AggreProt: a web server for predicting and engineering aggregation prone regions in proteins.

Recombinant proteins play pivotal roles in numerous applications including industrial biocatalysts or therapeutics. Despite the recent progress in computational protein structure prediction, protein solubility and reduced aggregation propensity remain challenging attributes to design. Identification of aggregation-prone regions is essential for understanding misfolding diseases or designing efficient protein-based technologies, and as such has a great socio-economic impact. Here, we introduce AggreProt, a user-friendly webserver that automatically exploits an ensemble of deep neural networks to predict aggregation-prone regions (APRs) in protein sequences. Trained on experimentally evaluated hexapeptides, AggreProt compares to or outperforms state-of-the-art algorithms on two independent benchmark datasets. The server provides per-residue aggregation profiles along with information on solvent accessibility and transmembrane propensity within an intuitive interface with interactive sequence and structure viewers for comprehensive analysis. We demonstrate AggreProt efficacy in predicting differential aggregation behaviours in proteins on several use cases, which emphasize its potential for guiding protein engineering strategies towards decreased aggregation propensity and improved solubility. The webserver is freely available and accessible at https://loschmidt.chemi.muni.cz/aggreprot/.

Thermodynamic characterization of amyloid polymorphism by microfluidic transient incomplete separation.

Amyloid fibrils of proteins such as α-synuclein are a hallmark of neurodegenerative diseases and much research has focused on their kinetics and mechanisms of formation. The question as to the thermodynamic stability of such structures has received much less attention. Here, we newly utilize the principle of transient incomplete separation of species in laminar flow in combination with chemical depolymerization for the quantification of amyloid fibril stability. The relative concentrations of fibrils and monomer at equilibrium are determined through an in situ separation of these species based on their different diffusivity inside a microfluidic capillary. The method is highly sample economical, using much less than a microliter of sample per data point and its only requirement is the presence of aromatic residues (W, Y) because of its label-free nature, which makes it widely applicable. Using this method, we investigate the differences in thermodynamic stability between different fibril polymorphs of α-synuclein and quantify these differences for the first time. Importantly, we show that fibril formation can be under kinetic or thermodynamic control and that a change in solution conditions can both stabilise and destabilise amyloid fibrils. Taken together, our results establish the thermodynamic stability as a well-defined and key parameter that can contribute towards a better understanding of the physiological roles of amyloid fibril polymorphism.

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.

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.

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.

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.

SoluProt: prediction of soluble protein expression in Escherichia coli.

MOTIVATION: Poor protein solubility hinders the production of many therapeutic and industrially useful proteins. Experimental efforts to increase solubility are plagued by low success rates and often reduce biological activity. Computational prediction of protein expressibility and solubility in Escherichia coli using only sequence information could reduce the cost of experimental studies by enabling prioritization of highly soluble proteins. RESULTS: A new tool for sequence-based prediction of soluble protein expression in E.coli, SoluProt, was created using the gradient boosting machine technique with the TargetTrack database as a training set. When evaluated against a balanced independent test set derived from the NESG database, SoluProt's accuracy of 58.5% and AUC of 0.62 exceeded those of a suite of alternative solubility prediction tools. There is also evidence that it could significantly increase the success rate of experimental protein studies. SoluProt is freely available as a standalone program and a user-friendly webserver at https://loschmidt.chemi.muni.cz/soluprot/. AVAILABILITY AND IMPLEMENTATION: https://loschmidt.chemi.muni.cz/soluprot/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Haloalkane Dehalogenases From Marine Organisms.

Haloalkane dehalogenases degrade halogenated compounds to corresponding alcohols by a hydrolytic mechanism. These enzymes are being intensively investigated as model systems in experimental and in silico studies of enzyme mechanism and evolution, but also hold importance as useful biocatalysts for a number of biotechnological applications. Haloalkane dehalogenases originate from various organisms including bacteria (degraders, symbionts, or pathogens), eukaryotes, and archaea. Several members of this enzyme family have been found in marine organisms. The marine environment represents a good source of enzymes with novel properties, because of its diverse living conditions. A number of novel dehalogenases isolated from marine environments show interesting characteristics such as high activity, unusually broad substrate specificity, stability, or selectivity. In this chapter, the overview of haloalkane dehalogenases from marine organisms is presented and their characteristics are summarized together with an overview of the methods for their identification and biochemical characterization.

CalFitter: a web server for analysis of protein thermal denaturation data.

Despite significant advances in the understanding of protein structure-function relationships, revealing protein folding pathways still poses a challenge due to a limited number of relevant experimental tools. Widely-used experimental techniques, such as calorimetry or spectroscopy, critically depend on a proper data analysis. Currently, there are only separate data analysis tools available for each type of experiment with a limited model selection. To address this problem, we have developed the CalFitter web server to be a unified platform for comprehensive data fitting and analysis of protein thermal denaturation data. The server allows simultaneous global data fitting using any combination of input data types and offers 12 protein unfolding pathway models for selection, including irreversible transitions often missing from other tools. The data fitting produces optimal parameter values, their confidence intervals, and statistical information to define unfolding pathways. The server provides an interactive and easy-to-use interface that allows users to directly analyse input datasets and simulate modelled output based on the model parameters. CalFitter web server is available free at https://loschmidt.chemi.muni.cz/calfitter/.

