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Staff member

Mariano Martín Gomez

Protein phase transitions in health and disease
Staff member publications

Martín, Mariano, Lissmatz, Alice, Bolognesi, Benedetta, (2026). High-throughput methods for studying protein self-assembly CURRENT OPINION IN STRUCTURAL BIOLOGY 101, 103353

Martín, Mariano, Bolognesi, Benedetta, (2025). Massive mutagenesis reveals an incomplete amyloid motif in Bri2 that turns amyloidogenic upon C-terminal extension PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 122, e2415521122

Bolognesi, Benedetta

JTD

Thompson, Mike, Martín, Mariano, Olmo, Trinidad Sanmartín, Rajesh, Chandana, Koo, Peter K., Bolognesi, Benedetta, Lehner, Ben, (2025). Massive experimental quantification allows interpretable deep learning of protein aggregation Science Advances 11, eadt5111

Protein aggregation is a pathological hallmark of more than 50 human diseases and a major problem for biotechnology. Methods have been proposed to predict aggregation from sequence, but these have been trained and evaluated on small and biased experimental datasets. Here we directly address this data shortage by experimentally quantifying the aggregation of >100,000 protein sequences. This unprecedented dataset reveals the limited performance of existing computational methods and allows us to train CANYA, a convolution-attention hybrid neural network that accurately predicts aggregation from sequence. We adapt genomic neural network interpretability analyses to reveal CANYA’s decision-making process and learned grammar. Our results illustrate the power of massive experimental analysis of random sequence-spaces and provide an interpretable and robust neural network model to predict aggregation.

JTD

Groeneweg, Stefan, van Geest, Ferdy S., Martín, Mariano, Dias, Mafalda, Frazer, Jonathan, Medina-Gomez, Carolina, Sterenborg, Rosalie BTM., Wang, Hao, Dolcetta-Capuzzo, Anna, de Rooij, Linda J., Teumer, Alexander, Abaci, Ayhan, van den Akker, Erica LT., Ambegaonkar, Gautam P., Armour, Christine M., Bacos, Iiuliu, Bakhtiani, Priyanka, Barca, Diana, Bauer, Andrew J., van den Berg, Sjoerd AA., van den Berge, Amanda, Bertini, Enrico, van Beynum, Ingrid M., Brunetti-Pierri, Nicola, Brunner, Doris, Cappa, Marco, Cappuccio, Gerarda, Castellotti, Barbara, Castiglioni, Claudia, Chatterjee, Krishna, Chesover, Alexander, Christian, Peter, Coenen-van der Spek, Jet, de Coo, Irenaeus FM., Coutant, Regis, Craiu, Dana, Crock, Patricia, DeGoede, Christian, Demir, Korcan, Dewey, Cheyenne, Dica, Alice, Dimitri, Paul, Dremmen, Marjolein HG., Dubey, Rachana, Enderli, Anina, Fairchild, Jan, Gallichan, Jonathan, Garibaldi, Luigi, George, Belinda, Gevers, Evelien F., Greenup, Erin, Hackenberg, Annette, Halász, Zita, Heinrich, Bianka, Hurst, Anna C., Huynh, Tony, Isaza, Amber R., Klosowska, Anna, van der Knoop, Marieke M., Konrad, Daniel, Koolen, David A., Krude, Heiko, Kulkarni, Abhishek, Laemmle, Alexander, LaFranchi, Stephen H., Lawson-Yuen, Amy, Lebl, Jan, Leeuwenburgh, Selmar, Linder-Lucht, Michaela, López Martí, Anna, Lorea, Cláudia F., Lourenço, Charles M., Lunsing, Roelineke J., Lyons, Greta, Malikova, Jana Krenek, Mancilla, Edna E., McCormick, Kenneth L., McGowan, Anne, Mericq, Veronica, Lora, Felipe Monti, Moran, Carla, Muller, Katalin E., Nicol, Lindsey E., Oliver-Petit, Isabelle, Paone, Laura, Paul, Praveen G., Polak, Michel, Porta, Francesco, Poswar, Fabiano O., Reinauer, Christina, Rozenkova, Klara, Seckold, Rowen, Seven Menevse, Tuba, Simm, Peter, Simon, Anna, Singh, Yogen, Spada, Marco, Stals, Milou AM., Stegenga, Merel T., Stoupa, Athanasia, Subramanian, Gopinath M., Szeifert, Lilla, Tonduti, Davide, Turan, Serap, Vanderniet, Joel, van der Walt, Adri, Wémeau, Jean-Louis, van Wermeskerken, Anne-Marie, Wierzba, Jolanta, de Wit, Marie-Claire Y., Wolf, Nicole I., Wurm, Michael, Zibordi, Federica, Zung, Amnon, Zwaveling-Soonawala, Nitash, Rivadeneira, Fernando, Meima, Marcel E., Marks, Debora S., Nicola, Juan P., Chen, Chi-Hua, Medici, Marco, Visser, WEdward, (2025). Mapping variants in thyroid hormone transporter MCT8 to disease severity by genomic, phenotypic, functional, structural and deep learning integration Nature Communications 16, 2479

Visser, W. Edward

JTD