Computational and Structural Biotechnology Journal

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Computational and Structural Biotechnology Journal

Computational and Structural Biotechnology Journal (ISSN 2001-0370) is an online journal publishing research articles and reviews after full peer review.

Computational and Structural Biotechnology Journal (CSBJ) is an open access journal (Impact Factor 4.8) and is composed of four specialty sections:
• General • Smart Hospital • Nanoscience and Advanced Materials • Quantum Biology and Biophotonics. All articles are published, without barriers to access, immediately upon acceptance. The journal places a strong emphasis on functional and mechanistic understanding of how molecular components in a biological process work together through the application of computational methods. Structural data may provide such insights, but they are not a pre-requisite for publication in the journal. Specific areas of interest include, but are not limited to:

• Structure and function of proteins, nucleic acids and other macromolecules
• Structure and function of multi-component complexes
• Protein folding, processing and degradation
• Enzymology
• Computational and structural studies of plant systems
• Microbial Informatics
• Genomics
• Proteomics
• Metabolomics
• Algorithms and Hypothesis in Bioinformatics
• Computational Chemistry & Drug Discovery
• Microscopy and Molecular Imaging
• Nanotechnology

While all general topics related to Computational and Structural Biology are welcomed, the editors reserve the right to pre-screen submissions based on the suitability of the topic of a submission and, therefore, the right as whether a manuscript will be processed/reviewed or not. Even though experimental validation is not required for publication, reliability and significance of biological discovery are validated and enriched by experimental studies. The journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence, and enables the rapid publication of papers under the following categories:

• Research articles
• Review articles
• Mini Reviews
• Highlights
• Communications
• Software/Web server articles
• Methods articles
• Database articles
• Book Reviews
• Meeting Reviews

28/08/2026

Computational and Structural Biotechnology Journal (CSBJ), a Science Partner Journal published in collaboration with AAAS, is inviting submissions for its upcoming special issue:

“𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗼𝗿 𝗡𝗲𝘅𝘁-𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗕𝗶𝗼-𝗗𝗮𝘁𝗮 𝗠𝗶𝗻𝗶𝗻𝗴”

📌 Topics include (but are not limited to):
• Artificial intelligence-driven analysis of large-scale biological and clinical datasets
• Protein and RNA structure prediction using deep learning approaches
• Gene and genome annotation with machine learning techniques
• Biomedical and clinical text mining and natural language processing
• Information retrieval and knowledge extraction from biomedical databases
• Genomic, transcriptomic, and multi-omics data integration and analysis
• Human microbiome data mining and modeling
• Medical ontologies and semantic data integration
• Named entity recognition and concept extraction in biomedical text
• Discovery of sequence and structural motifs
• Modeling of biochemical pathways and biological networks
• Image mining in medical and healthcare informatics
• AI-driven approaches in drug discovery, systems biology, and biomedical workflows

⏳ Submission Deadline: 30 November 2026

🔗 For more info about the Call for Papers, please visit: https://spj.science.org/journal/csbj/si/deep-learning-bio-data

🧠 Guest Editors:
• Sunjae Lee, Korea Advanced Institute of Science and Technology, South Korea
• Sejoon Lee, Seoul National University Bundang Hospital, South Korea
• Sunyong Yoo, Chonnam National University, South Korea
• Junho Kim, Sungkyunkwan University, South Korea

; KAIST, 분당서울대학교병원, 전남대학교, 성균관대학교-Sungkyunkwan University, Computational and Structural Biotechnology Journal, Science Partner Journals, AAAS - The American Association for the Advancement of Science

28/08/2026

🤖 Can artificial intelligence reveal how bacterial strains differ beneath the surface?

This study introduces a Flux-to-AI framework that combines genome-scale metabolic modeling, machine learning, and metabolism-and-expression (ME) modeling to predict strain-specific metabolic capabilities in Pseudomonas.

📝 Carlos Focil-Espinosa, Christopher Dalldorf, Diego Martinez, Alejandro Zepeda, Cristal Zuniga

🔗 Predicting Strain-Specific Metabolic Capabilities in the Genus Pseudomonas with a Flux-to-AI Approach Reveals Hidden Cell Envelope Properties. Computational and Structural Biotechnology Journal (CSBJ). DOI: https://doi.org/10.34133/csbj.0190

📚 CSBJ - A Science Partner Journal: https://spj.science.org/journal/csbj

San Diego State University, Facultad de Ingeniería Química UADY, Universidad Autónoma de Yucatán UADY, Tecnológico Nacional de México, Great Lakes Bioenergy Research Center, Computational and Structural Biotechnology Journal, Science Partner Journals, AAAS - The American Association for the Advancement of Science,

27/08/2026

🧬 Can we trust membrane protein annotations when databases tell different stories?

