Nordquist, E. | Search bioRxiv

bioRxiv2024

Small molecule NS11021 promotes BK channel activation by increasing inner pore hydration

The Ca2+ and voltage-gated big potassium (BK) channels are implicated in various diseases including heart disease, asthma, epilepsy and cancer, but remains an elusive drug target. A class of negatively charged activators (NCAs) have been demonstrated to promote the activation of several potassium channels including BK channels by binding to the hydrophobic inner pore; yet the underlying molecular mechanism of action remains poorly understood. In this work, we analyze the binding mode and potential activation mechanism of a specific NCA named NS11021 using atomistic simulations. The results show that NS11021 binding to the pore in deactivated BK channels is nonspecific and dynamic. The binding free energy of -8.3{+/-}0.7 kcal/mol (KD = 0.3-3.1 M) calculated using umbrella sampling agrees quantitatively with the experimental EC50 range of 0.4-2.1 M. The bound NS11021 remains dynamic and is distal from the filter to significantly impact its conformation. Instead, NS11021 binding significantly enhances the pore hydration due to the charged tetrazol-phenyl group, thereby promoting the opening of the hydrophobic gate. We further show that the free energy barrier to K+ permeation is reduced by [~]3 kcal/mol regardless of the binding pose, which could explain the [~]62-fold increase in the intrinsic opening of BK channels measured experimentally. Taken together, these results support that the molecular mechanism of NS11021 derives from increasing the hydration level of the conformationally closed pore, which does not depend on specific binding and likely explains the ability of NCAs to activate multiple K+ channels. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=188 SRC="FIGDIR/small/597166v1_unfig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@a55487org.highwire.dtl.DTLVardef@509a95org.highwire.dtl.DTLVardef@1d4e2baorg.highwire.dtl.DTLVardef@1714b4e_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2023

Incorporating physics to overcome data scarcity in predictive modeling of protein function: a case study of BK channels

Machine learning has played transformative roles in numerous chemical and biophysical problems such as protein folding where large amount of data exists. Nonetheless, many important problems remain challenging for data-driven machine learning approaches due to the limitation of data scarcity. One approach to overcome data scarcity is to incorporate physical principles such as through molecular modeling and simulation. Here, we focus on the big potassium (BK) channels that play important roles in cardiovascular and neural systems. Many mutants of BK channel are associated with various neurological and cardiovascular diseases, but the molecular effects are unknown. The voltage gating properties of BK channels have been characterized for 473 site-specific mutations experimentally over the last three decades; yet, these functional data by themselves remain far too sparse to derive a predictive model of BK channel voltage gating. Using physics-based modeling, we quantify the energetic effects of all single mutations on both open and closed states of the channel. Together with dynamic properties derived from atomistic simulations, these physical descriptors allow the training of random forest models that could reproduce unseen experimentally measured shifts in gating voltage, {Delta}V1/2, with a RMSE [~] 32 mV and correlation coefficient of R [~] 0.7. Importantly, the model appears capable of uncovering nontrivial physical principles underlying the gating of the channel, including a central role of hydrophobic gating. The model was further evaluated using four novel mutations of L235 and V236 on the S5 helix, mutations of which are predicted to have opposing effects on V1/2 and suggest a key role of S5 in mediating voltage sensor-pore coupling. The measured {Delta}V1/2 agree quantitatively with prediction for all four mutations, with a high correlation of R = 0.92 and RMSE = 18 mV. Therefore, the model can capture nontrivial voltage gating properties in regions where few mutations are known. The success of predictive modeling of BK voltage gating demonstrates the potential of combining physics and statistical learning for overcoming data scarcity in nontrivial protein function prediction. Author SummaryDeep machine learning has brought many exciting breakthroughs in chemistry, physics and biology. These models require large amount of training data and struggle when the data is scarce. The latter is true for predictive modeling of the function of complex proteins such as ion channels, where only hundreds of mutational data may be available. Using the big potassium (BK) channel as a biologically important model system, we demonstrate that a reliable predictive model of its voltage gating property could be derived from only 473 mutational data by incorporating physics-derived features, which include dynamic properties from molecular dynamics simulations and energetic quantities from Rosetta mutation calculations. We show that the final random forest model captures key trends and hotspots in mutational effects of BK voltage gating, such as the important role of pore hydrophobicity. A particularly curious prediction is that mutations of two adjacent residues on the S5 helix would always have opposite effects on the gating voltage, which was confirmed by experimental characterization of four novel mutations. The current work demonstrates the importance and effectiveness of incorporating physics in predictive modeling of protein function with scarce data.

bioinformatics↗

bioRxiv2022

Genomic resolution of cryptic species diversity in chipmunks

Discovery of cryptic species is essential to understanding the process of speciation and assessing the impacts of anthropogenic stressors. Here, we used genomic data to test for cryptic species diversity within an ecologically well-known radiation of North American rodents, western chipmunks (Tamias). We assembled a de novo reference genome for a single species (Tamias minimus) combined with new and published targeted sequence-capture data for 21,551 autosomal and 493 X-linked loci sampled from 121 individuals spanning 22 species. We identified at least two cryptic lineages corresponding with an isolated subspecies of least chipmunk (T. minimus grisescens) and with a restricted subspecies of the yellow-pine chipmunk (T. amoenus cratericus) known only from around the extensive Craters of the Moon lava flow. Additional population-level sequence data revealed that the so-called Crater chipmunk is a distinct species that is abundant throughout the coniferous forests of southern Idaho. This cryptic lineage does not appear to be most closely related to the ecologically and phenotypically similar yellow-pine chipmunk but does show evidence for recurrent hybridization with this and other species.

evolutionary biology↗