EGFR和4-苯胺喹唑啉类抑制剂之间相互作用模式的研究

采用分子动力学和MM/PBSA相结合的方法预测了表皮生长因子受体和4-苯胺喹 啉类抑制剂的相互作用模式。在分子动力学采样的基础上,采用MM/PBSA的方法分 别预测了四种可能结合模式下表皮生长因子受体和4-苯胺喹唑啉类抑制剂间的结合 自由能。在MM/PBSA计算中,受体和抑制剂之间的非键相互作用能采用分子力学 (MM)的方法得到;溶剂效应中极性部分对自由能的贡献通过解Possion- Boltzmanne (PB)方程的方法得到;溶液效应中非极性部分对自由能的贡献则通过 分子表面积计算(SA)的方法得到。计算表明,在四种结合模式下,表皮生长因子受 体和4-苯胺喹唑啉类抑制剂之间的结合自由能有较大的差别。在最佳的相互作用模 式中,抑制剂的苯胺部分位于活性口袋的底部,能够与受体残基的非极性侧链产生 很强的范德华和疏水相互作用。抑制剂喹唑啉环上的N(1)原子能够和Met-769上的 NH形成稳定的氢键,而抑制剂上的N(3)原子则和周围的一个水分子形成氢键。同时 ,抑制剂双环上的取代基团也能和活性口袋外部的部分残基形成一定的范德华和疏 水相互作用。最佳结合模式能够很好地解释已有抑制剂结构和活性间的关系。     

PTPlB和1，2-萘醌类抑制剂的分子动力学模拟

采用分子动力学和分子力学相结合的方法，研究了一类1，2-萘醌类抑制剂与酪氨酸蛋白磷酸酯酶PTP1B之间的相互作用模式．计算得到的抑制剂和靶酶之间的相互作用模式显示范德华相互作用、疏水相互作用以及氢键作用是主要的作用模式．计算结果还表明抑制剂和PTP1B的相互作用能△E越低，抑制剂活性越高．通过计算各种能量对△E的贡献，以及对复合物结构参数的分析，发现抑制剂和受体之间疏水相互作用是造成抑制剂活性差别的主要原因．这为设计其他非酸类抑制剂提供了信息．     

PTP1B和1,2-萘醌类抑制剂的分子动力学模拟

采用分子动力学和分子力学相结合的方法 ,研究了一类 1,2 萘醌类抑制剂与酪氨酸蛋白磷酸酯酶PTP1B之间的相互作用模式 .计算得到的抑制剂和靶酶之间的相互作用模式显示范德华相互作用、疏水相互作用以及氢键作用是主要的作用模式 .计算结果还表明抑制剂和PTP1B的相互作用能ΔE越低 ,抑制剂活性越高 .通过计算各种能量对ΔE的贡献 ,以及对复合物结构参数的分析 ,发现抑制剂和受体之间疏水相互作用是造成抑制剂活性差别的主要原因 .这为设计其他非酸类抑制剂提供了信息     

Hydroxamate类抑制剂与MMP-2的分子对接研究

通过分子动力学模拟研究了MMP-2和hydroxamate抑制剂之间的作用模式。在分子动力学模拟中,对于催化区的锌离子和其共价结合的配体(包括抑制剂和组氨酸)采用了键合的模型。从模拟的结果可以看到,R^1取代基和MMP-2的S1疏水口袋中的部分残基能形成很好的几何匹配,从而可以产生很强的范德华和疏水相互作用。模拟结果也表明,两个抑制剂和MMP-2之间分别能形成5个和8个氢键,抑制剂B比A活性更高的原因就是能够形成更加有利氢键作用模式。在整个模拟过程中,催化锌都能保持好的五配位形式,配位键的长度也处于稳定的状态,预测得到的MMP-2和其抑制剂的相互作用模式对于全新抑制剂的设计提供了非常重要的结构信息。     

表皮生长因子受体酪氨酸激酶抑制剂的药效团研究

根据一系列表皮生长因子受体酪氨酸激酶抑制剂的三维定量构效关系研究，得 到了该类抑制剂的药效团，研究结果与Novartis的药效团模型相当类似．药效团包 括一个氢键受体，一个氢键给体，一个疏水区和一个带有氯或溴原子药效团对于研 究表皮生长因子受体酪氨酸激酶抑制剂结构与活性的关系具有重要的意义．通过三 维数据库搜索可能会得到新的先导化合物．     

作用于秋水仙碱位点的微管蛋白抑制剂的结合模式研究与结构模型的构建

采用对接的方法建立了秋水仙碱位点抑制剂与微管蛋白的结合模式, 并构建了其结构模型. 结果表明: 抑制剂主要借助于与口袋I和II的疏水作用, 以及同α-Thr178, α-Val181和β-Cys241之间的氢键来实现与微管蛋白的结合. 根据抑制剂的结合构象, 将抑制剂的结构分为A, B以及AB间的桥连三个部分, 从而建立了由A部分中的疏水中心H1、氢键受体A1, B部分中的疏水中心H2、疏水基团H3和极性原子P以及桥连结构中的氢键受体A2组成的结构模型. 并指出H1与H2对活性的影响因素分别为疏水基团的体积和平面特征, 而桥连部分则应以刚性的形式保证AB处于桥连的同侧(即顺式构象). 还提出在A2与loop区之间存在一个的潜在氢键受体A3. 研究结果为设计新型小分子微管蛋白抑制剂提供指导.     

