Tool For Protein Engineering Research Articles

Dirichlet latent modelling enables effective learning and sampling of the functional protein design space

Engineering proteins with desired functions and biochemical properties is pivotal for biotechnology and drug discovery. While computational methods based on evolutionary information are reducing the experimental burden by designing targeted libraries of functional variants, they still have a low success rate when the desired protein has few or very remote homologous sequences. Here we propose an autoregressive model, called Temporal Dirichlet Variational Autoencoder (TDVAE), which exploits the mathematical properties of the Dirichlet distribution and temporal convolution to efficiently learn high-order information from a functionally related, possibly remotely similar, set of sequences. TDVAE is highly accurate in predicting the effects of amino acid mutations, while being significantly 90% smaller than the other state-of-the-art models. We then use TDVAE to design variants of the human alpha galactosidase enzymes as potential treatment for Fabry disease. Our model builds a library of diverse variants which retain sequence, biochemical and structural properties of the wildtype protein, suggesting they could be suitable for enzyme replacement therapy. Taken together, our results show the importance of accurate sequence modelling and the potential of autoregressive models as protein engineering and analysis tools.

Prediction of mutation-induced protein stability changes based on the geometric representations learned by a self-supervised method

BackgroundThermostability is a fundamental property of proteins to maintain their biological functions. Predicting protein stability changes upon mutation is important for our understanding protein structure–function relationship, and is also of great interest in protein engineering and pharmaceutical design.ResultsHere we present mutDDG-SSM, a deep learning-based framework that uses the geometric representations encoded in protein structure to predict the mutation-induced protein stability changes. mutDDG-SSM consists of two parts: a graph attention network-based protein structural feature extractor that is trained with a self-supervised learning scheme using large-scale high-resolution protein structures, and an eXtreme Gradient Boosting model-based stability change predictor with an advantage of alleviating overfitting problem. The performance of mutDDG-SSM was tested on several widely-used independent datasets. Then, myoglobin and p53 were used as case studies to illustrate the effectiveness of the model in predicting protein stability changes upon mutations. Our results show that mutDDG-SSM achieved high performance in estimating the effects of mutations on protein stability. In addition, mutDDG-SSM exhibited good unbiasedness, where the prediction accuracy on the inverse mutations is as well as that on the direct mutations.ConclusionMeaningful features can be extracted from our pre-trained model to build downstream tasks and our model may serve as a valuable tool for protein engineering and drug design.

Genetically Encoded Photocaged Proteinogenic and Non-Proteinogenic Amino Acids.

Photocaged amino acids could be genetically encoded into proteins via genetic code expansion (GCE) and constitute unique tools for innovative protein engineering. There are a number of photocaged proteinogenic amino acids that allow strategic conversion of proteins into their photocaged variants, thus enabling spatiotemporal and non-invasive regulation of protein functions using light. Meanwhile, there are a hand of photocaged non-proteinogenic amino acids that address the challenges in directly encoding certain non-canonical amino acids (ncAAs) that structurally resemble proteinogenic ones or possess highly reactive functional groups. Herein, we would like to summarize the efforts in encoding photocaged proteinogenic and non-proteinogenic amino acids, hoping to draw more attention to this fruitful and exciting scientific campaign.

Combining Complementarity and Binding Energetics in the Assessment of Protein Interactions: EnCPdock-A Practical Manual.

The combined effect of shape and electrostatic complementarities (Sc, EC) at the interface of the interacting protein partners (PPI) serves as the physical basis for such associations and is a strong determinant of their binding energetics. EnCPdock (https://www.scinetmol.in/EnCPdock/) presents a comprehensive web platform for the direct conjoint comparative analyses of complementarity and binding energetics in PPIs. It elegantly interlinks the dual nature of local (Sc) and nonlocal complementarity (EC) in PPIs using the complementarity plot. It further derives an AI-based ΔGbinding with a prediction accuracy comparable to the state of the art. This book chapter presents a practical manual to conceptualize and implement EnCPdock with its various features and functionalities, collectively having the potential to serve as a valuable protein engineering tool in the design of novel protein interfaces.