Computer-assisted engineering of hyperstable fibroblast growth factor 2.

Fibroblast growth factors (FGFs) serve numerous regulatory functions in complex organisms, and their corresponding therapeutic potential is of growing interest to academics and industrial researchers alike. However, applications of these proteins are limited due to their low stability. Here we tackle this problem using a generalizable computer-assisted protein engineering strategy to create a unique modified FGF2 with nine mutations displaying unprecedented stability and uncompromised biological function. The data from the characterization of stabilized FGF2 showed a remarkable prediction potential of in silico methods and provided insight into the unfolding mechanism of the protein. The molecule holds a considerable promise for stem cell research and medical or pharmaceutical applications.

Exploration of Protein Unfolding by Modelling Calorimetry Data from Reheating.

Studies of protein unfolding mechanisms are critical for understanding protein functions inside cells, de novo protein design as well as defining the role of protein misfolding in neurodegenerative disorders. Calorimetry has proven indispensable in this regard for recording full energetic profiles of protein unfolding and permitting data fitting based on unfolding pathway models. While both kinetic and thermodynamic protein stability are analysed by varying scan rates and reheating, the latter is rarely used in curve-fitting, leading to a significant loss of information from experiments. To extract this information, we propose fitting both first and second scans simultaneously. Four most common single-peak transition models are considered: (i) fully reversible, (ii) fully irreversible, (iii) partially reversible transitions, and (iv) general three-state models. The method is validated using calorimetry data for chicken egg lysozyme, mutated Protein A, three wild-types of haloalkane dehalogenases, and a mutant stabilized by protein engineering. We show that modelling of reheating increases the precision of determination of unfolding mechanisms, free energies, temperatures, and heat capacity differences. Moreover, this modelling indicates whether alternative refolding pathways might occur upon cooling. The Matlab-based data fitting software tool and its user guide are provided as a supplement.

Metagenome-derived haloalkane dehalogenases with novel catalytic properties.

Haloalkane dehalogenases (HLDs) are environmentally relevant enzymes cleaving a carbon-halogen bond in a wide range of halogenated pollutants. PCR with degenerate primers and genome-walking was used for the retrieval of four HLD-encoding genes from groundwater-derived environmental DNA. Using specific primers and the environmental DNA as a template, we succeeded in generating additional amplicons, resulting altogether in three clusters of sequences with each cluster comprising 8-13 closely related putative HLD-encoding genes. A phylogenetic analysis of the translated genes revealed that three HLDs are members of the HLD-I subfamily, whereas one gene encodes an enzyme from the subfamily HLD-II. Two metagenome-derived HLDs, eHLD-B and eHLD-C, each from a different subfamily, were heterologously produced in active form, purified and characterized in terms of their thermostability, pH and temperature optimum, quaternary structure, substrate specificity towards 30 halogenated compounds, and enantioselectivity. eHLD-B and eHLD-C showed striking differences in their activities, substrate preferences, and tolerance to temperature. Profound differences were also determined in the enantiopreference and enantioselectivity of these enzymes towards selected substrates. Comparing our data with those of known HLDs revealed that eHLD-C exhibits a unique combination of high thermostability, high activity, and an unusually broad pH optimum, which covers the entire range of pH 5.5-8.9. Moreover, a so far unreported high thermostability for HLDs was determined for this enzyme at pH values lower than 6.0. Thus, eHLD-C represents an attractive and novel biocatalyst for biotechnological applications.

Discovery of Novel Haloalkane Dehalogenase Inhibitors.

Haloalkane dehalogenases (HLDs) have recently been discovered in a number of bacteria, including symbionts and pathogens of both plants and humans. However, the biological roles of HLDs in these organisms are unclear. The development of efficient HLD inhibitors serving as molecular probes to explore their function would represent an important step toward a better understanding of these interesting enzymes. Here we report the identification of inhibitors for this enzyme family using two different approaches. The first builds on the structures of the enzymes' known substrates and led to the discovery of less potent nonspecific HLD inhibitors. The second approach involved the virtual screening of 150,000 potential inhibitors against the crystal structure of an HLD from the human pathogen Mycobacterium tuberculosis H37Rv. The best inhibitor exhibited high specificity for the target structure, with an inhibition constant of 3 µM and a molecular architecture that clearly differs from those of all known HLD substrates. The new inhibitors will be used to study the natural functions of HLDs in bacteria, to probe their mechanisms, and to achieve their stabilization.