The use of consensus sequence information to engineer stability and activity in proteins

Engineering thermostability of industrial enzymes for enhanced application performance

Thermostability is a key factor for the industrial application of enzymes. This review categorizes enzymes by their applications and discusses the importance of engineering thermostability for practical use. It summarizes fundamental theories and recent advancements in enzyme thermostability modification, including directed evolution, semi-rational design, and rational design. Directed evolution uses high-throughput screening to generate random mutations, while semi-rational design combines hotspot identification with screening. Rational design focuses on key residues to enhance stability by improving rigidity, foldability, and reducing aggregation. The review also covers rational strategies like engineering folding energy, surface charge, machine learning methods, and consensus design, along with tools that support these approaches. Practical examples are critically assessed to highlight the benefits and limitations of these strategies. Finally, the challenges and potential contributions of artificial intelligence in enzyme thermostability engineering are discussed.

Design of ancestral mammalian alkaline phosphatase bearing high stability and productivity

Mammalian alkaline phosphatase (AP) is widely used in diagnostics and molecular biology but its widespread use is impaired because it is difficult to express in Escherichia coli and has low thermostability. To overcome these challenges, we employed sequence-based protein redesign methods, specifically full consensus design (FCD) and ancestral sequence reconstruction (ASR), to create APs with enhanced properties. Biochemical analyses revealed that these newly designed APs exhibited improved levels of expression in their active form and increased thermostability compared to bovine intestinal AP isozyme II (bIAPII), without impeding enzymatic activity. Notably, the activity in culture broth of the designed APs was an order of magnitude higher than that of bIAPII, and their thermal stability increased by 13°C-17°C (measured as T50). We also assessed 28 single-point mutants of bIAPII to identify regions influencing thermostability and expression level; these mutations were common in the engineered APs but not in bIAPII. Specific mutations, such as T413E and G402S, enhanced thermostability, whereas increasing the expression level required multiple mutations. This suggests that a synergistic effect is required to enhance the expression level. Mutations enhancing thermostability were located in the crown domain, while those improving expression were close to the dimer interface, indicating distinct mechanisms underpinning these enhancements.

IMPORTANCE

Sequence-based protein redesign methods, such as full consensus design (FCD) and ancestral sequence reconstruction (ASR), have the potential to construct new enzymes utilizing protein sequence data registered in a rapidly expanding sequence database. The high thermostability of these enzymes would expand their application in diagnostics and molecular biology. These enzymes have also demonstrated a high level of active expression by Escherichia coli. These characteristics make these APs attractive candidates for industrial application. In addition, different amino acid residues are primarily responsible for thermal stability and active expression, suggesting important implications for strategies for designing enzymes by FCD and ASR.

Inside-Out Rational Design of Ornithine Cyclodeaminase RlOCD from Rhizobium leguminosarum by a Multiregion Synergy Strategy for Efficient Synthesis of l-Pipecolic Acid

Lysine cyclodeaminase (LCD)-mediated synthesis of l-pipecolic acid (l-PA) from l-lysine (l-Lys) is a promising approach. However, only one LCD has been reported, and its inadequate activity limits industrial applications. To address this problem, a substrate analogue-guided enzyme mining strategy was employed. A novel ornithine cyclodeaminase (OCD) from Rhizobium leguminosarum (RlOCD) was identified in combination with directed macrogenomic approaches. RlOCD displayed a conversion rate of 28% at a substrate loading as high as 1000 mM. A multiregion synergy strategy consisting of pocket reshaping, dynamical cross-correlation matrix-guided coevolutionary design, and surface modification was used to design RlOCD from the inside-out. A quadruple mutant (V93C/L119C/I170T/R90L) designated Mu4 with significantly increased activity was obtained, which showed a 28.46-fold increase in the catalytic efficiency. The conversion of Mu4 was 91% within 10 h at 1000 mM (146.19 g L-1) loading. The space-time yield of 282.1 g L-1 d-1 is the highest level ever reported. Molecular dynamics simulations and interaction analyses revealed that efficient pocket expansion and unique conformational rearrangements increased the affinity for the substrate, resulting in a more catalytically active conformation. This study expands the toolbox for the production of l-PA and demonstrates the effectiveness and potential of Mu4 for its production.

