Biology subjects

Damborsky, J.

Publications and source records attributed to Damborsky, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Structural mechanism governing radiationless energy transfer in Renilla bioluminescence

The nonradiative transport of electronic excitation from one chromophore to another, known as resonance energy transfer, lies at the root of photochemical processes in biology. Unlike photosynthesis, bioluminescence converts chemical energy into light through an enzymatic oxygenation of an energy-rich luciferin. In glowing cnidarians, the energy is relocated from an excited oxyluciferin to a fluorescent protein, shifting the colour and enhancing the quantum yield of a photogenic reaction. How protein-chromophore complexes assemble during this interplay in real space, and what this association entails for function, are unknown. Here, we report co-crystal structures of a 120-kilodalton energy-transfer complex from the luminescent soft coral Renilla reniformis. We find a heterotetrameric 2:2 assembly composed of two coelenteramide-loaded luciferases (RrLuc) docked at opposite sides of a head-to-tail dimer of green fluorescent protein (RrGFP). The edge-to-edge distance between donor and acceptor chromophores is below 3 nm, favouring the Forster-type radiationless energy transfer. Furthermore, RrGFP serves not only as a colour-switchable antenna and luminescence amplifier but also tunes the efficiency of luciferase catalysis by controlling its inherent dynamics. Our results provide detailed spatial information about intermolecular dipole-dipole coupling in Renilla bioluminescence, including the arrangement of donor-acceptor pairs that secure excited-state energy transfer with exquisite precision.

biochemistry↗

bioRxiv2026

Open or closed? Exploring the conformational heterogeneity of human plasminogen across multiple resolution scales

Plasminogen (Plg), the zymogen of the fibrinolytic protease plasmin, is a multidomain, conformationally-rich protein that plays a crucial role in targeted thrombolysis for stroke and heart attack treatments. Yet, the conformational state that affects binding to Plg activators and fibrinolytic activity is rarely validated, contributing to poor reproducibility and failures in drug development. Here, we establish a structural biology workflow to characterize human Plg suspended in an aqueous solution, preserving it in a near-native state and eliminating the need for crystal growth. Two variants, cleavage-resistant Plg-RV (R561A) and catalytically inactive Plg-CAT (S741A), were expressed in mammalian cells. Both variants were correctly folded and stable (Tm {approx} 60.6{degrees}C) and, unlike commercial plasma-derived Plg, were resistant to staphylokinase-mediated activation. Small-angle X-ray scattering and hydrogen-deuterium exchange mass spectrometry revealed substantial conformational heterogeneity. The recombinantly-produced Plg variants adopted a closed conformation, exhibiting a good fit to the closed structure determined by X-ray diffraction. Conversely, the plasma-derived Plg populated an extended, open-like state. Cryo-EM analysis of the Plg-RV variant yielded a 4.4 [A] resolution map, and a rigid-body-fitted model revealed the closed-state architecture. Our findings demonstrate that rigorous structural validation of Plg is essential for future functional studies and rational development of next-generation thrombolytic agents.

biochemistry↗

bioRxiv2026

CascadeMAP: Autonomous Closed-loop Optimization of Enzyme Cascades via Microfluidics, Machine Learning and Agentic AI

Enzyme cascades enable complex biochemical transformations, but their optimization is resource-intensive, requiring navigation through high-dimensional parameter spaces encompassing reaction conditions, enzyme ratios, and buffer composition. Here we introduce CascadeMAP, an autonomous microfluidic platform for closed-loop optimization of enzyme cascades, integrating high-throughput microfluidics with Bayesian optimization and multi-agent AI system. We demonstrate the platform across two cascades: (i) a glycerol detection pathway monitored by fluorescence and (ii) a 1,2,3-trichloropropane degradation pathway monitored by label-free Raman spectroscopy providing orthogonal detection modalities. Bayesian optimization identified optimal conditions three times faster than Design of Experiments. Multi-agent AI system automated hypothesis generation, processing 11 GB of experimental data, pattern recognition, and insight synthesis. Operating without human intervention for 7 days, CascadeMAP processed [~]220,000 reactions across [~]7,400 different conditions. This capability establishes a generalizable framework for the autonomous optimization of enzyme cascades and metabolic pathways and accelerates the development of biocatalytic and synthetic biological systems.

