酶分子设计常用策略和进展

酶分子的生物学功能很大程度上是由其三维空间结构和所处溶剂环境共同决定的。因此,优化酶分子的结构性质以及探索其性质最优的溶剂环境是改善酶分子功能以及进行理性设计的一个可行途径。从实际应用的角度来看,分子设计方法可以为酶工程提供一种有效的解决方案。目前,酶分子设计有两个重要的研究方向,包括提高酶分子的催化活力和优化其稳定性。同时,对酶分子设计方法的研究也有助于对蛋白质生物学机理的探索。在近些年的学术界酶分子设计案例中,生物信息学方法得到广泛的应用。本文系统地总结基于生物信息学的酶分子设计方法的背景、策略和一些经典案例。     

数据与计算驱动的蛋白质元件预测和设计

为实现特定合成生物系统,需要使用恰当的蛋白质元件,即具有所需的特异性分子识别、酶催化活性等功能的天然蛋白质或工程改造蛋白质。以转录因子所识别的DNA序列的预测以及蛋白质-小分子特异性结合口袋的预测和设计为例,介绍计算方法在蛋白质功能预测和设计中的作用。强调了不同类型计算工具的整合以及它们与生物背景知识整合、计算方法通用性和准确性之间的平衡;讨论了有待解决的问题、计算的潜力和新方法的发展需求。     

酶启动和调控自组装超分子水凝胶

超分子间的弱相互作用使自组装超分子水凝胶的结构比较容易改变。用酶启动和调控超分子水凝胶的自组装不仅能在原位上对超分子水凝胶结构进行调整和控制,而且具有很好的生物选择性,有望制造出生物医学上所需要的材料,并能够控制生物体系中一些重要的生物过程。本文对酶启动和调控自组装超分子水凝胶的两类过程进行了总结,并以磷酸酯酶、β-内酰胺酶、嗜热菌蛋白酶、脂肪酶、基质金属蛋白酶和磷酸酯酶/激酶等酶为例,综述了如何设计和使用酶来启动和调控小分子的自组装超分子水凝胶。     

基于序列和结构分析的酶热稳定性改造策略*

功能酶被广泛应用于食品、化工、医药等领域,但却容易受高温环境限制,导致催化效率降低。以分子改造为目的的蛋白质工程技术是解决这一问题的关键环节,其能够对酶结构和功能进行改造,获得热稳定性好的工业酶。传统的定向进化方法只能依靠随机突变进行人工筛选,具有效率低、针对性差等缺点;理性设计作为酶热稳定性改造的主要方法,可借助各种计算机程序和软件预测潜在突变位点,但其要求对酶的催化机制、热稳定性机制有深入了解。对于大多数天然酶而言,酶的序列和晶体结构是最容易获取的信息,也是预测功能的重要基础。从酶的序列和晶体结构入手,重点介绍了共识突变、基于序列偏好性的突变、截短柔性区域、优化分子内相互作用力、刚化催化活性区域及计算机辅助筛选柔性位点等常用策略,这些策略具有筛选效率高、改造准确性高、实用性强等优点。结合多种酶的热稳定性改造案例进行分析,旨在为不同酶的改造策略选择提供有效参考,同时也为工业酶的耐热性研究提供理论支持。     

体外多酶分子机器的现状和最新进展

体外多酶分子机器遵循所设计的多酶催化路径,将若干种纯化或部分纯化的酶元件进行合理的优化与适配,高效地在体外将特定的底物转化为目标化合物。体外多酶分子机器反应系统呈现元件化和模块化的特点,在设计、组装和调控方面具有较高的自由度。近年来,体外多酶分子机器在实现反应过程的精准调控和提高产品得率方面的优势逐渐体现,展示了其在生物制造领域重要的应用潜力。对体外多酶分子机器的相关研究已成为合成生物学的一个重要分支领域,日益受到广泛的关注。文中系统地综述了基于酶元件/模块的体外多酶分子机器的构建策略,以及改善该分子机器中酶元件/模块之间适配性的研究进展,并分析了该生物制造平台的发展前景与挑战。     

