Engineering a more thermostable blue light photo receptor Bacillus subtilis YtvA LOV domain by a computer aided rational design method

Application of an alchemical free energy method for the prediction of thermostable DuraPETase variants

Non-equilibrium (NEQ) alchemical free energy calculations are an emerging tool for accurately predicting changes in protein folding free energy resulting from amino acid mutations. In this study, this method in combination with the Rosetta ddg monomer tool was applied to predict more thermostable variants of the polyethylene terephthalate (PET) degrading enzyme DuraPETase. The Rosetta ddg monomer tool efficiently enriched promising mutations prior to more accurate prediction by NEQ alchemical free energy calculations. The relative change in folding free energy of 96 single amino acid mutations was calculated by NEQ alchemical free energy calculation. Experimental validation of ten of the highest scoring variants identified two mutations (DuraPETaseS61M and DuraPETaseS223Y) that increased the melting temperature (Tm) of the enzyme by up to 1 °C. The calculated relative change in folding free energy showed an excellent correlation with experimentally determined Tm resulting in a Pearson's correlation coefficient of r =  - 0.84. Limitations in the prediction of strongly stabilizing mutations were, however, encountered and are discussed. Despite these challenges, this study demonstrates the practical applicability of NEQ alchemical free energy calculations in prospective enzyme engineering projects. KEY POINTS: • Rosetta ddg monomer enriches stabilizing mutations in a library of DuraPETase variants • NEQ free energy calculations accurately predict changes in Tm of DuraPETase • The DuraPETase variants S223Y, S42M, and S61M have increased Tm.

Reengineering of a flavin-binding fluorescent protein using ProteinMPNN

Recent advances in machine learning techniques have led to development of a number of protein design and engineering approaches. One of them, ProteinMPNN, predicts an amino acid sequence that would fold and match user-defined backbone structure. Its performance was previously tested for proteins composed of standard amino acids, as well as for peptide- and protein-binding proteins. In this short report, we test whether ProteinMPNN can be used to reengineer a non-proteinaceous ligand-binding protein, flavin-based fluorescent protein CagFbFP. We fixed the native backbone conformation and the identity of 20 amino acids interacting with the chromophore (flavin mononucleotide, FMN) while letting ProteinMPNN predict the rest of the sequence. The software package suggested replacing 36-48 out of the remaining 86 amino acids so that the resulting sequences are 55%-66% identical to the original one. The three designs that we tested experimentally displayed different expression levels, yet all were able to bind FMN and displayed fluorescence, thermal stability, and other properties similar to those of CagFbFP. Our results demonstrate that ProteinMPNN can be used to generate diverging unnatural variants of fluorescent proteins, and, more generally, to reengineer proteins without losing their ligand-binding capabilities.

Understanding large scale sequencing datasets through changes to protein folding

The expansion of high-quality, low-cost sequencing has created an enormous opportunity to understand how genetic variants alter cellular behaviour in disease. The high diversity of mutations observed has however drawn a spotlight onto the need for predictive modelling of mutational effects on phenotype from variants of uncertain significance. This is particularly important in the clinic due to the potential value in guiding clinical diagnosis and patient treatment. Recent computational modelling has highlighted the importance of mutation induced protein misfolding as a common mechanism for loss of protein or domain function, aided by developments in methods that make large computational screens tractable. Here we review recent applications of this approach to different genes, and how they have enabled and supported subsequent studies. We further discuss developments in the approach and the role for the approach in light of increasingly high throughput experimental approaches.

Fixing Flavins: Hijacking a Flavin Transferase for Equipping Flavoproteins with a Covalent Flavin Cofactor

Most flavin-dependent enzymes contain a dissociable flavin cofactor. We present a new approach for installing in vivo a covalent bond between a flavin cofactor and its host protein. By using a flavin transferase and carving a flavinylation motif in target proteins, we demonstrate that "dissociable" flavoproteins can be turned into covalent flavoproteins. Specifically, four different flavin mononucleotide-containing proteins were engineered to undergo covalent flavinylation: a light-oxygen-voltage domain protein, a mini singlet oxygen generator, a nitroreductase, and an old yellow enzyme-type ene reductase. Optimizing the flavinylation motif and expression conditions led to the covalent flavinylation of all four flavoproteins. The engineered covalent flavoproteins retained function and often exhibited improved performance, such as higher thermostability or catalytic performance. The crystal structures of the designed covalent flavoproteins confirmed the designed threonyl-phosphate linkage. The targeted flavoproteins differ in fold and function, indicating that this method of introducing a covalent flavin-protein bond is a powerful new method to create flavoproteins that cannot lose their cofactor, boosting their performance.

