Biology subjects

Sexson, N.

Publications and source records attributed to Sexson, N..

3 recordsLinked to original sources

bioRxiv2026

SpaceBio Knowledge Hub: A LiteratOmics Platform for Microgravity and Space Biology Research

Space biology literature is growing exponentially. Existing infrastructure has not kept pace with organizing, synthesizing, and disseminating this knowledge. We present SpaceBio SpaceBio Knowledge Hub (www.spacebio.space), an integrated digital ecosystem that combines artificial intelligence, real-time data integration, and open-access infrastructure to advance research, education, and collaboration in microgravity, space biology and space exploration. The platform applies AI-driven approaches including natural language processing, machine learning, and automated content generation to construct a semantic atlas of the field. The atlas reveals the hierarchical thematic organization underlying microgravity-induced biological responses, space mission infrastructure, planetary science, and astrobiology. As part of this effort, SpaceBio is moving toward the construction of a LiteratOmics framework for microgravity, and space biology a systematic, AI-enabled approach to mining, integrating, and structuring the primary literature generated by omics-driven spaceflight research, treating the scientific literature itself as a navigable data layer alongside genomic, transcriptomic, and proteomic datasets. Built on a scalable, cloud-based architecture with a user-centered interface, SpaceBio supports literature exploration, data integration, and knowledge discovery for researchers, educators, students, industry partners, and citizen scientists. The platform also functions as a community-building ecosystem. It integrates hands-on research initiatives, AI-generated educational content, pilot data science projects, and social responsibility programs that broaden participation without compromising scientific rigor. AI-enabled digital environments can transform fragmented literature into a navigable knowledge landscape. SpaceBio accelerates research productivity, strengthens STEM education, and supports the global space life sciences community as human space exploration enters in the most ambitious era.

scientific communication and education↗

bioRxiv2025

InteracTor: Feature Engineering and Explainable AI for Profiling Protein Structure-Interaction-Function Relationships

Characterizing protein families structural and functional diversity is essential for understanding their biological roles. Traditional analyses often focus on primary and secondary structures, which may not fully capture complex protein interactions. Here we introduce InteracTor, a novel toolkit that extracts multimodal features from protein three-dimensional (3D) structures, including interatomic interactions like hydrogen bonds, van der Waals forces, and hydrophobic contacts. By integrating Explainable AI (XAI) techniques, we quantified the importance of the extracted features in the classification of protein structural and functional families. InteracTors interpretable features enable mechanistic insights into the determinants of protein structure, function, and dynamics, offering a transparent means to assess their predictive power within machine learning models. Interatomic interaction features extracted by InteracTor demonstrated superior predictive power for protein family classification compared to features based solely on primary or secondary structure, revealing the importance of considering specific tertiary contacts in computational protein analysis. This work provides a robust framework for future studies aiming to enhance the capabilities of models for protein function prediction and drug discovery. AUTHOR SUMMARYInteracTor is a computational toolkit designed to enhance our understanding of protein structure and function by focusing on three-dimensional (3D) structural interactions. Unlike traditional approaches that primarily rely on sequence or secondary structure data, InteracTor extracts biologically meaningful features such as hydrogen bonds, van der Waals forces, and hydrophobic contacts, which are critical for protein stability and dynamics. By integrating these features into machine learning models alongside explainable AI methods, InteracTor provides interpretable insights into how specific structural interactions influence protein behavior. Our results demonstrate that tertiary structure features significantly improve the accuracy of protein family classification compared to sequence-based methods alone, underscoring the importance of considering 3D interactions in computational protein analyses. The toolkits modular design makes it adaptable for diverse applications, including drug discovery and protein engineering. In a broader context, InteracTor bridges the gap between computational biology and practical applications in medicine and biotechnology by offering a transparent and robust framework for analyzing proteins at a molecular level. This work represents a step forward in leveraging structural data to advance predictive modeling and biological discovery.

bioinformatics↗

bioRxiv2024

InteracTor: A new integrative feature extraction toolkit for improved characterization of protein structural properties

Understanding the structural and functional diversity of protein families is crucial for elucidating their biological roles. Traditional analyses often focus on primary and secondary structures, which include amino acid sequences and local folding patterns like alpha helices and beta sheets. However, primary and secondary structures alone may not fully represent the complex interactions within proteins. To address this limitation, we developed a new algorithm (InteracTor) to analyze proteins by extracting features from their three-dimensional (3D) structures. The toolkit extracts interatomic interaction features such as hydrogen bonds, van der Waals interactions, and hydrophobic contacts, which are crucial for understanding protein dynamics, structure, and function. Incorporating 3D structural data and interatomic interaction features provides a more comprehensive understanding of protein structure and function, potentially enhancing downstream predictive modeling capabilities. By using the extracted features in Mutual Information scoring (MI), Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), and hierarchical clustering analysis as use cases, we identified clear separations among protein structural families, highlighting distinct functional aspects. Our analysis revealed that interatomic interaction features were more informative than protein secondary structure features, providing insights into potential structural and functional properties. These findings underscore the significance of considering tertiary structure in protein analysis, offering a robust framework for future studies aiming at enhancing the capabilities of models for protein function prediction and drug discovery.

bioinformatics↗