Acute Noise Causes Down-Regulation of ECM Protein Expression in Guinea Pig Cochlea.

Proteomics technology reveals the marker proteins, potential pathogenesis, and intervention targets after noise-induced hearing loss. To study the differences in cochlea protein expression before and after noise exposure using proteomics to reveal the pathological mechanism of noise-induced hearing loss (NIHL). A guinea pig NIHL model was established to test the ABR thresholds before and after noise exposure. The proteomics technology was used to study the mechanism of differential protein expression in the cochlea by noise stimulation. The average hearing threshold of guinea pigs on the first day after noise exposure was 57.00 ± 6.78 dB Sound pressure level (SPL); the average hearing threshold on the seventh day after noise exposure was 45.83 ± 6.07 dB SPL. The proteomics technology identified 3122 different inner ear proteins, of which six proteins related to the hearing were down-regulation: Tenascin C, Collagen Type XI alpha two chains, Collagen Type II alpha one chain, Thrombospondin 2, Collagen Type XI alpha one chain and Ribosomal protein L38, and are enriched in protein absorption, focal adhesion, and extracellular matrix receptor pathways. Impulse noise can affect the expression of differential proteins through focal adhesion pathways. This data can provide an experimental basis for the research on the prevention and treatment of NIHL.

Enhancing Biomolecular Simulations with Hybrid Potentials Incorporating NMR Data.

Some recent advances in biomolecular simulation and global optimization have used hybrid restraint potentials, where harmonic restraints that penalize conformations inconsistent with experimental data are combined with molecular mechanics force fields. These hybrid potentials can be used to improve the performance of molecular dynamics, structure prediction, energy landscape sampling, and other computational methods that rely on the accuracy of the underlying force field. Here, we develop a hybrid restraint potential based on NapShift, an artificial neural network trained to predict protein nuclear magnetic resonance (NMR) chemical shifts from sequence and structure. In addition to providing accurate predictions of experimental chemical shifts, NapShift is fully differentiable with respect to atomic coordinates, which allows us to use it for structural refinement. By employing NapShift to predict chemical shifts from the protein conformation at each simulation step, we can compute an energy penalty and the corresponding hybrid restraint forces based on the difference between the predicted values and the experimental chemical shifts. The performance of the hybrid restraint potential was benchmarked using both basin-hopping global optimization and molecular dynamics simulations. In each case, the NapShift hybrid potential improved the accuracy, leading to better structure prediction via basin-hopping and increased local stability in molecular dynamics simulations. Our results suggest that neural network hybrid potentials based on NMR observables can enhance a broad range of molecular simulation methods, and the prediction accuracy will improve as more experimental training data become available.

Morphology changes in the cochlea of impulse noise-induced hidden hearing loss.

BACKGROUND: This study was focused on impulse noise induces hidden hearing loss. OBJECTIVES: This study was designed to determine the morphology changes of noise-induced hidden hearing loss (NIHHL). METHOD: Fifteen guinea pigs were divided into three groups: noise-induced hidden hearing loss (NIHHL) group, noise-induced hearing loss (NIHL) group, and normal control group. For the NIHHL group, guinea pigs were exposed to 15 times of impulse noise with peak intensity of 163 dB SPL at one time. For the NIHL group, animals were exposed to two rounds of 100 times impulse noise, and the time interval is 24 h. Auditory brain response (ABR) was tested before, immediately, 24 h, one week, and one month after noise exposure to evaluate cochlear physiology changes. One month after noise exposure, all guinea pigs in three groups were sacrificed, and basement membranes were carefully dissected immediately after ABR tests. The cochlea samples were observed by transmission electron microscopy (TEM) to find out the morphology changes. RESULT: The ABR results showed that 15 times of impulse noise exposure could cause NIHHL in guinea pigs and 200 times could cause completely hearing loss. Impulse noise exposure could cause a dramatic increase of mitochondria in the inner hair cell. The structures of ribbon synapse and heminode were also obviously impaired compared to the normal group. The nerve fiber and myelin sheath remained intact after impulse noise exposure. CONCLUSION: This research revealed that impulse noise could cause hidden hearing loss, and the changes in inner hair cells, ribbon synapse, and heminode all played a vital role in the pathogenesis of hidden hearing loss.

Implicit Solvents for the Polarizable Atomic Multipole AMOEBA Force Field.

Computational protein design, ab initio protein/RNA folding, and protein-ligand screening can be too computationally demanding for explicit treatment of solvent. For these applications, implicit solvent offers a compelling alternative, which we describe here for the polarizable atomic multipole AMOEBA force field based on three treatments of continuum electrostatics: numerical solutions to the nonlinear and linearized versions of the Poisson-Boltzmann equation (PBE), the domain-decomposition conductor-like screening model (ddCOSMO) approximation to the PBE, and the analytic generalized Kirkwood (GK) approximation. The continuum electrostatics models are combined with a nonpolar estimator based on novel cavitation and dispersion terms. Electrostatic model parameters are numerically optimized using a least-squares style target function based on a library of 103 small-molecule solvation free energy differences. Mean signed errors for the adaptive Poisson-Boltzmann solver (APBS), ddCOSMO, and GK models are 0.05, 0.00, and 0.00 kcal/mol, respectively, while the mean unsigned errors are 0.70, 0.63, and 0.58 kcal/mol, respectively. Validation of the electrostatic response of the resulting implicit solvents, which are available in the Tinker (or Tinker-HP), OpenMM, and Force Field X software packages, is based on comparisons to explicit solvent simulations for a series of proteins and nucleic acids. Overall, the emergence of performative implicit solvent models for polarizable force fields opens the door to their use for folding and design applications.

Structural Insights into Hearing Loss Genetics from Polarizable Protein Repacking.

Hearing loss is associated with ∼8100 mutations in 152 genes, and within the coding regions of these genes are over 60,000 missense variants. The majority of these variants are classified as "variants of uncertain significance" to reflect our inability to ascribe a phenotypic effect to the observed amino acid change. A promising source of pathogenicity information is biophysical simulation, although input protein structures often contain defects because of limitations in experimental data and/or only distant homology to a template. Here, we combine the polarizable atomic multipole optimized energetics for biomolecular applications force field, many-body optimization theory, and graphical processing unit acceleration to repack all deafness-associated proteins and thereby improve average structure MolProbity score from 2.2 to 1.0. We then used these optimized wild-type models to create over 60,000 structures for missense variants in the Deafness Variation Database, which are being incorporated into the Deafness Variation Database to inform deafness pathogenicity prediction. Finally, this work demonstrates that advanced polarizable atomic multipole force fields are efficient enough to repack the entire human proteome.