Statistical modeling to quantify the uncertainty of FoldX-predicted protein folding and binding stability

BACKGROUND

Computational methods of predicting protein stability changes upon missense mutations are invaluable tools in high-throughput studies involving a large number of protein variants. However, they are limited by a wide variation in accuracy and difficulty of assessing prediction uncertainty. Using a popular computational tool, FoldX, we develop a statistical framework that quantifies the uncertainty of predicted changes in protein stability.

RESULTS

We show that multiple linear regression models can be used to quantify the uncertainty associated with FoldX prediction for individual mutations. Comparing the performance among models with varying degrees of complexity, we find that the model precision improves significantly when we utilize molecular dynamics simulation as part of the FoldX workflow. Based on the model that incorporates information from molecular dynamics, biochemical properties, as well as FoldX energy terms, we can generally expect upper bounds on the uncertainty of folding stability predictions of ± 2.9 kcal/mol and ± 3.5 kcal/mol for binding stability predictions. The uncertainty for individual mutations varies; our model estimates it using FoldX energy terms, biochemical properties of the mutated residue, as well as the variability among snapshots from molecular dynamics simulation.

CONCLUSIONS

Using a linear regression framework, we construct models to predict the uncertainty associated with FoldX prediction of stability changes upon mutation. This technique is straightforward and can be extended to other computational methods as well.

Fine spectral tuning of a flavin-binding fluorescent protein for multicolor imaging

Flavin-binding fluorescent proteins are promising genetically encoded tags for microscopy. However, spectral properties of their chromophores (riboflavin, flavin mononucleotide, and flavin adenine dinucleotide) are notoriously similar even between different protein families, which limits applications of flavoproteins in multicolor imaging. Here, we present a palette of 22 finely tuned fluorescent tags based on the thermostable LOV domain from Chloroflexus aggregans. We performed site saturation mutagenesis of three amino acid positions in the flavin-binding pocket, including the photoactive cysteine, to obtain variants with fluorescence emission maxima uniformly covering the wavelength range from 486 to 512 nm. We demonstrate three-color imaging based on spectral separation and two-color fluorescence lifetime imaging of bacteria, as well as two-color imaging of mammalian cells (HEK293T), using the proteins from the palette. These results highlight the possibility of fine spectral tuning of flavoproteins and pave the way for further applications of flavin-binding fluorescent proteins in fluorescence microscopy.

MutateX: an automated pipeline for in silico saturation mutagenesis of protein structures and structural ensembles

Mutations, which result in amino acid substitutions, influence the stability of proteins and their binding to biomolecules. A molecular understanding of the effects of protein mutations is both of biotechnological and medical relevance. Empirical free energy functions that quickly estimate the free energy change upon mutation (ΔΔG) can be exploited for systematic screenings of proteins and protein complexes. In silico saturation mutagenesis can guide the design of new experiments or rationalize the consequences of known mutations. Often software such as FoldX, while fast and reliable, lack the necessary automation features to apply them in a high-throughput manner. We introduce MutateX, a software to automate the prediction of ΔΔGs associated with the systematic mutation of each residue within a protein, or protein complex to all other possible residue types, using the FoldX energy function. MutateX also supports ΔΔG calculations over protein ensembles, upon post-translational modifications and in multimeric assemblies. At the heart of MutateX lies an automated pipeline engine that handles input preparation, parallelization and outputs publication-ready figures. We illustrate the MutateX protocol applied to different case studies. The results of the high-throughput scan provided by our tools can help in different applications, such as the analysis of disease-associated mutations, to complement experimental deep mutational scans, or assist the design of variants for industrial applications. MutateX is a collection of Python tools that relies on open-source libraries. It is available free of charge under the GNU General Public License from https://github.com/ELELAB/mutatex.

