Protein engineering by highly parallel screening of computationally designed variants

Robust genome and cell engineering via in vitro and in situ circularized RNAs

Circularization can improve RNA persistence, yet simple and scalable approaches to achieve this are lacking. Here we report two methods that facilitate the pursuit of circular RNAs (cRNAs): cRNAs developed via in vitro circularization using group II introns, and cRNAs developed via in-cell circularization by the ubiquitously expressed RtcB protein. We also report simple purification protocols that enable high cRNA yields (40-75%) while maintaining low immune responses. These methods and protocols facilitate a broad range of applications in stem cell engineering as well as robust genome and epigenome targeting via zinc finger proteins and CRISPR-Cas9. Notably, cRNAs bearing the encephalomyocarditis internal ribosome entry enabled robust expression and persistence compared with linear capped RNAs in cardiomyocytes and neurons, which highlights the utility of cRNAs in these non-dividing cells. We also describe genome targeting via deimmunized Cas9 delivered as cRNA and a long-range multiplexed protein engineering methodology for the combinatorial screening of deimmunized protein variants that enables compatibility between persistence of expression and immunogenicity in cRNA-delivered proteins. The cRNA toolset will aid research and the development of therapeutics.

Massively Parallel Screening of Toll/Interleukin-1 Receptor (TIR)-Derived Peptides Reveals Multiple Toll-Like Receptors (TLRs)-Targeting Immunomodulatory Peptides

Toll-like receptors (TLRs) are critical regulators of the immune system, and altered TLR responses lead to a variety of inflammatory diseases. Interference of intracellular TLR signaling, which is mediated by multiple Toll/interleukin-1 receptor (TIR) domains on all TLRs and TLR adapters, is an effective therapeutic strategy against immune dysregulation. Peptides that inhibit TIR-TIR interactions by fragmenting interface residues have potential as therapeutic decoys. However, a systematic method for discovering TIR-targeting moieties has been elusive, limiting exploration of the vast, unsequenced space of the TIR domain family. A comprehensive parallel screening method is developed to uncover novel TIR-binding peptides derived from previously unexplored surfaces on a wide range of TIR domains. A large peptide library is constructed, named TIR surfacesome, by tiling surface sequences of the large TIR domain family and screening against MALTIR and MyD88TIR, TIRs of two major TLR adaptor proteins, resulting in the discovery of hundreds of TIR-binding peptides. The selected peptides inhibited TLR signaling and demonstrated anti-inflammatory effects in macrophages, and therapeutic potential in mouse inflammatory models. This approach may facilitate the development of TLR-targeted therapeutics.

Engineering Escherichia coli for Efficient De Novo Synthesis of Salidroside

Salidroside is a high-value plant-derived glycoside with diverse biological activities, but the main industrial salidroside production method, extraction from Rhodiola plants, is insufficient to meet the growing market demand. The biosynthetic route via microbial fermentation is a sustainable and eco-friendly alternative method. De novo synthesis of the precursor tyrosol was established by introducing the ARO10 and ADH6 genes. Systematic metabolic engineering resulted in 3.0 g/L tyrosol, but accumulated tyrosol inhibited cell growth. Adaptive evolution produced an evolved strain with a 10.0% higher OD600 and a 3.3 g/L tyrosol titer. Introducing glucosyltransferase AtUGT85A1, and overexpressing phosphoglucose mutase pgm and UDP-glucose pyrophosphorylase galU, achieved de novo synthesis of salidroside. Furthermore, AtUGT85A1 was semirationally engineered, resulting in the A21G mutation, which enhanced salidroside production by 31.2%. The optimally engineered strain produced 16.8 g/L salidroside with 0.4 g/(L h) productivity in a 5 L bioreactor. This study laid a foundation for future industrial production of salidroside and provided important guidance for efficient biosynthesis of other tyrosol derivatives.

