CoVAMPnet: Comparative Markov State Analysis for Studying Effects of Drug Candidates on Disordered Biomolecules.

Computational study of the effect of drug candidates on intrinsically disordered biomolecules is challenging due to their vast and complex conformational space. Here, we developed a comparative Markov state analysis (CoVAMPnet) framework to quantify changes in the conformational distribution and dynamics of a disordered biomolecule in the presence and absence of small organic drug candidate molecules. First, molecular dynamics trajectories are generated using enhanced sampling, in the presence and absence of small molecule drug candidates, and ensembles of soft Markov state models (MSMs) are learned for each system using unsupervised machine learning. Second, these ensembles of learned MSMs are aligned across different systems based on a solution to an optimal transport problem. Third, the directional importance of inter-residue distances for the assignment to different conformational states is assessed by a discriminative analysis of aggregated neural network gradients. This final step provides interpretability and biophysical context to the learned MSMs. We applied this novel computational framework to assess the effects of ongoing phase 3 therapeutics tramiprosate (TMP) and its metabolite 3-sulfopropanoic acid (SPA) on the disordered Aß42 peptide involved in Alzheimer's disease. Based on adaptive sampling molecular dynamics and CoVAMPnet analysis, we observed that both TMP and SPA preserved more structured conformations of Aß42 by interacting nonspecifically with charged residues. SPA impacted Aß42 more than TMP, protecting α-helices and suppressing the formation of aggregation-prone ß-strands. Experimental biophysical analyses showed only mild effects of TMP/SPA on Aß42 and activity enhancement by the endogenous metabolization of TMP into SPA. Our data suggest that TMP/SPA may also target biomolecules other than Aß peptides. The CoVAMPnet method is broadly applicable to study the effects of drug candidates on the conformational behavior of intrinsically disordered biomolecules.

Bacterial Lactonases ZenA with Noncanonical Structural Features Hydrolyze the Mycotoxin Zearalenone.

Zearalenone (ZEN) is a mycoestrogenic polyketide produced by Fusarium graminearum and other phytopathogenic members of the genus Fusarium. Contamination of cereals with ZEN is frequent, and hydrolytic detoxification with fungal lactonases has been explored. Here, we report the isolation of a bacterial strain, Rhodococcus erythropolis PFA D8-1, with ZEN hydrolyzing activity, cloning of the gene encoding α/ß hydrolase ZenA encoded on the linear megaplasmid pSFRL1, and biochemical characterization of nine homologues. Furthermore, we report site-directed mutagenesis as well as structural analysis of the dimeric ZenARe of R. erythropolis and the more thermostable, tetrameric ZenAScfl of Streptomyces coelicoflavus with and without bound ligands. The X-ray crystal structures not only revealed canonical features of α/ß hydrolases with a cap domain including a Ser-His-Asp catalytic triad but also unusual features including an uncommon oxyanion hole motif and a peripheral, short antiparallel ß-sheet involved in tetramer interactions. Presteady-state kinetic analyses for ZenARe and ZenAScfl identified balanced rate-limiting steps of the reaction cycle, which can change depending on temperature. Some new bacterial ZEN lactonases have lower KM and higher kcat than the known fungal ZEN lactonases and may lend themselves to enzyme technology development for the degradation of ZEN in feed or food.

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.

Recognition and coacervation of G-quadruplexes by a multifunctional disordered region in RECQ4 helicase.

Biomolecular polyelectrolyte complexes can be formed between oppositely charged intrinsically disordered regions (IDRs) of proteins or between IDRs and nucleic acids. Highly charged IDRs are abundant in the nucleus, yet few have been functionally characterized. Here, we show that a positively charged IDR within the human ATP-dependent DNA helicase Q4 (RECQ4) forms coacervates with G-quadruplexes (G4s). We describe a three-step model of charge-driven coacervation by integrating equilibrium and kinetic binding data in a global numerical model. The oppositely charged IDR and G4 molecules form a complex in the solution that follows a rapid nucleation-growth mechanism leading to a dynamic equilibrium between dilute and condensed phases. We also discover a physical interaction with Replication Protein A (RPA) and demonstrate that the IDR can switch between the two extremes of the structural continuum of complexes. The structural, kinetic, and thermodynamic profile of its interactions revealed a dynamic disordered complex with nucleic acids and a static ordered complex with RPA protein. The two mutually exclusive binding modes suggest a regulatory role for the IDR in RECQ4 function by enabling molecular handoffs. Our study extends the functional repertoire of IDRs and demonstrates a role of polyelectrolyte complexes involved in G4 binding.

