Sequence-function Relationships Research Articles

Deep mutational scanning reveals sequence to function constraints for SWEET family transporters.

Protein science is entering a transformative phase enabled by deep mutational scans that provide an unbiased view of the residue level interactions that mediate function. However, it has yet to be extensively used to characterize the mutational and evolutionary landscapes of plant proteins. Here, we apply the method to explore sequence-function relationships within the sugar transporter AtSWEET13. DMS results describe how mutational interrogation throughout different regions of the protein affects AtSWEET13 abundance and transport function. Our results identify novel transport-enhancing mutations that are validated using the FRET sensor assays. Extending DMS results to phylogenetic analyses reveal the role of transmembrane helix 4 (TM4) which makes the SWEET family transporters distinct from prokaryotic SemiSWEETs. We show that transmembrane helix 4 is intolerant to motif swapping with other clade-specific SWEET TM4 compositions, despite accommodating single point-mutations towards aromatic and charged polar amino acids. We further show that the transfer learning approaches based on physics and ML based In silico variant prediction tools have limited utility for engineering plant proteins as they were unable to reproduce our experimental results. We conclude that DMS can produce datasets which, when combined with the right predictive computational frameworks, can direct plant engineering efforts through derivative phenotype selection and evolutionary insights.

An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries

Antibodies are engineerable quantities in medicine. Learning antibody molecular recognition would enable the in silico design of high affinity binders against nearly any proteinaceous surface. Yet, publicly available experiment antibody sequence-binding datasets may not contain the mutagenic, antigenic, or antibody sequence diversity necessary for deep learning approaches to capture molecular recognition. In part, this is because limited experimental platforms exist for assessing quantitative and simultaneous sequence-function relationships for multiple antibodies. Here we present MAGMA-seq, an integrated technology that combines multiple antigens and multiple antibodies and determines quantitative biophysical parameters using deep sequencing. We demonstrate MAGMA-seq on two pooled libraries comprising mutants of nine different human antibodies spanning light chain gene usage, CDR H3 length, and antigenic targets. We demonstrate the comprehensive mapping of potential antibody development pathways, sequence-binding relationships for multiple antibodies simultaneously, and identification of paratope sequence determinants for binding recognition for broadly neutralizing antibodies (bnAbs). MAGMA-seq enables rapid and scalable antibody engineering of multiple lead candidates because it can measure binding for mutants of many given parental antibodies in a single experiment.

Machine learning enabled design features of antimicrobial peptides selectively targeting peri-implant disease progression

Peri-implantitis is a complex infectious disease that manifests as progressive loss of alveolar bone around the dental implants and hyper-inflammation associated with microbial dysbiosis. Using antibiotics in treating peri-implantitis is controversial because of antibiotic resistance threats, the non-selective suppression of pathogens and commensals within the microbial community, and potentially serious systemic sequelae. Therefore, conventional treatment for peri-implantitis comprises mechanical debridement by nonsurgical or surgical approaches with adjunct local microbicidal agents. Consequently, current treatment options may not prevent relapses, as the pathogens either remain unaffected or quickly re-emerge after treatment. Successful mitigation of disease progression in peri-implantitis requires a specific mode of treatment capable of targeting keystone pathogens and restoring bacterial community balance toward commensal species. Antimicrobial peptides (AMPs) hold promise as alternative therapeutics through their bacterial specificity and targeted inhibitory activity. However, peptide sequence space exhibits complex relationships such as sparse vector encoding of sequences, including combinatorial and discrete functions describing peptide antimicrobial activity. In this paper, we generated a transparent Machine Learning (ML) model that identifies sequence-function relationships based on rough set theory using simple summaries of the hydropathic features of AMPs. Comparing the hydropathic features of peptides according to their differential activity for different classes of bacteria empowered predictability of antimicrobial targeting. Enriching the sequence diversity by a genetic algorithm, we generated numerous candidate AMPs designed for selectively targeting pathogens and predicted their activity using classifying rough sets. Empirical growth inhibition data is iteratively fed back into our ML training to generate new peptides, resulting in increasingly more rigorous rules for which peptides match targeted inhibition levels for specific bacterial strains. The subsequent top scoring candidates were empirically tested for their inhibition against keystone and accessory peri-implantitis pathogens as well as an oral commensal bacterium. A novel peptide, VL-13, was confirmed to be selectively active against a keystone pathogen. Considering the continually increasing number of oral implants placed each year and the complexity of the disease progression, prevalence of peri-implant diseases continues to rise. Our approach offers transparent ML-enabled paths towards developing antimicrobial peptide-based therapies targeting the changes in the microbial communities that can beneficially impact disease progression.