The emerging role of deubiquitylating enzyme USP21 as a potential therapeutic target in cancer

Although certain members of the Ubiquitin-specific peptidases (USPs) have been recognized as promising therapeutic targets for various diseases, research progress regarding USP21 has been relatively sluggish in its early stages. USP21 is a crucial member of the USPs subfamily, involved in diverse cellular processes such as apoptosis, DNA repair, and signal transduction. Research findings from the past decade demonstrate that USP21 mediates the deubiquitination of multiple well-known target proteins associated with critical cellular processes relevant to both disease and homeostasis, particularly in various cancers.This reviewcomprehensively summarizes the structure and biological functions of USP21 with an emphasis on its role in tumorigenesis, and elucidates the advances on the discovery of tens of small-molecule inhibitors targeting USP21, which suggests that targeting USP21 may represent a potential strategy for cancer therapy.

Empowering Protein Engineering through Recombination of Beneficial Substitutions

Directed evolution stands as a seminal technology for generating novel protein functionalities, a cornerstone in biocatalysis, metabolic engineering, and synthetic biology. Today, with the development of various mutagenesis methods and advanced analytical machines, the challenge of diversity generation and high-throughput screening platforms is largely solved, and one of the remaining challenges is: how to empower the potential of single beneficial substitutions with recombination to achieve the epistatic effect. This review overviews experimental and computer-assisted recombination methods in protein engineering campaigns. In addition, integrated and machine learning-guided strategies were highlighted to discuss how these recombination approaches contribute to generating the screening library with better diversity, coverage, and size. A decision tree was finally summarized to guide the further selection of proper recombination strategies in practice, which was beneficial for accelerating protein engineering.

What Strengthens Protein-Protein Interactions: Analysis and Applications of Residue Correlation Networks

Identifying residues critical to protein-protein binding and efficient design of stable and specific protein binders are challenging tasks. Extending beyond the direct contacts in a protein-protein binding interface, our study employs computational modeling to reveal the essential network of residue interactions and dihedral angle correlations critical in protein-protein recognition. We hypothesized that mutating residues exhibiting highly correlated dynamic motion within the interaction network could efficiently optimize protein-protein interactions to create tight and selective protein binders. We tested this hypothesis using the ubiquitin (Ub) and MERS coronaviral papain-like protease (PLpro) complex, since Ub is a central player in multiple cellular functions and PLpro is an antiviral drug target. Our designed ubiquitin variant (UbV) hosting three mutated residues displayed a ∼3,500-fold increase in functional inhibition relative to wild-type Ub. Further optimization of two C-terminal residues within the Ub network resulted in a KD of 1.5 nM and IC50 of 9.7 nM for the five-point Ub mutant, eliciting 27,500-fold and 5,500-fold enhancements in affinity and potency, respectively, as well as improved selectivity, without destabilizing the UbV structure. Our study highlights residue correlation and interaction networks in protein-protein interactions, and introduces an effective approach to design high-affinity protein binders for cell biology research and future therapeutics.

Engineering Proteins for Cell Entry

Proteins are essential for life, as they participate in all vital processes in the body. In the past decade, delivery of active proteins to specific cells and organs has attracted increasing interest. However, most proteins cannot enter the cytoplasm due to the cell membrane acting as a natural barrier. To overcome this challenge, various proteins have been engineered to acquire cell-penetrating capacity by mimicking or modifying natural shuttling proteins. In this review, we provide an overview of the different types of engineered cell-penetrating proteins such as cell-penetrating peptides, supercharged proteins, receptor-binding proteins, and bacterial toxins. We also discuss some strategies for improving endosomal escape such as pore formation, the proton sponge effect, and hijacking intracellular trafficking pathways. Finally, we introduce some novel methods and technologies for designing and detecting engineered cell-penetrating proteins.