Characterization and rational engineering of an alkaline-tolerant azoreductase derived from Roseibium sp. H3510 for enhanced decolorization of azo dyes

rAzoR2326, an azoreductase derived from Roseibium sp. H3510, functions as an FMN-dependent homodimer utilizing NADH as cofactor. It demonstrated maximum activity at 45 °C and retained moderate activity above 50 °C, exhibiting stability from pH 7-10. Evolution and structure guided rational design of wild-type rAzoR2326 (WT) efficiently yielded 6 single-point mutants with improved thermostability and activity from a 22-variant library. Further combinatorial mutation led to mutant M20 with substantially enhanced thermostability (15-fold longer half-life at 50 °C) and activity (3.24-fold higher kcat/Km). M20 exhibited superior catalytic properties for decolorizing Allura Red compared to WT. Specifically, its decolorization capacity at pH 10.0 was 4.26-fold higher than WT. Additionally, M20 demonstrated remarkable thermostability, retaining 76.83 % decolorization activity for Allura Red after 120 min at 50 °C, whereas WT nearly lost all catalytic activity under the same conditions. Molecular dynamics simulations revealed the structural changes in M20, such as improved hydrogen bonding and a new C-H···π interaction, led to a more compact and rigid enzyme structure. This resulted in a more stable FMN-binding pocket and substrate tunnel, thereby improving the catalytic stability and activity of M20. Given its enhanced dye decolorization ability and alkaline tolerance, M20 shows promise as a biocatalyst for treating azo dye effluents.

Engineering the next-generation synthetic cell factory driven by protein engineering

Synthetic cell factory offers substantial advantages in economically efficient production of biofuels, chemicals, and pharmaceutical compounds. However, to create a high-performance synthetic cell factory, precise regulation of cellular material and energy flux is essential. In this context, protein components including enzymes, transcription factor-based biosensors and transporters play pivotal roles. Protein engineering aims to create novel protein variants with desired properties by modifying or designing protein sequences. This review focuses on summarizing the latest advancements of protein engineering in optimizing various aspects of synthetic cell factory, including: enhancing enzyme activity to eliminate production bottlenecks, altering enzyme selectivity to steer metabolic pathways towards desired products, modifying enzyme promiscuity to explore innovative routes, and improving the efficiency of transporters. Furthermore, the utilization of protein engineering to modify protein-based biosensors accelerates evolutionary process and optimizes the regulation of metabolic pathways. The remaining challenges and future opportunities in this field are also discussed.

Ancestral Reconstruction and the Evolution of Protein Energy Landscapes

A protein's sequence determines its conformational energy landscape. This, in turn, determines the protein's function. Understanding the evolution of new protein functions therefore requires understanding how mutations alter the protein energy landscape. Ancestral sequence reconstruction (ASR) has proven a valuable tool for tackling this problem. In ASR, one phylogenetically infers the sequences of ancient proteins, allowing characterization of their properties. When coupled to biophysical, biochemical, and functional characterization, ASR can reveal how historical mutations altered the energy landscape of ancient proteins, allowing the evolution of enzyme activity, altered conformations, binding specificity, oligomerization, and many other protein features. In this article, we review how ASR studies have been used to dissect the evolution of energy landscapes. We also discuss ASR studies that reveal how energy landscapes have shaped protein evolution. Finally, we propose that thinking about evolution from the perspective of an energy landscape can improve how we approach and interpret ASR studies.

Engineering styrene biosynthesis: designing a functional trans-cinnamic acid decarboxylase in Pseudomonas

We are interested in converting second generation feedstocks into styrene, a valuable chemical compound, using the solvent-tolerant Pseudomonas putida DOT-T1E as a chassis. Styrene biosynthesis takes place from L-phenylalanine in two steps: firstly, L-phenylalanine is converted into trans-cinnamic acid (tCA) by PAL enzymes and secondly, a decarboxylase yields styrene. This study focuses on designing and synthesizing a functional trans-cinnamic acid decarboxylase in Pseudomonas putida. To achieve this, we utilized the "wholesale" method, involving deriving two consensus sequences from multi-alignments of homologous yeast ferulate decarboxylase FDC1 sequences with > 60% and > 50% identity, respectively. These consensus sequences were used to design Pseudomonas codon-optimized genes named psc1 and psd1 and assays were conducted to test the activity in P. putida. Our results show that the PSC1 enzyme effectively decarboxylates tCA into styrene, whilst the PSD1 enzyme does not. The optimal conditions for the PSC1 enzyme, including pH and temperature were determined. The L-phenylalanine DOT-T1E derivative Pseudomonas putida CM12-5 that overproduces L-phenylalanine was used as the host for expression of pal/psc1 genes to efficiently convert L-phenylalanine into tCA, and the aromatic carboxylic acid into styrene. The highest styrene production was achieved when the pal and psc1 genes were co-expressed as an operon in P. putida CM12-5. This construction yielded styrene production exceeding 220 mg L-1. This study serves as a successful demonstration of our strategy to tailor functional enzymes for novel host organisms, thereby broadening their metabolic capabilities. This breakthrough opens the doors to the synthesis of aromatic hydrocarbons using Pseudomonas putida as a versatile biofactory.