Extreme dependence of Chloroflexus aggregans LOV domain thermo- and photostability on the bound flavin species

Light-oxygen-voltage (LOV) domains are common photosensory modules that found many applications in fluorescence microscopy and optogenetics. Here, we show that the Chloroflexus aggregans LOV domain can bind different flavin species (lumichrome, LC; riboflavin, RF; flavin mononucleotide, FMN; flavin adenine dinucleotide, FAD) during heterologous expression and that its physicochemical properties depend strongly on the nature of the bound flavin. We show that whereas the dissociation constants for different chromophores are similar, the melting temperature of the protein reconstituted with single flavin species varies from ~ 60 °C for LC to ~ 81 °C for FMN, and photobleaching half-times vary almost 100-fold. These observations serve as a caution for future studies of LOV domains in non-native conditions yet raise the possibility of fine-tuning various properties of LOV-based fluorescent probes and optogenetic tools by manipulating the chromophore composition.

Effects of Proline Substitutions on the Thermostable LOV Domain from Chloroflexus aggregans

Light-oxygen-voltage (LOV) domains are ubiquitous photosensory modules found in proteins from bacteria, archaea and eukaryotes. Engineered versions of LOV domains have found widespread use in fluorescence microscopy and optogenetics, with improved versions being continuously developed. Many of the engineering efforts focused on the thermal stabilization of LOV domains. Recently, we described a naturally thermostable LOV domain from Chloroflexus aggregans. Here we show that the discovered protein can be further stabilized using proline substitution. We tested the effects of three mutations, and found that the melting temperature of the A95P mutant is raised by approximately 2 °C, whereas mutations A56P and A58P are neutral. To further evaluate the effects of mutations, we crystallized the variants A56P and A95P, while the variant A58P did not crystallize. The obtained crystal structures do not reveal any alterations in the proteins other than the introduced mutations. Molecular dynamics simulations showed that mutation A58P alters the structure of the respective loop (Aβ-Bβ), but does not change the general structure of the protein. We conclude that proline substitution is a viable strategy for the stabilization of the Chloroflexus aggregans LOV domain. Since the sequences and structures of the LOV domains are overall well-conserved, the effects of the reported mutations may be transferable to other proteins belonging to this family.

Beyond the Green Fluorescent Protein: Biomolecular Reporters for Anaerobic and Deep-Tissue Imaging

Fluorescence imaging represents cornerstone technology for studying biological function at the cellular and molecular levels. The technology's centerpiece is a prolific collection of genetic reporters based on the green fluorescent protein (GFP) and related analogs. More than two decades of protein engineering have endowed the GFP repertoire with an incredible assortment of fluorescent proteins, allowing scientists immense latitude in choosing reporters tailored to various cellular and environmental contexts. Nevertheless, GFP and derivative reporters have specific limitations that hinder their unrestricted use for molecular imaging. These challenges have inspired the development of new reporter proteins and imaging mechanisms. Here, we review how these developments are expanding the frontiers of reporter gene techniques to enable nondestructive studies of cell function in anaerobic environments and deep inside intact animals-two important biological contexts that are fundamentally incompatible with the use of GFP-based reporters.

A thermostable flavin-based fluorescent protein from Chloroflexus aggregans: a framework for ultra-high resolution structural studies

A new thermostable fluorescent protein is shown to be a promising model for ultra-high resolution structural studies of LOV domains and for application as a fluorescent reporter.

FoldX as Protein Engineering Tool: Better Than Random Based Approaches?

Improving protein stability is an important goal for basic research as well as for clinical and industrial applications but no commonly accepted and widely used strategy for efficient engineering is known. Beside random approaches like error prone PCR or physical techniques to stabilize proteins, e.g. by immobilization, in silico approaches are gaining more attention to apply target-oriented mutagenesis. In this review different algorithms for the prediction of beneficial mutation sites to enhance protein stability are summarized and the advantages and disadvantages of FoldX are highlighted. The question whether the prediction of mutation sites by the algorithm FoldX is more accurate than random based approaches is addressed.

