Research Description
How a single mutation can disrupt protein function and propagate throughout scales, from cells to tissues and the whole organism causing diseases like cancer? How the change of just a few atoms in an oncogene can trigger the formation of a tumor? Howis the sequence of a protein related to its shape and function? We aim to answer these fundamental questions by exploring the molecular basis of mendelian diseases and cancer at the deepest level, in terms of protein structures and their motions at the atomic scale. In the same way as animal shapes are selected by evolution to fly, run or swim, each protein fold has been evolutionarily selected to perform certain motions, the so-called 鈥渃onformational-changes鈥, which dictate biological function. In cancer cells, proteins also mutate and quickly evolve their 鈥減henotypes鈥 - their conformational dynamics and resulting cellular function - in order to adapt to their environment and promote oncogenic growth. To reveal the key dynamic information contained in cancer mutation patterns, we develop simulation methods (; ) and servers () and integrate them with structural determination techniques (SAXS, cryoEM), in vitro and in vivo experiments. Using this interdisciplinary approach, we discovered that EGFR mutations in brain tumors converge to acquire a similar conformation, which is antagonistic from mutations in lung cancer and respond to different drugs. Our mechanistic insights set a rational basis for synergistic drug combinations that trap this glioblastoma-specific EGFR conformation, triggering tumor regression in animal tumor models (; ). Our goal is to go beyond conventional oncogenes like EGFR and perform a complete conformational profiling of tumors, which will be essential to identify, group and rationally target evolutionarily selected mutations with conformation-specific drugs. We are also interested in mendelian disease mutational patterns, which can reveal essential aspects of protein function and we have shown unexpectedly overlap with mutations found in human tumors ().
On a broader view, we believe that understanding biomolecular dynamics, protein-protein interactions and folding, and their perturbations in human diseases is essential to connect the structural scale with higher-level observations in molecular biology and medicine and push the boundaries of biophysical research and our understanding of life mechanisms. Apart of deriving testable predictions from simulations, we have experience helping to rationalize experimental data at the structure/ dynamics- level (; ; ; ) and are always interested to hear about potential collaborations.
More information on our research may be found .
Available positions
We are an inter-disciplinary group at the crossroads of computational biophysics & structural biology to generate new insights on protein function and malfunction to be tested in vitro and in vivo. If you are interested to join or just curious to know more about our research, please contact Laura Orellana
Press releases
Projects and Research Lines
We develop algorithms to predict protein conformational changes and the impact of mutations on them, focusing specially on cancer:
1) Computational multiscale methods to study protein conformational changes:
- Coarse-grained & atomistic simulation methods: Normal Mode Analysis, Elastic Networks, Langevin Dynamics, classical Molecular Dynamics, Principal Component & Network Analysis of structural ensembles, machine learning, sampling algorithms.
- In silico mutational screening: Development of algorithms and tools to identify 鈥渄ynamically hot鈥 mutations shifting protein conformation.
2) Protein evolution, cancer mutations and conformational dynamics:
- Rationalization of mutational patterns for known oncogenes: conformational convergence in Tyrosine Kinase Receptors (EGFR)
- Discovery of new cancer drivers & biomarkers including house-keeping and tissue-specific proteins
Servers:
Selected Publications
Mhashal A.R., Yoluk O., Orellana L.
Frontiers in Biomolecular Sciences, 2022 (Accepted)
Orellana L
Front Mol Biosci 2019 ;6():117
Orellana L
Mol Cell Oncol 2019 ;6(5):e1630798
Orellana L, Thorne AH, Lema R, Gustavsson J, Parisian AD, Hospital A, et al
Proc Natl Acad Sci U S A 2019 05;116(20):10009-10018
Binder ZA, Thorne AH, Bakas S, Wileyto EP, Bilello M, Akbari H, et al
Cancer Cell 2018 07;34(1):163-177.e7
Orellana L, Yoluk O, Carrillo O, Orozco M, Lindahl E
Nat Commun 2016 08;7():12575
Book Chapters
Orellana L,* Mhashal A. & Emperador A. (2022).
Advances in Protein Molecular and Structural Biology Methods. Eds. Tripathi T & Dubey V. Elsevier, USA (In Press). [ISBN: 978-032-39-0264-9].
Recent Collaborations
Thulasingam M, Orellana L, Nji E, Ahmad S, Rinaldo-Matthis A, Haeggstr枚m JZ
Nat Commun 2021 03;12(1):1728
Matsuoka R, Fudim R, Jung S, Zhang C, Bazzone A, Chatzikyriakidou Y, Nomura N, Iwata S, Orellana L, Bernstein O., Drew D.
bioRxiv (2021)