Engineering of large numbers of highly specific homing endonucleases that induce recombination on novel DNA targets

The last decade has seen the emergence of a universal method for precise and efficient genome engineering. This method relies on the use of sequence-specific endonucleases such as homing endonucleases. The structures of several of these proteins are known, allowing for site-directed mutagenesis of residues essential for DNA binding. Here, we show that a semi-rational approach can be used to derive hundreds of novel proteins from I-CreI, a homing endonuclease from the LAGLIDADG family. These novel endonucleases display a wide range of cleavage patterns in yeast and mammalian cells that in most cases are highly specific and distinct from I-CreI. Second, rules for protein/DNA interaction can be inferred from statistical analysis. Third, novel endonucleases can be combined to create heterodimeric protein species, thereby greatly enhancing the number of potential targets. These results describe a straightforward approach for engineering novel endonucleases with tailored specificities, while preserving the activity and specificity of natural homing endonucleases, and thereby deliver new tools for genome engineering.     

AlphaFold – A Personal Perspective on the Impact of Machine Learning

I outline how over my career as a protein scientist Machine Learning has impacted my area of science and one of my pastimes, chess, where there are some interesting parallels. In 1968, modelling of three-dimensional structures was initiated based on a known structure as a template, the problem of the pathway of protein folding was posed and bets were taken in the emerging field of Machine Learning on whether computers could outplay humans at chess. Half a century later, Machine Learning has progressed from using computational power combined with human knowledge in solving problems to playing chess without human knowledge being used, where it has produced novel strategies. Protein structures are being solved by Machine Learning based on human-derived knowledge but without templates. There is much promise that programs like AlphaFold based on Machine Learning will be powerful tools for designing entirely novel protein folds and new activities. But, will they produce novel ideas on protein folding pathways and provide new insights into the principles that govern folds?     

Gene expression profiling in osteoblast biology: bioinformatic tools

This review focuses on using microarray data on a clonal osteoblast cell model to demonstrate how various current and future bioinformatic tools can be used to understand, at a more global or comprehensible level, how cells grow and differentiate. In this example, BMP2 was used to stimulate growth and differentiation of osteoblast to a mineralized matrix. A discussion is included on various methods for clustering gene expression data, statistical evaluation of data, and various new tools that can be used to derive deeper insight into a particular biological problem. How these tools can be obtained is also discussed. New tools for the biologists to compare their datasets with others, as well as examples of future bioinformatic tools that can be used for developing gene networks and pathways for a given set of data are included and discussed.     

Engineered Allosteric Regulation of Protein Function

Allosteric regulation of proteins has been utilized to study various aspects of cell signaling, from unicellular events to organism-wide phenotypes. However, traditional methods of allosteric regulation, such as constitutively active mutants and inhibitors, lack tight spatiotemporal control. This often leads to unintended signaling consequences that interfere with data interpretation. To overcome these obstacles, researchers employed protein engineering approaches that enable tight control of protein function through allosteric mechanisms. These methods provide high specificity as well as spatial and temporal precision in regulation of protein activity in vitro and in vivo. In this review, we focus on the recent advancements in engineered allosteric regulation and discuss the various bioengineered allosteric techniques available now, from chimeric GPCRs to chemogenetic and optogenetic switches. We highlight the benefits and pitfalls of each of these techniques as well as areas in which future improvements can be made. Additionally, we provide a brief discussion on implementation of engineered allosteric regulation approaches, demonstrating that these tools can shed light on elusive biological events and have the potential to be utilized in precision medicine.     

