1. CLOSING DISCUSSION Advances and challenges in Protein-RNA: recognition, regulation, and prediction Discussion facilitator: Manny Ares Banff International Research Station for Mathematical Innovation and Discovery

2. 34 half hour presentations over ~3.5 days Equivalent to 17 hours of instruction -> nearly half of a university course; Normally this might be spread over a two month period. What have we experienced?

3. Overarching problems we would all like to see solved: What are the rules of engagement that govern meetings between the protein world and the RNA world? And then how does the quality of these meetings influence the success or failure of the biological phenomena that depend on them? 34 half hour presentations over ~3.5 days Equivalent to 17 hours of instruction -> nearly half of a university course; Normally this might be spread over a two month period. What have we experienced?

4. 34 half hour presentations over ~3.5 days Equivalent to 17 hours of instruction -> nearly half of a university course; Normally this might be spread over a two month period. What have we experienced? Overarching problems we would all like to see solved: What are the rules of engagement that govern meetings between the protein world and the RNA world? If we could predict this,we might be able to predict this And then how does the quality of these meetings influence the success or failure of the biological phenomena that depend on them?

5. 34 half hour presentations over ~3.5 days Equivalent to 17 hours of instruction -> nearly half of a university course; Normally this might be spread over a two month period. What have we experienced? Overarching problems we would all like to see solved: What are the rules of engagement that govern meetings between the protein world and the RNA world? If we could predict this,we might be able to predict this And then how does the quality of these meetings influence the success or failure of the biological phenomena that depend on them? Ultimate goal; systems level understanding of post-transcriptional gene control, and the power to do intelligent engineering.

6. Several distinct approaches to solving these two problems Global capture of protein- RNA binding sites in vivo (RIPs & CLIPs) Comprehensive assignment of binding motifs to known sequence specific RBPs (the Periodic Table of the [ribo]Elements) Capture of noncannonical RNA binding proteins in vivo Solving structures of RNAs and proteins, and RNP complexes Observations on impact of RBP expression and function on downstream gene expression events In vivo In vitro In silico Prediction methods, classifiers, modeling, redesign, engineer

7. Several distinct approaches to solving these two problems Global capture of protein- RNA binding sites in vivo (RIPs & CLIPs) Comprehensive assignment of binding motifs to known sequence specific RBPs (the Periodic Table of the [ribo]Elements) Capture of noncannonical RNA binding proteins in vivo Solving structures of RNAs and proteins, and RNP complexes Observations on impact of RBP expression and function on downstream gene expression events In vivo In vitro In silico Prediction methods, classifiers, modeling, redesign, engineer

8. Several distinct approaches to solving these two problems Global capture of protein- RNA binding sites in vivo (RIPs & CLIPs) Comprehensive assignment of binding motifs to known sequence specific RBPs (the Periodic Table of the [ribo]Elements) Capture of noncannonical RNA binding proteins in vivo Solving structures of RNAs and proteins, and RNP complexes Observations on impact of RBP expression and function on downstream gene expression events In vivo In vitro In silico Prediction methods, classifiers, modeling, redesign, engineer

9. Several distinct approaches to solving these two problems Global capture of protein- RNA binding sites in vivo (RIPs & CLIPs) Comprehensive assignment of binding motifs to known sequence specific RBPs (the Periodic Table of the [ribo]Elements) Capture of noncannonical RNA binding proteins in vivo Solving structures of RNAs and proteins, and RNP complexes Observations on impact of RBP expression and function on downstream gene expression events In vivo In vitro In silico Prediction methods, classifiers, modeling, redesign, engineer

10. Some future challenges associated with these distinct approaches Global capture of protein- RNA binding sites in vivo (RIPs & CLIPs) Capture of noncannonical RNA binding proteins in vivo Observations on impact of RBP expression and function on downstream gene expression events

11. Some future challenges associated with these distinct approaches Global capture of protein- RNA binding sites in vivo (RIPs & CLIPs) Capture of noncannonical RNA binding proteins in vivo Observations on impact of RBP expression and function on downstream gene expression events False negatives, Sensitivity is transcript abundance dependent Expression space is very sparsely represented Doing good on HEK293! Overexpression phenomena may drive false positive rate

12. Some future challenges associated with these distinct approaches Global capture of protein- RNA binding sites in vivo (RIPs & CLIPs) Capture of noncannonical RNA binding proteins in vivo Observations on impact of RBP expression and function on downstream gene expression events False negatives, Sensitivity is transcript abundance dependent Expression space is very sparsely represented Doing good on HEK293! Overexpression phenomena may drive false positive rate Really interesting right now since we don’t know too much. Need to see motifs? Where on the protein is the RNA binding? Can we add these to the periodic table?

13. Some future challenges associated with these distinct approaches Global capture of protein- RNA binding sites in vivo (RIPs & CLIPs) Capture of noncannonical RNA binding proteins in vivo Observations on impact of RBP expression and function on downstream gene expression events False negatives, Sensitivity is transcript abundance dependent Expression space is very sparsely represented Doing good on HEK293! Overexpression phenomena may drive false positive rate Really interesting right now since we don’t know too much. Need to see motifs? Where on the protein is the RNA binding? Can we add these to the periodic table? Still quite idiosyncratic, very cell type dependent, and systems where interesting things are observed may not have complete data from CLIP

14. Some future challenges associated with these distinct approaches Comprehensive assignment of binding motifs to known sequence specific RBPs (the Periodic Table of the [ribo]Elements) Solving structures of RNAs and proteins, and RNP complexes

15. Some future challenges associated with these distinct approaches Comprehensive assignment of binding motifs to known sequence specific RBPs (the Periodic Table of the [ribo]Elements) Solving structures of RNAs and proteins, and RNP complexes First pass assignments with single domain constructs, but wondering about multiple domain effects. Motifs derived from near equilibrium conditions so probably represent off-rates when kinetics may be more important in vivo Otherwise this is an essential look up tool!

16. Some future challenges associated with these distinct approaches Comprehensive assignment of binding motifs to known sequence specific RBPs (the Periodic Table of the [ribo]Elements) Solving structures of RNAs and proteins, and RNP complexes First pass assignments with single domain constructs, but wondering about multiple domain effects. Motifs derived from near equilibrium conditions so probably represent off-rates when kinetics may be more important in vivo Otherwise this is an essential look up tool! Yes we need more structures! More structures with multiple domains that have significant impact on binding and specificity -> getting at combinatorics. Molecular dynamics is becoming increasingly important for understanding binding, in particular from “variable” regions in the basic fold.

17. Some future challenges associated with these distinct approaches Prediction methods, classifiers, modeling, redesign, engineer

18. Some future challenges associated with these distinct approaches Prediction methods, classifiers, modeling, redesign, engineer Rich set of very imaginative approaches! Standards for accepting that a prediction is good seem high. Problem: a lack of structures -> the deeper the true set the better the learning. Problem for some approaches: lack of good negative examples for some classifier approaches.

19. Something I still have questions about What’s the deal with these unstructured regions? Kato et al. 2012 Cell 149:753 They seem to form gels and strange aggregates. They have specificity of some kind including homotypic interactions and heterotypic interactions of various types. These interactions are mediated by residues in the unstructured regions, including through phosphorylation. RNA is also trapped (?) bound (?) in these gels. There are some cellular bodies that seem to represent a manifestation of this: P-bodies, P-granules, stress granules, plaques. Associated with normal function and disease.

20. THANKS!! YAEL, GABRIELE & BIRS