bioRxiv Science⌕ Search

Biology subjects

Woods, H.

Publications and source records attributed to Woods, H..

13 recordsLinked to original sources

Predicting Binding Affinities for the Binding Domain of Hyperpolarization-Activated Cyclic Nucleotide-Gated Channel Isoforms Using Free-Energy Perturbation

Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels are are a family of voltage-gated, cyclic-nucleotide modulated Na+/K+ channels that regulate spontaneous rhythmic electrical activity in both the heart and the brain. Understanding differences in the responsiveness to cyclic adenosine monophosphate (cAMP) modulation between HCN isoforms would offer insight into the specific binding interactions that drive channel activation. Using all-atom molecular dynamics (MD) simulations and the free-energy perturbation (FEP) approach, we determined the absolute binding free energy of cAMP to the the cyclicnucleotide-binding domain (CNBD) of HCN isoforms 1-4. By studying the free-energy of ligand binding to the various isoforms of HCN, our study advances the understanding of HCN channel activation and modulation mechanisms. Overall, our work offers insight into explaining differences in channel sensitivity across the isoforms of HCN.

biophysics↗

The Overlap Area as a Novel Measure of Effect Size in Neuroscience Research

In experimental biomedical research, a common concern is whether a manipulation produces a biologically meaningful effect. Another concern is whether effect sizes are reliable, statistically significant, and generalizable. Traditional effect size measures, such as Cohens d, quantify mean differences but ignore variance heterogeneity between groups. This can result in biased effect size estimates and a lack of thresholds for statistical significance testing. Motivated by this, we introduce a novel effect size measure, termed overlap area (OA), which quantifies the difference between the population distributions of the experimental and control groups. A robust Bayesian method estimated OA, and random resampling determined OA thresholds for statistical significance in single and replicated experiments. Simulations confirmed the approachs sensitivity and robustness. Applied to a real-world dataset, OA revealed that environmental enrichment affects the mouse brain synaptome. Moreover, we developed an open-source toolbox supporting OA as a powerful new measurement for conducting robust, reliable, and reproducible analyses of manipulation effects in neuroscience and related fields.

neuroscience↗

Experiences are encoded by brainwide reprogramming of synaptome architecture

Synaptome architecture describes the spatiotemporal distribution of highly diverse excitatory synapses throughout the brain. Whether and how this architecture is impacted by experience is key to understanding its role in learning and memory. We found that environmental enrichment and monocular visual deprivation drive large-scale, type-and subtype-specific reorganisation of excitatory synapses in more than one hundred brain regions. Each experience modifies distinct subsets of synapses, with patterns aligned with protein turnover rates and connectome architecture. These reorganisations occur during development and adulthood, revealing a conserved mechanism of synaptome plasticity across the lifespan. Our findings support a population-selection model in which experience drives adaptation by selectively modifying synapse varieties, generating a distributed trace of past experiences. Our results also point to synaptome architecture as a shared framework integrating experience, lifespan changes, sleep, genetic variation and disease.

neuroscience↗

Structural dynamics of sphingosine kinase 1 regulation and inhibition

Sphingosine kinase 1 (SK1) generates sphingosine-1-phosphate, a bioactive lipid implicated in cancer and other diseases. Despite its clinical importance, the structural and dynamic basis of SK1 regulation and inhibition remains poorly understood. Using integrated spectroscopic and computational approaches, we uncover conformational transitions that govern substrate entry, catalysis, and inhibitor binding. Phosphorylation of Ser225 reconfigures the regulatory loop and reshuffles salt bridges, priming SK1 for membrane engagement and catalytic activity. We identify a previously uncharacterized catalytic intermediate with a distinct conformation and a highly dynamic lipid-binding loop 1 (LBL-1), sensitive to potent inhibitors such as PF-543. These inhibitors not only stabilize non-catalytic states but also induce LBL-mediated dimerization, blocking membrane binding and substrate access. Our findings reveal a multilayered regulatory mechanism driven by structural flexibility and establish a novel inhibitory paradigm. This framework provides critical insight into SK1 regulation and a foundation for developing next-generation SK1-targeted therapeutics.

biophysics↗

PRMix: Primary Region Mix Augmentation and Benchmark Dataset for Precise Whole Mouse Brain Anatomical Delineation

