Putting engineering back into protein engineering: bioinformatic approaches to catalyst design

Hydrogen-Bonded Thiol Undergoes Unconventional Excited-State Intramolecular Proton-Transfer Reactions

The chapter on the thiol-related hydrogen bond (H-bond) and its excited-state intramolecular proton-transfer (ESIPT) reaction was recently opened where compound 4'-diethylamino-3-mercaptoflavone (3NTF) undergoes ESIPT in both cyclohexane solution and solid, giving a 710 nm tautomer emission with an anomalously large Stokes shift of 12,230 cm-1. Considering the thiol H-bond to be unconventional compared to the conventional Pauling-type -OH or -NH H-bond, it is thus essential and timely to probe its fundamental difference between their ESIPT. However, thiol-associated ESIPT tends to be nonemissive due to the dominant nπ* character of the tautomeric lowest excited state. Herein, based on the 3-mercaptoflavone scaffold and π-elongation concept, a new series of 4'-substituted-7-diethylamino-3-mercaptoflavones, NTFs, was designed and synthesized with varied H-bond strength and 690-720 nm tautomeric emission upon ultraviolet (UV) excitation in cyclohexane. The order of their H-bonding strength was experimentally determined to be N-NTF < O-NTF < H-NTF < F-NTF, while the rate of -SH ESIPT measured by fluorescence upconversion was F-NTF (398 fs)-1 < H-NTF (232 fs)-1 < O-NTF (123 fs)-1 < N-NTF (101 fs)-1 in toluene. Unexpectedly, the strongest H-bonded F-NTF gives the slowest ESIPT, which does not conform to the traditional ESIPT model. The results are rationalized by the trend of carbonyl oxygen basicity rather than -SH acidity. Namely, the thiol acidity relevant to the H-bond strength plays a minor role in the driving force of ESIPT. Instead, the proton-accepting strength governs ESIPT. That is to say, the noncanonical thiol H-bonding system undergoes an unconventional type of ESIPT.

In vitro 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 in vitro 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.

Effects of solvent polarity on the novel excited-state intramolecular thiol proton transfer and photophysical property compared with the oxygen proton transfer

Recently, the dual-fluorescent phenomena of excited state intramolecular thiol proton transfer (ESIPT) for 3-thiolflavone derivative (3NTF) were reported by Chou and coworkers for the first time [J. Am. Chem. Soc. 143 (2021) 12715-12724], which opened a new chapter in the field of ESIPT. Based on density functional theory (DFT) and time-dependent density functional theory (TDDFT), the proton transfer processes of 3NTF in toluene, dichloromethane and acetonitrile were studied. By optimizing the structure of the ground (S0) state and first excited (S1) state of 3NTF in different solvents, the hydrogen-bond parameters and proton-transfer potential energy curves were calculated. It was shown that although photo-excitation enhanced the intramolecular hydrogen bonding strength and thus promoted the occurrence of ESIPT, the solvent polarities inhibited the enhancement of the hydrogen bond of S1 state, which was not conducive to ESIPT. The electron spectra analyses were consistent with experimental data, which confirmed the rationality of molecular configurations. The time-evolved excited state dynamics simulation was performed based on the optimized structure of 3NTF, indicating that the ESIPT was an ultrafast photochemical reaction less than 180 fs. Moreover, we compared the potential energy surfaces of ESIPT, electronic structures based on natural transition orbitals (NTOs) method and electron-hole isosurfaces for the 3NTF and the traditional flavone molecule (3NHF), concluded that the unusually large Stokes shift fluorescence of 3NTF was mainly caused by the coupling of ESIPT and twisting intramolecular charge transfer (TICT), and the 3NTF isomer had the more nπ* character in the electron transition process. The nπ* ICT significantly increased with the decrease of solvent polarities, affecting the molecular photophysical properties, this made it more widely used in biomedical, photochemical, materials science and other fields.

A unified cost-effective method for the construction of reliable potential energy surfaces for H2S and H2O clusters

A DFT-based methodology has been used to construct the potential energy surface of H2S clusters up to pentamers. Geometrical parameters and energetics show very good agreement with the existing experimental and high-level theoretical results. Distinct stable conformers of three dimers, six trimers, eleven tetramers and twenty-three pentamers have been identified. Both S-HS H-bond and SS interactions are identified in dimers, trimers and pentamers, while no SS interactions could be found in any of the 11 tetramer conformers. The binding energies of the most stable dimer, trimer, tetramer and pentamer are -1.66, -5.21, -8.57 and -12.54 kcal mol-1, respectively. The PES has been found to be exceedingly flat and the energy gap between the most and the least stable conformers was found to be only 0.09, 2.13, 1.65 and 1.13 kcal mol-1, from the dimer to the pentamer, respectively. The proposed method has also been used for water clusters up to the pentamer. The results obtained were found to agree closely with the existing results. Only one conformer was found for the water dimer, whereas four, five and fifteen conformers were obtained for the trimer, tetramer and pentamer, respectively. Atoms in molecular calculations were found to corroborate with the geometric and energetic results for both clusters.

