Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/pengzhangzhi/Awesome-Computational-Structural-Biology

A curated list of awesome self-learning materials in Computational Structural Biology, such as sources, tutorials, etc.
https://github.com/pengzhangzhi/Awesome-Computational-Structural-Biology

List: Awesome-Computational-Structural-Biology

alphafold2 awesome awesome-list bioinformatics computational-biology deep-learning deep-neural-networks

Last synced: 3 months ago
JSON representation

A curated list of awesome self-learning materials in Computational Structural Biology, such as sources, tutorials, etc.

Awesome Lists containing this project

README 

        
# Introduction

This is a list of resources (courses, tutorials, etc.) in the field of Computational and Structural Biology. This is for those with no background in biology but who want to work in this field. 



**I highly expect you to contribute to this project if you have any ideas on related topics. One person can go fast, but a group of people can go further.**



# Structural Biology



The first step is to learn the very fundamental concepts in biology, like what is an amino acid? What are proteins made of? You don't have to memorize every detail, but it is essential to be familiarized with those conceptual words. After this stage, you should understand the basic structure of proteins, e.g., the four levels of structure. 



- [Computational Structural Biology: Winter 2022](https://www.cs.ucdavis.edu/~koehl/Teaching/ECS129)



- [Workshop: Machine Learning in Structural Biology (neurips.cc)](https://neurips.cc/virtual/2021/workshop/21869)



- [Course 81855: Workshop in Computational Structural Biology (huji.ac.il)](https://www.cs.huji.ac.il/w~fora/81855/)



- ["生物学" 免费教程 - Structural Biology 101: A Beginner' 's Guide | Udemy](https://www.udemy.com/course/sbio-101/)



- [Introduction to proteins: structure, function, and motion TAU online course | Mysite (bentalab.com)](https://www.bentalab.com/tau-online-course)



- This article provides useful resources about Protein Structural Biology. [How to Introduce Students to Protein Structural Biology (routledge.com)](https://www.routledge.com/blog/article/how-to-introduce-students-to-protein-structural-biology)



- [rsgturkey/Computational_Structural_Biology_Workshop (github.com)](https://github.com/rsgturkey/Computational_Structural_Biology_Workshop) 



- - Video: [Introduction to Computational Structural Biology Workshop - Day 1 - Part 1 - YouTube](https://www.youtube.com/watch?v=sy7dOi1tFhQ)



- [Structural Biology 228 | Computational Structural Biology (stanford.edu)](https://web.stanford.edu/class/sbio228/index.html)



- [CSCI4969-6969 Machine Learning in Bioinformatics | Zaki Home Page (rpi.edu)](https://www.cs.rpi.edu/~zaki/courses/mlib/)



- [Structural Bioinformatics && Modelling – Bonvin Lab](https://www.bonvinlab.org/education/molmod/)



- Biological Macromolecules [computational medicine - Biology (google.com)](https://sites.google.com/view/computationalmedicine/home/background-materials/biology)



- - DNA [part 1](https://www.youtube.com/watch?v=NX0ZPtB_QFY) and [part 2](https://www.youtube.com/watch?v=2-nCSLMGwhI) (Khan Academy)

  - [RNA](https://www.youtube.com/watch?v=jUUJSOM1ihU) (Khan Academy)

  - Protein [Structure ](https://www.youtube.com/watch?v=gaMi3299bQk)(Gerry Bergstrom) and [Function](https://www.youtube.com/watch?v=eVmLvbB6L18) (Khan Academy)

  - [Carbohydrates and Lipids](https://www.youtube.com/watch?v=f4Gicf7ONGA) (Craig Savage)

  - Khan Academy [Macromolecules](https://www.google.com/url?q=https%3A%2F%2Fwww.khanacademy.org%2Fscience%2Fbiology%2Fmacromolecules&sa=D&sntz=1&usg=AOvVaw2LkrCIcJUc-uevGr3iFdDR)

  - Khan Academy [Biomolecules](https://www.google.com/url?q=https%3A%2F%2Fwww.khanacademy.org%2Ftest-prep%2Fmcat%2Fbiomolecules&sa=D&sntz=1&usg=AOvVaw2lZNNQ1hEF4maCsZa5Bb5y)

