Biology subjects

Smith, M. S.

Publications and source records attributed to Smith, M. S..

7 recordsLinked to original sources

bioRxiv2026

Neuronal EphB2 signaling drives persistent neuropathic pain following spinal cord injury

Neuropathic pain after spinal cord injury reflects persistent hyperexcitability in the spinal cord dorsal horn, yet the molecular drivers sustaining this maladaptive state are unknown. Using an antibody microarray of dorsal horn tissue from mice six weeks after cervical contusion spinal cord injury, we found persistent upregulation of Eph-ephrin signaling, including increased EphB1, EphB2 and EphB3 expression and phosphorylation. Reversible chemogenetic inhibition of EphB kinase activity, using an EphB1/2/3 analog-sensitive knock-in mouse, selectively reversed established mechanical allodynia without affecting thermal hyperalgesia or motor function and also shifted dorsal horn signaling away from pain sensitization-associated pathways. Among EphB receptors, EphB2 showed the most consistent and robust injury-induced increase in expression within dorsal horn. Although EphB2 transcript levels increased in both dorsal horn neurons and astrocytes, conditional deletion of EphB2 only in dorsal horn neurons, but not in astrocytes, reversed established mechanical allodynia and reduced dorsal horn neuronal activation. These findings identify EphB signaling, and neuronal EphB2 in particular, as a mechanism that actively maintains pain hypersensitivity after spinal cord injury.

neuroscience↗

bioRxiv2025

A Cooperative Model for Symmetric Ligand Binding to Protein Fibrils

A hallmark of neurodegenerative diseases like Alzheimers Disease (AD) and chronic traumatic encephalopathy (CTE) is the presence of toxic protein aggregates in neurons. In AD and CTE specifically, the protein tau forms insoluble fibrils that are hundreds of nanometers in length. Intriguingly, recent experimental structures suggest that tau ligands like the disaggregator EGCG and positron emission tomography (PET) tracers like GTP-1 and MK-6240 bind to tau fibrils in long stacks reflecting the symmetry of the protein across many binding sites. In these stacks, each ligand makes more contact with its symmetry mates than it does with the protein. To interpret the binding of these molecules and new ligands, we must understand the effects of the cooperativity between sites and the entropy coming from the number of sites. Here, we investigate a nearest-neighbors model of cooperativity and use statistical mechanics to derive binding isotherms for saturation and competition experiments. This model allows us to relate measured EC50 so IC50 and so values to the intrinsic binding affinity to a single site and to cooperativity across sites in ways resembling the Cheng-Prusoff Equation. Depending on the degree of cooperativity between molecular species, this model permits solutions that lack the steep binding curves expected from cooperative systems and even solutions resembling 2-site systems. We finally consider conditions for a fibrils detection in a PET scan and practical matters of fitting this models parameters to data.

biophysics↗

bioRxiv2024

The impact of Library Size and Scale of Testing on Virtual Screening

Virtual libraries for ligand discovery have recently increased 10,000-fold, and this is thought to have improved hit rates and potencies from library docking. This idea has not, however, been experimentally tested in direct comparisons of larger-vs-smaller libraries. Meanwhile, though libraries have exploded, the scale of experimental testing has little changed, with often only dozens of high-ranked molecules investigated, making interpretation of hit rates and affinities uncertain. Accordingly, we docked a 1.7 billion molecule virtual library against the model enzyme AmpC {beta}-lactamase, testing 1,521 new molecules and comparing the results to the same screen with a library of 99 million molecules, where only 44 molecules were tested. Encouragingly, the larger screen outperformed the smaller one: hit rates improved by two-fold, more new scaffolds were discovered, and potency improved. Overall, 50-fold more inhibitors were found, supporting the idea that there are many more compounds to be discovered than are being tested. With so many compounds evaluated, we could ask how the results vary with number tested, sampling smaller sets at random from the 1521. Hit rates and affinities were highly variable when we only sampled dozens of molecules, and it was only when we included several hundred molecules that results converged. As docking scores improved, so too did the likelihood of a molecule binding; hit rates improved steadily with docking score, as did affinities. This also appeared true on re-analysis of large-scale results against the {sigma}2 and dopamine D4 receptors. It may be that as the scale of both the virtual libraries and their testing grows, not only are better ligands found but so too does our ability to rank them.

