蛋白质结构与功能研究中的分子模拟技术

分子模拟技术为蛋白质的研究提供了一种崭新的手段,在理论上解决了结构预测和功能分析以及蛋白质工程实施方面所面临的难题。它在蛋白质的结构预测和模建工作中占有举足轻重的地位,实现了生物技术与计算机技术的完美结合。本文简要阐述了该技术的基本步骤和工作原理,并以目前应用最广的生物大分子领域的商品化分子模拟软件Accelrys公司基于Linux系统开发的InsightII为例,介绍了相关程序模块的功能和作用,同时结合该技术在蛋白质的结构预测和模建、结构与功能关系分析、分子设计等过程中的开发与应用,加以具体说明和展望。     

蛋白酶抑制剂反应环区的结构研究

蛋白质分子表面环区常在蛋白质功能和行为中起重要作用.我们对一些丝氨酸蛋白酶抑制剂的反应环区和已知的限制性水解部位做了结构比较研究.研究结果表明,蛋白质抑制剂反应部位所处的环区具有独特的结构,使其能够对相应蛋白酶发挥很高的抑制作用.     

蛋白质结构预测方法探析

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。     

基于结构比较的蛋白质模建系统及其评估

对蛋白质三维结构的同源模建的所有关键模块作了详细研究后,现已整合成蛋白质结构模建的软件系统ＰＭＯＤＥＬＬＩＮＧ。它分主链模建和侧链安装两部分,共七个模块组成。经１１族同源蛋白的模建实验的回顾性检表明,与目前流行的商品软件相比,具有操作自动化,模建精度高和运行时间少的优点。文中还对蝮蛇蛇毒磷酯酶,细胞因子受体ＣＤ４０的配体ｇｐ３９,小鼠巨细胞蛋白酶和人白介素１３等至今未知结构的蛋白作了预测性模建。     

基于知识的蛋白质结构预测

介绍了近几年基于知识的蛋白质三维结构预测方法及其进展．目前,基于知识的结构预测方法主要有两类,一类是同源蛋白模建,这种技术比较成熟,模建的结果可靠性比较高,但只适用于同源性比较高的目标序列的模建;另一类方法即蛋白质逆折叠技术,主要包括3D profile方法和基于势函数的方法,给出的是目标蛋白质的空间走向,它主要可用于序列同源性比较低的蛋白质的结构预测．     

鼠抗人纤维蛋白抗体单链Fv片断的三维结构模建

用Biosym公司开发的计算机辅助分子设计系统模建了鼠抗人纤维蛋白抗体Fv片断的三维结构。Fv是由重链可变区(Vh)和轻链可变区(Vl)两个结构域组成的具有抗原结合能力的最小抗体片断。先分别模建了Vh和Vl两个结构域,然后搭建出Fv片断的整体三维结构,并对模建的结构进行了分子力学和动力学优化。对结构的合理性验证显示模建结构是合理的。本研究为鼠抗人纤维蛋白抗体Fv片断的人源化的分子设计打下了基础。     

枯草杆菌色氨酰tRNA合成酶的同源模建

色氨酰tRNA合成酶（TrpRS）在蛋白质合成系统中具有非常重要的地位。通过蛋白质同源模建得到了枯草杆菌（Bacillus subtilis）色氨酰tRNA合成酶的三维结构。研究表明合成酶的二级结构含有16个α螺旋和5个β折叠,唯一的色氨酸Trp^92位于两个亚基的界面上。模建结果对配基结合位点和活性位点以及可能与tRNA^Trp结合的方式也给出了预测。     

自然氨基酸的溶剂可及性标准

提出一种新的计算蛋白质溶剂可及面积的方法.利用该方法计算了二十个氨基酸在三肽模型中的可及面积,与他人的结果作了比较.提供了一套在分子模建中很有用的数据.     