Thermal Stabilization of a Bacterial Zn(II)-Dependent Phospholipase C through Consensus Sequence Design

Proteins' extraordinary performance in recognition and catalysis has led to their use in a range of applications. However, proteins obtained from natural sources are oftentimes not suitable for direct use in industrial or diagnostic setups. Natural proteins, evolved to optimally perform a task in physiological conditions, usually lack the stability required to be used in harsher conditions. Therefore, the alteration of the stability of proteins is commonly pursued in protein engineering studies. Here, we achieved a substantial thermal stabilization of a bacterial Zn(II)-dependent phospholipase C by consensus sequence design. We retrieved and analyzed sequenced homologues from different sources, selecting a subset of examples for expression and characterization. A non-natural consensus sequence showed the highest stability and activity among those tested. Comparison of the stability parameters of this stabilized mutant and other natural variants bearing similar mutations allows us to pinpoint the sites most likely to be responsible for the enhancement. Point mutations in these sites alter the unfolding process of the consensus sequence. We show that the stabilized version of the protein retains full activity even in harsh oil degumming conditions, making it suitable for industrial applications.

Comparative analysis of reconstructed ancestral proteins with their extant counterparts suggests primitive life had an alkaline habitat

To understand the origin and early evolution of life it is crucial to establish characteristics of the primordial environment that facilitated the emergence and evolution of life. One important environmental factor is the pH of the primordial environment. Here, we assessed the pH-dependent thermal stabilities of previously reconstructed ancestral nucleoside diphosphate kinases and ribosomal protein uS8s. The selected proteins were likely to be present in ancient organisms such as the last common ancestor of bacteria and that of archaea. We also assessed the thermal stability of homologous proteins from extant acidophilic, neutralophilic, and alkaliphilic microorganisms as a function of pH. Our results indicate that the reconstructed ancestral proteins are more akin to those of extant alkaliphilic bacteria, which display greater stability under alkaline conditions. These findings suggest that the common ancestors of bacterial and archaeal species thrived in an alkaline environment. Moreover, we demonstrate the reconstruction method employed in this study is a valuable technique for generating alkali-tolerant proteins that can be used in a variety of biotechnological and environmental applications.

Reverse transcription loop-mediated isothermal amplification (RT-LAMP) primer design based on Indonesia SARS-CoV-2 RNA sequence

BACKGROUND

The COVID-19 pandemic has highlighted the importance of tracking cases by using various methods such as the Reverse transcription loop-mediated isothermal amplification (RT-LAMP) which is a fast, simple, inexpensive, and accurate mass tracker. However, there have been no reports about the development of RT-LAMP primer designs that use genome sequences of viruses from Indonesia. Therefore, this study aimed to design an RT-LAMP primer using SARS-CoV-2 genome sequences from Indonesia and several other countries representing five continents in the world, as well as genomes from five Variants of Concern (VOC).

RESULT

The results showed that the consensus sequence of 70 SARS-CoV-2 virus sequences was obtained with a length of 29,982 bases. The phylogenetic test confirmed that the consensus sequence had a close kinship with the SARS-CoV-2 Wuhan Isolate. Furthermore, the SimPlot analysis showed that there was a high genetic diversity of sequences from the Coronaviridae tribal virus at base sequences of 1,500-5,000, 6,500-7,500, and 23,300-25,500. A total of 139 sets of primers were obtained from the primer design with 4 sets namely T1_6, T1_9, T4_7, and T4_52 having the best characteristics. Based on the secondary structure analysis test on 4 sets of primers, T1_6 and T1_9 were predicted not to form secondary structures at RT-LAMP operational temperatures. The primer set T1_9 showed better specificity in BLAST NCBI and eLAMP BLAST tests.