  - Khan Academy Medicine Playlist on [Proteins](https://www.youtube.com/playlist?list=PLbKSbFnKYVY0By5uwg3eAmGeuynvGqCQw)

  - Khan Academy Medicine Playlist on [DNA and RNA](https://www.youtube.com/playlist?list=PLbKSbFnKYVY1nxvjyBMsq7PJ5Br0AMU8K)

  - Khan Academy Medicine Playlist on [Lipids, and Carbohydrates](https://www.youtube.com/playlist?list=PLbKSbFnKYVY0SLyKM6jd5uNJzzJYp2HJ8)

  - DNA Learning Center Playlist on [DNA Structure and Function](https://www.youtube.com/playlist?list=PLAD3DE96CA98E831E)



# Computational Biology



- [Tutorials | Computational Biology Core (uconn.edu)](https://bioinformatics.uconn.edu/resources-and-events/tutorials-2/#)



- ["生物信息学" 免费教程 - Conducting Introductory Computational Biology Research | Udemy](https://www.udemy.com/course/conducting-intro-comp-bio-research/)



- [Spring 2021 6.874 Computational Systems Biology: Deep Learning in the Life Sciences (mit6874.github.io)](https://mit6874.github.io/)



- - This is a great course, but it focuses more on the genome



- [Syllabus | Foundations of Computational and Systems Biology | Biology | MIT OpenCourseWare](https://ocw.mit.edu/courses/7-91j-foundations-of-computational-and-systems-biology-spring-2014/pages/syllabus/)



# Alphafold 2



'Morden' computational methods for biology are dominated mainly by neural networks, the most famous is Alphafold2.



Alphafold2 achieves an atomic level of accuracy in the protein structure prediction task. It gives a solution to a problem that has not been answered for 50 years. The model capacity is gigantic, and the design is sophisticated. To understand the paper, I list several tutorials that describe the mechanisms of Alphafold2. I think that many operations in Alphafold2 are no biologically or physically meaningful; instead, they just try to find a method to scale up the model capacity effectively and thus achieve better performance. But it does not mean I don't like Alphafold2. The uses of the structure module and the representation of the atom's 3D coordinate are as brilliant as hard to understand.



- [AlphaFold: a solution to a 50-year-old grand challenge in biology (deepmind.com)](https://www.deepmind.com/blog/alphafold-a-solution-to-a-50-year-old-grand-challenge-in-biology)

- [Highly accurate protein structure prediction with AlphaFold | Nature](https://www.nature.com/articles/s41586-021-03819-2)

- Video tutorial

- - Chiniese Tutorial: [AlphaFold 2 论文精读【论文精读】*哔哩哔哩*bilibili](https://www.bilibili.com/video/BV1oR4y1K7Xr?spm_id_from=333.337.search-card.all.click)

  - [AlphaFold and the Grand Challenge to solve protein folding - YouTube](https://www.youtube.com/watch?v=nGVFbPKrRWQ)

- Article

- - [AlphaFold 2 is here: what's behind the structure prediction miracle | Oxford Protein Informatics Group (blopig.com)](https://www.blopig.com/blog/2021/07/alphafold-2-is-here-whats-behind-the-structure-prediction-miracle/)

