Targeted removal of the FA2 site on human albumin prevents fatty acid-mediated inhibition of Zn2+ binding.

Zinc is required for virtually all biological processes. In plasma, Zn2+ is predominantly transported by human serum albumin (HSA), which possesses two Zn2+-binding sites of differing affinities (sites A and B). Fatty acids (FAs) are also transported by HSA, with seven structurally characterized FA-binding sites (named FA1-FA7) known. FA binding inhibits Zn2+-HSA interactions, in a manner that can impact upon hemostasis and cellular zinc uptake, but the degree to which binding at specific FA sites contributes to this inhibition is unclear. Wild-type HSA and H9A, H67A, H247A, and Y150F/R257A/S287A (FA2-KO) mutant albumins were expressed in Pichia pastoris. Isothermal titration calorimetry studies revealed that the Zn2+-binding capacity at the high-affinity Zn2+ site (site A) was reduced in H67A and H247A mutants, with site B less affected. The H9A mutation decreased Zn2+ binding at the lower-affinity site, establishing His9 as a site B ligand. Zn2+ binding to HSA and H9A was compromised by palmitate, consistent with FA binding affecting site A. 13C-NMR experiments confirmed that the FA2-KO mutations prohibited FA binding at site FA2. Zn2+ binding to the FA2-KO mutant was unaffected by myristate, suggesting binding at FA2 is solely responsible for inhibition. Molecular dynamics studies identified the steric obstruction exerted by bound FA in site FA2, which impedes the conformational change from open (FA-loaded) to closed (FA-free) states, required for Zn2+ to bind at site A. The successful targeting of the FA2 site will aid functional studies exploring the interplay between circulating FA levels and plasma Zn2+ speciation in health and disease.

PinMyMetal: A hybrid learning system to accurately model metal binding sites in macromolecules.

Metal ions are vital components in many proteins for the inference and engineering of protein function, with coordination complexity linked to structural (4-residue predominate), catalytic (3-residue predominate), or regulatory (2-residue predominate) roles. Computational tools for modeling metal ions in protein structures, especially for transient, reversible, and concentration-dependent regulatory sites, remain immature. We present PinMyMetal (PMM), a sophisticated hybrid machine learning system for predicting zinc ion localization and environment in macromolecular structures. Compared to other predictors, PMM excels in predicting regulatory sites (median deviation of 0.34 Å), demonstrating superior accuracy in locating catalytic sites (median deviation of 0.27 Å) and structural sites (median deviation of 0.14 Å). PMM assigns a certainty score to each predicted site based on local structural and physicochemical features independent of homolog presence. Interactive validation through our server, CheckMyMetal, expands PMM's scope, enabling it to pinpoint and validates diverse functional zinc sites from different structure sources (predicted structures, cryo-EM and crystallography). This facilitates residue-wise assessment and robust metal binding site design. The lightweight PMM system demands minimal computing resources and is available at https://PMM.biocloud.top. While currently trained on zinc, the PMM workflow can easily adapt to other metals through expanded training data.

The current role and evolution of X-ray crystallography in drug discovery and development.

INTRODUCTION: Macromolecular X-ray crystallography and cryo-EM are currently the primary techniques used to determine the three-dimensional structures of proteins, nucleic acids, and viruses. Structural information has been critical to drug discovery and structural bioinformatics. The integration of artificial intelligence (AI) into X-ray crystallography has shown great promise in automating and accelerating the analysis of complex structural data, further improving the efficiency and accuracy of structure determination. AREAS COVERED: This review explores the relationship between X-ray crystallography and other modern structural determination methods. It examines the integration of data acquired from diverse biochemical and biophysical techniques with those derived from structural biology. Additionally, the paper offers insights into the influence of AI on X-ray crystallography, emphasizing how integrating AI with experimental approaches can revolutionize our comprehension of biological processes and interactions. EXPERT OPINION: Investing in science is crucially emphasized due to its significant role in drug discovery and advancements in healthcare. X-ray crystallography remains an essential source of structural biology data for drug discovery. Recent advances in biochemical, spectroscopic, and bioinformatic methods, along with the integration of AI techniques, hold the potential to revolutionize drug discovery when effectively combined with robust data management practices.

Continuous Validation Across Macromolecular Structure Determination Process.