Ubiquitin Engineering for Interrogating the Ubiquitin-Proteasome System and Novel Therapeutic Strategies

Protein turnover, a highly regulated process governed by the ubiquitin-proteasome system (UPS), is essential for maintaining cellular homeostasis. Dysregulation of the UPS has been implicated in various diseases, including viral infections and cancer, making the proteins in the UPS attractive targets for therapeutic intervention. However, the functional and structural redundancies of UPS enzymes present challenges in identifying precise drug targets and achieving target selectivity. Consequently, only 26S proteasome inhibitors have successfully advanced to clinical use thus far. To overcome these obstacles, engineered peptides and proteins, particularly engineered ubiquitin, have emerged as promising alternatives. In this review, we examine the impact of engineered ubiquitin on UPS and non-UPS proteins, as well as on viral enzymes. Furthermore, we explore their potential to guide the development of small molecules targeting novel surfaces, thereby expanding the range of druggable targets.

Robust Design of Effective Allosteric Activators for Rsp5 E3 Ligase Using the Machine Learning Tool ProteinMPNN

We used the deep learning tool ProteinMPNN to redesign ubiquitin (Ub) as a specific and functionally stimulating/enhancing binder of the Rsp5 E3 ligase. We generated 20 extensively mutated─up to 37 of 76 residues─recombinant Ub variants (UbVs), named R1 to R20, displaying well-folded structures and high thermal stabilities. These UbVs can also form stable complexes with Rsp5, as predicted using AlphaFold2. Three of the UbVs bound to Rsp5 with low micromolar affinity, with R4 and R12 effectively enhancing the Rsp5 activity six folds. AlphaFold2 predicts that R4 and R12 bind to Rsp5's exosite in an identical manner to the Rsp5-Ub template, thereby allosterically activating Rsp5-Ub thioester formation. Thus, we present a virtual solution for rapidly and cost-effectively designing UbVs as functional modulators of Ub-related enzymes.

Protein engineering via sequence-performance mapping

Discovery and evolution of new and improved proteins has empowered molecular therapeutics, diagnostics, and industrial biotechnology. Discovery and evolution both require efficient screens and effective libraries, although they differ in their challenges because of the absence or presence, respectively, of an initial protein variant with the desired function. A host of high-throughput technologies-experimental and computational-enable efficient screens to identify performant protein variants. In partnership, an informed search of sequence space is needed to overcome the immensity, sparsity, and complexity of the sequence-performance landscape. Early in the historical trajectory of protein engineering, these elements aligned with distinct approaches to identify the most performant sequence: selection from large, randomized combinatorial libraries versus rational computational design. Substantial advances have now emerged from the synergy of these perspectives. Rational design of combinatorial libraries aids the experimental search of sequence space, and high-throughput, high-integrity experimental data inform computational design. At the core of the collaborative interface, efficient protein characterization (rather than mere selection of optimal variants) maps sequence-performance landscapes. Such quantitative maps elucidate the complex relationships between protein sequence and performance-e.g., binding, catalytic efficiency, biological activity, and developability-thereby advancing fundamental protein science and facilitating protein discovery and evolution.

What Strengthens Protein-Protein Interactions: Analysis and Applications of Residue Correlation Networks

Identifying critical residues in protein-protein binding and efficiently designing stable and specific protein binders is challenging. In addition to direct contacts in a protein-protein binding interface, our study employs computation modeling to reveal the essential network of residue interaction and dihedral angle correlation critical in protein-protein recognition. We propose that mutating residues regions exhibited highly correlated motions within the interaction network can efficiently optimize protein-protein interactions to create tight and selective protein binders. We validated our strategy using ubiquitin (Ub) and MERS coronaviral papain-like protease (PLpro) complexes, where Ub is one central player in many cellular functions and PLpro is an antiviral drug target. Our designed UbV with 3 mutated residues resulted in a ~3,500-fold increase in functional inhibition, compared with the wild-type Ub. Further optimization by incorporating 2 more residues within the network, the 5-point mutant achieved a KD of 1.5 nM and IC50 of 9.7 nM. The modification led to a 27,500-fold and 5,500-fold enhancements in affinity and potency, respectively, as well as improved selectivity, without destabilizing the UbV structure. Our study highlights residue correlation and interaction networks in protein-protein interaction, introduces an effective approach to design high affinity protein binders for cell biology and future therapeutics solutions.

