Computer-aided engineering of stabilized fibroblast growth factor 21.

FGF21 is an endocrine signaling protein belonging to the family of fibroblast growth factors (FGFs). It has emerged as a molecule of interest for treating various metabolic diseases due to its role in regulating glucogenesis and ketogenesis in the liver. However, FGF21 is prone to heat, proteolytic, and acid-mediated degradation, and its low molecular weight makes it susceptible to kidney clearance, significantly reducing its therapeutic potential. Protein engineering studies addressing these challenges have generally shown that increasing the thermostability of FGF21 led to improved pharmacokinetics. Here, we describe the computer-aided design and experimental characterization of FGF21 variants with enhanced melting temperature up to 15 °C, uncompromised efficacy at activation of MAPK/ERK signaling in Hep G2 cell culture, and ability to stimulate proliferation of Hep G2 and NIH 3T3 fibroblasts cells comparable with FGF21-WT. We propose that stabilizing the FGF21 molecule by rational design should be combined with other reported stabilization strategies to maximize the pharmaceutical potential of FGF21.

ChannelsDB 2.0: a comprehensive database of protein tunnels and pores in AlphaFold era.

ChannelsDB 2.0 is an updated database providing structural information about the position, geometry and physicochemical properties of protein channels-tunnels and pores-within deposited biomacromolecular structures from PDB and AlphaFoldDB databases. The newly deposited information originated from several sources. Firstly, we included data calculated using a popular CAVER tool to complement the data obtained using original MOLE tool for detection and analysis of protein tunnels and pores. Secondly, we added tunnels starting from cofactors within the AlphaFill database to enlarge the scope of the database to protein models based on Uniprot. This has enlarged available channel annotations ∼4.6 times as of 1 September 2023. The database stores information about geometrical features, e.g. length and radius, and physico-chemical properties based on channel-lining amino acids. The stored data are interlinked with the available UniProt mutation annotation data. ChannelsDB 2.0 provides an excellent resource for deep analysis of the role of biomacromolecular tunnels and pores. The database is available free of charge: https://channelsdb2.biodata.ceitec.cz.

InVADo: Interactive Visual Analysis of Molecular Docking Data.

Molecular docking is a key technique in various fields like structural biology, medicinal chemistry, and biotechnology. It is widely used for virtual screening during drug discovery, computer-assisted drug design, and protein engineering. A general molecular docking process consists of the target and ligand selection, their preparation, and the docking process itself, followed by the evaluation of the results. However, the most commonly used docking software provides no or very basic evaluation possibilities. Scripting and external molecular viewers are often used, which are not designed for an efficient analysis of docking results. Therefore, we developed InVADo, a comprehensive interactive visual analysis tool for large docking data. It consists of multiple linked 2D and 3D views. It filters and spatially clusters the data, and enriches it with post-docking analysis results of protein-ligand interactions and functional groups, to enable well-founded decision-making. In an exemplary case study, domain experts confirmed that InVADo facilitates and accelerates the analysis workflow. They rated it as a convenient, comprehensive, and feature-rich tool, especially useful for virtual screening.

PredictONCO: a web tool supporting decision-making in precision oncology by extending the bioinformatics predictions with advanced computing and machine learning.

PredictONCO 1.0 is a unique web server that analyzes effects of mutations on proteins frequently altered in various cancer types. The server can assess the impact of mutations on the protein sequential and structural properties and apply a virtual screening to identify potential inhibitors that could be used as a highly individualized therapeutic approach, possibly based on the drug repurposing. PredictONCO integrates predictive algorithms and state-of-the-art computational tools combined with information from established databases. The user interface was carefully designed for the target specialists in precision oncology, molecular pathology, clinical genetics and clinical sciences. The tool summarizes the effect of the mutation on protein stability and function and currently covers 44 common oncological targets. The binding affinities of Food and Drug Administration/ European Medicines Agency -approved drugs with the wild-type and mutant proteins are calculated to facilitate treatment decisions. The reliability of predictions was confirmed against 108 clinically validated mutations. The server provides a fast and compact output, ideal for the often time-sensitive decision-making process in oncology. Three use cases of missense mutations, (i) K22A in cyclin-dependent kinase 4 identified in melanoma, (ii) E1197K mutation in anaplastic lymphoma kinase 4 identified in lung carcinoma and (iii) V765A mutation in epidermal growth factor receptor in a patient with congenital mismatch repair deficiency highlight how the tool can increase levels of confidence regarding the pathogenicity of the variants and identify the most effective inhibitors. The server is available at https://loschmidt.chemi.muni.cz/predictonco.

