白色念珠菌N-肉豆蔻酰基转移酶中药物结合位点的MCSS分析

采用多拷贝同时搜寻方法(MCSS)分析得到了CaNMT活性位点的疏水区域、氢键结合位点和负电性区域. MCSS计算结果显示, CaNMT活性位点有两个疏水性比较强的区域: 一个由Tyr107, Tyr109, Val108, Phe117, Phe123, Ala127, Phe176和Leu337等残基组成; 另一个由Phe115, Phe240和Phe339组成. CaNMT活性位点发现有两个氢键作用区域, 其中Tyr119, His227, Asn392和Leu451是与已有抑制剂的氢键结合位点, Tyr107, Asn175, Thr211和Asp412是新发现的氢键结合位点, 而且在NMT家族中高度稳定, 它们对设计新结构类型的CaNMT抑制剂具有重要作用. Leu451是负电性兼氢键作用位点, 是抑制剂设计时所必需考虑的位点.     

非对称二甲基精氨酸二甲胺水解酶-2的理论研究

利用同源模建的方法,构建了DDAH-2的三位结构.并与L-瓜氨酸、L-高半胱氨酸分别进行对接研究.其中,Asp77,Gly269,Glu27和Arg96是与L-瓜氨酸相互作用较强的残基,Asp77,His171,Asp125和Ala270是与-L高半胱氨酸相互作用较强的残基.尤其Asp77是在两种复合物中同时起重要作用的氨基酸,并且它与抑制剂之间都形成了氢键.     

漆酶与酚类模式底物的结合及反应活性的理论研究

通过生物信息学分析、分子动力学模拟及量子化学计算,对21种邻对位取代酚类模式底物与漆酶的结合能力以及反应活性进行了探讨.生物信息学结构比对分析发现漆酶的活性口袋含有Asp/Glu206,Asn/His208,Asn264,Gly392和His458等保守的氨基酸残基(氨基酸残基编号以Trametes versicolor漆酶为例,PDB:1KYA);采用MM-GBSA方法计算了21种酚类模式底物与T.versicolor漆酶的结合自由能.分子力学计算结果表明,漆酶与底物的结合力主要来自Asp206和Asn264等残基与底物分子形成的分子间氢键,并且Phe265残基和酚类底物的芳香环形成π-π相互作用.量子化学计算表明,芳环上取代基的推拉电子效应显著影响协同电子转移的底物去质子化过程,其中推电子能力较强的—NH2,—OH,—OCH3和—CH CHCH3等基团能够明显增强酚羟基反应活性,而吸电子的—CONH2和—Cl则具有相反的效应.     

ACE抑制三肽与ACE相互作用的分子机制

4种食源性三肽IRP(Ile-Arg-Pro),IKP(Ile-Lys-Pro),GRP(Gly-Arg-Pro),IRA(Ile-ArgAla)的ACE抑制活性已得到实验证实,但其与ACE的相互作用模式与分子机制尚不清楚,本研究采用柔性分子对接方法解决这一问题。分子对接结果表明:4种三肽与ACE有相似的作用模式,氢键、亲水、疏水、静电等作用力共同对三肽与ACE的结合存在贡献,但以氢键作用为主;ACE分子中Lys511,His513,Tyr520,Tyr523等氨基酸残基为其与肽结合的重要结合位点;ACE抑制三肽中氮端氨基和碳端羧基对其抑制活性影响显著,其中氮端氨基的作用更为重要。通过以上分子机理研究可为开发强活性ACE抑制肽提供理论指导。     

人类谷胱甘肽硫转移酶家族等位基因蛋白B与抑制剂作用模型的理论研究

采用分子动力学模拟方法系统地研究了谷胱甘肽硫转移酶家族(Glutathione S-transferases,GSTs)的等位基因蛋白B(GSTP1*B)与抑制剂利尿酸(EA)以及EA的谷胱甘肽(GSH)共轭物EAG(I),EAG(O)的具体结合方式.抑制剂及其谷胱甘肽共轭物与蛋白的相互作用能计算结果及分子动力学轨迹的统计分析结果表明,GSTP1*B与EA的谷胱甘肽共轭物的结合能力优于其与EA的结合能力,Phe8,Arg13,Trp38和Tyr108是作用过程中的关键残基,对稳定抑制剂及其谷胱甘肽共轭物在GSTP1*B的G和H位点的构象具有重要的作用.通过对构象的统计分析发现,残基Phe8和Tyr108与GSTP1*B酶对抑制剂的选择性密切相关.     

