深度突变扫描技术在蛋白研究中的应用

作为细胞结构与功能的中心参与者,蛋白质一直是生命科学研究的中心主题。分析蛋白质序列变异对其结构、功能的影响,是研究蛋白的重要手段之一。近年一种称为深度突变扫描(deep mutational scanning,DMS)的技术被广泛应用于蛋白研究领域,其通过高丰度DNA文库在蛋白特定区域平行引入成千上万种突变,经筛选后,利用高通量测序为每一种突变打分,从而揭示序列与功能之间的相关性。深度突变扫描以其高通量、快速简易、节省人工等特点,已经成为蛋白质功能研究以及蛋白工程改造的一种重要方法,目前已在蛋白进化、抗体改造、致病突变鉴定等蛋白研究的多个领域广泛应用。本综述简要概括了深度突变扫描技术的原理,重点介绍了其在哺乳动物细胞中的应用,同时分析了目前的技术瓶颈,旨在为相关研究提供参考。     

Enzyme optimization: moving from blind evolution to statistical exploration of sequence-function space

Directed evolution is a powerful tool for the creation of commercially useful enzymes, particularly those approaches that are based on in vitro recombination methods, such as DNA shuffling. Although these types of search algorithms are extraordinarily efficient compared with purely random methods, they do not explicitly represent or interrogate the genotype-phenotype relationship and are essentially blind in nature. Recently, however, researchers have begun to apply multivariate statistical techniques to model protein sequence-function relationships and guide the evolutionary process by rapidly identifying beneficial diversity for recombination. In conjunction with state-of-the-art library generation methods, the statistical approach to sequence optimization is now being used routinely to create enzymes efficiently for industrial applications.     

Coevolutionary Landscape of Kinase Family Proteins: Sequence Probabilities and Functional Motifs

The protein kinase catalytic domain is one of the most abundant domains across all branches of life. Although kinases share a common core function of phosphoryl-transfer, they also have wide functional diversity and play varied roles in cell signaling networks, and for this reason are implicated in a number of human diseases. This functional diversity is primarily achieved through sequence variation, and uncovering the sequence-function relationships for the kinase family is a major challenge. In this study we use a statistical inference technique inspired by statistical physics, which builds a coevolutionary “Potts” Hamiltonian model of sequence variation in a protein family. We show how this model has sufficient power to predict the probability of specific subsequences in the highly diverged kinase family, which we verify by comparing the model’s predictions with experimental observations in the Uniprot database. We show that the pairwise (residue-residue) interaction terms of the statistical model are necessary and sufficient to capture higher-than-pairwise mutation patterns of natural kinase sequences. We observe that previously identified functional sets of residues have much stronger correlated interaction scores than are typical.     

Depicting a protein's two faces: GPCR classification by phylogenetic tree-based HMMs

Related proteins with similar biological functions generally share common features, allowing us to extract the common sequence features. These common features enable us to build statistical models that can be used to classify proteins, to predict new members, and to study the sequence-function relationship of this protein function group. Although evolution underlies the basis of multiple sequence analysis methods, most methods ignore phylogenetic relationships and the evolutionary process in building these statistical models. Previously we have shown that a phylogenetic tree-based profile hidden Markov model (T-HMM) is superior in generating a profile for a group of similar proteins. In this study we used the method to generate common features of G protein-coupled receptors (GPCRs). The profile generated by T-HMM gives high accuracy in GPCR function classification, both by ligand and by coupled G protein.     

Recent and future perspectives on engineering interferons and other cytokines as therapeutics

As crucial mediators and regulators of our immune system, cytokines are involved in a broad range of biological processes and are implicated in various disease pathologies. The field of cytokine therapeutics has gained much momentum from the maturation of conventional protein engineering methodologies such as structure-based designs and/or directed evolution, which is further aided by the advent of in silico protein designs and characterization. Just within the past 5 years, there has been an explosion of proof-of-concept, preclinical, and clinical studies that utilize an armory of protein engineering methods to develop cytokine-based drugs. Here, we highlight the key engineering strategies undertaken by recent studies that aim to improve the pharmacodynamic and pharmacokinetic profile of interferons and other cytokines as therapeutics.     

DNase II is a member of the phospholipase D superfamily

MOTIVATION: DNase II is an endodeoxyribonuclease involved in apoptosis and essential for the mammalian development. Despite the understanding of biochemical properties of this enzyme, its structure and relationships to other protein families remain unknown. RESULTS: Using protein fold-recognition we found that DNase II exhibits a catalytic domain common to the phospholipase D superfamily. Our model explains the available experimental data and provides the first structural platform for sequence-function analyses of this important nuclease.     