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.

Computer-assisted stabilization of fibroblast growth factor FGF-18.

The fibroblast growth factors (FGF) family holds significant potential for addressing chronic diseases. Specifically, recombinant FGF18 shows promise in treating osteoarthritis by stimulating cartilage formation. However, recent phase 2 clinical trial results of sprifermin (recombinant FGF18) indicate insufficient efficacy. Leveraging our expertise in rational protein engineering, we conducted a study to enhance the stability of FGF18. As a result, we obtained a stabilized variant called FGF18-E4, which exhibited improved stability with 16 °C higher melting temperature, resistance to trypsin and a 2.5-fold increase in production yields. Moreover, the FGF18-E4 maintained mitogenic activity after 1-week incubation at 37 °C and 1-day at 50 °C. Additionally, the inserted mutations did not affect its binding to the fibroblast growth factor receptors, making FGF18-E4 a promising candidate for advancing FGF-based osteoarthritis treatment.

Illuminating the mechanism and allosteric behavior of NanoLuc luciferase.

NanoLuc, a superior ß-barrel fold luciferase, was engineered 10 years ago but the nature of its catalysis remains puzzling. Here experimental and computational techniques are combined, revealing that imidazopyrazinone luciferins bind to an intra-barrel catalytic site but also to an allosteric site shaped on the enzyme surface. Structurally, binding to the allosteric site prevents simultaneous binding to the catalytic site, and vice versa, through concerted conformational changes. We demonstrate that restructuration of the allosteric site can boost the luminescent reaction in the remote active site. Mechanistically, an intra-barrel arginine coordinates the imidazopyrazinone component of luciferin, which reacts with O2 via a radical charge-transfer mechanism, and then it also protonates the resulting excited amide product to form a light-emitting neutral species. Concomitantly, an aspartate, supported by two tyrosines, fine-tunes the blue color emitter to secure a high emission intensity. This information is critical to engineering the next-generation of ultrasensitive bioluminescent reporters.

Atypical homodimerization revealed by the structure of the (S)-enantioselective haloalkane dehalogenase DmmarA from Mycobacterium marinum.

Haloalkane dehalogenases (HLDs) are a family of α/ß-hydrolase fold enzymes that employ SN2 nucleophilic substitution to cleave the carbon-halogen bond in diverse chemical structures, the biological role of which is still poorly understood. Atomic-level knowledge of both the inner organization and supramolecular complexation of HLDs is thus crucial to understand their catalytic and noncatalytic functions. Here, crystallographic structures of the (S)-enantioselective haloalkane dehalogenase DmmarA from the waterborne pathogenic microbe Mycobacterium marinum were determined at 1.6 and 1.85 Šresolution. The structures show a canonical αßα-sandwich HLD fold with several unusual structural features. Mechanistically, the atypical composition of the proton-relay catalytic triad (aspartate-histidine-aspartate) and uncommon active-site pocket reveal the molecular specificities of a catalytic apparatus that exhibits a rare (S)-enantiopreference. Additionally, the structures reveal a previously unobserved mode of symmetric homodimerization, which is predominantly mediated through unusual L5-to-L5 loop interactions. This homodimeric association in solution is confirmed experimentally by data obtained from small-angle X-ray scattering. Utilizing the newly determined structures of DmmarA, molecular modelling techniques were employed to elucidate the underlying mechanism behind its uncommon enantioselectivity. The (S)-preference can be attributed to the presence of a distinct binding pocket and variance in the activation barrier for nucleophilic substitution.

Advancing Enzyme's Stability and Catalytic Efficiency through Synergy of Force-Field Calculations, Evolutionary Analysis, and Machine Learning.

Thermostability is an essential requirement for the use of enzymes in the bioindustry. Here, we compare different protein stabilization strategies using a challenging target, a stable haloalkane dehalogenase DhaA115. We observe better performance of automated stabilization platforms FireProt and PROSS in designing multiple-point mutations over the introduction of disulfide bonds and strengthening the intra- and the inter-domain contacts by in silico saturation mutagenesis. We reveal that the performance of automated stabilization platforms was still compromised due to the introduction of some destabilizing mutations. Notably, we show that their prediction accuracy can be improved by applying manual curation or machine learning for the removal of potentially destabilizing mutations, yielding highly stable haloalkane dehalogenases with enhanced catalytic properties. A comparison of crystallographic structures revealed that current stabilization rounds were not accompanied by large backbone re-arrangements previously observed during the engineering stability of DhaA115. Stabilization was achieved by improving local contacts including protein-water interactions. Our study provides guidance for further improvement of automated structure-based computational tools for protein stabilization.