CONCLUSION

This study obtained a primer set of T1_9 with base sequence F3: CACTGAGACTCATTGATGCTATG, B3: CCAACCGTCTCTAAGAAACTCT, F2: GTTCACATCTGATTTGGCTACT, F1c: GAAGTCAACTGAACAACACCACCT, B2: CCTTCCTTAAACTTCTCTTCAAGC, B1c: GTGGCTAACTAACATCTTTGGCACT, LB: TGAAAACAAACCCGCCGTCCTTG, which meets the ideal parameters and has the best specificity. Therefore, it is recommended for use in further tests to recognize SARS-CoV-2 from Indonesia, other five continents, as well as five VOCs, including the new Omicron sub-variant.

Thermal Stability Enhancement of L-Asparaginase from Corynebacterium glutamicum Based on a Semi-Rational Design and Its Effect on Acrylamide Mitigation Capacity in Biscuits

Acrylamide is present in thermally processed foods, and it possesses toxic and carcinogenic properties. L-asparaginases could effectively regulate the formation of acrylamide at the source. However, current L-asparaginases have drawbacks such as poor thermal stability, low catalytic activity, and poor substrate specificity, thereby restricting their utility in the food industry. To address this issue, this study employed consensus design to predict the crucial residues influencing the thermal stability of Corynebacterium glutamicum L-asparaginase (CgASNase). Subsequently, a combination of site-point saturating mutation and combinatorial mutation techniques was applied to generate the double-mutant enzyme L42T/S213N. Remarkably, L42T/S213N displayed significantly enhanced thermal stability without a substantial impact on its enzymatic activity. Notably, its half-life at 40 °C reached an impressive 13.29 ± 0.91 min, surpassing that of CgASNase (3.24 ± 0.23 min). Moreover, the enhanced thermal stability of L42T/S213N can be attributed to an increased positive surface charge and a more symmetrical positive potential, as revealed by three-dimensional structural simulations and structure comparison analyses. To assess the impact of L42T/S213N on acrylamide removal in biscuits, the optimal treatment conditions for acrylamide removal were determined through a combination of one-way and orthogonal tests, with an enzyme dosage of 300 IU/kg flour, an enzyme reaction temperature of 40 °C, and an enzyme reaction time of 30 min. Under these conditions, compared to the control (464.74 ± 6.68 µg/kg), the acrylamide reduction in double-mutant-enzyme-treated biscuits was 85.31%, while the reduction in wild-type-treated biscuits was 68.78%. These results suggest that L42T/S213N is a promising candidate for industrial applications of L-asparaginase.

High-Accuracy Prediction of Stabilizing Surface Mutations to the Three-Helix Bundle, UBA(1), with EmCAST

The accurate modeling of energetic contributions to protein structure is a fundamental challenge in computational approaches to protein analysis and design. We describe a general computational method, EmCAST (empirical Cα stabilization), to score and optimize the sequence to the structure in proteins. The method relies on an empirical potential derived from the database of the Cα dihedral angle preferences for all possible four-residue sequences, using the data available in the Protein Data Bank. Our method produces stability predictions that naturally correlate one-to-one with the experimental results for solvent-exposed mutation sites. EmCAST predicted four mutations that increased the stability of a three-helix bundle, UBA(1), from 2.4 to 4.8 kcal/mol by optimizing residues in both helices and turns. For a set of eight variants, the predicted and experimental stabilizations correlate very well (R2 = 0.97) with a slope near 1 and with a 0.16 kcal/mol standard error for EmCAST predictions. Tests against literature data for the stability effects of surface-exposed mutations show that EmCAST outperforms the existing stability prediction methods. UBA(1) variants were crystallized to verify and analyze their structures at an atomic resolution. Thermodynamic and kinetic folding experiments were performed to determine the magnitude and mechanism of stabilization. Our method has the potential to enable the rapid, rational optimization of natural proteins, expand the analysis of the sequence/structure relationship, and supplement the existing protein design strategies.