In silico approaches for the identification of potential allergens among hypothetical proteins from Alternaria alternata and its functional annotation

Direct exposure to the fungal species Alternaria alternata is a major risk factor for the development of asthma, allergic rhinitis, and inflammation. As of November 23rd 2020, the NCBI protein database showed 11,227 proteins from A. alternata genome as hypothetical proteins (HPs). Allergens are the main causative of several life-threatening diseases, especially in fungal infections. Therefore, the main aim of the study is to identify the potentially allergenic inducible proteins from the HPs in A. alternata and their associated functional assignment for the complete understanding of the complex biological systems at the molecular level. AlgPred and Structural Database of Allergenic Proteins (SDAP) were used for the prediction of potential allergens from the HPs of A. alternata. While analyzing the proteome data, 29 potential allergens were predicted by AlgPred and further screening in SDAP confirmed the allergic response of 10 proteins. Extensive bioinformatics tools including protein family classification, sequence-function relationship, protein motif discovery, pathway interactions, and intrinsic features from the amino acid sequence were used to successfully predict the probable functions of the 10 HPs. The functions of the HPs are characterized as chitin-binding, ribosomal protein P1, thaumatin, glycosyl hydrolase, and NOB1 proteins. The subcellular localization and signal peptide prediction of these 10 proteins has further provided additional information on localization and function. The allergens prediction and functional annotation of the 10 proteins may facilitate a better understanding of the allergenic mechanism of A. alternata in asthma and other diseases. The functional domain level insights and predicted structural features of the allergenic proteins help to understand the pathogenesis and host immune tolerance. The outcomes of the study would aid in the development of specific drugs to combat A. alternata infections.

Toward physics-based precision medicine: Exploiting protein dynamics to design new therapeutics and interpret variants.

The goal of precision medicine is to utilize our knowledge of the molecular causes of disease to better diagnose and treat patients. However, there is a substantial mismatch between the small number of food and drug administration (FDA)-approved drugs and annotated coding variants compared to the needs of precision medicine. This review introduces the concept of physics-based precision medicine, a scalable framework that promises to improve our understanding of sequence-function relationships and accelerate drug discovery. We show that accounting for the ensemble of structures a protein adopts in solution with computer simulations overcomes many of the limitations imposed by assuming a single protein structure. We highlight studies of protein dynamics and recent methods for the analysis of structural ensembles. These studies demonstrate that differences in conformational distributions predict functional differences within protein families and between variants. Thanks to new computational tools that are providing unprecedented access to protein structural ensembles, this insight may enable accurate predictions of variant pathogenicity for entire libraries of variants. We further show that explicitly accounting for protein ensembles, with methods like alchemical free energy calculations or docking to Markov state models, can uncover novel lead compounds. To conclude, we demonstrate that cryptic pockets, or cavities absent in experimental structures, provide an avenue to target proteins that are currently considered undruggable. Taken together, our review provides a roadmap for the field of protein science to accelerate precision medicine.

Identifying LasR Quorum Sensors with Improved Signal Specificity by Mapping the Sequence-Function Landscape.