- Survey paper: [Machine learning in protein structure prediction (sciencedirectassets.com)](https://pdf.sciencedirectassets.com/272030/1-s2.0-S1367593121X0004X/1-s2.0-S1367593121000508/main.pdf?X-Amz-Security-Token=IQoJb3JpZ2luX2VjEIj%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLWVhc3QtMSJGMEQCIEWNJH%2FUfYSMaYWskBRKdTh18bQp47Q4oBMnlGHEW5RiAiB3rXuPdqCdeD20InbYQ3yYHsU5NiYaZObTCjAJh4myoCrbBAiw%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F8BEAQaDDA1OTAwMzU0Njg2NSIMQjl6jFtfsNjYn3bKKq8E2duLtITN23qRjysa3xNAafq6Te%2Blzm2qG6jMye6d9tzTxj4LhKvDsBugaG0FQ5wDSAYo64l85%2BXbGriYSHt23oO%2Fw%2Bck335Sl%2BSKr%2FGkWl0KSDNY%2F72g7jUeLBbUhli7783r9wZqNUEAdiwZ3yjsrCbRKJvNxd4AOEOMYGl5%2Fc24u5cCSvcZdT%2BwGWm2HxG70FR18W9zSvOp6FzLV6HLo8G05YB44tJ8c42BEubSMcAXAVZxhOSZr2hAGCOMNFd8ryINOJEGej3wnPIhUimte3s9oNnZHgjbKGX2liuSXWA4t6s9QeWgwayvKv20dVZL%2BnhvQcDJVvIz2XKKW%2FL7JOHYhrNNkQ5CuK%2FoPxE1jSG8cVkVBD2KLCd7k9h7kOQOKwNMR9%2BCS5mS4OkG2oK6V26hSsAH6%2FUyVAZAbRyuO8hxVQEOXnylIaJErmHm9im4Y9IjKNYiw2NATUlIXzRnGt9hf0tR5%2BOEm0Q%2F6y4AUhYJg4WBdu5oOk2gJJSbhB06nw%2FWmwZHv5QU5JIFn%2BAcDSEg5Q7ozY1eoIHiPnp0AZ8dg1lV6Ae66Pj15w1FX6z%2BMiElXpUW0JmrwBDTe25kKBPDpVyGXcIhDxCT8lAvOfckpEZNdg5PISgKY%2F8it4BgB%2FkTEB6XEV4Ke%2BLYNp8pdABKFl50HZYzpY6n4lDDni0RcqjqOoOEyGQmYSj6bseyTomU5LKPEdTqBSjV9IxtZ5Bk%2FC915UKolzoJlT8%2BnjCripOWBjqqAZLrtfdZVitOsyDXd3pFLFNyhhorrgbTe6td6F0RaaxRq6pCGUn5KrxCc0ZTo%2BxeJ4%2B3jEdcxUgMBg7Xi7di2PwGDoyVk7fglD5voWGxkUlufJLjifkFWf4TxXwEx%2Fd8f%2BtV6efXn4DrOA6aRdv%2FiHqMN3xLYAUaqDqKBAy5AsaBDYDCRd7ow5Bz081RJCqDe5RTRhaor1Px3FwdY0MfHNLqqLxmNyk8Sf67&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20220706T004351Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTY52ETLVUP%2F20220706%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=7da34859bd9818d505424913cb630c083158562accedb4c3ab4a2746291d4bf3&hash=819075f69b419604b96b88b5b2d796bd9f2bafd3ae1861f04f59fb37651e3e11&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S1367593121000508&tid=spdf-504717dd-d586-47c2-92fb-abad7d13e5cf&sid=1138a55d69e3214e7d597c09df17d38b1074gxrqa&type=client&ua=51565457510751550306&rr=72644f00ead69842)

- Code Implementation

- - [lucidrains/alphafold2: To eventually become an unofficial Pytorch implementation / replication of Alphafold2, as details of the architecture get released (github.com)](https://github.com/lucidrains/alphafold2)  This code is not 100% pure implementation of Alphafold2, where many modifications and improvement are devloped for better performance.

  - [aqlaboratory/openfold: Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2 (github.com)](https://github.com/aqlaboratory/openfold)



# Practices



**"What I cannot create, I do not understand."** -**Richard Feynman**.



As I have stated, most current methods focus on developing neural networks for bio problems. If you have a fair understanding of deep learning, you can find a paper to read and start your project. If you are a beginner in deep learning (DL), there is a long way to go. Many good materials on deep learning are publicly available; please look them up. Either way, I recommend you reproduce a SOTA method in the field of computational biology. If you don't know what paper to reproduce, I think Alphafold2 is a great starting point! That will ground you up for your project.



I also list several beginner-level excises below.



- [sinadadmand/CHBI522: Computational Structural Biology (github.com)](https://github.com/sinadadmand/CHBI522)

- [pjmartel/compbio: Materials for the computational structural biology course. (github.com)](https://github.com/pjmartel/compbio)



# Cutting-edge research



- [bioRxiv.org - the preprint server for Biology](https://www.biorxiv.org/)



- [A collection of new and exciting preprints and papers in the world of computational structural biology Folded Weekly (folded-weekly.netlify.app)](https://folded-weekly.netlify.app/)



- awesome lists



- [yangkky/Machine-learning-for-proteins: Listing of papers about machine learning for proteins. (github.com)](https://github.com/yangkky/Machine-learning-for-proteins)

- [Bio Lists](https://github.com/biolists)