biophysics↗

bioRxiv2023

Docking for molecules that bind in a symmetric stack with SymDOCK

Discovering ligands for amyloid fibrils, such as those formed by the tau protein, is an area of much current interest. In recent structures, ligands bind in stacks in the tau fibrils to reflect the rotational and translational symmetry of the fibril itself; in these structures the ligands make few interactions with the protein but interact extensively with each other. To exploit this symmetry and stacking, we developed SymDOCK, a method to dock molecules that follow the proteins symmetry. For each prospective ligand pose, we apply the symmetry operation of the fibril to generate a self-interacting and fibril-interacting stack, checking that doing so will not cause a clash between the original molecule and its image. Absent a clash, we retain that pose and add the ligand-ligand van der Waals energy to the ligands docking score (here using DOCK3.8). We can check these geometries and energies using an implementation of ANI, a neural network-based quantum-mechanical evaluation of the ligand stacking energies. In retrospective calculations, symmetry docking can reproduce the poses of three tau PET tracers whose structures have been determined. More convincingly, in a prospective study SymDOCK predicted the structure of the PET tracer MK-6240 bound in a symmetrical stack to AD PHF tau before that structure was determined; the docked pose was used to determine how MK-6240 fit the cryo-EM density. In proof-of-concept studies, SymDOCK enriched known ligands over property-matched decoys in retrospective screens without sacrificing docking speed, and can address large library screens that seek new symmetrical stackers. Future applications of this approach will be considered.

biophysics↗

bioRxiv2023

Cryo-EM structure of Alzheimer disease tau filaments with PET ligand MK-6240

Positron Emission Tomography (PET) ligands have advanced Alzheimers disease (AD) diagnosis and treatment. Using autoradiography and cryo-EM, we identified AD brain tissue with elevated tau burden, purified filaments, and determined the structure of second-generation high avidity PET ligand MK-6240 at 2.31 [A] resolution, which bound at a 1:1 ratio within the cleft of tau paired-helical filament (PHF), engaging with glutamine 351, lysine K353, and isoleucine 360. This information elucidates the basis of MK-6240 PET in quantifying PHF deposits in AD and may facilitate the structure-based design of superior ligands against tau amyloids.

neuroscience↗

bioRxiv2021

Ligand Strain Energy in Large Library Docking

While small molecule internal strain is crucial to molecular docking, using it in evaluating ligand scores has remained elusive. Here, we investigate a technique that calculates strain using relative torsional populations in the Cambridge Structural Database, enabling fast pre-calculation of these energies. In retrospective studies of large docking screens of the dopamine D4 receptor and of AmpC {beta}-lactamase, where close to 600 docking hits were tested experimentally, including such strain energies improved hit rates by preferentially reducing high-scoring decoy molecules that were strained. In a 40 target subset of the DUD-E benchmark, we found two thresholds that usefully distinguished between ligands and decoys: one based on the total strain energy of the small molecules, and one based on the maximum strain allowed for any given torsion within them. Using these criteria, about 75% of the benchmark targets had improved enrichment after strain filtering. Relying on pre-calculated population distributions, this approach is rapid, taking less than 0.04 second to evaluate a conformation on a standard core, making it pragmatic for pre-calculating strain in even ultra-large libraries. Since it is scoring function agnostic, it may be useful to multiple docking approaches; it is openly available at http://tldr.docking.org

bioinformatics↗

bioRxiv2020

Probability-based methods for outlier detection in replicated high-throughput biological data

A problem that arises frequently in high-throughput biological studies is the assessment of technical reproducibility of data obtained under homogeneous experimental conditions. This is an important problem considering the significant growth in the number of high-throughput technologies that have become available to the researcher in the past decade. Although certain ad hoc and graphical methods for determining data quality have been in existence, these methods lack statistical rigor and are not broadly applicable across different technologies. There is an inherent need for the quantitative evaluation of the reproducibility of technical replicates from high-throughput compound and siRNA screening, next-generation sequencing and other modern "omics" studies. To this end, we have developed an approach that accounts for technical variability and potential asymmetry that arise naturally in the distribution of replicate data, and aids in the identification of outliers. Our methods for outlier detection rely on flexible statistical models, employ maximum likelihood methods for estimation and are broadly applicable to a variety of high-throughput biological studies. We discuss an adaptation of these methods when there are multiple replicates as well as current limitations to these techniques. We illustrate our methods using experimental data from high-throughput compound screening and protein expression studies as well as simulated data. Our methods are implemented in the R package replicateOutliers and are currently available at github.com/matthew-seth-smith/replicateOutliers.

bioinformatics↗