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Protein Data Bank: A Comprehensive Review of 3D Structure Holdings and Worldwide Utilization by Researchers, Educators, and Students
The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), funded by the United States National Science Foundation, National Institutes of Health, and Department of Energy, supports structural biologists and Protein Data Bank (PDB) data users around the world. The RCSB PDB, a founding member of the Worldwide Protein Data Bank (wwPDB) partnership, serves as the US data center for the global PDB archive housing experimentally-determined three-dimensional (3D) structure data for biological macromolecules. As the wwPDB-designated Archive Keeper, RCSB PDB is also responsible for the security of PDB data and weekly update of the archive. RCSB PDB serves tens of thousands of data depositors (using macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro-electron diffraction) annually working on all permanently inhabited continents. RCSB PDB makes PDB data available from its research-focused web portal at no charge and without usage restrictions to many millions of PDB data consumers around the globe. It also provides educators, students, and the general public with an introduction to the PDB and related training materials through its outreach and education-focused web portal. This review article describes growth of the PDB, examines evolution of experimental methods for structure determination viewed through the lens of the PDB archive, and provides a detailed accounting of PDB archival holdings and their utilization by researchers, educators, and students worldwide.
Journal Article
Digital project practice for banking and fintech
New technology and changes in the regulatory framework have had a significant impact; various new players have emerged, and new business models have evolved. API-based ecosystems have become the new normal and collaboration in the financial and banking industry has reached new levels. Digital Project Practice for Banking and FinTech focuses on technology changes in the financial industry and their implications for business practice. A combination of practical experience in the field as well as academic research, the book explores a wide range of topics in the multifaceted landscape of FinTech. It examines the industry's various dimensions, implications, and potential based on academic research and practice. From project management in the digital era to the regulation and supervision of FinTech companies, the book delves into distinct aspects of this dynamic field, offering valuable insights and practical knowledge. It provides an in-depth overview of various unfolding developments and how to deal with and benefit from them. The book begins by exploring the unique challenges and opportunities project management presents in the digital era. It examines the evolving role of project management and provides strategies for effectively navigating the complexities of digital transformation initiatives. The book then covers such topics as: Financial Technology Canvas, a powerful tool for facilitating effective communication within fintech teams Process automation implementation in the financial sector and related benefits, challenges, and best practices to drive operational efficiency and enhance customer experiences Robotic process automation in financial institutions Cyptoeconomics and its potential implications for the diffusion of payment technologies The efficiency and risk factors associated with digital disruption in the banking sector. At its core, this book is about real-world practice in the digital banking industry. It is a source of different perspectives and diverse experiences from the global financial and banking industry.
Book
RCSB Protein Data Bank: supporting research and education worldwide through explorations of experimentally determined and computationally predicted atomic level 3D biostructures
The Protein Data Bank (PDB) was established as the first open-access digital data resource in biology and medicine in 1971 with seven X-ray crystal structures of proteins. Today, the PDB houses >210 000 experimentally determined, atomic level, 3D structures of proteins and nucleic acids as well as their complexes with one another and small molecules ( e.g. approved drugs, enzyme cofactors). These data provide insights into fundamental biology, biomedicine, bioenergy and biotechnology. They proved particularly important for understanding the SARS-CoV-2 global pandemic. The US-funded Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) and other members of the Worldwide Protein Data Bank (wwPDB) partnership jointly manage the PDB archive and support >60 000 `data depositors' (structural biologists) around the world. wwPDB ensures the quality and integrity of the data in the ever-expanding PDB archive and supports global open access without limitations on data usage. The RCSB PDB research-focused web portal at https://www.rcsb.org/ (RCSB.org) supports millions of users worldwide, representing a broad range of expertise and interests. In addition to retrieving 3D structure data, PDB `data consumers' access comparative data and external annotations, such as information about disease-causing point mutations and genetic variations. RCSB.org also provides access to >1 000 000 computed structure models (CSMs) generated using artificial intelligence/machine-learning methods. To avoid doubt, the provenance and reliability of experimentally determined PDB structures and CSMs are identified. Related training materials are available to support users in their RCSB.org explorations.
