In the year 1971, the world’s biggest structural biology collaboration name—RCSB, was formed to gather all the structural biologists at a single platform and then extended out to be the world’s most extensive structural data repository named RCSB-PDB (https://www.rcsb.org/) that has provided the service for more than 50 years and continues its legacy for the discoveries and repositories for structural data. The RCSB has evolved from being a collaboratory network to a full-fledged database and tool with a huge list of protein structures, nucleic acid-containing structures, ModelArchive and AlphaFold structures, and the best is that it is expanding day by day with computational advancement with tools and visual experiences. In this review article, we have discussed how RCSB has been a successful collaboratory network, its expansion in each decade and how it has helped the ground-breaking research. The PDBs tools that are helping the researchers, yearly data deposition, validation, processing and the suggestions that can help the developer improve for upcoming years are also discussed. This review will help future researchers understand the complete history of RCSB and its developments in each decade and how various future collaborative networks can be developed in various scientific areas and can be successful by keeping RCSB as a case study.

Machine Learning (ML) is a field of Artificial Intelligence (AI) that uses algorithms to learn and improve from data without being explicitly programmed automatically. ML algorithms can be trained on large datasets to recognise patterns and make predictions or decisions. In immunoinformatics, ML algorithms analyse and interpret immune-related data, such as protein sequences, gene expression data, and clinical outcomes, and the modern era is shifting towards targeted therapy predictions by utilising it. In this review, we have extensively reviewed various literature and identified the key areas of immunoinformatics where AI is being used and is being explored to take over the role. Some examples of how AI is used in immunoinformatics include- Identifying potential vaccine targets that use algorithms to analyse protein sequences and other data to identify potential vaccine targets, such as antigens capable of eliciting an immune response, predicting disease outcomes that are used to analyse patient data, such as gene expression and clinical outcomes, of predicting the likelihood of a patient developing a particular immune-related disease, designing personalised therapies where robust algorithms can analyse patient data and identify personalised treatment strategies most likely adequate for a particular patient. AI has also been explored for analysing complex immunoinformatics datasets where the multiple algorithms can analyse large, complex ones and identify patterns and relationships that may not be immediately apparent to humans. Overall, AI in immunoinformatics can revolutionise how we understand and treat immune-related diseases and help researchers make critical scientific discoveries more quickly and efficiently, and this review will provide a comprehensive view of various AI applications in immunoinformatics.