Biology subjects

Shang, T.

Publications and source records attributed to Shang, T..

9 recordsLinked to original sources

bioRxiv2026

ctDNA Release Heterogeneity in Luminal Breast Cancer Revealed by Single-Cell Multi-Omics Spatial Analysis

Backgroud Breast cancer is a leading cause of cancer-related mortality, and HR+/HER2-; is the predominant subtype. Molecular pathology, including PIK3CA and ESR1 mutation, informs prognosis and treatment selection. Circulating tumor DNA (ctDNA) offers a non-invasive approach for cancer monitoring, but its clinical utility is limited by tissue plasma discordance, reflecting heterogeneous ctDNA release. The cellular and spatial determinants of this release remain poorly understood, particularly in HR+/HER2-; subtype, where heterogeneity across subclones complicates liquid biopsy interpretation. Results To identify the cellular and spatial determinants of ctDNA release, we developed scMASTER, integrating single-cell transcriptomes and mutations, spatial transcriptomics and matched tumor and plasma DNA sequencing. In ten patients with non-metastatic HR+/HER2- breast cancer, eight recurrent malignant epithelial states showed distinct ctDNA release potential. Antigen-presentation, stress, mitosis and mesenchymal-like states showed high release. PIK3CA and ESR1 mutations were linked to distinct functional states and altered ctDNA detectability. Three spatial architectures, EMT-enriched regions, intraductal luminal progenitors and avascular hypoxic zones, showed high release. Tumor-microenvironment interactions were bidirectional: anti-tumoral immunity correlated with reduced release, whereas immunomodulatory components correlated with increased release. A stratification model based on these features identified high-risk, immunosuppressed patients with poor prognosis, validated across external cohorts. Conclusions Our study establishes a single-cell spatial framework for decoding ctDNA release heterogeneity, linking tumor states, somatic mutations, spatial architecture, and immune interactions to ctDNA release. These findings provide mechanistic insights into tissue-plasma discordance, offer a biological rationale for interpreting ctDNA dynamics, refine patient risk stratification and liquid biopsy-based monitoring in HR+/HER2-; breast cancer.

cancer biology↗

bioRxiv2026

Rapid functional classification of cardiac genetic variants directly informs precision cardiology

Large-scale clinical genome sequencing yields vast numbers of variants of unknown significance (VUSs). The high frequency of VUSs and the paucity of platforms to characterize their functional impact pose significant challenges for clinical decision making. Here, we present an integrated end-to-end platform, REVi-SCOPE (Rapid evaluation of variants in single cells by optogenetics and prime editing), for characterization of the impact of VUSs on cardiac physiology. Our strategy consists of (1) introduction of variants directly into wild-type (WT) human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) via prime editing; (2) optogenetic assessment of calcium and membrane voltage dynamics in single hiPSC-CMs within the pool of edited and unedited cells; and (3) in situ single-cell genotyping of the phenotyped hiPSC-CMs with single-allele resolution. By optimizing and integrating each of these steps, we created a platform that enables VUS characterization in 10 days. We validated the REVi-SCOPEs capabilities by analyzing the properties of established arrhythmogenic variants. We then used REVi-SCOPE to reveal the functional impact of a VUS, TRPM4A320V, identified in a child with a conduction block. Together, our results show that REVi-SCOPE enables functional characterization of VUSs linked to cardiac arrhythmias with unprecedented throughput.

bioengineering↗

bioRxiv2025

MeDCycFold: A Rosetta Distillation Model to Accelerate Structure Prediction of Cyclic Peptides with Backbone N-methylation and D-amino Acids