Repurposing a plant peptide cyclase for targeted lysine acylation

Transpeptidases are powerful tools for protein engineering but are largely restricted to acting at protein backbone termini. Alternative enzymatic approaches for internal protein labelling require bulky recognition motifs or non-proteinogenic reaction partners, potentially restricting which proteins can be modified or the types of modification that can be installed. Here we report a strategy for labelling lysine side chain ε-amines by repurposing an engineered asparaginyl ligase, which naturally catalyses peptide head-to-tail cyclization, for versatile isopeptide ligations that are compatible with peptidic substrates. We find that internal lysines with an adjacent leucine residue mimic the conventional N-terminal glycine–leucine substrate. This dipeptide motif enables efficient intra- or intermolecular ligation through internal lysine side chains, minimally leaving an asparagine C-terminally linked to the lysine side chain via an isopeptide bond. The versatility of this approach is demonstrated by the chemoenzymatic synthesis of peptides with non-native C terminus-to-side chain topology and the conjugation of chemically modified peptides to recombinant proteins.

Protein Engineering with Lightweight Graph Denoising Neural Networks.

Protein engineering faces challenges in finding optimal mutants from a massive pool of candidate mutants. In this study, we introduce a deep-learning-based data-efficient fitness prediction tool to steer protein engineering. Our methodology establishes a lightweight graph neural network scheme for protein structures, which efficiently analyzes the microenvironment of amino acids in wild-type proteins and reconstructs the distribution of the amino acid sequences that are more likely to pass natural selection. This distribution serves as a general guidance for scoring proteins toward arbitrary properties on any order of mutations. Our proposed solution undergoes extensive wet-lab experimental validation spanning diverse physicochemical properties of various proteins, including fluorescence intensity, antigen-antibody affinity, thermostability, and DNA cleavage activity. More than 40% of ProtLGN-designed single-site mutants outperform their wild-type counterparts across all studied proteins and targeted properties. More importantly, our model can bypass the negative epistatic effect to combine single mutation sites and form deep mutants with up to seven mutation sites in a single round, whose physicochemical properties are significantly improved. This observation provides compelling evidence of the structure-based model's potential to guide deep mutations in protein engineering. Overall, our approach emerges as a versatile tool for protein engineering, benefiting both the computational and bioengineering communities.

Geometric Antibody Engineering Reveals the Spatial Factor on the Efficacy of Bispecific T Cell Engagers.

Bispecific antibodies (BsAbs) represent an emerging class of biologics that can recognize two different antigens or epitopes. T-cell engagers (TcEs) bind two targets in trans on the cell surface of the effector and target cell to induce proximal immune effects, opening exciting windows for immunotherapies. To date, the engineering of BsAbs has been mainly focused on tuning the molecular weight and valency. However, the effects of spatial factors on the biological functions of BsAbs have been less explored due to the lack of biochemical methods to precisely manipulate protein geometry. Here, we studied the geometric effects of the TcEs. First, by genetically inserting rigidly designed ankyrin repeat proteins into TcEs, we revealed that the efficacy progressively decreased as the spacer distance of the two binding domains increased. Then, we constructed 26 pairs of TcEs with the same size but varying orientations using click chemistry-mediated conjugation at different mutation sites. We found that linear ligation sites play a minor role in modulating cell-killing efficacy. Next, we rendered the TcEs' advanced topology by cyclization chemistry using the SpyTag/SpyCatcher pair or sortase ligation approaches. Cyclized TcEs were generally more potent than their linear counterparts. Particularly, sortase A cyclized TcEs, bearing a minimal tagging motif, exhibited better cell-killing efficacy in vitro and improved stability both in vitro and in vivo compared to the linear TcE. This work combines modern bioconjugation chemistry and protein engineering tools for antibody engineering, shedding light on the elusive spatial factors of BsAbs functionality.

How Nitric Oxide Hindered the Search for Hemoglobin-Based Oxygen Carriers as Human Blood Substitutes.

The search for a clinically affordable substitute of human blood for transfusion is still an unmet need of modern society. More than 50 years of research on acellular hemoglobin (Hb)-based oxygen carriers (HBOC) have not yet produced a single formulation able to carry oxygen to hemorrhage-challenged tissues without compromising the body's functions. Of the several bottlenecks encountered, the high reactivity of acellular Hb with circulating nitric oxide (NO) is particularly arduous to overcome because of the NO-scavenging effect, which causes life-threatening side effects as vasoconstriction, inflammation, coagulopathies, and redox imbalance. The purpose of this manuscript is not to add a review of candidate HBOC formulations but to focus on the biochemical and physiological events that underly NO scavenging by acellular Hb. To this purpose, we examine the differential chemistry of the reaction of NO with erythrocyte and acellular Hb, the NO signaling paths in physiological and HBOC-challenged situations, and the protein engineering tools that are predicted to modulate the NO-scavenging effect. A better understanding of two mechanisms linked to the NO reactivity of acellular Hb, the nitrosylated Hb and the nitrite reductase hypotheses, may become essential to focus HBOC research toward clinical targets.