Thermostability enhancement and insight of L-asparaginase from Mycobacterium sp. via consensus-guided engineering

Acrylamide alleviation in food has represented as a critical issue due to its neurotoxic effect on human health. L-Asparaginase (ASNase, EC 3.5.1.1) is considered a potential additive for acrylamide alleviation in food. However, low thermal stability hinders the application of ASNase in thermal food processing. To obtain highly thermal stable ASNase for its industrial application, a consensus-guided approach combined with site-directed saturation mutation (SSM) was firstly reported to engineer the thermostability of Mycobacterium gordonae L-asparaginase (GmASNase). The key residues Gly97, Asn159, and Glu249 were identified for improving thermostability. The combinatorial triple mutant G97T/N159Y/E249Q (TYQ) displayed significantly superior thermostability with half-life values of 61.65 ± 8.69 min at 50 °C and 5.12 ± 1.66 min at 55 °C, whereas the wild-type was completely inactive at these conditions. Moreover, its T_m value increased by 8.59 °C from parent wild-type. Interestingly, TYQ still maintained excellent catalytic efficiency and specific activity. Further molecular dynamics and structure analysis revealed that the additional hydrogen bonds, increased hydrophobic interactions, and favorable electrostatic potential were essential for TYQ being in a more rigid state for thermostability enhancement. These results suggested that our strategy was an efficient engineering approach for improving fundamental properties of GmASNase and offering GmASNase as a potential agent for efficient acrylamide mitigation in food industry. KEY POINTS: • The thermostability of GmASNase was firstly improved by consensus-guided engineering. • The half-life and T_m value of triple mutant TYQ were significantly increased. • Insight on improved thermostability of TYQ was revealed by MD and structure analysis.

Recent advances in simultaneous thermostability-activity improvement of industrial enzymes through structure modification

Engineered thermostable microbial enzymes are widely employed to catalyze chemical reactions in numerous industrial sectors. Although high thermostability is a prerequisite of industrial applications, enzyme activity is usually sacrificed during thermostability improvement. Therefore, it is vital to select the common and compatible strategies between thermostability and activity improvement to reduce mutants̕ libraries and screening time. Three functional protein engineering approaches, including directed evolution, rational design, and semi-rational design, are employed to manipulate protein structure on a genetic basis. From a structural standpoint, integrative strategies such as increasing substrate affinity; introducing electrostatic interaction; removing steric hindrance; increasing flexibility of the active site; N- and C-terminal engineering; and increasing intramolecular and intermolecular hydrophobic interactions are well-known to improve simultaneous activity and thermostability. The current review aims to analyze relevant strategies to improve thermostability and activity simultaneously to circumvent the thermostability and activity trade-off of industrial enzymes.

Efficient Exploration of Sequence Space by Sequence-Guided Protein Engineering and Design

The rapid growth of sequence databases over the past two decades means that protein engineers faced with optimizing a protein for any given task will often have immediate access to a vast number of related protein sequences. These sequences encode information about the evolutionary history of the protein and the underlying sequence requirements to produce folded, stable, and functional protein variants. Methods that can take advantage of this information are an increasingly important part of the protein engineering tool kit. In this Perspective, we discuss the utility of sequence data in protein engineering and design, focusing on recent advances in three main areas: the use of ancestral sequence reconstruction as an engineering tool to generate thermostable and multifunctional proteins, the use of sequence data to guide engineering of multipoint mutants by structure-based computational protein design, and the use of unlabeled sequence data for unsupervised and semisupervised machine learning, allowing the generation of diverse and functional protein sequences in unexplored regions of sequence space. Altogether, these methods enable the rapid exploration of sequence space within regions enriched with functional proteins and therefore have great potential for accelerating the engineering of stable, functional, and diverse proteins for industrial and biomedical applications.