Computational and artificial intelligence-based methods for antibody development

Due to their high target specificity and binding affinity, therapeutic antibodies are currently the largest class of biotherapeutics. The traditional largely empirical antibody development process is, while mature and robust, cumbersome and has significant limitations. Substantial recent advances in computational and artificial intelligence (AI) technologies are now starting to overcome many of these limitations and are increasingly integrated into development pipelines. Here, we provide an overview of AI methods relevant for antibody development, including databases, computational predictors of antibody properties and structure, and computational antibody design methods with an emphasis on machine learning (ML) models, and the design of complementarity-determining region (CDR) loops, antibody structural components critical for binding.

Insights Into the Properties, Biological Functions, and Regulation of USP21

Deubiquitylating enzymes (DUBs) antagonize ubiquitination by removing ubiquitin from their substrates. The role of DUBs in controlling various physiological and pathological processes has been extensively studied, and some members of DUBs have been identified as potential therapeutic targets in diseases ranging from tumors to neurodegeneration. Ubiquitin-specific protease 21 (USP21) is a member of the ubiquitin-specific protease family, the largest subfamily of DUBs. Although USP21 was discovered late and early research progress was slow, numerous studies in the last decade have gradually revealed the importance of USP21 in a wide variety of biological processes. In particular, the pro-carcinogenic effect of USP21 has been well elucidated in the last 2 years. In the present review, we provide a comprehensive overview of the current knowledge on USP21, including its properties, biological functions, pathophysiological roles, and cellular regulation. Limited pharmacological interventions for USP21 have also been introduced, highlighting the importance of developing novel and specific inhibitors targeting USP21.

Machine learning-coupled combinatorial mutagenesis enables resource-efficient engineering of CRISPR-Cas9 genome editor activities

The genome-editing Cas9 protein uses multiple amino-acid residues to bind the target DNA. Considering only the residues in proximity to the target DNA as potential sites to optimise Cas9’s activity, the number of combinatorial variants to screen through is too massive for a wet-lab experiment. Here we generate and cross-validate ten in silico and experimental datasets of multi-domain combinatorial mutagenesis libraries for Cas9 engineering, and demonstrate that a machine learning-coupled engineering approach reduces the experimental screening burden by as high as 95% while enriching top-performing variants by ∼7.5-fold in comparison to the null model. Using this approach and followed by structure-guided engineering, we identify the N888R/A889Q variant conferring increased editing activity on the protospacer adjacent motif-relaxed KKH variant of Cas9 nuclease from Staphylococcus aureus (KKH-SaCas9) and its derived base editor in human cells. Our work validates a readily applicable workflow to enable resource-efficient high-throughput engineering of genome editor’s activity.

Screening combinatorial mutants is too massive for wet-lab experiment alone. Here the authors present a machine learning-coupled combinatorial mutagenesis approach to vastly reduce experimental burden for engineering Cas9 genome editing enzymes.

Deep generative modeling for protein design

Deep learning approaches have produced substantial breakthroughs in fields such as image classification and natural language processing and are making rapid inroads in the area of protein design. Many generative models of proteins have been developed that encompass all known protein sequences, model specific protein families, or extrapolate the dynamics of individual proteins. Those generative models can learn protein representations that are often more informative of protein structure and function than hand-engineered features. Furthermore, they can be used to quickly propose millions of novel proteins that resemble the native counterparts in terms of expression level, stability, or other attributes. The protein design process can further be guided by discriminative oracles to select candidates with the highest probability of having the desired properties. In this review, we discuss five classes of generative models that have been most successful at modeling proteins and provide a framework for model guided protein design.