Comparison of Fungal Thermophilic and Mesophilic Catalase-Peroxidases for Their Antioxidative Properties.

Catalase-peroxidases (KatGs) are unique bifunctional oxidoreductases that contain heme in their active centers allowing both the peroxidatic and catalatic reaction modes. These originally bacterial enzymes are broadly distributed among various fungi allowing them to cope with reactive oxygen species present in the environment or inside the cells. We used various biophysical, biochemical, and bioinformatics methods to investigate differences between catalase-peroxidases originating in thermophilic and mesophilic fungi from different habitats. Our results indicate that the architecture of the active center with a specific post-translational modification is highly similar in mesophilic and thermophilic KatG and also the peroxidatic acitivity with ABTS, guaiacol, and L-DOPA. However, only the thermophilic variant CthedisKatG reveals increased manganese peroxidase activity at elevated temperatures. The catalatic activity releasing molecular oxygen is comparable between CthedisKatG and mesophilic MagKatG1 over a broad temperature range. Two constructed point mutations in the active center were performed selectively blocking the formation of described post-translational modification in the active center. They exhibited a total loss of catalatic activity and changes in the peroxidatic activity. Our results indicate the capacity of bifunctional heme enzymes in the variable reactivity for potential biotech applications.

pyCaverDock: Python implementation of the popular tool for analysis of ligand transport with advanced caching and batch calculation support.

SUMMARY: Access pathways in enzymes are crucial for the passage of substrates and products of catalysed reactions. The process can be studied by computational means with variable degrees of precision. Our in-house approximative method CaverDock provides a fast and easy way to set up and run ligand binding and unbinding calculations through protein tunnels and channels. Here we introduce pyCaverDock, a Python3 API designed to improve user experience with the tool and further facilitate the ligand transport analyses. The API enables users to simplify the steps needed to use CaverDock, from automatizing setup processes to designing screening pipelines. AVAILABILITY AND IMPLEMENTATION: pyCaverDock API is implemented in Python 3 and is freely available with detailed documentation and practical examples at https://loschmidt.chemi.muni.cz/caverdock/.

Domino-like effect of C112R mutation on ApoE4 aggregation and its reduction by Alzheimer's Disease drug candidate.

BACKGROUND: Apolipoprotein E (ApoE) ε4 genotype is the most prevalent risk factor for late-onset Alzheimer's Disease (AD). Although ApoE4 differs from its non-pathological ApoE3 isoform only by the C112R mutation, the molecular mechanism of its proteinopathy is unknown. METHODS: Here, we reveal the molecular mechanism of ApoE4 aggregation using a combination of experimental and computational techniques, including X-ray crystallography, site-directed mutagenesis, hydrogen-deuterium mass spectrometry (HDX-MS), static light scattering and molecular dynamics simulations. Treatment of ApoE ε3/ε3 and ε4/ε4 cerebral organoids with tramiprosate was used to compare the effect of tramiprosate on ApoE4 aggregation at the cellular level. RESULTS: We found that C112R substitution in ApoE4 induces long-distance (> 15 Å) conformational changes leading to the formation of a V-shaped dimeric unit that is geometrically different and more aggregation-prone than the ApoE3 structure. AD drug candidate tramiprosate and its metabolite 3-sulfopropanoic acid induce ApoE3-like conformational behavior in ApoE4 and reduce its aggregation propensity. Analysis of ApoE ε4/ε4 cerebral organoids treated with tramiprosate revealed its effect on cholesteryl esters, the storage products of excess cholesterol. CONCLUSIONS: Our results connect the ApoE4 structure with its aggregation propensity, providing a new druggable target for neurodegeneration and ageing.

Identification, characterization, and engineering of glycosylation in thrombolytics.

Cardiovascular diseases, such as myocardial infarction, ischemic stroke, and pulmonary embolism, are the most common causes of disability and death worldwide. Blood clot hydrolysis by thrombolytic enzymes and thrombectomy are key clinical interventions. The most widely used thrombolytic enzyme is alteplase, which has been used in clinical practice since 1986. Another clinically used thrombolytic protein is tenecteplase, which has modified epitopes and engineered glycosylation sites, suggesting that carbohydrate modification in thrombolytic enzymes is a viable strategy for their improvement. This comprehensive review summarizes current knowledge on computational and experimental identification of glycosylation sites and glycan identity, together with methods used for their reengineering. Practical examples from previous studies focus on modification of glycosylations in thrombolytics, e.g., alteplase, tenecteplase, reteplase, urokinase, saruplase, and desmoteplase. Collected clinical data on these glycoproteins demonstrate the great potential of this engineering strategy. Outstanding combinatorics originating from multiple glycosylation sites and the vast variety of covalently attached glycan species can be addressed by directed evolution or rational design. Directed evolution pipelines would benefit from more efficient cell-free expression and high-throughput screening assays, while rational design must employ structure prediction by machine learning and in silico characterization by supercomputing. Perspectives on challenges and opportunities for improvement of thrombolytic enzymes by engineering and evolution of protein glycosylation are provided.