Programmable intercellular signaling using components of naturally occurring quorum sensing can allow for coordinated functions to be engineered in microbial consortia. LuxR-type transcriptional regulators are widely used for this purpose and are activated by homoserine lactone (HSL) signals. However, they often suffer from imperfect molecular discrimination of structurally similar HSLs, causing misregulation within engineered consortia containing multiple HSL signals. Here, we studied one such example, the regulator LasR from Pseudomonas aeruginosa. We elucidated its sequence-function relationship for ligand specificity using targeted protein engineering and multiplexed high-throughput biosensor screening. A pooled combinatorial saturation mutagenesis library (9,486 LasR DNA sequences) was created by mutating six residues in LasR's β5 sheet with single, double, or triple amino acid substitutions. Sort-seq assays were performed in parallel using cognate and noncognate HSLs to quantify each corresponding sensor's response to each HSL signal, which identified hundreds of highly specific variants. Sensor variants identified were individually assayed and exhibited up to 60.6-fold (p = 0.0013) improved relative activation by the cognate signal compared to the wildtype. Interestingly, we uncovered prevalent mutational epistasis and previously unidentified residues contributing to signal specificity. The resulting sensors with negligible signal crosstalk could be broadly applied to engineer bacteria consortia.

Self-driving laboratories to autonomously navigate the protein fitness landscape

Protein engineering has nearly limitless applications across chemistry, energy and medicine, but creating new proteins with improved or novel functions remains slow, labor-intensive and inefficient. Here we present the Self-driving Autonomous Machines for Protein Landscape Exploration (SAMPLE) platform for fully autonomous protein engineering. SAMPLE is driven by an intelligent agent that learns protein sequence–function relationships, designs new proteins and sends designs to a fully automated robotic system that experimentally tests the designed proteins and provides feedback to improve the agent’s understanding of the system. We deploy four SAMPLE agents with the goal of engineering glycoside hydrolase enzymes with enhanced thermal tolerance. Despite showing individual differences in their search behavior, all four agents quickly converge on thermostable enzymes. Self-driving laboratories automate and accelerate the scientific discovery process and hold great potential for the fields of protein engineering and synthetic biology.

Intrinsically disordered regions of the Msn2 transcription factor encode multiple functions using interwoven sequence grammars.

Intrinsically disordered regions (IDRs) are abundant in eukaryotic proteins, but their sequence-function relationship remains poorly understood. IDRs of transcription factors (TFs) can direct promoter selection and recruit coactivators, as shown for the budding yeast TF Msn2. To examine how IDRs encode both these functions, we compared genomic binding specificity, coactivator recruitment, and gene induction amongst a large set of designed Msn2-IDR mutants. We find that both functions depend on multiple regions across the>600AA IDR. Yet, transcription activity was readily disrupted by mutations that showed no effect on the Msn2 binding specificity. Our data attribute this differential sensitivity to the integration of a relaxed, composition-based code directing binding specificity with a more stringent, motif-based code controlling the recruitment of coactivators and transcription activity. Therefore, Msn2 utilizes interwoven sequence grammars for encoding multiple functions, suggesting a new IDR design paradigm of potentially general use.

Synthetic Homoserine Lactone Sensors for Gram-Positive Bacillus subtilis Using LuxR-Type Regulators.

A universal biochemical signal for bacterial cell-cell communication could facilitate programming dynamic responses in diverse bacterial consortia. However, the classical quorum sensing paradigm is that Gram-negative and Gram-positive bacteria generally communicate via homoserine lactones (HSLs) or oligopeptide molecular signals, respectively, to elicit population responses. Here, we create synthetic HSL sensors for Gram-positive Bacillus subtilis 168 using allosteric LuxR-type regulators (RpaR, LuxR, RhlR, and CinR) and synthetic promoters. Promoters were combinatorially designed from different sequence elements (-35, -16, -10, and transcriptional start regions). We quantified the effects of these combinatorial promoters on sensor activity and determined how regulator expression affects its activation, achieving up to 293-fold activation. Using the statistical design of experiments, we identified significant effects of promoter regions and pairwise interactions on sensor activity, which helped to understand the sequence-function relationships for synthetic promoter design. We present the first known set of functional HSL sensors (≥20-fold dynamic range) in B. subtilis for four different HSL chemical signals: p-coumaroyl-HSL, 3-oxohexanoyl-HSL, n-butyryl-HSL, and n-(3-hydroxytetradecanoyl)-HSL. This set of synthetic HSL sensors for a Gram-positive bacterium can pave the way for designable interspecies communication within microbial consortia.

