Surface grafting onto template‐assembled synthetic protein scaffolds in molecular recognition
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Creating functional biological molecules de novo requires a detailed understanding of the intimate relationship between primary sequence, folding mechanism, and packing topology, and remains up to now a most challenging goal in protein design and mimicry. As a consequence, the use of well-defined robust macromolecules as scaffolds for the introduction of function by grafting surface residues has become a major objective in protein engineering and de novo design. In this article, the concept of scaffolds is demonstrated on some selected examples, illustrating that novel types of functional molecules can be generated. Reengineered proteins and, most notably, de novo designed peptide scaffolds exhibiting molecular function, are ideal tools for structure–function studies and as leads in drug design. © 2001 John Wiley & Sons, Inc. Biopolymers (Pept Sci) 55: 451–458, 2000Keywords:
Folding (DSP implementation)
Protein design
Sequence (biology)
Protein Engineering
Molecular mimicry
Protein Engineering
Protein design
Biocatalysis
Directed Molecular Evolution
Site-directed mutagenesis
Folding (DSP implementation)
Molecular engineering
Denaturation (fissile materials)
Metabolic Engineering
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Protein engineering, which aims to improve the properties and functions of proteins, holds great research significance and application value. However, current models that predict the effects of amino acid substitutions often perform poorly when evaluated for precision. Recent research has shown that ProteinMPNN, a large-scale pre-training sequence design model based on protein structure, performs exceptionally well. It is capable of designing mutants with structures similar to the original protein. When applied to the field of protein engineering, the diverse designs for mutation positions generated by this model can be viewed as a more precise mutation range.
Protein Engineering
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Recent advances in computation-based protein engineering offer opportunities to introduce or modify the biophysical characteristics of proteins at will. The power of computational design comes from the ability to surpass the combinatorial and physical limitations inherent to laboratory-based high-throughput or trial-and-error methods. As a result, modifications that require significant changes to the amino acid sequence of a protein are now accessible to the protein engineering community. Hydrophobic cores of proteins have been repacked to increase their thermostability. Binding sites in proteins have been modified to increase affinity or alter specificity for proteins, peptides, and small molecules. Enzymes have been designed de novo. Non-natural protein folds have been created. For the most part, these achievements have been applied to proteins that make good model systems in academic settings. How can these computational methods be applied to therapeutically relevant proteins? This review will focus on the ground-breaking achievements of computation-based protein engineering and on recent applications of rational design to improve therapeutic proteins. Keywords: computational design, rational design, protein engineering, antibody engineering, protein therapeutics, antibody therapeutics
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It is hypothesized that protein domains evolved from smaller intrinsically stable subunits via combinatorial assembly. Illegitimate recombination of fragments that encode protein subunits could have quickly led to diversification of protein folds and their functionality. This evolutionary concept presents an attractive strategy to protein engineering, e.g., to create new scaffolds for enzyme design. We previously combined structurally similar parts from two ancient protein folds, the (βα)(8)-barrel and the flavodoxin-like fold. The resulting "hopeful monster" differed significantly from the intended (βα)(8)-barrel fold by an extra β-strand in the core. In this study, we ask what modifications are necessary to form the intended structure and what potential this approach has for the rational design of functional proteins. Guided by computational design, we optimized the interface between the fragments with five targeted mutations yielding a stable, monomeric protein whose predicted structure was verified experimentally. We further tested binding of a phosphorylated compound and detected that some affinity was already present due to an intact phosphate-binding site provided by one fragment. The affinity could be improved quickly to the level of natural proteins by introducing two additional mutations. The study illustrates the potential of recombining protein fragments with unique properties to design new and functional proteins, offering both a possible pathway of protein evolution and a protocol to rapidly engineer proteins for new applications.
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This chapter discusses several studies from various classes of metalloenzymes, in which a functional property of the protein, such as electron transfer or catalysis, was either predictably improved or completely altered, or cases where new functionality was engineered into a protein scaffold by modifying the protein cage, as opposed to the catalytic site itself. The chapter focuses on inorganic chemistry, special emphasis is given to metalloenzymes and, where available, the methods used in each study in order to serve as a guide for future studies in metalloenzyme design and engineering. The recent progress in rational design of protein cages for alternative enzymatic functions, and in particular, studies that have strived to predictably alter or impart new functionality to metalloenzymes, have led to important advances in our knowledge of how metalloenzymes work and have set the groundwork for producing designer proteins with a selected function and high rates of catalysis.
