Substrate-Specific Screening for Mutational Hotspots Using Biased Molecular Dynamics Simulations

Design of a Genetically Programmable and Customizable Protein Scaffolding System for the Hierarchical Assembly of Robust, Functional Macroscale Materials

Inspired by the properties of natural protein-based biomaterials, protein nanomaterials are increasingly designed with natural or engineered peptides or with protein building blocks. Few examples describe the design of functional protein-based materials for biotechnological applications that can be readily manufactured, are amenable to functionalization, and exhibit robust assembly properties for macroscale material formation. Here, we designed a protein-scaffolding system that self-assembles into robust, macroscale materials suitable for in vitro cell-free applications. By controlling the coexpression in Escherichia coli of self-assembling scaffold building blocks with and without modifications for covalent attachment of cross-linking cargo proteins, hybrid scaffolds with spatially organized conjugation sites are overproduced that can be readily isolated. Cargo proteins, including enzymes, are rapidly cross-linked onto scaffolds for the formation of functional materials. We show that these materials can be used for the in vitro operation of a coimmobilized two-enzyme reaction and that the protein material can be recovered and reused. We believe that this work will provide a versatile platform for the design and scalable production of functional materials with customizable properties and the robustness required for biotechnological applications.

Choose Your Own Adventure: A Comprehensive Database of Reactions Catalyzed by Cytochrome P450 BM3 Variants

Cytochrome P450 BM3 monooxygenase is the topic of extensive research as many researchers have evolved this enzyme to generate a variety of products. However, the abundance of information on increasingly diversified variants of P450 BM3 that catalyze a broad array of chemistry is not in a format that enables easy extraction and interpretation. We present a database that categorizes variants by their catalyzed reactions and includes details about substrates to provide reaction context. This database of >1500 P450 BM3 variants is downloadable and machine-readable and includes instructions to maximize ease of gathering information. The database allows rapid identification of commonly reported substitutions, aiding researchers who are unfamiliar with the enzyme in identifying starting points for enzyme engineering. For those actively engaged in engineering P450 BM3, the database, along with this review, provides a powerful and user-friendly platform to understand, predict, and identify the attributes of P450 BM3 variants, encouraging the further engineering of this enzyme.

Mechanism-Guided Computational Design of ω-Transaminase by Reprograming of High-Energy-Barrier Steps

ω-Transaminases (ω-TAs) show considerable potential for the synthesis of chiral amines. However, their low catalytic efficiency towards bulky substrates limits their application, and complicated catalytic mechanisms prevent precise enzyme design. Herein, we address this challenge using a mechanism-guided computational enzyme design strategy by reprograming the transition and ground states in key reaction steps. The common features among the three high-energy-barrier steps responsible for the low catalytic efficiency were revealed using quantum mechanics (QM). Five key residues were simultaneously tailored to stabilize the rate-limiting transition state with the aid of the Rosetta design. The 14 top-ranked variants showed 16.9-143-fold improved catalytic activity. The catalytic efficiency of the best variant, M9 (Q25F/M60W/W64F/I266A), was significantly increased, with a 1660-fold increase in k_cat /K_m and a 1.5-26.8-fold increase in turnover number (TON) towards various indanone derivatives.

Integrating dynamics into enzyme engineering

Enzyme engineering has become a widely adopted practice in research labs and industry. In parallel, the past decades have seen tremendous strides in characterizing the dynamics of proteins, using a growing array of methodologies. Importantly, links have been established between the dynamics of proteins and their function. Characterizing the dynamics of an enzyme prior to, and following, its engineering is beginning to inform on the potential of 'dynamic engineering', i.e. the rational modification of protein dynamics to alter enzyme function. Here we examine the state of knowledge at the intersection of enzyme engineering and protein dynamics, describe current challenges and highlight pioneering work in the nascent area of dynamic engineering.