打分矩阵方法在β-发夹模体识别中的应用

用打分矩阵的五种算法,从蛋白质的氨基酸序列出发,以氨基酸为参数,分别对3088个同源性小于40％的蛋白质和2878个同源性小于33％的蛋白质的β-发夹模体片断进行了识别。利用self—consistency和10—fold cross—validation两种方法对五种算法进行检验,结果表明,Claverie（1996）算法相对较好,对3088个蛋白质的平均识别精度达到了77．7％和76．1％。     

源于红树林的新型脂肪酶Lip906生物信息学分析

本研究利用生物信息学在线软件对脂肪酶Lip906的二级结构和模体信息进行预测,同时对其三级结构进行同源建模和模建结果质量评价,预测该蛋白质的活性位点信息,旨在从蛋白质序列特征和分子结构水平理解其在酯类水解过程中的作用。结果表明,模建的Lip906蛋白结构品质较高,具有7段α-螺旋和2组β-折叠结构,是一个典型的α/β类蛋白,表面呈弱负电势分布;Lip906蛋白具有5个不同模体,可能参与不同生化反应或执行不同的功能。这些研究结果对理解Lip906蛋白功能以及配基结合位点定位非常重要,也为脂肪酶Lip906的突变设计提供了理论基础。     

Refinement of unreliable local regions in template-based protein models

Contemporary template-based modeling techniques allow applications of modeling methods to vast biological problems. However, they tend to fail to provide accurate structures for less-conserved local regions in sequence even when the overall structure can be modeled reliably. We call these regions unreliable local regions (ULRs). Accurate modeling of ULRs is of enormous value because they are frequently involved in functional specificity. In this article, we introduce a new method for modeling ULRs in template-based models by employing a sophisticated loop modeling technique. Combined with our previous study on protein termini, the method is applicable to refinement of both loop and terminus ULRs. A large-scale test carried out in a blind fashion in CASP9 (the 9th Critical Assessment of techniques for protein structure prediction) shows that ULR structures are improved over initial template-based models by refinement in more than 70% of the successfully detected ULRs. It is also notable that successful modeling of several long ULRs over 12 residues is achieved. Overall, the current results show that a careful application of loop and terminus modeling can be a promising tool for model refinement in template-based modeling.     

Multiple copy sampling in protein loop modeling: computational efficiency and sensitivity to dihedral angle perturbations.

Multiple copy sampling and the bond scaling-relaxation technique are combined to generate 3-dimensional conformations of protein loop segments. The computational efficiency and sensitivity to initial loop copy dispersion are analyzed. The multicopy loop modeling method requires approximately 20-50% of the computational time required by the single-copy method for the various protein segments tested. An analytical formula is proposed to estimate the computational gain prior to carrying out a multicopy simulation. When 7-residue loops within flexible proteins are modeled, each multicopy simulation can sample a set of loop conformations with initial dispersions up to +/- 15 degrees for backbone and +/- 30 degrees for side-chain rotatable dihedral angles. The dispersions are larger for shorter and smaller for longer and/or surface loops. The degree of convergence of loop copies during a simulation can be used to complement commonly used target functions (such as potential energy) for distinguishing between native and misfolded conformations. Furthermore, this convergence also reflects the conformational flexibility of the modeled protein segment. Application to simultaneously building all 6 hypervariable loops of an antibody is discussed.     

A composite score for predicting errors in protein structure models

Reliable prediction of model accuracy is an important unsolved problem in protein structure modeling. To address this problem, we studied 24 individual assessment scores, including physics-based energy functions, statistical potentials, and machine learning-based scoring functions. Individual scores were also used to construct approximately 85,000 composite scoring functions using support vector machine (SVM) regression. The scores were tested for their abilities to identify the most native-like models from a set of 6000 comparative models of 20 representative protein structures. Each of the 20 targets was modeled using a template of <30% sequence identity, corresponding to challenging comparative modeling cases. The best SVM score outperformed all individual scores by decreasing the average RMSD difference between the model identified as the best of the set and the model with the lowest RMSD (DeltaRMSD) from 0.63 A to 0.45 A, while having a higher Pearson correlation coefficient to RMSD (r=0.87) than any other tested score. The most accurate score is based on a combination of the DOPE non-hydrogen atom statistical potential; surface, contact, and combined statistical potentials from MODPIPE; and two PSIPRED/DSSP scores. It was implemented in the SVMod program, which can now be applied to select the final model in various modeling problems, including fold assignment, target-template alignment, and loop modeling.     

