Abstract. The Martini coarse-grained (CG) force field enables efficient simulations of biomolecular systems but cannot reliably maintain folded protein str

PhD (f/m/d)

The kinetochore--the molecular machine that couples chromosomes to spindle microtubules--performs an essential, tightly regulated function, yet varies dramatically across eukaryotes. Microtubules are ...

Research assistant (praedoc) (f/m/d) DM-786

Cross-linking mass spectrometry (XL-MS) has emerged as a powerful quantitative technique for probing intra-protein structural information as well as protein-protein interactions at an unprecedented sc...

Design and scale experimental datasets for ML Develop workflows that connect noisy experimental signals to actionable model insights 1. Bridging Models with Real-World Experimental Signals Develop met...

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Protein conformational dynamics and ligand binding processes are fundamental to biological function, yet their systematic sampling and thermodynamic characterization remain challenging. Here, we present a distance-restraint-guided inference method that extends AlphaFold3-like diffusion model frameworks to predict protein structures at specified conformational states. By restraining intergroup distances─defined as geometric centroid distances between atom groups─during the reverse diffusion process, our method enables systematic sampling along reaction coordinates without model retraining. We implemented this approach in Boltz-2 and demonstrated its effectiveness on three model proteins that undergo open-closed conformational transitions, as well as on a protein-peptide dissociation pathway. Compared with conventional approaches that induce conformational diversity by manipulating the input multiple sequence alignments, our method achieved more uniform coverage of conformational space while maintaining high structural quality as assessed by both learning-based confidence metrics and stereochemistry-based validation. By combining distance-restrained sampling with molecular dynamics simulations, we constructed free energy landscapes and quantitatively estimated binding free energies. Altogether, our approach bridges deep learning-based structure prediction and physics-based simulations, providing an efficient strategy for characterizing the dynamics of biomolecules.

Salary for this Role: From £53,025 per annum plus benefits, subject to skills and experience Job Title: Senior Data Scientist Reports to: Radoslav Enchev Closing Date: 06/May/2026 23.59 GMT Job Descri...

Force fields for molecular dynamics are usually developed manually, limiting their transferability and making systematic exploration of functional forms challenging. We developed a graph neural networ...

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Despite considerable advances in computational chemistry, bridging the gap between the accuracy of all-atom molecular dynamics (AA-MD) and the high-throughput capabilities of docking remains an unsolv...

Nature Communications - Tubulin conservation presents a challenge to understanding microtubules’ diverse functions in eukaryotes. Here, the authors characterize the structures of P....

The Martini coarse-grained (CG) force field enables efficient simulations of biomolecular systems but cannot reliably maintain folded protein structures. To stabilize proteins during simulation, Martini is typically combined with structure-based force fields such as elastic network models (ENMs) or Go̅ models. While these approaches preserve global folds and capture protein flexibility, their ability to reproduce conformational dynamics remains unclear. Here, we evaluate Martini 3 combined with ENMs or Go̅ models on three folded proteins and show that both approaches struggle to sample the conformational space observed in atomistic simulations, even when uniform interaction strengths or equilibrium bond distances are adjusted. This limitation arises from the assumption of a uniform interaction network, in which all Go̅-bonds are assigned the same ϵ value, and therefore have the same potential depth. To overcome this, we present a fully automated, perturbation-based optimization approach for Go̅ networks, PoGo̅, that iteratively refines a nonuniform Go̅ network against a precomputed atomistic free-energy landscape in essential conformational space. Moreover, we demonstrate that our approach can also be used to optimize ENMs. In both cases, convergence is rapid and yields CG ensembles in close agreement with reference atomistic simulations. As a cross-validation, the optimization also improves the root-mean-square fluctuation profile.