The architecture of the mouse brain shares remarkable similarities with the human brain, making it an essential model for studying brain pathologies, synaptic diversity, and regional specialization. A key step in such studies involves registering molecular images to reference brain atlases, a process hindered by the difficulty of accurately delineating brain regions. Toward this, we have curated a collection of high-resolution, dual-fluorescence microscopy images, termed as dual-fluorescence mouse brain microscopy (DMBM) dataset, complemented by expert annotations of 118 subregions. This dataset provides unprecedented insights into the molecular and structural complexity of the mouse brain. However, its full potential for detailed whole-brain analysis is compromised by challenges such as boundary ambiguity and sample scarcity in existing automated segmentation methods, prompting the development of the primary region mix (PRMix) augmentation method. PRMix is specifically designed to expand these datasets, enhance the realism of synthetic data and minimize overlap between adjacent regions. Our approach, together with the curated dataset, achieves superior segmentation performance across the mouse brain compared with existing methods, setting a new benchmark in brain imaging research. Code and data are available at https://git-pages.ecdf.ed.ac.uk/dmbm-datasets-5c13cd/.

neuroscience↗

Divergent Pairwise Epistasis in the Context of Unstable Membrane Protein Variants

Many membrane proteins are prone to misfolding, which compromises their functional expression at the plasma membrane. This is particularly true for the mammalian gonadotropin-releasing hormone receptor GPCRs (GnRHR). We recently demonstrated that evolutionary GnRHR modifications appear to have coincided with adaptive changes in cotranslational folding efficiency. Though protein stability is known to shape evolution, it is unclear how cotranslational folding constraints modulate the synergistic, epistatic interactions between mutations. We therefore compared the pairwise interactions formed by mutations that disrupt the membrane topology (V276T) or tertiary structure (W107A) of GnRHR. Using deep mutational scanning, we evaluated how the plasma membrane expression of these variants is modified by hundreds of secondary mutations. An analysis of 251 mutants in three genetic backgrounds reveals that V276T and W107A form distinct epistatic interactions that depend on both the severity and the mechanism of destabilization. V276T forms predominantly negative epistatic interactions with destabilizing mutations in soluble loops. In contrast, W107A forms positive interactions with mutations in both loops and transmembrane domains that reflect the diminishing impacts of the destabilizing mutations in variants that are already unstable. These findings reveal how epistasis is remodeled by conformational defects in membrane proteins and in unstable proteins more generally. Impact StatementMany of the synergistic interactions between mutations within a gene are modified in the context of unstable proteins in a manner that depends on how the variants promote misfolding in the cell.

biochemistry↗

Modeling membrane geometries implicitly in Rosetta

Interactions between membrane proteins (MPs) and lipid bilayers are critical for many cellular functions. In the Rosetta molecular modeling suite, the implicit membrane energy function is based on a "slab" model, which represent the membrane as a flat bilayer. However, in nature membranes often have a curvature that is important for function and/or stability. Even more prevalent, in structural biology research MPs are reconstituted in model membrane systems such as micelles, bicelles, nanodiscs, or liposomes. Thus, we have modified the existing membrane energy potentials within the RosettaMP framework to allow users to model MPs in different membrane geometries. We show that these modifications can be utilized in core applications within Rosetta such as structure refinement, protein-protein docking, and protein design. For MPs structures found in curved membranes, refining these structures in curved, implicit membranes produces higher quality models with structures closer to experimentally determined structures. For MP systems embedded in multiple membranes, representing both membranes results in more favorable scores compared to only representing one of the membranes. Modeling MPs in geometries mimicking the membrane model system used in structure determination can improve model quality and model discrimination.

biophysics↗

Molecular Basis for Variations in the Sensitivity of Pathogenic Rhodopsin Variants to 9-cis-Retinal

Over 100 mutations in the rhodopsin gene have been linked to a spectrum of retinopathies that include retinitis pigmentosa and congenital stationary night blindness. Though most of these variants exhibit a loss of function, the molecular defects caused by these underlying mutations vary considerably. In this work, we utilize deep mutational scanning to quantitatively compare the plasma membrane expression of 123 known pathogenic rhodopsin variants in the presence and absence of the stabilizing cofactor 9-cis-retinal. We identify 69 retinopathy variants, including 20 previously uncharacterized variants, that exhibit diminished plasma membrane expression in HEK293T cells. 67 of these apparent class II variants exhibit a measurable increase in expression in the presence of 9-cis-retinal. However, the magnitude of the response to this molecule varies considerably across this spectrum of mutations. Evaluation of the observed shifts in relation to thermodynamic estimates for the coupling between binding and folding suggests underlying differences in stability constrains the magnitude of their response to retinal. Nevertheless, estimates from computational modeling suggest many of the least sensitive variants also directly compromise binding. Finally, we evaluate the functional properties of three previous uncharacterized, retinal-sensitive variants ({Delta}N73, S131P, and R135G) and show that two retain residual function in vitro. Together, our results provide a comprehensive experimental characterization of the proteostatic properties of retinopathy variants and their response to retinal.

