The bacterial melibiose permease (MelB) belongs to the glycoside-pentoside-hexuronide:cation symporter family, a part of the major facilitator superfamily (MFS). Structural information regarding glycoside-pentoside-hexuronide:cation symporter family transporters and other Na(+)-coupled permeases within MFS has been lacking, although a wealth of biochemical and biophysical data are available. Here we present the three-dimensional crystal structures of Salmonella typhimurium MelBSt in two conformations, representing an outward partially occluded and an outward inactive state of MelBSt. MelB adopts a typical MFS fold and contains a previously unidentified cation-binding motif. Three conserved acidic residues form a pyramidal-shaped cation-binding site for Na(+), Li(+) or H(+), which is in close proximity to the sugar-binding site. Both cosubstrate-binding sites are mainly contributed by the residues from the amino-terminal domain. These two structures and the functional data presented here provide mechanistic insights into Na(+)/melibiose symport. We also postulate a structural foundation for the conformational cycling necessary for transport catalysed by MFS permeases in general.

Precision medicine—of which precision prescribing is a core component—is becoming a new frontier in today’s healthcare. Both artificial intelligence (AI) and machine learning (ML) have the potential to enhance our understanding of data and therefore our ability to accurately diagnose and treat patients. By leveraging these technologies and processes, we can uncover associations between a person’s genomic makeup and their health, identify biomarkers associated with diseases, fine-tune patient selection for clinical trials, reduce costs, and accelerate drug discovery and vaccine development. Although real-world data pose challenges in terms of collection, representation, and missing or inaccurate data sets, the integration of precision medicine into healthcare is critical. Clearly, precision medicine can benefit from health information innovations that empower decision-making at the patient level. Healthcare fusion is an example of an innovative framework and process [K Zhai et al. ECKM 2022, 20(3), pp. 179–192]. Data science and process improvement are also expected to play a role in resource planning and operational efficiency for optimal patient-centered care. Driving this transformation are advances in ‘omics’ technologies, digital devices, and imaging capabilities, along with an arsenal of powerful analytics tools working across a multitude of institutions and stakeholders. Encompassing this entire ecosystem, medicine will be evidence-based and driven by three key components: (1) Data curation through clinical diagnostics and behavioral apps that capture health and disease states; (2) Individualized solutions driven by advanced data analytics and personalized therapies; and (3) Business models that deliver value and incentivize growth. The aim of this paper is to present a novel conceptual framework to leverage AI and enhance information flow to serve the patient as per components one and two.

Arginine kinase is a member of the phosphagen kinase family that includes creatine kinase and likely shares a common reaction mechanism in catalyzing the buffering of cellular ATP energy levels. Abstraction of a proton from the substrate guanidinium by a catalytic base has long been thought to be an early mechanistic step. The structure of arginine kinase as a transition state analog complex (Zhou, G., Somasundaram, T., Blanc, E., Parthasarathy, G., Ellington, W. R., and Chapman, M. S. (1998) Proc. Natl. Acad. Sci. U. S. A. 95, 8449–8454) showed that Glu-225 and Glu-314 were the only potential catalytic residues contacting the phosphorylated nitrogen. In the present study, these residues were changed to Asp, Gln, and Val or Ala in several single and multisite mutant enzymes. These mutations had little impact on the substrate binding constants. The effect upon activity varied with reductions in k_cat between 3000-fold and less than 2-fold. The retention of significant activity in some mutants contrasts with published studies of homologues and suggests that acid-base catalysis by these residues may enhance the rate but is not absolutely essential. Crystal structures of mutant enzymes E314D at 1.9 Å and E225Q at 2.8 Å resolution showed that the precise alignment of substrates is subtly distorted. Thus, pre-ordering of substrates might be just as important as acid-base chemistry, electrostatics, or other potential effects in the modest impact of these residues upon catalysis.