Improving the thermostability by introduction of arginines on the surface of α-L-rhamnosidase (r-Rha1) from Aspergillus niger

To improve the thermostability of α-L-rhamnosidase (r-Rha1), an enzyme previously identified from Aspergillus niger JMU-TS528, multiple arginine (Arg) residues were introduced into the r-Rha1 sequence to replace several lysine (Lys) residues that located on the surface of the folded r-Rha1. Hinted by in silico analysis, five surface Lys residues (K134, K228, K406, K440, K573) were targeted to produce a list of 5 single-residue mutants and 4 multiple-residue mutants using site-directed mutagenesis. Among these mutants, a double Lys to Arg mutant, i.e. K406R/K573R, showed the best thermostability improvement. The half-life of this mutant's enzyme activity increased 3 h at 60 °C, 23 min at 65 °C, and 3.5 min at 70 °C, when compared with the wild type. The simulated protein structure based interaction analysis and molecular dynamics calculation indicate that the thermostability improvement of the mutant K406R-K573R was possibly due to the extra hydrogen bonds, the additional cation-π interactions, and the relatively compact conformation. With the enhanced thermostability, the α-L-rhamnosidase mutant, K406R-K573R, has potentially broadened the r-Rha1 applications in food processing industry.

Computational tools help improve protein stability but with a solubility tradeoff

Accurately predicting changes in protein stability upon amino acid substitution is a much sought after goal. Destabilizing mutations are often implicated in disease, whereas stabilizing mutations are of great value for industrial and therapeutic biotechnology. Increasing protein stability is an especially challenging task, with random substitution yielding stabilizing mutations in only ∼2% of cases. To overcome this bottleneck, computational tools that aim to predict the effect of mutations have been developed; however, achieving accuracy and consistency remains challenging. Here, we combined 11 freely available tools into a meta-predictor (meieringlab.uwaterloo.ca/stabilitypredict/). Validation against ∼600 experimental mutations indicated that our meta-predictor has improved performance over any of the individual tools. The meta-predictor was then used to recommend 10 mutations in a previously designed protein of moderate thermodynamic stability, ThreeFoil. Experimental characterization showed that four mutations increased protein stability and could be amplified through ThreeFoil's structural symmetry to yield several multiple mutants with >2-kcal/mol stabilization. By avoiding residues within functional ties, we could maintain ThreeFoil's glycan-binding capacity. Despite successfully achieving substantial stabilization, however, almost all mutations decreased protein solubility, the most common cause of protein design failure. Examination of the 600-mutation data set revealed that stabilizing mutations on the protein surface tend to increase hydrophobicity and that the individual tools favor this approach to gain stability. Thus, whereas currently available tools can increase protein stability and combining them into a meta-predictor yields enhanced reliability, improvements to the potentials/force fields underlying these tools are needed to avoid gaining protein stability at the cost of solubility.

Recent Advances in Development of Genetically Encoded Fluorescent Sensors

Genetically encoded fluorescent sensors are essential tools in modern biological research, and recent advances in fluorescent proteins (FPs) have expanded the scope of sensor design and implementation. In this review we compare different sensor platforms, including Förster resonance energy transfer (FRET) sensors, fluorescence-modulated single FP-based sensors, translocation sensors, complementation sensors, and dimerization-based sensors. We discuss elements of sensor design and engineering for each platform, including the incorporation of new types of FPs and sensor screening techniques. Finally, we summarize the wide range of sensors in the literature, exploring creative new sensor architectures suitable for different applications.

Effect of Oxidative Damage on the Stability and Dimerization of Superoxide Dismutase 1

