I am a dedicated researcher with a strong foundation in biochemistry, molecular biology, and computational chemistry. As a Postdoctoral Fellow at City of Hope’s Beckman Research Institute, my work focuses on advancing drug discovery by leveraging computational models to study complex protein-small molecule interactions, particularly in the areas of cancer and metabolic disease research.
My research combines machine learning, molecular dynamics simulations, and protein modeling to accelerate the drug development process and reduce research costs. With a Ph.D. in Biochemistry from Clark University, I am driven by a commitment to improve therapeutic options for diseases with high unmet needs, including cancer and diabetes.
Clark University, Worcester, MA | 2016 - 2023
Focused on molecular dynamics simulations and protein modeling, contributing to groundbreaking insights in E3 ubiquitin ligase and insulin receptor dynamics.
Capital Normal University, Beijing, China | 2012 - 2016
Solid foundation in biochemistry and molecular biology, sparking an interest in computational approaches to life science challenges.
City of Hope, Beckman Research Institute | 2023 - Present
Investigating lipid interactions with β2 adrenergic receptors to inform targeted drug development for complex diseases.
City of Hope, Beckman Research Institute | 2023 - Present
Conducted advanced research on degrader drugs, including PROTAC (proteolysis-targeting chimera) molecules and molecular glues, a pioneering class of therapeutics designed to selectively degrade harmful proteins.
Clark University | 2016 - 2022
Led classes in introductory and physical chemistry, supporting student learning through hands-on experience in scientific principles.
Clark University | 2019 - 2022
Performed molecular dynamics simulations to analyze 340,000 conformations, identifying allosteric signaling pathways. Developed a novel visualization tool for tracking critical residues, advancing cancer therapeutic targeting.
Clark University | 2016 - 2023
Conducted all-atom and coarse-grained simulations to understand insulin receptor activation, proposing a new signaling mechanism for applications in metabolic disease therapies.
Clark University | 2020 - 2021
Collaborated with medicinal chemists to design ligands for GPCR targets, leveraging loop modeling, QSAR analysis, and molecular docking to enhance drug targeting precision.
Clark University | 2018 - 2022
Conducted small molecule MD simulations and collaborated on molecule design to develop bifunctional tools for amyloid detection, aiding in neurodegenerative disease research.
Clark University | 2022 - 2023
Collaborated with biochemists; Carried out protein structure prediction using machine learning models; Performed secondary structure prediction; Predicted intrinsically disordered region and binding sites; Performed implicit solvent MD simulations; Combined computational results and NMR data to determine residual structures.
Studying protein degradation through E3 ubiquitin ligase dynamics. My research applies molecular dynamics simulations to identify pathways for targeting “undruggable” cancer-related proteins, opening new avenues for cancer therapeutics.
Focused on G-protein-coupled receptors, especially β2 adrenergic receptor interactions. This work aids in designing drugs targeting cardiovascular, immune, and nervous systems by enhancing the precision of receptor-targeted treatments.
Exploring the insulin receptor’s role in glucose regulation to advance therapies for diabetes and metabolic diseases. My research aims to develop precision treatments addressing insulin resistance and related health impacts.
Utilizing machine learning models to predict protein-ligand interactions, optimize drug candidates, and refine molecular designs. This approach accelerates drug discovery while reducing costs and improving treatment specificity.
Developing computational models to study protein-small molecule dynamics in cancer treatment. This research enhances targeted cancer therapies by simulating molecular interactions, helping identify promising therapeutic compounds.
Specializing in molecular dynamics and protein structure prediction, I work on simulating complex protein conformations to support accurate modeling of therapeutic targets, which is essential for advanced drug discovery.
iScience, 2024. DOI: 10.1016/j.isci.2024.110086
Investigates lipid interactions with β2-adrenergic receptors to uncover conformational changes critical to therapeutic targeting of GPCRs.
Upcoming Submission, 2023
Analyzes structural dynamics of insulin receptors, proposing a novel mechanism in metabolic signaling with implications for diabetes treatments.
Journal of Chemical Information and Modeling, 2022. DOI: 10.1021/acs.jcim.2c01022
Utilizes molecular dynamics to map pathways within c-Cbl, advancing targeted therapeutic strategies for cancer-related proteins.
Tetrahedron, 2022. DOI: 10.1016/j.tet.2022.132817
Collaborative study on amyloid-binding molecules, providing insights into molecular design for neurodegenerative disease interventions.
Protein Science, 2021; Conference abstract. DOI: 10.1002/pro.4191
Explores the structural dynamics of c-Cbl, contributing to foundational knowledge in cancer-related protein interactions.
Presented at the Protein Society 37th Annual Symposium, this award recognizes Tianyi’s groundbreaking work in protein dynamics and his contributions to advancing cancer therapeutics through computational modeling.
Clark University awarded Tianyi this honor in 2021 for his excellence in both teaching and research, highlighting his commitment to advancing biochemistry and computational biology.
Received for outstanding research contributions and a poster presentation at the Protein Society 35th Annual Symposium in 2021. This award celebrates his exploration of protein dynamics and the structural properties of unactivated c-Cbl.
Tianyi actively contributes to the scientific community by serving as a peer reviewer for high-impact journals such as BioMed Research International, BMC Bioinformatics, and Frontiers in Genetics, furthering advancements in computational biology.
Tianyi has presented his research at prominent conferences, including the ACS Fall 2022 conference and the Protein Society’s Annual Symposium. His presentations covered innovations in protein dynamics, molecular modeling, and cancer-related receptor studies, underscoring his leadership in computational chemistry.
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