基于结构比较的蛋白质同源模建系统及其评估Ⅱ侧链的安装

至今,有关蛋白质侧链的同源模建,除了在本体模板上安装侧链和少数限制条件下在同源模板上安装侧链的报道外,系统的研究和实施似乎还未见报道。本软件系统ＰＭＯＤＥＬＩＮＧ采用同源移植和“死端排除“相结合的侧链安装策略,对与模板蛋白相应践基具有相似大小和形状的目标残基采用直接移植的方法。其余铡链则用广义“死端排除定则”安装。经众多蛋白的测试,达到了较好的模建品质。     

基于结构比较的蛋白质同源模建系统及其评估Ⅱ侧链的安装

至今,有关蛋白质侧链的同源模建,除了在本体模板上安装侧链和少数限制条件下在同源模板上安装侧链的报道外,系统的研究和实施似乎还未见报道。本软件系统ＰＭＯＤＥＬＩＮＧ采用同源移植和“死端排除“相结合的侧链安装策略,对与模板蛋白相应践基具有相似大小和形状的目标残基采用直接移植的方法。其余铡链则用广义“死端排除定则”安装。经众多蛋白的测试,达到了较好的模建品质。     

蓖麻毒素A链突变体的设计和结构模建

蓖麻毒素Ａ链（ＲＴＡ）有抑制蛋白质合成的功能,可用作“生物导弹”的弹头,但其免疫原性较强。我们根据国外所做的ＲＴＡ连续突变的实验结果,以及ＰＤＢ库中ＲＴＡ及其同源蛋白的结构信息,设计了一个ＲＴＡ突变体,缺失的５个片段与功能及结构保守性关系较小。然后,我们用同源模建的方法对设计出的ＲＴＡ突变体进行三维结构模建,初步验证表明模型基本合理     

蓖麻毒素A链突变体的设计和结构模建

蓖麻毒素Ａ链（ＲＴＡ）有抑制蛋白质合成的功能,可用作“生物导弹”的弹头,但其免疫原性较强。我们根据国外所做的ＲＴＡ连续突变的实验结果,以及ＰＤＢ库中ＲＴＡ及其同源蛋白的结构信息,设计了一个ＲＴＡ突变体,缺失的５个片段与功能及结构保守性关系较小。然后,我们用同源模建的方法对设计出的ＲＴＡ突变体进行三维结构模建,初步验证表明模型基本合理     

基于结构比较的蛋白质模建系统及其评估：Ⅲ．灵敏的蛋白质结构评估方法

本文按二级结构、疏水性和侧链氢键把蛋白质的局部结构环境划分为６４类,按环境依赖的残基替代频数表构成残基与环境的兼容性分数表,可用来评估一个蛋白质的整体折叠正确与否和检测蛋白质折叠的局部错误。与著名的商品软件Ｐｒｏｆｉｌｅ－３Ｄ相比,此方法不仅能对整体折叠作出合理判断,而且对局部错误的检测更为灵敏。它已成为我们的蛋白质结构模建系统ＰＭＯＤＥＬＩＮＧ的重要模块。     

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．     

蛋白质结构比较的微分几何方法

本文提出了一种利用蛋白质主链结构的微分几何描述实现蛋白质结构比较的新方法,即用主链的曲率和扰率刻划蛋白质结构有局部构象,再按动态规划算法实现最佳比较。通过对珠蛋白,天冬氨酸蛋白酶和丝氨酸蛋白酶的多重比较表明这个方法既适用于近缘同源蛋白又适用于近缘同源蛋白又适用于远缘同源蛋白的结构比较。     

第三届理论生物物理学术会议会议纪要

中国生物物理学会理论生物物理专业委员会于1992年11月1日—2日在上海生化所举行了第三届理论生物物理学术会议,内容是“蛋白质工程、基因组分析和非线性生物学”参加人数近四十人,其中大都是30岁以下的硕士和博士。宣读的论文已经与国际同行接轨,其主要贡献是:1.蛋白质结构联配-863项目已经制成BX“蛋白质结构联配软件”,从而可能实现远缘同源蛋白质的分子模建。至今的同源分子模建属于近缘同源模建。     

