TKSA-MC: A web server for rational mutation through the optimization of protein charge interactions

Seq2Topt: a sequence-based deep learning predictor of enzyme optimal temperature

An accurate deep learning predictor is needed for enzyme optimal temperature (${T}_{opt}$), which quantitatively describes how temperature affects the enzyme catalytic activity. In comparison with existing models, a new model developed in this study, Seq2Topt, reached a superior accuracy on ${T}_{opt}$ prediction just using protein sequences (RMSE = 12.26°C and R2 = 0.57), and could capture key protein regions for enzyme ${T}_{opt}$ with multi-head attention on residues. Through case studies on thermophilic enzyme selection and predicting enzyme ${T}_{opt}$ shifts caused by point mutations, Seq2Topt was demonstrated as a promising computational tool for enzyme mining and in-silico enzyme design. Additionally, accurate deep learning predictors of enzyme optimal pH (Seq2pHopt, RMSE = 0.88 and R2 = 0.42) and melting temperature (Seq2Tm, RMSE = 7.57 °C and R2 = 0.64) were developed based on the model architecture of Seq2Topt, suggesting that the development of Seq2Topt could potentially give rise to a useful prediction platform of enzymes.

Enhancement of the Thermostability of Microbacterium Esterase by Combinatorial Rational Design

The esterase EstSIT01 from Microbacterium can catalyze the asymmetric hydrolysis of meso-dimethyl ester to produce the crucial chiral intermediate (4S, 5R)-hemimethyl ester for d-biotin synthesis. Despite its high yields and stereoselectivity, the low thermostability of EstSIT01 limits its practical application. Herein, two kinds of rational strategies were combined to enhance the thermostability of EstSIT01. Based on the Surface Residue Substitution (SRS) method, two variants (G215A and G316A) with improved thermostability and one mutant (G293A) with superior activity were identified from nine candidates. According to the Consensus Mutation method, two mutants (E301P and A332P) with enhanced thermostability were identified from six candidates. However, the combined mutation failed to yield mutants surpassing the best single mutant, E301P, in terms of thermostability. The combined mutant E301P/G215A and E301P/G215A/G293A exhibited a slight enhancement in enzyme activity relative to E301P, while also exhibiting improved thermostability compared to the wild-type EstSIT01. Compared with the wild-type esterase, the thermal inactivation half-lives (t1/2) of mutant E301P were enhanced 1.4-fold, 2.4-fold and 1.8-fold at 45 °C, 55 °C, and 65 °C, respectively. The optimal reaction temperature and pH for mutant E301P remained consistent with those of the wild type, at 40 °C and 10.0, respectively. The Km of E301P was 0.22 ± 0.03 mM and the kcat was 5.1 ± 0.28 s-1. Further analysis indicated that the free energies of G215A, G293A and E301P were decreased by 0.91, 0.308 and 1.1049 kcal/mol, respectively, compared to the wild-type EstSIT01. The interaction analysis revealed that the substitution of glutamic acid with proline at position 301 enhanced the hydrophobic interactions within the protein. The decreased free energies and the increased hydrophobic interactions were well correlated with the enhanced stability in these mutants.

Exploring the molecular pathways of the activation process in PPARγ recurrent bladder cancer mutants

The intricate involvement of Peroxisome Proliferator-Activated Receptor Gamma (PPARγ) in glucose homeostasis and adipogenesis is well-established. However, its role in cancer, particularly luminal bladder cancer, remains debated. The overexpression and activation of PPARγ are implicated in tumorigenesis. Specific gain-of-function mutations (M280I, I290M, and T475M) within the ligand-binding domain of PPARγ are associated with bladder cancer and receptor activation. The underlying molecular pathways prompted by these mutations remain unclear. We employed a dual-basin structure-based model (db-SBM) to explore the conformational dynamics between the inactive and active states of PPARγ and examined the effects of the M280I, I290M, and T475M mutations. Our findings, consistent with the existing literature, reveal heightened ligand-independent transcriptional activity in the I290M and T475M mutants. Both mutants showed enhanced stabilization of the active state compared to the wild-type receptor, with the I290M mutation promoting a specific transition route, making it a prime candidate for further study. Electrostatic analysis identified residues K303 and E488 as pivotal in the I290M activation cascade. Biophysical assays confirmed that disrupting the K303-E488 interaction reduced the thermal stabilization characteristic of the I290M mutation. Our study demonstrates the predictive capabilities of combining simulation and cheminformatics methods, validated by biochemical experiments, to gain insights into molecular activation mechanisms and identify target residues for protein modulation.