sMolBoxes: Dataflow Model for Molecular Dynamics Exploration.

We present sMolBoxes, a dataflow representation for the exploration and analysis of long molecular dynamics (MD) simulations. When MD simulations reach millions of snapshots, a frame-by-frame observation is not feasible anymore. Thus, biochemists rely to a large extent only on quantitative analysis of geometric and physico-chemical properties. However, the usage of abstract methods to study inherently spatial data hinders the exploration and poses a considerable workload. sMolBoxes link quantitative analysis of a user-defined set of properties with interactive 3D visualizations. They enable visual explanations of molecular behaviors, which lead to an efficient discovery of biochemically significant parts of the MD simulation. sMolBoxes follow a node-based model for flexible definition, combination, and immediate evaluation of properties to be investigated. Progressive analytics enable fluid switching between multiple properties, which facilitates hypothesis generation. Each sMolBox provides quick insight to an observed property or function, available in more detail in the bigBox View. The case studies illustrate that even with relatively few sMolBoxes, it is possible to express complex analytical tasks, and their use in exploratory analysis is perceived as more efficient than traditional scripting-based methods.

Fully automated virtual screening pipeline of FDA-approved drugs using Caver Web.

Protein tunnels are essential in transporting small molecules into the active sites of enzymes. Tunnels' geometrical and physico-chemical properties influence the transport process. The tunnels are attractive hot spots for protein engineering and drug development. However, studying the ligand binding and unbinding using experimental techniques is challenging, while in silico methods come with their limitations, especially in the case of resource-demanding virtual screening pipelines. Caver Web 1.2 is a new version of the web server combining the capabilities for the detection of protein tunnels with the calculation of the ligand trajectories. The new version of the Caver Web server was expanded with the ability to fetch novel ligands from the Integrated Database of Small Molecules and with the fully automated virtual screening pipeline allowing for the fast evaluation of the predefined set of over 4,300 currently approved drugs. The virtual screening pipeline is accompanied by a comprehensive user interface, making it a viable service for the broader spectrum of companies and the academic user community. The web server is freely available for academic use at https://loschmidt.chemi.muni.cz/caverweb.

Structural Insights into (Tere)phthalate-Ester Hydrolysis by a Carboxylesterase and Its Role in Promoting PET Depolymerization.

TfCa, a promiscuous carboxylesterase from Thermobifida fusca, was found to hydrolyze polyethylene terephthalate (PET) degradation intermediates such as bis(2-hydroxyethyl) terephthalate (BHET) and mono-(2-hydroxyethyl)-terephthalate (MHET). In this study, we elucidated the structures of TfCa in its apo form, as well as in complex with a PET monomer analogue and with BHET. The structure-function relationship of TfCa was investigated by comparing its hydrolytic activity on various ortho- and para-phthalate esters of different lengths. Structure-guided rational engineering of amino acid residues in the substrate-binding pocket resulted in the TfCa variant I69W/V376A (WA), which showed 2.6-fold and 3.3-fold higher hydrolytic activity on MHET and BHET, respectively, than the wild-type enzyme. TfCa or its WA variant was mixed with a mesophilic PET depolymerizing enzyme variant [Ideonella sakaiensis PETase (IsPETase) PM] to degrade PET substrates of various crystallinity. The dual enzyme system with the wild-type TfCa or its WA variant produced up to 11-fold and 14-fold more terephthalate (TPA) than the single IsPETase PM, respectively. In comparison to the recently published chimeric fusion protein of IsPETase and MHETase, our system requires 10% IsPETase and one-fourth of the reaction time to yield the same amount of TPA under similar PET degradation conditions. Our simple dual enzyme system reveals further advantages in terms of cost-effectiveness and catalytic efficiency since it does not require time-consuming and expensive cross-linking and immobilization approaches.

SoluProtMutDB: A manually curated database of protein solubility changes upon mutations.

