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1.
假设分子对接面的紧密堆积类似于蛋白质内部的紧密堆积,因此用于蛋白质内部的侧链构象预测方法,如死端排除法,可应用于分子对接面内的侧链构象预测。应用9个晶体结构对这一假设进行检验。结果表明假设基本正确。对2个蛋白酶和抑制剂的应用比较成功。9个配体中的7个有正确的均方根差的趋势。还发现受体结构的柔性较小,说明由于对接面的紧密堆积产生的侧链构象变化很小。根据这些结果,提出一个新的分子对接流程图,即在刚体对  相似文献   

2.
一般的蛋白质对接程序能够提供大量的待选构象,但其中仅含有少量的正确构象。现在对接的主要工作在于如何从这些大量构象中挑出正确构象。我们先前的研究工作证明蛋白质界面比非界面表面具有更高的能量。在这里,我们使用由chen等人提出的一个用于检验、设计对接程序的蛋白质复合物标准库中的非抗原-抗体复合物,将侧链能量运用到对接中,并比较了侧链能量和残基配对倾向性、残基组成倾向性、残基保守性在对接中的表现。单独使用这四项的正确构象的平均百排分位排序分别为:38.6±19.6、26.3±20.8、22.7±16.6和37.8±26.1,但是对于个别蛋白,侧链能量的表现要优于其它的三个参数。我们将四个参数综合起来考虑,发展了一个新的打分函数,平均百排分位排序为22.2±7.8,并且提高了筛选效率。  相似文献   

3.
蛋白质-蛋白质对接中打分函数的研究   总被引:1,自引:0,他引:1  
通过分析蛋白质-蛋白质间的静电、疏水作用和熵效应与相对于晶体结构的蛋白质主链原子的均方根偏差(RMSD)的相关性,定量地考查了它们在蛋白质-蛋白质对接中作为打分函数评价近天然构象的能力。对7个蛋白质复合物体系的分析表明,就水化能而言,原子接触势模型(ACE)优于原子水化参数模型(ASP),且修正的ACE模型具有更好的评价近天然构象的能力;水化能与静电能结合对评价能力有进一步的提高。最后,我们将静电和修正的ACE水化能结合作为打分函数用于36个蛋白质复合物体系的对接研究,进一步证实了这两种能量项的组合能有效地将近天然结构从分子对接模式中区分出来。  相似文献   

4.
蛋白质结构预测方法探析   总被引:1,自引:0,他引:1  
刘云玲  陶兰 《生物信息学》2007,5(4):185-186
首先介绍了蛋白质结构预测中的三种理论方法,然后对同源蛋白质结构预测中侧链构造和环区构建中涉及到的主要方法进行了探讨,对非同源蛋白质结构预测中空间构象搜寻涉及到的主要算法进行了分析比较。  相似文献   

5.
预测蛋白质—蛋白质复合物结构的软对接算法   总被引:1,自引:0,他引:1  
提出了一种有效的软对接算法 ,用于在已知受体和配体三维结构的条件下预测蛋白质 蛋白质复合物的结构。该算法的分子模型基于Janin提出的简化蛋白质模型 ,并在此基础上有所改进。对蛋白质分子表面的柔性氨基酸残基Arg、Lys、Asp、Glu和Met进行了特殊处理 ,通过软化分子表面的方式考虑了它们的侧链柔性。采用双重过滤技术来排除不合理的对接结构 ,此过滤技术是以复合物界面几何互补性和残基成对偏好性为标准提出的。对所得到的构象进行能量优化 ,之后用打分函数对这些结构进行排序 ,挑选出与复合物天然结构接近的构象。该打分函数包括静电、疏水和范德华相互作用能。用此算法对 2 6个复合物进行了结构预测 ,均找到了近天然结构 ,其中有 2 0个复合物的近天然结构排在了前 10位。改进的分子模型可以在一定程度上描述蛋白质表面残基侧链的柔性 ;双重过滤技术使更多的近天然结构保留下来 ,从而提高了算法成功预测的可能性 ;打分函数可以较合理地评价对接结构。总之 ,此种软对接算法能够对蛋白质分子识别的研究提供有益的帮助。  相似文献   