Chapter Open for the Excited-State Intramolecular Thiol Proton Transfer in the Room-Temperature Solution

We report here, for the first time, the experimental observation on the excited-state intramolecular proton transfer (ESIPT) reaction of the thiol proton in room-temperature solution. This phenomenon is demonstrated by a derivative of 3-thiolflavone (3TF), namely, 2-(4-(diethylamino)phenyl)-3-mercapto-4H-chromen-4-one (3NTF), which possesses an -S-H···O═ intramolecular H-bond (denoted by the dashed line) and has an S₁ absorption at 383 nm. Upon photoexcitation, 3NTF exhibits a distinctly red emission maximized at 710 nm in cyclohexane with an anomalously large Stokes shift of 12 230 cm^-1. Upon methylation on the thiol group, 3MeNTF, lacking the thiol proton, exhibits a normal Stokes-shifted emission at 472 nm. These, in combination with the computational approaches, lead to the conclusion of thiol-type ESIPT unambiguously. Further time-resolved study renders an unresolvable (<180 fs) ESIPT rate for 3NTF, followed by a tautomer emission lifetime of 120 ps. In sharp contrast to 3NTF, both 3TF and 3-mercapto-2-(4-(trifluoromethyl)phenyl)-4H-chromen-4-one (3FTF) are non-emissive. Detailed computational approaches indicate that all studied thiols undergo thermally favorable ESIPT. However, once forming the proton-transferred tautomer, the lone-pair electrons on the sulfur atom brings non-negligible nπ* contribution to the S₁' state (prime indicates the proton-transferred tautomer), for which the relaxation is dominated by the non-radiative deactivation. For 3NTF, the extension of π-electron delocalization by the diethylamino electron-donating group endows the S₁' state primarily in the ππ* configuration, exhibiting the prominent tautomer emission. The results open a new chapter in the field of ESIPT, covering the non-canonical sulfur intramolecular H-bond and its associated ESIPT at ambient temperature.

Synthetic biology for the directed evolution of protein biocatalysts: navigating sequence space intelligently

The amino acid sequence of a protein affects both its structure and its function. Thus, the ability to modify the sequence, and hence the structure and activity, of individual proteins in a systematic way, opens up many opportunities, both scientifically and (as we focus on here) for exploitation in biocatalysis. Modern methods of synthetic biology, whereby increasingly large sequences of DNA can be synthesised de novo, allow an unprecedented ability to engineer proteins with novel functions. However, the number of possible proteins is far too large to test individually, so we need means for navigating the 'search space' of possible protein sequences efficiently and reliably in order to find desirable activities and other properties. Enzymologists distinguish binding (Kd) and catalytic (kcat) steps. In a similar way, judicious strategies have blended design (for binding, specificity and active site modelling) with the more empirical methods of classical directed evolution (DE) for improving kcat (where natural evolution rarely seeks the highest values), especially with regard to residues distant from the active site and where the functional linkages underpinning enzyme dynamics are both unknown and hard to predict. Epistasis (where the 'best' amino acid at one site depends on that or those at others) is a notable feature of directed evolution. The aim of this review is to highlight some of the approaches that are being developed to allow us to use directed evolution to improve enzyme properties, often dramatically. We note that directed evolution differs in a number of ways from natural evolution, including in particular the available mechanisms and the likely selection pressures. Thus, we stress the opportunities afforded by techniques that enable one to map sequence to (structure and) activity in silico, as an effective means of modelling and exploring protein landscapes. Because known landscapes may be assessed and reasoned about as a whole, simultaneously, this offers opportunities for protein improvement not readily available to natural evolution on rapid timescales. Intelligent landscape navigation, informed by sequence-activity relationships and coupled to the emerging methods of synthetic biology, offers scope for the development of novel biocatalysts that are both highly active and robust.