分子动力学模拟残基突变对芳香烃受体配体结合区的影响

芳香烃受体(Aryl hydrocarbon receptor,AhR)属于配体依赖性的转录因子蛋白。本文通过对AhR配体结合区域(Ligand binding domain,LBD)的结构功能及物种特异性分析,发现在其结合腔口有一些关键残基可能起到"门控"作用,进一步将野生型(WT)和3个突变模型(Phe289Ala、Tyr316Ala、Ile319Ala)进行分子动力学模拟,从蛋白稳定性、蛋白结构变化、蛋白结合腔变化及蛋白和配体结合能力4个方面分析3个残基的门控作用。研究发现,Phe289、Tyr316、Ile319氨基酸残基通过形成疏水作用为AhR LBD起到"门控"作用;而将这些氨基酸分别突变后,其蛋白稳定性降低,整体运动性增加,配体亲和力减弱,其中Tyr316、Ile319对腔内体积影响较大,Phe289使腔内环境稳定性降低。本研究可为基于芳香烃受体的药物设计提供相关理论指导。     

促δ-波睡眠肽的结构与功能的研究

用固相法合成了促δ-波睡眠肽Trp-Ala-Gly-Gly-Asp-Ala-Ser-Glu(DSIP)及其十四种类似物和三个短肽,研究了结构与功能的关系,类似物的设计,主要考虑在分子中引入D-氨基酸以抑制酶的作用和增强稳定性,以及引入疏水侧链氨基酸如Phe和Trp等。位置的修饰主要在1,3,4,5,8和9位,即:D-Trp[1],Tyr[1],Tyr[1]Phe[5],D-Trp[1]Phe[8],Trp[3,4],D-Trp[3,4],D-Trp[1,3,4]Phe[8],D-Glu[9],D-pF-Phe[3,4]Phe[8]D-Glu[9],Phe[5],Glu[5]Asp[9],Tyr[5]Asp[9],Ala[7]和Asp[9]-DSIP以及Trp-Ala-Gly-Gly-Asp,Trp-Ala-Gly-Gly-Glu和Trp-Gly-Glu.合成肽的纯度经氨基酸组成分析、元素分析、薄层层析以及纸电泳鉴定。生物试验表明D-Trp[1],Tyr[1],Tyr[1]Phe[5],Ala[7]-DSIP无促眠活性;而Phe[5]-DSIP的促眠活性与DSIP相接近,其他类似物的生物试验结果将另文发表。     

FKBP12蛋白与其抑制剂结合的分子动力学模拟和结合自由能计算

FKBP12 (FK506-binding protein-12)是一种具有神经保护和促神经再生作用的蛋白. 采用分子动力学模拟取样, 运用MM-GBSA方法计算了FKBP12和3个抑制剂(GPI-1046, 308和107)的绝对结合自由能, GPI-1046的结合能最小, 308小于107的结合能. 通过能量分解的方法考察了FKBP12蛋白的主要残基与抑制剂之间的相互作用和识别, 计算结果表明: 3个抑制剂具有相似的结合模式, Ile56和Tyr82主要表现为氢键作用, Tyr26, Phe46, Val55, Ile56, Trp59, Tyr82, Tyr87和Phe99形成疏水作用区. 计算结果和实验结果吻合.     

基于分子动力学模拟提高嗜热磷酸三酯酶样内酯酶非特异性底物活力的理论研究

采用分子动力学模拟和拉伸分子动力学模拟方法, 结合分子力学-广义玻恩表面积(MM-GB/SA)方法进行自由能计算和结构交互指纹分析, 研究了模拟过程中非特异性底物(对氧磷/内酯)分别与嗜热磷酸三酯酶样内酯酶(SsoPox)野生型和突变体(W263F/W263T)结合的构象变化, 分析了Loop8中重要残基Trp263的突变提高SsoPox非特异性底物活力的原因, 发现其能够影响门控残基Phe229的构象变化, 导致活性口袋入口变宽(Phe229与Tyr99之间的距离变大), 使对氧磷和内酯更容易结合到蛋白质的活性位点上; Asp256和Arg223形成盐桥的几率高于野生型(WT)SsoPox, 在Arg223(位于Loop7)的协助下质子更加高效地从活性中心的Asp256(位于Loop8)传递到溶剂中去, 因而能够提高SsoPox水解底物的效率. 通过比较2个野生型复合物的结构稳定性和结合自由能差异, 发现在模拟过程中SsoPox与内酯的复合物体系更加稳定并且具有更低的结合自由能, 有利于SsoPox识别底物并使其埋在活性部位的疏水环境中, 促进氢氧化物亲核进攻底物的亲电中心. 因此, 底物与酶稳定的相互作用可能是SsoPox具有天然内酯酶活性的原因之一.     

