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Molecular docking is used to anticipate the optimal orientation of a particular molecule to a target to form a stable complex. It makes predictions about the 3D structure of any complex based on the binding characteristics of the ligand and the target receptor usually a protein. It is an exceptionally useful tool, which is used as a model to study how ligands attach to proteins. Docking can also be used for studying the interaction of ligands and proteins to analyze inhibitory efficacy. The ligand may also be a protein, making it possible to study interactions between two different proteins using the numerous docking tools available for basic research on protein interactions. The protein-protein docking is a crucial approach to understanding the protein interactions and predicting the structure of protein complexes that have not yet been experimentally determined. Moreover, the protein-protein interactions can predict the function of target proteins and the drug-like properties of molecules. Therefore, protein docking assists in uncovering insights into protein interactions and also aids in a better understanding of molecular pathways/mechanisms. This chapter comprehends the various tools for protein-protein docking (pairwise and multiple), including their methodologies and analysis of output as results.
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http://dx.doi.org/10.1007/978-1-0716-3985-6_5 | DOI Listing |
Front Immunol
September 2025
Department of Thoracic Surgery, Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, Guangdong, China.
Background: Lung cancer remains the leading cause of cancer-related mortality globally, primarily due to late-stage diagnosis, molecular heterogeneity, and therapy resistance. Key biomarkers such as EGFR, ALK, KRAS, and PD-1 have revolutionized precision oncology; however, comprehensive structural and clinical validation of these targets is crucial to enhance therapeutic efficacy.
Methods: Protein sequences for EGFR, ALK, KRAS, and PD-1 were retrieved from UniProt and modeled using SWISS-MODEL to generate high-confidence 3D structures.
Chem Sci
August 2025
Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University Shanghai 200240 China
Predicting Antibody-Antigen (Ab-Ag) docking and structure-based design represent significant long-term and therapeutically important challenges in computational biology. We present SAGERank, a general, configurable deep learning framework for antibody design using Graph Sample and Aggregate Networks. SAGERank successfully predicted the majority of epitopes in a cancer target dataset.
View Article and Find Full Text PDFBiotechnol Appl Biochem
September 2025
Emergency Intensive Care Medicine Center, Shandong University of Traditional Chinese Medicine Affiliated Hospital, Jinan, China.
Background: Differentially expressed genes (DEGs) have been known to provide important information on disease mechanisms and potential therapeutic targets. The traditional Chinese medicine (TCM) offers a large reservoir of bioactive compounds that could modulate at these targets. This study is an attempt to investigate the biomarkers in Sepsis and COVID-19 using gene expression analysis and molecular modeling validation of TCM-derived candidate compounds targeting key DEGs associated with sepsis.
View Article and Find Full Text PDFPhytother Res
September 2025
Department of Pharmacy, Shanghai General Hospital Jiuquan Hospital (The People's Hospital of Jiuquan), Jiuquan, China.
To evaluate the efficacy and explore the potential mechanism of curcumin for the treatment and prevention of NSCLC. We searched six databases thoroughly for articles published before December 2024. Stata 15.
View Article and Find Full Text PDFComput Biol Chem
September 2025
Department of Bioengineering and Biotechnology, Birla Institute of Technology, Mesra, Ranchi, India. Electronic address:
Women are susceptible to hormonal imbalances and endocrine-related disorders such as Polycystic Ovary Syndrome (PCOS), Ovarian Cancer (OC), and Major Depressive Disorder (MDD). This study aims to identify gene-level interconnections among these conditions using omics-based bioinformatic approaches. Publicly available GEO datasets, viz.
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