Biology subjects

Chen, B. Y.

Publications and source records attributed to Chen, B. Y..

6 recordsLinked to original sources

bioRxiv2023

Depletion of tropomyosin 1.6 promotes matrix-degrading phenotype in TGF-β1-induced myofibroblast

Fibroblasts can be transformed into myofibroblasts under pro-fibrotic conditions, which is characterized by increased contractility and reduced matrix degradation. The relationship between contractile activity and matrix degradation is not fully understood. We found that TGF-{beta}1-induced myofibroblast activation occurs on a culture dish, favoring stress fiber formation and inhibiting podosome structures due to high matrix stiffness. To mimic physiological conditions, we cultured fibroblasts on collagen gel. Blocking actomyosin signaling significantly reduced TGF-{beta}1-induced myofibroblast activation. Tpm1.6, an actomyosin-associated contractile unit, was specifically upregulated by TGF-{beta}1 on soft collagen substrates. Depletion of Tpm1.6 attenuated TGF-{beta}1-induced increase of -SMA, N-cadherin, and {beta}1-integrin, indicating its crucial role in early myofibroblast activation during fibrosis progression. Tpm1.6 depletion reduced TGF-{beta}1-induced cell contractility and enhanced collagen degradation. Notably, in Tpm1.6-depleted fibroblasts, TGF-{beta}1 triggered formation of distinct -SMA dot structures enriched with MMP9, promoting collagen degradation. Our study highlights the pivotal role of Tpm1.6 in TGF-{beta}1-induced myofibroblast activation and collagen degradation. Depletion of Tpm1.6 triggers robust collagen degradation through distinct -SMA dots, presenting a potential therapeutic approach for chronic kidney disease.

cell biology↗

bioRxiv2023

DeepVASP-S: a deep-learning tool for explaining steric mechanism that controls binding specificity

DeepVASP-S is a computational tool that leverages the capabilities of convolutional neural networks (CNNs) to analyze steric aspects of protein-ligand interactions and predict amino acid contributions to binding specificity. This tool combines structural bioinformatics with machine learning to address the complex problem of understanding how specific amino acids contribute to the specificity of binding. Here, we use this tool to predict subclasses for Enolase and Serine Protease according to their binding specificity, and explain it through the underlying steric mechanism. The strength of DeepVASP-S lies in its ability to identify and highlight these specific regions, providing researchers with insights into the molecular determinants of protein function and interaction. Such information is extremely valuable for the field of drug design, as it enables the creation of more targeted and effective therapeutics with minimized side effects. The approach taken by DeepVASP-S represents a significant step forward in computational biochemistry, merging high-resolution 3D structural data with the predictive power of machine learning to unlock a deeper understanding of protein-ligand interactions.

bioinformatics↗

bioRxiv2023

MechPPI: Binding Mechanism-based Machine-Learning tool for Predicting Protein-Protein Binding Affinity Changes Upon Mutations

Protein-protein interactions are essential for various biological processes, including signal transduction, metabolism, vesicle transport, and mitogenic processes. Its crucial to consider them within the context of their interactions with other proteins to understand protein function. Mutations in proteins can affect their binding affinity to partner proteins by introducing various effects, such as changes in hydrophobic regions, electrostatic interactions, or hydrogen bonds. Assessing the impact of mutations on protein interactions can have implications for disease susceptibility and drug efficacy. Understanding the impact of mutations on protein-protein interactions and predicting binding affinity changes computationally can benefit both basic biology and drug development. Different computational methods offer varying levels of accuracy and efficiency, and the choice of method depends on the specific research goals and available resources. We developed MechPPI, a tool that can use potential mechanism features underlying mutation to predict the binding affinity change upon mutation. We showed MechPPI can accurately predict binding affinity change upon a single mutation, and results demonstrate the potential of MechPPI as a powerful and useful computational tool in protein design and engineering.

bioinformatics↗

bioRxiv2023

A Bond-Anchored Network Structure Alignment (BANSA) Method for Graphical Analysis of Protein-Protein Interfacial Regions

We introduce a method to analyze and compare intermolecular bonds formed between protein-protein interactions. Utilizing the DiffBond software, we calculate potential intermolecular bonds, such as ionic bonds, hydrogen bonds, and salt bridges, based on amino acid structural and spatial parameters. This results in a graphical representation of bonds termed a bond network for each protein pair interaction. We then introduce an innovative strategy, the Bond-anchored Network Structural Alignment (BANSA), to align these networks using bond formation as anchor points. This alignment process uses the Root Mean Square Deviation (RMSD) to quantitatively assess the similarity between molecular structures. We validate the BANSA approach using several forms of analysis including a heatmap analysis, which provides a consolidated view of the entire bond network as well as a thorough comparison with existing literature. The results highlight the methods potential to offer insights into molecular interactions across various protein pairs without a need for direct modelling of protein-protein interactions.

bioinformatics↗

bioRxiv2021

DeepVASP-E: A Flexible Analysis of Electrostatic Isopotentials for Finding and Explaining Mechanisms that Control Binding Specificity

Amino acids that play a role in binding specificity can be identified with many methods, but few techniques identify the biochemical mechanisms by which they act. To address a part of this problem, we present DeepVASP-E, an algorithm that can suggest electrostatic mechanisms that influence specificity. DeepVASP-E uses convolutional neural networks to classify an electrostatic representation of ligand binding sites into specificity categories. It also uses class activation mapping to identify regions of electrostatic potential that are salient for classification. We hypothesize that electrostatic regions that are salient for classification are also likely to play a biochemical role in achieving specificity. Our findings, on two families of proteins with electrostatic influences on specificity, demonstrate that large salient regions can identify amino acids that have an electrostatic role in binding, and that DeepVASP-E is an effective classifier of ligand binding sites.

bioinformatics↗

bioRxiv2019

Glucose metabolism promotes neonatal heart regeneration

The mammalian heart switches its main metabolic substrate from glucose to fatty acids shortly after birth. This metabolic switch coincides with the loss of regenerative capacity in the heart. However, it is unknown whether glucose metabolism itself regulates heart regeneration. Here, we report that glucose metabolism is a determinant of regenerative capacity in the neonatal mammalian heart. Cardiac-specific overexpression of Glut1, the embryonic form of constitutively active glucose transporter, resulted in an increase in glucose uptake and concomitant glycogen storage in postnatal heart. Upon cryoinjury, Glut1 transgenic hearts showed higher regenerative capacity with less fibrosis than non-transgenic control hearts. Interestingly, flow cytometry analysis revealed two distinct populations of ventricular cardiomyocytes: Tnnt2-high and Tnnt2-low cardiomyocytes, the latter of which showed significantly higher mitotic activity in response to high intracellular glucose in Glut1 transgenic hearts. Metabolic profiling shows that Glut1-transgenic hearts have a significant increase in the glucose metabolites upon injury, and inhibition of the nucleotide biosynthesis abrogated the regenerative advantage of high intra-cardiomyocyte glucose level. Our data suggest that the increased in glucose metabolism promotes cardiac regeneration in neonatal mouse heart.

developmental biology↗