6.
Kunitz 型丝氨酸蛋白酶抑制剂结构与功能研究   总被引:2,自引:0,他引:2  
蛋白酶抑制剂在酶学及蛋白质的结构与功能关系研究中有重要意义,Kunitz型丝氨酸蛋白酶抑制剂是其中最重要的,也是研究最广泛的蛋白酶抑制剂之一.该类蛋白酶抑制剂三维结构高度保守:由一个明显的疏水核心、三对高度保守的二硫键桥、三链β-折叠和一个N端3 10螺旋及一个C端α-螺旋组成.3对二硫键对分子空间结构的稳定起着非常重要的作用.这一类型抑制剂有5个主要的活性位点:P1、P1’、P3、P3’、P4,它们都位于一个溶剂暴露的环上.P1位点是抑制作用的关键活性位点,抑制剂的专一性由P1位点氨基酸残基的性质决定;P1’位点氨基酸残基的侧链大小对抑制剂.酶的结合常数有很大影响,用大的侧链残基取代会导致结合常数降低;P4位点残基被取代经常产生负效应,会导致活性区域环的构象发生很大改变,从而影响酶与抑制剂的结合.  相似文献   

7.
蛋白质分子间相互作用与识别是当前生命科学研究的热点,分子对接方法是研究这一问题的有效手段.为了推进分子对接方法的发展,欧洲生物信息学中心组织了国际蛋白质复合物结构预测(CAPRI)竞赛.通过参加CAPRI竞赛,逐步摸索出了一套用于蛋白质复合物结构预测的集成蛋白质一蛋白质分子对接方法HoDock,它包括结合位点预测、初始复合物结构采集、精细复合物结构采集、结构成簇和打分排序以及最终复合物结构挑选等主要步骤.本文以最近的CAPRI Target 39为例,具体说明该方法的主要步骤和应用.该方法在CAPRI Target 39竞赛中取得了比较好的结果,预测结构Model 10是所有参赛小组提交的366个结构中仅有的3个正确结构之一,其配体均方根偏差(L_Rmsd)为0.25nm.在对接过程中,首先用理论预测和实验信息相结合的方法来寻找蛋白质结合位点残基,确认CAPRI Target 39A链的A31TRP和A191HIS,B链的B512ARG和B531ARG为可能结合位点残基.同时,用ZDock程序做不依赖结合位点的初步全局刚性对接.然后,根据结合位点信息进行初步局部刚性对接,从全局和局部对接中挑出了11个初始对接复合物结构.进而,用改进的Rosetta Dock程序做精细位置约束对接,并对每组对接中打分排序前200的结构进行成簇聚类.最后,综合分析打分、成簇和结合位点三方面的信息,得到10个蛋白质复合物结构.竞赛结果表明,A191HIS,B512ARG和B531ARG三个结合位点残基预测正确,提交的10个蛋白质复合物结构中有5个复合物受体一配体界面残基预测成功率较高.与其他参赛小组的对接结果比较,表明HoDock方法具有一定优势.这些结果说明我们提出的集成分子对接方法有助于提高蛋白质复合物结构预测的准确率.  相似文献   

8.
蛋白质侧链的预测   总被引:1,自引:1,他引:0  
根据Desmet的死端排除定则制作了一种蛋白质侧链观测的快速软件系统,用一批实例结构数据和不同的侧链转子来检验上定则的可行性;并讨论预测的RMS与溶剂可及性和结构元素的关系。  相似文献   

9.
阿维链霉菌在复合培养基中生长时合成大量蛋白酶以满足菌体分解有机氮源进行生长代谢的需要。而大量蛋白酶的存在对二维电泳的蛋白质组分析细胞蛋白质样品的提取带来了很大的困难。根据阿维链霉菌胞内蛋白酶的组成,以EDTA、PMSF、Bestatin、Pepstatin和E-64等5种蛋白酶抑制剂为基础,通过单因子实验和正交实验优化得到了高效的蛋白酶抑制剂复合配方。验证实验表明,该复合蛋白酶抑制剂在阿维链霉菌细胞蛋白质样品提取中,具有良好的蛋白酶活性抑制效果。  相似文献   

10.
利用蛋白质主链的极性分数及主链二面角为参量,构建了一种基于蛋白质结构数据库的势函数。将该势函数应用于蛋白质反向折叠研究中,发现该函数可成功地将蛋白质分子的天然构象从构建的构象库中识别出来;将一目标序列与构象库的每一可能的构象匹配,并用该势函数计算相应的能量,结果表明对绝大多数蛋白质分子来说,天然的构象的能量值总是最低。此外,该函数还将一些序列相似性较低,而结构相似性较高的蛋白质分子识别出来。我们认  相似文献   