ModLoop: automated modeling of loops in protein structures

SUMMARY: ModLoop is a web server for automated modeling of loops in protein structures. The input is the atomic coordinates of the protein structure in the Protein Data Bank format, and the specification of the starting and ending residues of one or more segments to be modeled, containing no more than 20 residues in total. The output is the coordinates of the non-hydrogen atoms in the modeled segments. A user provides the input to the server via a simple web interface, and receives the output by e-mail. The server relies on the loop modeling routine in MODELLER that predicts the loop conformations by satisfaction of spatial restraints, without relying on a database of known protein structures. For a rapid response, ModLoop runs on a cluster of Linux PC computers. AVAILABILITY: The server is freely accessible to academic users at http://salilab.org/modloop     

Enhanced thermostability of methyl parathion hydrolase from Ochrobactrum sp. M231 by rational engineering of a glycine to proline mutation

Protein thermostability can be increased by some glycine to proline mutations in a target protein. However, not all glycine to proline mutations can improve protein thermostability, and this method is suitable only at carefully selected mutation sites that can accommodate structural stabilization. In this study, homology modeling and molecular dynamics simulations were used to select appropriate glycine to proline mutations to improve protein thermostability, and the effect of the selected mutations was proved by the experiments. The structure of methyl parathion hydrolase (MPH) from Ochrobactrum sp. M231 (Ochr-MPH) was constructed by homology modeling, and molecular dynamics simulations were performed on the modeled structure. A profile of the root mean square fluctuations of Ochr-MPH was calculated at the nanosecond timescale, and an eight-amino acid loop region (residues 186-193) was identified as having high conformational fluctuation. The two glycines nearest to this region were selected as mutation targets that might affect protein flexibility in the vicinity. The structures and conformational fluctuations of two single mutants (G194P and G198P) and one double mutant (G194P/G198P) were modeled and analyzed using molecular dynamics simulations. The results predicted that the mutant G194P had the decreased conformational fluctuation in the loop region and might increase the thermostability of Ochr-MPH. The thermostability and kinetic behavior of the wild-type and three mutant enzymes were measured. The results were consistent with the computational predictions, and the mutant G194P was found to have higher thermostability than the wild-type enzyme.     

Loopholes and missing links in protein modeling

This paper provides an unbiased comparison of four commercially available programs for loop sampling, Prime, Modeler, ICM, and Sybyl, each of which uses a different modeling protocol. The study assesses the quality of results and examines the relative strengths and weaknesses of each method. The set of loops to be modeled varied in length from 4-12 amino acids. The approaches used for loop modeling can be classified into two methodologies: ab initio loop generation (Modeler and Prime) and database searches (Sybyl and ICM). Comparison of the modeled loops to the native structures was used to determine the accuracy of each method. All of the protocols returned similar results for short loop lengths (four to six residues), but as loop length increased, the quality of the results varied among the programs. Prime generated loops with RMSDs <2.5 A for loops up to 10 residues, while the other three methods met the 2.5 A criteria at seven-residue loops. Additionally, the ability of the software to utilize disulfide bonds and X-ray crystal packing influenced the quality of the results. In the final analysis, the top-ranking loop from each program was rarely the loop with the lowest RMSD with respect to the native template, revealing a weakness in all programs to correctly rank the modeled loops.     

in-silico study of arylalkylamine-nacetyltransferase enzyme to regulate circadian rhythmicity