biochemistry↗

bioRxiv2026

ProtXAI: Explainable AI Reveals Structural Determinants of Protein Dynamics

Molecular dynamics simulations provide atomistic views of protein motions, but conventional analyses often struggle with extracting subtle mechanistic insights from complex trajectories. Here, we present an integrated framework, ProtXAI, combining molecular dynamics and explainable artificial intelligence (XAI), to identify residue-level determinants of conformational change across diverse protein systems. By leveraging inter-residue distance dynamics, deep learning, and sequential relevance propagation, the approach captures both local fluctuations and long-range communication pathways within protein structures. We applied this framework to three mechanistically distinct systems: apolipoprotein E4 (ApoE4), staphylokinase (SAK) variants, and an ancestral luciferase. Across these applications, our XAI-based approach recovered experimentally supported dynamic hotspots: ligand-responsive hinges in ApoE4, mutation-dependent flexibility shifts in SAK, and evolutionary redistribution of motions in the luciferase. ProtXAI also revealed additional long-range couplings not accessible to classical analysis. Together, these findings demonstrate that combining molecular dynamics with XAI provides a general and scalable strategy for dissecting protein dynamics and uncovering structural determinants of function, stability, and evolutionary changes without prior bias. This approach thus advances the current methodological repertoire for analysing proteins and their intrinsic properties. HighlightsMolecular dynamics simulations are increasingly accessible, yet scalable tools for comparative analysis remain limited. We demonstrate that machine learning coupled with explainable AI can automatically extract structural determinants of protein dynamics from trajectories. ProtXAI identifies key dynamic regions across diverse scenarios, including comparison of protein variants, understanding ligand modulation, and single-trajectory analysis. ProtXAI enables scalable, unbiased interpretation of long trajectories, providing an alternative to manual, time-intensive analysis.

bioinformatics↗

bioRxiv2026

Investigation of Protein Melting Temperature Prediction with Cross-Method Validation on Biophysical Data

MotivationProtein melting temperature (Tm) prediction accelerates the discovery of thermostable enzymes which are crucial for industrial biotechnology often requiring harsh reaction conditions. Experimental determination of Tm remains labour-intensive and varies across techniques, motivating the development of in silico predictors. Mass-spectrometry datasets such as Meltome Atlas now enable large-scale Tm prediction with models based on deep learning, but model generalisation across diverse experimental datasets has not been systematically tested. ResultsWe evaluated the generalisability of state-of-the-art deep learning approaches and explored ESM-based embeddings for Tm prediction. To this end, we assembled the ProMelt training dataset (45 441 proteins) and five independent biophysics-based validation datasets. Our analysis revealed substantial differences between proteomics- and biophysics-based Tm measurements, highlighting the challenge of cross-domain generalisation. Existing state-of-the-art predictors trained on large-scale proteomics datasets showed reduced performance on biophysics-based validation sets. Our fine-tuned embedding-based models, particularly LoRA-adapted ESM-2 (TmProt 1.0), outperformed state-of-the-art predictors in identifying thermostable proteins (Tm[≥] 60 {degrees}C) across heterogeneous datasets, achieving AUC scores of 0.75-0.77. We also demonstrated that the available models could be used efficiently in the sequence prioritization task. AvailabilityThe TmProt web server is available at https://loschmidt.chemi.muni.cz/tmprot/. Source code and data are available at https://github.com/loschmidt/TmProt.

bioinformatics↗

bioRxiv2026

Uncovering Functional Distant Mutations by Ultra-High-Throughput Screening of Dehalogenases

Conformational dynamics play a central role in enzyme function by controlling substrate access and productive binding. Yet mutations that beneficially modulate these properties are difficult to identify. Here, we used ultrahigh-throughput fluorescence-activated droplet sorting (FADS) with a bulky fluorogenic substrate derived from coumarin (COU-3) to impose steric selection pressure on the haloalkane dehalogenase LinB. Screening a focused library yielded five single substitutions located 11.5-15.5 [A] from the catalytic centre. Variant I138N showed a fourfold increase in catalytic efficiency toward COU-3 through reduced KM and increased kcat, associated with increased cap-domain flexibility and facilitated substrate entry. In contrast, variant P208S markedly reduced substrate inhibition and shifted specificity toward bulkier iodinated haloalkanes by reshaping its tunnel environment. Integrated kinetic and structural analyses revealed that screening with bulky substrates directs selection toward distal regions controlling substrate access and unproductive binding. These findings demonstrate that ultrahigh-throughput FADS can reveal dynamic mechanisms of enzyme adaptation that remain difficult to predict by rational design. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=183 SRC="FIGDIR/small/713925v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@782038org.highwire.dtl.DTLVardef@8b43f3org.highwire.dtl.DTLVardef@11a403eorg.highwire.dtl.DTLVardef@6fcaea_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2026