This study presents MetaMP, a membrane protein reconciliation and benchmarking platform that harmonizes fragmented annotations from MPstruc, RCSB PDB, OPM, and UniProt into a unified, searchable resource. By making cross database discrepancies explicit, traceable, and biologically interpretable, MetaMP provides researchers with a reproducible framework for annotation harmonization, expert guided curation, quality control, and membrane protein benchmarking.

📝 Ebenezer Awotoro, Chisom Anyabolu, Florian Schwarz, Johannes Tauscher, Dominik Heider, Katharina Ladewig, Christel Le Bon, Karine Moncoq, Bruno Miroux, Georges Hattab

🔗 MetaMP Ecosystem for Unified, Auditable, and Benchmark-Ready Data for Reliable Membrane Protein Annotation. Computational and Structural Biotechnology Journal (CSBJ). DOI: https://doi.org/10.34133/csbj.0165

📚 CSBJ - A Science Partner Journal: https://spj.science.org/journal/csbj

Computational and Structural Biotechnology Journal, Science Partner Journals, AAAS - The American Association for the Advancement of Science,

26/08/2026

🧬 How can we make nucleosome profiling more scalable, quantitative, and reproducible?

This study presents a scalable MNase-seq framework for nucleosome profiling across pluripotent stem cells and cardiomyocyte models. By systematically optimizing experimental conditions and introducing a cost-effective yeast spike-in strategy for quantitative normalization, the proposed workflow addresses key challenges in MNase-seq, including technical variability, high cell input requirements, and limited standardization. The approach makes nucleosome profiling more accessible across diverse biological models.

📝 Chris Thekkedam, David T. Humphreys, Marina Naval-Sanchez, Amy M. Nicks, Richard P. Harvey, Osvaldo Contreras

🔗 A Scalable MNase-seq Framework for Reproducible Nucleosome Profiling across Pluripotent Stem Cell and Cardiomyocyte Models. Computational and Structural Biotechnology Journal (CSBJ). DOI: https://doi.org/10.34133/csbj.0204

📚 CSBJ - A Science Partner Journal: https://spj.science.org/journal/csbj

Victor Chang Cardiac Research Institute, The University of Queensland, UNSW Medicine & Health, UNSW, Computational and Structural Biotechnology Journal, Science Partner Journals, AAAS - The American Association for the Advancement of Science,

25/08/2026

Computational and Structural Biotechnology Journal (CSBJ), a 𝘚𝘤𝘪𝘦𝘯𝘤𝘦 𝘗𝘢𝘳𝘵𝘯𝘦𝘳 𝘑𝘰𝘶𝘳𝘯𝘢𝘭 published in collaboration with AAAS, is inviting submissions for its upcoming special issue:

“𝗖𝗼𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗮𝗻𝗱 𝗘𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝗮𝗹 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝘀 𝗶𝗻 𝗖𝗮𝗿𝗯𝗼𝗵𝘆𝗱𝗿𝗮𝘁𝗲-𝗔𝗰𝘁𝗶𝘃𝗲 𝗘𝗻𝘇𝘆𝗺𝗲𝘀”

📌 Topics include (but are not limited to):
• Genome mining, metagenomics, and multi-omics approaches for CAZyme discovery
• Bioinformatics tools, databases, computational pipelines, and annotation frameworks
• Structure prediction, molecular dynamics simulations, and mechanistic studies of CAZymes
• Artificial intelligence, machine learning, and foundation models for enzyme function prediction and protein engineering
• Discovery and engineering of glycoside hydrolases, glycosyltransferases, carbohydrate esterases, polysaccharide lyases, and auxiliary activity enzymes
• Lytic polysaccharide monooxygenases (LPMOs): structure, mechanism, evolution, and applications
• Protein engineering, directed evolution, and de novo enzyme design
• High-throughput screening and advanced experimental and biophysical approaches for enzyme characterization
• Microbial carbohydrate metabolism, microbiomes, and host–microbe interactions
• Synthetic biology and metabolic engineering of carbohydrate-active pathways
• CAZymes in environmental sustainability, circular bioeconomy, and biomass valorization
• Industrial applications in bioenergy, biorefineries, food biotechnology, pharmaceuticals, and biomaterials
• Comparative genomics and evolutionary analysis of CAZyme repertoires
• Emerging computational and experimental strategies for novel CAZyme discovery and functional validation

⏳ Submission Deadline: 31 May 2027

🔗 For more info about the Call for Papers, please visit: https://spj.science.org/journal/csbj/si/carbohydrate-active-enzymes

🧠 Guest Editors:
• Ragothaman Yennamalli, Jawaharlal Nehru University, JNU, India
• Madhuprakash Jogi, University of Hyderabad, India

Computational and Structural Biotechnology Journal, Science Partner Journals, AAAS - The American Association for the Advancement of Science,

24/06/2026

🛰️ What happens to human biology on planets with different magnetic fields?