During their life cycle, proteins are subject to different modifications involving reactive oxygen species. Such oxidative damage to proteins may lead to the formation of insoluble aggregates and cytotoxicity and is associated with age-related disorders including neurodegenerative diseases, cancer, and diabetes. Superoxide dismutase 1 (SOD1), a key antioxidant enzyme in human cells, is particularly susceptible to such modifications. Moreover, this homodimeric metalloenzyme has been directly linked to both familial and sporadic amyotrophic lateral sclerosis (ALS), a devastating, late-onset motor neuronal disease, with more than 150 ALS-related mutations in the SOD1 gene. Importantly, oxidatively damaged SOD1 aggregates have been observed in both familial and sporadic forms of the disease. However, the molecular mechanisms as well as potential implications of oxidative stress in SOD1-induced cytotoxicity remain elusive. In this study, we examine the effects of oxidative modification on SOD1 monomer and homodimer stability, the key molecular properties related to SOD1 aggregation. We use molecular dynamics simulations in combination with thermodynamic integration to study microscopic-level site-specific effects of oxidative "mutations" at the dimer interface, including lysine, arginine, proline and threonine carbonylation, and cysteine oxidation. Our results show that oxidative damage of even single residues at the interface may drastically destabilize the SOD1 homodimer, with several modifications exhibiting a comparable effect to that of the most drastic ALS-causing mutations known. Additionally, we show that the SOD1 monomer stability decreases upon oxidative stress, which may lead to partial local unfolding and consequently to increased aggregation propensity. Importantly, these results suggest that oxidative stress may play a key role in development of ALS, with the mutations in the SOD1 gene being an additional factor.

Mapping networks of light-dark transition in LOV photoreceptors

MOTIVATION

In optogenetics, designing modules of long or short signaling state lifetime is necessary for control over precise cellular events. A critical parameter for designing artificial or synthetic photoreceptors is the signaling state lifetime of photosensor modules. Design and engineering of biologically relevant artificial photoreceptors is based on signaling mechanisms characteristic of naturally occurring photoreceptors. Therefore identifying residues important for light-dark transition is a definite first step towards rational design of synthetic photoreceptors. A thorough grasp of detailed mechanisms of photo induced signaling process would be immensely helpful in understanding the behaviour of organisms.

RESULTS

Herein, we introduce the technique of differential networks. We identify key biological interactions, using light-oxygen-voltage domains of all organisms whose dark and light state crystal structures are simultaneously available. Even though structural differences between dark and light states are subtle (other than the covalent bond formation between flavin chromophore and active site Cysteine), our results successfully capture functionally relevant residues and are in complete agreement with experimental findings from literature. Additionally, using sequence-structure alignments, we predict functional significance of interactions found to be important from network perspective yet awaiting experimental validation. Our approach would not only help in minimizing extensive photo-cycle kinetics procedure but is also helpful in providing first-hand information on the fundamentals of photo-adaptation and rational design of synthetic photoreceptors in optogenetics.

CONTACT

devrani.dbs@presiuniv.ac.in or soumen@jcbose.ac.in

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Rigidity Emerges during Antibody Evolution in Three Distinct Antibody Systems: Evidence from QSFR Analysis of Fab Fragments

The effects of somatic mutations that transform polyspecific germline (GL) antibodies to affinity mature (AM) antibodies with monospecificity are compared among three GL-AM Fab pairs. In particular, changes in conformational flexibility are assessed using a Distance Constraint Model (DCM). We have previously established that the DCM can be robustly applied across a series of antibody fragments (VL to Fab), and subsequently, the DCM was combined with molecular dynamics (MD) simulations to similarly characterize five thermostabilizing scFv mutants. The DCM is an ensemble based statistical mechanical approach that accounts for enthalpy/entropy compensation due to network rigidity, which has been quite successful in elucidating conformational flexibility and Quantitative Stability/Flexibility Relationships (QSFR) in proteins. Applied to three disparate antibody systems changes in QSFR quantities indicate that the VH domain is typically rigidified, whereas the VL domain and CDR L2 loop become more flexible during affinity maturation. The increase in CDR H3 loop rigidity is consistent with other studies in the literature. The redistribution of conformational flexibility is largely controlled by nonspecific changes in the H-bond network, although certain Arg to Asp salt bridges create highly localized rigidity increases. Taken together, these results reveal an intricate flexibility/rigidity response that accompanies affinity maturation.

Antibodies are protective proteins used by the immune system to recognize and neutralize foreign objects through interactions with a specific part of the target, called an antigen. Antibody structures are Y-shaped, contain multiple protein chains, and include two antigen-binding sites. The binding sites are located at the end of the Fab fragments, which are the upward facing arms of the Y-structure. The Fab fragments maintain binding affinity by themselves, and are thus often used as surrogates to student antibody-antigen interactions. High affinity antibodies are produced during the course of an immune response by successive mutations to germline gene-encoded antibodies. Germline antibodies are more likely to be polyspecific, whereas the affinity maturation process yields monoclonal antibodies that bind specifically to the target antigen. In this work, we use a computational Distance Constraint Model to characterize how mechanical properties change as three disparate germline antibodies are converted to affinity mature. Our results reveal a rich set of mechanical responses throughout the Fab structure. Nevertheless, increased rigidity in the VH domain is consistently observed, which is consistent with the transition from polyspecificity to monospecificity. That is, flexible antibody structures are able to recognize multiple antigens, while increased affinity and specificity is achieved—in part—by structural rigidification.