Investigating the Conformational Flexibility of Staphylokinase Across Multiple Time Scales

Cardiovascular diseases, including ischemic stroke, necessitate improved thrombolytic agents. A microbe-encoded plasminogen activator staphylokinase (SAK) is a promising alternative to the widely used tissue plasminogen activator (tPA) due to its high fibrin specificity and low production cost. To overcome potential immunogenicity hampering its use in clinical settings, the low-immunogenic variants SAK SY155 and SAK THR174 were previously engineered. However, the molecular basis underlying their reduced immunogenicity is not understood and requires detailed elucidation. Here, we determine molecular structures and compare flexibility between low-immunogenic and immunogenic SAK variants, using a combination of experimental and computational structural techniques. Our analyses show that all variants share the canonical SAK fold and retain similar plasminogen activation kinetics, despite the number of introduced substitutions. Crucially, the low-immunogenic variants exhibit distinct flexibility profiles, with SAK THR174 showing substantially increased flexibility in the H1 helix and B3 region. SAK SY155 exhibits an increased flexibility in the H1-B3 loop and propensity to homodimerize. These flexibility changes are found in the known immunogenic epitopes. Our multi-scale flexibility analysis provides the molecular explanation for the reduced immunogenicity, altered thermostability, and retained fibrinolytic function of the engineered variants. This information is critical for the design of next-generation thrombolytics.

biochemistry↗

bioRxiv2026

Fibrin Selective Alteplase with Improved Thrombolysis and Inhibition Resistance Engineered by Rational Design

Thrombolytic enzymes represent an important class of proteolytic biocatalysts for medical applications, yet currently used FDA-approved variants, including alteplase and tenecteplase, remain limited by suboptimal catalytic efficiency, off-target activity, and susceptibility to inhibition. These limitations reflect the complexity of enzyme function in physiological environments, where therapeutic performance depends on the simultaneous optimization of multiple catalytic and biophysical properties. Here, we introduce a multi-objective enzyme engineering strategy for the design of next-generation thrombolytic proteases, explicitly targeting multiple properties required for therapeutic performance. Our approach combines computer-aided design, evolutionary reconstruction, and literature-guided mutation selection to improve catalytic activity, fibrin selectivity, inhibition resistance, and functional lifetime within a single workflow. This framework is coupled with systematic biochemical characterization, in vitro evaluation of clot penetration and fibrinolytic activity, and in vivo validation of efficacy and safety. By addressing multiple performance parameters simultaneously, this strategy enables efficient navigation of trade-offs that typically limit enzyme optimization. Using this approach, we identify Brnoteplase as a lead variant with enhanced fibrin selectivity, improved resistance to inhibition, and superior clot penetration, resulting in increased effective catalytic lifetime and enabling bolus administration. In vivo studies demonstrate enhanced thrombolysis and recanalization with reduced hemorrhagic complications. These findings provide a broadly applicable framework for designing proteolytic biocatalysts suitable for complex biological environments.

biochemistry↗

bioRxiv2026

Biochemical and Immunological Properties of Engineered Low-Immunogenic Staphylokinases for Next-Generation Thrombolytic Therapy

Staphylokinase (SAK) is a highly fibrin-specific plasminogen activator with significant potential as a safe and affordable thrombolytic. Yet, its clinical translation can be limited by potential immunogenicity. To accelerate the development of improved thrombolytics, a critical step is identifying the most suitable molecular template. Therefore, we performed a comparative analysis of biochemical and immunological properties of three engineered low-immunogenic variants (SAK SY155, SAK THR174, and SAK STAR FRIDA) and two wild-types (SAK STAR and SAK 42D), using a newly established panel of assays. All variants retained potent thrombolytic activity, with SAK SY155 displaying the highest catalytic efficiency and fibrin-clot permeability. However, this advantage did not fully translate into improved clot reduction under flow conditions. Comprehensive immunological profiling, including T lymphocyte proliferation, dendritic cell maturation, mouse immunization models, and human serum reactivity tests, confirmed decreased immunogenicity for two low-immunogenic variants. Overall, low-immunogenic SAK SY155 emerged as the most promising template for rational engineering of next-generation thrombolytics.

biochemistry↗

bioRxiv2025

SoluProtMut: Siamese Deep Learning for Predicting Solubility Effects of Protein Mutations with Experimental Validation

