Yang Li
RNA plays critical roles in diverse cellular processes, from gene regulation to enzymatic activity. Accurate RNA structure prediction is essential to understand its biological function. The integration of co-evolutionary analysis with advanced deep learning approaches can help us predict better RNA structures. Beyond prediction, RNA design enables us to create RNA molecules with desired properties. Our lab focuses on both RNA structure prediction and design through these complementary computational approaches, aiming to unlock the full potential of RNA molecules in medicine and biotechnology.
liyangum@nus.edu.sg
Special Fellow, Cancer Science Institute of Singapore
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We have been focusing on protein and RNA structure prediction and design through advanced deep learning approaches. More specifically, we are interested in:
1. Co-evolution analysis and learning Co-evolution analysis aims to model interactions between residues in an unsupervised manner. Initially, scientists employed statistical physics-based approaches, such as the Potts model, to analyze these interactions. More recently, language models have been introduced to capture non-linear relationships and learn interactions across multiple systems. We believe that the application of language models in co-evolution analysis remains largely underexplored, especially when trying to make connections between alignments, attentions, and contacts. Moreover, co-evolution analysis has consistently proven very useful in protein structure prediction and design. It is an exciting research area that can help us understand biomolecules at different levels.
2. Biomolecule structure prediction Protein and RNA structure prediction has long been a hot topic in computational biology. With AlphaFold series models, many challenges in this domain appear to have been addressed, at least for protein monomer structures. However, significant opportunities remain in modeling complex structures. Currently, the accuracy and performance of complex structure predictions are still limited. Co-evolution should be another key factor in modeling complex structures. Our focus is in understanding intermolecular co-evolution and modelling the detailed interactions between biomolecules.
3. Conditional sequence and structure design Meanwhile, molecular structure and sequence design represent another promising direction, particularly with the advancements in AlphaFold-based structural modeling. Researchers can now quickly evaluate the structural feasibility of their designs. Even more exciting is the potential to transition these designed molecules into practical applications, which is also a major focus of our lab and represents an exciting frontier in computational biology.
| 1. van Wietmarschen N*, Sridharan S*, et al., Nussenzweig A, “Repeat expansions confer WRN dependence in microsatellite-unstable cancers”, Nature, Volume 586, pp 292–298(2020).
2. Feng X, Tubbs A, Zhang C, Tang M, Sridharan S, Wang C, et al., Chen J, “ATR inhibition potentiates ionizing radiation-induced interferon response via cytosolic nucleic acid sensing pathways”, EMBO J (2020)39:e104036 3. Yin M, Baslan T, Walker RL, Zhu YJ, Freeland A, Matsukawa T, Sridharan S, et al., Aplan PD, “A unique mutator phenotype reveals complementary oncogenic lesions leading to acute leukemia”, JCI Insight, Volume 4, Issue 23, 17 October 2019. 4. Tubbs A*, Sridharan S*, et al., Nussenzweig A, “Dual Roles of Poly(dA:dT) Tracts in Replication Initiation and Fork Collapse”, Cell, Volume 174, Issue 5, 23 August 2018, pp 1127-1142. 5. Canela A, Sridharan S, Sciascia N, Tubbs A, Meltzer P, Sleckman BP, Nussenzweig A, “DNA Breaks and End Resection Measured Genome-wide by End Sequencing”, Molecular Cell, Volume 63, Issue 5, pp 898–911, September 2016. |