Protein Structure Prediction

via “end-to-end differentiable protein structure prediction from sequence”

* 📰 2022: [ChatGPT: Optimizing Language Models For Dialogue (ChatGPT)](https://openai.com/blog/chatgpt/)

Unique: Uses a hybrid architecture combining MSA embeddings (capturing evolutionary information) with pairwise distance and angle predictions in a single differentiable model, trained on ~170k PDB structures. Achieves CASP14 accuracy (GDT_TS ~87%) without requiring template-based homology modeling, a paradigm shift from traditional physics-based or template-dependent methods.

vs others: Outperforms RoseTTAFold and I-TASSER on CASP benchmarks with faster inference and more reliable confidence estimates (pLDDT), while being fully open-source and requiring no manual template selection unlike older homology modeling approaches.

via “protein structure format standardization and conversion”

Dataset by Emmyc2. 5,64,739 downloads.

Unique: Centralizes protein structure preprocessing in a single versioned dataset, eliminating the need for individual researchers to implement custom PDB parsing and normalization logic

vs others: More reliable than ad-hoc PDB parsing scripts because it enforces consistent preprocessing; more accessible than raw PDB files which require domain expertise to handle correctly

via “protein-sequence-annotation-and-function-prediction”

A large language model for science. Can summarize academic literature, solve math problems, generate Wiki articles, write scientific code, annotate molecules and proteins, and more. [Model API](https://github.com/paperswithcode/galai).

via “protein-structure-prediction”

via “protein-structure-prediction”

via “protein fold stability prediction and optimization”

via “biological-sequence-prediction”

via “3d rna structure prediction”