Development of tools for assisting structural bioinformatics

The explosion of biological data, particularly in structural bioinformatics, has driven the need for flexible and interoperable tools capable of addressing increasingly complex challenges. To meet these demands, computational tool development has evolved from solving individual problems to embracing the FAIR (Findable, Accessible, Interoperable, Reusable) principles, ensuring software is both scalable and adaptable to the broader bioinformatics landscape. This evolution marks a significant shift in how bioinformatics software is crafted, fostering improved data management, reproducibility, and cross-disciplinary collaboration. In structural bioinformatics, where analyzing intricate protein structures and interactions often involves multiple computational stages, adhering to FAIR principles is particularly crucial. These guidelines facilitate seamless data exchange between tools and ensure consistent, reproducible results—essential for collaborative research and large-scale projects. Tools designed with accessibility and interoperability in mind can be smoothly integrated into existing workflows, increasing research efficiency and accelerating scientific discovery. In this work, different tools—DockingPie, PyPCN, AlPaCas, and PyMod—have been developed to address key challenges in protein structure prediction, molecular docking, protein interaction analysis, and CRISPR-Cas system-related applications. The development of these software solutions, which span various topics, integrates seamlessly into the field of structural bioinformatics, leveraging the features that define and shape this domain.