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。     

云南切梢小蠹气味结合蛋白的同源模建

【目的】为了研究云南切梢小蠹Tomicus yunnanensis气味结合蛋白(Odorant binding proteins,OBPs)参与其嗅觉识别过程的机理奠定基础,本文对云南切梢小蠹OBP进行了同源模建及评价分析。【方法】采用同源模建方法构建了云南切梢小蠹OBP的三维结构,并利用Procheck、Verify_3D和ERRAT等方法评估了构建模型的可靠性。【结果】对优化后的模建蛋白的结果分析发现,序列中有6个保守的半胱氨酸位点,具有气味结合蛋白的典型特征,并且Procheck、Verify_3D和ERRAT对模型的评价分数较高,结构具有很高的可能性。【结论】基于已知的同源蛋白结构信息和模建蛋白的一级序列信息,预测序列已知但结构未知的蛋白空间结构,为今后研究该云南切梢小蠹气味结合蛋白的功能结构域奠定基础。     

Incorporation of evolutionary information into Rosetta comparative modeling

Prediction of protein structures from sequences is a fundamental problem in computational biology. Algorithms that attempt to predict a structure from sequence primarily use two sources of information. The first source is physical in nature: proteins fold into their lowest energy state. Given an energy function that describes the interactions governing folding, a method for constructing models of protein structures, and the amino acid sequence of a protein of interest, the structure prediction problem becomes a search for the lowest energy structure. Evolution provides an orthogonal source of information: proteins of similar sequences have similar structure, and therefore proteins of known structure can guide modeling. The relatively successful Rosetta approach takes advantage of the first, but not the second source of information during model optimization. Following the classic work by Andrej Sali and colleagues, we develop a probabilistic approach to derive spatial restraints from proteins of known structure using advances in alignment technology and the growth in the number of structures in the Protein Data Bank. These restraints define a region of conformational space that is high-probability, given the template information, and we incorporate them into Rosetta's comparative modeling protocol. The combined approach performs considerably better on a benchmark based on previous CASP experiments. Incorporating evolutionary information into Rosetta is analogous to incorporating sparse experimental data: in both cases, the additional information eliminates large regions of conformational space and increases the probability that energy-based refinement will hone in on the deep energy minimum at the native state.     

The leucine-rich repeat as a protein recognition motif

Leucine-rich repeats (LRRs) are 20-29-residue sequence motifs present in a number of proteins with diverse functions. The primary function of these motifs appears to be to provide a versatile structural framework for the formation of protein-protein interactions. The past two years have seen an explosion of new structural information on proteins with LRRs. The new structures represent different LRR subfamilies and proteins with diverse functions, including GTPase-activating protein rna1p from the ribonuclease-inhibitor-like subfamily; spliceosomal protein U2A', Rab geranylgeranyltransferase, internalin B, dynein light chain 1 and nuclear export protein TAP from the SDS22-like subfamily; Skp2 from the cysteine-containing subfamily; and YopM from the bacterial subfamily. The new structural information has increased our understanding of the structural determinants of LRR proteins and our ability to model such proteins with unknown structures, and has shed new light on how these proteins participate in protein-protein interactions.     

On the characterization of energy networks of proteins

The construction of a realistic theoretical model of proteins is determinant for improving the computational simulations of their structural and functional aspects. Modeling proteins as a network of non-covalent connections between the atoms of amino acid residues has shown valuable insights into these macromolecules. The energy-related properties of protein structures are known to be very important in molecular dynamics. However, these same properties have been neglected when the protein structures are modeled as networks of atoms and amino acid residues. A new approach for the construction of protein models based on a network of atoms is presented. This method, based on interatomic interaction, takes into account the energy and geometric aspects of the protein structures that were not employed before, such as atomic occlusion inside the protein, the use of solvation, protein modeling and analysis, and the use of energy potentials to estimate the energies of interatomic non-covalent contacts. As a result, we achieved a more realistic network model of proteins. This model has the virtue of being more robust in face of different unknown variables that usually are arbitrarily estimated. We were able to determine the most connected residues of all the proteins studied, so that we are now in a better condition to study their structural role.     