biochemistry↗

Towards Generalizable Predictions for the Effects of Mutations on G-Protein Coupled Receptor Expression

Missense mutations that compromise the plasma membrane expression (PME) of integral membrane proteins (MPs) are the root cause of numerous genetic diseases. Differentiation of this class of mutations from those that specifically modify the activity of the folded protein has proven useful for the development and targeting of precision therapeutics. Nevertheless, it remains challenging to predict the effects of mutations on the stability and/ or expression of MPs. In this work, we utilize deep mutational scanning data to train a series of artificial neural networks to predict the effects of mutations on the PME of the G-protein coupled receptor (GPCR) rhodopsin from structural and/ or evolutionary features. We show that our best performing network, which we term PMEpred, can differentiate pathogenic rhodopsin variants that induce misfolding from those that primarily compromise signaling. This network also generates statistically significant predictions for the effects of mutations on the PME of another GPCR ({beta}2 adrenergic receptor) but not for an unrelated voltage-gated potassium channel (KCNQ1). Notably, our analyses of these networks suggest structural features alone are generally sufficient to recapitulate the observed mutagenic trends. Moreover, our findings imply that networks trained in this manner may be generalizable to proteins that share a common fold. Implications of our findings for the design of mechanistically specific genetic predictors are discussed.

biophysics↗

Structural comparative modeling of multi-domain ΔF508 CFTR

Cystic Fibrosis (CF) is a common genetic disease caused by mutations in the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR), an epithelial anion channel expressed in several vital organs. Absence of functional CFTR results in imbalanced osmotic equilibrium and subsequent mucus build up in the lungs - which increases the risk of infection and eventually causes death. CFTR is an ATP binding cassette (ABC) transporter composed of two transmembrane domains (TMDs), two nucleotide binding domains (NBDs), and an unstructured regulatory domain. The most prevalent patient mutation is the deletion of F508 ({Delta}F508), making {Delta}F508 CFTR the primary target for current FDA approved CF therapies. However, no experimental multi-domain {Delta}F508 CFTR structure has been determined and few studies have modeled {Delta}F508 using multi-domain WT CFTR structures. Here, we used cryo-EM density data and Rosetta comparative modeling (RosettaCM) to compare a {Delta}F508 model with published experimental data on CFTR NBD1 thermodynamics. We then apply this modeling method to generate multi-domain WT and {Delta}F508 CFTR structural models. These models demonstrate the destabilizing effects of {Delta}F508 on NBD1 and the NBD1/TMD interface in both the closed and open conformation of CFTR. Furthermore, we modeled {Delta}F508/R1070W and {Delta}F508 bound to the CFTR corrector VX-809. Our models reveal the stabilizing effects of R1070W and VX-809 on multi-domain models of {Delta}F508 CFTR and pave the way for rational design of additional drugs that target {Delta}F508 CFTR for treatment of CF. Author SummaryProteins three-dimension shape determines their function, so when genetic mutation compromises the shape of vital proteins, it may cause disease. Such is the case in Cystic Fibrosis, a chronic genetic disease caused by mutations in the protein Cystic Fibrosis Transmembrane Conductance Regulator. Here, we work backwards from the shape of the wild-type protein - found in healthy people, to computationally model the shape of the most common Cystic Fibrosis mutant. Our computer models reveal distinct defects in the shape of the mutant Cystic Fibrosis Transmembrane Conductance Regulator protein in the area surrounding the mutation. We also model an important FDA approved Cystic Fibrosis drug, VX-809, into the mutant protein structure and show how VX-809 stabilizes the protein around the location of the mutation. The method we developed will pave the way for computational drug design for Cystic Fibrosis.

bioinformatics↗

Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks

Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. High performance computing cluster integration allows these benchmarks to run continuously and automatically. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. The framework and design concepts presented here are valuable for developers and users of any type of scientific software and for the scientific community to create reproducible methods. Specific examples highlight the utility of this framework and the comprehensive documentation illustrates the ease of adding new tests in a matter of hours.

