Intrinsically disordered regions (IDRs) mediate protein interactions through poorly understood mechanisms. We studied the retinoblastoma protein (pRb) and its interaction with the SV40 Large T antigen (LTSV40), a viral oncoprotein that displaces E2F factors. Using molecular dynamics and umbrella sampling, we show the LTSV40 LXCXE motif is part of a conserved Order–Motif–IDR architecture. The ordered N-terminal region drives initial pRb recognition via induced folding, adding over 6 kcal/mol to affinity. Simultaneously, the C-terminal IDR undergoes a bent-to-extended transition, sterically occluding the pRb AB cleft to prevent E2F binding. These coupled phenomena are evolutionarily conserved across 14 polyomaviruses, as confirmed by AlphaFold and MobiDB, suggesting a common pRb inactivation strategy. These results highlight a broader challenge for the field: functionally decisive IDR behaviors such as binding-induced folding and steric occlusion are not yet representable within current interaction data models, even when complementary evidence exists in resources such as DisProt or MobiDB. Bridging this gap through systematic integration of IDR annotations into databases such as IntAct and Complex Portal, supported by projection onto AlphaFold3-predicted complex structures, would enable community-scale identification of complexes where disorder is mechanistically decisive. ECCB 2026 online poster platform
Carla Luciana Padilla Franzotti, Nicolás Palópoli, Gustavo Pierdominici‐Sottile et al.· Zenodo (CERN European Organi...· 0 citations
CIR-DDG, a lightweight residual adapter that combines a fixed base prediction with 22 interpretable descriptors of cross-chain distance, contact density and site--partner context, is introduced, showing that the learned geometric correction generalizes beyond SKEMPI thermodynamic measurements.
Weizhen Yu, Zhi-Heng Zou, Yonggui Huang et al.· 0 citations
This work reviews the computational and experimental approaches that disentangle folding and function at scale, revealing a dark energy component and providing new insights into how biological information flows from sequence to structure to function and back to sequence.
Ezequiel A. Galpern, Federico Caamaño, Ignacio E. Sánchez et al.· 0 citations
The current work highlighted the potential of polymer-based excipient systems for developing high-concentration injectable suspensions of proteins and their combinations on the viscosity, injectability, and stability.
Chanakya D. Patil, Yi-Jing Huang, K. Arte et al.· International journal of pha...· 0 citations
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Endothelial DANCR deficiency promotes atherosclerotic plaque instability through activation of the RPL22/p53 pathway, suggesting DANCR as a potential protective factor and therapeutic target in atherosclerosis.
Shu-Ting Wang, Qi-Yue Zhang, Rong-Xia Li et al.· Journal of Advanced Research· 0 citations
This research investigates the potential role of quantum entanglement in biological information processing. We explore the feasibility of simulating quantum effects within biological molecules and examining the influence of entanglement on fundamental processes such as DNA sequence recognition and protein folding. The core claim centers on demonstrating how entanglement could provide a mechanism for enhanced computational capabilities within biological systems. This work contributes to the emerging field of quantum biology by proposing a novel framework for understanding biological phenomena through the lens of quantum mechanics, specifically focusing on the emergent properties of entanglement. The theoretical analysis presented here lays the groundwork for future experimental investigations and offers a new perspective on the complexities of life.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Predicting protein three-dimensional structures from their amino acid sequences remains a grand challenge in computational biology. Traditional methods have struggled to accurately capture the complex, long-range interactions that govern protein folding. This work proposes a novel approach utilizing Graph Neural Networks (GNNs) to address this challenge through a fragment assembly paradigm. We hypothesize that proteins can be effectively predicted by learning to assemble smaller, interacting fragments based on their local structural characteristics. Our GNN learns to represent individual protein fragments as graphs, capturing their local interactions via node features (amino acid types, residue connections) and edge features (distances, angles). The network then predicts the optimal assembly order of these fragments, ultimately generating a predicted protein structure. This approach avoids the need for explicit conformational search and leverages the powerful representation learning capabilities of GNNs. We demonstrate the feasibility and potential of this approach, outlining a framework for future development and exploration.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This record contains the code-associated data and figures for "Tensor-based Approximation of Molecular Kinetics: Generator Learning, Reaction Coordinates and Incremental Updating". It provides the precomputed data, results, and figures behind four case studies: a 3D Lemon-Slice toy system, and molecular dynamics trajectories of the fast-folding proteins Chignolin (CLN025) and NTL9. Each case study demonstrates a tensor-train (TT) based approach to gEDMD (generator Extended Dynamic Mode Decomposition) for estimating the generator of molecular dynamics, including comparisons against a dense reference method, PCCA+ soft-state assignment, an incremental TT-SVD update scheme, and truncation/bandwidth sensitivity studies. The corresponding code is available at: https://github.com/fnueske/tensor_gedmd
Feliks Nüske, Peter Benner, Minakshi Verma· Zenodo (CERN European Organi...· 0 citations
A fundamental theoretical framework for the development of specialized foods that possess safe swallowing and nutritional attributes is offered and the polarity of the tryptophan residue microenvironment alters and impeding the further folding of unfolded protein structures during heat-induced gelation is examined.
Wei Wang, Qing Shao, Li-Fei Wang et al.· Foods· 0 citations
The XGBoost model developed in this study achieved a relatively high AUC of 0.901, indicating that the model’s ability to confirm prolonged PACU stay remains somewhat insufficient, and can be used as an auxiliary screening tool in clinical rather than a definitive diagnostic tool.
Na Zhu, Xiang Xiong, Xuan-Zhao Wu et al.· BMC Anesthesiology· 0 citations
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