几种新型树脂对猪血粉水解液中混合氨基酸分离性能研究

本文就AAS-1.AAS-2.和D371等新型树脂对猪血粉水解液的脱色及氨基酸的分离性能进行了研究;并将这些树脂同工业品树脂配合使用,分离出了Arg、Lys、His、Phe、Tyr、Glu、Asp和部分其它中性氨基酸。     

Amino acids of ricin and its polypeptides

Ricin and its corresponding polypeptides (A & B chain) were purified from castor seed. The molecular weight of ricin subunits were 29,000 and 28,000 daltons. The amino acids in ricin determined were Asp45 The22 Ser40 Glu53 Cys4 Gly96 His5 Ile21 Leu33 Lys20 Met4 Phe13 Pro37 Tyr11 Ala45 Val23 Arg20 indicating that ricin contains approximately 516 amino acid residues. The amino acids of the two subunits of ricin A and B chains were Asp23 The12 Ser21 Glu29 Cys2 Gly48 His3 Ile12, Leu17 Lys10 Met2 Phe6 Pro17 Tyr7 Ala35 Val13 Arg13 while in B chain the amino acids were Asp22 The10 Ser19 Glu25 Cys2 Gly47 His1 Ile10, Leu15 Lys11 Met1 Phe7 Pro6 Tyr5 Ala32Val11 Arg10. The total helical content of ricin came around 53.6% which is a new observation.     

VEGFR-2 与抑制剂Sunitinib 的分子对接及分子动力学研究

用分子对接方法研究了VEGFR-2 和抑制剂Sunitinib 的相互作用模式, 并对其复合物进行了10 ns 的分子动力学(Molecular Dynamics, MD)模拟. 结果表明, 抑制剂Sunitinib 能与VEGFR-2 中位于活性空腔的Glu885, Ile888, His1026,Asp1028, Asp1046 五个氨基酸残基形成疏水作用; 另外, VEGFR-2 中His1026, Cys1024, Asp1046 三个氨基酸残基能与Sunitinib 形成三个作用强度不同的氢键. 这些基团之间的相互作用是Sunitinib 抑制VEGFR-2 活性的关键因素. 研究结果可为VEGFR-2 抑制剂的结构改良、分子设计、合成提供理论参考, 并有助于寻找活性更高、效果更好的抗肿瘤药物.     

Quantum mechanical studies of residue-specific hydrophobic interactions in p53-MDM2 binding

Quantum chemistry calculations at the levels of MP2/cc-pVDZ and MP2/cc-PVTZ have been carried out to study residue-specific interactions at the hydrophobic p53-MDM2 binding interface. The result of the calculation, based on structures from nanosecond molecular dynamics simulation, revealed that (19)Phe, (22)Leu, and (23)Trp of p53 have the strongest binding interaction with MDM2 followed by (26)Leu and (27)Pro. The specific residues of MDM2 that have dominant binding interactions with p53 are specifically identified to be (51)Lys, (54)Leu, (62)Met, (67)Tyr, (72)Gln, (94)Lys, (96)His, and (100)Tyr. The p53-MDM2 binding interaction is dominated by van der Waals interaction and to a lesser degree by electrostatic interaction. The MP2 results are in generally good agreement with those from the force field calculation while the DFT/B3LYP calculation failed to give attractive interaction energies for certain residue-residue interactions due to the lack of dispersion energy.     

The Catalytic Mechanism of Fluoroacetate Dehalogenase: A Computational Exploration of Biological Dehalogenation

The biological dehalogenation of fluoroacetate carried out by fluoroacetate dehalogenase is discussed by using quantum mechanical/molecular mechanical (QM/MM) calculations for a whole‐enzyme model of 10 800 atoms. Substrate fluoroacetate is anchored by a hydrogen‐bonding network with water molecules and the surrounding amino acid residues of Arg105, Arg108, His149, Trp150, and Tyr212 in the active site in a similar way to haloalkane dehalogenase. Asp104 is likely to act as a nucleophile to attack the α‐carbon of fluoroacetate, resulting in the formation of an ester intermediate, which is subsequently hydrolyzed by the nucleophilic attack of a water molecule to the carbonyl carbon atom. The cleavage of the strong C? F bond is greatly facilitated by the hydrogen‐bonding interactions between the leaving fluorine atom and the three amino acid residues of His149, Trp150, and Tyr212. The hydrolysis of the ester intermediate is initiated by a proton transfer from the water molecule to His271 and by the simultaneous nucleophilic attack of the water molecule. The transition state and produced tetrahedral intermediate are stabilized by Asp128 and the oxyanion hole composed of Phe34 and Arg105.     