Molecular insights into TP53 mutation (p. Arg267Trp) and its connection to Choroid Plexus Carcinomas and Li-Fraumeni Syndrome

BACKGROUND

Choroid plexus carcinomas (CPCs) are rare malignant tumors primarily affecting pediatric patients and often co-occur with Li-Fraumeni Syndrome (LFS), an inherited predisposition to early-onset malignancies in multiple organ systems. LFS is closely linked to TP53 mutations, with germline TP53 gene mutations present in approximately 75% of Li-Fraumeni syndrome families and 25% of Li-Fraumeni-like syndrome families. Individuals with TP53 mutations also have an elevated probability of carrying mutations in BRCA1 and BRCA2 genes.

OBJECTIVE

To investigate the structural and functional implications of the TP53: 799C > T, p. (Arg267Trp) missense mutation, initially identified in a Saudi family, and understand its impact on TP53 functionality and related intermolecular interactions.

METHODS

Computational analyses were conducted to examine the structural modifications resulting from the TP53: 799C > T, p. (Arg267Trp) mutation. These analyses focused on the mutation's impact on hydrogen bonding, ionic interactions, and the specific interaction with Cell Cycle and Apoptosis Regulator 2 (CCAR2), as annotated in UniProt.

RESULTS

The study revealed that the native Arg267 residue is critical for a salt bridge interaction with glutamic acid at position 258. The mutation-induced charge alteration has the potential to disrupt this ionic bonding. Additionally, the mutation is located within an amino acid region crucial for interaction with CCAR2. The altered properties of the amino acid within this domain may affect its functionality and disrupt this interaction, thereby impacting the regulation of catalytic enzyme activity.

CONCLUSIONS

Our findings highlight the intricate intermolecular interactions governing TP53 functionality. The TP53: 799C > T, p. (Arg267Trp) mutation causes structural modifications that potentially disrupt critical ionic bonds and protein interactions, offering valuable insights for the development of targeted mutants with distinct functional attributes. These insights could inform therapeutic strategies for conditions associated with TP53 mutations.

Rational design engineering of a more thermostable Sulfurihydrogenibium yellowstonense carbonic anhydrase for potential application in carbon dioxide capture technologies

Implementing hyperthermostable carbonic anhydrases into CO2 capture and storage technologies in order to increase the rate of CO2 absorption from the industrial flue gases is of great importance from technical and economical points of view. The present study employed a combination of in silico tools to further improve thermostability of a known thermostable carbonic anhydrase from Sulfurihydrogenibium yellowstonense. Experimental results showed that our rationally engineered K100G mutant not only retained the overall structure and catalytic efficiency but also showed a 3 °C increase in the melting temperature and a two-fold improvement in the enzyme half-life at 85 °C. Based on the molecular dynamics simulation results, rearrangement of salt bridges and hydrogen interactions network causes a reduction in local flexibility of the K100G variant. In conclusion, our study demonstrated that thermostability can be improved through imposing local structural rigidity by engineering a single-point mutation on the surface of the enzyme.

Recent advances in structure-based enzyme engineering for functional reconstruction

Structural information can help engineer enzymes. Usually, specific amino acids in particular regions are targeted for functional reconstruction to enhance the catalytic performance, including activity, stereoselectivity, and thermostability. Appropriate selection of target sites is the key to structure-based design, which requires elucidation of the structure-function relationships. Here, we summarize the mutations of residues in different specific regions, including active center, access tunnels, and flexible loops, on fine-tuning the catalytic performance of enzymes, and discuss the effects of altering the local structural environment on the functions. In addition, we keep up with the recent progress of structure-based approaches for enzyme engineering, aiming to provide some guidance on how to take advantage of the structural information.

