Biosystems design by directed evolution

Biomedical applications of solid-binding peptides and proteins

Over the past decades, solid-binding peptides (SBPs) have found multiple applications in materials science. In non-covalent surface modification strategies, solid-binding peptides are a simple and versatile tool for the immobilization of biomolecules on a vast variety of solid surfaces. Especially in physiological environments, SBPs can increase the biocompatibility of hybrid materials and offer tunable properties for the display of biomolecules with minimal impact on their functionality. All these features make SBPs attractive for the manufacturing of bioinspired materials in diagnostic and therapeutic applications. In particular, biomedical applications such as drug delivery, biosensing, and regenerative therapies have benefited from the introduction of SBPs. Here, we review recent literature on the use of solid-binding peptides and solid-binding proteins in biomedical applications. We focus on applications where modulating the interactions between solid materials and biomolecules is crucial. In this review, we describe solid-binding peptides and proteins, providing background on sequence design and binding mechanism. We then discuss their application on materials relevant for biomedicine (calcium phosphates, silicates, ice crystals, metals, plastics, and graphene). Although the limited characterization of SBPs still represents a challenge for their design and widespread application, our review shows that SBP-mediated bioconjugation can be easily introduced into complex designs and on nanomaterials with very different surface chemistries.

High-throughput biochemistry in RNA sequence space: predicting structure and function

RNAs are central to fundamental biological processes in all known organisms. The set of possible intramolecular interactions of RNA nucleotides defines the range of alternative structural conformations of a specific RNA that can coexist, and these structures enable functional catalytic properties of RNAs and/or their productive intermolecular interactions with other RNAs or proteins. However, the immense combinatorial space of potential RNA sequences has precluded predictive mapping between RNA sequence and molecular structure and function. Recent advances in high-throughput approaches in vitro have enabled quantitative thermodynamic and kinetic measurements of RNA-RNA and RNA-protein interactions, across hundreds of thousands of sequence variations. In this Review, we explore these techniques, how they can be used to understand RNA function and how they might form the foundations of an accurate model to predict the structure and function of an RNA directly from its nucleotide sequence. The experimental techniques and modelling frameworks discussed here are also highly relevant for the sampling of sequence-structure-function space of DNAs and proteins.

Match_Motif: A rapid computational tool to assist in protein-protein interaction design

In order to generate protein assemblies with a desired function, the rational design of protein-protein binding interfaces is of significant interest. Approaches based on random mutagenesis or directed evolution may involve complex experimental selection procedures. Also, molecular modeling approaches to design entirely new proteins and interactions with partner molecules can involve large computational efforts and screening steps. In order to simplify at least the initial effort for designing a putative binding interface between two proteins the Match_Motif approach has been developed. It employs the large collection of known protein-protein complex structures to suggest interface modifications that may lead to improved binding for a desired input interaction geometry. The approach extracts interaction motifs based on the backbone structure of short (four residues) segments and the relative arrangement with respect to short segments on the partner protein. The interaction geometry is used to search through a database of such motifs in known stable bound complexes. All matches are rapidly identified (within a few seconds) and collected and can be used to guide changes in the interface that may lead to improved binding. In the output, an alternative interface structure is also proposed based on the frequency of occurrence of side chains at a given interface position in all matches and based on sterical considerations. Applications of the procedure to known complex structures and alternative arrangements are presented and discussed. The program, data files, and example applications can be downloaded from https://www.groups.ph.tum.de/t38/downloads/.