合成生物学技术在环境保护中的应用

合成生物学是一个基于生物学和工程学原理的科学领域,其目的是重新设计和重组微生物,以优化或创建具有增强功能的新生物系统。该领域利用分子工具、系统生物学和遗传框架的重编程,从而构建合成途径以获得具有替代功能的微生物。传统上,合成生物学方法通常旨在开发具有成本效益的微生物细胞工厂进而从可再生资源中生产化学物质。然而,近年来合成生物学技术开始在环境保护中发挥着更直接的作用。本综述介绍了基因工程中的合成生物学工具,讨论了基于基因工程的微生物修复策略,强调了合成生物学技术可以通过响应特定污染物进行生物修复来保护环境。其中,规律间隔成簇短回文重复序列(Clustered Regularly Interspersed Short Palindromic Repeats, CRISPR)技术在基因工程细菌和古细菌的生物修复中得到了广泛应用,生物修复领域也出现了很多新的先进技术,包括生物膜工程、人工微生物群落的构建、基因驱动、酶和蛋白质工程等。有了这些新的技术和工具,生物修复将成为当今最好和最有效的污染物去除方式之一。     

Tyrphostins: tyrosine kinase blockers as novel antiproliferative agents and dissectors of signal transduction.

Protein tyrosine kinases (PTKs) are members of a growing family of oncoproteins and protooncoproteins that play a pivotal role in normal and abnormal proliferative processes. This hallmark identifies these unique proteins as potential targets for antiproliferative therapy. This review discusses the current status of PTK inhibitors, with special emphasis on tyrphostins as antiproliferative agents and as potential drugs for cancers, leukemias, psoriasis, and restenosis as well as other proliferative conditions. The development of tyrphostins as selective signal blockers can be viewed as a first step toward the development of "smart" cocktails as antiproliferative agents. Each of these custom-made cocktails will be aimed at proliferative conditions whose transduction pathways can be characterized by molecular tools. The review also discusses the use of PTK blockers as tools to study signal transduction processes in which protein tyrosine kinases are implicated.     

Protein design for pathway engineering

Design and construction of biochemical pathways has increased the complexity of biosynthetically-produced compounds when compared to single enzyme biocatalysis. However, the coordination of multiple enzymes can introduce a complicated set of obstacles to overcome in order to achieve a high titer and yield of the desired compound. Metabolic engineering has made great strides in developing tools to optimize the flux through a target pathway, but the inherent characteristics of a particular enzyme within the pathway can still limit the productivity. Thus, judicious protein design is critical for metabolic and pathway engineering. This review will describe various strategies and examples of applying protein design to pathway engineering to optimize the flux through the pathway. The proteins can be engineered for altered substrate specificity/selectivity, increased catalytic activity, reduced mass transfer limitations through specific protein localization, and reduced substrate/product inhibition. Protein engineering can also be expanded to design biosensors to enable high through-put screening and to customize cell signaling networks. These strategies have successfully engineered pathways for significantly increased productivity of the desired product or in the production of novel compounds.     

Recursive domains in proteins.

The domain is a fundamental unit of protein structure. Numerous studies have analyzed folding patterns in protein domains of known structure to gain insight into the underlying protein folding process. Are such patterns a haphazard assortment or are they similar to sentences in a language, which can be generated by an underlying grammar? Specifically, can a small number of intuitively sensible rules generate a large class of folds, including feasible new folds? In this paper, we explore the extent to which four simple rules can generate the known all‐β folds, using tools from graph theory. As a control, an exhaustive set of β‐sandwiches was tested and found to be largely incompatible with such a grammar. The existence of a protein grammar has potential implications for both the mechanism of folding and the evolution of domains.     

Establishing synthesis pathway-host compatibility via enzyme solubility

Current pathway synthesis tools identify possible pathways that can be added to a host to produce the desired target molecule through the exploration of abstract metabolic and reaction network space. However, not many of these tools explore gene-level information required to physically realize the identified synthesis pathways, and none explore enzyme-host compatibility. Developing tools that address this disconnect between abstract reactions/metabolic design space and physical genetic sequence design space will enable expedited experimental efforts that avoid exploring unprofitable synthesis pathways. This work describes a workflow, termed Probabilistic Pathway Assembly with Solubility Confidence Scores (ProPASS), which links synthesis pathway construction with the exploration of the physical design space as imposed by the availability of enzymes with predicted characterized activities within the host. Predicted protein solubility propensity scores are used as a confidence level to quantify the compatibility of each pathway enzyme with the host Escherichia coli (E. coli). This study also presents a database, termed Protein Solubility Database (ProSol DB), which provides solubility confidence scores in E. coli for 240,016 characterized enzymes obtained from UniProtKB/Swiss-Prot. The utility of ProPASS is demonstrated by generating genetic implementations of heterologous synthesis pathways in E. coli that target several commercially useful biomolecules.     