Journal Article
Time to End Physician Sexual Abuse of Patients: Calling the U.S. Medical Community to Action
Despite the strict prohibition against all forms of sexual relations between physicians and their patients, some physicians cross this bright line and abuse their patients sexually. The true extent of sexual abuse of patients by physicians in the U.S. health care system is unknown. An analysis of National Practitioner Data Bank reports of adverse disciplinary actions taken by state medical boards, peer-review sanctions by institutions, and malpractice payments shows that a very small number of physicians have faced “reportable” consequences for this unethical behavior. However, physician self-reported data suggest that the problem occurs at a higher rate. We discuss the factors that can explain why such sexual abuse of patients is a persistent problem in the U.S. health care system. We implore the medical community to begin a candid discussion of this problem and call for an explicit zero-tolerance standard against sexual abuse of patients by physicians. This standard must be coupled with regulatory, institutional, and cultural changes to realize its promise. We propose initial recommendations toward that end.
Journal Article
The Protein Data Bank archive as an open data resource
The Protein Data Bank archive was established in 1971, and recently celebrated its 40th anniversary (Berman et al. in Structure 20:391,
2012
). An analysis of interrelationships of the science, technology and community leads to further insights into how this resource evolved into one of the oldest and most widely used open-access data resources in biology.
Journal Article
Biometrics : challenges, trends and opportunities
\"Biometrics has played a crucial role in the advancement of payment systems and identity authentication in recent decades (Liebana-Cabanillas et al., 2022). From its earliest applications, such as the use of fingerprints to identify individuals, to more recent advances in facial recognition and voice analysis, biometrics has become a fundamental tool in the protection and security of financial transactions (Hartoneva, 2020). In 2019, various biometric technologies in non-financial industries also increased significantly, primarily due to the growth of online payments. In addition, it was the most important year for the industry in terms of harmonising the Big Data market. As a result, biometrics have gained wide acceptance in a variety of sectors, including banking (Szczuko et al., 2022), mobile payments (Sulaiman et al., 2022), IT security (Rahman et al., 2022), travel (Lehto et al., 2023) and even healthcare (Mitchell et al., 2023). Companies are increasingly embracing biometrics as an additional layer of security and convenience for their customers, providing an enhanced user experience and reducing the risk of fraud and phishing\"-- Provided by publisher.
BOOK
Integrating machine learning and encryption for effective data management in blood bank supply chains
The security and efficient management of healthcare data—especially in blood bank supply chains are of paramount importance due to the sensitive, diverse, and time-critical nature of the information involved. Existing approaches frequently fall short in balancing data protection, computational efficiency, and compliance with privacy regulations. This study introduces a robust, privacy-preserving framework that integrates AES-GCM encryption and hash-block storage to ensure secure cloud-based data handling. A novel component of this framework is the host-proof storage feature selector, which dynamically identifies sensitive features from healthcare datasets for secure cloud storage without compromising data usability. The framework employs the Banyan Tree Growth Optimization algorithm to fine-tune the hyperparameters of the XGBoost classifier, significantly enhancing prediction accuracy and minimizing processing time. To ensure trust and transparency in data retrieval, an Integrity Verification Block incorporating a Third-Party Auditor (TPA) is designed, using SVM-based feature matching to validate data authenticity within the cloud. Experimental evaluation on real-world healthcare datasets demonstrates the proposed system’s high effectiveness across multiple metrics. The BTGO-optimized XGBoost model achieved a classification accuracy of 99%, a reduction in error rate, and a 37% improvement in processing time compared to baseline models. Encryption latency averaged 0.23 s, and integrity verification via TPA was completed within 0.12 s. These results highlight the system’s ability to improve data reliability, security, and regulatory compliance while ensuring scalability and efficiency in cloud environments. Overall, the proposed model addresses critical limitations in existing solutions and offers a practical, secure, and high-performance approach to healthcare data management in real-world cloud-based applications. The framework achieved 99.72% classification accuracy, with AES-GCM encryption latency reduced to 0.23s and third-party verification performed in under 0.12s, validating the model’s real-time effectiveness in secure healthcare data management. The proposed model achieved a classification accuracy of 99.72%, outperforming baseline methods such as SVM and RF by over 6%. Compared to similar encryption-integrated models, our approach demonstrated 2–3× lower latency and real-time verifiability, confirming its suitability for cloud-based healthcare applications.
Journal Article