Biology subjects

Kravchenko, A.

Publications and source records attributed to Kravchenko, A..

4 recordsLinked to original sources

bioRxiv2026

The development of coarse and fine feature tuning in infant visual cortex

Objects can be recognized from visual features at multiple spatial scales, from coarse global shape to fine local detail. How infant visual cortex encodes these features across the visual hierarchy, however, remains unknown. We combined awake infant fMRI with developmentally inspired deep neural networks (DNNs) trained and tested with different levels of image blur to probe cortical tuning to coarse and fine visual features. V1 showed mature tuning to coarse and fine features by 2 months. In contrast, infant extrastriate cortex (V2-V4) followed a protracted developmental trajectory and was biased toward coarse object features relative to adults. This relative bias persisted at 9 months and was not readily explained by developing acuity. The sustained coarse-feature bias contrasted with DNNs, in which low-spatial-frequency representations took longest to converge with visual cortex during training, revealing distinct developmental biases in object-feature tuning in biological and artificial visual systems.

neuroscience↗

bioRxiv2026

Integrating the MARTINI2 Coarse-Grained Force Field into HADDOCK3 for Faster Modelling of Large Biomolecular Complexes

The integration of coarse-grained (CG) approaches into docking workflows offers a powerful strategy for modelling large biomolecular assemblies with reduced computational costs. We present here the implementation of the MARTINI2 coarse-grained force field into the HADDOCK3 integrative modelling platform. This development enables the use of the CG representations and parameters within HADDOCK3 for efficient sampling and scoring of large protein-protein complexes. The implementation takes advantage of the modular and flexible architecture of HADDOCK3, allowing a seamless combination of MARTINI2 representation with the various modules. Conversion from and to all-atom models is integrated into the coarse-grained modelling workflow. The performance of the protocol is first assessed on protein-protein and protein-DNA benchmarks and then illustrated on a few representative large-scale systems, demonstrating a significant reduction in computational costs while maintaining biologically relevant accuracy.

bioinformatics↗

bioRxiv2025

HADDOCK3: A modular and versatile platform for integrative modelling of biomolecular complexes

HADDOCK is a widely used resource for integrative modelling of a variety of biomolecular complexes that is able to incorporate experimental knowledge into physics-based calculations during complex prediction, refinement, scoring and analysis. Here we introduce HADDOCK3, the new modular version of the program, in which the original, parameterisable albeit rigid pipeline has been first broken down in a catalogue of independent modules and then enriched with powerful analysis tools and third-party integrations. Thanks to this increased flexibility, HADDOCK3 can now handle multiple integrative modelling scenarios, providing a valuable, physics-based tool to enrich and complement the predictions made by machine learning algorithms in the post-AlphaFold era. We present examples of successful applications of HADDOCK3 that were not feasible with the previous versions of HADDOCK, highlighting its expanded capabilities. The HADDOCK3 software source code is freely available from the GitHub repository (https://github.com/haddocking/haddock3) and comes with an online user guide (www.bonvinlab.org/haddock3-user-manual). All example data described in this manuscript are available at https://github.com/haddocking/haddock3-paper-data.

bioinformatics↗

bioRxiv2024

Root-pore interactions, the underestimated driver for rhizosphere structure and rhizosheath development

Physical characteristics of rhizosphere and rhizosheath, i.e. root-adhering soil, are crucial for plant performance. Yet, the drivers of the rhizospheres structural properties and their relationships with rhizosheath development remain unclear. We used X-ray computed micro-tomography (i) to explore two drivers of rhizosphere porosity: root-induced changes vs. preferential root growth into soil with certain pore characteristics and (ii) to estimate their contributions to rhizosphere macroporosity gradients and rhizosheath formation. Rhizosheath development was assessed in relation to rhizosphere macroporosity and rhizodeposition after {superscript 1}C labeling. Our results confirmed that both root-induced changes and growth preferences shape rhizosphere structure, with their relative significance depending on the inherent macropore availability. In intact soils, growth preferences were the dominant factor, while in sieved soils the root-induced changes became equally important. Rhizosheath formation was associated with roots compacting their surrounding and releasing carbon. However, no correlation was found between rhizosheath formation and the actual rhizosphere, i.e., the volume of soil adjacent to the roots. The study offers new process-level understanding of rhizosphere porosity gradients, while emphasizing caution in interpreting root growth data from sieved soil studies. Similarly, traditional destructively sampled rhizosheath may not fully capture the true characteristics of the actual rhizosphere, underscoring importance of intact-soil analyses.

biophysics↗