FireProt 2.0: web-based platform for the fully automated design of thermostable proteins

Thermostable proteins find their use in numerous biomedical and biotechnological applications. However, the computational design of stable proteins often results in single-point mutations with a limited effect on protein stability. However, the construction of stable multiple-point mutants can prove difficult due to the possibility of antagonistic effects between individual mutations. FireProt protocol enables the automated computational design of highly stable multiple-point mutants. FireProt 2.0 builds on top of the previously published FireProt web, retaining the original functionality and expanding it with several new stabilization strategies. FireProt 2.0 integrates the AlphaFold database and the homology modeling for structure prediction, enabling calculations starting from a sequence. Multiple-point designs are constructed using the Bron-Kerbosch algorithm minimizing the antagonistic effect between the individual mutations. Users can newly limit the FireProt calculation to a set of user-defined mutations, run a saturation mutagenesis of the whole protein or select rigidifying mutations based on B-factors. Evolution-based back-to-consensus strategy is complemented by ancestral sequence reconstruction. FireProt 2.0 is significantly faster and a reworked graphical user interface broadens the tool's availability even to users with older hardware. FireProt 2.0 is freely available at http://loschmidt.chemi.muni.cz/fireprotweb.

Unveiling mutation effects on the structural dynamics of the main protease from SARS-CoV-2 with hybrid simulation methods

The main protease of SARS-CoV-2 (called M^pro or 3CL^pro) is essential for processing polyproteins encoded by viral RNA. Several M^pro mutations were found in SARS-CoV-2 variants, which are related to higher transmissibility, pathogenicity, and resistance to neutralization antibodies. Macromolecules adopt several favored conformations in solution depending on their structure and shape, determining their dynamics and function. In this study, we used a hybrid simulation method to generate intermediate structures along the six lowest frequency normal modes and sample the conformational space and characterize the structural dynamics and global motions of WT SARS-CoV-2 M^pro and 48 mutations, including mutations found in P.1, B.1.1.7, B.1.351, B.1.525 and B.1.429+B.1.427 variants. We tried to contribute to the elucidation of the effects of mutation in the structural dynamics of SARS-CoV-2 M^pro. A machine learning analysis was performed following the investigation regarding the influence of the K90R, P99L, P108S, and N151D mutations on the dimeric interface assembling of the SARS-CoV-2 M^pro. The parameters allowed the selection of potential structurally stable dimers, which demonstrated that some single surface aa substitutions not located at the dimeric interface (K90R, P99L, P108S, and N151D) are able to induce significant quaternary changes. Furthermore, our results demonstrated, by a Quantum Mechanics method, the influence of SARS-CoV-2 M^pro mutations on the catalytic mechanism, confirming that only one of the chains of the WT and mutant SARS-CoV-2 M^pros are prone to cleave substrates. Finally, it was also possible to identify the aa residue F140 as an important factor related to the increasing enzymatic reactivity of a significant number of SARS-CoV-2 M^pro conformations generated by the normal modes-based simulations.

Probing the Energy Landscape of Spectrin R15 and R16 and the Effects of Non-native Interactions

Understanding the details of a protein folding mechanism can be a challenging and complex task. One system with an interesting folding behavior is the α-spectrin domain, where the R15 folds three-orders of magnitude faster than its homologues R16 and R17, despite having similar structures. The molecular origins that explain these folding rate differences remain unclear, but our previous work revealed that a combined effect produced by non-native interactions could be a reasonable cause for these differences. In this study, we explore further the folding process by identifying the molecular paths, metastable states, and the collective motions that lead these unfolded proteins to their native state conformation. Our results uncovered the differences between the folding pathways for the wild-type R15 and R16 and an R16 mutant. The metastable ensembles that speed down the folding were identified using an energy landscape visualization method (ELViM). These ensembles correspond to similar experimentally reported configurations. Our observations indicate that the non-native interactions are also associated with secondary structure misdocking. This computational methodology can be used as a fast, straightforward protocol for shedding light on systems with unclear folding or conformational traps.

