binds to the human angiotensin-converting enzyme 2 (ACE2) receptor for viral entry. However, it is not completely understood how these mutations alter the interaction and binding strength between the Omicron RBD and ACE2. In this study, we used a combined steered molecular dynamics (SMD) simulation and experimental microscale thermophoresis (MST) approach to quantify the interaction between Omicron RBD and ACE2. We report that the Omicron brings an enhanced RBD-ACE2 interface through N501Y, Q498R, and T478K mutations; the changes further lead to unique interaction patterns, reminiscing the features of previously dominated variants, Alpha (N501Y) and Delta (L452R and T478K). Among the Q493K and Q493R, we report that Q493R shows stronger binding to ACE2 than Q493K due to increased interactions. Our MST data confirmed that the Omicron mutations in RBD are associated with a five-fold higher binding affinity to ACE2 compared to the RBD of the original strain. In conclusion, our results could help explain the Omicron variant's prevalence in human populations, as higher interaction forces or affinity for ACE2 likely promote greater viral binding and internalization, leading to increased infectivity.     

Adaptation of a fast Fourier transform-based docking algorithm for protein design

Designing proteins with novel protein/protein binding properties can be achieved by combining the tools that have been developed independently for protein docking and protein design. We describe here the sequence-independent generation of protein dimer orientations by protein docking for use as scaffolds in protein sequence design algorithms. To dock monomers into sequence-independent dimer conformations, we use a reduced representation in which the side chains are approximated by spheres with atomic radii derived from known C2 symmetry-related homodimers. The interfaces of C2-related homodimers are usually more hydrophobic and protein core-like than the interfaces of heterodimers; we parameterize the radii for docking against this feature to capture and recreate the spatial characteristics of a hydrophobic interface. A fast Fourier transform-based geometric recognition algorithm is used for docking the reduced representation protein models. The resulting docking algorithm successfully predicted the wild-type homodimer orientations in 65 out of 121 dimer test cases. The success rate increases to approximately 70% for the subset of molecules with large surface area burial in the interface relative to their chain length. Forty-five of the predictions exhibited less than 1 A C(alpha) RMSD compared to the native X-ray structures. The reduced protein representation therefore appears to be a reasonable approximation and can be used to position protein backbones in plausible orientations for homodimer design.