Forty Years of Directed Evolution and its Continuously Evolving Technology Toolbox - A Review of the Patent Landscape

Generating functional protein variants with novel or improved characteristics has been a goal of the biotechnology industry and life sciences, for decades. Rational design and directed evolution are two major pathways to achieve the desired ends. Whilst rational protein design approach has made substantial progress, the idea of using a method based on cycles of mutagenesis and natural selection to develop novel binding proteins, enzymes and structures has attracted great attention. Laboratory evolution of proteins/enzymes requires new tools and analytical approaches to create genetic diversity and identifying variants with desired traits. In this pursuit, construction of sufficiently large libraries of target molecules to search for improved variants and the need for new protocols to alter the properties of target molecules has been a continuing challenge in the directed evolution experiments. This review will discuss the in vivo and in vitro gene diversification tools, library screening or selection approaches, and artificial intelligence/machine-learning-based strategies to mutagenesis developed in the last forty years to accelerate the natural process of evolution in creating new functional protein variants, optimization of microbial strains and transformation of enzymes into industrial machines. Analyzing patent position over these techniques and mechanisms also constitutes an integral and distinctive part of this review. The aim is to provide an up-to-date resource/technology toolbox for research-based and pharmaceutical companies to discover the boundaries of competitor's intellectual property (IP) portfolio, their freedom-to-operate in the relevant IP landscape, and the need for patent due diligence analysis to rule out whether use of a particular patented mutagenesis method, library screening/selection technique falls outside the safe harbor of experimental use exemption. While so doing, we have referred to some recent cases that emphasize the significance of selecting a suitable gene diversification strategy in directed evolution experiments. This article is protected by copyright. All rights reserved.

SCONES: Self-Consistent Neural Network for Protein Stability Prediction Upon Mutation

Engineering proteins to have desired properties by mutating amino acids at specific sites is commonplace. Such engineered proteins must be stable to function. Experimental methods used to determine stability at throughputs required to scan the protein sequence space thoroughly are laborious. To this end, many machine learning based methods have been developed to predict thermodynamic stability changes upon mutation. These methods have been evaluated for symmetric consistency by testing with hypothetical reverse mutations. In this work, we propose transitive data augmentation, evaluating transitive consistency with our new S^transitive data set, and a new machine learning based method, the first of its kind, that incorporates both symmetric and transitive properties into the architecture. Our method, called SCONES, is an interpretable neural network that predicts small relative protein stability changes for missense mutations that do not significantly alter the structure. It estimates a residue's contributions toward protein stability (ΔG) in its local structural environment, and the difference between independently predicted contributions of the reference and mutant residues is reported as ΔΔG. We show that this self-consistent machine learning architecture is immune to many common biases in data sets, relies less on data than existing methods, is robust to overfitting, and can explain a substantial portion of the variance in experimental data.

The Unexplored Importance of Fleeting Chiral Intermediates in Enzyme-Catalyzed Reactions

Decades of extensive research efforts by biochemists, organic chemists, and protein engineers have led to an understanding of the basic mechanisms of essentially all known types of enzymes, but in a formidable number of cases an essential aspect has been overlooked. The occurrence of short-lived chiral intermediates formed by symmetry-breaking of prochiral precursors in enzyme catalyzed reactions has been systematically neglected. We designate these elusive species as fleeting chiral intermediates and analyze such crucial questions as "Do such intermediates occur in homochiral form?" If so, what is the absolute configuration, and why did Nature choose that particular stereoisomeric form, even when the isolable final product may be achiral? Does the absolute configuration of a chiral product depend in any way on the absolute configuration of the fleeting chiral precursor? How does this affect the catalytic proficiency of the enzyme? If these issues continue to be unexplored, then an understanding of the mechanisms of many enzyme types remains incomplete. We have systematized the occurrence of these chiral intermediates according to their structures and enzyme types. This is followed by critical analyses of selected case studies and by final conclusions and perspectives. We hope that the fascinating concept of fleeting chiral intermediates will attract the attention of scientists, thereby opening an exciting new research field.