Hydroxamate类抑制剂与MMP-3的结合自由能的计算

用自由能微扰方法(FEP)计算了两种hydroxamate类的抑制剂和MMP-3的相对结合自由能。在计算中,对于催化区的锌离子与其共价结合的配体(包括抑制和组氨酸)采用了键合的模型,抑制剂和周围配体的部分电荷的计算采用两步静电势收敛方法。自由能计算采用了慢增长(Slowgrowth)和固定窗口增长(Fixedwidthwindowgrowth)两种方法,并且在每次计算中都采用了双向采样(Double-widesampling)的策略。两种方法计算得到的相对结合自由能都能和实验值很好的符合。同时从动力学模拟的得到的分子轨迹得到了抑制剂和受体之间相互作用模式,抑制剂的P1部分可以和受体的S1'口袋形成很强范德华和疏水相互作用,P1上的苯环可以和Tyr223上的苯环形成较好的π键堆积相互作用。     

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（英文）

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1ns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.     

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.     

新型酪氨酸激酶小分子抑制的三维药效团研究

通过CATALYST软件包得到了两类HER2抑制的三维药效团模型。尽管亚苄基丙二腈化合物和3-取代吲哚啉-2-酮系列化合物具有完全不同的骨架结构,但得到的药效团却具有共同的特性,这表明当这两类抑制剂和受体发生相互作用时,采用了相似的结合模式。共同的药效团模型包括一个氢键受体,一个氢键给体,一个脂肪类疏水团以及一个芳香类疏水团。根据药效团模型,我们还进行了三维构效关系的研究,结果表明得到的药效团模型具有很好的预测能力(线性回归系数R≈0.96)。药效团模型对于研究酪氨酸激酶小分子抑制剂的结构与活性关系,以及评估和预测此类未知化合物活性具人重要的意义。     

Virtual Screening of FDA-Approved Drugs for Enhanced Binding with Mitochondrial Aldehyde Dehydrogenase

Mitochondrial aldehyde dehydrogenase (ALDH2) is a potential target for the treatment of substance use disorders such as alcohol addiction. Here, we adopted computational methods of molecular dynamics (MD) simulation, docking, and molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) analysis to perform a virtual screening of FDA-approved drugs, hitting potent inhibitors against ALDH2. Using MD-derived conformations as receptors, butenafine (net charge q = +1 e) and olaparib (q = 0) were selected as promising compounds with a low toxicity and a binding strength equal to or stronger than previously reported potent inhibitors of daidzin and CVT-10216. A few negatively charged compounds were also hit from the docking with the Autodock Vina software, while the MM-PBSA analysis yielded positive binding energies (unfavorable binding) for these compounds, mainly owing to electrostatic repulsion in association with a negatively charged receptor (q = −6 e for ALDH2 plus the cofactor NAD⁺). This revealed a deficiency of the Vina scoring in dealing with strong charge–charge interactions between binding partners, due to its built-in protocol of not using atomic charges for electrostatic interactions. These observations indicated a requirement of further verification using MD and/or MM-PBSA after docking prediction. The identification of key residues for the binding implied that the receptor residues at the bottom and entrance of the substrate-binding hydrophobic tunnel were able to offer additional interactions with different inhibitors such as π-π, π-alkyl, van der Waals contacts, and polar interactions, and that the rational use of these interactions is beneficial to the design of potent inhibitors against ALDH2.     

乙酰胆碱酯酶AChE与1,7-二氮杂咔唑抑制剂的作用机理的分子动力学模拟

通过分子对接、分子动力学(MD)模拟以及成键自由能分析方法,从原子水平上模拟研究了3种1,7-二氮杂咔唑衍生物(分别记为M1、M2和M3)与ACh E的结合模式及相互作用机理,分析和讨论了研究体系的静电相互作用和范德华相互作用(vd W)。用MM-PBSA方法计算的3种抑制剂与ACh E之间的结合自由能与抑制剂的实验生物活性数据(IC50值)相对应。分析结果表明,残基S286与抑制剂之间形成的氢键作用有利于抑制剂与ACh E之间的结合。范德华相互作用,尤其是抑制剂与关键残基W279和Y334的作用,对抑制剂与ACh E之间的结合自由能有较大的贡献,在区分抑制剂M1(或M2)和M3的生物活性上发挥着重要的作用。     

