Biology subjects

Yong, E. H.

Publications and source records attributed to Yong, E. H..

2 recordsLinked to original sources

bioRxiv2026

Accessing pore-blocker bound and open conformations of TMEM16A using PIP2-assisted adaptive sampling

Ion channels are promising targets for drug discovery due to their diverse physiological functions. The database of ion channel structures has grown exponentially over the last decade due to advances in structure-determination techniques. However, not all ion channel conformations have been determined, and not all druggable conformations can be modelled as thermodynamically stable under simulation conditions. This greatly limits conformation-specific drug targeting. In this study, we used an endogenous regulator of ion channels, phosphatidylinositol-4,5-bisphosphate (PIP2), as a computational tool to probe the conductive conformation of the TMEM16A calcium-activated chloride channel distinctive from the available structures. By using the PIP2-binding conformation from a coarse-grained model, followed by fluctuation-amplified specific traits (FAST) adaptive sampling in an all-atom configuration, the system transitioned from a closed state to a thermodynamically stable conductive state. The transition also highlights the importance of PIP2 in TM6 helical kink, the opening of the outer gate and the alpha helix on I551. The modelled structure also displays the experimental conductance at sub-300 mV. Using an accelerated weighted histogram (AWH), the binding sites of 1PBC, A9C, niclosamide, and Ani9 pore blockers were determined and validated against previous experimental studies. This paves the way to structure-specific drug development, as overactivation of TMEM16A is correlated with many diseases, such as pulmonary hypertension and ischemic stroke. Together, this study highlights the importance of lipids in stabilising ion channel conformations for targeted drug design and introduces a novel approach to expand the therapeutic targeting of ion channels.

biophysics↗

bioRxiv2025

Hierarchical Analysis of RNA Secondary Structures with Pseudoknots Based on Sections

Predicting RNA structures containing pseudoknots remains computationally challenging due to high processing costs and complexity. While standard methods for pseudoknot prediction require O(N6) time complexity, we present a hierarchical approach that significantly reduces computational cost while maintaining prediction accuracy. Our method analyzes RNA structures by dividing them into contiguous regions of unpaired bases derived from known secondary structures ("sections"). We examine pseudoknot interactions between sections using a nearest-neighbor energy model and dynamic programming. The algorithm scales as O(n2{ell}4), offering substantial computational advantages over existing global prediction methods. Our analysis of 726 transfer messenger RNA and 455 Ribonuclease P RNA sequences reveals that biologically relevant pseudoknots are highly concentrated among section pairs with large minimum free energy gains. Over 90% of connected section pairs appear within just the top 3% of section pairs ranked by MFE gain. For 2-clusters, our method achieves high prediction accuracy with sensitivity exceeding 0.90 and positive predictive value above 0.80. For 3-clusters, we discovered asymmetric behavior where "former" section pairs (formed early in the sequence) predict accurately, while "latter" section pairs show different formation patterns. The hierarchical section-based approach demonstrates that local energy considerations can effectively predict pseudoknot formations between unpaired regions. Our work provides strong evidence for the effectiveness of local energy calculations in pseudoknot prediction and offers insights into the dynamic processes governing RNA structure formation.

bioinformatics↗