Ajjarapu, K. | bioRxiv Science

bioRxiv2026

An isogenic single-cell atlas of familial Parkinson's disease mutations reveals convergent changes in dopamine neurons

Summary/AbstractFamilial Parkinsons disease (PD) is caused by mutations in more than twenty genes that affect diverse cellular pathways, including mitochondrial quality control, lysosomal function, and vesicular trafficking. A central question is how mutations impacting these distinct pathways converge to cause the selective degeneration of dopamine neurons. Human pluripotent stem cell (hPSC)-based disease models provide a valuable system to study this; however, systematic comparison of the pathogenic effects of different mutations has been limited by genetic background variability. To address this, we generated an isogenic single-cell transcriptomic atlas of fourteen familial PD mutations comprising more than 200,000 hPSC-derived midbrain specified cells. Integrated analysis revealed mutation-specific transcriptional signatures alongside shared dysregulated genes and modules that converge on mitochondrial homeostasis, endolysosomal degradation, and iron/ferroptosis pathways. Differentially expressed genes were significantly enriched for PD GWAS-implicated genes in dopamine neurons, bridging monogenic and sporadic PD genetic risk and highlighting a shared downstream state across multiple mutations. Finally, cells with a DNAJC6 mutation, which is associated with juvenile-onset parkinsonism, exhibited alteration of neurodevelopmental and psychiatric disorder risk genes, providing a transcriptional correlate for neurodevelopmental features observed in early-onset PD. Together, this resource enables molecular stratification of familial PD mutations and provides a foundational benchmarking data set.

genomics↗

bioRxiv2025

Scalable prediction of symmetric protein complex structures

All life relies on proteins to function, yet accurately modeling protein structures that exceed {approx} 10, 000 amino acids or have higher-order geometries remains difficult. Existing solutions are limited to specific scenarios, require considerable computational resources, or are otherwise unscalable. Consequently, many large, disease-relevant protein complexes in the human proteome, as well as nearly all viruses and numerous other classes, are impractical to model with high fidelity for drug development. To modulate these protein complexes and viruses, structural information is eminently valuable, and often essential. In the last two years, machine learning based-tools that can generate binders to a given target structure with high hit rates have emerged. Combined with high-throughput screening, these technologies can far outpace traditional drug discovery. However, they cannot function well without accurate models of their target structures. Thus, to unlock the full power of AI-driven drug discovery, a scalable method must be developed to predict large protein complex structures. To overcome this bottleneck, we introduce Plica-1, a physics-based method to rapidly and accurately predict the structure of arbitrarily large, symmetric protein complexes. Validated across 4 major symmetry classes (icosahedral, tetrahedral, octahedral, and cyclic), the method consistently achieves near-experimental levels of accuracy, i.e., RMSD < 5[A]. In test cases, the method runs in < 5 minutes on consumer hardware, 103-105 times faster than the closest comparable software. The largest structure currently built, at {approx}40,000 amino acids, is > 8 times the limit of existing machine learning methods. The results demonstrate that protein complexes can be modeled at significantly improved speeds and scales, making Plica-1 a promising tool for protein engineering and drug development.

bioengineering↗