Enzymatic Conjugation of Modified RNA Fragments by Ancestral RNA Ligase AncT4_2

Oligonucleotide therapeutics have great potential as a next-generation approach to treating intractable diseases. Large quantities of modified DNA/RNA containing xenobiotic nucleic acids (XNAs) must be synthesized before clinical application. In this study, the ancestral RNA ligase AncT4_2 was designed by ancestral sequence reconstruction (ASR) to perform the conjugation reaction of modified RNA fragments. AncT4_2 had superior properties to native RNA ligase 2 from T4 phage (T4Rnl2), including high productivity, a >2.5-fold-higher turnover number, and >10°C higher thermostability. One remarkable point is the broad substrate selectivity of AncT4_2; the activity of AncT4_2 toward 17 of the modified RNA fragments was higher than that of T4Rnl2. The activity was estimated by measuring the conjugation reaction of two RNA strands, 3'-OH (12 bp) and 5'-PO4 (12 bp), in which the terminal and penultimate positions of the 3'-OH fragment and the first and second positions of the 5'-PO4 fragment were substituted by 2'-fluoro, 2'-O-methyl, 2'-O-methoxyethyl, and 2'-H, respectively. The enzymatic properties of AncT4_2 allowed the enzyme to conjugate large quantities of double-stranded RNA coding for patisiran (>400 μM level), which was formed by four RNA fragments containing 2'-OMe-substituted nucleic acids. Structural analysis of modeled AncT4_2 suggested that protein dynamics were changed by mutation to Gly or indel during ASR and that this may positively impact the conjugation of modified RNA fragments with the enzyme. AncT4_2 is expected to be a key biocatalyst in synthesizing RNA therapeutics by an enzymatic reaction. IMPORTANCE RNA therapeutics is one of the next-generation medicines for treating various diseases. Our designed ancestral RNA ligase AncT4_2 exhibited excellent enzymatic properties, such as high thermal stability, productivity, specific activity, and broad substrate selectivity compared to native enzymes. These advantages create the potential for AncT4_2 to be applied in conjugating the modified RNA fragments containing various xenobiotic nucleic acids. In addition, patisiran, a known polyneuropathy therapeutic, could be synthesized from four fragmented oligonucleotides at a preparative scale. Taken together, these findings indicate AncT4_2 could open the door to synthesizing RNA therapeutics by enzymatic reaction at large-scale production.

Singular value decomposition of protein sequences as a method to visualize sequence and residue space

Singular value decomposition (SVD) of multiple sequence alignments (MSAs) is an important and rigorous method to identify subgroups of sequences within the MSA, and to extract consensus and covariance sequence features that define the alignment and distinguish the subgroups. This information can be correlated to structure, function, stability, and taxonomy. However, the mathematics of SVD is unfamiliar to many in the field of protein science. Here, we attempt to present an intuitive yet comprehensive description of SVD analysis of MSAs. We begin by describing the underlying mathematics of SVD in a way that is both rigorous and accessible. Next, we use SVD to analyze sequences generated with a simplified model in which the extent of sequence conservation and covariance between different positions is controlled, to show how conservation and covariance produce features in the decomposed coordinate system. We then use SVD to analyze alignments of two protein families, the homeodomain and the Ras superfamilies. Both families show clear evidence of sequence clustering when projected into singular value space. We use k-means clustering to group MSA sequences into specific clusters, show how the residues that distinguish these clusters can be identified, and show how these clusters can be related to taxonomy and function. We end by providing a description a set of Python scripts that can be used for SVD analysis of MSAs, displaying results, and identifying and analyzing sequence clusters. These scripts are freely available on GitHub.

The stability and dynamics of computationally designed proteins

Protein stability, dynamics and function are intricately linked. Accordingly, protein designers leverage dynamics in their designs and gain insight to their successes and failures by analyzing their proteins' dynamics. Molecular dynamics (MD) simulations are a powerful computational tool for quantifying both local and global protein dynamics. This review highlights studies where MD simulations were applied to characterize the stability and dynamics of designed proteins and where dynamics were incorporated into computational protein design. First, we discuss the structural basis underlying the extreme stability and thermostability frequently observed in computationally designed proteins. Next, we discuss examples of designed proteins, where dynamics were not explicitly accounted for in the design process, whose coordinated motions or active site dynamics, as observed by MD simulation, enhanced or detracted from their function. Many protein functions depend on sizeable or subtle conformational changes, so we finally discuss the computational design of proteins to perform a specific function that requires consideration of motion by multi-state design.

Structural plasticity in the loop region of engineered lipocalins with novel ligand specificities, so-called Anticalins

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The lipocalins exhibit four structurally variable loops at one end of a β-barrel.

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Binding sites for diverse ligands occur in the natural lipocalin family members.

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Loop reshaping via combinatorial protein design leads to novel ligand specificities.

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Many crystal structures of Anticalins derived from the Lcn2 scaffold are available.