Putting engineering back into protein engineering: bioinformatic approaches to catalyst design

Complex multivariate engineering problems are commonplace and not unique to protein engineering. Mathematical and data-mining tools developed in other fields of engineering have now been applied to analyze sequence-activity relationships of peptides and proteins and to assist in the design of proteins and peptides with specified properties. Decreasing costs of DNA sequencing in conjunction with methods to quickly synthesize statistically representative sets of proteins allow modern heuristic statistics to be applied to protein engineering. This provides an alternative approach to expensive assays or unreliable high-throughput surrogate screens.     

Protein evolution with dependence among codons due to tertiary structure

Markovian models of protein evolution that relax the assumption of independent change among codons are considered. With this comparatively realistic framework, an evolutionary rate at a site can depend both on the state of the site and on the states of surrounding sites. By allowing a relatively general dependence structure among sites, models of evolution can reflect attributes of tertiary structure. To quantify the impact of protein structure on protein evolution, we analyze protein-coding DNA sequence pairs with an evolutionary model that incorporates effects of solvent accessibility and pairwise interactions among amino acid residues. By explicitly considering the relationship between nonsynonymous substitution rates and protein structure, this approach can lead to refined detection and characterization of positive selection. Analyses of simulated sequence pairs indicate that parameters in this evolutionary model can be well estimated. Analyses of lysozyme c and annexin V sequence pairs yield the biologically reasonable result that amino acid replacement rates are higher when the replacements lead to energetically favorable proteins than when they destabilize the proteins. Although the focus here is evolutionary dependence among codons that is associated with protein structure, the statistical approach is quite general and could be applied to diverse cases of evolutionary dependence where surrogates for sequence fitness can be measured or modeled.     

Evaluation of models for the evolution of protein sequences and functions under structural constraint

In the field of evolutionary structural genomics, methods are needed to evaluate why genomes evolved to contain the fold distributions that are observed. In order to study the effects of population dynamics in the evolved genomes we need fast and accurate evolutionary models which can analyze the effects of selection, drift and fixation of a protein sequence in a population that are grounded by physical parameters governing the folding and binding properties of the sequence. In this study, various knowledge-based, force field, and statistical methods for protein folding have been evaluated with four different folds: SH2 domains, SH3 domains, Globin-like, and Flavodoxin-like, to evaluate the speed and accuracy of the energy functions. Similarly, knowledge-based and force field methods have been used to predict ligand binding specificity in SH2 domain. To demonstrate the applicability of these methods, the dynamics of evolution of new binding capabilities by an SH2 domain is demonstrated.     

Systems for in vivo hypermutation: a quest for scale and depth in directed evolution

Traditional approaches to the directed evolution of genes of interest (GOIs) place constraints on the scale of experimentation and depth of evolutionary search reasonably achieved. Engineered genetic systems that dramatically elevate the mutation of target GOIs in vivo relieve these constraints by enabling continuous evolution, affording new strategies in the exploration of sequence space and fitness landscapes for GOIs. We describe various in vivo hypermutation systems for continuous evolution, discuss how different architectures for in vivo hypermutation facilitate evolutionary search scale and depth in their application to problems in protein evolution and engineering, and outline future opportunities for the field.     

The state-of-the-art strategies of protein engineering for enzyme stabilization

Enzymes generated by natural recruitment and protein engineering have greatly contribute in various sets of applications. However, their insufficient stability is a bottleneck that limit the rapid development of biocatalysis. Novel approaches based on precise and global structural dissection, advanced gene manipulation, and combination with the multidisciplinary techniques open a new horizon to generate stable enzymes efficiently. Here, we comprehensively introduced emerging advances of protein engineering strategies for enzyme stabilization. Then, we highlighted practical cases to show importance of enzyme stabilization in pharmaceutical and industrial applications. Combining computational enzyme design with molecular evolution will hold considerable promise in this field.     

几种定向进化技术的比较及文库构建策略

蛋白质定向进化在蛋白质工程中取得了令人瞩目的成就,其核心技术---随机突变库构建技术已成为近年来体外定向进化研究的热点。在概述定向进化基本原理基础上,对几种随机突变技术进行了介绍、分类和比较,并对突变库的特征及构建策略予以分析和描述 。     

酶的分子定向进化及其应用

酶的分子定向进化是20世纪90年代初兴起的一种蛋白质工程的新策略,是一种在生物体外模拟自然进化过程的、具有一定目的性的快速改造蛋A质的方法.该方法引起了生物催化技术领域的又一次革命.目前分子定向进化技术已被广泛应用于工业、农业及制药业等的相关领域.本文详细综述了酶的分子定向进化的概念、过程、基本策略及其核心技术,并着重介绍了酶的分子定向进化技术在提高酶的活力、稳定性、底物特异性和对映体选择性等几方面的应用及取得的相关成果.     