11.
Computational prediction of side‐chain conformation is an important component of protein structure prediction. Accurate side‐chain prediction is crucial for practical applications of protein structure models that need atomic‐detailed resolution such as protein and ligand design. We evaluated the accuracy of eight side‐chain prediction methods in reproducing the side‐chain conformations of experimentally solved structures deposited to the Protein Data Bank. Prediction accuracy was evaluated for a total of four different structural environments (buried, surface, interface, and membrane‐spanning) in three different protein types (monomeric, multimeric, and membrane). Overall, the highest accuracy was observed for buried residues in monomeric and multimeric proteins. Notably, side‐chains at protein interfaces and membrane‐spanning regions were better predicted than surface residues even though the methods did not all use multimeric and membrane proteins for training. Thus, we conclude that the current methods are as practically useful for modeling protein docking interfaces and membrane‐spanning regions as for modeling monomers. Proteins 2014; 82:1971–1984. © 2014 Wiley Periodicals, Inc.  相似文献   

12.
The goal of this article is to reduce the complexity of the side chain search within docking problems. We apply six methods of generating side chain conformers to unbound protein structures and determine their ability of obtaining the bound conformation in small ensembles of conformers. Methods are evaluated in terms of the positions of side chain end groups. Results for 68 protein complexes yield two important observations. First, the end‐group positions change less than 1 Å on association for over 60% of interface side chains. Thus, the unbound protein structure carries substantial information about the side chains in the bound state, and the inclusion of the unbound conformation into the ensemble of conformers is very beneficial. Second, considering each surface side chain separately in its protein environment, small ensembles of low‐energy states include the bound conformation for a large fraction of side chains. In particular, the ensemble consisting of the unbound conformation and the two highest probability predicted conformers includes the bound conformer with an accuracy of 1 Å for 78% of interface side chains. As more than 60% of the interface side chains have only one conformer and many others only a few, these ensembles of low‐energy states substantially reduce the complexity of side chain search in docking problems. This approach was already used for finding pockets in protein–protein interfaces that can bind small molecules to potentially disrupt protein–protein interactions. Side‐chain search with the reduced search space will also be incorporated into protein docking algorithms. Proteins 2012. © 2011 Wiley Periodicals, Inc.  相似文献   

13.
14.
Bordner AJ  Gorin AA 《Proteins》2007,68(2):488-502
Computational prediction of protein complex structures through docking offers a means to gain a mechanistic understanding of protein interactions that mediate biological processes. This is particularly important as the number of experimentally determined structures of isolated proteins exceeds the number of structures of complexes. A comprehensive docking procedure is described in which efficient sampling of conformations is achieved by matching surface normal vectors, fast filtering for shape complementarity, clustering by RMSD, and scoring the docked conformations using a supervised machine learning approach. Contacting residue pair frequencies, residue propensities, evolutionary conservation, and shape complementarity score for each docking conformation are used as input data to a Random Forest classifier. The performance of the Random Forest approach for selecting correctly docked conformations was assessed by cross-validation using a nonredundant benchmark set of X-ray structures for 93 heterodimer and 733 homodimer complexes. The single highest rank docking solution was the correct (near-native) structure for slightly more than one third of the complexes. Furthermore, the fraction of highly ranked correct structures was significantly higher than the overall fraction of correct structures, for almost all complexes. A detailed analysis of the difficult to predict complexes revealed that the majority of the homodimer cases were explained by incorrect oligomeric state annotation. Evolutionary conservation and shape complementarity score as well as both underrepresented and overrepresented residue types and residue pairs were found to make the largest contributions to the overall prediction accuracy. Finally, the method was also applied to docking unbound subunit structures from a previously published benchmark set.  相似文献   

15.
《Proteins》2018,86(5):581-591
We compare side chain prediction and packing of core and non‐core regions of soluble proteins, protein‐protein interfaces, and transmembrane proteins. We first identified or created comparable databases of high‐resolution crystal structures of these 3 protein classes. We show that the solvent‐inaccessible cores of the 3 classes of proteins are equally densely packed. As a result, the side chains of core residues at protein‐protein interfaces and in the membrane‐exposed regions of transmembrane proteins can be predicted by the hard‐sphere plus stereochemical constraint model with the same high prediction accuracies (>90%) as core residues in soluble proteins. We also find that for all 3 classes of proteins, as one moves away from the solvent‐inaccessible core, the packing fraction decreases as the solvent accessibility increases. However, the side chain predictability remains high (80% within ) up to a relative solvent accessibility, , for all 3 protein classes. Our results show that % of the interface regions in protein complexes are “core”, that is, densely packed with side chain conformations that can be accurately predicted using the hard‐sphere model. We propose packing fraction as a metric that can be used to distinguish real protein‐protein interactions from designed, non‐binding, decoys. Our results also show that cores of membrane proteins are the same as cores of soluble proteins. Thus, the computational methods we are developing for the analysis of the effect of hydrophobic core mutations in soluble proteins will be equally applicable to analyses of mutations in membrane proteins.  相似文献   