ProtaBank: A repository for protein design and engineering data

We present ProtaBank, a repository for storing, querying, analyzing, and sharing protein design and engineering data in an actively maintained and updated database. ProtaBank provides a format to describe and compare all types of protein mutational data, spanning a wide range of properties and techniques. It features a user‐friendly web interface and programming layer that streamlines data deposition and allows for batch input and queries. The database schema design incorporates a standard format for reporting protein sequences and experimental data that facilitates comparison of results across different data sets. A suite of analysis and visualization tools are provided to facilitate discovery, to guide future designs, and to benchmark and train new predictive tools and algorithms. ProtaBank will provide a valuable resource to the protein engineering community by storing and safeguarding newly generated data, allowing for fast searching and identification of relevant data from the existing literature, and exploring correlations between disparate data sets. ProtaBank invites researchers to contribute data to the database to make it accessible for search and analysis. ProtaBank is available at https://protabank.org .     

用于蛋白质间相互作用研究的新型双杂交系统

近年来,一些不依赖于转录因子活性的新型双杂交系统相继建立,如分离的泛素系统、蛋白质片段互补分析、阻遏物重构分析和SOS恢复系统等。同利用转录因子活性的酵母双杂交系统相似,这些方法也利用了一些活性蛋白的结构与功能特点来研究蛋白质间相互作用,这些活性蛋白不是转录因子,但也可在结构上进行分离可通过重构使其生物活性得以恢复。由于这些新型双杂交系统的各自特点,使得它们成为酵母双杂交系统的有益补充和研究蛋白质间相互作用的有力工具。     

Engineering protein self-assembling in protein-based nanomedicines for drug delivery and gene therapy

Lack of targeting and improper biodistribution are major flaws in current drug-based therapies that prevent reaching high local concentrations of the therapeutic agent. Such weaknesses impose the administration of high drug doses, resulting in undesired side effects, limited efficacy and enhanced production costs. Currently, missing nanosized containers, functionalized for specific cell targeting will be then highly convenient for the controlled delivery of both conventional and innovative drugs. In an attempt to fill this gap, health-focused nanotechnologies have put under screening a growing spectrum of materials as potential components of nanocages, whose properties can be tuned during fabrication. However, most of these materials pose severe biocompatibility concerns. We review in this study how proteins, the most versatile functional macromolecules, can be conveniently exploited and adapted by conventional genetic engineering as efficient building blocks of fully compatible nanoparticles for drug delivery and how selected biological activities can be recruited to mimic viral behavior during infection. Although engineering of protein self-assembling is still excluded from fully rational approaches, the exploitation of protein nano-assemblies occurring in nature and the direct manipulation of protein–protein contacts in bioinspired constructs open intriguing possibilities for further development. These methodologies empower the construction of new and potent vehicles that offer promise as true artificial viruses for efficient and safe nanomedical applications.     

Proteomic approach to aging research

The scope of the current paper is to review existing and potential applications of proteomic analysis to aging research. The focus will lie on the unique opportunities of high-throughput studies for uncovering specific alterations in protein expression, protein complexes or protein modifications caused by biological aging. The result of such studies will outline aging phenotypes and potentially indicate pathways involved in the pathogenesis of age-associated disfunctions. Specific attention is paid to the illustrations of successful applications of proteomic technologies and potential applications of new proteomic concepts to biogerontological studies.     

