RESEARCH
Data-driven research for biomedical discovery, safer healthcare, and translational impact.
The Irina Moreira Lab carries out interdisciplinary research at the interface of computational structural biology, biophysics, and artificial intelligence. Our work focuses on understanding biological systems at the molecular level, with particular attention to how structure, dynamics, and interactions shape function in both health and disease.
A central theme across our projects is the quantitative characterisation of biomolecular systems using a combination of physics-based and data-driven approaches. By bringing together molecular modelling, enhanced sampling methods, and statistical learning, we investigate the conformational landscapes of proteins and how these are influenced by mutations, ligands, and the surrounding environment. This allows us to identify the mechanistic determinants of stability, specificity, and functional regulation.
We are especially interested in the structural and dynamic basis of protein function, including membrane proteins, signalling complexes, and large macromolecular assemblies. Our projects often focus on systems of high biomedical relevance, such as G protein-coupled receptors, enzyme complexes, and protein–protein interaction networks, using multi-scale simulation strategies supported by high-performance computing.
Another important research direction is the study of genetic variation and its effects on protein structure and function. We develop computational frameworks to predict the impact of mutations, particularly in the context of rare disease, by integrating structural descriptors, energetic calculations, and machine learning models. In doing so, we aim to move beyond static representations and capture the dynamic, ensemble nature of biomolecules.
Method development is also at the heart of our work. We design new algorithms for biomolecular modelling, improve scoring functions for binding affinity prediction, and create robust pipelines for large-scale data integration and analysis. Alongside this, we explore the use of artificial intelligence in biology, with a strong preference for interpretable, physically grounded models that combine predictive power with mechanistic insight.
Our projects currently span protein structure and dynamics, molecular recognition, membrane protein systems, mutation impact, rare disease mechanisms, and computational method development. Through active collaborations with academic, clinical, and multidisciplinary partners, we aim to connect modelling with experimental validation and ensure that our research remains both rigorous and relevant.
Interested in our work or in collaborating with us?