bioinformatics↗

Systematic Profiling of Temperature- and Retinal-Sensitive Rhodopsin Variants by Deep Mutational Scanning

Membrane protein variants with diminished conformational stability often exhibit enhanced cellular expression at reduced growth temperatures. The expression of "temperature-sensitive" variants is also typically sensitive to corrector molecules that bind and stabilize the native conformation. In this work, we employ deep mutational scanning to compare the effects of reduced growth temperature and an investigational corrector (9-cis-retinal) on the plasma membrane expression of 700 rhodopsin variants in HEK293T cells. We find that the change in expression at reduced growth temperatures is correlated with the response to retinal among variants bearing mutations within a hydrophobic transmembrane domain (TM2). The most sensitive variants within this helix appear to disrupt a network of hydrogen bonds that stabilizes a native helical kink. By comparison, mutants that alter a polar transmembrane domain (TM7) exhibit weaker responses to temperature and retinal that are poorly correlated. Statistical analyses suggest this insensitivity primarily arises from an abundance of mutations that enhance its membrane integration, stabilize its native conformation, and/ or perturb the retinal binding pocket. Finally, we show that the characteristics of purified temperature- and retinal-sensitive variants suggest that the proteostatic effects of retinal may be manifested during translation and cotranslational folding. Together, our findings elucidate various factors that mediate the sensitivity of genetic variants to temperature and to small molecule correctors.

biophysics↗

Upgraded molecular models of the human KCNQ1 potassium channel

The voltage-gated potassium channel KCNQ1 (KV7.1) assembles with the KCNE1 accessory protein to generate the slow delayed rectifier current, IKS, which is critical for membrane repolarization as part of the cardiac action potential. Loss-of-function (LOF) mutations in KCNQ1 are the most common cause of congenital long QT syndrome (LQTS), type 1 LQTS, an inherited genetic predisposition to cardiac arrhythmia and sudden cardiac death. A detailed structural understanding of KCNQ1 is needed to elucidate the molecular basis for KCNQ1 LOF in disease and to enable structure-guided design of new anti-arrhythmic drugs. In this work, advanced structural models of human KCNQ1 in the resting/closed and activated/open states were developed by Rosetta homology modeling guided by newly available experimentally-based templates: X. leavis KCNQ1 and resting voltage sensor structures. Using molecular dynamics (MD) simulations, the models capability to describe experimentally established channel properties including state-dependent voltage sensor gating charge interactions and pore conformations, PIP2 binding sites, and voltage sensor - pore domain interactions were validated. Rosetta energy calculations were applied to assess the models utility in interpreting mutation-evoked KCNQ1 dysfunction by predicting the change in protein thermodynamic stability for 50 characterized KCNQ1 variants with mutations located in the voltage-sensing domain. Energetic destabilization was successfully predicted for folding-defective KCNQ1 LOF mutants whereas wild type-like mutants had no significant energetic frustrations, which supports growing evidence that mutation-induced protein destabilization is an especially common cause of KCNQ1 dysfunction. The new KCNQ1 Rosetta models provide helpful tools in the study of the structural mechanisms of KCNQ1 function and can be used to generate structure-based hypotheses to explain KCNQ1 dysfunction.\n\nAuthor SummaryCardiac rhythm is maintained by synchronized electrical impulses conducted throughout the heart. The potassium ion channel KCNQ1 is important for the repolarization phase of the cardiac action potential that underlies these electrical impulses. Heritable mutations in KCNQ1 can lead to channel loss-of-function (LOF) and predisposition to a life-threatening cardiac arrhythmia. Knowledge of the three-dimensional structure of KCNQ1 is important to understand how mutations lead to LOF and to support structurally-guided design of new anti-arrhythmic drugs. In this work, we present the development and validation of molecular models of human KCNQ1 inferred by homology from the structure of frog KCNQ1. Models were developed for the open channel state in which potassium ions can pass through the channel and the closed state in which the channel is not conductive. Using molecular dynamics simulations, interactions in the voltage-sensing and pore domain of KCNQ1 and with the membrane lipid PIP2 were analyzed. Energy calculations for KCNQ1 mutations in the voltage-sensing domain reveled that most of the mutations that lead to LOF cause energetic destabilization of the KCNQ1 protein. The results support both the utility of the new models and growing evidence that mutation-induced protein destabilization is a common cause of KCNQ1 dysfunction.

biophysics↗