Three-dimensional structures of membrane proteins from genomic sequencing

We show that amino acid covariation in proteins, extracted from the evolutionary sequence record, can be used to fold transmembrane proteins. We use this technique to predict previously unknown 3D structures for 11 transmembrane proteins (with up to 14 helices) from their sequences alone. The prediction method (EVfold_membrane) applies a maximum entropy approach to infer evolutionary covariation in pairs of sequence positions within a protein family and then generates all-atom models with the derived pairwise distance constraints. We benchmark the approach with blinded de novo computation of known transmembrane protein structures from 23 families, demonstrating unprecedented accuracy of the method for large transmembrane proteins. We show how the method can predict oligomerization, functional sites, and conformational changes in transmembrane proteins. With the rapid rise in large-scale sequencing, more accurate and more comprehensive information on evolutionary constraints can be decoded from genetic variation, greatly expanding the repertoire of transmembrane proteins amenable to modeling by this method.     

The use of soluble protein structures in modeling helical proteins in a layered membrane

Major advances have been made in the prediction of soluble protein structures, led by the knowledge-based modeling methods that extract useful structural trends from known protein structures and incorporate them into scoring functions. The same cannot be reported for the class of transmembrane proteins, primarily due to the lack of high-resolution structural data for transmembrane proteins, which render many of the knowledge-based method unreliable or invalid. We have developed a method that harnesses the vast structural knowledge available in soluble protein data for use in the modeling of transmembrane proteins. At the core of the method, a set of transmembrane protein decoy sets that allow us to filter and train features recognized from soluble proteins for transmembrane protein modeling into a set of scoring functions. We have demonstrated that structures of soluble proteins can provide significant insight into transmembrane protein structures. A complementary novel two-stage modeling/selection process that mimics the two-stage helical membrane protein folding was developed. Combined with the scoring function, the method was successfully applied to model 5 transmembrane proteins. The root mean square deviations of the predicted models ranged from 5.0 to 8.8?Å to the native structures.     

Assessing the reliability of sequence similarities detected through hydrophobic cluster analysis

Hydrophobic cluster analysis (HCA) has long been used as a tool to detect distant homologies between protein sequences, and to classify them into different folds. However, it relies on expert human intervention, and is sensitive to subjective interpretations of pattern similarities. In this study, we describe a novel algorithm to assess the similarity of hydrophobic amino acid distributions between two sequences. Our algorithm correctly identifies as misattributions several HCA-based proposals of structural similarity between unrelated proteins present in the literature. We have also used this method to identify the proper fold of a large variety of sequences, and to automatically select the most appropriate structure for homology modeling of several proteins with low sequence identity to any other member of the protein data bank. Automatic modeling of the target proteins based on these templates yielded structures with TM-scores (vs. experimental structures) above 0.60, even without further refinement. Besides enabling a reliable identification of the correct fold of an unknown sequence and the choice of suitable templates, our algorithm also shows that whereas most structural classes of proteins are very homogeneous in hydrophobic cluster composition, a tenth of the described families are compatible with a large variety of hydrophobic patterns. We have built a browsable database of every major representative hydrophobic cluster pattern present in each structural class of proteins, freely available at http://www2.ufp.pt/ pedros/HCA_db/index.htm.     

Analytical ultracentrifugation combined with X-ray and neutron scattering: Experiment and modelling

Analytical ultracentrifugation and solution scattering provide different multi-parameter structural and compositional information on proteins. The joint application of the two methods supplements high resolution structural studies by crystallography and NMR. We summarise the procedures required to obtain equivalent ultracentrifugation and X-ray and neutron scattering data. The constrained modelling of ultracentrifugation and scattering data is important to confirm the experimental data analysis and yields families of best-fit molecular models for comparison with crystallography and NMR structures. This modelling of ultracentrifugation and scattering data is described in terms of starting models, their conformational randomisation in trial-and-error fits, and the identification of the final best-fit models. Seven applications of these methods are described to illustrate the current state-of-the-art. These include the determination of antibody solution structures (the human IgG4 subclass, and oligomeric forms of human IgA and its secretory component), the solution structures of the complement proteins of innate immunity (Factor H and C3/C3u) and their interactions with macromolecular ligands (C-reactive protein), and anionic polysaccharides (heparin). Complementary features of joint ultracentrifugation and scattering experiments facilitate an improved understanding of crystal structures (illustrated for C3/C3u, C-reactive protein and heparin). If a large protein or its complex cannot be crystallised, the joint ultracentrifugation-scattering approach provides a means to obtain an overall macromolecular structure.     

