The need to implement FAIR principles in biomolecular simulations

Autor: Amaro, Rommie, Åqvist, Johan, Bahar, Ivet, Battistini, Federica, Bellaiche, Adam, Beltran, Daniel, Biggin, Philip C., Bonomi, Massimiliano, Bowman, Gregory R., Bryce, Richard, Bussi, Giovanni, Carloni, Paolo, Case, David, Cavalli, Andrea, Chang, Chie-En A., Cheatham III, Thomas E., Cheung, Margaret S., Chipot, Cris, Chong, Lillian T., Choudhary, Preeti, Cisneros, Gerardo Andres, Clementi, Cecilia, Collepardo-Guevara, Rosana, Coveney, Peter, Covino, Roberto, Crawford, T. Daniel, Peraro, Matteo Dal, de Groot, Bert, Delemotte, Lucie, De Vivo, Marco, Essex, Jonathan, Fraternali, Franca, Gao, Jiali, Gelpí, Josep Lluís, Gervasio, Francesco Luigi, Gonzalez-Nilo, Fernando Danilo, Grubmüller, Helmut, Guenza, Marina, Guzman, Horacio V., Harris, Sarah, Head-Gordon, Teresa, Hernandez, Rigoberto, Hospital, Adam, Huang, Niu, Huang, Xuhui, Hummer, Gerhard, Iglesias-Fernández, Javier, Jensen, Jan H., Jha, Shantenu, Jiao, Wanting, Jorgensen, William L., Kamerlin, Shina Caroline Lynn, Khalid, Syma, Laughton, Charles, Levitt, Michael, Limongelli, Vittorio, Lindahl, Erik, Lindorff-Larsen, Kresten, Loverde, Sharon, Lundborg, Magnus, Luo, Yun Lyna, Luque, Francisco Javier, Lynch, Charlotte I., MacKerell, Alexander, Magistrato, Alessandra, Marrink, Siewert J., Martin, Hugh, McCammon, J. Andrew, Merz, Kenneth, Moliner, Vicent, Mulholland, Adrian, Murad, Sohail, Naganathan, Athi N., Nangia, Shikha, Noe, Frank, Noy, Agnes, Oláh, Julianna, O'Mara, Megan, Ondrechen, Mary Jo, Onuchic, José N., Onufriev, Alexey, Osuna, Silvia, Panchenko, Anna R., Pantano, Sergio, Parish, Carol, Parrinello, Michele, Perez, Alberto, Perez-Acle, Tomas, Perilla, Juan R., Pettitt, B. Montgomery, Pietropalo, Adriana, Piquemal, Jean-Philip, Poma, Adolfo, Praprotnik, Matej, Ramos, Maria J., Ren, Pengyu, Reuter, Nathalie, Roitberg, Adrian, Rosta, Edina, Rovira, Carme, Roux, Benoit, Röthlisberger, Ursula, Sanbonmatsu, Karissa Y., Schlick, Tamar, Shaytan, Alexey K., Simmerling, Carlos, Smith, Jeremy C., Sugita, Yuji, Świderek, Katarzyna, Taiji, Makoto, Tao, Peng, Tikhonova, Irina G., Tirado-Rives, Julian, Tunón, Inaki, Van Der Kamp, Marc W., Van der Spoel, David, Velankar, Sameer, Voth, Gregory A., Wade, Rebecca, Warshel, Ariel, Welborn, Valerie Vaissier, Wetmore, Stacey, Wong, Chung F., Yang, Lee-Wei, Zacharias, Martin, Orozco, Modesto

This letter illustrates the opinion of the molecular dynamics (MD) community on the need to adopt a new FAIR paradigm for the use of molecular simulations. It highlights the necessity of a collaborative effort to create, establish, and sustain a data

Externí odkaz: http://arxiv.org/abs/2407.16584

Accelerating Time-to-Science by Streaming Detector Data Directly into Perlmutter Compute Nodes

Autor: Welborn, Samuel S., Enders, Bjoern, Harris, Chris, Ercius, Peter, Bard, Deborah J.

Recent advancements in detector technology have significantly increased the size and complexity of experimental data, and high-performance computing (HPC) provides a path towards more efficient and timely data processing. However, movement of large d

Externí odkaz: http://arxiv.org/abs/2403.14352

Comparison of fibroblast growth factor 19 concentrations between dogs with and without gallbladder mucoceles

Autor: Vy Ngoc Yen Truong, Chin‐Chi Liu, Jillian Myers, Mayzie Miller, Amanda Yang, Jeongha Lee, Nancy Welborn, Andrea N. Johnston

Publikováno v: Journal of Veterinary Internal Medicine, Vol 38, Iss 5, Pp 2518-2522 (2024)

Abstract Background Fibroblast growth factor 19 (FGF19) is an enterohepatic hormone the synthesis of which is stimulated by bile acid activation of the nuclear farnesoid X receptor (FXR) in ileal enterocytes. Increased production of FGF19 downregulat

Externí odkaz: https://doaj.org/article/7427de78142749d0b7b3b47acc536725

TomoPyUI: a user-friendly tool for rapid tomography alignment and reconstruction

Autor: Samuel S. Welborn, Molleigh B. Preefer, Johanna Nelson Weker

Publikováno v: Journal of Synchrotron Radiation, Vol 31, Iss 4, Pp 979-986 (2024)

The management and processing of synchrotron and neutron computed tomography data can be a complex, labor-intensive and unstructured process. Users devote substantial time to both manually processing their data (i.e. organizing data/metadata, applyin

Externí odkaz: https://doaj.org/article/f13df00171794d828fc5ab2c28f3e2c6

OrbNet Denali: A machine learning potential for biological and organic chemistry with semi-empirical cost and DFT accuracy

Autor: Christensen, Anders S., Sirumalla, Sai Krishna, Qiao, Zhuoran, O'Connor, Michael B., Smith, Daniel G. A., Ding, Feizhi, Bygrave, Peter J., Anandkumar, Animashree, Welborn, Matthew, Manby, Frederick R., Miller III, Thomas F.

We present OrbNet Denali, a machine learning model for electronic structure that is designed as a drop-in replacement for ground-state density functional theory (DFT) energy calculations. The model is a message-passing neural network that uses symmet

Externí odkaz: http://arxiv.org/abs/2107.00299