Protein solubility is an attractive engineering target primarily due to its relation to yields in protein production and manufacturing. Moreover, better knowledge of the mutational effects on protein solubility could connect several serious human diseases with protein aggregation. However, we have limited understanding of the protein structural determinants of solubility, and the available data have mostly been scattered in the literature. Here, we present SoluProtMutDB - the first database containing data on protein solubility changes upon mutations. Our database accommodates 33 000 measurements of 17 000 protein variants in 103 different proteins. The database can serve as an essential source of information for the researchers designing improved protein variants or those developing machine learning tools to predict the effects of mutations on solubility. The database comprises all the previously published solubility datasets and thousands of new data points from recent publications, including deep mutational scanning experiments. Moreover, it features many available experimental conditions known to affect protein solubility. The datasets have been manually curated with substantial corrections, improving suitability for machine learning applications. The database is available at loschmidt.chemi.muni.cz/soluprotmutdb.

Multiple Substrate Binding Mode-Guided Engineering of a Thermophilic PET Hydrolase.

Thermophilic polyester hydrolases (PES-H) have recently enabled biocatalytic recycling of the mass-produced synthetic polyester polyethylene terephthalate (PET), which has found widespread use in the packaging and textile industries. The growing demand for efficient PET hydrolases prompted us to solve high-resolution crystal structures of two metagenome-derived enzymes (PES-H1 and PES-H2) and notably also in complex with various PET substrate analogues. Structural analyses and computational modeling using molecular dynamics simulations provided an understanding of how product inhibition and multiple substrate binding modes influence key mechanistic steps of enzymatic PET hydrolysis. Key residues involved in substrate-binding and those identified previously as mutational hotspots in homologous enzymes were subjected to mutagenesis. At 72 °C, the L92F/Q94Y variant of PES-H1 exhibited 2.3-fold and 3.4-fold improved hydrolytic activity against amorphous PET films and pretreated real-world PET waste, respectively. The R204C/S250C variant of PES-H1 had a 6.4 °C higher melting temperature than the wild-type enzyme but retained similar hydrolytic activity. Under optimal reaction conditions, the L92F/Q94Y variant of PES-H1 hydrolyzed low-crystallinity PET materials 2.2-fold more efficiently than LCC ICCG, which was previously the most active PET hydrolase reported in the literature. This property makes the L92F/Q94Y variant of PES-H1 a good candidate for future applications in industrial plastic recycling processes.

COMPARISON OF THE BACKGROUND GAMMA-RAY SPECTRUM FROM SOIL OBTAINED BY DIRECT MEASUREMENT AND BY CALCULATION USING MCNP6 WITH NAI(TL) SCINTILLATION DETECTOR.

Efficiency calibration is an essential part of any gamma-spectrometric or non-spectrometric measurement. The article describes all steps leading to the reconstruction of the gamma-ray spectrum from the soil - mathematical calibration of the NaI(Tl) detector and optimization of the model in the MCNP6 code, determination of radionuclide vector of soil around NPPs in Slovakia, calculation of the detector response matrix using the calculation code MCNP6 and finally the reconstruction of the gamma-ray spectrum. The results were compared in the energy range of 50-1620 keV for the spectrum obtained by direct measurement of the soil sample and for the spectrum obtained by calculation using the code MCNP6. The relative deviation in the whole spectrum between direct measurement and calculation is not more than 2%, which can be considered as the satisfactory result. The detector model thus created can be used to calculate the response matrix for any geometry arrangement.

Hidden Potential of Highly Efficient and Widely Accessible Thrombolytic Staphylokinase.

Stroke burden is substantially increasing but current therapeutic drugs are still far from ideal. Here we highlight the vast potential of staphylokinase as an efficient, fibrin-selective, inexpensive, and evolvable thrombolytic agent. The emphasis is escalated by new recent findings. Staphylokinase nonimmunogenic variant was proven noninferior to alteplase in a clinical trial, with decreased risk of intracranial hemorrhage and the advantage of single bolus administration. Furthermore, our detailed kinetic analysis revealed a new staphylokinase limiting bottleneck whose elimination might provide up to 1000-fold higher activity than the clinically approved alteplase. This knowledge of limitations unlocks new possibilities for improvements that are now achievable by the community of protein engineers who have the required expertise and are ready to transform staphylokinase into a powerful molecule. Collectively, the noninferiority and safety of nonimmunogenic staphylokinase together with the newly identified effectivity limitation make staphylokinase a perfect candidate for further exploration, modification, and advancement to make it the next-generation widely accessible thrombolytic drug effectively treating stroke all around the world, including middle- and low-income countries.