This study explores how quantum-level magnetic interactions may have shaped the evolution of life on Earth through the “radical pair mechanism,” a process where weak magnetic fields influence electron spin dynamics, reactive oxygen species (ROS), and biological signaling. The authors propose that biological systems may have evolutionarily fine-tuned magnetic parameters to function optimally within Earth’s geomagnetic field, opening new perspectives on quantum biology, health, disease, and even space exploration.

📝 Betony Adams, Abbas Hassasfar, Ilya Sinayskiy, Alistair Nunn, Geoffrey Guy, Francesco Petruccione

🔗 Quantum Effects in Evolution: Terrestrial Fine-Tuning of Magnetic Parameters. Computational and Structural Biotechnology Journal (CSBJ). DOI: https://doi.org/10.34133/csbj.0125

📚 CSBJ Quantum Biology and Biophotonics: https://spj.science.org/journal/csbj/qbio

Stellenbosch University, University of Westminster, The National Institute for Theoretical and Computational Sciences, University of KwaZulu-Natal, Computational and Structural Biotechnology Journal, Science Partner Journals, AAAS - The American Association for the Advancement of Science

20/06/2026

📈 𝗖𝗦𝗕𝗝 𝗔𝗰𝗵𝗶𝗲𝘃𝗲𝘀 𝗡𝗲𝘄 𝗠𝗶𝗹𝗲𝘀𝘁𝗼𝗻𝗲𝘀 𝗶𝗻 𝗜𝗺𝗽𝗮𝗰𝘁 𝗮𝗻𝗱 𝗩𝗶𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆

We are pleased to share that the Computational and Structural Biotechnology Journal (CSBJ) has received an Impact Factor of 4.8 (2025) in the latest Journal Citation Reports™ 2026 released by Clarivate.

CSBJ has also achieved a 2025 CiteScore of 8.2 according to Scopus and is now ranked #8 among journals in the Structural Biology category.

These achievements reflect the quality, relevance, and growing impact of the research published in CSBJ. More importantly, they represent the collective efforts of our scientific community.

🙏 We extend our sincere gratitude to our Editors-in-Chief, Associate Editors, Editorial Team Members, reviewers, authors, and readers for their dedication, expertise, and continued support. Their contributions have been instrumental in establishing CSBJ as a trusted platform for interdisciplinary research at the interface of biotechnology, computational sciences, and emerging technologies.

🔬 𝗔 𝗛𝗼𝗺𝗲 𝗳𝗼𝗿 𝗜𝗻𝘁𝗲𝗿𝗱𝗶𝘀𝗰𝗶𝗽𝗹𝗶𝗻𝗮𝗿𝘆 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵

CSBJ welcomes high-quality submissions across four dedicated sections:

🧬 General: Advancing the functional and mechanistic understanding of biological systems through computational, theoretical, and data-driven approaches.

🏥 Smart Hospital: Exploring digital health, artificial intelligence, automation, and next-generation technologies transforming healthcare delivery and hospital systems.

⚛️ Nanoscience & Advanced Materials: Bridging nanotechnology, materials science, chemistry, physics, and biomedical engineering to develop innovative solutions with real-world impact.

💡 Quantum Biology & Biophotonics: Investigating quantum phenomena in biological systems and advancing optical and photonic technologies for biomedical and health applications.

Together, these sections provide a broad and interdisciplinary forum for research that addresses fundamental biological questions while driving technological innovation.

🚀 𝗘𝗮𝘀𝗶𝗲𝗿 𝗧𝗵𝗮𝗻 𝗘𝘃𝗲𝗿 𝘁𝗼 𝗦𝘂𝗯𝗺𝗶𝘁

To support researchers and accelerate scientific communication, CSBJ offers several author-friendly submission pathways.

🔄 bioRxiv-to-Journal (B2J) Transfer Service: Authors can now transfer manuscripts directly from bioRxiv to CSBJ through the bioRxiv-to-Journal (B2J) service. Manuscripts, metadata, and author information can be transferred seamlessly, eliminating the need to re-upload files or re-enter submission details. This streamlined workflow helps researchers move efficiently from preprint dissemination to peer-reviewed publication while supporting the principles of open science.