Cyclic peptides with backbone N-methylated amino acids(BNMeAAs) and D-amino acids(D-AAs) have gained attention for their stability, membrane permeability, and other therapeutic potentials. Currently, Rosetta can predict their structures using energy calculations, but this method is heavily time-consuming. Moreover, structural data for cyclic peptides containing BNMeAAs and D-AAs are extremely insufficient to build a data-driven structure prediction model. To address these problems, we propose MeDCycFold, a deep learning-based Rosetta distillation model by fine-tuning the AlphaFold model. First, a cyclic peptide structure dataset is constructed using Rosetta by sampling massive conformations for cyclic peptides with BNMeAAs and D-AAs and evaluating their energy scores. Then, the AlphaFold model is fine-tuned with the extended 56 BNMeAAs and D-AAs. Besides, a relative position cyclic matrix is introduced for head-to-tail cyclization in the cyclic peptides. Finally, a force field is employed to reduce clashes in the predicted structures. Empirical experiments show that our proposed MeDCycFold speeds up structure prediction by 49 times while maintaining the prediction accuracy comparable to Rosetta, which can greatly accelerate the development of cyclic peptide drugs.

bioinformatics↗

bioRxiv2025

HighPlay：Cyclic Peptide Sequence Design Based on Reinforcement Learning and Protein Structure Prediction

The structural diversity and good biocompatibility of cyclic peptides has led to their emergence as potential therapeutic agents. Traditional cyclic peptide design relies on natural template modification and combinatorial chemical library screening, but suffers from bottlenecks such as limited molecular diversity, high cost, and time-consuming.AI technologies have improved design efficiency by predicting target binding patterns and generating scaffold structures, but they still require extensive laboratory screening. In this study, we propose HighPlay, which integrates reinforcement learning (Monte Carlo Tree Search) with the HighFold structure prediction model to design cyclic peptide sequences for protein targets, dynamically exploring the sequence space without the need of predefined target information. The model was applied to the design of cyclic peptide sequences for three different targets, which were screened and verified by molecular dynamics simulation, and showed good binding affinity. Specifically, the cyclic peptide sequences designed for TEAD4 target showed micromolar-level affinity in further experimental validation.

bioinformatics↗

bioRxiv2025

Predicting the structures of cyclic peptides containing unnatural amino acids by HighFold2

Cyclic peptides containing unnatural amino acids possess many excellent properties and have become promising candidates in drug discovery. Therefore, accurately predicting the three-dimensional structures of cyclic peptides containing unnatural residues will significantly advance the development of cyclic peptide-based therapeutics. Although deep learning-based structural prediction models have made tremendous progress, these models still cannot predict the structures of cyclic peptides containing unnatural amino acids. To address this gap, we introduce a novel model, HighFold2, built upon the AlphaFold-Multimer framework. HighFold2 first extends the pre-defined rigid groups and their initial atomic coordinates from natural amino acids to unnatural amino acids, thus enabling structural prediction for these residues. Then, it incorporates an additional neural network to characterize the atom-level features of peptides, allowing for multi-scale modeling of peptide molecules while enabling the distinction between various unnatural amino acids. Besides, HighFold2 constructs a relative position encoding matrix for cyclic peptides based on different cyclization constraints. Except for training using spatial structures with unnatural amino acids, HighFold2 also parameterizes the unnatural amino acids to relax the predicted structure by energy minimization for clash elimination. Extensive empirical experiments demonstrate that HighFold2 can accurately predict the three-dimensional structures of cyclic peptide monomers containing unnatural amino acids and their complexes with proteins, with the median RMSD for C reaching 1.891 [A]. All these results indicate the effectiveness of HighFold2, representing a significant advancement in cyclic peptide-based drug discovery.

bioinformatics↗

bioRxiv2024

NCPepFold: Accurate Prediction of Non-canonical Cyclic Peptide Structures via Cyclization Optimization with Multigranular Representation

