Biology subjects

Pereira, G. P.

Publications and source records attributed to Pereira, G. P..

3 recordsLinked to original sources

bioRxiv2026

Signal peptidase complexes set species-specific rules for signal peptide cleavage

Protein secretion is essential for cell function and begins when signal peptides direct nascent proteins into the secretory pathway, where they are cleaved by the signal peptidase complex (SPC). Although this pathway is highly conserved, signal peptides do not always function efficiently across species, and the molecular basis for this incompatibility remains unclear. Here, we show that human signal peptides with longer hydrophobic cores efficiently target and translocate nascent proteins in both yeast and human but are frequently left uncleaved in yeast, identifying signal peptide cleavage as a major barrier to cross-species compatibility. Molecular dynamics (MD) simulations reveal that, despite their highly conserved architectures, yeast and human SPCs generate distinct membrane-thinning profiles near the signal peptide-binding region. Our results support a model in which SPC-lipid interactions tune the local membrane environment to accommodate signal peptides of distinct hydrophobic core length, providing a biophysical mechanism for species-specific signal peptide recognition. These findings offer a mechanistic framework for understanding and potentially engineering protein secretion across diverse expression hosts.

cell biology↗

bioRxiv2025

Single-cell and spatially resolved atlas of pancreatic cancer reveals immunophenotypes associated with clinical outcome

Pancreatic ductal adenocarcinoma (PDAC), accounting for 90% of pancreatic neoplasms, is characterized by its poor prognosis, with a 5-year survival rate of only 12%. Most patients are diagnosed with metastatic or locally advanced disease, leaving only 15% eligible for curative resection. PDAC exhibits resistance to chemotherapy, targeted therapies, and immunotherapy, largely due to its highly heterogeneous tumor microenvironment (TME). In this study, we performed an integrative analysis of publicly available scRNA, spatial transcriptomics, and bulk RNA sequencing datasets to investigate the influence of TME composition and tumor architecture on PDAC progression, treatment response, and clinical outcomes. We identified TME subtypes with distinct cellular compositions, functional signatures, and immunomodulatory cell-cell interactions. Spatially distinct cellular niches and gene modules revealed heterogeneity across primary tumors and metastatic lesions. Deconvolution of these spatial niches in a large cohort of bulk RNA samples uncovered unique clusters associated with patient survival, providing novel insights into TME biology and its clinical implications. These findings underscore the importance of integrating multi-omics approaches to unravel the complexity of the PDAC TME and highlight its potential to inform therapeutic strategies and improve patient outcomes.

cancer biology↗

bioRxiv2024

AlphaFold-Multimer struggles in predicting PROTAC-mediated protein-protein interfaces

Proteolysis Targeting Chimeras (PROTACs) are heterobifunctional molecules composed by ligands binding to a target protein and a E3-ligase complex, connected by a linker, that induce proximity-based target protein degradation. PROTACs are promising alternatives to conventional drugs against cancer. Predicting PROTAC-mediated complexes is often the first step for in silico PROTAC design pipelines. AlphaFold2 (AF2) revolutionized structural biology, enabling the prediction of multimeric protein structures. However, we previously noted that AF2 fails to predict PROTAC-mediated complexes. Here, we investigate the potential causes of this limitation. We consider a set of 326 protein heterodimers orthogonal to the AF2 training set, and evaluate AF2 models focusing on the interface size and presence of interface ligand. Our results show that AF2-multimer predictions are sensitive to the size of the interface to predict even in the absence of ligands, with the majority of models being incorrect for the smallest interfaces. We also benchmark both AF2 and AF3 on a set of 28 PROTAC-mediated dimers and show that AF3 does not significantly improve upon the accuracy of AF2. The low accuracy of AF2 on complexes with small interfaces has strong implications for computational pipelines for PROTAC design, as these stabilize typically small interfaces, and more generally on any prediction task that involves small interfaces.

bioinformatics↗