Protein engineering via Bayesian optimization-guided evolutionary algorithm and robotic experiments

Directed protein evolution applies repeated rounds of genetic mutagenesis and phenotypic screening and is often limited by experimental throughput. Through in silico prioritization of mutant sequences, machine learning has been applied to reduce wet lab burden to a level practical for human researchers. On the other hand, robotics permits large batches and rapid iterations for protein engineering cycles, but such capacities have not been well exploited in existing machine learning-assisted directed evolution approaches. Here, we report a scalable and batched method, Bayesian Optimization-guided EVOlutionary (BO-EVO) algorithm, to guide multiple rounds of robotic experiments to explore protein fitness landscapes of combinatorial mutagenesis libraries. We first examined various design specifications based on an empirical landscape of protein G domain B1. Then, BO-EVO was successfully generalized to another empirical landscape of an Escherichia coli kinase PhoQ, as well as simulated NK landscapes with up to moderate epistasis. This approach was then applied to guide robotic library creation and screening to engineer enzyme specificity of RhlA, a key biosynthetic enzyme for rhamnolipid biosurfactants. A 4.8-fold improvement in producing a target rhamnolipid congener was achieved after examining less than 1% of all possible mutants after four iterations. Overall, BO-EVO proves to be an efficient and general approach to guide combinatorial protein engineering without prior knowledge.

A coarse-grained xDLVO model for colloidal protein-protein interactions

Colloidal protein-protein interactions (PPIs) of attractive and repulsive nature modulate the solubility of proteins, their aggregation, precipitation and crystallization. Such interactions are very important for many biotechnological processes, but are complex and hard to control, therefore, difficult to be understood in terms of measurements alone. In diluted protein solutions, PPIs can be estimated from the osmotic second virial coefficient, B22, which has been calculated using different methods and levels of theory. The most popular approach is based on the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory and its extended versions, i.e. xDLVO. Despite much efforts, these models are not fully quantitative and must be fitted to experiments, which limits their predictive value. Here, we report an extended xDLVO-CG model, which extends existing models by a coarse-grained representation of proteins and the inclusion of an additional ion-protein dispersion interaction term. We demonstrate for four proteins, i.e. lysozyme (LYZ), subtilisin (Subs), bovine serum albumin (BSA) and immunoglobulin (IgG1), that semi-quantitative agreement with experimental values without the need to fit to experimental B22 values. While most likely not the final step in the nearly hundred years of research in PPIs, xDLVO-CG is a step towards predictive PPIs calculations that are transferable to different proteins.

Recent advances in user-friendly computational tools to engineer protein function

Progress in technology and algorithms throughout the past decade has transformed the field of protein design and engineering. Computational approaches have become well-engrained in the processes of tailoring proteins for various biotechnological applications. Many tools and methods are developed and upgraded each year to satisfy the increasing demands and challenges of protein engineering. To help protein engineers and bioinformaticians navigate this emerging wave of dedicated software, we have critically evaluated recent additions to the toolbox regarding their application for semi-rational and rational protein engineering. These newly developed tools identify and prioritize hotspots and analyze the effects of mutations for a variety of properties, comprising ligand binding, protein-protein and protein-nucleic acid interactions, and electrostatic potential. We also discuss notable progress to target elusive protein dynamics and associated properties like ligand-transport processes and allosteric communication. Finally, we discuss several challenges these tools face and provide our perspectives on the further development of readily applicable methods to guide protein engineering efforts.

