protein-nets

Project objective

• Explore protein encoding (https://github.com/ethanmoyer/ptnstrerrpredict/blob/master/ptn.py)
• Data set is through Protein Data Bank through API calls (https://www.wwpdb.org/)

Paper 1: Functional Protein Structure Annotation using a Deep Colvolution Generative Adversarial Network

• Create a deep convolution generalized advesarial network (DCGAN) to generate and discriminate between functional and non-functional protein structures.

Conference: https://www.bhi-bsn-2021.org/

Paper 2: Novel Protein Structure Generation according to Specific Function using a Deep Colvolution Generative Adversarial Network

• Use a DCGAN to generate and discriminate between proteins with a particular function, such as ligand binding, RNA cleaving, etc

Paper 3: Optiminal Protein Structure Folding using a Noisy Autoencoder

• Iteratively train and test an autoencoder network on increadinly messed-up decoy protein structures and explore the extent to which an autoencoder can refold them.

Tasks

[x] - Define protein structures of interest, search space, and data base focus ()
[x] - Build database using list of four-letter protein structure IDs ()
[] - Download proteins from PDB and encode protein structures into grid structure (Ethan, )
[x] - Determine feature set based on encoding and decide if any more features should be included ()
[] - Train GAN/DCGAN on encoded protein structures ()
[] - Perform experiemnts/gather results to determine how well the model performed ()
[] - Explore the explainability of the model ()
[] - Generate figures for paper and sample test cases to illistrate how the model performs on an individual protein structure ()

Paper #1 roles

Ethan: Data set, protein encoding, Isamu: Results Jeff: background/GAN architecture, torsion angle Mali: Introduction Alisha: Background of protein structure prediction, abstract Yigit: Related work/GAN architecture Adam: Background of protein structure prediction

Agenda

Plan for April 5th, 2021

• Introduce GANs (Jeff)
• Introduce protein structure encoding, prediction, mapping (Ethan)
  • Measuring Protein Structure using Machine Learning:
  • https://www.mabc2020.com/post/measuring-protein-structure-deviation-using-machine-learning

Plan for April 14th, 2021

• Introduce GitHub for project management (Yigit)
• Assign taks for paper based on individual interest (Ethan)
  • Paper:
  • https://www.overleaf.com/read/nkfdszdmkjqf

Plan for April 21st, 2021

• Aim to have paper #1 on DCGAN for functional vs nonfunctional protein structure annotation done and submitted by Sunday (18 April 2021)
• Discuss autoencoder project and more functional annotation of protein

Resources

• https://papers.nips.cc/paper/2018/file/afa299a4d1d8c52e75dd8a24c3ce534f-Paper.pdf
• https://arxiv.org/pdf/2004.07119.pdf
• https://arxiv.org/pdf/1511.06434.pdf
• https://proteinstructures.com/structure/ramachandran-plot/