3D RNA and Functional Interactions from Evolutionary Couplings.

By Caleb Weinreb, Adam J. Riesselman, John Ingraham, Torsten Gross, Christopher Sander, Debora Marks. 2015

166 citations recorded by the source corpus.

Non-coding RNAs are ubiquitous, but the discovery of new RNA gene sequences far outpaces the research on the structure and functional interactions of these RNA gene sequences. We mine the evolutionary sequence record to derive precise information about the function and structure of RNAs and RNA-protein complexes. As in protein structure prediction, we use maximum entropy global probability models of sequence co-variation to infer evolutionarily constrained nucleotide-nucleotide interactions within RNA molecules and nucleotide-amino acid interactions in RNA-protein complexes. The predicted contacts allow all-atom blinded 3D structure prediction at good accuracy for several known RNA structures and RNA-protein complexes. For unknown structures, we predict contacts in 160 non-coding RNA families. Beyond 3D structure prediction, evolutionary couplings help identify important functional interactions-e.g., at switch points in riboswitches and at a complex nucleation site in HIV. Aided by increasing sequence accumulation, evolutionary coupling analysis can accelerate the discovery of functional interactions and 3D structures involving RNA.

Subfields: Molecular Biology

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In Molecular Biology

1. Martin Weigt
2. H. Szurmant
3. Jinbo Xu
4. Jianzhu Ma
5. Alexander Schug
6. Jian Peng
7. Sheng Wang
8. Guido Uguzzoni

Citations in the corpus

• High-resolution de novo structure prediction from primary sequence (2022, methodology): “An important observation is that the deeper layers of current transformer-based PLMs focus relatively more on learning protein contacts, whereas the secondary structure information is spread more evenly across both shallow and deep layers ( 15 ).”
• Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks (2017, methodology): “…applied to protein complex assembly and interactions, disordered region structure prediction, RNA structure prediction, and mutagenesis analysis (Ovchinnikov et al., 2014; Uguzzoni et al., 2017; Hopf et al., 2014, 2017; Toth-Petroczy et al., 2016; De Leonardis et al., 2015; Weinreb et al., 2016).”
• Sampling native-like structures of RNA-protein complexes through Rosetta folding and docking (2018, unknown): “…listed in parentheses): 1DFU (1B75) (Luand Steitz, 2000; Stoldt et al., 1998), 1B7F (3SXL) (Crowder et al., 1999; Handa et al., 1999), 1JBS (1AQZ) (Yang et al., 2001; Yang and Moffat, 1996), 1P6V (1K8H) (Dong et al., 2002; Gutmann et al., 2003), 1WPU (1WPV) (Kumarevel et al., 2005), 1WSU (1LVA)…”
• Structured states of disordered proteins from genomic sequences (2016, unknown): “We then ran our secondary structure method on the ECs from the above PLM runs on the PFAM alignments and predicted a secondary structure using our propensity score (Secondary structure prediction).”
• Large-scale identification of coevolution signals across homo-oligomeric protein interfaces by direct coupling analysis (2017, background): “Such information has proven useful in the prediction of tertiary protein structures (10-13), conformational transitions (14-16), and similarly RNA structures (17, 18).”
• FARFAR2: Improved de novo Rosetta prediction of complex global RNA folds (2020, unknown): “…recent scoring methods taking advantage of artificial neural networks and evolutionary coupling information (Li et al., 2019; Wang et al., 2019; Weinreb et al., 2016) and alternative high-resolution refinement procedures (Tan et al., 2018; Watkins et al., 2018) that might be pipelined with…”