Improving the Thermostability of Xylanase A from Bacillus subtilis by Combining Bioinformatics and Electrostatic Interactions Optimization

The rational improvement of the enzyme catalytic activity is one of the most significant challenges in biotechnology. Most conventional strategies used to engineer enzymes involve selecting mutations to increase their thermostability. Determining good criteria for choosing these substitutions continues to be a challenge. In this work, we combine bioinformatics, electrostatic analysis, and molecular dynamics to predict beneficial mutations that may improve the thermostability of XynA from Bacillus subtilis. First, the Tanford-Kirkwood surface accessibility method is used to characterize each ionizable residue contribution to the protein native state stability. Residues identified to be destabilizing were mutated with the corresponding residues determined by the consensus or ancestral sequences at the same locations. Five mutants (K99T/N151D, K99T, S31R, N151D, and K154A) were investigated and compared with 12 control mutants derived from experimental approaches from the literature. Molecular dynamics results show that the mutants exhibited folding temperatures in the order K99T > K99T/N151D > S31R > N151D > WT > K154A. The combined approaches employed provide an effective strategy for low-cost enzyme optimization needed for large-scale biotechnological and medical applications.

Electrostatic interaction optimization improves catalytic rates and thermotolerance on xylanases

Understanding the aspects that contribute to improving proteins' biochemical properties is of high relevance for protein engineering. Properties such as the catalytic rate, thermal stability, and thermal resistance are crucial for applying enzymes in the industry. Different interactions can influence those biochemical properties of an enzyme. Among them, the surface charge-charge interactions have been a target of particular attention. In this study, we employ the Tanford-Kirkwood solvent accessibility model using the Monte Carlo algorithm (TKSA-MC) to predict possible interactions that could improve stability and catalytic rate of a WT xylanase (XynA^WT) and its M6 xylanase (XynA^M6) mutant. The modeling prediction indicates that mutating from a lysine in position 99 to a glutamic acid (K99E) favors the native state stabilization in both xylanases. Our lab results showed that mutated xylanases had their thermotolerance and catalytic rate increased, which conferred higher processivity of delignified sugarcane bagasse. The TKSA-MC approach employed here is presented as an efficient computational-based design strategy that can be applied to improve the thermal resistance of enzymes with industrial and biotechnological applications.

Web-based tools for computational enzyme design

Enzymes are in high demand for very diverse biotechnological applications. However, natural biocatalysts often need to be engineered for fine-tuning their properties towards the end applications, such as the activity, selectivity, stability to temperature or co-solvents, and solubility. Computational methods are increasingly used in this task, providing predictions that narrow down the space of possible mutations significantly and can enormously reduce the experimental burden. Many computational tools are available as web-based platforms, making them accessible to non-expert users. These platforms are typically user-friendly, contain walk-throughs, and do not require deep expertise and installations. Here we describe some of the most recent outstanding web-tools for enzyme engineering and formulate future perspectives in this field.

Predicting the stability of mutant proteins by computational approaches: an overview

A very large number of computational methods to predict the change in thermodynamic stability of proteins due to mutations have been developed during the last 30 years, and many different web servers are currently available. Nevertheless, most of them suffer from severe drawbacks that decrease their general reliability and, consequently, their applicability to different goals such as protein engineering or the predictions of the effects of mutations in genetic diseases. In this review, we have summarized all the main approaches used to develop these tools, with a survey of the web servers currently available. Moreover, we have also reviewed the different assessments made during the years, in order to allow the reader to check directly the different performances of these tools, to select the one that best fits his/her needs, and to help naïve users in finding the best option for their needs.

The Role of Electrostatics and Folding Kinetics on the Thermostability of Homologous Cold Shock Proteins

Understanding which aspects contribute to the thermostability of proteins is a challenge that has persisted for decades, and it is of great relevance for protein engineering. Several types of interactions can influence the thermostability of a protein. Among them, the electrostatic interactions have been a target of particular attention. Aiming to explore how this type of interaction can affect protein thermostability, this paper investigated four homologous cold shock proteins from psychrophilic, mesophilic, thermophilic, and hyperthermophilic organisms using a set of theoretical methodologies. It is well-known that electrostatics as well as hydrophobicity are key-elements for the stabilization of these proteins. Therefore, both interactions were initially analyzed in the native structure of each protein. Electrostatic interactions present in the native structures were calculated with the Tanford-Kirkwood model with solvent accessibility, and the amount of hydrophobic surface area buried upon folding was estimated by measuring both folded and extended structures. On the basis of Energy Landscape Theory, the local frustration and the simplified alpha-carbon structure-based model were modeled with a Debye-Hückel potential to take into account the electrostatics and the effects of an implicit solvent. Thermodynamic data for the structure-based model simulations were collected and analyzed using the Weighted Histogram Analysis and Stochastic Diffusion methods. Kinetic quantities including folding times, transition path times, folding routes, and Φ values were also obtained. As a result, we found that the methods are able to qualitatively infer that electrostatic interactions play an important role on the stabilization of the most stable thermophilic cold shock proteins, showing agreement with the experimental data.

