Merge pull request #112 from ngastellu/patch-1

Update project-32-AiChemMcGill.md

Author: sgbaird

_projects/project-32-AiChemMcGill.md

@@ -3,22 +3,28 @@ number: 32 # leave as-is, maintainers will adjust
 title: Efficient Protein Mutagenisis using Bayesian Optimization
 topic: real-world
 team_leads:
- - Benjamin Weiser (McGill University)
+- Benjamin Weiser (McGill University) @BenjaminWeiser
+- Jérôme Genzling (McGill University) @JGenzling
+- Nicolas Gastellu (McGill University) @ngastellu
+- Sylvester Zhang (McGill University) @sylvester-zhang
 
 # Comment these lines by prepending the pound symbol (#) to each line to hide these elements
-contributors:
-  - Jérôme Genzling
-  - Nicolas Gastellu
-  - Tao Liu
-  - Sylvester Zg
+#contributors:
+ 
 
-# github: AC-BO-Hackathon/<your-repo-name>
-# youtube_video: <your-video-id>
+github: AC-BO-Hackathon/project-AiChemMcGill 
+youtube_video: fxpDX7Wmdc0
 
 ---
 
 This project focuses on developing a Bayesian optimization workflow for protein mutagenesis enhancing protein binding affinity. Using predictions generated by the model such as the one outlined in the study from from Rube et al. (Nat. Biotech., 2022), our approach aims to optimize protein mutagenesis for biologics and enzyme engineering applications. By integrating predictive models with Bayesian optimization, we seek to efficiently guide the design of high-affinity protein variants through the vast search space of potential nucleotides mutations.
 
+Check out our social media post on [LinkedIn](https://www.linkedin.com/feed/update/urn:li:activity:7182104774291460096/) for an overview of the project!
+
 References:
 
 1. Rube, H.T., Rastogi, C., Feng, S. et al. Prediction of protein–ligand binding affinity from sequencing data with interpretable machine learning. Nat Biotechnol 40, 1520–1527 (2022). https://doi.org/10.1038/s41587-022-01307-0
+2. Blanchard, Andrew E., et al. "Language models for the prediction of SARS-CoV-2 inhibitors." The International Journal of High Performance Computing Applications 36.5-6 (2022): 587-602.
+3. Matthew J Bick, Per J Greisen, Kevin J Morey, Mauricio S Antunes, David La, Banumathi Sankaran, Luc Reymond, Kai Johnsson, June I Medford, David Baker (2017) Computational design of environmental sensors for the potent opioid fentanyl eLife 6:e28909
+
+