Xingcheng Lin

A biophysical framework for accurately identifying antigen single-amino acid escape variants and corresponding variant-specific compensatory TCR sequences

This paper develops a biophysical computational framework to identify antigen escape variants and compensatory TCR sequences by integrating mutagenesis data and structural modeling.

Charge Imbalance Drives Salt-Optimized Nucleosome Phase Separation under Physiological Conditions

The paper uses simulations to show that charge imbalance in nucleosomes drives phase separation under physiological salt conditions, influenced by DNA mechanics and histone modifications.

Biophysical modeling for accurate T cell specificity prediction of viral and tumor antigens

The paper develops a biophysical model to predict T cell receptor specificity for viral and tumor antigens using structural data and sequence-based clustering.

R-loop Prediction Reveals Generalization Limits of DNA Foundation Models Beyond Regulatory Genomics

The paper evaluates DNA foundation models' ability to predict R-loop-forming sequences, finding they perform well in some settings but struggle with cross-platform and cross-species generalization.

Active regulation of the epidermal growth factor receptor by the membrane bilayer

This study shows that the lipid composition of the membrane actively regulates EGFR conformation and signaling, independent of ligand binding, with implications for cancer and normal cell function.

Efficient RNA Folding Simulation via a Structure-Based Single-Site-Per-Nucleotide Model

The paper introduces an efficient RNA folding model that accurately simulates RNA hairpin unfolding and reproduces experimental thermodynamic properties.

Nucleosome condensate and linker DNA alter chromatin folding pathways and rates

This study maps chromatin folding pathways and rates using a coarse-grained model, showing that linker DNA length and nucleosome condensate affect stability and structure.

mIDEA: An Interpretable Structure-Sequence Model for Methylation-Dependent Protein-DNA Binding Sensitivity

mIDEA predicts and interprets protein-methylated-DNA interactions using a structure-aware energy matrix, improving binding-site prediction with methylation data.

IRIS Integrates Sparse Sequence, Experimental, and AI-Predicted Structures for Protein-RNA Affinity Prediction and Motif Discovery

IRIS predicts protein-RNA binding affinities and motifs using sequence, structural, and AI-predicted data without requiring large-scale affinity measurements.

Connected Chromatin Amplifies Acetylation-modulated Nucleosome Interactions

The paper simulates how H4K16 acetylation affects nucleosome stability and chromatin structure through computational modeling.

Interpretable Protein-DNA Interactions Captured by Structure-Sequence Optimization

The paper introduces IDEA, a model that predicts DNA recognition sites and binding affinities using structure-sequence optimization, enabling physicochemical interpretation of protein-DNA interactions.