About Me

Hi, my name is Siyuan Jiang. I am an undergraduate student at Tsinghua University, Tanwei College, majoring in Chemical Biology for Pharmaceutical Science.

My research interests center on computational protein engineering, especially model-driven protein design, virtual screening, evolutionary analysis, and functional redesign.

Education

Tsinghua University, Tanwei College
B.S. in Chemical Biology for Pharmaceutical Science, 2023 - 2027 (expected)

GPA: 3.81/4.00
Selected coursework: Bioinformatics, Fundamentals of Programming (Python & C++), Chemical Biology, AI in Healthcare, Medicinal Chemistry

Research Interests

My primary research interests include:

Protein Design Models: Developing generative and predictive models for protein sequence, structure, and function by integrating structural priors, evolutionary information, and experimental feedback.
Virtual Screening: Building efficient screening pipelines for protein and peptide candidates to reduce computational bottlenecks before high-precision structural validation.
Functional Engineering: Using evolutionary and functional analyses to guide rational modification of enzymes, binders, and other engineered proteins.

Research Experience

High-Throughput Screening of Functional Food-Derived Peptides
School of Life Sciences, THU | Advisor: Assoc. Researcher Yafei Yuan | Oct 2025 - Now

Developed a multi-stage screening pipeline for bioactive food-derived peptides targeting specific proteins, addressing the computational bottleneck of direct high-precision docking.
Constructed and trained a lightweight pre-screening model to filter massive peptide libraries and enrich candidates for downstream analysis.
Integrated AlphaFold 3 for high-precision structural validation of top candidates, followed by wet-lab assays to verify binding affinity.

Spatiotemporal Evolution of the Gamma-Secretase Complex
School of Life Sciences, THU | Advisor: Prof. Yigong Shi | Jul 2025 - Now

Constructed eukaryotic phylogenetic trees for the gamma-secretase complex and traced the PS1 subunit from prokaryotic homologs to clarify its deep evolutionary origin.
Batch-predicted approximately 1,500 structures with AlphaFold 3 and analyzed the stepwise assembly order of gamma-secretase subunits during evolution.
Applied statistical methods and deep learning tools to identify mechanistically important residues in gamma-secretase and explore their potential association with Alzheimer’s disease.

T-cell Specific In-situ CRISPR Screening System
School of Pharmaceutical Sciences, THU | Advisor: Prof. Xuebin Liao | Jul 2025 - Oct 2025

Addressed the limitations of traditional CRISPR screening, where scarce hematopoietic stem cells impede in-situ studies.
Leveraged infinitely proliferative mouse embryonic stem cells with lineage-specific gene editing to establish an in-situ screening platform for immune cells.
The platform aims to dissect T cell exhaustion regulation and metabolic-epigenetic coupling during memory T cell differentiation.

iGEM Competition: In-situ RNA Quantification Tool
Department of Chemical Engineering, THU | Advisor: Prof. Chun Li | Aug 2024 - Oct 2024

Developed an in-situ RNA quantification tool in yeast based on the ADAR protein, offering advantages over qPCR in speed, convenience, and non-destructive measurement.
The tool facilitates better understanding of real-time gene expression within living cells.
The project was awarded a Gold Medal at the 2024 iGEM Competition.

Selected Projects

Machine Learning for Cancer Diagnosis using Plasma small RNAs

Developed a bioinformatics pipeline with Python, R, and scikit-learn to diagnose cancer from plasma small RNA-seq data.
Implemented L1-regularized feature selection over 100 stratified samples to identify stable biomarkers; the resulting 4-piRNA colorectal cancer model achieved a test AUC of 0.802.
Validated a Rare Abundance Genes strategy and built a scoring model that achieved an AUC of 0.971.

Protein-Peptide Interaction Prediction via Generative Docking & GNNs

Developed a generative-discriminative framework coupling diffusion-based docking (RAPiDock) with a Transformer-GNN scorer (ITN).
Implemented multi-instance learning on 3D bipartite graphs to enable structure-aware, interpretable binding prediction.
Outperformed sequence-based baselines in AUC and enrichment on pMHC I and SH3-peptide systems.

Honors and Awards

WeiGuang Program, Individual Excellence Award, Tsinghua University (Jun 2025)
Gold Award for the Practical Detachment, Tanwei College (Jan 2025)
Comprehensive Excellence Award, Tsinghua University Scholarship, Top 20% (Nov 2024)
Gold Medal, International Genetically Engineered Machine Competition (iGEM) (Oct 2024)

Skills

Programming: Python, C++, R, Java, LaTeX
Frameworks: PyTorch
Languages: English (TOEFL 94)

Hobbies

Beyond research, I maintain an active lifestyle and diverse interests:

Sports: I am a member of the Tanwei College Badminton Team and the Tsinghua University Diving Association. I also enjoy running and swimming.
Arts & Culture: I love Rock Music, J-Pop, and Sci-Fi films (e.g., The Matrix, Star Wars).