📝 Your Paper-Your Way: CSBJ follows the "Your Paper-Your Way" policy, making initial submission straightforward and flexible. Our goal is simple: allow researchers to focus on the science, not on formatting.

📢 𝗪𝗲 𝗜𝗻𝘃𝗶𝘁𝗲 𝗬𝗼𝘂𝗿 𝗠𝗮𝗻𝘂𝘀𝗰𝗿𝗶𝗽𝘁 𝗦𝘂𝗯𝗺𝗶𝘀𝘀𝗶𝗼𝗻𝘀

As CSBJ continues to expand its global reach and scientific influence, we invite researchers, innovators, and interdisciplinary teams to submit their latest findings to the journal.

Whether your work advances computational biology, biotechnology, structural biology, digital health, nanoscience, advanced materials, quantum biology, biophotonics, or related fields, CSBJ provides a rigorous, visible, and author-friendly platform for sharing impactful discoveries with the international scientific community.

We look forward to receiving your submissions and working together to advance scientific knowledge and innovation.

🔗 For more information, please visit: https://spj.science.org/page/csbj/for-authors

📊 𝟮𝟬𝟮𝟱 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀

✅ Impact Factor (JCR 2026): 4.8
✅ CiteScore (Scopus 2025): 8.2
✅ CiteScore Rank in Structural Biology: #8

Computational and Structural Biotechnology Journal, Science Partner Journals, AAAS - The American Association for the Advancement of Science

09/06/2026

🤖 Have we built AI powerful enough to design proteins, but too complicated for most scientists to use?

This study introduces BioPipelines, an open-source framework that simplifies computational protein and ligand design by integrating more than 40 AI-driven and bioinformatics tools into streamlined workflows. By reducing the technical barriers associated with protein engineering and drug discovery, BioPipelines enables experimental scientists to harness advanced computational methods with minimal programming expertise.

📝 Gianluca Quargnali, Pablo Rivera-Fuentes

🔗 BioPipelines: Accessible Computational Protein and Ligand Design for Chemical Biologists. Computational and Structural Biotechnology Journal (CSBJ). DOI: https://doi.org/10.34133/csbj.0129

📚 CSBJ - A Science Partner Journal: https://spj.science.org/journal/csbj

Universität Zürich, Computational and Structural Biotechnology Journal, Science Partner Journals, AAAS - The American Association for the Advancement of Science

09/06/2026

🧬 If structure determines function, how can we optimize DNA therapeutics without accurately predicting their 3D shape?

This study evaluated current computational methods for predicting the 3D structures of single-stranded DNA (ssDNA) oligonucleotides, addressing one of the major unresolved challenges in structural biology. Accurate ssDNA structure prediction has the potential to accelerate the development of aptamers, biosensors, genome editing tools, and DNA-based therapeutics.

📝 Selma Bengaouer, Thomas Binet, Stéphane Octave, Séverine Padiolleau-Lefèvre, Bérangère Avalle, Irene Maffucci

🔗 Predicting Single-Stranded DNA Oligonucleotides 3D Structures: An Open Issue. Computational and Structural Biotechnology Journal (CSBJ). DOI: https://doi.org/10.34133/csbj.0127

📚 CSBJ - A Science Partner Journal: https://spj.science.org/journal/csbj

UTC - Université de Technologie de Compiègne, Computational and Structural Biotechnology Journal, Science Partner Journals, AAAS - The American Association for the Advancement of Science

09/06/2026

🦠 Can a redesigned bacterial platform reshape the future of protein engineering?

This study introduces an optimized quantitative bacterial two-hybrid (qB2H) system for assessing protein-protein interactions (PPIs), providing researchers with a robust and scalable platform for protein engineering, AI-driven discovery, and therapeutic development.

📝 Antoine Guyot, Emma Maillard, Kelly Ferreira-Pinto, Laure Plançon-Arnould, Aravindan Arun Nadaradjane, Raphaël Guérois, Francoise Ochsenbein, Loïc Martin, Oscar Henrique Pereira Ramos

🔗 Optimized Quantitative Bacterial Two-Hybrid (qB2H) for Protein–Protein Interaction Assessment. Computational and Structural Biotechnology Journal (CSBJ). DOI: https://doi.org/10.34133/csbj.0098

📚 CSBJ - A Science Partner Journal: https://spj.science.org/journal/csbj

Computational and Structural Biotechnology Journal, Science Partner Journals, AAAS - The American Association for the Advancement of Science

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