Harnessing generative AI to decode enzyme catalysis and evolution for enhanced engineering.

Enzymes, as paramount protein catalysts, occupy a central role in fostering remarkable progress across numerous fields. However, the intricacy of sequence-function relationships continues to obscure our grasp of enzyme behaviors and curtails our capabilities in rational enzyme engineering. Generative artificial intelligence (AI), known for its proficiency in handling intricate data distributions, holds the potential to offer novel perspectives in enzyme research. Generative models could discern elusive patterns within the vast sequence space and uncover new functional enzyme sequences. This review highlights the recent advancements in employing generative AI for enzyme sequence analysis. We delve into the impact of generative AI in predicting mutation effects on enzyme fitness, catalytic activity and stability, rationalizing the laboratory evolution of de novo enzymes, and decoding protein sequence semantics and their application in enzyme engineering. Notably, the prediction of catalytic activity and stability of enzymes using natural protein sequences serves as a vital link, indicating how enzyme catalysis shapes enzyme evolution. Overall, we foresee that the integration of generative AI into enzyme studies will remarkably enhance our knowledge of enzymes and expedite the creation of superior biocatalysts.

Engineering phenylalanine ammonia lyase to limit feedback inhibition by cinnamate and enhance biotransformation.

Phenylalanine ammonia-lyase (PAL) is a crucial enzyme for various biotechnology applications, such as producing phenols, antioxidants, and nutraceuticals. However, feedback inhibition from its product, cinnamic acid, limits its forward reaction rate. Therefore, this study aims to address the feedback inhibition in PAL using enzyme engineering strategies. Random and site-directed mutagenesis approaches were utilized to screen mutant enzymes with ameliorated tolerance against cinnamic acid. A thermotolerant and cinnamate-tolerant mutant was rationally identified using a high throughput screening method and subsequent biochemical characterization. We evaluated cinnamate affinity among the seven rationally selected mutations, and the T102E mutation was identified as the most promising mutant. This mutant showed a six-fold reduction in the affinity of PAL for cinnamic acid and a two-fold increase in operational stability compared with native PAL. Furthermore, the enzyme was immobilized on carbon nanotubes to increase its robustness and reusability. The immobilized mutant PAL showed greater efficiency in the deamination of phenylalanine present in protein hydrolysate than its free form. The rationale behind the enhancement of cinnamate tolerance was validated using molecular dynamic simulations. Overall, the knowledge of the sequence-function relationship of PAL was applied to drive enzyme engineering to develop highly tolerant PAL.

GRNA-SeqRET: a universal tool for targeted and genome-scale gRNA design and sequence extraction for prokaryotes and eukaryotes.

High-throughput genetic screening is frequently employed to rapidly associate gene with phenotype and establish sequence-function relationships. With the advent of CRISPR technology, and the ability to functionally interrogate previously genetically recalcitrant organisms, non-model organisms can be investigated using pooled guide RNA (gRNA) libraries and sequencing-based assays to quantitatively assess fitness of every targeted locus in parallel. To aid the construction of pooled gRNA assemblies, we have developed an in silico design workflow for gRNA selection using the gRNA Sequence Region Extraction Tool (gRNA-SeqRET). Built upon the previously developed CCTop, gRNA-SeqRET enables automated, scalable design of gRNA libraries that target user-specified regions or whole genomes of any prokaryote or eukaryote. Additionally, gRNA-SeqRET automates the bulk extraction of any regions of sequence relative to genes or other features, aiding in the design of homology arms for insertion or deletion constructs. We also assess in silico the application of a designed gRNA library to other closely related genomes and demonstrate that for very closely related organisms Average Nucleotide Identity (ANI) > 95% a large fraction of the library may be of relevance. The gRNA-SeqRET web application pipeline can be accessed at https://grna.jgi.doe.gov. The source code is comprised of freely available software tools and customized Python scripts, and is available at https://bitbucket.org/berkeleylab/grnadesigner/src/master/ under a modified BSD open-source license (https://bitbucket.org/berkeleylab/grnadesigner).

Deep Mutational Scanning of an Oxygen-Independent Fluorescent Protein CreiLOV for Comprehensive Profiling of Mutational and Epistatic Effects.

