We use computational tools to study and predict biological structures at the molecular level. We use a combination of mathematical modeling, data analysis, molecular dynamics and biophysical concepts to advance our understanding of how atomic structure affects dynamics and ultimately function. Please find below a list of representative publications for this focus area in our lab:
Jonathan Martin, Marcos Lequerica Mateos, José N Onuchic, Ivan Coluzza, Faruck MorcosMachine learning in biological physics: From biomolecular prediction to design Proceedings of the National Academy of Sciences. 121 (27), e2311807121, 2024.
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Sophia Alvarez, Charisse M Nartey, Nicholas Mercado, Jose Alberto de la Paz, Tea Huseinbegovic, Faruck MorcosIn vivo functional phenotypes from a computational epistatic model of evolution Proceedings of the National Academy of Sciences. 121 (6), e2308895121, 2024.
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Charisse M Nartey, Hyun Jo Koo, Caroline Laurendon, Hana Z Shaik, Paul O’maille, Joseph P Noel, Faruck Morcos Coevolutionary Information Captures Catalytic Functions and Reveals Divergent Roles of Terpene Synthase Interdomain Connections Biochemistry. 63 (3), 355-366, 2024.
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Cheyenne Ziegler, Jonathan Martin, Claude Sinner, Faruck Morcos Latent generative landscapes as maps of functional diversity in protein sequence space Nature Communications. 14 (1), 2222, 2023.
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Aishwarya Krishnamohan, George L Hamilton, Rajen Goutam, Hugo Sanabria, Faruck Morcos Coevolution and smfret enhances conformation sampling and fret experimental design in tandem pdz1–2 proteins The Journal of Physical Chemistry B. 127 (4), 884-898, 2023.
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Krithika Ravishankar, Xianli Jiang, Emmett M Leddin, Faruck Morcos, G Andrés Cisneros Computational compensatory mutation discovery approach: Predicting a PARP1 variant rescue mutation Biophysical Journal. 121 (19), 3663-3673, 2022.
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Hsichuan Chi, Qin Zhou, Jasmine N. Tutol, Shelby M. Phelps, Jessica Lee, Paarth Kapadia, Faruck Morcos*, and Sheel C. Dodani* Coupling a Live Cell Directed Evolution Assay with Coevolutionary Landscapes to Engineer an Improved Fluorescent Rhodopsin Chloride Sensor ACS Synthetic Biology. Vol. 11, (4), 1627–1638, 2022. (*Co-corresponding)
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Xian-Li Jiang, Rey P Dimas, Clement TY Chan*, Faruck Morcos* Coevolutionary methods enable robust design of modular repressors by reestablishing intra-protein interactions Nature Communications. Vol. 12 (1), 1-8, 2021.(*Co-corresponding)
News: [UNT News]
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Nicole N Thadani, Qin Zhou, Kiara Reyes Gamas, Susan Butler, Carlos Bueno, Nicholas P Schafer, Faruck Morcos, Peter G Wolynes, Junghae Suh Frustration and Direct-Coupling Analyses to Predict Formation and Function of Adeno-Associated Virus Biophysical Journal. 120 (3), 489-503, 2021.
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Ricardo N. dos Santos, Xianli Jiang, Leandro Martínez, Faruck Morcos Coevolutionary Signals and Structure-Based Models for the Prediction of Protein Native Conformations Computational Methods in Protein Evolution, Methods in Molecular Biology Vol. 1851. Springer Science+Business Media. 2018
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Qin Zhou, Nikesh Kunder, José Alberto De la Paz, Alexandra E. Lasley, Vandita D. Bhat, Faruck Morcos* and Zachary T. Campbell* Global pairwise RNA interaction landscapes reveal core features of protein recognition Nature Communications. 9, Article number: 2511. 2018 (* Co-corresponding)
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Ricardo N dos Santos, Allan J R Ferrari, Hugo C R de Jesus, Fábio C Gozzo, Faruck Morcos, Leandro Martínez. Enhancing protein fold determination by exploring the complementary information of chemical cross-linking and coevolutionary signals. Bioinformatics. 2018.
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Jeffrey K. Noel, Faruck Morcos and Jose N. Onuchic. Sequence co-evolutionary information is a natural partner to minimally-frustrated models of biomolecular dynamics F1000Research 5(F1000 Faculty Rev):106. 5:13652, 2016.
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Biman Jana, Faruck Morcos and Jose N. Onuchic. From Structure to Function: the Convergence of Structure Based Models and Co-evolutionary Information. Physical Chemistry Chemical Physics. Vol. 16, No. 14, Pages 6496-507, 2014
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Faruck Morcos, Biman Jana, Terence Hwa and Jose N. Onuchic. Coevolutionary signals across protein lineages help capture multiple protein conformations. Proc Natl Acad Sci USA. Vol 110, No. 51, p. 20533-20538. 2013
Press Highlight in ScienceDaily: Proteins' passing phases revealed
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Joanna I. Sulkowska*, Faruck Morcos*, Martin Weigt, Terence Hwa and Jose N. Onuchic. Genomics-aided structure prediction. Proc Natl Acad Sci USA. Vol. 109, No. 26, p. 10340-45, 2012. (*equivalent contribution)
Protein residues kiss, don't tell: Genomes reveal contacts, scientists refine methods for protein-folding prediction
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Faruck Morcos*, Andrea Pagnani*, Bryan Lunt, Arianna Bertolino, Debora S. Marks, Chris Sander, Riccardo Zecchina,
Jose N. Onuchic, Terence Hwa and Martin Weigt. Direct-coupling analysis of residue coevolution captures native contacts across many protein families. Proc Nat Acad Sci USA. Vol. 108, No. 49, E1293-E1301, 2011. (*equivalent contribution)
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Faruck Morcos, R. Lopez, S. Chatterjee, P. Brenner, C. McClendon, J. Zintsmaster, M. Ercsey-Ravasz, C.R. Sweet,
M. Jacobson, J. Peng and J. Izaguirre. Modeling conformational ensembles of slow functional motions in Pin1-WW. PLoS Computational Biology. 6(12): e1001015, 2010.
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Zaher Dawy, Faruck Morcos, Johanna Weindl and Jakob C. Mueller. Translation initiation modeling and mutational analysis based on the 3-end of the Escherichia coli 16S rRNA sequence. Elsevier BioSystems. 96 (1), p. 58-64, 2009
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S. Flores, K. Keating, J. Painter, F. Morcos, K. Nguyen, E. Merrit, L. Kuhn and M. Gerstein. HingeMaster: normal mode hinge prediction approach and integration of complementary predictors. Proteins: Structure, Function, and Bioinformatics. Vol. 73, Issue 2, Pages 299-319, Nov, 2008
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Zaher Dawy, Pavol Hanus, Johanna Weindl, Janis Dingel, Faruck Morcos. On genomic coding theory. European Transactions on Telecommunications , John Wiley & Sons, Ltd. Vol. 18, Issue 8, pp. 873-879, 2007
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