BACKGROUND & METHODOLOGY: Cancer is a unique disease in which cells acquire positive traits for survival, mirroring species evolution. Classical methods to identify driver mutations detect signatures of Darwinian selection like focal 3D-clusters of dozens of mutations in protein structures. Understanding whether such mutations have functional impact e.g. by triggering activation requires knowledge of the relevant protein conformations as well of its inter-conversion pathways (Orellana et al., 2016), which is challenging for structural and computational methods alike (Orellana, 2019a, 2021a). To sample the conformational landscape of mutations we integrate coarse-grained (CG) and atomistic molecular dynamics (MD) simulations to generate testable predictions. Using this approach we revealed how in brain tumors, missense and deletion mutations of the oncogene EGFR evolutionarily converge to a similar intermediate state (Binder et al., 2018; Orellana, 2019b, 2021b; Orellana et al., 2019a). To screen “hot” mutations we develop Elastic Network algorithms, Langevin and discrete MD simulations (Emperador and Orozco, 2017; Emperador et al., 2015). CG- pre-screened mutations are subject to further MD to define their functional impact, which is evaluated by comparison with experimental structures trapped in biologically-relevant conformations (Orellana et al., 2016, 2019b; Thulasingam et al., 2021). We are also exploring machine learning approaches to analyze cancer mutational clusters. RESEARCH TEAM: The Protein Dynamics and Cancer Lab, led by Dr. Laura Orellana, is a newly stablished (Nov. 2020) team focused on the study of disease mutations from a conformational perspective. The PI joined KI after being awarded a 6-years tenure-track Assistant Professor Position with a start-up grant (7M SEK total). She has published several corresponding author papers in high-impact journals (NCOMMS, PNAS, Cancer Cell), often integrating coarse-grained and atomistic simulations with experiments, specially in cancer (Binder et al., 2018; Orellana, 2019; Orellana et al., 2019b). She is recognized as an emerging leader in computational and structural biology, receiving multiple invitations to review the field (Orellana, 2019a), and was appointed as expert evaluator for the Barcelona Supercomputing Center Life Sciences & Medicine Committee. Our first postdoc opening attracted 18 excellent applicants from Sweden, Canada, Korea, India and other countries. We have recruited one senior postdoc, one researcher and two MSc students, and we are part of an initiative to create a Structural Biology Unit with other top structural groups at KI (Petzold, Jovine and Landreh Labs). The requested resources are essential to get our research group at KI started. REFERENCES Mhashal A., Yoluk O. & Orellana L. (2022) Front.Mol.Biosci. Binder et al. (2018) Cancer Cell 34, 163-177.e7. Emperador & Orozco (2017) JCTC 13, 1454–1461. Emperador et al (2015) JCTC 11, 5929–5938. Orellana* (2019a) Front. Mol. Biosci. 6, 117. REVIEW Orellana* (2019b) Mol. Celllular Oncol. REVIEW Orellana* (2021a) Computational Techniques to Study Protein Dynamics and Conformations. In Advances in Protein Molecular and Structural Biology Methods, T. Tripathi, ed. (Elsevier). CHAPTER Orellana* (2021b). Curr. Res. Struct. Biol. Orellana* et al (2016) NCOMMS 7, 12575. Orellana* et al (2019) PNAS 116. Orellana* et al (2019) Bioinformatics 35, 3505–3507 Thulasingam, Orellana et al (2021) NCOMMS