Oxygen-independent, flavin mononucleotide-based fluorescent proteins (FbFPs) are promising alternatives to green fluorescent protein in anaerobic contexts. Deep mutational scanning performs systematic profiling of protein sequence-function relationships but has not been applied to FbFPs. Focusing on CreiLOV from Chlamydomonas reinhardtii, we created and analyzed two comprehensive mutant collections: (1) single-residue, site-saturation mutagenesis libraries covering all 118 residues; and (2) a full combinatorial metagenesis library among 20 mutations at 15 residues, where mutation and residue selection was based on single-site mutagenesis results. Notably, the second type of library is indispensable to study higher-order epistasis but underrepresented in the literature. Using optimized FACS-seq assays, 2,185 (>92.5%) out of 2,360 possible single-site mutants and 165,428 (>89.7%) out of 184,320 possible combinatorial mutants were reliably assigned with fitness values. We constructed statistical and machine-learning models to analyze the CreiLOV data set, enabling accurate fitness prediction of higher-order mutants using lower-order mutagenesis data. In addition, we successfully isolated CreiLOV variants with improved fluorescence quantum yield and thermostability. This work provides new empirical data and design rules to engineer combinatorial protein variants.

Mutational scan inferred binding energetics and structure in intrinsically disordered protein CcdA.

Unlike globular proteins, mutational effects on the function of Intrinsically Disordered Proteins (IDPs) are not well-studied. Deep Mutational Scanning of a yeast surface displayed mutant library yields insights into sequence-function relationships in the CcdA IDP. The approach enables facile prediction of interface residues and local structural signatures of the bound conformation. In contrast to previous titration-based approaches which use a number of ligand concentrations, we show that use of a single rationally chosen ligand concentration can provide quantitative estimates of relative binding constants for large numbers of protein variants. This is because the extended interface of IDP ensures that energetic effects of point mutations are spread over a much smaller range than for globular proteins. Our data also provides insights into the much-debated role of helicity and disorder in partner binding of IDPs. Based on this exhaustive mutational sensitivity dataset, a rudimentary model was developed in an attempt to predict mutational effects on binding affinity of IDPs that form alpha-helical structures upon binding.

Community science designed ribosomes with beneficial phenotypes

Functional design of ribosomes with mutant ribosomal RNA (rRNA) can expand opportunities for understanding molecular translation, building cells from the bottom-up, and engineering ribosomes with altered capabilities. However, such efforts are hampered by cell viability constraints, an enormous combinatorial sequence space, and limitations on large-scale, 3D design of RNA structures and functions. To address these challenges, we develop an integrated community science and experimental screening approach for rational design of ribosomes. This approach couples Eterna, an online video game that crowdsources RNA sequence design to community scientists in the form of puzzles, with in vitro ribosome synthesis, assembly, and translation in multiple design-build-test-learn cycles. We apply our framework to discover mutant rRNA sequences that improve protein synthesis in vitro and cell growth in vivo, relative to wild type ribosomes, under diverse environmental conditions. This work provides insights into rRNA sequence-function relationships and has implications for synthetic biology.

SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering

Deep learning has been widely used for protein engineering. However, it is limited by the lack of sufficient experimental data to train an accurate model for predicting the functional fitness of high-order mutants. Here, we develop SESNet, a supervised deep-learning model to predict the fitness for protein mutants by leveraging both sequence and structure information, and exploiting attention mechanism. Our model integrates local evolutionary context from homologous sequences, the global evolutionary context encoding rich semantic from the universal protein sequence space and the structure information accounting for the microenvironment around each residue in a protein. We show that SESNet outperforms state-of-the-art models for predicting the sequence-function relationship on 26 deep mutational scanning datasets. More importantly, we propose a data augmentation strategy by leveraging the data from unsupervised models to pre-train our model. After that, our model can achieve strikingly high accuracy in prediction of the fitness of protein mutants, especially for the higher order variants (> 4 mutation sites), when finetuned by using only a small number of experimental mutation data (< 50). The strategy proposed is of great practical value as the required experimental effort, i.e., producing a few tens of experimental mutation data on a given protein, is generally affordable by an ordinary biochemical group and can be applied on almost any protein.

