arXiv subjects

Yang Ha

Publications and source records attributed to Yang Ha.

3 recordsLinked to original sources

arXiv2025

Structural Flexibility of the TCF7L2-DNA Complex with the Type 2 Diabetes SNP rs7903146

The single nucleotide polymorphism (SNP) rs7903146 in the TCF7L2 gene has been determined as one of the strongest common genetic risk factors for Type 2 Diabetes (T2D). The location of the SNP in a non-coding region suggests a regulatory mechanism, meaning the SNP doesn't change the protein's own structure but rather affects how the TCF7L2 protein binds to DNA to control other genes. This binding, however, is highly dependent on the shape and flexibility of the DNA. This study aims to reveal the atomic-level effects of the SNP's cytosine-to-thymine substitution on the TCF7L2-DNA complex. We first utilized AlphaFold to generate individual high-confidence structures of the TCF7L2 protein and two 15-base pair DNA duplexes: one containing the reference C allele and one containing the variant T allele. These structures were then used as inputs for Neurosnap's Boltz2 deep learning model to generate two complete protein-DNA complexes of the TCF7L2 HMG-box bound to each DNA variant. Using the iMODS server, we conducted a Normal Mode Analysis (NMA) to predict and compare large-scale flexibility and differences in interactions between the complexes. The protein-DNA interface was dissected using PDBsum to locate atomic contacts, clefts, and interaction maps. Overall, our results show that the T allele variant exhibits increased global stiffness with a higher eigenvalue and reduced flexibility, suggesting that the SNP disrupts the mechanism and biomechanical balance needed for efficient TCF7L2-DNA binding, thus affecting downstream gene regulation.

q-bio.BM↗

arXiv2023

Predicting Drug Solubility Using Different Machine Learning Methods -- Linear Regression Model with Extracted Chemical Features vs Graph Convolutional Neural Network

Predicting the solubility of given molecules remains crucial in the pharmaceutical industry. In this study, we revisited this extensively studied topic, leveraging the capabilities of contemporary computing resources. We employed two machine learning models: a linear regression model and a graph convolutional neural network (GCNN) model, using various experimental datasets. Both methods yielded reasonable predictions, with the GCNN model exhibiting the highest level of performance. However, the present GCNN model has limited interpretability while the linear regression model allows scientists for a greater in-depth analysis of the underlying factors through feature importance analysis, although more human inputs and evaluations on the overall dataset is required. From the perspective of chemistry, using the linear regression model, we elucidated the impact of individual atom species and functional groups on overall solubility, highlighting the significance of comprehending how chemical structure influences chemical properties in the drug development process. It is learned that introducing oxygen atoms can increase the solubility of organic molecules, while almost all other hetero atoms except oxygen and nitrogen tend to decrease solubility.

q-bio.QM↗

arXiv2023

Developing novel ligands with enhanced binding affinity for the sphingosine 1-phosphate receptor 1 using machine learning

Multiple sclerosis (MS) is a debilitating neurological disease affecting nearly one million people in the United States. Sphingosine-1-phosphate receptor 1, or S1PR1, is a protein target for MS. Siponimod, a ligand of S1PR1, was approved by the FDA in 2019 for MS treatment, but there is a demonstrated need for better therapies. To this end, we finetuned an autoencoder machine learning model that converts chemical formulas into mathematical vectors and generated over 500 molecular variants based on siponimod, out of which 25 compounds had higher predicted binding affinity to S1PR1. The model was able to generate these ligands in just under one hour. Filtering these compounds led to the discovery of six promising candidates with good drug-like properties and ease of synthesis. Furthermore, by analyzing the binding interactions for these ligands, we uncovered several chemical properties that contribute to high binding affinity to S1PR1. This study demonstrates that machine learning can accelerate the drug discovery process and reveal new insights into protein-drug interactions.

cs.LG↗