Redesigning methionyl-tRNA synthetase for β-methionine activity with adaptive landscape flattening and experiments.

Amino acids (AAs) with a noncanonical backbone would be a valuable tool for protein engineering, enabling new structural motifs and building blocks. To incorporate them into an expanded genetic code, the first, key step is to obtain an appropriate aminoacyl-tRNA synthetase. Currently, directed evolution is not available to optimize AAs with noncanonical backbones, since an appropriate selective pressure has not been discovered. Computational protein design (CPD) is an alternative. We used a new CPD method to redesign MetRS and increase its activity towards β-Met, which has an extra backbone methylene. The new method considered a few active site positions for design and used a Monte Carlo exploration of the corresponding sequence space. During the exploration, a bias energy was adaptively learned, such that the free energy landscape of the apo enzyme was flattened. Enzyme variants could then be sampled, in the presence of the ligand and the bias energy, according to their β-Met binding affinities. Eighteen predicted variants were chosen for experimental testing; 10 exhibited detectable activity for β-Met adenylation. Top predicted hits were characterized experimentally in detail. Dissociation constants, catalytic rates, and Michaelis constants for both α-Met and β-Met were measured. The best mutant retained a preference for α-Met over β-Met; however, the preference was reduced, compared to the wildtype, by a factor of 29. For this mutant, high resolution crystal structures were obtained in complex with both α-Met and β-Met, indicating that the predicted, active conformation of β-Met in the active site was retained.

A project-oriented biochemistry laboratory for protein engineering and structure-function using small laccase enzyme from Streptomyces coelicolor.

An understanding of structure-function relationships in proteins is essential for modern biochemical studies. The integration of common freely accessible bioinformatics tools available online with the knowledge of protein-engineering tools provide a fundamental understanding of the application of protein structure-function for biochemical research. In order for students to apply their prior knowledge of recombinant protein technology into the understanding of protein structure-function relationships, we developed a semester-long project-oriented biochemistry laboratory experience that is the second laboratory course of a series. For easier integration of knowledge and application, we organized this course into four sequential modules: protein structure visualization/modification, mutagenesis target identification, site-directed mutagenesis, and mutant protein expression, purification, and characterization. These tasks were performed on the protein small laccase (SLAC) that was cloned and characterized by students in the previous semester during the first biochemistry laboratory course of the series. This goal-oriented project-based approach helped students apply their prior knowledge to newly introduced techniques to understand protein structure-function relationships in this research-like laboratory setting. A student assessment before and after the course demonstrated an overall increase in learning and enthusiasm for this topic.

Invitro continuous protein evolution empowered by machine learning and automation.

Directed evolution has become one of the most successful and powerful tools for protein engineering. However, the efforts required for designing, constructing, and screening a large library of variants can be laborious, time-consuming, and costly. With the recent advent of machine learning (ML) in the directed evolution of proteins, researchers can now evaluate variants in silico and guide a more efficient directed evolution campaign. Furthermore, recent advancements in laboratory automation have enabled the rapid execution of long, complex experiments for high-throughput data acquisition in both industrial and academic settings, thus providing the means to collect a large quantity of data required to develop ML models for protein engineering. In this perspective, we propose a closed-loop invitro continuous protein evolution framework that leverages the best of both worlds, ML and automation, and provide a brief overview of the recent developments in the field.

A synthetic biology approach to engineering circuits in immune cells.

A synthetic circuit in a biological system involves the designed assembly of genetic elements, biomolecules, or cells to create a defined function. These circuits are central in synthetic biology, enabling the reprogramming of cellular behavior and the engineering of cells with customized responses. In cancer therapeutics, engineering T cells with circuits have the potential to overcome the challenges of current approaches, for example, by allowing specific recognition and killing of cancer cells. Recent advances also facilitate engineering integrated circuits for the controlled release of therapeutic molecules at specified locations, for example, in a solid tumor. In this review, we discuss recent strategies and applications of synthetic receptor circuits aimed at enhancing immune cell functions for cancer immunotherapy. We begin by introducing the concept of circuits in networks at the molecular and cellular scales and provide an analysis of the development and implementation of several synthetic circuits in T cells that have the goal to overcome current challenges in cancer immunotherapy. These include specific targeting of cancer cells, increased T-cell proliferation, and persistence in the tumor microenvironment. By harnessing the power of synthetic biology, and the characteristics of certain circuit architectures, it is now possible to engineer a new generation of immune cells that recognize cancer cells, while minimizing off-target toxicities. We specifically discuss T-cell circuits for antigen density sensing. These circuits allow targeting of solid tumors that share antigens with normal tissues. Additionally, we explore designs for synthetic circuits that could control T-cell differentiation or T-cell fate as well as the concept of synthetic multicellular circuits that leverage cellular communication and division of labor to achieve improved therapeutic efficacy. As our understanding of cell biology expands and novel tools for genome, protein, and cell engineering are developed, we anticipate further innovative approaches to emerge in the design and engineering of circuits in immune cells.