C.U.R.R.F. (Codon Usage regarding Restriction Finder): a free Java(®)-based tool to detect potential restriction sites in both coding and non-coding DNA sequences

The synthesis of complete genes is becoming a more and more popular approach in heterologous gene expression. Reasons for this are the decreasing prices and the numerous advantages in comparison to classic molecular cloning methods. Two of these advantages are the possibility to adapt the codon usage to the host organism and the option to introduce restriction enzyme target sites of choice. C.U.R.R.F. (Codon Usage regarding Restriction Finder) is a free Java(®)-based software program which is able to detect possible restriction sites in both coding and non-coding DNA sequences by introducing multiple silent or non-silent mutations, respectively. The deviation of an alternative sequence containing a desired restriction motive from the sequence with the optimal codon usage is considered during the search of potential restriction sites in coding DNA and mRNA sequences as well as protein sequences. C.U.R.R.F is available at http://www.zvm.tu-dresden.de/die_tu_dresden/fakultaeten/fakultaet_mathematik_und_naturwissenschaften/fachrichtung_biologie/mikrobiologie/allgemeine_mikrobiologie/currf.

Computational protein engineering: bridging the gap between rational design and laboratory evolution

Enzymes are tremendously proficient catalysts, which can be used as extracellular catalysts for a whole host of processes, from chemical synthesis to the generation of novel biofuels. For them to be more amenable to the needs of biotechnology, however, it is often necessary to be able to manipulate their physico-chemical properties in an efficient and streamlined manner, and, ideally, to be able to train them to catalyze completely new reactions. Recent years have seen an explosion of interest in different approaches to achieve this, both in the laboratory, and in silico. There remains, however, a gap between current approaches to computational enzyme design, which have primarily focused on the early stages of the design process, and laboratory evolution, which is an extremely powerful tool for enzyme redesign, but will always be limited by the vastness of sequence space combined with the low frequency for desirable mutations. This review discusses different approaches towards computational enzyme design and demonstrates how combining newly developed screening approaches that can rapidly predict potential mutation “hotspots” with approaches that can quantitatively and reliably dissect the catalytic step can bridge the gap that currently exists between computational enzyme design and laboratory evolution studies.

Engineering genes for predictable protein expression

The DNA sequence used to encode a polypeptide can have dramatic effects on its expression. Lack of readily available tools has until recently inhibited meaningful experimental investigation of this phenomenon. Advances in synthetic biology and the application of modern engineering approaches now provide the tools for systematic analysis of the sequence variables affecting heterologous expression of recombinant proteins. We here discuss how these new tools are being applied and how they circumvent the constraints of previous approaches, highlighting some of the surprising and promising results emerging from the developing field of gene engineering.

The imminent role of protein engineering in synthetic biology

Protein engineering has for decades been a powerful tool in biotechnology for generating vast numbers of useful enzymes for industrial applications. Today, protein engineering has a crucial role in advancing the emerging field of synthetic biology, where metabolic engineering efforts alone are insufficient to maximize the full potential of synthetic biology. This article reviews the advancements in protein engineering techniques for improving biocatalytic properties to optimize engineered pathways in host systems, which are instrumental to achieve high titer production of target molecules. We also discuss the specific means by which protein engineering has improved metabolic engineering efforts and provide our assessment on its potential to continue to advance biology engineering as a whole.

Enzyme optimization: moving from blind evolution to statistical exploration of sequence-function space

Directed evolution is a powerful tool for the creation of commercially useful enzymes, particularly those approaches that are based on in vitro recombination methods, such as DNA shuffling. Although these types of search algorithms are extraordinarily efficient compared with purely random methods, they do not explicitly represent or interrogate the genotype-phenotype relationship and are essentially blind in nature. Recently, however, researchers have begun to apply multivariate statistical techniques to model protein sequence-function relationships and guide the evolutionary process by rapidly identifying beneficial diversity for recombination. In conjunction with state-of-the-art library generation methods, the statistical approach to sequence optimization is now being used routinely to create enzymes efficiently for industrial applications.

Better library design: data-driven protein engineering

Data-driven protein engineering is increasingly used as an alternative to rational design and combinatorial engineering because it uses available knowledge to limit library size, while still allowing for the identification of unpredictable substitutions that lead to large effects. Recent advances in computational modeling and bioinformatics, as well as an increasing databank of experiments on functional variants, have led to new strategies to choose particular amino acid residues to vary in order to increase the chances of obtaining a variant protein with the desired property. Strategies for limiting diversity at each position, design of small sub-libraries, and the performance of scouting experiments, have also been developed or even automated, further reducing the library size.