Directed evolution approaches for optogenetic tool development

Photoswitchable proteins enable specific molecular events occurring in complex biological settings to be probed in a rapid and reversible fashion. Recent progress in the development of photoswitchable proteins as components of optogenetic tools has been greatly facilitated by directed evolution approaches in vitro, in bacteria, or in yeast. We review these developments and suggest future directions for this rapidly advancing field.

SYNBIP: synthetic binding proteins for research, diagnosis and therapy

The success of protein engineering and design has extensively expanded the protein space, which presents a promising strategy for creating next-generation proteins of diverse functions. Among these proteins, the synthetic binding proteins (SBPs) are smaller, more stable, less immunogenic, and better of tissue penetration than others, which make the SBP-related data attracting extensive interest from worldwide scientists. However, no database has been developed to systematically provide the valuable information of SBPs yet. In this study, a database named ‘Synthetic Binding Proteins for Research, Diagnosis, and Therapy (SYNBIP)’ was thus introduced. This database is unique in (a) comprehensively describing thousands of SBPs from the perspectives of scaffolds, biophysical & functional properties, etc.; (b) panoramically illustrating the binding targets & the broad application of each SBP and (c) enabling a similarity search against the sequences of all SBPs and their binding targets. Since SBP is a human-made protein that has not been found in nature, the discovery of novel SBPs relied heavily on experimental protein engineering and could be greatly facilitated by in-silico studies (such as AI and computational modeling). Thus, the data provided in SYNBIP could lay a solid foundation for the future development of novel SBPs. The SYNBIP is accessible without login requirement at both official (https://idrblab.org/synbip/) and mirror (http://synbip.idrblab.net/) sites.

Computer-aided understanding and engineering of enzymatic selectivity

Enzymes offering chemo-, regio-, and stereoselectivity enable the asymmetric synthesis of high-value chiral molecules. Unfortunately, the drawback that naturally occurring enzymes are often inefficient or have undesired selectivity toward non-native substrates hinders the broadening of biocatalytic applications. To match the demands of specific selectivity in asymmetric synthesis, biochemists have implemented various computer-aided strategies in understanding and engineering enzymatic selectivity, diversifying the available repository of artificial enzymes. Here, given that the entire asymmetric catalytic cycle, involving precise interactions within the active pocket and substrate transport in the enzyme channel, could affect the enzymatic efficiency and selectivity, we presented a comprehensive overview of the computer-aided workflow for enzymatic selectivity. This review includes a mechanistic understanding of enzymatic selectivity based on quantum mechanical calculations, rational design of enzymatic selectivity guided by enzyme-substrate interactions, and enzymatic selectivity regulation via enzyme channel engineering. Finally, we discussed the computational paradigm for designing enzyme selectivity in silico to facilitate the advancement of asymmetric biosynthesis.

Recent advances in user-friendly computational tools to engineer protein function

Progress in technology and algorithms throughout the past decade has transformed the field of protein design and engineering. Computational approaches have become well-engrained in the processes of tailoring proteins for various biotechnological applications. Many tools and methods are developed and upgraded each year to satisfy the increasing demands and challenges of protein engineering. To help protein engineers and bioinformaticians navigate this emerging wave of dedicated software, we have critically evaluated recent additions to the toolbox regarding their application for semi-rational and rational protein engineering. These newly developed tools identify and prioritize hotspots and analyze the effects of mutations for a variety of properties, comprising ligand binding, protein-protein and protein-nucleic acid interactions, and electrostatic potential. We also discuss notable progress to target elusive protein dynamics and associated properties like ligand-transport processes and allosteric communication. Finally, we discuss several challenges these tools face and provide our perspectives on the further development of readily applicable methods to guide protein engineering efforts.