The Molecular Dynamic Simulation of Zolpidem Interaction with Gamma Aminobutyric Acid Type A Receptor

Our goal was to generate the extracellular domain of gamma‐aminobutyric acid type A receptor (GABA_A receptor) by comparative modeling and to study the interaction of zolpidem with the GABA_A receptor. The modeling strategy was verified to provide reasonable 3‐dimensional coordinates. These coordinates helped to combine all the subunits well. The benzodiazepine (BZ) binding site was located in a binding pocket between the α₁ and γ₂ subunits of the GABA_A receptor. Zolpidem was selected to dock into the binding site. In our study, the residues of the binding pocket were suggested to be αHis129, αTyr187, αGly228, αThr234, αTyr237, γMet96, γPhe116, γSer130, γGly143, and γMet169. By the calculation of the docking module, the conformation of zolpidem docking in the BZ binding site was investigated. A hydrogen bond was found at γArg136 when zolpidem's conformation was in rank 2 of the docking score. The contracted binding pocket showed residues at αHis129, αTyr187, αGly228, αTyr237, γPhe116, and γMet169. Zolpidem docking in a contracted binding pocket might generate a hydrogen bond in α His 129.     

Theoretical Investigation on Binding Process of Allophanate to Allophanate Hydrolase

Several molecular simulation methods were integrated to investigate the detailed binding process of allophanate to allophanate hydrolase and predict their stable complex structure. The optimal enzyme-substrate complex conformation demonstrates that along with Arg307 and Tyr299, Gly124 is also one of the key anchor residues in the stable complex. The energetic calculation suggests the existence of an intermediate state in the enzyme-substrate binding process. The further atomic-level investigation illuminates that Tyr299, Arg307 and Ser172 can stabilize the substrate in the intermediate state. By this token, the residues Arg307 and Tyr299 function in both binding process and getting stable state.     

Molecular Modeling and Docking of Mannose-binding Lectin from Lycoris radiata

Lycoris radiata mannose-binding lectin（LRL） is a protein which binds mannose residues specifically. The maturation peptide and three mannose-binding domains（residues 49-57, 80-88 and 113--121） of LRL were identified by sequence analysis. The 3D structure of LRL constructed by homology modeling shaped a flstular triangular prism. Three flanks of the prism are mainly composed of β-sheets and each flank has a mannose-binding domain. According to the docking and dynamics simulation, the bindings of residues 49--57 and 80--88 with mannose are more stable than that of residues 113--121 with it. The key residues for binding mannose are Gin80, Asp82, Ash84 and Tyr88. The study preliminarily analyzed the interaction sites and mechanism of LRL with mannoses, which could be useful for the study on insect-resistance and related drug discovery of LRL.     

Thermodynamics of stacking interactions in proteins

Using a database of 6166 experimental structures taken from the Protein Data Bank, we have studied pair interactions between planar residues (Phe, Tyr, His, Arg, Glu and Asp) in proteins, known as pi-pi interactions. On the basis of appropriate coordinates defining the mutual arrangement of two residues, we have calculated 2-D potentials of mean force aimed at determining the stability of the most probable structures for aromatic-aromatic, aromatic-cation and aromatic-anion bound pairs. Our analysis reveals the thermodynamic relevance and the ubiquity of stacked complexes in proteins.     

Molecular modeling of Protein A affinity chromatography

The properties of the complex between fragment B of Protein A and the Fc domain of IgG were investigated adopting molecular dynamics with the intent of providing useful insight that might be exploited to design mimetic ligands with properties similar to those of Protein A. Simulations were performed both for the complex in solution and supported on an agarose surface, which was modeled as an entangled structure constituted by two agarose double chains. The energetic analysis was performed by means of the molecular mechanics Poisson Boltzmann surface area (MM/PBSA), molecular mechanics generalized Born surface area (MM/GBSA), and the linear interaction energy (LIE) approaches. An alanine scan was performed to determine the relative contribution of Protein A key amino acids to the complex interaction energy. It was found that three amino acids play a dominant role: Gln 129, Phe 132 and Lys 154, though also four other residues, Tyr 133, Leu 136, Glu 143 and Gln 151 contribute significantly to the overall binding energy. A successive molecular dynamics analysis of Protein A re-organization performed when it is not in complex with IgG has however shown that Phe 132 and Tyr 133 interact among themselves establishing a significant π–π interaction, which is disrupted upon formation of the complex with IgG and thus reduces consistently their contribution to the protein–antibody bond. The effect that adsorbing fragment B of Protein A on an agarose support has on the stability of the protein–antibody bond was investigated using a minimal molecular model and compared to a similar study performed for a synthetic ligand. It was found that the interaction with the surface does not hinder significantly the capability of Protein A to interact with IgG, while it is crucial for the synthetic ligand. These results indicate that ligand–surface interactions should be considered in the design of new synthetic affinity ligands in order to achieve results comparable to those of Protein A right from the ligand design stage.