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.

The SAP domain of Ku facilitates its efficient loading onto DNA ends.

The evolutionarily conserved DNA repair complex Ku serves as the primary sensor of free DNA ends in eukaryotic cells. Its rapid association with DNA ends is crucial for several cellular processes, including non-homologous end joining (NHEJ) DNA repair and telomere protection. In this study, we conducted a transient kinetic analysis to investigate the impact of the SAP domain on individual phases of the Ku-DNA interaction. Specifically, we examined the initial binding, the subsequent docking of Ku onto DNA, and sliding of Ku along DNA. Our findings revealed that the C-terminal SAP domain of Ku70 facilitates the initial phases of the Ku-DNA interaction but does not affect the sliding process. This suggests that the SAP domain may either establish the first interactions with DNA, or stabilize these initial interactions during loading. To assess the biological role of the SAP domain, we generated Arabidopsis plants expressing Ku lacking the SAP domain. Intriguingly, despite the decreased efficiency of the ΔSAP Ku complex in loading onto DNA, the mutant plants exhibited full proficiency in classical NHEJ and telomere maintenance. This indicates that the speed with which Ku loads onto telomeres or DNA double-strand breaks is not the decisive factor in stabilizing these DNA structures.

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.

In-depth analysis of biocatalysts by microfluidics: An emerging source of data for machine learning.

Nowadays, the vastly increasing demand for novel biotechnological products is supported by the continuous development of biocatalytic applications that provide sustainable green alternatives to chemical processes. The success of a biocatalytic application is critically dependent on how quickly we can identify and characterize enzyme variants fitting the conditions of industrial processes. While miniaturization and parallelization have dramatically increased the throughput of next-generation sequencing systems, the subsequent characterization of the obtained candidates is still a limiting process in identifying the desired biocatalysts. Only a few commercial microfluidic systems for enzyme analysis are currently available, and the transformation of numerous published prototypes into commercial platforms is still to be streamlined. This review presents the state-of-the-art, recent trends, and perspectives in applying microfluidic tools in the functional and structural analysis of biocatalysts. We discuss the advantages and disadvantages of available technologies, their reproducibility and robustness, and readiness for routine laboratory use. We also highlight the unexplored potential of microfluidics to leverage the power of machine learning for biocatalyst development.

Droplet-Based Microfluidic Temperature-Jump Platform for the Rapid Assessment of Biomolecular Kinetics.

Protein folding, unfolding, and aggregation are important in a variety of biological processes and intimately linked to "protein misfolding diseases". The ability to perform experiments at different temperatures allows the extraction of important information regarding the kinetics and thermodynamics of such processes. Unfortunately, conventional stopped-flow methods are difficult to implement, generate limited information, and involve complex sample handling. To address this issue, we present a temperature-controlled droplet-based microfluidic platform that allows measurement of reaction kinetics on millisecond to second timescales and at temperatures between ambient and 90 °C. The utility of the microfluidic platform for measuring fast biomolecular kinetics at high temperatures is showcased through the investigation of the unfolding kinetics of haloalkane dehalogenases and the elongation of fibrils composed of the amyloid ß peptide associated with Alzheimer's disease. In addition, a deep-ultraviolet (UV) fluorescence microscope was developed for the on-chip recording of protein intrinsic fluorescence spectrum originating from aromatic amino acid residues. We envision that the developed optofluidic platform will find wide applicability in the analysis of biological processes, such as protein refolding and phase separation.

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.

Mechanism-Based Strategy for Optimizing HaloTag Protein Labeling.

HaloTag labeling technology has introduced unrivaled potential in protein chemistry and molecular and cellular biology. A wide variety of ligands have been developed to meet the specific needs of diverse applications, but only a single protein tag, DhaAHT, is routinely used for their incorporation. Following a systematic kinetic and computational analysis of different reporters, a tetramethylrhodamine- and three 4-stilbazolium-based fluorescent ligands, we showed that the mechanism of incorporating different ligands depends both on the binding step and the efficiency of the chemical reaction. By studying the different haloalkane dehalogenases DhaA, LinB, and DmmA, we found that the architecture of the access tunnels is critical for the kinetics of both steps and the ligand specificity. We showed that highly efficient labeling with specific ligands is achievable with natural dehalogenases. We propose a simple protocol for selecting the optimal protein tag for a specific ligand from the wide pool of available enzymes with diverse access tunnel architectures. The application of this protocol eliminates the need for expensive and laborious protein engineering.

CalFitter 2.0: Leveraging the power of singular value decomposition to analyse protein thermostability.

