{Reference Type}: Journal Article
{Title}: HSADab: A comprehensive database for human serum albumin.
{Author}: Zheng L;Zeng Z;Zhao Y;Liu X;Huai Z;Zhang X;Sun Z;Zhang JZH;
{Journal}: Int J Biol Macromol
{Volume}: 277
{Issue}: 0
{Year}: 2024 Oct 29
{Factor}: 8.025
{DOI}: 10.1016/j.ijbiomac.2024.134289
{Abstract}: Human Serum Albumin (HSA), the most abundant protein in human body fluids, plays a crucial role in the transportation, absorption, metabolism, distribution, and excretion of drugs, significantly influencing their therapeutic efficacy. Despite the importance of HSA as a drug target, the available data on its interactions with external agents, such as drug-like molecules and antibodies, are limited, posing challenges for molecular modeling investigations and the development of empirical scoring functions or machine learning predictors for this target. Furthermore, the reported entries in existing databases often contain major inconsistencies due to varied experiments and conditions, raising concerns about data quality. To address these issues, a pioneering database, HSADab, was established through an extensive review of >30,000 scientific publications published between 1987 and 2023. The database encompasses over 5000 affinity data points at multiple temperatures and >130 crystal structures, including both ligand-bound and apo forms. The current HSADab resource (www.hsadab.cn) serves as a reliable foundation for validating molecular simulation protocols, such as traditional virtual screening workflows using docking, end-point, and al-chemical free energy techniques. Additionally, it provides a valuable data source for the implementation of machine learning predictors, including plasma protein binding models and plasma protein-based drug design models.