Stability and CDR Composition Biases Enrich Binder Functionality Landscapes

The rugged protein sequence-function landscape complicates efforts, both in nature and in the laboratory, to evolve protein function. Protein library diversification must strike a balance between sufficient variegation to thoroughly sample alternative functionality versus the probability of mutant destabilization below an expressible threshold. In this work, we explore the sequence-function landscape in the context of screening for molecular recognition from an Ig scaffold library. The fibronectin type III domain is used to explore the impact of two sequence diversification strategies: (a) partial wild-type conservation at structurally important positions within the paratope region and (b) tailored amino acid composition mimicking antibody binding-site composition at putative paratope positions. Structurally important positions within the paratope region were identified through stability, structural, and phylogenetic analyses and partially or fully conserved in sequence. To achieve tailored antibody-like diversity, we designed a set of skewed nucleotide mixtures yielding codons approximately matching the distribution observed in antibody complementarity-determining regions without incurring the expense of triphosphoramidite-based construction. These design elements were explored via comparison of three library designs: a random library, a library with wild-type bias in the DE loop only and tyrosine-serine diversity elsewhere, and a library with wild-type bias at 11 positions and the antibody-inspired amino acid distribution. Using pooled libraries for direct competition in a single tube, selection and maturation of binders to seven targets yielded 19 of 21 clones that originated from the structurally biased, tailored-diversity library design. Sequence analysis of the selected clones supports the importance of both tailored compositional diversity and structural bias. In addition, selection of both well and poorly expressed clones from two libraries further elucidated the impact of structural bias.     

Efficient manipulations of synonymous mutations for controlling translation rate: an analytical approach

Gene translation is a central process in all living organism with important ramifications to almost every biomedical field. Previous systems evolutionary studies in the field have demonstrated that in many organisms coding sequence features undergo selection to optimize this process. In the current study, we report for the first time analytical proofs related to the various aspects of this process and its optimality. Among our results we show that coding sequences with mono- tonic increasing profiles of translation efficiency (i.e., with slower codons near the 5'UTR), mathematically optimize ribosomal allocation by minimizing the number of ribosomes needed for translating a codon per time unit. Thus, the genomic translation efficiency profile reported in previous studies for many organisms is optimal in this sense. In addition, we show that improving translation efficiency of a codon in a gene may result in a decrease in the translation rate of other genes, demonstrating that the relation between codon bias and protein translation rate is less trivial than was assumed before. Based on these observations we describe an efficient heuristic for designing coding sequences with specific translation efficiency and minimal ribosomal allocation for heterologous gene expression. We demonstrate how this heuristic can be used in biotechnology for engineering a heterologous gene before expressing it in a new host.     

Enzyme engineering: Reshaping the biocatalytic functions

Enzyme engineering is a powerful tool to fine-tune the enzymes. It is a technique by which the stability, activity, and specificity of the enzymes can be altered. The characteristic properties of an enzyme can be amended by immobilization and protein engineering. Among them, protein engineering is the most promising, as in addition to amending the stability and activity, it is the only way to modulate the specificity and stereoselectivity of enzymes. The current review sheds light on protein engineering and the approaches applied for it on the basis of the degree of knowledge of structure and function of enzymes. Enzymes, which have been engineered are also discussed in detail and categorized on the basis of their respective applications. This will give a better insight into the revolutionary changes brought by protein engineering of enzymes in various industrial and environmental processes.     

Directed evolution of novel protein functions

Directed evolution has been successfully used to engineer proteins for basic and applied biological research. However, engineering of novel protein functions by directed evolution remains an overwhelming challenge. This challenge may come from the fact that multiple simultaneously or synergistic mutations are required for the creation of a novel protein function. Here we review the key developments in engineering of novel protein functions by using either directed evolution or a combined directed evolution and rational or computational design approach. Specific attention will be paid to a molecular evolution model for generation of novel proteins. The engineered novel proteins should not only broaden the range of applications of proteins but also provide new insights into protein structure-function relationship and protein evolution.     

A practical guide for the computational selection of residues to be experimentally characterized in protein families

In recent years, numerous biocomputational tools have been designed to extract functional and evolutionary information from multiple sequence alignments (MSAs) of proteins and genes. Most biologists working actively on the characterization of proteins from a single or family perspective use the MSA analysis to retrieve valuable information about amino acid conservation and the functional role of residues in query protein(s). In MSAs, adjustment of alignment parameters is a key point to improve the quality of MSA output. However, this issue is frequently underestimated and/or misunderstood by scientists and there is no in-depth knowledge available in this field. This brief review focuses on biocomputational approaches complementary to MSA to help distinguish functional residues in protein families. These additional analyses involve issues ranging from phylogenetic to statistical, which address the detection of amino acids pivotal for protein function at any level. In recent years, a large number of tools has been designed for this very purpose. Using some of these relevant, useful tools, we have designed a practical pipeline to perform in silico studies with a view to improving the characterization of family proteins and their functional residues. This review-guide aims to present biologists a set of specially designed tools to study proteins. These tools are user-friendly as they use web servers or easy-to-handle applications. Such criteria are essential for this review as most of the biologists (experimentalists) working in this field are unfamiliar with these biocomputational analysis approaches.