Gene Designer: a synthetic biology tool for constructing artificial DNA segments

Background

Direct synthesis of genes is rapidly becoming the most efficient way to make functional genetic constructs and enables applications such as codon optimization, RNAi resistant genes and protein engineering. Here we introduce a software tool that drastically facilitates the design of synthetic genes.

Results

Gene Designer is a stand-alone software for fast and easy design of synthetic DNA segments. Users can easily add, edit and combine genetic elements such as promoters, open reading frames and tags through an intuitive drag-and-drop graphic interface and a hierarchical DNA/Protein object map. Using advanced optimization algorithms, open reading frames within the DNA construct can readily be codon optimized for protein expression in any host organism. Gene Designer also includes features such as a real-time sliding calculator of oligonucleotide annealing temperatures, sequencing primer generator, tools for avoidance or inclusion of restriction sites, and options to maximize or minimize sequence identity to a reference.

Conclusion

Gene Designer is an expandable Synthetic Biology workbench suitable for molecular biologists interested in the de novo creation of genetic constructs.

Analysis of sequence-reactivity space for protein-protein interactions

Sequence-reactivity space is defined by the relationships between amino acid type choices at some residue positions in a protein and the reactivities of the resulting variants. We are studying Kazal superfamily serine proteinase inhibitors, under substitution of any combination of residue types at 10 binding-region positions. Reactivities are defined by the standard free energy of association for an inhibitor against an enzyme, and we are interested in both the strength (the free energy value) and specificity (relative free energy values for one inhibitor against different enzymes). Characterizing the structure of such a space poses several interesting questions: (1) How many sequences achieve particular strength and specificity characteristics? (2) What is the best such sequence? (3) What are some nearly-as-good alternatives? (4) What are their common residue type characteristics (e.g., conservation and correlation)? Although these problems are all highly combinatorial in nature, this article develops an efficient, integrated mechanism to address them under a data-driven model that predicts reactivity for given sequences. We employ sampling and a novel deterministic distribution propagation algorithm, in order to determine both the reactivity distribution and sequence composition statistics; integer programming and a novel branch-and-bound search algorithm, in order to optimize sequences and enumerate near-optimal sequences; and correlation-based sequence decomposition, in order to identify sequence motifs. We demonstrate the value of our mechanism in analyzing the Kazal superfamily sequence-reactivity space, providing insights into the underlying biochemistry and suggesting hypotheses for further experimental consideration. In general, our mechanism offers a valuable tool for investigating the available degrees of freedom in protein design within a combined computational-experimental framework.

Outlook for cellulase improvement: screening and selection strategies

Cellulose is the most abundant renewable natural biological resource, and the production of biobased products and bioenergy from less costly renewable lignocellulosic materials is important for the sustainable development of human beings. A reduction in cellulase production cost, an improvement in cellulase performance, and an increase in sugar yields are all vital to reduce the processing costs of biorefineries. Improvements in specific cellulase activities for non-complexed cellulase mixtures can be implemented through cellulase engineering based on rational design or directed evolution for each cellulase component enzyme, as well as on the reconstitution of cellulase components. Here, we review quantitative cellulase activity assays using soluble and insoluble substrates, and focus on their advantages and limitations. Because there are no clear relationships between cellulase activities on soluble substrates and those on insoluble substrates, soluble substrates should not be used to screen or select improved cellulases for processing relevant solid substrates, such as plant cell walls. Cellulase improvement strategies based on directed evolution using screening on soluble substrates have been only moderately successful, and have primarily targeted improvement in thermal tolerance. Heterogeneity of insoluble cellulose, unclear dynamic interactions between insoluble substrate and cellulase components, and the complex competitive and/or synergic relationship among cellulase components limit rational design and/or strategies, depending on activity screening approaches. Herein, we hypothesize that continuous culture using insoluble cellulosic substrates could be a powerful selection tool for enriching beneficial cellulase mutants from the large library displayed on the cell surface.

Engineered protein function by selective amino acid diversification

Almost all protein engineering methods rely upon making changes to naturally occurring proteins that already possess some of the desired properties. This will probably remain the case as long as we lack a complete understanding of the way that an amino acid sequence gives rise to a protein with a precisely defined biological function. Common to all methods for altering an existing protein is the selection of a subset of amino acids in the protein for variation and a choice of which substitutions to make at each position. Variants are then tested empirically and further variants are created based upon their performance. Differences between protein engineering methods are the ways in which amino acids are chosen for variation, the protocols followed for creating the variants, and how information regarding variant properties is used in creating subsequent variants. In this article, we describe these differences and provide examples of how the experimental parameters of specific projects determine which method is most suitable.