The interruption of amino acids supply has emerged as a promising anti-tumor strategy, with research focusing on the metabolic vulnerabilities of cancer cells. Depletion of essential amino acids necessary for tumor growth has shown potential in inhibiting cancer cell proliferation and inducing cell death. This approach has garnered significant interest in the field of cancer metabolism and targeted therapies, with studies exploring the mechanisms underlying amino acid depletion’s anti-tumor effects and its potential as a therapeutic strategy across different cancer types.

Advancing our collective understanding in this area is crucial for identifying novel therapeutic targets and developing effective anti-cancer interventions. Recent advances have elucidated the intricate metabolic pathways involved in amino acid utilization by cancer cells, shedding light on the potential for targeted disruption of amino acid metabolism and supply as a strategy to impede tumor growth. Furthermore, studies have highlighted the interplay between amino acid depletion and the tumor microenvironment, offering insights into the broader implications of this approach for cancer therapy.

Original Research articles and reviews in this field are invited.

This Collection supports and amplifies research related to SDG 3 (Good Health and Well-being).

Structural bioinformatics plays a crucial role in modern computational biology, driving breakthroughs in drug discovery, protein engineering, and understanding biomolecular interactions, as well as disease-causing modifications of these biomolecules. With the rapid development of Artificial Intelligence (AI)-based methods, enhanced molecular simulations, and integrative modeling approaches, this field is experiencing unprecedented progress.

This special issue aims to highlight cutting-edge research and novel applications in structural bioinformatics.

Structural bioinformatics is a broad field focused on applying computational approaches to proteins of biological and biomedical interest. It also addresses machine and deep learning methods dedicated to structural prediction, which have become essential for advancing protein structure and conformational modeling. New techniques for studying biomolecular interactions offer promising ways to better characterize protein-protein and protein-ligand interactions and understand their functional implications. Another important focus is computational drug design, which includes structure-based and AI-driven approaches for drug discovery and repurposing, as well as ligand docking, and molecular dynamics simulations. The field also includes integrative structural biology, combining experimental data and computational methods for multi-scale modeling of biomolecular systems. Finally, the development of structural databases, algorithms, and visualization tools remains central to supporting research in this field. This special issue aims to highlight the diverse yet interconnected and innovative aspects of structural bioinformatics.

We invite researchers to submit original research articles, reviews, and perspective papers that advance the field of structural bioinformatics.