Enhanced Thermostability of D-Psicose 3-Epimerase from Clostridium bolteae through Rational Design and Engineering of New Disulfide Bridges

D-psicose 3-epimerase (DPEase) catalyzes the isomerization of D-fructose to D-psicose (aka D-allulose, a low-calorie sweetener), but its industrial application has been restricted by the poor thermostability of the naturally available enzymes. Computational rational design of disulfide bridges was used to select potential sites in the protein structure of DPEase from Clostridium bolteae to engineer new disulfide bridges. Three mutants were engineered successfully with new disulfide bridges in different locations, increasing their optimum catalytic temperature from 55 to 65 °C, greatly improving their thermal stability and extending their half-lives (t_1/2) at 55 °C from 0.37 h to 4−4.5 h, thereby greatly enhancing their potential for industrial application. Molecular dynamics simulation and spatial configuration analysis revealed that introduction of a disulfide bridge modified the protein hydrogen–bond network, rigidified both the local and overall structures of the mutants and decreased the entropy of unfolded protein, thereby enhancing the thermostability of DPEase.

Directed Evolution: Methodologies and Applications

Directed evolution aims to expedite the natural evolution process of biological molecules and systems in a test tube through iterative rounds of gene diversifications and library screening/selection. It has become one of the most powerful and widespread tools for engineering improved or novel functions in proteins, metabolic pathways, and even whole genomes. This review describes the commonly used gene diversification strategies, screening/selection methods, and recently developed continuous evolution strategies for directed evolution. Moreover, we highlight some representative applications of directed evolution in engineering nucleic acids, proteins, pathways, genetic circuits, viruses, and whole cells. Finally, we discuss the challenges and future perspectives in directed evolution.

Flow Biocatalysis: A Challenging Alternative for the Synthesis of APIs and Natural Compounds

Biocatalysts represent an efficient, highly selective and greener alternative to metal catalysts in both industry and academia. In the last two decades, the interest in biocatalytic transformations has increased due to an urgent need for more sustainable industrial processes that comply with the principles of green chemistry. Thanks to the recent advances in biotechnologies, protein engineering and the Nobel prize awarded concept of direct enzymatic evolution, the synthetic enzymatic toolbox has expanded significantly. In particular, the implementation of biocatalysts in continuous flow systems has attracted much attention, especially from industry. The advantages of flow chemistry enable biosynthesis to overcome well-known limitations of “classic” enzymatic catalysis, such as time-consuming work-ups and enzyme inhibition, as well as difficult scale-up and process intensifications. Moreover, continuous flow biocatalysis provides access to practical, economical and more sustainable synthetic pathways, an important aspect for the future of pharmaceutical companies if they want to compete in the market while complying with European Medicines Agency (EMA), Food and Drug Administration (FDA) and green chemistry requirements. This review focuses on the most recent advances in the use of flow biocatalysis for the synthesis of active pharmaceutical ingredients (APIs), pharmaceuticals and natural products, and the advantages and limitations are discussed.

Computational design of noncanonical amino acid-based thioether staples at N/C-terminal domains of multi-modular pullulanase for thermostabilization in enzyme catalysis

Enzyme thermostabilization is considered a critical and often obligatory step in biosynthesis, because thermostability is a significant property of enzymes that can be used to evaluate their feasibility for industrial applications. However, conventional strategies for thermostabilizing enzymes generally introduce non-covalent interactions and/or natural covalent bonds caused by natural amino acid substitutions, and the trade-off between the activity and stability of enzymes remains a challenge. Here, we developed a computationally guided strategy for constructing thioether staples by incorporating noncanonical amino acid (ncAA) into the more flexible N/C-terminal domains of the multi-modular pullulanase from Bacillus thermoleovorans (BtPul) to enhance its thermostability. First, potential thioether staples located in the N/C-terminal domains of BtPul were predicted using RosettaMatch. Next, eight variants involving stable thioether staples were precisely predicted using FoldX and Rosetta ddg_monomer. Six positive variants were obtained, of which T73(O2beY)-171C had a 157% longer half-life at 70 °C and an increase of 7.0 °C in T m, when compared with the wild-type (WT). T73(O2beY)-171C/T126F/A72R exhibited an even more improved thermostability, with a 211% increase in half-life at 70 °C and a 44% enhancement in enzyme activity compared with the WT, which was attributed to further optimization of the local interaction network. This work introduces and validates an efficient strategy for enhancing the thermostability and activity of multi-modular enzymes.