Rapid Computational Prediction of Thermostabilizing Mutations for G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) are highly dynamic and often denature when extracted in detergents. Deriving thermostable mutants has been a successful strategy to stabilize GPCRs in detergents, but this process is experimentally tedious. We have developed a computational method to predict the position of the thermostabilizing mutations for a given GPCR sequence. We have validated the method against experimentally measured thermostability data for single mutants of the β₁-adrenergic receptor (β₁AR), adenosine A_2A receptor (A_2AR) and neurotensin receptor 1 (NTSR1). To make these predictions we started from homology models of these receptors of varying accuracies and generated an ensemble of conformations by sampling the rigid body degrees of freedom of transmembrane helices. Then, an all-atom force field function was used to calculate the enthalpy gain, known as the “stability score” upon mutation of every residue, in these receptor structures, to alanine. For all three receptors, β₁AR, A_2AR, and NTSR1, we observed that mutations of hydrophobic residues in the transmembrane domain to alanine that have high stability scores correlate with high experimental thermostability. The prediction using the stability score improves when using an ensemble of receptor conformations compared to a single structure, showing that receptor flexibility is important. We also find that our previously developed LITiCon method for generating conformation ensembles is similar in performance to predictions using ensembles obtained from microseconds of molecular dynamics simulations (which is computationally hundred times slower than LITiCon). We improved the thermostability prediction by including other properties such as residue-based stress and the extent of allosteric communication by each residue in the stability score. Our method is the first step toward a computational method for rapid prediction of thermostable mutants of GPCRs.

Computational library design for increasing haloalkane dehalogenase stability

We explored the use of a computational design framework for the stabilization of the haloalkane dehalogenase LinB. Energy calculations, disulfide bond design, molecular dynamics simulations, and rational inspection of mutant structures predicted many stabilizing mutations. Screening of these in small mutant libraries led to the discovery of seventeen point mutations and one disulfide bond that enhanced thermostability. Mutations located in or contacting flexible regions of the protein had a larger stabilizing effect than mutations outside such regions. The combined introduction of twelve stabilizing mutations resulted in a LinB mutant with a 23 °C increase in apparent melting temperature (Tm,app , 72.5 °C) and an over 200-fold longer half-life at 60 °C. The most stable LinB variants also displayed increased compatibility with co-solvents, thus allowing substrate conversion and kinetic resolution at much higher concentrations than with the wild-type enzyme.

Exploring the functional residues in a flavin-binding fluorescent protein using deep mutational scanning

Flavin mononucleotide (FMN)-based fluorescent proteins are versatile reporters that can monitor various cellular processes in both aerobic and anaerobic conditions. However, the understanding of the role of individual amino acid residues on the protein function has been limited and has restricted the development of better functional variants. Here we examine the functional amino acid residues of Escherichia coli flavin mononucleotide binding fluorescent protein (EcFbFP) using the application of high-throughput sequencing of functional variants, termed deep mutational scanning. The variants were classified into 329 function-retained (FR) and 259 function-loss (FL) mutations, and further the mutational enrichment in each amino acid residues was weighed to find the functionally important residues of EcFbFP. We show that the crucial amino acid residues of EcFbFP lie among the FMN-binding pocket, turns and loops of the protein where conformation changes occur, and spatially clustered residues near the E56-K97 salt bridges. In addition, the mutational sensitivity of the critical residues was confirmed by site-directed mutagenesis. The deep mutational scanning of EcFbFP has demonstrated important implications for constructing better functioning protein variants.

The photophysics of LOV-based fluorescent proteins – new tools for cell biology

In this study photophysical characteristics of LOV-based fluorescent proteins which are essential for analytic methods as well as imaging approaches have been comparatively analyzed in detail.