Biology subjects

Kohout, P.

Publications and source records attributed to Kohout, P..

7 recordsLinked to original sources

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

Niche constraints drive differences between mycorrhizal fungal guilds in future range shifts

Mycorrhizal fungi are a diverse and ubiquitous group of plant symbionts whose distribution strongly influences ecosystem function across the globe. Yet, until now, we do not have quantitative data on the range sizes of different mycorrhizal fungal taxa, limiting our capacity to forecast future shifts in community composition and function. Here, we use 115,924 DNA sequence-derived observations to map the distribution of 651 common mycorrhizal fungal taxa and forecast future changes to their range sizes. We demonstrate that climate, plant cover, and soil factors, particularly mean annual temperature, net primary productivity, and soil organic carbon, exert major control over mycorrhizal fungal distributions. Based on these drivers, the ecological niches of mycorrhizal fungi consistently differ between arbuscular and ectomycorrhizal functional guilds. Arbuscular mycorrhizal fungal taxa generally occupy a wider niche breadth than ectomycorrhizal fungi, occurring across larger ranges of climate, soil, plant cover, topography, and disturbance conditions. Our models also predict widespread decreases in the suitable range size of mycorrhizal fungal taxa under projected future global climates, with average ranges decreasing by 13.8% or ~2.2 million km2 under high emissions scenarios (ssp5-8.5). This decrease in projected range size will be most pronounced for ectomycorrhizal fungi, strongly linked to constraints from their smaller overall niches. By generating a global atlas of common mycorrhizal fungi and their associated environmental niche, we establish a critical baseline for widely suspected declines in global fungal biodiversity.

ecology↗

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

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↗

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

Closing the gap: Examining the impact of source habitat proximity on plant and soil microbial communities in post-mining spoil heap primary succession

Revegetation of barren substrates is often determined by the composition and distance of the nearest plant community, serving as a source of colonising propagules. Whether such dispersal effect can be observed during the development of soil microbial communities, is not clear. In this study, we aimed to elucidate which factors structure plant and soil bacterial and fungal communities during primary succession on a limestone quarry spoil heap, focusing on the effect of distance to the adjoining xerophilous grassland. We established a grid of 35 plots covering three successional stages - initial barren substrate, early successional community and late successional grassland ecosystem, the latter serving as the primary source of soil colonization. On these plots, we performed vegetation surveys of plant community composition and collected soil cores to analyse soil chemical properties and bacterial and fungal community composition. The composition of early successional plant community was significantly affected by the proximity of the source late successional community, however, the effect weakened when the distance exceeded 20 m. Early successional microbial communities were structured mainly by the local plant community composition and soil chemical properties, with minimal contribution of the source community proximity. These results show that on small spatial scales, species migration is an important determinant of plant community composition during primary succession while the establishment of soil microbial communities is not limited by dispersal and is primarily driven by local biotic and abiotic conditions.

microbiology↗

bioRxiv2020

GlobalFungi: Global database of fungal records from high-throughput-sequencing metabarcoding studies

Fungi are key players in vital ecosystem services, spanning carbon cycling, decomposition, symbiotic associations with cultural and wild plants and pathogenicity. The high importance of fungi in the ecosystem processes contrasts with the incompleteness of understanding of the patterns of fungal biogeography and the environmental factors that drive it. To close this gap of knowledge, we have here collected and validated data published on the composition of soil fungal communities in terrestrial environments including soil and plant-associated habitats and made them publicly accessible through a user interface at http://globalfungi.com. The GlobalFungi database contains over 650 million observations of fungal sequences across >20 000 samples with geographical locations and additional metadata contained in 207 original studies with millions of unique sequence variants of the fungal internal transcribed spacers (ITS) 1 and 2 representing fungal species and genera. As it is, the study represents the most comprehensive atlas of fungal distribution on the global scale open to further additions.

ecology↗