Biology subjects

Cullen, M.

Publications and source records attributed to Cullen, M..

4 recordsLinked to original sources

bioRxiv2026

Capturing secondary structure in coarse grained intrinsically disordered proteins with simulations driven by chemical shifts.

A major challenge when investigating intrinsically disordered proteins (IDPs) pertains to understanding how secondary structure formation across otherwise disordered ensembles, relates to function. While coarse-grained (CG) simulation methods are instrumental to describe ensemble-to-function relationships in IDPs, they remain largely incapable of sampling secondary structure. We here present NapshiftC, an artificial neural network capable of modelling NMR chemical shifts from C-mapped CG protein conformations, leveraging its predictions to restrain simulations and yield experimentally-informed IDP ensembles that contain secondary structure. We applied NapshiftC on a large set of proteins in isolation or condensed phase, and found that, when secondary structure is present, CS incorporation yields improved agreements with smFRET spectroscopy and resolves the over-reliance of CG models on near linear conformers. By incorporating observables that are routinely collected for IDPs but never coupled to CG simulations, NapshiftC ultimately informs on both local and global conformational features of function-defining IDP ensembles.

biophysics↗

bioRxiv2025

Integrating NMR restraints into coarse-grained simulations: toward accurate conformational ensembles of complex protein systems

Structural dynamics play critical roles for the biological activity of protein molecules. Characterising the inherent conformational landscapes of these macromolecules remains a major experimental and computational challenge, particularly for heterogeneous and transient systems such as intrinsically disordered proteins, membrane-associated assemblies and disordered fuzzy coats of amyloid aggregates. In this context, coarse-grained (CG) molecular dynamics simulations have enabled accessing to extended timescales and large system sizes, however, their reduced resolution and simplified interaction potentials often limit the structural accuracy. Here, we introduce Martini3-NMR, an integrative framework that incorporates nuclear magnetic resonance (NMR) observables directly into CG protein force fields. Using artificial neural networks to model NMR chemical shifts at the CG level, and integrating these data with NOE restraints, we define an approach to significantly enhance the accuracy of CG simulations while maintaining their elevated sampling efficiency, thereby resulting in a substantially improved description of protein conformational ensembles. We demonstrate the broad applicability of Martini3-NMR by generating CG ensembles for a diverse range of systems and complex processes such as protein folding from misfolded states, oligomer disassembly within lipid bilayers and the topology of disordered fuzzy regions at amyloid fibril surfaces, which were found to display condensate-like structural and dynamical properties. By enabling an experimentally driven and computationally efficient exploration of protein conformational landscapes, Martini3-NMR provides a novel general framework for investigating dynamic, heterogeneous and multiscale biomolecular processes. This approach opens to significant new opportunities for extending CG simulations towards a more quantitative understanding of the relationship between molecular structure, dynamics and biological function.

biophysics↗

bioRxiv2020

A cross-species drug discovery pipeline to identify and validate new treatments for osteosarcoma

PurposeOsteosarcoma is a rare but aggressive bone cancer that occurs primarily in children. Like other rare cancers, treatment advances for osteosarcoma have stagnated, with little improvement in survival for the past several decades. Developing new treatments has been hampered by extensive genomic heterogeneity and limited access to patient samples to study the biology of this complex disease. Experimental designTo overcome these barriers, we combined the power of comparative oncology with patient-derived models of cancer and high-throughput chemical screens in a cross-species drug discovery pipeline. ResultsCoupling in vitro high-throughput drug screens on low-passage and established cell lines with in vivo validation in patient-derived xenografts we identify the proteasome and CRM1 nuclear export pathways as therapeutic sensitivities in osteosarcoma, with dual inhibition of these pathways inducing synergistic cytotoxicity. ConclusionsThese collective efforts provide an experimental framework and set of new tools for osteosarcoma and other rare cancers to identify and study new therapeutic vulnerabilities.

cancer biology↗

bioRxiv2020

Human papillomavirus 16 positive cervical cancer in Guatemala: The D2 and D3 sublineages differ in integration rate and age of diagnosis

Human papillomavirus (HPV) 16 displays substantial sequence variation; four HPV16 lineages (A, B, C, D) have been described, as well as multiple sub-lineages. To identify molecular events associated with HPV16 carcinogenesis we evaluated viral variation, the integration of HPV16, and somatic mutation in 96 cervical cancer samples from Guatemala. A total of 64% (60/94) of the samples had integrated HPV16 sequences, and integration was associated with an earlier age of diagnosis (P=0.0007) and pre-menopausal disease. HPV16 integration sites were broadly distributed in the genome but in one tumor, HPV16 integrated into the promoter of the interferon regulatory factor 4 (IRF4) gene, which plays an important role in the regulation of the interferon response to viral infection. The HPV16 D2 and D3 sub-lineages were found in 23% and 30% of the tumors, respectively and were significantly associated with adenocarcinoma. D2-positive tumors had a higher rate of integration (P=0.011), earlier age of diagnosis (P=0.012), and a lower rate of somatic mutation (P=0.03). Whereas D3-positive tumors are less likely to integrate, have later age-of-diagnosis, and a higher rate of somatic mutation. In conclusion, Guatemalan cervical tumors have a high frequency of the very high-risk HPV16 D2 and D3 sub-lineages and cervical cancer patients with these variants of HPV16 differ in histology, age of- diagnosis, integration, and somatic mutation frequency. In summary, related lineages of HPV16 have different features of oncogenicity.

cancer biology↗