Biology subjects

Mateos, D. L.

Publications and source records attributed to Mateos, D. L..

4 recordsLinked to original sources

bioRxiv2024

Exploring voltage-gated sodium channel conformations and protein-protein interactions using AlphaFold2

Voltage-gated sodium (NaV) channels are vital regulators of electrical activity in excitable cells. Given their importance in physiology, NaV channels are key therapeutic targets for treating numerous conditions, yet developing subtype-selective drugs remains challenging due to the high sequence and structural conservation among NaV subtypes. Recent advances in cryo-electron microscopy have resolved most human NaV channels, providing valuable insights into their structure and function. However, limitations persist in fully capturing the complex conformational states that underlie NaV channel gating and modulation. This study explores the capability of AlphaFold2 to sample multiple NaV channel conformations and assess AlphaFold Multimers accuracy in modeling interactions between the NaV -subunit and its protein partners, including auxiliary {beta}-subunits and calmodulin. We enhance conformational sampling to explore NaV channel conformations using a subsampled multiple sequence alignment approach and varying the number of recycles. Our results demonstrate that AlphaFold2 models multiple NaV channel conformations, including those observed in experimental structures, states that have not been described experimentally, and potential intermediate states. Correlation and clustering analyses uncover coordinated domain behavior and recurrent state ensembles. Furthermore, AlphaFold Multimer models NaV complexes with auxiliary {beta}-subunits and calmodulin with high accuracy, and the presence of protein partners significantly alters both the modeled conformational landscape of the NaV -subunit and the coupling between its functional states. These findings highlight the potential of deep learning-based methods to expand our understanding of NaV channel structure, gating, and modulation, while also underscoring the limitations of predicted models that remain hypotheses until validated by experimental data. SummaryLopez-Mateos et al.s study demonstrates AlphaFold2s potential to sample multiple states of human NaV channels. Additionally, NaV -subunit interactions with {beta}-subunits and calmodulin reshape NaV -subunit conformational landscape. This study reveals potential of deep learning methods to model structural diversity of ion channels.

biophysics↗

bioRxiv2023

Computationally Driven Design of Novel BACE-1 Inhibitors for Alzheimer's Disease

Alzheimers disease is the most common form of dementia affecting 35 million people globally. One of the major efforts in the development of a treatment for Alzheimers is to reduce the rate of plaque formation, the common hallmark of Alzheimers disease. The protease BACE-1 has been demonstrated to play a role in catalyzing plaque formation and is therefore a major drug target. Here we report potential new drug candidates building upon Verubecestat, developed through computationally driven molecular modeling methods. Both designed molecules have improved docking scores relative to Verubecestat when modeled in the BACE-1 active site, and therefore present potential new leads for more effective therapeutics to combat Alzheimers disease.

biochemistry↗

bioRxiv2023

Computational Drug Design of Novel Agonists of the μ-Opioid Receptor to Inhibit Pain Signaling

Opioids such as Morphine, Codeine, Hydrocodone, and Oxycodone target the -opioid receptor, a G-protein-coupled receptor (GPCR), blocking the transmission of nociceptive signals. In this study, four opioids were analyzed for ADMET properties and molecular interactions with a GPCR crystal structure (PDB ID: 8EF6). This aided in the computational design of two novel drug candidates with improved docking scores and ADMET properties when compared to Hydrocodone. Homology analysis indicated that a Mus musculus (house mouse) animal model could be used in the preclinical studies of these drug candidates in the development of safer and more effective opioid drugs for pain management with reduced side effects.

molecular biology↗

bioRxiv2023

Elucidating Molecular Mechanisms of Protoxin-2 State-specific Binding to the Human NaV1.7 Channel

Human voltage-gated sodium (hNaV) channels are responsible for initiating and propagating action potentials in excitable cells and mutations have been associated with numerous cardiac and neurological disorders. hNaV1.7 channels are expressed in peripheral neurons and are promising targets for pain therapy. The tarantula venom peptide protoxin-2 (PTx2) has high selectivity for hNaV1.7 and serves as a valuable scaffold to design novel therapeutics to treat pain. Here, we used computational modeling to study the molecular mechanisms of the state-dependent binding of PTx2 to hNaV1.7 voltage-sensing domains (VSDs). Using Rosetta structural modeling methods, we constructed atomistic models of the hNaV1.7 VSD II and IV in the activated and deactivated states with docked PTx2. We then performed microsecond-long all-atom molecular dynamics (MD) simulations of the systems in hydrated lipid bilayers. Our simulations revealed that PTx2 binds most favorably to the deactivated VSD II and activated VSD IV. These state-specific interactions are mediated primarily by PTx2s residues R22, K26, K27, K28, and W30 with VSD as well as the surrounding membrane lipids. Our work revealed important protein-protein and protein-lipid contacts that contribute to high-affinity state-dependent toxin interaction with the channel. The workflow presented will prove useful for designing novel peptides with improved selectivity and potency for more effective and safe treatment of pain. SummaryNaV1.7, a voltage-gated sodium channel, plays a crucial role in pain perception and is specifically targeted by PTx2, which serves as a template for designing pain therapeutics. In this study, Ngo et al. employed computational modeling to evaluate the state-dependent binding of PTx2 to NaV1.7.

biophysics↗