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De novo protein design offers templates for engineering tailor-made protein functions and orthogonal protein interaction networks for synthetic biology research. Various computational methods have been developed to introduce functional sites in known protein structures. De novo designed protein scaffolds provide further opportunities for functional protein design. Here we demonstrate the rational design of novel tumor necrosis factor alpha (TNFα) binding proteins using a home-made grafting program AutoMatch. We grafted three key residues from a virus 2L protein to a de novo designed small protein, DS119, with consideration of backbone flexibility. The designed proteins bind to TNFα with micromolar affinities. We further optimized the interface residues with RosettaDesign and significantly improved the binding capacity of one protein Tbab1-4. These designed proteins inhibit the activity of TNFα in cellular luciferase assays. Our work illustrates the potential application of the de novo designed protein DS119 in protein engineering, biomedical research, and protein sequence-structure-function studies.
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α1 -Antitrypsin (α1 -AT) serves as an archetypal example for the serine proteinase inhibitor (serpin) protein family and has been used as a scaffold for protein engineering for >35 years. Techniques used to engineer α1 -AT include targeted mutagenesis, protein fusions, phage display, glycoengineering, and consensus protein design. The goals of engineering have also been diverse, ranging from understanding serpin structure-function relationships, to the design of more potent or more specific proteinase inhibitors with potential therapeutic relevance. Here we summarize the history of these protein engineering efforts, describing the techniques applied to engineer α1 -AT, specific mutants of interest, and providing an appended catalog of the >200 α1 -AT mutants published to date.
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Abstract Protein structure design and engineering is a research endeavour in which proteins with predicted structure and function are constructed in the laboratory through rational design, combinatorial selection or combination of both approaches. It is built upon our knowledge about the structure and function of proteins and can be accomplished either from scratch ( de novo design) or based on native scaffolds (redesign). The area of protein design is an exciting and rapidly growing field, advancing from the design of simple protein structures, to those that are more complicated and recently to the designs of functional proteins. Design of artificial proteins containing unnatural amino acids, backbone linkages or cofactors have also been reported, making it possible to prepare proteins with structural and functional properties beyond those of native proteins. These advances bring us closer to realising the dream of tailor‐made artificial enzymes with high catalytic efficiency and selectivity for biotechnological and pharmaceutical applications. Key Concepts: Protein structure design and engineering is a research endeavour in which proteins with predicted structure and function are constructed in the laboratory. The protein design field can be organised into two complimentary approaches: rational design and combinatorial selection of the desired protein. Rational protein design strategies can involve designing a protein from scratch ( de novo design) or redesigning a protein with native scaffolds to achieve new structures and functions. Combinatorial approaches can involve sampling a large population of proteins to select the desired one or it can involve numerous rounds of randomised mutation followed by selection to fine‐tune properties being selected for. Techniques are available that allow the incorporation of unnatural moieties, such as unnatural amino acids, backbone linkages or cofactors, into proteins. The field has made a big stride recently in designing functional proteins.
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By constructing mutant libraries and utilizing high-throughput screening methods, directed evolution has emerged as the most popular strategy for protein design nowadays. In the past decade, taking advantages of computer performance and algorithms, computer-assisted protein design has rapidly developed and become a powerful method of protein engineering. Based on the simulation of protein structure and calculation of energy function, computational design can alter the substrate specificity and improve the thermostability of enzymes, as well as de novo design of artificial enzymes with expected functions. Recently, machine learning and other artificial intelligence technologies have also been applied to computational protein engineering, resulting in a series of remarkable applications. Along the lines of protein engineering, this paper reviews the progress and applications of computer-assisted protein design, and current trends and outlooks of the development.定向进化通过建立突变体文库与高通量筛选方法，快速提升蛋白的特定性质，是目前蛋白质工程最为常用的蛋白质设计改造策略。近十年随着计算机运算能力大幅提升以及先进算法不断涌现，计算机辅助蛋白质设计改造得到了极大的重视和发展，成为蛋白质工程新开辟的重要方向。以结构模拟与能量计算为基础的蛋白质计算设计不但能改造酶的底物特异性与热稳定性，还可从头设计具有特定功能的人工酶。近年来机器学习等人工智能技术也被应用于计算机辅助蛋白质设计改造，并取得瞩目的成绩。文中介绍了蛋白质工程的发展历程，重点评述当前计算机辅助蛋白质设计改造方面的进展与应用，并展望其未来发展方向。.
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