lncRNAs功能注释和预测

随着测序技术的发展,在各种哺乳动物中发现越来越多的长非编码RNAs（long non-coding RNAs,lncRNAs）,但是大部分lncRNAs的功能却未知.鉴于lncRNAs在众多生物过程如免疫反应、发育和基因印迹中表现出对蛋白编码基因和其它非编码RNAs的重要调节作用,对lncRNAs的功能研究也成为生物学家和生物信息学家研究的热点. 其中,功能注释和预测是目前研究lncRNAs功能的主要方法之一.本文主要对lncRNAs功能注释和预测方法的研究进展作一综述,包括以下几个方面：基于共表达网络的方法、基于miRNAs的方法、基于蛋白质结合的方法、基于表观遗传修饰的方法以及基于ceRNA网络的方法. 为进一步研究lncRNAs的功能提供参考,同时为开发更加有效的注释或预测方法提供线索.     

分子动态模拟及其在生物大分子研究中的应用

生物分子动念模拟技术是运用计算机对生物大分子的结构、功能、质子运动轨迹以及生物分子间的相互作用进行预测,是研究生物分子结构和功能的重要手段.该文介绍分子动态技术的原理及其在生命科学研究中的应用和研究进展,分析目前存在的问题,并提出对未来工作的展望.     

RNA的生物催化功能及其研究进展

长期以来,我们一直认为生命过程中的所有化学反应是在生物催化剂——酶的作用下进行的,而所有的酶都是蛋白质或带有辅基的蛋白质。但近年研究发现,某些RNA分子也具有“酶”的生物催化功能,它们能在一定条件下催化自身或其它RNA分子发生化学反应。Cech(1981,1986)据此提出了RNA生物催化剂的概念,将这些具有酶活性的RNA称之为核酶(ribozyme)。本文简要介绍几种RNA分子的生物催化功能及其研究进展。     

应用计算机预测核糖体16s rRNA三维折叠模型

1964年Levinthal等开创了电子计算机在生物大分子结构研究中的应用。此后二十年,由于计算机分子图形技术的不断进步,而成功地发展了一批计算机分子图形系统和相应的软件,并对一些生物大分子高级结构模型进行了预测,从而为生物大分子的结构与功能研究,生物技术的发展提供新的有效手段。本文将介绍这一研究领域的一个实例,用计算机预测核糖体16srRNA三维折叠模型,以此领略这一领域的概况。     

生物分子网络弹性研究进展

弹性是生物分子网络重要且基础的属性之一,一方面弹性赋予生物分子网络抵抗内部噪声与环境干扰并维持其自身基本功能的能力,另一方面,弹性为网络状态的恢复制造了阻力。生物分子网络弹性研究试图回答如下3个问题：a. 生物分子网络弹性的产生机理是什么？b. 弹性影响下生物分子网络的状态如何发生转移？c. 如何预测生物网络状态转换临界点,以防止系统向不理想的状态演化？因此,研究生物分子网络弹性有助于理解生物系统内部运作机理,同时对诸如疾病发生临界点预测、生物系统状态逆转等临床应用具有重要的指导意义。鉴于此,本文主要针对以上生物分子网络弹性领域的3个热点研究问题,在研究方法和生物学应用上进行了系统地综述,并对未来生物分子网络弹性的研究方向进行了展望。     

Using Chou's amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes

With the rapid increment of protein sequence data, it is indispensable to develop automated and reliable predictive methods for protein function annotation. One approach for facilitating protein function prediction is to classify proteins into functional families from primary sequence. Being the most important group of all proteins, the accurate prediction for enzyme family classes and subfamily classes is closely related to their biological functions. In this paper, for the prediction of enzyme subfamily classes, the Chou's amphiphilic pseudo-amino acid composition [Chou, K.C., 2005. Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics 21, 10-19] has been adopted to represent the protein samples for training the 'one-versus-rest' support vector machine. As a demonstration, the jackknife test was performed on the dataset that contains 2640 oxidoreductase sequences classified into 16 subfamily classes [Chou, K.C., Elrod, D.W., 2003. Prediction of enzyme family classes. J. Proteome Res. 2, 183-190]. The overall accuracy thus obtained was 80.87%. The significant enhancement in the accuracy indicates that the current method might play a complementary role to the exiting methods.     