基因工程功能化丝蛋白生物材料及其在生物医学中的应用

丝蛋白生物材料具有优异的力学性能、良好的生物相容性及可降解性,在生物医学领域具有巨大的应用潜力。现有丝蛋白生物材料在结构和功能方面的相关知识,为设计合成新型丝蛋白生物材料提供了理论基础。此外,利用基因工程技术可将编码新肽或结构域的基因序列添加到编码丝蛋白的基因序列中,以获得具有新功能的丝蛋白生物材料,并更好地满足现代生物医学的需求。文中总结了基因工程功能化的丝蛋白生物材料在生物医学领域中的应用现状和发展前景。     

Structural and functional characterization of solute binding proteins for aromatic compounds derived from lignin: p‐Coumaric acid and related aromatic acids

Lignin comprises 15–25% of plant biomass and represents a major environmental carbon source for utilization by soil microorganisms. Access to this energy resource requires the action of fungal and bacterial enzymes to break down the lignin polymer into a complex assortment of aromatic compounds that can be transported into the cells. To improve our understanding of the utilization of lignin by microorganisms, we characterized the molecular properties of solute binding proteins of ATP‐binding cassette transporter proteins that interact with these compounds. A combination of functional screens and structural studies characterized the binding specificity of the solute binding proteins for aromatic compounds derived from lignin such as p‐coumarate, 3‐phenylpropionic acid and compounds with more complex ring substitutions. A ligand screen based on thermal stabilization identified several binding protein clusters that exhibit preferences based on the size or number of aromatic ring substituents. Multiple X‐ray crystal structures of protein–ligand complexes for these clusters identified the molecular basis of the binding specificity for the lignin‐derived aromatic compounds. The screens and structural data provide new functional assignments for these solute‐binding proteins which can be used to infer their transport specificity. This knowledge of the functional roles and molecular binding specificity of these proteins will support the identification of the specific enzymes and regulatory proteins of peripheral pathways that funnel these compounds to central metabolic pathways and will improve the predictive power of sequence‐based functional annotation methods for this family of proteins.Proteins 2013; 81:1709–1726. © 2013 Wiley Periodicals, Inc.     

Applications of genetically-encoded biosensors for the construction and control of biosynthetic pathways

Cells are filled with biosensors, molecular systems that measure the state of the cell and respond by regulating host processes. In much the same way that an engineer would monitor a chemical reactor, the cell uses these sensors to monitor changing intracellular environments and produce consistent behavior despite the variable environment. While natural systems derive a clear benefit from pathway regulation, past research efforts in engineering cellular metabolism have focused on introducing new pathways and removing existing pathway regulation. Synthetic biology is a rapidly growing field that focuses on the development of new tools that support the design, construction, and optimization of biological systems. Recent advances have been made in the design of genetically-encoded biosensors and the application of this class of molecular tools for optimizing and regulating heterologous pathways. Biosensors to cellular metabolites can be taken directly from natural systems, engineered from natural sensors, or constructed entirely in vitro. When linked to reporters, such as antibiotic resistance markers, these metabolite sensors can be used to report on pathway productivity, allowing high-throughput screening for pathway optimization. Future directions will focus on the application of biosensors to introduce feedback control into metabolic pathways, providing dynamic control strategies to increase the efficient use of cellular resources and pathway reliability.     

Proteomic analysis of cellular signaling

Protein phosphorylation events are key regulators of cellular signaling processes. In the era of functional genomics, rational drug design programs demand large-scale high-throughput analysis of signal transduction cascades. Significant improvements in the area of mass spectrometry-based proteomics have provided exciting opportunities for rapid progress toward global protein phosphorylation analysis. This review summarizes several recent advances made in the field of phosphoproteomics with an emphasis placed on mass spectrometry instrumentation, enrichment methods and quantification strategies. In the near future, these technologies will provide a tool that can be used for quantitative investigation of signal transduction pathways to generate new insights into biologic systems.     

Protein design in systems metabolic engineering for industrial strain development

Accelerating the process of industrial bacterial host strain development, aimed at increasing productivity, generating new bio-products or utilizing alternative feedstocks, requires the integration of complementary approaches to manipulate cellular metabolism and regulatory networks. Systems metabolic engineering extends the concept of classical metabolic engineering to the systems level by incorporating the techniques used in systems biology and synthetic biology, and offers a framework for the development of the next generation of industrial strains. As one of the most useful tools of systems metabolic engineering, protein design allows us to design and optimize cellular metabolism at a molecular level. Here, we review the current strategies of protein design for engineering cellular synthetic pathways, metabolic control systems and signaling pathways, and highlight the challenges of this subfield within the context of systems metabolic engineering.