Progress in the Conversion of Ginsenoside Rb1 into Minor Ginsenosides Using β-Glucosidases.

In recent years, minor ginsenosides have received increasing attention due to their outstanding biological activities, yet they are of extremely low content in wild ginseng. Ginsenoside Rb1, which accounts for 20% of the total ginsenosides, is commonly used as a precursor to produce minor ginsenosides via β-glucosidases. To date, many research groups have used different approaches to obtain β-glucosidases that can hydrolyze ginsenoside Rb1. This paper provides a compilation and analysis of relevant literature published mainly in the last decade, focusing on enzymatic hydrolysis pathways, enzymatic characteristics and molecular mechanisms of ginsenoside Rb1 hydrolysis by β-glucosidases. Based on this, it can be concluded that: (1) The β-glucosidases that convert ginsenoside Rb1 are mainly derived from bacteria and fungi and are classified as glycoside hydrolase (GH) families 1 and 3, which hydrolyze ginsenoside Rb1 mainly through the six pathways. (2) Almost all of these β-glucosidases are acidic and neutral enzymes with molecular masses ranging from 44-230 kDa. Furthermore, the different enzymes vary widely in terms of their optimal temperature, degradation products and kinetics. (3) In contrast to the GH1 β-glucosidases, the GH3 β-glucosidases that convert Rb1 show close sequence-function relationships. Mutations affecting the substrate binding site might alter the catalytic efficiency of enzymes and yield different prosapogenins. Further studies should focus on elucidating molecular mechanisms and improving overall performances of β-glucosidases for better application in food and pharmaceutical industries.

Rationally Controlling Selective Steroid Hydroxylation via Scaffold Sampling of a P450 Family

Many steroids are important pharmaceutically active compounds, while cytochrome P450 monooxygenases (CYPs) are attractive enzymes for applications in steroidal drug synthesis. However, the catalytic efficiency of existing P450s is not routinely high enough, as well as the molecular basis for selectivity control is unclear, which severely restrict their real applications. Here, a 16β steroid-hydroxylase CYP109B4 from Bacillus sonorensis is identified with excellent selectivity and activity. The crystallization and structural analysis of CYP109B4 reveal potential three “hotspot” residues (V84, V292, and S387) responsible for selectivity control. Then, guided by the sequence–function relationships revealed from the mutability landscape construction on the three residues, focused rational iterative site-specific mutagenesis (FRISM) and limited iterative saturation mutagenesis were performed, which provide variant B4-M7 (L240V/S387F/V84L/V292S/I291T/M290F/F294I) with completely switched regioselectivity from 16β to 15β. The subsequent computational analysis uncovers insights into the substrate binding modes in CYP109B4 and its variants, which further confirms the critical role of the “hotspot” residues for selectivity control. Finally, the generality of conserved-“hotspots”-mediated selectivity control is demonstrated by performing scaffold sampling between a panel of CYP109B members. Overall, in addition to the present chemical results, our study provides guidance in rationally designing more excellent P450 biocatalysts for potential practical (industrial) applications.

Comprehensive Characterization of Missense Variation in Coagulation Factor IX Reveals Features Necessary for Secretion and γ-Carboxylation

