bioRxivBioinformaticsPreprint
Harnessing A Unified Multi-modal SequenceModeling to unveil Protein-DNA Interdependency
Mingchen LiiD, Yuchen ReniD, Peng Ye, Jiabei ChengiD, Xinzhu MaiD, Yuchen CaiiD, Wanli OuyangiD, Bozitao ZhongiD, Banghao WuiD, Nanqing DongiD, Liang HongiD, Pan TaniD
Abstract
The interdependent orchestration of proteins, coding DNA sequences (CDS), and adjacent regulatory DNA elements underpins biological functionality, where evolutionary constraints shape codon usage, transcriptional regulation, and structural stability. We present ProDMM, a multimodal framework that deciphers these systemic relationships through unified sequence modeling. Integrating a context-aware encoder with conditional generation, our architecture captures codependent patterns between protein motifs and cis-regulatory syntax. ProDMMs encoder demonstrates superior predictive capabilities in forecasting pathway-level phenotypes and protein expression profiles, outperforming conventional single-modality approaches. Its decoder generates synthetically optimized regulatory elements by resolving spatial dependencies between coding sequences and adjacent non-coding regions, enabling transcriptional fine-tuning unavailable to sequence-agnostic methods. By resolving how localized CDS-NCDS-protein interdependencies propagate to system-level phenotypes, ProDMM establishes a paradigm for holistic biological sequence engineering. This approach enables coordinated optimization of translation efficiency, enzymatic activity, and pathway flux, offering transformative potential for designing tailored biosynthesis systems and advancing sustainable bioproduction strategies.
- assay: DNA methylation
- assay: proteomics
- research focus: gene regulatory network inference
- research focus: protein structure prediction
- research focus: drug response / drug discovery
- approach: deep learning
- Citations
- 1
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