Protein engineering for natural product biosynthesis: expanding diversity for therapeutic applications

In this dispensation of the fourth industrial revolution, protein engineering has become a popular approach for increasing enzymatic activity, stability, and titer in the biosynthesis of natural products. This is attributed to its numerous advantages (over direct isolation from plants or via chemical synthesis), including decreasing or eliminating reaction byproducts, high precision, moderate handling of intricate and chemically unstable chemicals, overall reusability, and cost efficiency. Recently, protein engineering tools have advanced to redesign and enhance natural product biosynthesis. These methods include direct evolution, substrate engineering, medium engineering, enzyme engineering and immobilization, structure-assisted protein engineering, and advanced computational. Recent successes in implementing these emerging protein engineering technologies were critically discussed in this article. Also, the advantages, limitations, and applications in industrial and medical biotechnology were discussed. Last, future research directions and potential were also highlighted.

Serum albumin binding knob domains engineered within a VH framework III bispecific antibody format and as chimeric peptides.

Serum albumin binding is an established mechanism to extend the serum half-life of antibody fragments and peptides. The cysteine rich knob domains, isolated from bovine antibody ultralong CDRH3, are the smallest single chain antibody fragments described to date and versatile tools for protein engineering. Here, we used phage display of bovine immune material to derive knob domains against human and rodent serum albumins. These were used to engineer bispecific Fab fragments, by using the framework III loop as a site for knob domain insertion. By this route, neutralisation of the canonical antigen (TNFα) was retained but extended pharmacokinetics in-vivo were achieved through albumin binding. Structural characterisation revealed correct folding of the knob domain and identified broadly common but non-cross-reactive epitopes. Additionally, we show that these albumin binding knob domains can be chemically synthesised to achieve dual IL-17A neutralisation and albumin binding in a single chemical entity. This study enables antibody and chemical engineering from bovine immune material, via an accessible discovery platform.

Sortase E-mediated site-specific immobilization of green fluorescent protein and xylose dehydrogenase on gold nanoparticles

Sortase, a bacterial transpeptidase enzyme, is an attractive tool for protein engineering due to its ability to break a peptide bond at a specific site and then reform a new bond with an incoming nucleophile. Here, we present the immobilization of two recombinant proteins, enhanced green fluorescent protein (eGFP) and xylose dehydrogenase (XylB) over triglycine functionalized PEGylated gold nanoparticles (AuNPs) using C. glutamicum sortase E. For the first time, we used a new class of sortase from a non-pathogenic organism for sortagging. The site-specific conjugation of proteins with LAHTG-tagged sequences on AuNPs via covalent cross-linking was successfully detected by surface-enhanced Raman scattering (SERS) and UV–vis spectral analysis. The sortagging was initially validated by an eGFP model protein and later with the xylose dehydrogenase enzyme. The catalytic activity, stability, and reusability of the immobilized XylB were studied with the bioconversion of xylose to xylonic acid. When compared to the free enzyme, the immobilized XylB was able to retain 80% of its initial activity after four sequential cycles and exhibited no significant variations in instability after each cycle for about 72 h. These findings suggest that C. glutamicum sortase could be useful for immobilizing site-specific proteins/enzymes in biotransformation applications for value-added chemical production.

Biophysical Interpretation of Evolutionary Consequences on the SARS-CoV2 Main Protease through Molecular Dynamics Simulations and Network Topology Analysis.

In this study, we present a combined analysis procedure between atomistic molecular dynamics (MD) simulations and network topology to obtain more understanding on the evolutionary consequences on protein stability and substrate binding of the main protease enzyme of SARS-CoV2. Communicability matrices of the protein residue networks (PRNs) were extracted from MD trajectories of both Mpro enzymes in complex with the nsp8/9 peptide substrate to compare the local communicability within both proteases that would affect the enzyme function, along with biophysical details on global protein conformation, flexibility, and contribution of amino acid side chains to both intramolecular and intermolecular interactions. The analysis displayed the significance of the mutated residue 46 with the highest communicability gain to the binding pocket closure. Interestingly, the mutated residue 134 with the highest communicability loss corresponded to a local structural disruption of the adjacent peptide loop. The enhanced flexibility of the disrupted loop connecting to the catalytic residue Cys145 introduced an extra binding mode that brought the substrate in proximity and could facilitate the reaction. This understanding might provide further help in the drug development strategy against SARS-CoV2 and prove the capability of the combined techniques of MD simulations and network topology analysis as a "reverse" protein engineering tool.