Artificial intelligence-based peptide structure prediction methods have revolutionized biomolecular science. However, restricting predictions to peptides composed solely of 20 natural amino acids significantly limits their practical application, as such peptides often demonstrate poor stability under physiological conditions. Here, we present NCPepFold, a computational approach that can utilize a specific cyclic position matrix to directly predict the structure of cyclic peptides with non-canonical amino acids. By integrating multi-granularity information at the residue- and atomic-level, along with fine-tuning techniques, NCPepFold significantly improves prediction accuracy, with the average peptide RMSD for cyclic peptides being 1.640 [A]. In summary, this is a novel deep learning model designed specifically for cyclic peptides with non-canonical amino acids without length restrictions, offering great potential for peptide drug design and advancing biomedical research.

bioinformatics↗

bioRxiv2024

Reliable amplification of highly repetitive or low complexity sequence DNA enabled by superhelicase-mediated isothermal amplification

PCR is a cornerstone of molecular biology, but many biologically important DNA templates remain difficult to amplify. Long tandem repeats, low-complexity tracts, and sequences with extreme base composition often yield low product levels, smeared bands, stutter products, or truncated amplicons. These failures can arise because repeated cycles of thermal denaturation and reannealing promote off-register annealing, polymerase slippage, secondary-structure formation, and incomplete extension. Previously, we developed SSB-Helicase Assisted Rapid PCR (SHARP), an isothermal amplification method in which an engineered superhelicase and single-stranded DNA-binding protein replace the thermal melting step of PCR with enzymatic strand separation. Here, we tested whether SHARP can improve amplification of templates that are refractory to conventional PCR. SHARP robustly amplified up to six identical tandem repeats of the Widom 601 nucleosome-positioning sequence and up to 35 identical ankyrin repeats, targets that were poorly amplified by conventional PCR under the conditions tested. SHARP also amplified templates with extreme base composition, including a 95% AT-rich template and GC-rich templates (up to ~95% GC), as well as a 99-repeat CGG tract associated with Fragile X syndrome and a CAG/CAA repeat tract from mutant HTT exon 1 associated with Huntington disease. Together, these results show that helicase-driven isothermal amplification can expand access to repetitive and compositionally extreme DNA sequences that are challenging for conventional thermocycling-based PCR.

bioengineering↗

bioRxiv2023

GAPS: Geometric Attention-based Networks for Peptide Binding Sites Identification by the Transfer Learning Approach

The identification of protein-peptide binding sites significantly advances our understanding of their interaction. Recent advancements in deep learning have profoundly transformed the prediction of protein-peptide binding sites. In this work, we describe the Geometric Attention-based networks for Peptide binding Sites identification (GAPS). The GAPS constructs atom representations using geometric feature engineering and employs various attention mechanisms to update pertinent biological features. In addition, the transfer learning strategy is implemented for leveraging the pre-trained protein-protein binding sites information to enhance training of the protein-peptide binding sites recognition, taking into account the similarity of proteins and peptides. Consequently, GAPS demonstrates state-of-the-art (SOTA) performance in this task. Our model also exhibits exceptional performance across several expanded experiments including predicting the apo protein-peptide, the protein-cyclic peptide, and the predicted protein-peptide binding sites. Overall, the GAPS is a powerful, versatile, stable method suitable for diverse binding site predictions.

bioinformatics↗

bioRxiv2023

Highfold: accurately predicting cyclic peptide monomers and complexes with AlphaFold

In recent years, cyclic peptides have gained growing traction as a therapeutic modality owing to their diverse biological activities. Understanding the structures of these cyclic peptides and their complexes can provide valuable insights. However, experimental observation needs much time and money, and there still are many limitations to CADD methods. As for DL-based models, the scarcity of training data poses a formidable challenge in predicting cyclic peptides and their complexes. In this work, we present "High-fold," an AlphaFold-based algorithm that addresses this issue. By incorporating pertinent information about head-to-tailed circular and disulfide bridge structures, Highfold reaches the best performance in comparison to other various approaches. This model enables accurate prediction of cyclic peptides and their complexes, making a step to-wards resolving its structure-activity research.

bioinformatics↗