Circadian Rhythmicity is present in the sleeping and breeding patterns of animals, including human beings and also related with brain wave activity, hormone production, cell regeneration and other biological activities. Melatonin is thought to play important roles in regulating circadian rhytmicity of the animals. Arylalkylamine-N-acetyltransferase (AANAT) is an enzyme which is responsible for the melatonin metabolism. In this study AANAT enzyme is targeted for the control of sleeping sickness and other irregular circadian rhythmicity by regulating the melatonin formation. AANAT protein 3D-structure was modeled, followed by loop modeling, refinement through energy minimization processes by molecular dynamics simulation and validation. Analysis of the Ramachandran plot shows 90.9% amino acids falls in the allowed region. The modeled protein was docked with N-Acetyl Serotonin. Combinatorial library was generated by using N-Acetyl Serotonin as a reference molecule and molecules having 80% similarity to N-Acetyl Serotonin was selected from Zinc database. These molecules were virtually screened by MOLEGRO virtual docker and top 5 molecules were selected and docked by using AutoDock. The AutoDock result shows that the ZINC01587152 molecule is having best interactions with the receptor protein. On the basis of this study we can suggest that the ZINC01587152 molecule is the best ligand against AANAT enzyme. It may be further synthesized and tested for sleep related disorders.     

Efficient methods for filtering and ranking fragments for the prediction of structurally variable regions in proteins

The prediction of protein 3D structures close to insertions and deletions or, more generally, loop prediction, is still one of the major challenges in homology modeling projects. In this article, we developed ranking criteria and selection filters to improve knowledge-based loop predictions. These criteria were developed and optimized for a test data set containing 678 insertions and deletions. The examples are, in principle, predictable from the used loop database with an RMSD < 1 A and represent realistic modeling situations. Four noncorrelated criteria for the selection of fragments are evaluated. A fast prefilter compares the distance between the anchor groups in the template protein with the stems of the fragments. The RMSD of the anchor groups is used for fitting and ranking of the selected loop candidates. After fitting, repulsive close contacts of loop candidates with the template protein are used for filtering, and fragments with backbone torsion angles, which are unfavorable according to a knowledge-based potential, are eliminated. By the combined application of these filter criteria to the test set, it was possible to increase the percentage of predictions with a global RMSD < 1 A to over 50% among the first five ranks, with average global RMSD values for the first rank candidate that are between 1.3 and 2.2 A for different loop lengths. Compared to other examples described in the literature, our large numbers of test cases are not self-predictions, where loops are placed in a protein after a peptide loop has been cut out, but are attempts to predict structural changes that occur in evolution when a protein is affected by insertions and deletions.     

Building proteins from C alpha coordinates using the dihedral probability grid Monte Carlo method.

Dihedral probability grid Monte Carlo (DPG-MC) is a general-purpose method of conformational sampling that can be applied to many problems in peptide and protein modeling. Here we present the DPG-MC method and apply it to predicting complete protein structures from C alpha coordinates. This is useful in such endeavors as homology modeling, protein structure prediction from lattice simulations, or fitting protein structures to X-ray crystallographic data. It also serves as an example of how DPG-MC can be applied to systems with geometric constraints. The conformational propensities for individual residues are used to guide conformational searches as the protein is built from the amino-terminus to the carboxyl-terminus. Results for a number of proteins show that both the backbone and side chain can be accurately modeled using DPG-MC. Backbone atoms are generally predicted with RMS errors of about 0.5 A (compared to X-ray crystal structure coordinates) and all atoms are predicted to an RMS error of 1.7 A or better.     

Structure-based stabilization of an enzyme: the case of penicillin acylase from Alcaligenes faecalis

The modeled structure of penicillin acylase from Alcaligenes faecali (AFPGA) was constructed by comparative modeling with the Modeller program. Candidate positions that could be replaced with cysteine were estimated by scanning the modeled structure of AFPGA with the program MODIP (modeling disulfide bond in protein). The mutant Q3C/P751C had a higher optimum temperature by three degrees than that of the wild type AFPGA. The half life of the double mutant Q3C/P751C at 55 degrees C was increased by 50%. To our knowledge, this was the first structure-based genetic modification of AFPGA.