Fluorescent Protein Variants Generated by Reassembly between Skeleton and Chromophore

Fluorescent proteins (FPs) can be used as intrinsic molecular tags to track the dynamic activity in live cells. To obtain variants in an available and massive manner is always a challenge. Here, we adopted a computer-based microarray synthesis method to realize the reassembly between the chromophore and the skeleton. DNAWorks was used to segment the input FP templates into a set of overlapping oligonucleotides (20-43 mer) with a balanced annealing temperature, G + C content, and codon frequency. The constitution of the chromophore was kept in the same section by switching the divided sites during segmentation and the codon was optimized to further keep the balanced parameters. The designed oligonucleotides were synthesized on photo-programmable microfluidic arrays. Sequence analysis and the subsequent conditional induced expression of FPs revealed that oligonucleotides were highly reassembled. Spectra, photostability, and molecular size detection of randomly selected variants showed that they were distinct monomeric proteins that preserved photoactivity. Our study provides an effective means of obtaining FP variants based on a computer-designed parallel synthesis.

CRISPR/Cas9-facilitated engineering with growth-coupled and sensor-guided in vivo screening of enzyme variants for a more efficient chorismate pathway in E. coli

Protein engineering plays an increasingly important role in developing new and optimizing existing metabolic pathways for biosynthesis. Conventional screening approach of libraries of gene and enzyme variants is often done using a host strain under conditions not relevant to the cultivation or intracellular conditions of the later production strain. This does not necessarily result in the identification of the best enzyme variant for in vivo use in the production strain. In this work, we propose a method which integrates CRISPR/Cas9-facilitated engineering of the target gene(s) with growth-coupled and sensor-guided in vivo screening (CGSS) for protein engineering and pathway optimization. The efficiency of the method is demonstrated for engineering 3-deoxy-D-arabino-heptulosonate-7-phosphate (DAHP) synthase AroG, a key enzyme in the chorismate pathway for the synthesis of aromatic amino acids (AAAs), to obtain variants of AroG (AroG^fbr) with increased resistance to feedback inhibition of Phe. Starting from a tryptophan (Trp)-producing E. coli strain (harboring a reported Phe-resistant AroG variant AroG^S180F), the removal of all the endogenous DAHP synthases makes the growth of this strain dependent on the activity of an introduced AroG variant. The different catalytic efficiencies of AroG variants lead to different intracellular concentration of Trp which is sensed by a Trp biosensor (TnaC-eGFP). Using the growth rate and the signal strength of the biosensor as criteria, we successfully identified several novel Phe-resistant AroG variants (including the best one AroG^D6G−D7A) which exhibited higher specific enzyme activity than that of the reference variant AroG^S180F at the presence of 40 mM Phe. The replacement of AroG^S180F with the newly identified AroG^D6G−D7A in the Trp-producing strain significantly improved the Trp production by 38.5% (24.03 ± 1.02 g/L at 36 h) in a simple fed-batch fermentation.

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A novel approach for phenotype-focused and product-targeted in vivo screening of enzyme variants.

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AroG variant with high resistance to feedback inhibition of phenylalanine.

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Tryptophan production in E. coli improved by 38.5% with the new variant AroG^D6G−D7A.

Predicting the Effect of Mutations on Protein Folding and Protein-Protein Interactions

The function of a protein is largely determined by its three-dimensional structure and its interactions with other proteins. Changes to a protein's amino acid sequence can alter its function by perturbing the energy landscapes of protein folding and binding. Many tools have been developed to predict the energetic effect of amino acid changes, utilizing features describing the sequence of a protein, the structure of a protein, or both. Those tools can have many applications, such as distinguishing between deleterious and benign mutations and designing proteins and peptides with attractive properties. In this chapter, we describe how to use one of such tools, ELASPIC, to predict the effect of mutations on the stability of proteins and the affinity between proteins, in the context of a human protein-protein interaction network. ELASPIC uses a wide range of sequential and structural features to predict the change in the Gibbs free energy for protein folding and protein-protein interactions. It can be used both through a web server and as a stand-alone application. Since ELASPIC was trained using homology models and not crystal structures, it can be applied to a much broader range of proteins than traditional methods. It can leverage precalculated sequence alignments, homology models, and other features, in order to drastically lower the amount of time required to evaluate individual mutations and make tractable the analysis of millions of mutations affecting the majority of proteins in a genome.