Multiplexed Assays of Variant Effect (MAVEs) are a powerful tool for assessing the effects of variation in proteins to understand sequence-function relationships, inform biological structures, and infer pathogenicity in clinical settings. We wished to use MAVEs to study the functional impact of all possible missense variants on coagulation factor IX (FIX). Variation in the F9 gene can cause decreased levels of functional FIX, leading to the bleeding disorder hemophilia B. However, genes that can be studied with MAVEs were limited to those that encode intracellular or membrane-bound proteins. This is because each variant protein must be physically connected to the genome of the cell of origin to allow functional protein selection and subsequent DNA sequencing to measure individual variant effects. However, FIX is a secreted plasma protein that does not remain physically associated with the cell that expresses it, making it incompatible with current MAVE methods. We hypothesized that cell surface display, as has been used in yeast and phage, could be adapted to establish the connection between FIX protein and F9 DNA variation needed for MAVEs in mammalian cells. Thus, we developed a FIX mammalian cell surface display system in HEK-293 cells by fusing FIX protein to a single pass transmembrane domain with a C-terminal linker. We measured FIX expression in our mammalian cell-surface display system using flow cytometry and multiple monoclonal, fluorescent anti-FIX antibodies to different FIX domains. We introduced a library of nearly all ~10,000 FIX missense variants into this system to profile each variant's effect on FIX function. Antibody-stained cells were then sorted into quartile bins by FACS. Variants in each bin were then deeply sequenced and scored based on their frequency within each bin. Scores were then used to assign functional significance to each FIX variant. To detect variants that impact FIX secretion, we used both antibodies to both FIX heavy chain and a strep II epitope engineered into the C-terminal linker. With the anti-FIX heavy chain antibody, we found that approximately 36.2% of variants were lowly secreted and that secretion scores alone correlated well with FIX antigen data reported in the Factor IX (F9) Gene Variant Database. We further found that proline and cysteine substitutions had strong negative effects on FIX expression. The anti-strep II antibody yielded similar results, except that cysteine substitution was generally better-tolerated. We concluded that the FIX heavy chain antibody binding is particularly sensitive to loss of function due to cysteine substitutions. FIX post-translational modification, particularly γ-carboxylation of the GLA domain, is critical for normal FIX function. A γ-carboxylation-sensitive anti-FIX monoclonal antibody can detect properly γ-carboxylated FIX. In our system, binding of this γ-carboxylation-sensitive antibody was lost when cells were grown in the presence of warfarin, indicating that our displayed FIX is γ-carboxylated. We used this carboxylation-sensitive antibody to detect FIX variants associated with reduced FIX carboxylation-sensitive antibody binding relative to secretion. As expected, the majority of variants with disproportionately low γ-carboxylation were located within the propeptide and GLA domains. Thus, we developed a novel membrane-tethered mammalian display system to study the functional impact of all possible missense variation in the secreted coagulation protein FIX. Our results provide direct functional evidence for loss of function in a high proportion (39.6%) of all possible FIX missense variants, either by decreasing secretion or by loss of γ-carboxylation.

Characterization of proteins from the 3N5M family reveals an operationally stable amine transaminase

Amine transaminases (ATA) convert ketones into optically active amines and are used to prepare active pharmaceutical ingredients and building blocks. Novel ATA can be identified in protein databases due to the extensive knowledge of sequence-function relationships. However, predicting thermo- and operational stability from the amino acid sequence is a persisting challenge and a vital step towards identifying efficient ATA biocatalysts for industrial applications. In this study, we performed a database mining and characterized selected putative enzymes of the β-alanine:pyruvate transaminase cluster (3N5M) — a subfamily with so far only a few described members, whose tetrameric structure was suggested to positively affect operational stability. Four putative transaminases (TA-1: Bilophilia wadsworthia, TA-5: Halomonas elongata, TA-9: Burkholderia cepacia, and TA-10: Burkholderia multivorans) were obtained in a soluble form as tetramers in E. coli. During comparison of these tetrameric with known dimeric transaminases we found that indeed novel ATA with high operational stabilities can be identified in this protein subfamily, but we also found exceptions to the hypothesized correlation that a tetrameric assembly leads to increased stability. The discovered ATA from Burkholderia multivorans features a broad substrate specificity, including isopropylamine acceptance, is highly active (6 U/mg) in the conversion of 1-phenylethylamine with pyruvate and shows a thermostability of up to 70 °C under both, storage and operating conditions. In addition, 50% (v/v) of isopropanol or DMSO can be employed as co-solvents without a destabilizing effect on the enzyme during an incubation time of 16 h at 30 °C.Key points• Database mining identified a thermostable amine transaminase in the β-alanine:pyruvate transaminase subfamily.• The tetrameric transaminase tolerates 50% DMSO and isopropanol under operating conditions at 30 °C.• A tetrameric structure is not necessarily associated with a higher operational stabilityGraphical abstract