Biology subjects

Damborsky, J.

Publications and source records attributed to Damborsky, J..

29 records · Page 2Linked to original sources

bioRxiv2024

Anticipating Protein Evolution with Successor Sequence Predictor

The quest to predict and understand protein evolution has been hindered by limitations on both the theoretical and the experimental fronts. Most existing theoretical models of evolution are descriptive, rather than predictive, leaving the final modifications in the hands of researchers. Existing experimental techniques to help probe the evolutionary sequence space of proteins, such as directed evolution, are resource-intensive and require specialised skills. We present the Successor Sequence Predictor (SSP) as an innovative solution. Successor Sequence Predictor is an in silico method that mimics laboratory-based protein evolution by reconstructing a proteins evolutionary history and suggesting future amino acid substitutions based on trends observed in that history through carefully selected physicochemical descriptors. This approach enhances specialised proteins by predicting mutations that improve desired properties, such as thermostability, activity, and solubility. Successor Sequence Predictor can thus be used as a general protein engineering tool to develop practically useful proteins. The code of the Successor Sequence Predictor is provided, and the design of mutations will be also possible via an easy-to-use web server https://loschmidt.chemi.muni.cz/fireprotasr/. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=150 SRC="FIGDIR/small/598054v1_ufig1.gif" ALT="Figure 1"> View larger version (70K): org.highwire.dtl.DTLVardef@c0cc9forg.highwire.dtl.DTLVardef@1ba19aforg.highwire.dtl.DTLVardef@ec341forg.highwire.dtl.DTLVardef@15205a6_HPS_FORMAT_FIGEXP M_FIG C_FIG Tracing Evolutions Pathway to the Future

bioinformatics↗

bioRxiv2024

Prediction of Aggregation Prone Regions in Proteins Using Deep Neural Networks and Their Suppression by Computational Design

Identification of aggregation-prone regions in proteins and their suppression through mutations is a powerful strategy to enhance protein solubility and yield, significantly expanding their application potential. Here, we developed a deep neural network-based predictor AggreProt, that generates a residue-level aggregation profile for protein sequences. The model outperformed or matched current state-of-the-art algorithms, as validated on two independent datasets comprising hexapeptides and full-length proteins with annotated aggregation-prone regions. We further validated the model experimentally using a set of 34 hexapeptides identified in the model protein haloalkane dehalogenase LinB, along with seven proteins from the AmyPro database. Experimental results agreed with our predictions in 79% of cases and also revealed inaccuracies in some database annotations. Finally, the algorithms utility was demonstrated by identifying aggregation-prone regions in the LinB enzyme and designing mutations to suppress aggregation in its exposed regions. The resulting variants exhibited reduced aggregation propensity, improved solubility, and up to a 100% increase in yield compared to the wild type. AggreProt is freely available to the scientific community via a user- friendly web server: https://loschmidt.chemi.muni.cz/aggreprot.

bioinformatics↗

bioRxiv2023

Brain map of aging-induced alterations in membrane ganglioside pattern

Recent efforts to develop comprehensive metabolome and proteome brain atlases for animal models have yielded significant progress. However, the ganglioside (GSs) profile of these models remains largely unexplored. As essential components of the brain, GSs play a crucial role in neuronal function. To address this gap in knowledge, we conducted an in-depth analysis of the young and adult rat brain, as well as its major brain regions, using ultra-high performance liquid chromatography and tandem mass spectrometry in positive and negative ion modes. We also analyzed GSs in cerebrospinal fluid (CSF) and serum from matched samples. Our findings indicate a shift in the ratio of a-series to b-series GSs with age, along with region-specific changes accompanied by aging. This could improve our understanding of brain aging and neurodegenerative diseases. Our study complements existing brain atlases of lipidome and protein expression and highlights the importance of further investigating the mechanisms underlying these GSs changes and the potential therapeutic implications of our findings.

neuroscience↗

bioRxiv2023

Large-scale Annotation of Biochemically Relevant Pockets and Tunnels in Cognate Enzyme-Ligand Complexes

Tunnels in enzymes with buried active sites are key structural features allowing the entry of substrates and the release of products, thus contributing to the catalytic efficiency. Targeting the bottlenecks of protein tunnels is also a powerful protein engineering strategy. However, the identification of functional tunnels in multiple protein structures is a non-trivial task that can only be addressed computationally. We present a pipeline integrating automated structural analysis with an in-house machine-learning predictor for the annotation of protein pockets, followed by the calculation of the energetics of ligand transport via biochemically relevant tunnels. A thorough validation using eight distinct molecular systems revealed that CaverDock analysis of ligand un/binding is on par with time-consuming molecular dynamics simulations, but much faster. The optimized and validated pipeline was applied to annotate more than 17,000 cognate enzyme-ligand complexes. Analysis of ligand un/binding energetics indicates that the top priority tunnel has the most favourable energies in 75 % of cases. Moreover, energy profiles of cognate ligands revealed that a simple geometry analysis can correctly identify tunnel bottlenecks only in 50 % of cases. Our study provides essential information for the interpretation of results from tunnel calculation and energy profiling in mechanistic enzymology and protein engineering. We formulated several simple rules allowing identification of biochemically relevant tunnels based on the binding pockets, tunnel geometry, and ligand transport energy profiles.

bioinformatics↗

bioRxiv2023

Effects of Alzheimer's Disease Drug Candidates on Disordered Aβ42 Dissected by Comparative Markov State Analysis (CoVAMPnet)

