Multimeric protein interaction and complex prediction: Structure, dynamics and function.

Journal: Computational and structural biotechnology journal
Published Date:

Abstract

Understanding the structure, interactions, dynamics, and functions of multimeric protein complexes is essential for studying multimeric protein complexes, with broad implications for disease mechanisms and drug design, and other areas of biomedical research. Although remarkable achievements have been made in monomer prediction in recent years, protein multimers prediction remains a crucial yet challenging area due to their complex structures, diverse physicochemical properties, and limited experimental data. This review encompasses recent advancements in multimer research, providing an overview of classical concepts and methodologies and the key differences from monomer prediction methods. It further explores state-of-the-art advances in CASP16, including predictions of unknown stoichiometries, supercomplexes, conformational ensembles. This review also delves into the contributions of AlphaFold2 & 3 to multimer prediction, highlighting both the successes and limitations, particularly in handling functional protein-protein interactions and dynamical conformations. Recent deep learning methods and their applications in multimer interaction analysis and quality assessment are discussed, along with insights into future research directions, such as improving prediction accuracy, enabling functional interpretation of protein-protein interactions, and reconstructing protein mechanisms.

Authors

  • Da Lu
    Beijing Key Laboratory for Design and Evaluation Technology of Advanced Implantable & Interventional Medical Devices, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
  • Shuhong Yu
    Department of Oncology, Renmin Hospital of Wuhan University, Wuhan, 430060, China.
  • Yixiang Huang
    Mathematical Intelligence Application LAB, Institute for Mathematical Sciences, Renmin University of China, Beijing, China.
  • Xinqi Gong

Keywords

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