A PxL motif promotes timely cell cycle substrate dephosphorylation by the Cdc14 phosphatase

The cell division cycle consists of a series of temporally ordered events. Cell cycle kinases and phosphatases provide key regulatory input, but how the correct substrate phosphorylation and dephosphorylation timing is achieved is incompletely understood. Here we identify a PxL substrate recognition motif that instructs dephosphorylation by the budding yeast Cdc14 phosphatase during mitotic exit. The PxL motif was prevalent in Cdc14-binding peptides enriched in a phage display screen of native disordered protein regions. PxL motif removal from the Cdc14 substrate Cbk1 delays its dephosphorylation, whereas addition of the motif advances dephosphorylation of otherwise late Cdc14 substrates. Crystal structures of Cdc14 bound to three PxL motif substrate peptides provide a molecular explanation for PxL motif recognition on the phosphatase surface. Our results illustrate the sophistication of phosphatase-substrate interactions and identify them as an important determinant of ordered cell cycle progression.

Combining Rosetta with molecular dynamics (MD): A benchmark of the MD-based ensemble protein design

Computational protein design is a set of procedures for computing amino acid sequences that will fold into a specified structure. Rosetta Design, a commonly used software for protein design, allows for the effective identification of sequences compatible with a given backbone structure, while molecular dynamics (MD) simulations can thoroughly sample near-native conformations. We benchmarked a procedure in which Rosetta design is started on MD-derived structural ensembles and showed that such a combined approach generates 20-30% more diverse sequences than currently available methods with only a slight increase in computation time. Importantly, the increase in diversity is achieved without a loss in the quality of the designed sequences assessed by their resemblance to natural sequences. We demonstrate that the MD-based procedure is also applicable to de novo design tasks started from backbone structures without any sequence information. In addition, we implemented a protocol that can be used to assess the stability of designed models and to select the best candidates for experimental validation. In sum our results demonstrate that the MD ensemble-based flexible backbone design can be a viable method for protein design, especially for tasks that require a large pool of diverse sequences.

Global analysis of protein folding using massively parallel design, synthesis, and testing

Proteins fold into unique native structures stabilized by thousands of weak interactions that collectively overcome the entropic cost of folding. Although these forces are "encoded" in the thousands of known protein structures, "decoding" them is challenging because of the complexity of natural proteins that have evolved for function, not stability. We combined computational protein design, next-generation gene synthesis, and a high-throughput protease susceptibility assay to measure folding and stability for more than 15,000 de novo designed miniproteins, 1000 natural proteins, 10,000 point mutants, and 30,000 negative control sequences. This analysis identified more than 2500 stable designed proteins in four basic folds-a number sufficient to enable us to systematically examine how sequence determines folding and stability in uncharted protein space. Iteration between design and experiment increased the design success rate from 6% to 47%, produced stable proteins unlike those found in nature for topologies where design was initially unsuccessful, and revealed subtle contributions to stability as designs became increasingly optimized. Our approach achieves the long-standing goal of a tight feedback cycle between computation and experiment and has the potential to transform computational protein design into a data-driven science.

Development of a Dual-Functional Hydrogel Using RGD and Anti-VEGF Aptamer

Synthetic molecular libraries hold great potential to advance the biomaterial development. However, little effort is made to integrate molecules with molecular recognition abilities selected from different libraries into a single biomolecular material. The purpose of this work is to incorporate peptides and nucleic acid aptamers into a porous hydrogel to develop a dual-functional biomaterial. The data show that an anti-integrin peptide can promote the attachment and growth of endothelial cells in a 3D porous poly(ethylene glycol) hydrogel and an antivascular endothelial growth factor aptamer can sequester and release VEGF of high bioactivity. Importantly, the dual-functional porous hydrogel enhances the growth and survival of endothelial cells. This work demonstrates that molecules selected from different synthetic libraries can be integrated into one system for the development of novel biomaterials.