The importance of the quantitative description of protein unfolding and aggregation for the rational design of stability or understanding the molecular basis of protein misfolding diseases is well established. Protein thermostability is typically assessed by calorimetric or spectroscopic techniques that monitor different complementary signals during unfolding. The CalFitter webserver has already proved integral to deriving invaluable energy parameters by global data analysis. Here, we introduce CalFitter 2.0, which newly incorporates singular value decomposition (SVD) of multi-wavelength spectral datasets into the global fitting pipeline. Processed time- or temperature-evolved SVD components can now be fitted together with other experimental data types. Moreover, deconvoluted basis spectra provide spectral fingerprints of relevant macrostates populated during unfolding, which greatly enriches the information gains of the CalFitter output. The SVD analysis is fully automated in a highly interactive module, providing access to the results to users without any prior knowledge of the underlying mathematics. Additionally, a novel data uploading wizard has been implemented to facilitate rapid and easy uploading of multiple datasets. Together, the newly introduced changes significantly improve the user experience, making this software a unique, robust, and interactive platform for the analysis of protein thermal denaturation data. The webserver is freely accessible at https://loschmidt.chemi.muni.cz/calfitter.

Computer-aided engineering of staphylokinase toward enhanced affinity and selectivity for plasmin.

Cardio- and cerebrovascular diseases are leading causes of death and disability, resulting in one of the highest socio-economic burdens of any disease type. The discovery of bacterial and human plasminogen activators and their use as thrombolytic drugs have revolutionized treatment of these pathologies. Fibrin-specific agents have an advantage over non-specific factors because of lower rates of deleterious side effects. Specifically, staphylokinase (SAK) is a pharmacologically attractive indirect plasminogen activator protein of bacterial origin that forms stoichiometric noncovalent complexes with plasmin, promoting the conversion of plasminogen into plasmin. Here we report a computer-assisted re-design of the molecular surface of SAK to increase its affinity for plasmin. A set of computationally designed SAK mutants was produced recombinantly and biochemically characterized. Screening revealed a pharmacologically interesting SAK mutant with ∼7-fold enhanced affinity toward plasmin, ∼10-fold improved plasmin selectivity and moderately higher plasmin-generating efficiency in vitro. Collectively, the results obtained provide a framework for SAK engineering using computational affinity-design that could pave the way to next-generation of effective, highly selective, and less toxic thrombolytics.

Tools for computational design and high-throughput screening of therapeutic enzymes.

Therapeutic enzymes are valuable biopharmaceuticals in various biomedical applications. They have been successfully applied for fibrinolysis, cancer treatment, enzyme replacement therapies, and the treatment of rare diseases. Still, there is a permanent demand to find new or better therapeutic enzymes, which would be sufficiently soluble, stable, and active to meet specific medical needs. Here, we highlight the benefits of coupling computational approaches with high-throughput experimental technologies, which significantly accelerate the identification and engineering of catalytic therapeutic agents. New enzymes can be identified in genomic and metagenomic databases, which grow thanks to next-generation sequencing technologies exponentially. Computational design and machine learning methods are being developed to improve catalytically potent enzymes and predict their properties to guide the selection of target enzymes. High-throughput experimental pipelines, increasingly relying on microfluidics, ensure functional screening and biochemical characterization of target enzymes to reach efficient therapeutic enzymes.

Substrate inhibition by the blockage of product release and its control by tunnel engineering.

Substrate inhibition is the most common deviation from Michaelis-Menten kinetics, occurring in approximately 25% of known enzymes. It is generally attributed to the formation of an unproductive enzyme-substrate complex after the simultaneous binding of two or more substrate molecules to the active site. Here, we show that a single point mutation (L177W) in the haloalkane dehalogenase LinB causes strong substrate inhibition. Surprisingly, a global kinetic analysis suggested that this inhibition is caused by binding of the substrate to the enzyme-product complex. Molecular dynamics simulations clarified the details of this unusual mechanism of substrate inhibition: Markov state models indicated that the substrate prevents the exit of the halide product by direct blockage and/or restricting conformational flexibility. The contributions of three residues forming the possible substrate inhibition site (W140A, F143L and I211L) to the observed inhibition were studied by mutagenesis. An unusual synergy giving rise to high catalytic efficiency and reduced substrate inhibition was observed between residues L177W and I211L, which are located in different access tunnels of the protein. These results show that substrate inhibition can be caused by substrate binding to the enzyme-product complex and can be controlled rationally by targeted amino acid substitutions in enzyme access tunnels.

Engineering the protein dynamics of an ancestral luciferase.

