Designing de novo retroaldolase catalysts

Evolutionary history of native proteins, shaping observed sequences by complex interplay between mutational drift, maintaining stability and developing functionality, often complicates rationalization of protein engineering experiments making it hard to learn even from large datasets available with advent of high throughput screening and deep-sequencing technologies. Use of de novo protein scaffolds for gain of function design projects should, arguably, allow better understanding of fundamental principles underlying implementation of this function in nature and application of these principles to new protein engineering problems. Computational design of enzymatic activity in the de novo built idealized protein scaffolds instead of natural proteins from PDB has a promising advantages of avoiding limitations associated with evolutionary history and virtually unlimited number of geometric variants that can be generated for given scaffold to accommodate catalytic machinery. I am going to present computational strategy used to design de novo proteins with enzymatic activity and experimental data collected using recently identified de novo designed beta-barrels catalyzing retro-aldolase reaction. This information helps to narrow down range of catalytic mechanisms compatible with the structural model, which in turn help to highlight features and interactions potentially important for catalysis

Design and optimization of enzymatic activity in a de novo β-barrel scaffold

While native scaffolds offer a large diversity of shapes and topologies for enzyme engineering, their often unpredictable behavior in response to sequence modification makes de novo generated scaffolds an exciting alternative. Here we explore the customization of the backbone and sequence of a de novo designed eight stranded β-barrel protein to create catalysts for a retro-aldolase model reaction. We show that active and specific catalysts can be designed in this fold and use directed evolution to further optimize activity and stereoselectivity. Our results support previous suggestions that different folds have different inherent amenability to evolution and this property could account, in part, for the distribution of natural enzymes among different folds

Multiscale Systems, Homogenization, and Rough Paths:VAR75 2016: Probability and Analysis in Interacting Physical Systems

In recent years, substantial progress was made towards understanding convergence of fast-slow deterministic systems to stochastic differential equations. In contrast to more classical approaches, the assumptions on the fast flow are very mild. We survey the origins of this theory and then revisit and improve the analysis of Kelly-Melbourne [Ann. Probab. Volume 44, Number 1 (2016), 479-520], taking into account recent progress in $p$-variation and c\`adl\`ag rough path theory.Comment: 27 pages. Minor corrections. To appear in Proceedings of the Conference in Honor of the 75th Birthday of S.R.S. Varadha

A computational method for design of connected catalytic networks in proteins

Computational design of new active sites has generally proceeded by geometrically defining interactions between the reaction transition state(s) and surrounding side‐chain functional groups which maximize transition‐state stabilization, and then searching for sites in protein scaffolds where the specified side‐chain–transition‐state interactions can be realized. A limitation of this approach is that the interactions between the side chains themselves are not constrained. An extensive connected hydrogen bond network involving the catalytic residues was observed in a designed retroaldolase following directed evolution. Such connected networks could increase catalytic activity by preorganizing active site residues in catalytically competent orientations, and enabling concerted interactions between side chains during catalysis, for example, proton shuffling. We developed a method for designing active sites in which the catalytic side chains, in addition to making interactions with the transition state, are also involved in extensive hydrogen bond networks. Because of the added constraint of hydrogen‐bond connectivity between the catalytic side chains, to find solutions, a wider range of interactions between these side chains and the transition state must be considered. Our new method starts from a ChemDraw‐like two‐dimensional representation of the transition state with hydrogen‐bond donors, acceptors, and covalent interaction sites indicated, and all placements of side‐chain functional groups that make the indicated interactions with the transition state, and are fully connected in a single hydrogen‐bond network are systematically enumerated. The RosettaMatch method can then be used to identify realizations of these fully‐connected active sites in protein scaffolds. The method generates many fully‐connected active site solutions for a set of model reactions that are promising starting points for the design of fully‐preorganized enzyme catalysts.ISSN:0961-8368ISSN:1469-896

Design and optimization of enzymatic activity in a de novo β-barrel scaffold

While native scaffolds offer a large diversity of shapes and topologies for enzyme engineering, their often unpredictable behavior in response to sequence modification makes de novo generated scaffolds an exciting alternative. Here we explore the customization of the backbone and sequence of a de novo designed eight stranded beta-barrel protein to create catalysts for a retro-aldolase model reaction. We show that active and specific catalysts can be designed in this fold and use directed evolution to further optimize activity and stereoselectivity. Our results support previous suggestions that different folds have different inherent amenability to evolution and this property could account, in part, for the distribution of natural enzymes among different folds.ISSN:1469-896XISSN:0961-836