Massively parallel de novo protein design for targeted therapeutics

De novo protein design holds promise for creating small stable proteins with shapes customized to bind therapeutic targets. We describe a massively parallel approach for designing, manufacturing and screening mini-protein binders, integrating large-scale computational design, oligonucleotide synthesis, yeast display screening and next-generation sequencing. We designed and tested 22,660 mini-proteins of 37-43 residues that target influenza haemagglutinin and botulinum neurotoxin B, along with 6,286 control sequences to probe contributions to folding and binding, and identified 2,618 high-affinity binders. Comparison of the binding and non-binding design sets, which are two orders of magnitude larger than any previously investigated, enabled the evaluation and improvement of the computational model. Biophysical characterization of a subset of the binder designs showed that they are extremely stable and, unlike antibodies, do not lose activity after exposure to high temperatures. The designs elicit little or no immune response and provide potent prophylactic and therapeutic protection against influenza, even after extensive repeated dosing.

Data driven flexible backbone protein design

Protein design remains an important problem in computational structural biology. Current computational protein design methods largely use physics-based methods, which make use of information from a single protein structure. This is despite the fact that multiple structures of many protein folds are now readily available in the PDB. While ensemble protein design methods can use multiple protein structures, they treat each structure independently. Here, we introduce a flexible backbone strategy, FlexiBaL-GP, which learns global protein backbone movements directly from multiple protein structures. FlexiBaL-GP uses the machine learning method of Gaussian Process Latent Variable Models to learn a lower dimensional representation of the protein coordinates that best represent backbone movements. These learned backbone movements are used to explore alternative protein backbones, while engineering a protein within a parallel tempered MCMC framework. Using the human ubiquitin-USP21 complex as a model we demonstrate that our design strategy outperforms current strategies for the interface design task of identifying tight binding ubiquitin variants for USP21.

Site-specific inhibition of the small ubiquitin-like modifier (SUMO)-conjugating enzyme Ubc9 selectively impairs SUMO chain formation

Posttranslational modifications by small ubiquitin-like modifiers (SUMOs) regulate many cellular processes, including genome integrity, gene expression, and ribosome biogenesis. The E2-conjugating enzyme Ubc9 catalyzes the conjugation of SUMOs to ϵ-amino groups of lysine residues in target proteins. Attachment of SUMO moieties to internal lysines in Ubc9 itself can further lead to the formation of polymeric SUMO chains. Mono- and poly-SUMOylations of target proteins provide docking sites for distinct adapter and effector proteins important for regulating discrete SUMO-regulated pathways. However, molecular tools to dissect pathways depending on either mono- or poly-SUMOylation are largely missing. Using a protein-engineering approach, we generated high-affinity SUMO2 variants by phage display that bind the back side binding site of Ubc9 and function as SUMO-based Ubc9 inhibitors (SUBINs). Importantly, we found that distinct SUBINs primarily inhibit poly-SUMO chain formation, whereas mono-SUMOylation was not impaired. Proof-of-principle experiments demonstrated that in a cellular context, SUBINs largely prevent heat shock-triggered poly-SUMOylation. Moreover, SUBINs abrogated arsenic-induced degradation of promyelocytic leukemia protein. We propose that the availability of the new chain-selective SUMO inhibitors reported here will enable a thorough investigation of poly-SUMO-mediated cellular processes, such as DNA damage responses and cell cycle progression.

How do miniproteins fold?

A high-throughput study yields libraries of miniproteins that help to explain how proteins are stabilized