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 A{beta}42 peptide involved in Alzheimers disease. Based on adaptive sampling molecular dynamics and CoVAMPnet analysis, we observed that both TMP and SPA preserved more structured conformations of A{beta}42 by interacting non-specifically with charged residues. SPA impacted A{beta}42 more than TMP, protecting -helices and suppressing the formation of aggregation-prone {beta}-strands. Experimental biophysical analyses showed only mild effects of TMP/SPA on A{beta}42, and activity enhancement by the endogenous metabolization of TMP into SPA. Our data suggest that TMP/SPA may also target other biomolecules than A{beta} peptides. The CoVAMPnet method is broadly applicable to study the effects of drug candidates on the conformational behavior of intrinsically disordered biomolecules. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/523007v2_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@13eea16org.highwire.dtl.DTLVardef@17a6bd1org.highwire.dtl.DTLVardef@3c6b33org.highwire.dtl.DTLVardef@a20444_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2022

Illuminating the mechanism and allosteric behavior of NanoLuc luciferase

NanoLuc, a superior {beta}-barrel fold luciferase, was engineered 10 years ago but the nature of its catalysis remains puzzling. Here experimental and computational techniques were 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.

biochemistry↗

bioRxiv2022

Domino-like Effect of C112R Mutation on APOE4 Aggregation and Its Suppression by Alzheimer's Disease Drug Candidate

BackgroundApolipoprotein E (ApoE) {varepsilon}4 genotype is the most prevalent risk factor for late-onset Alzheimers Disease (AD). Although ApoE4 differs from its non-pathological ApoE3 isoform only by the C112R mutation, the molecular mechanism of its proteinopathy is unknown. MethodsHere, 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 {varepsilon}3/{varepsilon}3 and {varepsilon}4/{varepsilon}4 cerebral organoids with tramiprosate was used to compare the effect of tramiprosate on ApoE4 aggregation at the cellular level. ResultsWe found that C112R substitution in ApoE4 induces long-distance (>15 [A]) 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 {varepsilon}4/{varepsilon}4 cerebral organoids treated with tramiprosate revealed its effect on cholesteryl esters, the storage products of excess cholesterol. ConclusionsOur results connect the ApoE4 structure with its aggregation propensity, providing a new druggable target for neurodegeneration and ageing.

neuroscience↗

bioRxiv2022

A non-conventional archaeal fluorinase identified by in silico mining catalyzes the fastest natural nucleophilic fluorination

Fluorinases, the only enzymes known to catalyze the transfer of fluorine to an organic molecule, are essential biocatalysts for the sustainable synthesis of valuable organofluorines. However, the few fluorinases identified so far have low turnover rates that hamper biotechnological applications. Here, we isolated and characterized putative fluorinases retrieved from systematic in silico mining and identified a non-conventional archaeal enzyme from Methanosaeta sp. that mediates the highest fluorination rate reported to date. Furthermore, we demonstrate enhanced production of fluoronucleotides in vivo in a bacterial host engineered with this archaeal fluorinase, paving the way towards synthetic metabolism for efficient biohalogenations. Fastest biological SN2 fluorination

synthetic biology↗

bioRxiv2022

A catalytic mechanism for Renilla-type bioluminescence

The widely used coelenterazine-powered Renilla luciferase was discovered over 40 years ago but the oxidative mechanism by which it generates blue photons remains unclear. Here we decipher Renilla-type bioluminescence through crystallographic, spectroscopic, and computational experiments. Structures of ancestral and extant luciferases complexed with the substrate-like analogue azacoelenterazine or a reaction product were obtained, providing unprecedented snapshots of coelenterazine-to-coelenteramide oxidation. Bound coelenterazine adopts a Y-shaped conformation, enabling the deprotonated imidazopyrazinone component to attack O2 via a radical charge-transfer mechanism. A high emission intensity is secured by an aspartate from a conserved proton-relay system, which protonates the excited coelenteramide product. Another aspartate on the rim of the catalytic pocket fine-tunes the electronic state of coelenteramide and promotes the formation of the blue light-emitting phenolate anion. The results obtained also reveal structural features distinguishing flash-type from glow-type bioluminescence, providing insights that will guide the engineering of next-generation luciferase-luciferin pairs for ultrasensitive optical bioassays.

biochemistry↗

bioRxiv2021

Increased Occurrence of Treponema spp. and Double-species Infections in Patients with Alzheimer's Disease

ObjectiveAlthough the link between microbial infections and Alzheimers disease (AD) has been demonstrated in multiple studies, the involvement of pathogens in the development of AD remains unclear. Therefore, this theory beckons further systematic investigation. In this study, we have examined the association between the 10 most widely discussed viral and bacterial pathogens found in serum and cerebrospinal fluid (CSF) from patients with AD. MethodsWe have used an in-house developed multiplex PCR kit for simultaneous detection of five bacterial and five viral pathogens in serum and CSF from 50 AD patients and 53 healthy controls. Data analysis was performed with multiple statistical methods: Fishers exact test, chisquare goodness of fit test, and one-sample proportion test. ResultsWe observed an increased frequency of AD patients tested positive for Treponema spp. (AD: 62.2%; CTRL: 30.3%; p-value = 0.007). Furthermore, we confirmed a significantly higher prevalence of cases with two and more simultaneous infections in AD patients compared to controls (AD: 24%; CTRL 7.5%; p-value = 0.029). The studied pathogens were widespread equally in serum and CSF. Borrelia burgdorferi, human herpesvirus 7, and human cytomegalovirus were not detected in any of the studied samples. DiscussionAn increased prevalence of Treponema spp. and double-species infections in AD patients compared to the healthy controls provides further evidence of the association between microbial infections and AD. Paralleled analysis of multiple sample specimens provides complementary information and is advisable for future studies.

microbiology↗

bioRxiv2017

Computer-Assisted Engineering of Hyperstable Fibroblast Growth Factor 2

Fibroblast growth factors (FGFs) play 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 in vivo and in vitro. 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.

bioengineering↗