Protein dynamics are often invoked in explanations of enzyme catalysis, but their design has proven elusive. Here we track the role of dynamics in evolution, starting from the evolvable and thermostable ancestral protein AncHLD-RLuc which catalyses both dehalogenase and luciferase reactions. Insertion-deletion (InDel) backbone mutagenesis of AncHLD-RLuc challenged the scaffold dynamics. Screening for both activities reveals InDel mutations localized in three distinct regions that lead to altered protein dynamics (based on crystallographic B-factors, hydrogen exchange, and molecular dynamics simulations). An anisotropic network model highlights the importance of the conformational flexibility of a loop-helix fragment of Renilla luciferases for ligand binding. Transplantation of this dynamic fragment leads to lower product inhibition and highly stable glow-type bioluminescence. The success of our approach suggests that a strategy comprising (i) constructing a stable and evolvable template, (ii) mapping functional regions by backbone mutagenesis, and (iii) transplantation of dynamic features, can lead to functionally innovative proteins.

Exploration of enzyme diversity: High-throughput techniques for protein production and microscale biochemical characterization.

Enzymes are being increasingly utilized for acceleration of industrially and pharmaceutically critical chemical reactions. The strong demand for finding robust and efficient biocatalysts for these applications can be satisfied via the exploration of enzyme diversity. The first strategy is to mine the natural diversity, represented by millions of sequences available in the public genomic databases, by using computational approaches. Alternatively, metagenomic libraries can be targeted experimentally or computationally to explore the natural diversity of a specific environment. The second strategy, known as directed evolution, is to generate man-made diversity in the laboratory using gene mutagenesis and screen the constructed library of mutants. The selected hits must be experimentally characterized in both strategies, which currently represent the rate-limiting step in the process of diversity exploration. The traditional techniques used for biochemical characterization are time-demanding, cost, and sample volume ineffective, and low-throughput. Therefore, the development and implementation of high-throughput experimental methods are essential for discovering novel enzymes. This chapter describes the experimental protocols employing the combination of robust production and high-throughput microscale biochemical characterization of enzyme variants. We validated its applicability against the model enzyme family of haloalkane dehalogenases. These protocols can be adapted to other enzyme families, paving the way towards the functional characterization and quick identification of novel biocatalysts.

A Haloalkane Dehalogenase from Saccharomonospora viridis Strain DSM 43017, a Compost Bacterium with Unusual Catalytic Residues, Unique (S)-Enantiopreference, and High Thermostability.

Haloalkane dehalogenases can cleave a carbon-halogen bond in a broad range of halogenated aliphatic compounds. However, a highly conserved catalytic pentad composed of a nucleophile, a catalytic base, a catalytic acid, and two halide-stabilizing residues is required for their catalytic activity. Only a few family members, e.g., DsaA, DmxA, or DmrB, remain catalytically active while employing a single halide-stabilizing residue. Here, we describe a novel haloalkane dehalogenase, DsvA, from a mildly thermophilic bacterium, Saccharomonospora viridis strain DSM 43017, possessing one canonical halide-stabilizing tryptophan (W125). At the position of the second halide-stabilizing residue, DsvA contains the phenylalanine F165, which cannot stabilize the halogen anion released during the enzymatic reaction by a hydrogen bond. Based on the sequence and structural alignments, we identified a putative second halide-stabilizing tryptophan (W162) located on the same α-helix as F165, but on the opposite side of the active site. The potential involvement of this residue in DsvA catalysis was investigated by the construction and biochemical characterization of the three variants, DsvA01 (F165W), DsvA02 (W162F), and DsvA03 (W162F and F165W). Interestingly, DsvA exhibits a preference for the (S)- over the (R)-enantiomers of ß-bromoalkanes, which has not been reported before for any characterized haloalkane dehalogenase. Moreover, DsvA shows remarkable operational stability at elevated temperatures. The present study illustrates that protein sequences possessing an unconventional composition of catalytic residues represent a valuable source of novel biocatalysts.IMPORTANCE The present study describes a novel haloalkane dehalogenase, DsvA, originating from a mildly thermophilic bacterium, Saccharomonospora viridis strain DSM 43017. We report its high thermostability, remarkable operational stability at high temperatures, and an (S)-enantiopreference, which makes this enzyme an attractive biocatalyst for practical applications. Sequence analysis revealed that DsvA possesses an unusual composition of halide-stabilizing tryptophan residues in its active site. We constructed and biochemically characterized two single point mutants and one double point mutant and identified the noncanonical halide-stabilizing residue. Our study underlines the importance of searching for noncanonical catalytic residues in protein sequences.