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Graphical analysis reveals high structural plasticity of the lipocalin loop region.

Anticalins are generated via combinatorial protein design on the basis of the lipocalin protein scaffold and constitute a novel class of small and robust engineered binding proteins that offer prospects for applications in medical therapy as well as in vivo diagnostics as an alternative to antibodies. The lipocalins are natural binding proteins with diverse ligand specificities which share a simple architecture with a central eight-stranded antiparallel β-barrel and an α-helix attached to its side. At the open end of the β-barrel, four structurally variable loops connect the β-strands in a pair-wise manner and, together, shape the ligand pocket. Using targeted random mutagenesis in combination with molecular selection techniques, this loop region can be reshaped to generate pockets for the tight binding of various ligands ranging from small molecules over peptides to proteins. While such Anticalin proteins can be derived from different natural lipocalins, the human lipocalin 2 (Lcn2) scaffold proved particularly successful for the design of binding proteins with novel specificities and, over the years, more than 20 crystal structures of Lcn2-based Anticalins have been elucidated. In this graphical structural biology review we illustrate the conformational variability that emerged in the loop region of these functionally diverse artificial binding proteins in comparison with the natural scaffold. Our present analysis provides picturesque evidence of the high structural plasticity around the binding site of the lipocalins which explains the proven tolerance toward excessive mutagenesis, thus demonstrating remarkable resemblance to the complementarity-determining region of antibodies (immunoglobulins).

Comprehensive mutagenesis to identify amino acid residues contributing to the difference in thermostability between two originally thermostable ancestral proteins

Further improvement of the thermostability of inherently thermostable proteins is an attractive challenge because more thermostable proteins are industrially more useful and serve as better scaffolds for protein engineering. To establish guidelines that can be applied for the rational design of hyperthermostable proteins, we compared the amino acid sequences of two ancestral nucleoside diphosphate kinases, Arc1 and Bac1, reconstructed in our previous study. Although Bac1 is a thermostable protein whose unfolding temperature is around 100°C, Arc1 is much more thermostable with an unfolding temperature of 114°C. However, only 12 out of 139 amino acids are different between the two sequences. In this study, one or a combination of amino acid(s) in Bac1 was/were substituted by a residue(s) found in Arc1 at the same position(s). The best mutant, which contained three amino acid substitutions (S108D, G116A and L120P substitutions), showed an unfolding temperature more than 10°C higher than that of Bac1. Furthermore, a combination of the other nine amino acid substitutions also led to improved thermostability of Bac1, although the effects of individual substitutions were small. Therefore, not only the sum of the contributions of individual amino acids, but also the synergistic effects of multiple amino acids are deeply involved in the stability of a hyperthermostable protein. Such insights will be helpful for future rational design of hyperthermostable proteins.

Design of a Full-Consensus Glutamate Decarboxylase and Its Application to GABA Biosynthesis

Glutamate decarboxylase (GAD) catalyses the decarboxylation of L-glutamate to gamma-aminobutyric acid (GABA). Improvement of the enzymatic properties of GAD is important for the low-cost synthesis of GABA. In this study, utilizing sequences of enzymes homologous with GAD from lactic acid bacteria, highly mutated GADs were designed using sequence-based protein design methods. Two mutated GADs, FcGAD and AncGAD, generated by full-consensus design and ancestral sequence reconstruction, had more desirable properties than native GADs. With respect to thermal stability, the half-life of the designed GADs was about 10 °C higher than that of native GAD. The productivity of FcGAD was considerably higher than those of known GADs; more than 250 mg/L of purified enzyme could be produced in the E. coli expression system. In a production test using 26.4 g of l-glutamate and 3.0 g of resting cells, 17.2 g of GABA could be prepared within one hour, without purification, in a one-pot synthesis.

Data-driven computational protein design

Computational protein design can generate proteins not found in nature that adopt desired structures and perform novel functions. Although proteins could, in theory, be designed with ab initio methods, practical success has come from using large amounts of data that describe the sequences, structures, and functions of existing proteins and their variants. We present recent creative uses of multiple-sequence alignments, protein structures, and high-throughput functional assays in computational protein design. Approaches range from enhancing structure-based design with experimental data to building regression models to training deep neural nets that generate novel sequences. Looking ahead, deep learning will be increasingly important for maximizing the value of data for protein design.