Molecular mechanisms and computational insights into human SGLTs: advancing toward selective SGLT1 inhibition

The sodium and glucose transporters (SGLTs) are integral membrane proteins crucial for glucose homeostasis, with SGLT1 and SGLT2 being widely studied as primary therapeutic targets. Despite SGLT2 inhibitors having been well clinically established, selective SGLT1 inhibition remains an unmet goal, although its potential in managing diabetes, cardiovascular disease, and cancer. Recent advances in structural biology, including cryo-electron microscopy and computational modeling approaches, have provided significant avenues into the molecular mechanisms of SGLTs and their inhibition. High-resolution structural data now reveal inhibitor binding modes and conformational dynamics, while molecular dynamics simulations, free energy calculations, and AlphaFold2 predictions further explain sodium coupling and conformational transitions. Notable differences between SGLT1 and SGLT2 include selectivity determinants, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup><mml:mrow><mml:mi>N</mml:mi> <mml:mi>a</mml:mi></mml:mrow> <mml:mrow><mml:mo>+</mml:mo></mml:mrow> </mml:msup> </mml:mrow> </mml:math> site occupancy, and gating mechanisms, which inform drug design but also pose challenges for achieving SGLT1 specificity. Homology modeling and MD simulations, strongly validated by cryo-EM, mutagenesis, and uptake/binding assays, are complemented by binding free energy calculations and 3D-RISM hydration analysis, with rising use of AlphaFold predicted models tied to experimental maps; key open questions include the absence of Na3 density in SGLT2, isoform-specific MAP17 dependence, and how differences in the central binding cavity of SGLT1 versus SGLT2 can be leveraged for selectivity. Integrating advanced computational approaches, including Artificial Intelligence and Machine Learning, offers promising avenues to explore inhibitor-induced conformational changes and advance the rational design of selective SGLT1 inhibitors. This review proposes a new framework for selective SGLT1 inhibitor development by aligning computational predictions with experimental validations.