Consensus design and engineering of an efficient and high-yield peptide asparaginyl ligase for protein cyclization and ligation

Plant legumains are Asn/Asp-specific endopeptidases (AEPs) that have diverse functions in plants. Peptide asparaginyl ligases (PALs) are a special legumain subtype that primarily catalyze peptide bond formation rather than hydrolysis. PALs are versatile protein engineering tools but are rarely found in nature. To overcome this limitation, here we describe a two-step method to design and engineer a high-yield and efficient recombinant PAL based on commonly found AEPs. We first constructed a consensus sequence derived from 1,500 plant legumains to design the evolutionarily stable legumain conLEG that could be produced in E. coli with 20-fold higher yield relative to that for natural legumains. We then applied the LAD (ligase-activity determinant) hypothesis to exploit conserved residues in PAL substrate-binding pockets and convert conLEG into conPAL1-3. Functional studies showed that conLEG is primarily a hydrolase, whereas conPALs are ligases. Importantly, conPAL3 is a super-efficient and broadly active PAL for protein cyclization and ligation.

Titrating Avidity of Yeast-Displayed Proteins Using a Transcriptional Regulator.

Yeast surface display is a valuable tool for protein engineering and directed evolution; however, significant variability in the copy number (i.e., avidity) of displayed variants on the yeast cell wall complicates screening and selection campaigns. Here, we report an engineered titratable display platform that modulates the avidity of Aga2-fusion proteins on the yeast cell wall dependent on the concentration of the anhydrotetracycline (aTc) inducer. Our design is based on a genomic Aga1 gene copy and an episomal Aga2-fusion construct both under the control of an aTc-dependent transcriptional regulator that enables stoichiometric and titratable expression, secretion, and display of Aga2-fusion proteins. We demonstrate tunable display levels over 2-3 orders of magnitude for various model proteins, including glucose oxidase enzyme variants, mechanostable dockerin-binding domains, and anti-PDL1 affibody domains. By regulating the copy number of displayed proteins, we demonstrate the effects of titratable avidity levels on several specific phenotypic activities, including enzyme activity and cell adhesion to surfaces under shear flow. Finally, we show that titrating down the display level allows yeast-based binding affinity measurements to be performed in a regime that avoids ligand depletion effects while maintaining small sample volumes, avoiding a well-known artifact in yeast-based binding assays. The ability to titrate the multivalency of proteins on the yeast cell wall through simple inducer control will benefit protein engineering and directed evolution methodology relying on yeast display for broad classes of therapeutic and diagnostic proteins of interest.

"Not-so-popular" orthogonal pairs in genetic code expansion.

During the past decade, genetic code expansion has been proved to be a powerful tool for protein studies and engineering. As the key part, a series of orthogonal pairs have been developed to site-specifically incorporate hundreds of noncanonical amino acids (ncAAs) into proteins by using bacteria, yeast, mammalian cells, animals, or plants as hosts. Among them, the pair of tyrosyl-tRNA synthetase/tRNATyr from Methanococcus jannaschii and the pair of pyrrolysyl-tRNA synthetase/tRNAPyl from Methanosarcina species are the most popular ones. Recently, other "not-so-popular" orthogonal pairs have started to attract attentions, because they can provide more choices of ncAA candidates and are necessary for simultaneous incorporation of multiple ncAAs into a single protein. Here, we summarize the development and applications of those "not-so-popular" orthogonal pairs, providing guidance for studying and engineering proteins.

A primer to directed evolution: current methodologies and future directions.

Directed evolution is one of the most powerful tools for protein engineering and functions by harnessing natural evolution, but on a shorter timescale. It enables the rapid selection of variants of biomolecules with properties that make them more suitable for specific applications. Since the first in vitro evolution experiments performed by Sol Spiegelman in 1967, a wide range of techniques have been developed to tackle the main two steps of directed evolution: genetic diversification (library generation), and isolation of the variants of interest. This review covers the main modern methodologies, discussing the advantages and drawbacks of each, and hence the considerations for designing directed evolution experiments. Furthermore, the most recent developments are discussed, showing how advances in the handling of ever larger library sizes are enabling new research questions to be tackled.