16.
Small molecule docking predicts the interaction of a small molecule ligand with a protein at atomic-detail accuracy including position and conformation the ligand but also conformational changes of the protein upon ligand binding. While successful in the majority of cases, docking algorithms including RosettaLigand fail in some cases to predict the correct protein/ligand complex structure. In this study we show that simultaneous docking of explicit interface water molecules greatly improves Rosetta’s ability to distinguish correct from incorrect ligand poses. This result holds true for both protein-centric water docking wherein waters are located relative to the protein binding site and ligand-centric water docking wherein waters move with the ligand during docking. Protein-centric docking is used to model 99 HIV-1 protease/protease inhibitor structures. We find protease inhibitor placement improving at a ratio of 9∶1 when one critical interface water molecule is included in the docking simulation. Ligand-centric docking is applied to 341 structures from the CSAR benchmark of diverse protein/ligand complexes [1]. Across this diverse dataset we see up to 56% recovery of failed docking studies, when waters are included in the docking simulation.  相似文献   

17.
Detection of protein complexes and their structures is crucial for understanding their role in the basic biology of organisms. Computational docking methods can provide researchers with a good starting point for the analysis of protein complexes. However, these methods are often not accurate and their results need to be further refined to improve interface packing. In this paper, we introduce a refinement method that incorporates evolutionary information into a novel scoring function by employing Evolutionary Trace (ET)-based scores. Our method also takes Van der Waals interactions into account to avoid atomic clashes in refined structures. We tested our method on docked candidates of eight protein complexes and the results suggest that the proposed scoring function helps bias the search toward complexes with native interactions. We show a strong correlation between evolutionary-conserved residues and correct interface packing. Our refinement method is able to produce structures with better lRMSD (least RMSD) with respect to the known complexes and lower energies than initial docked structures. It also helps to filter out false-positive complexes generated by docking methods, by detecting little or no conserved residues on false interfaces. We believe this method is a step toward better ranking and prediction of protein complexes.  相似文献   

18.
Reliable computational prediction of protein side chain conformations and the energetic impact of amino acid mutations are the key aspects for the optimization of biotechnologically relevant enzymatic reactions using structure‐based design. By improving the protein stability, higher yields can be achieved. In addition, tuning the substrate selectivity of an enzymatic reaction by directed mutagenesis can lead to higher turnover rates. This work presents a novel approach to predict the conformation of a side chain mutation along with the energetic effect on the protein structure. The HYDE scoring concept applied here describes the molecular interactions primarily by evaluating the effect of dehydration and hydrogen bonding on molecular structures in aqueous solution. Here, we evaluate its capability of side‐chain conformation prediction in classic remutation experiments. Furthermore, we present a new data set for evaluating “cross‐mutations,” a new experiment that resembles real‐world application scenarios more closely. This data set consists of protein pairs with up to five point mutations. Thus, structural changes are attributed to point mutations only. In the cross‐mutation experiment, the original protein structure is mutated with the aim to predict the structure of the side chain as in the paired mutated structure. The comparison of side chain conformation prediction (“remutation”) showed that the performance of HYDEprotein is qualitatively comparable to state‐of‐the art methods. The ability of HYDEprotein to predict the energetic effect of a mutation is evaluated in the third experiment. Herein, the effect on protein stability is predicted correctly in 70% of the evaluated cases. Proteins 2017; 85:1550–1566. © 2017 Wiley Periodicals, Inc.  相似文献   

19.
We have recently completed systematic molecular dynamics simulations of 807 different proteins representing 95% of the known autonomous protein folds in an effort we refer to as Dynameomics. Here we focus on the analysis of side chain conformations and dynamics to create a dynamic rotamer library. Overall this library is derived from 31,000 occurrences of each of 86,217 different residues, or 2.7 × 10(9) rotamers. This dynamic library has 74% overlap of rotamer distributions with rotamer libraries derived from static high-resolution crystal structures. Seventy-five percent of the residues had an assignable primary conformation, and 68% of the residues had at least one significant alternate conformation. The average correlation time for switching between rotamers ranged from 22 ps for Met to over 8 ns for Cys; this time decreased 20-fold on the surface of the protein and modestly for dihedral angles further from the main chain. Side chain S(2) axis order parameters were calculated and they correlated well with those derived from NMR relaxation experiments (R = 0.9). Relationships relating the S(2) axis order parameters to rotamer occupancy were derived. Overall the Dynameomics rotamer library offers a comprehensive depiction of side chain rotamer preferences and dynamics in solution, and more realistic distributions for dynamic proteins in solution at ambient temperature than libraries derived from crystal structures, in particular charged surface residues are better represented. Details of the rotamer library are presented here and the library itself can be downloaded at http://www.dynameomics.org.  相似文献   

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