Hashim M. Al-Hashimi, PhD
- Professor of Biochemistry and Molecular Biophysics
Credentials & Experience
Education & Training
- IB, 1992 United World Colleges of the Atlantic, Atlantic College, Wales, UK
- BS, 1995 Chemistry, Imperial College, London, UK
- PhD, 2000 Biophysical Chemistry, Yale University
- Fellowship: 2002 Memorial Sloan-Kettering Cancer Center
Honors & Awards
- 2021 Fellow of the Biophysical Society
- 2021 Sarkar lecture, Hospital for Sick Children Toronto Canada
- 2020 Fellow of the International Society of Magnetic Resonance
- 2020 NAS Award in Molecular Biology National Academy of Science
- 2015 James B. Duke Distinguished Professorship
- 2014 Akutsu Award of the Korean NMR Society
- 2013 Agilent Technologies Thought Leader Award
- 2013 Vilcek Prize for Creative Promise in Biomedical Science
- 2013 Future of Biophysics Burroughs Welcome Fund Symposium Biophysical Society 57th Meeting
- 2012 Founder’s Medal International Conference on Magnetic Resonance in Biological Systems
- 2012 Collegiate Professorship University of Michigan
- 2011 Popular Science Magazine ‘Brilliant 10’ scientists and engineers in USA
- 2009 LSA Excellence in Teaching Award University of Michigan
- 2009 Robert L Kuczkowski Faculty Career Enhancement Award
- 2006 National Science Foundation Career Award
- 2004 Ralph E Powe Junior Faculty Enhancement Award
Al-Hashimi is interested in developing a deep, quantitative, and predictive understanding of cellular processes based on the fundamental behaviors of nucleic acids and their interactions with protein binding partners. Over the past two decades, Al-Hashimi and his trainees developed approaches combining NMR spectroscopy, computational modeling, optical melting experiments, and chemical probing to determine 3D dynamic ensembles of RNA and DNA molecules at atomic resolution. Using dynamic ensembles of nucleic acids, the Al-Hashimi group has developed quantitative and predictive models for several fundamental biological processes, including DNA replication fidelity, Tat-dependent HIV-1 transcriptional activation, RNA folding, and the impact of post-transcriptional modifications such as m6A and Nm on translation, splicing, and RNA-protein interactions. These studies have reshaped structural biology, revealing dynamic ensembles as the fundamental behavior of biomolecules needed to understand and predict cellular activity quantitatively.
The Al-Hashimi lab is currently using dynamic ensembles to reconstitute the folding and cellular activities of viral and other non-coding RNAs, to determine the role of DNA structural dynamics in shaping the probabilities of mutagenesis and cancer, and to rationally design inhibitors targeting viral RNA regulatory elements in HIV-1 and SARS-CoV-2 as well as non-coding RNAs involved in cancer. Efforts include developing and applying high throughput sequencing-based approaches to map DNA structural dynamics genome-wide, developing high throughput assays for quantitatively measuring the activity of various steps in the HIV-1 lifecycle, and determining an atlas of dynamic ensembles for all nucleic acid building block motifs. A more recent area includes efforts to digitize biochemistry by describing biomolecules in terms of finite-state-computing machines and applying complexity theory to classifying biochemical reactions.
As a mentor, Al-Hashimi is strongly committed to creating a welcoming, enriching, and supportive environment for all his trainees and effectively preparing them for careers in science. To date, he has trained over thirty-five graduate students and postdoctoral fellows with backgrounds in biochemistry, cell biology, chemistry, computer science, physics, and mathematics. His alumni include heads of laboratories at universities in the US, France, Sweden, India, and China and scientists at many biotech companies, large pharmaceutical firms, and management consulting firms. Over 30 undergraduates have also worked in the Al-Hashimi laboratory.
- Mechanisms of Cancer-Causing Mutations
- NMR Spectroscopy
- Nucleic Acid Structural Dynamics In Vitro and In Vivo
- RNA Biology and Drug Discovery
Zhang, Q., Sun, X., Watt, E.D., & Al-Hashimi, H.M., Resolving the motional modes that code for RNA
adaptation. Science 311 (5761), 653-656 (2006).
Zhang, Q., Stelzer, A.C., Fisher, C.K., & Al-Hashimi, H.M., Visualizing spatially correlated dynamics that directs
RNA conformational transitions. Nature 450 (7173), 1263-1267 (2007).
Bailor, M.H., Sun, X., & Al-Hashimi, H.M., Topology links RNA secondary structure with global conformation,
dynamics, and adaptation. Science 327 (5962), 202-206 (2010).
Nikolova, E.N. et al., Transient Hoogsteen base pairs in canonical duplex DNA. Nature 470 (7335), 498-502
Dethoff, E.A., Chugh, J., Mustoe, A.M., & Al-Hashimi, H.M., Functional complexity and regulation through RNA
dynamics. Nature 482 (7385), 322-330 (2012).
Dethoff, E.A., Petzold, K., Chugh, J., Casiano-Negroni, A., & Al-Hashimi, H.M., Visualizing transient low-populated structures of RNA. Nature 491 (7426), 724-728 (2012).
Kimsey, I.J., Petzold, K., Sathyamoorthy, B., Stein, Z.W., & Al-Hashimi, H.M., Visualizing transient Watson-
Crick-like mispairs in DNA and RNA duplexes. Nature 519 (7543), 315-320 (2015).
Kimsey, I.J. et al., Dynamic basis for dG*dT misincorporation via tautomerization and ionization. Nature 554
(7691), 195-201 (2018).
Ganser, L.R. et al., High-performance virtual screening by targeting a high-resolution RNA dynamic ensemble.
Nat Struct Mol Biol 25 (5), 425-434 (2018).
Ganser, L.R., Kelly, M.L., Herschlag, D., & Al-Hashimi, H.M., The roles of structural dynamics in the cellular
functions of RNAs. Nat Rev Mol Cell Biol 20 (8), 474-489 (2019).
Shi, H. et al., Rapid and accurate determination of atomistic RNA dynamic ensemble models using NMR and
structure prediction. Nat Commun 11 (1), 5531 (2020).
Afek, A. et al., DNA mismatches reveal conformational penalties in protein-DNA recognition. Nature 587 (7833),
Ganser, L.R. et al., Probing RNA Conformational Equilibria within the Functional Cellular Context. Cell Rep 30
(8), 2472-2480.e2474 (2020).
Liu, B. et al., A quantitative model predicts how m(6)A reshapes the kinetic landscape of nucleic acid
hybridization and conformational transitions. Nat Commun 12 (1), 5201 (2021).