Interaction-site prediction for protein complexes: a critical assessment

MOTIVATION: Proteins function through interactions with other proteins and biomolecules. Protein-protein interfaces hold key information toward molecular understanding of protein function. In the past few years, there have been intensive efforts in developing methods for predicting protein interface residues. A review that presents the current status of interface prediction and an overview of its applications and project future developments is in order. SUMMARY: Interface prediction methods rely on a wide range of sequence, structural and physical attributes that distinguish interface residues from non-interface surface residues. The input data are manipulated into either a numerical value or a probability representing the potential for a residue to be inside a protein interface. Predictions are now satisfactory for complex-forming proteins that are well represented in the Protein Data Bank, but less so for under-represented ones. Future developments will be directed at tackling problems such as building structural models for multi-component structural complexes.     

机器学习在蛋白质功能预测领域的研究进展

蛋白质是有机生命体内不可或缺的化合物,在生命活动中发挥着多种重要作用,了解蛋白质的功能有助于医学和药物研发等领域的研究。此外,酶在绿色合成中的应用一直备受人们关注,但是由于酶的种类和功能多种多样,获取特定功能酶的成本高昂,限制了其进一步的应用。目前,蛋白质的具体功能主要通过实验表征确定,该方法实验工作繁琐且耗时耗力,同时,随着生物信息学和测序技术的高速发展,已测序得到的蛋白质序列数量远大于功能获得注释的序列数量,高效预测蛋白质功能变得至关重要。随着计算机技术的蓬勃发展,由数据驱动的机器学习方法已成为应对这些挑战的有效解决方案。本文对蛋白质功能及其注释方法以及机器学习的发展历程和操作流程进行了概述,聚焦于机器学习在酶功能预测领域的应用,对未来人工智能辅助蛋白质功能高效研究的发展方向提出了展望。     

生物信息学方法预测蛋白质相互作用网络中的功能模块

蛋白质相互作用是大多数生命过程的基础。随着高通量实验技术和计算机预测方法的发展,在各种生物中已获得了数目十分庞大的蛋白质相互作用数据,如何从中提取出具有生物学意义的数据是一项艰巨的挑战。从蛋白质相互作用数据出发获得相互作用网络进而预测出其中的功能模块,对于蛋白质功能预测、揭示各种生化反应过程的分子机理都有着极大的帮助。我们分类概括了用生物信息学预测蛋白质相互作用功能模块的方法,以及对这些方法的评价,并介绍了蛋白质相互作用网络比较的一些方法。     

动态分子网络的构建与分析

分子网络研究是从全局角度揭示生物系统的结构和功能的重要手段,现有的网络分析大部分是基于静态网络.实际上,在不同的环境条件、组织类型和疾病状态以及生长和分化的过程中,分子网络时刻都在发生变化.经过研究人员的努力,人们已经提出了一些可用于分析分子网络动态的生物信息学方法,如节点的动态性分类、动态蛋白质复合物的预测、条件特异子网的构建以及网络动态行为的模拟等.本文综述了动态分子网络的构建与分析方法.可以预见,动态网络分析将成为未来网络研究的标准模式.     

Testing computational prediction of missense mutation phenotypes: Functional characterization of 204 mutations of human cystathionine beta synthase

Predicting the phenotypes of missense mutations uncovered by large‐scale sequencing projects is an important goal in computational biology. High‐confidence predictions can be an aid in focusing experimental and association studies on those mutations most likely to be associated with causative relationships between mutation and disease. As an aid in developing these methods further, we have derived a set of random mutations of the enzymatic domains of human cystathionine beta synthase. This enzyme is a dimeric protein that catalyzes the condensation of serine and homocysteine to produce cystathionine. Yeast missing this enzyme cannot grow on medium lacking a source of cysteine, while transfection of functional human CBS into yeast strains missing endogenous enzyme can successfully complement for the missing gene. We used PCR mutagenesis with error‐prone Taq polymerase to produce 948 colonies and compared cell growth in the presence or absence of a cysteine source as a measure of CBS function. We were able to infer the phenotypes of 204 single‐site mutants, 79 of them deleterious and 125 neutral. This set was used to test the accuracy of six publicly available prediction methods for phenotype prediction of missense mutations: SIFT, PolyPhen, PMut, SNPs3D, PhD‐SNP, and nsSNPAnalyzer. The top methods are PolyPhen, SIFT, and nsSNPAnalyzer, which have similar performance. Using kernel discriminant functions, we found that the difference in position‐specific scoring matrix values is more predictive than the wild‐type PSSM score alone, and that the relative surface area in the biologically relevant complex is more predictive than that of the monomeric proteins. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