Computational approaches for protein function prediction: A combined strategy from multiple sequence alignment to molecular docking-based virtual screening

The functional characterization of proteins represents a daily challenge for biochemical, medical and computational sciences. Although finally proved on the bench, the function of a protein can be successfully predicted by computational approaches that drive the further experimental assays. Current methods for comparative modeling allow the construction of accurate 3D models for proteins of unknown structure, provided that a crystal structure of a homologous protein is available. Binding regions can be proposed by using binding site predictors, data inferred from homologous crystal structures, and data provided from a careful interpretation of the multiple sequence alignment of the investigated protein and its homologs. Once the location of a binding site has been proposed, chemical ligands that have a high likelihood of binding can be identified by using ligand docking and structure-based virtual screening of chemical libraries. Most docking algorithms allow building a list sorted by energy of the lowest energy docking configuration for each ligand of the library. In this review the state-of-the-art of computational approaches in 3D protein comparative modeling and in the study of protein–ligand interactions is provided. Furthermore a possible combined/concerted multistep strategy for protein function prediction, based on multiple sequence alignment, comparative modeling, binding region prediction, and structure-based virtual screening of chemical libraries, is described by using suitable examples. As practical examples, Abl-kinase molecular modeling studies, HPV-E6 protein multiple sequence alignment analysis, and some other model docking-based characterization reports are briefly described to highlight the importance of computational approaches in protein function prediction.     

Protein models: The Grand Challenge of protein docking

Characterization of life processes at the molecular level requires structural details of protein–protein interactions (PPIs). The number of experimentally determined protein structures accounts only for a fraction of known proteins. This gap has to be bridged by modeling, typically using experimentally determined structures as templates to model related proteins. The fraction of experimentally determined PPI structures is even smaller than that for the individual proteins, due to a larger number of interactions than the number of individual proteins, and a greater difficulty of crystallizing protein–protein complexes. The approaches to structural modeling of PPI (docking) often have to rely on modeled structures of the interactors, especially in the case of large PPI networks. Structures of modeled proteins are typically less accurate than the ones determined by X‐ray crystallography or nuclear magnetic resonance. Thus the utility of approaches to dock these structures should be assessed by thorough benchmarking, specifically designed for protein models. To be credible, such benchmarking has to be based on carefully curated sets of structures with levels of distortion typical for modeled proteins. This article presents such a suite of models built for the benchmark set of the X‐ray structures from the Dockground resource ( http://dockground.bioinformatics.ku.edu ) by a combination of homology modeling and Nudged Elastic Band method. For each monomer, six models were generated with predefined C^α root mean square deviation from the native structure (1, 2, …, 6 Å). The sets and the accompanying data provide a comprehensive resource for the development of docking methodology for modeled proteins. Proteins 2014; 82:278–287. © 2013 Wiley Periodicals, Inc.     

Automated minimization of steric clashes in protein structures

Molecular modeling of proteins including homology modeling, structure determination, and knowledge-based protein design requires tools to evaluate and refine three-dimensional protein structures. Steric clash is one of the artifacts prevalent in low-resolution structures and homology models. Steric clashes arise due to the unnatural overlap of any two nonbonding atoms in a protein structure. Usually, removal of severe steric clashes in some structures is challenging since many existing refinement programs do not accept structures with severe steric clashes. Here, we present a quantitative approach of identifying steric clashes in proteins by defining clashes based on the Van der Waals repulsion energy of the clashing atoms. We also define a metric for quantitative estimation of the severity of clashes in proteins by performing statistical analysis of clashes in high-resolution protein structures. We describe a rapid, automated, and robust protocol, Chiron, which efficiently resolves severe clashes in low-resolution structures and homology models with minimal perturbation in the protein backbone. Benchmark studies highlight the efficiency and robustness of Chiron compared with other widely used methods. We provide Chiron as an automated web server to evaluate and resolve clashes in protein structures that can be further used for more accurate protein design.