Small molecule probes to quantify the functional fraction of a specific protein in a cell with minimal folding equilibrium shifts

Although much is known about protein folding in buffers, it remains unclear how the cellular protein homeostasis network functions as a system to partition client proteins between folded and functional, soluble and misfolded, and aggregated conformations. Herein, we develop small molecule folding probes that specifically react with the folded and functional fraction of the protein of interest, enabling fluorescence-based quantification of this fraction in cell lysate at a time point of interest. Importantly, these probes minimally perturb a protein's folding equilibria within cells during and after cell lysis, because sufficient cellular chaperone/chaperonin holdase activity is created by rapid ATP depletion during cell lysis. The folding probe strategy and the faithful quantification of a particular protein's functional fraction are exemplified with retroaldolase, a de novo designed enzyme, and transthyretin, a nonenzyme protein. Our findings challenge the often invoked assumption that the soluble fraction of a client protein is fully folded in the cell. Moreover, our results reveal that the partitioning of destabilized retroaldolase and transthyretin mutants between the aforementioned conformational states is strongly influenced by cytosolic proteostasis network perturbations. Overall, our results suggest that applying a chemical folding probe strategy to other client proteins offers opportunities to reveal how the proteostasis network functions as a system to regulate the folding and function of individual client proteins in vivo

<i>De Novo</i>-Designed Enzymes as Small-Molecule-Regulated Fluorescence Imaging Tags and Fluorescent Reporters

Enzyme-based tags attached to a protein-of-interest (POI) that react with a small molecule, rendering the conjugate fluorescent, are very useful for studying the POI in living cells. These tags are typically based on endogenous enzymes, so protein engineering is required to ensure that the small-molecule probe does not react with the endogenous enzyme in the cell of interest. Here we demonstrate that <i>de novo</i>-designed enzymes can be used as tags to attach to POIs. The inherent bioorthogonality of the <i>de novo</i>-designed enzyme–small-molecule probe reaction circumvents the need for protein engineering, since these enzyme activities are not present in living organisms. Herein, we transform a family of <i>de novo</i>-designed retroaldolases into variable-molecular-weight tags exhibiting fluorescence imaging, reporter, and electrophoresis applications that are regulated by tailored, reactive small-molecule fluorophores

De novo design of luciferases using deep learning.

De novo enzyme design has sought to introduce active sites and substrate-binding pockets that are predicted to catalyse a reaction of interest into geometrically compatible native scaffolds1,2, but has been limited by a lack of suitable protein structures and the complexity of native protein sequence-structure relationships. Here we describe a deep-learning-based 'family-wide hallucination' approach that generates large numbers of idealized protein structures containing diverse pocket shapes and designed sequences that encode them. We use these scaffolds to design artificial luciferases that selectively catalyse the oxidative chemiluminescence of the synthetic luciferin substrates diphenylterazine3 and 2-deoxycoelenterazine. The designed active sites position an arginine guanidinium group adjacent to an anion that develops during the reaction in a binding pocket with high shape complementarity. For both luciferin substrates, we obtain designed luciferases with high selectivity; the most active of these is a small (13.9 kDa) and thermostable (with a melting temperature higher than 95 °C) enzyme that has a catalytic efficiency on diphenylterazine (kcat/Km = 106 M-1 s-1)&nbsp;comparable to that of native luciferases, but a much higher substrate specificity. The creation&nbsp;of highly active and specific biocatalysts from scratch with broad applications in biomedicine is a key milestone for computational enzyme design, and our approach should enable&nbsp;generation of a wide range of luciferases and other enzymes

Improving Protein Expression, Stability, and Function with ProteinMPNN

Natural proteins are highly optimized for function but are often difficult to produce at a scale suitable for biotechnological applications due to poor expression in heterologous systems, limited solubility, and sensitivity to temperature. Thus, a general method that improves the physical properties of native proteins while maintaining function could have wide utility for protein-based technologies. Here, we show that the deep neural network ProteinMPNN, together with evolutionary and structural information, provides a route to increasing protein expression, stability, and function. For both myoglobin and tobacco etch virus (TEV) protease, we generated designs with improved expression, elevated melting temperatures, and improved function. For TEV protease, we identified multiple designs with improved catalytic activity as compared to the parent sequence and previously reported TEV variants. Our approach should be broadly useful for improving the expression, stability, and function of biotechnologically important proteins