Searching for functional sites in protein structures

An ability to assign protein function from protein structure is important for structural genomics consortia. The complex relationship between protein fold and function highlights the necessity of looking beyond the global fold of a protein to specific functional sites. Many computational methods have been developed that address this issue. These include evolutionary trace methods, methods that involve the calculation and assessment of maximal superpositions, methods based on graph theory, and methods that apply machine learning techniques. Such function prediction techniques have been applied to the identification of enzyme catalytic triads and DNA-binding motifs.     

Sequence-based enzyme catalytic domain prediction using clustering and aggregated mutual information content

Characterizing enzyme sequences and identifying their active sites is a very important task. The current experimental methods are too expensive and labor intensive to handle the rapidly accumulating protein sequences and structure data. Thus accurate, high-throughput in silico methods for identifying catalytic residues and enzyme function prediction are much needed. In this paper, we propose a novel sequence-based catalytic domain prediction method using a sequence clustering and an information-theoretic approaches. The first step is to perform the sequence clustering analysis of enzyme sequences from the same functional category (those with the same EC label). The clustering analysis is used to handle the problem of widely varying sequence similarity levels in enzyme sequences. The clustering analysis constructs a sequence graph where nodes are enzyme sequences and edges are a pair of sequences with a certain degree of sequence similarity, and uses graph properties, such as biconnected components and articulation points, to generate sequence segments common to the enzyme sequences. Then amino acid subsequences in the common shared regions are aligned and then an information theoretic approach called aggregated column related scoring scheme is performed to highlight potential active sites in enzyme sequences. The aggregated information content scoring scheme is shown to be effective to highlight residues of active sites effectively. The proposed method of combining the clustering and the aggregated information content scoring methods was successful in highlighting known catalytic sites in enzymes of Escherichia coli K12 in terms of the Catalytic Site Atlas database. Our method is shown to be not only accurate in predicting potential active sites in the enzyme sequences but also computationally efficient since the clustering approach utilizes two graph properties that can be computed in linear to the number of edges in the sequence graph and computation of mutual information does not require much time. We believe that the proposed method can be useful for identifying active sites of enzyme sequences from many genome projects.     

Deciphering the preference and predicting the viability of circular permutations in proteins

Circular permutation (CP) refers to situations in which the termini of a protein are relocated to other positions in the structure. CP occurs naturally and has been artificially created to study protein function, stability and folding. Recently CP is increasingly applied to engineer enzyme structure and function, and to create bifunctional fusion proteins unachievable by tandem fusion. CP is a complicated and expensive technique. An intrinsic difficulty in its application lies in the fact that not every position in a protein is amenable for creating a viable permutant. To examine the preferences of CP and develop CP viability prediction methods, we carried out comprehensive analyses of the sequence, structural, and dynamical properties of known CP sites using a variety of statistics and simulation methods, such as the bootstrap aggregating, permutation test and molecular dynamics simulations. CP particularly favors Gly, Pro, Asp and Asn. Positions preferred by CP lie within coils, loops, turns, and at residues that are exposed to solvent, weakly hydrogen-bonded, environmentally unpacked, or flexible. Disfavored positions include Cys, bulky hydrophobic residues, and residues located within helices or near the protein's core. These results fostered the development of an effective viable CP site prediction system, which combined four machine learning methods, e.g., artificial neural networks, the support vector machine, a random forest, and a hierarchical feature integration procedure developed in this work. As assessed by using the hydrofolate reductase dataset as the independent evaluation dataset, this prediction system achieved an AUC of 0.9. Large-scale predictions have been performed for nine thousand representative protein structures; several new potential applications of CP were thus identified. Many unreported preferences of CP are revealed in this study. The developed system is the best CP viability prediction method currently available. This work will facilitate the application of CP in research and biotechnology.