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Call for papers - Computational inference of protein conformations and interactions

Guest Editors

Jacob D. Durrant, PhD, University of Pittsburgh, USA
Tatiana Galochkina, PhD, University Paris Cité & INSERM, France
Balachandran Manavalan, PhD, Sungkyunkwan University, South Korea

Submission Status: Open   |   Submission Deadline: 15 January 2025


BMC Biology is calling for submissions to our Collection on Computational inference of protein conformations and interactions.

This Collection welcomes submissions on the prediction of protein-protein interactions, protein/small-molecule interactions, and binding motifs. Additionally, we encourage submissions focusing on conformational dynamics and intrinsically disordered regions (IDRs). We welcome manuscripts describing the application of machine learning and deep learning to address these important questions.

Meet the Guest Editors

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Jacob D. Durrant, PhD, University of Pittsburgh, USA

Dr Jacob D. Durrant is an Associate Professor of Biological Sciences at the University of Pittsburgh. His research focuses on developing and applying computer-aided drug design (CADD) techniques to advance early-stage ligand discovery. Dr Durrant’s lab develops machine-learning and big-data tools to predict ligand poses and affinities, as well as simulation tools to study protein motions that impact ligand binding. Using these and related tools, he has discovered over seventy validated protein-binding small molecules targeting more than a dozen disease-relevant proteins. Dr Durrant’s work also aims to encourage broad tool adoption through open-source software development and an emphasis on usability.

Tatiana Galochkina, PhD, University Paris Cité & INSERM, France

Dr Tatiana Galochkina holds a PhD in Applied Mathematics from Université Lyon 1 and PhD in Mathematical and Physical Sciences from Lomonosov Moscow State University, and currently works as an Associate Professor in bioinformatics at Université Paris Cité, team DSIMB. Her research is focused on the analysis and prediction of protein dynamics and interactions with other molecules, in particular, with carbohydrates, using a combination of molecular modeling and machine learning approaches. Finally, Dr Galochkina is an author and teaches two master courses on machine learning applications to biological data at Université Paris Cité.

Balachandran Manavalan, PhD, Sungkyunkwan University, South Korea

Dr Balachandran Manavalan is an Assistant Professor at the Department of Integrative Biotechnology, Sungkyunkwan University (SKKU). He earned his PhD in Computational Biology from Ajou University in 2011 and subsequently served as a research fellow and research assistant professor at Korea Institute for Advanced Study and Ajou University School of Medicine. He established his research group at SKKU’s Department of Integrative Biotechnology in 2022. Dr Manavalan's research focuses on artificial intelligence, bioinformatics, machine learning, big data, proteomics, and functional genomics. His remarkable research achievements have placed him the top 2% highly cited researcher for the past four consecutive years, according to the Stanford University data.

About the Collection

BMC Biology is calling for submissions to our Collection on Computational inference of protein conformations and interactions.

With advancements in computational techniques and the exponential growth of available biological data, the prediction of protein conformational dynamics and interactions with diverse molecular targets has garnered significant attention. In silico tools can identify binding motifs and predict protein interactions with other proteins and small-molecule ligands (e.g., drugs, lipids, sugars, nucleotides), driving advancements in drug discovery and personalized medicine. 

This Collection welcomes submissions on the prediction of protein-protein interactions, protein/small-molecule interactions, and binding motifs. Additionally, we encourage submissions focusing on conformational dynamics and intrinsically disordered regions (IDRs). We welcome manuscripts describing the application of machine learning and deep learning to address these important questions.

Topics may include, but are not limited to:

  • Novel methods for predicting protein/protein and protein/small-molecule interactions
  • Analyses of protein interaction networks and pathways
  • Prediction of protein structure and conformational changes
  • Analysis of sequence and structural binding motifs
  • Reverse virtual screening approaches for identifying potential drug targets
  • Prediction and characterization of Intrinsically Disordered Regions (IDRs)


Image credit: © [M] Christoph Burgstedt / Getty Images / iStock

  1. Accurately identifying drug-target affinity (DTA) plays a pivotal role in drug screening, design, and repurposing in pharmaceutical industry. It not only reduces the time, labor, and economic costs associated ...

    Authors: Xin Zeng, Kai-Yang Zhong, Pei-Yan Meng, Shu-Juan Li, Shuang-Qing Lv, Meng-Liang Wen and Yi Li
    Citation: BMC Biology 2024 22:182
    Content type: Research Published on:

  2. Identification of potential drug-target interactions (DTIs) with high accuracy is a key step in drug discovery and repositioning, especially concerning specific drug targets. Traditional experimental methods f...

    Authors: Yongdi Zhu, Chunhui Ning, Naiqian Zhang, Mingyi Wang and Yusen Zhang
    Citation: BMC Biology 2024 22:156
    Content type: Research article Published on:

  3. Metabolite-associated cell communications play critical roles in maintaining human biological function. However, most existing tools and resources focus only on ligand-receptor interaction pairs where both par...

    Authors: Yuncong Zhang, Yu Yang, Liping Ren, Meixiao Zhan, Taoping Sun, Quan Zou and Yang Zhang
    Citation: BMC Biology 2024 22:152
    Content type: Research article Published on:

Submission Guidelines

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This Collection welcomes submission of original Research Articles. Should you wish to submit a different article type, please read our submission guidelines to confirm that type is accepted by the journal. To submit your manuscript to this Collection, please use our online submission system. During the submission process you will be asked whether you are submitting to a Collection, please select "Computational inference of protein conformations and interactions" from the dropdown menu.

Articles will undergo the journal’s standard peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.