Protein solubility is an attractive engineering target because it is a critical property influencing the scalability of protein production and the success of therapeutic proteins in biomedical applications. However, predicting solubility changes upon mutation in silico is challenging due to data heterogeneity and protein bias. Here, we explore how different sources of solubility data can be used for machine learning and present SoluProtMut, a Siamese deep geometric neural network trained to predict the impact of mutations on protein solubility. Our final model was trained exclusively on deep mutational scanning data. We compare our model with five established solubility prediction methods. The model achieves state-of-the-art performance on an independent dataset of various proteins, especially in predicting the effects of multipoint mutations (informedness of 26.5 %). Our findings also reaffirm that the scarcity of solubility data continues to hamper progress in this field. To address this limitation, we experimentally quantified solubility changes for hundreds of single-point and multipoint mutants of haloalkane dehalogenase. Complemented with recent deep-mutational-scanning data on myoglobin, we employed both these data for external validation. Although the generalization to unseen proteins remains limited, our findings demonstrate the potential of integrating high-throughput assays with deep learning to improve the accuracy and scope of solubility prediction. Highlights- We present a novel anti-symmetric Siamese architecture for mutational prediction on structures built on graph-based convolutional neural networks ensuring SE(3) invariance. - We demonstrate that a model trained only on single-point mutants of a single protein derived from high-throughput experiments generalizes to multipoint mutants of unseen proteins, achieving the state-of-the-art binary prediction informedness of 26.5 %. - We address the key limitation in the domain by extending the available data with 277 single-point and multipoint mutants of haloalkane dehalogenase labelled in-house and a selection of recently published 1037 single-point mutants of myoglobin. - We systematically study how different data subsets affect the performance of the trained models, revealing that including yeast-derived high-throughput data in training hampers generalization to low-throughput assays but recovers the performance on the yeast-derived myoglobin dataset.

bioinformatics↗

bioRxiv2025

Molecular trick to reverse SN2 mechanism in hydrolytic enzyme

Hydrolytic haloalkane dehalogenase enzymes catalyze an SN2 nucleophilic substitution to erase halogen substituents in organohalogen compounds. The acid-base-nucleophile triad secures irreversible SN2 displacement of the halogen for the hydroxyl derived from the water. Catalysis relies on the protonatable imidazole ring of the histidine base, and its substitution with an asparagine traps the enzyme in a covalently bound intermediate state, a principle exploited in the widely used HaloTag technology. In contrast, the histidine-to-phenylalanine substitution triggers reversibility of the SN2 mechanism, but the molecular trick by which it reprograms the catalytic pathway remains unknown. Here, we show that the phenylalanine at the site of the histidine base spatially disturbs the adjacent residues, leading to the remodeling of surrounding active-site loops. Consequently, rerouting of the access tunnels imparts distinctive kinetic behavior, featuring a reversible SN2 chemical step that facilitates transhalogenation reactions. This information is crucial for engineering next-generation biocatalysts for sustainable chemistry. HighlightsO_LICatalytic triad (glutamate-histidine-aspartate) secures irreversible SN2 mechanism C_LIO_LIHistidine-to-phenylalanine substitution in the triad leads to a reversible SN2 reaction C_LIO_LIEnzyme active site rerouting is a hallmark of the catalytic reprogramming C_LIO_LIBasis for designing biocatalysts for sustainable transhalogenation chemistry C_LI The bigger pictureHaloalkane dehalogenases catalyze the hydrolytic cleavage of the carbon-halogen bond in halogenated hydrocarbons, a feature used in a wide variety of industrial and biotechnological processes. In HaloTag technology, the catalytic histidine is replaced by an asparagine to enable a stable covalent bonding between a probe and a protein for biological imaging, affinity purification, etc. Remarkably, this originally irreversible SN2 process becomes fully reversible in a histidine-to-phenylalanine mutant. Moreover, halogen ion product is released at this stage, offering a greener alternative for transhalogenation reactions compared to the conventional synthetic approach and a way to recycle environmentally harmful halogenated compounds. However, the optimization of enzymes is needed for applications on an industrial scale. Therefore, we investigated the kinetics of the SN2 step and structural features of these two enzyme mutants, providing a basis for subsequent optimization efforts.

bioengineering↗

bioRxiv2025

Kinetic mechanism of Renilla luciferase guides induced-fit engineering for improved bioluminescence