乙酰胆碱酯酶AChE与1,7-二氮杂咔唑抑制剂的作用机理的分子动力学模拟

通过分子对接、分子动力学（MD）模拟以及成键自由能分析方法,从原子水平上模拟研究了3种1,7-二氮杂咔唑衍生物（分别记为M1、M2和M3）与AChE的结合模式及相互作用机理,分析和讨论了研究体系的静电相互作用和范德华相互作用（vdW）。用MM-PBSA方法计算的3种抑制剂与AChE之间的结合自由能与抑制剂的实验生物活性数据（IC₅₀值）相对应。分析结果表明,残基S286与抑制剂之间形成的氢键作用有利于抑制剂与AChE之间的结合。范德华相互作用,尤其是抑制剂与关键残基W279和Y334的作用,对抑制剂与AChE之间的结合自由能有较大的贡献,在区分抑制剂M1（或M2）和M3的生物活性上发挥着重要的作用。     

An analysis of the interactions between the Sem-5 SH3 domain and its ligands using molecular dynamics, free energy calculations, and sequence analysis.

The Src-homology-3 (SH3) domain of the Caenorhabditis elegans protein Sem-5 binds proline-rich sequences. It is reported that the SH3 domains broadly accept amide N-substituted residues instead of only recognizing prolines on the basis of side chain shape or rigidity. We have studied the interactions between Sem-5 and its ligands using molecular dynamics (MD), free energy calculations, and sequence analysis. Relative binding free energies, estimated by a method called MM/PBSA, between different substitutions at sites -1, 0, and +2 of the peptide are consistent with the experimental data. A new method to calculate atomic partial charges, AM1-BCC method, is also used in the binding free energy calculations for different N-substitutions at site -1. The results are very similar to those obtained from widely used RESP charges in the AMBER force field. AM1-BCC charges can be calculated more rapidly for any organic molecule than can the RESP charges. Therefore, their use can enable a broader and more efficient application of the MM/PBSA method in drug design. Examination of each component of the free energy leads to the construction of van der Waals interaction energy profiles for each ligand as well as for wild-type and mutant Sem-5 proteins. The profiles and free energy calculations indicate that the van der Waals interactions between the ligands and the receptor determine whether an N- or a Calpha-substituted residue is favored at each site. A VC value (defined as a product of the conservation percentage of each residue and its van der Waals interaction energy with the ligand) is used to identify several residues on the receptor that are critical for specificity and binding affinity. This VC value may have a potential use in identifying crucial residues for any ligand-protein or protein-protein system. Mutations at two of those crucial residues, N190 and N206, are examined. One mutation, N190I, is predicted to reduce the selectivity of the N-substituted residue at site -1 of the ligand and is shown to bind similarly with N- and Calpha-substituted residues at that site.     

Steroid binding by antibodies and artificial receptors: Exploration of theoretical methods to determine the origins of binding affinities and specificities

Binding mode calculations for complexes between an artificial paracyclophane receptor and digoxins, cholic acids as well as cortisone steroids show encapsulation of different ring combinations. Docking experiments were performed between the 26-10 antibody and digoxins. Coordination affinity arises from hydrophobic desolvation and van der Waals interactions rather than from hydrogen bonds. The specificity and affinity arises mainly from shape complementarity. Computed binding free energies and Kohonen neural network computations both point to physicochemical and structural similarities of natural antibodies and artificial receptors.     

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.     

Molecular dynamics simulation and free energy calculation studies of the binding mechanism of allosteric inhibitors with p38α MAP kinase

p38 MAP kinase is a promising target for anti-inflammatory treatment. The classical kinase inhibitors imatinib and sorafenib as well as BI-1 and BIRB-796 were reported to bind in the DFG-out form of human p38α, known as type II or allosteric kinase inhibitors. Although DFG-out conformation has attracted great interest in the design of type II kinase inhibitors, the structural requirements for binding and mechanism of stabilization of DFG-out conformation remain unclear. As allosteric inhibition is important to the selectivity of kinase inhibitor, herein the binding modes of imatinib, sorafenib, BI-1 and BIRB-796 to p38α were investigated by molecular dynamics simulation. Binding free energies were calculated by molecular mechanics/Poisson-Boltzmann surface area method. The predicted binding affinities can give a good explanation of the activity difference of the studied inhibitors. Furthermore, binding free energies decomposition analysis and further structural analysis indicate that the dominating effect of van der Waals interaction drives the binding process, and key residues, such as Lys53, Gly71, Leu75, Ile84, Thr106, Met109, Leu167, Asp168, and Phe169, play important roles by forming hydrogen bond, salt bridge, and hydrophobic interactions with the DFG-out conformation of p38α. Finally, we also conducted a detailed analysis of BI-1, imatinib, and sorafenib binding to p38α in comparison with BIRB-796 exploited for gaining potency as well as selectivity of p38 inhibitors. These results are expected to be useful for future rational design of novel type II p38 inhibitors.