Computational Enzyme Engineering Pipelines for Optimized Production of Renewable Chemicals

To enable a sustainable supply of chemicals, novel biotechnological solutions are required that replace the reliance on fossil resources. One potential solution is to utilize tailored biosynthetic modules for the metabolic conversion of CO₂ or organic waste to chemicals and fuel by microorganisms. Currently, it is challenging to commercialize biotechnological processes for renewable chemical biomanufacturing because of a lack of highly active and specific biocatalysts. As experimental methods to engineer biocatalysts are time- and cost-intensive, it is important to establish efficient and reliable computational tools that can speed up the identification or optimization of selective, highly active, and stable enzyme variants for utilization in the biotechnological industry. Here, we review and suggest combinations of effective state-of-the-art software and online tools available for computational enzyme engineering pipelines to optimize metabolic pathways for the biosynthesis of renewable chemicals. Using examples relevant for biotechnology, we explain the underlying principles of enzyme engineering and design and illuminate future directions for automated optimization of biocatalysts for the assembly of synthetic metabolic pathways.

Computational design of enzymes for biotechnological applications

Enzymes are the natural catalysts that execute biochemical reactions upholding life. Their natural effectiveness has been fine-tuned as a result of millions of years of natural evolution. Such catalytic effectiveness has prompted the use of biocatalysts from multiple sources on different applications, including the industrial production of goods (food and beverages, detergents, textile, and pharmaceutics), environmental protection, and biomedical applications. Natural enzymes often need to be improved by protein engineering to optimize their function in non-native environments. Recent technological advances have greatly facilitated this process by providing the experimental approaches of directed evolution or by enabling computer-assisted applications. Directed evolution mimics the natural selection process in a highly accelerated fashion at the expense of arduous laboratory work and economic resources. Theoretical methods provide predictions and represent an attractive complement to such experiments by waiving their inherent costs. Computational techniques can be used to engineer enzymatic reactivity, substrate specificity and ligand binding, access pathways and ligand transport, and global properties like protein stability, solubility, and flexibility. Theoretical approaches can also identify hotspots on the protein sequence for mutagenesis and predict suitable alternatives for selected positions with expected outcomes. This review covers the latest advances in computational methods for enzyme engineering and presents many successful case studies.

Selective CH bond functionalization with engineered heme proteins: new tools to generate complexity

CH functionalization is an attractive strategy to construct and diversify molecules. Heme proteins, predominantly cytochromes P450, are responsible for an array of CH oxidations in biology. Recent work has coupled concepts from synthetic chemistry, computation, and natural product biosynthesis to engineer heme protein systems to deliver products with tailored oxidation patterns. Heme protein catalysis has been shown to go well beyond these native reactions and now accesses new-to-nature CH transformations, including CN and CC bond forming processes. Emerging work with these systems moves us along the ambitious path of building complexity from the ubiquitous CH bond.

Computer-Guided Surface Engineering for Enzyme Improvement

Protein engineering strategies are often guided by our understanding of how the structure of a protein determines its function. However, our understanding is generally restricted to small regions of a protein, namely the active site and its immediate vicinity, while the remainder of the protein is something of an enigma. Studying highly homologous transaminases with strictly conserved active sites, but different substrate preferences and activities, we predict and experimentally validate that the surface of the protein far from the active site carries out a decisive role in substrate selectivity and catalytic efficiency. Using a unique molecular dynamics approach and novel trajectory analysis, we demonstrate the phenomenon of surface-directed ligand diffusion in this well-known protein family for the first time. Further, we identify the residues involved in directing substrate, design surface channel variants endowed for improved kinetic properties and establish a broadly applicable new approach for protein engineering.

Simulation-Guided Design of Cytochrome P450 for Chemo- and Regioselective Macrocyclic Oxidation

Engineering high chemo-, regio-, and stereoselectivity is a prerequisite for enzyme usage in organic synthesis. Cytochromes P450 can oxidize a broad range of substrates, including macrocycles, which are becoming popular scaffolds for therapeutic agents. However, a large conformational space explored by macrocycles not only reduces the selectivity of oxidation but also impairs computational enzyme design strategies based on docking and molecular dynamics (MD) simulations. We present a novel design workflow that uses enhanced-sampling Hamiltonian replica exchange (HREX) MD and focuses on quantifying the substrate binding for suggesting the mutations to be made. This computational approach is applied to P450 BM3 with the aim to shift regioselectively toward one of the numerous possible positions during β-cembrenediol oxidation. The predictions are experimentally tested and the resulting product distributions validate our design strategy, as single mutations led up to 5-fold regioselectivity increases. We thus conclude that the HREX-MD-based workflow is a promising tool for the identification of positions for mutagenesis aiming at P450 enzymes with improved regioselectivity.