Luciferases are widely used bioluminescent reporters, yet the molecular determinants of their catalytic efficiency and light-emission stability remain incompletely understood. Here, we reconstruct the complete catalytic pathway of Renilla luciferase by combining steady-state, transient, and temperature-dependent kinetics with crystallography and molecular simulations. We show that the enzyme is substantially undersaturated with oxygen (Km,O2 = 719 M), causing its true turnover number (kcat = 21.9 s-1) to be systematically underestimated. Concurrently, elevated oxygen drives irreversible enzyme inactivation after ~1,500 turnovers, revealing a fundamental trade-off that limits oxygen-affinity engineering. Instead, the genuine bottleneck of the catalytic cycle is the induced-fit conformational opening of the product-bound enzyme. Selective engineering of this transition step through rational loop grafting yielded a variant AncFT-L14 with enhanced catalytic efficiency and glow-type bioluminescence with substantially slower signal decay in cell lysates. Collectively, our results identify conformational dynamics as a primary tunable determinant of luciferase function. More broadly, this work establishes a mechanistically grounded framework for the development of next-generation bioluminescent tools and for engineering enzymes controlled by dynamically gated ligand exchange.

biochemistry↗

bioRxiv2025

Structural insights into the evolution of ABH-fold luciferases

The alpha/beta-hydrolase (ABH) superfamily is a widespread and functionally versatile protein fold recognized for its ability to adapt to diverse molecular functions across all three domains of life. One such spectacular example of evolutionary adaptation at the ABH-fold represents an acquisition of oxygenolytic luciferase reaction that occurred within the hydrolytic haloalkane dehalogenase family. The molecular details of this evolution remain puzzling. In this work, we determine crystal structures and explore dynamical behavior of a bifunctional ancestral ABH-fold enzyme, highlighting molecular features associated with the transition from hydrolytic to oxygenolytic catalysis at this fold. Structures showed a canonical {beta}-sandwich shielded with a helical cap domain. The catalytic pocket is voluminous enough to accommodate a bulky substrate. Molecular docking showed productive binding of the coelenterazine substrate, as well as the product coelenteramide. Molecular dynamics simulations demonstrated that the coelenterazine entry does not present a major energetic barrier and identified a preferred binding orientation important for oxygenolytic catalysis. Comparisons between ancestral and extant enzymes highlighted specific amino acids and sequence motifs characteristic for oxygenolytic luciferases. Collectively, our results provide an expanded view of the evolutionary transition in which ABH-fold enzymes, originally using water to cleave chemical bonds, adapted to utilize dioxygen for bioluminescence.

evolutionary biology↗

bioRxiv2025

Taurine Inhibits Apolipoprotein E4 Aggregation

Apolipoprotein E4 (ApoE4) is a major genetic risk factor in many neurodegenerative diseases, yet effective therapeutic strategies targeting its associated pathologies remain unresolved. The aggregation of ApoE4, a key pathological feature, can be attenuated by tramiprosate and its metabolite 3-sulfopropanoic acid. In this study, we investigated the potential of taurine, a close chemical analogue of tramiprosate, to modulate ApoE4-mediated pathological processes. Using an integrated approach--including molecular dynamics simulations, static light scattering, mass spectrometry, and cerebral organoid models--we investigated taurines effects on ApoE4 aggregation. We found that taurine effectively inhibits ApoE4 aggregation. Notably, taurine significantly ameliorates the pathophysiological characteristics of ApoE4, bringing its phenotype closer to the more benign ApoE3 variant. By leveraging its neuroprotective properties, taurine may offer effects comparable to tramiprosate and 3-sulfopropanoic acid, positioning it as an accessible and promising candidate for mitigating neurodegeneration, particularly in individuals with the high-risk ApoE4/E4 genotype.

biochemistry↗

bioRxiv2025

Mechanism-Guided Engineering of Fluorinase Unlocks EfficientNucleophilic Biofluorination

The fluorinase enzyme, the only known biocatalyst forming stable carbon-fluorine bonds, operates with extremely low efficiency, catalyzing one reaction every 2-12 minutes. This severely limits its utility for sustainable biofluorination, and its sluggish activity remains poorly understood. We suppressed its aggregation through directed mutagenesis and elucidated the kinetic mechanism using a novel mathematical framework that fits complex kinetic and oligomerization data. This analysis revealed that >80% of enzyme molecules are inactive under standard conditions due to two dead-end pathways. The designed W50F+A279R mutant preferentially formed hexamers and displayed enhanced catalytic efficiency in this oligomeric state. When coupled with mechanism-based optimization of the reaction medium, including enzymatic removal of the inhibitory product, the catalytic turnover rate reached 12.5 {+/-} 2.1 min-{superscript 1}, representing [~]60-fold increase compared with previously reported turnover rates of the wild-type enzyme. Our work provides a mechanistic blueprint for fluorinase enhancement and a generalizable mathematical framework for analyzing kinetics of multimeric enzymes.