The overall quality of the experimentally determined structures contained in the PDB is exceptionally high, mainly due to the continuous improvement of model building and structural validation programs. Improving reproducibility on a large scale requires expanding the concept of validation in structural biology and all other disciplines to include a broader framework that encompasses the entire project. A successful approach to science requires diligent attention to detail and a focus on the future. An earnest commitment to data availability and reuse is essential for scientific progress, be that by human minds or artificial intelligence.

Structural and biochemical characterisation of Co2+-binding sites on serum albumins and their interplay with fatty acids.

Serum albumin-Co2+ interactions are of clinical importance. They play a role in mediating the physiological effects associated with cobalt toxicity and are central to the albumin cobalt binding (ACB) assay for diagnosis of myocardial ischemia. To further understand these processes, a deeper understanding of albumin-Co2+ interactions is required. Here, we present the first crystallographic structures of human serum albumin (HSA; three structures) and equine serum albumin (ESA; one structure) in complex with Co2+. Amongst a total of sixteen sites bearing a cobalt ion across the structures, two locations were prominent, and they relate to metal-binding sites A and B. Site-directed mutagenesis and isothermal titration calorimetry (ITC) were employed to characterise sites on HSA. The results indicate that His9 and His67 contribute to the primary (putatively corresponding to site B) and secondary Co2+-binding sites (site A), respectively. The presence of additional multiple weak-affinity Co2+ binding sites on HSA was also supported by ITC studies. Furthermore, addition of 5 molar equivalents of the non-esterified fatty acid palmitate (C16:0) reduced the Co2+-binding affinity at both sites A and B. The presence of bound myristate (C14:0) in the HSA crystal structures provided insight into the fatty acid-mediated structural changes that diminish the affinity of the protein toward Co2+. Together, these data provide further support for the idea that ischemia-modified albumin corresponds to albumin with excessive fatty-acid loading. Collectively, our findings provide a comprehensive understanding of the molecular underpinnings governing Co2+ binding to serum albumin.

Structural biology and public health response to biomedical threats.

Over the course of the pandemic caused by SARS-CoV-2, structural biologists have worked hand in hand with groups developing vaccines and treatments. However, relying solely on in vitro and clinical studies may be insufficient to guide vaccination and treatment developments, and other healthcare policies during virus mutations or peaks in infections and fatalities. Therefore, it is crucial to track statistical data related to the number of infections, deaths, and vaccinations in specific regions and present it in an easy-to-understand way.

CMM-An enhanced platform for interactive validation of metal binding sites.

Metal ions bound to macromolecules play an integral role in many cellular processes. They can directly participate in catalytic mechanisms or be essential for the structural integrity of proteins and nucleic acids. However, their unique nature in macromolecules can make them difficult to model and refine, and a substantial portion of metal ions in the PDB are misidentified or poorly refined. CheckMyMetal (CMM) is a validation tool that has gained widespread acceptance as an essential tool for researchers working on metal-macromolecule complexes. CMM can be used during structure determination or to validate metal binding sites in structural models within the PDB. The functionalities of CMM have recently been greatly enhanced and provide researchers with additional information that can guide modeling decisions. The new version of CMM shows metals in the context of electron density maps and allows for on-the-fly refinement of metal binding sites. The improvements should increase the reproducibility of biomedical research. The web server is available at https://cmm.minorlab.org.

Optimal Solution to the Torsional Coefficient Fitting Problem in Force Field Parametrization.

Molecular modeling is an excellent tool for studying biological systems on the atomic scale. Depending on objects, which may be proteins, nucleic acids, or lipids, different force fields are recommended. The phospholipid bilayers constitute an example, in which behavior is extensively studied using molecular dynamics simulations due to limitations of experimental methods. The reliability of the results is strongly dependent on an appropriate description of these compounds. There are some deficiencies in the parametrization of intra- and intermolecular interactions that result in incorrect reproduction of phospholipid bilayer properties known from experimental studies, such as temperatures of phase transitions. Refinement of the force field parameters of nonbonded interactions present in the studied system is required to close these discrepancies. Such parameters as partial charges and torsional potential coefficients are crucial in this issue and not obtainable from experimental studies. This work presents a new fitting procedure for torsional coefficients that employs linear algebra theory and compares it with the Monte Carlo method. The proposed algebraic approach can be applied to any considered molecular system. In the manuscript, it is presented on the example of dimethyl phosphoric acid molecule. The advantages of our method encompass finding an optimal solution, the lack of additional parameters required by the algorithm, and significantly shorter computational time. Additionally, we indicate the importance of proper assignment of the partial charges.