Directed Evolution of Ornithine Cyclodeaminase Using an EvolvR-Based Growth-Coupling Strategy for Efficient Biosynthesis of l-Proline

l-Proline takes a significant role in the pharmaceutical and chemical industries as well as graziery. Typical biosynthesis of l-proline is from l-glutamate, involving three enzyme reactions as well as a spontaneous cyclization. Alternatively, l-proline can be also synthesized in l-ornithine and/or l-arginine producing strains by an ornithine aminotransferase (OCD). In this study, a strategy of directed evolution combining rare codon selection and pEvolvR was developed to screen OCD with high catalytic efficiency, improving l-proline production from l-arginine chassis cells. The mutations were generated by CRISPR-assisted DNA polymerases and were screened by growth-coupled rare codon selection system. OCD_{K205G/M86K/T162A} from Pseudomonas putida was identified with 2.85-fold increase in catalytic efficiency for the synthesis of l-proline. Furthermore, we designed and optimized RBS for the BaargI and Ppocd coupling cascade using RedLibs, as well as sRNA inhibition of argF to moderate l-proline biosynthesis in l-arginine overproducing Corynebacterium crenatum. The strain PS6 with best performance reached 15.3 g/L l-proline in the shake flask and showed a titer of 38.4 g/L in a 5 L fermenter with relatively low concentration of residual l-ornithine and/or l-arginine.

Biosystems Design by Machine Learning

Biosystems such as enzymes, pathways, and whole cells have been increasingly explored for biotechnological applications. However, the intricate connectivity and resulting complexity of biosystems poses a major hurdle in designing biosystems with desirable features. As -omics and other high throughput technologies have been rapidly developed, the promise of applying machine learning (ML) techniques in biosystems design has started to become a reality. ML models enable the identification of patterns within complicated biological data across multiple scales of analysis and can augment biosystems design applications by predicting new candidates for optimized performance. ML is being used at every stage of biosystems design to help find nonobvious engineering solutions with fewer design iterations. In this review, we first describe commonly used models and modeling paradigms within ML. We then discuss some applications of these models that have already shown success in biotechnological applications. Moreover, we discuss successful applications at all scales of biosystems design, including nucleic acids, genetic circuits, proteins, pathways, genomes, and bioprocesses. Finally, we discuss some limitations of these methods and potential solutions as well as prospects of the combination of ML and biosystems design.

Optically guided mass spectrometry to screen microbial colonies for directed enzyme evolution

Directed evolution is a well-established and powerful tool for enzyme engineering, which consists of iterative rounds of creating and screening a library of variants. In many cases, the ability to characterize these variants in high-throughput remains a bottleneck. In addition, profiling of desired candidates becomes even more challenging when engineering multiple enzymes in a biochemical pathway. In this chapter, we describe a label-free, high-throughput method for the engineering of multistep enzymatic reactions in bacterial colonies via optically guided matrix-assisted laser desorption/ionization time-of-flight (MALDI-ToF) mass spectrometry (MS). This method is able to detect products, reactants, and byproducts with high sensitivity and accuracy. We demonstrate its effectiveness in two applications related to natural product biosynthesis, including facile creation of analog of the peptidic antibiotic plantazolicin and rapid profiling of congeners of rhamnolipid. Computational algorithms were developed to process and visualize the resulting mass spectral data sets. In both cases, improved MS acquisition efficiency and information-rich insights were obtained through this technique on large populations of colonies at a rate of 1-2.5s per colony. This method should be generally applicable to high-throughput phenotyping of microbial libraries from a wide range of enzymatic reactions.