biochemistry↗

bioRxiv2024

Functional characterization of luciferase in a brittle star indicates parallel evolution influenced by genomic availability of haloalkane dehalogenase

Determining why convergent traits use distinct versus shared genetic components is crucial for understanding how evolutionary processes generate and sustain biodiversity. However, the factors dictating the genetic underpinnings of convergent traits remain incompletely understood. Here, we use heterologous protein expression, biochemical assays, and phylogenetic analyses to confirm the origin of a luciferase gene from haloalkane dehalogenases in the brittle star Amphiura filiformis. Through database searches and gene tree analyses, we also show a complex pattern of presence and absence of haloalkane dehalogenases across organismal genomes. These results first confirm parallel evolution across a vast phylogenetic distance, because octocorals like Renilla also use luciferase derived from haloalkane dehalogenases. This parallel evolution is surprising, even though previously hypothesized, because many organisms that also use coelenterazine as the bioluminescence substrate evolved completely distinct luciferases. The inability to detect haloalkane dehalogenases in the genomes of several bioluminescent groups suggests that the distribution of this gene family influences its recruitment as a luciferase. Together, our findings highlight how biochemical function and genomic availability help determine whether distinct or shared genetic components are used during the convergent evolution of traits like bioluminescence.

evolutionary biology↗

bioRxiv2024

Analysis of Mutations in Precision Oncology using The Automated, Accurate, and User-Friendly Web Tool PredictONCO

Next-generation sequencing technology has created many new opportunities for clinical diagnostics, but it faces the challenge of functional annotation of identified mutations. Various algorithms have been developed to predict the impact of missense variants that influence oncogenic drivers. However, computational pipelines that handle biological data must integrate multiple software tools, which can add complexity and hinder non-specialist users from accessing the pipeline. Here, we have developed an online user-friendly web server tool PredictONCO that is fully automated and has a low barrier to access. The tool models the structure of the mutant protein in the first step. Next, it calculates the protein stability change, pocket level information, evolutionary conservation, and changes in ionisation of catalytic amino acid residues, and uses them as the features in the machine-learning predictor. The XGBoost-based predictor was validated on an independent subset of held-out data, demonstrating areas under the receiver operating characteristic curve (ROC) of 0.95 and 0.94, and the average precision from the precision-recall curve 0.98 and 0.94 for structure-based and sequence-based predictions, respectively. Finally, PredictONCO calculates the docking results of small molecules approved by regulatory authorities. We demonstrate the applicability of the tool by presenting its usage for variants in two cancer-associated proteins, cellular tumour antigen p53 and fibroblast growth factor receptor FGFR1. Our free web tool will assist with the interpretation of data from next-generation sequencing and navigate treatment strategies in clinical oncology: https://loschmidt.chemi.muni.cz/predictonco/.

bioinformatics↗

bioRxiv2024

A Computational Workflow for Analysis of Missense Mutations in Precision Oncology

Every year, more than 19 million cancer cases are diagnosed, and this number continues to increase annually. Since standard treatment options have varying success rates for different types of cancer, understanding the biology of an individuals tumour becomes crucial, especially for cases that are difficult to treat. Personalised high-throughput profiling, using next-generation sequencing, allows for a comprehensive examination of biopsy specimens. Furthermore, the widespread use of this technology has generated a wealth of information on cancer-specific gene alterations. However, there exists a significant gap between identified alterations and their proven impact on protein function. Here, we present a bioinformatics pipeline that enables fast analysis of a missense mutations effect on stability and function in known oncogenic proteins. This pipeline is coupled with a predictor that summarises the outputs of different tools used throughout the pipeline, providing a single probability score, achieving the balanced accuracy above 86%. The pipeline incorporates a virtual screening method to suggest potential FDA/EMA-approved drugs to be considered for treatment. We showcase three case studies to demonstrate the timely utility of this pipeline. To facilitate access and analysis of cancer-related mutations, we have packaged the pipeline as a web server, which is freely available at https://loschmidt.chemi.muni.cz/predictonco/.

cancer biology↗