pH and Charged Mutations Modulate Cold Shock Protein Folding and Stability: A Constant pH Monte Carlo Study

The folding and stability of proteins is a fundamental problem in several research fields. In the present paper, we have used different computational approaches to study the effects caused by changes in pH and for charged mutations in cold shock proteins from Bacillus subtilis (Bs-CspB). First, we have investigated the contribution of each ionizable residue for these proteins to their thermal stability using the TKSA-MC, a Web server for rational mutation via optimizing the protein charge interactions. Based on these results, we have proposed a new mutation in an already optimized Bs-CspB variant. We have evaluated the effects of this new mutation in the folding energy landscape using structure-based models in Monte Carlo simulation at constant pH, SBM-CpHMC. Our results using this approach have indicated that the charge rearrangements already in the unfolded state are critical to the thermal stability of Bs-CspB. Furthermore, the conjunction of these simplified methods was able not only to predict stabilizing mutations in different pHs but also to provide essential information about their effects in each stage of protein folding.

Rational Design of Chymotrypsin Inhibitor 2 by Optimizing Non-Native Interactions

Rational design of proteins via mutagenesis is crucial for several biotechnological applications. A significant challenge of the computational strategies used to predict optimized mutations is to understand the influence of each amino acid during the folding process. In the present work, chymotrypsin inhibitor 2 (CI2) and several of its designed mutants have been simulated using a non-native hydrophobic and electrostatic potential as a structure-based Cα model. Through these simulations, we could identify the most critical folding stage to accelerate CI2 and also the charged residues responsible for providing its thermostability. The replacement of ionizable residues for hydrophobic ones tended to promote the formation of the CI2 secondary structure in the early transition state, which speeds up folding. However, this same replacement destabilized the native structure, and there was a decrease in the protein thermostability. Such a simple method proved to be capable of providing valuable information about thermodynamics and kinetics of CI2 and its mutations, thus being a fast alternative to the study of rational protein design.

A Dynamic Hydrophobic Core and Surface Salt Bridges Thermostabilize a Designed Three-Helix Bundle

Thermostable proteins are advantageous in industrial applications, as pharmaceuticals or biosensors, and as templates for directed evolution. As protein-design methodologies improve, bioengineers are able to design proteins to perform a desired function. Although many rationally designed proteins end up being thermostable, how to intentionally design de novo, thermostable proteins is less clear. UVF is a de novo-designed protein based on the backbone structure of the Engrailed homeodomain (EnHD) and is highly thermostable (T_m > 99°C vs. 52°C for EnHD). Although most proteins generally have polar amino acids on their surfaces and hydrophobic amino acids buried in their cores, protein engineers followed this rule exactly when designing UVF. To investigate the contributions of the fully hydrophobic core versus the fully polar surface to UVF’s thermostability, we built two hybrid, chimeric proteins combining the sets of buried and surface residues from UVF and EnHD. Here, we determined a structural, dynamic, and thermodynamic explanation for UVF’s thermostability by performing 4 μs of all-atom, explicit-solvent molecular dynamics simulations at 25 and 100°C, Tanford-Kirkwood solvent accessibility Monte Carlo electrostatic calculations, and a thermodynamic analysis of 40 temperature runs by the weighted-histogram analysis method of heavy-atom, structure-based models of UVF, EnHD, and both chimeric proteins. Our models showed that UVF was highly dynamic because of its fully hydrophobic core, leading to a smaller loss of entropy upon folding. The charged residues on its surface made favorable electrostatic interactions that contributed enthalpically to its thermostability. In the chimeric proteins, both the hydrophobic core and charged surface independently imparted thermostability.