Alphafold2 - Protein Structural Bioinformatics After CASP14
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This presentation discusses Alphafold2 about the algorithm in comparison with others and the time course of the impact and development that happened after the paper and software release. This slide was presented at IIBMP2021 https://iibmp2021.hamadalab.com/program/, a virtual conference in Tokyo, Japan on 9/27, 28, 2021
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Alphafold2 - Protein Structural Bioinformatics After CASP14
- 1. Alphafold2 - Protein Structural Bioinformatics After CASP14 Daisuke Kihara Department of Biological Sciences, Computer Science Purdue University https://kiharalab.org
- 2. How it started: CASP13 – CASP14 1 2
- 3. Alphafold1 (CASP13) & Alphafold2 (CAPSP14) 3 Sum of the Z-score of GDT-TS November 30, 2020
- 5. Alphafold1 (CASP13) 5 (Jumper J et al., Nature 2020) Jan. 15, 2020 Incomplete code release at Github
- 6. 6 (From CASP13 presentations by Jumper & Senior)
- 7. trRosetta 7 Submitted: Aug. 22, 2019 Appeared: Jan. 2, 2020 (Yang et al., PNAS, 2020)
- 8. AttentiveDist (Kiharalab) 8 Submitted (& bioRxiv) : Nov. 25, 2020 Appeared: April 7, 2021 (Jain A, et al., Scientific Reports, 2021) 1D Features: 1-hot encoding (20 aa) PSSM, HMM Predicted 2ndary structures & ASA (Spot-1D) 2D Features: Predicted contacts (CCMPred) Mutual Information Statistical pairwise potential CASP13, 43 FM and FM/TBM targets
- 9. Alphafold2 9 April 7, 2021 (Jumper J et al., Nature 2021) Input Sequence Embedding Structure building (Inference) code at Github & well-documented Suppl. Info. Released End-to-End
- 10. Input MSA ◉ DB Search ○ JackHMMER > Mgnify (Metagenome DB) ○ JackHMMER > Uniref90 ○ Hhblits > Uniclust30 + BFD (Metagenome DB) ○ Above are simply stacked. ◉ MSA processing (to reduce the memory usage) ○ MSA block deletion (MSA-level dropout) ○ Nseq sequences are randomly selected as MSA cluster centers ○ 15% are masked ○ Among masked: 10%, replaced to random amino acids; 10%, replaced from MSA profile; 10%, unchanged; 70%: targets for prediction. ○ Nextra_Seq selected for extra MSA sequences 10
- 12. 12 Evoformer Block (Sequence Embedding) “tri-amino acid contact (potential)” i j k “MSA Transformer”
- 14. 14 Structure Module (IPA) 8 blocks (shared weights)
- 15. Model Training & Loss Function ◉ Recycling (training & inference) ◉ MSA resampling (training & inference) ◉ Noisy Student Self-distillation ◉ Loss Function 15 Atom coordinates Main-chain atom coord. & angles Dist-ogram Predicted error If atoms are experiment ally determined Crystal structure statistics Masked MSA
- 16. RoseTTafold 16 April 7, 2021 (Baek M et al., Science 2021) Preparing input of SE(3) “MSA transformer”
- 17. Time Course 3 17
- 19. Alphafold US Patent 19 June 3, 2021
- 20. Alphafold2 Paper & Code Release 20 July 15, 2021 (Jumper J et al., Nature 2021) Input Sequence Embedding Structure building
- 21. Alphafold Database 21 July 22, 2021 (Tunyasuvunakool K. et al., Nature 2021) Database of protein models of 21 model organisms
- 22. Alphafold Use Case Reports (Twitter) ◉ Installation of the software ◉ Surprisingly accurate models of specific proteins ◉ Modeling of protein complexes ○ With Gly-linker; X-linker, manipulating residue number index ◉ Peptide docking ◉ Indication of disordered region, mutation.. 22 July - August, 2021
- 23. Alphafold-Related Papers (166 papers on bioRxiv since 12/01/2020) 23 July - August, 2021 Peptide docking Protein-protein docking Mutation Protein Design Folding Pathway Molecular Replacement Application
- 24. ColabFold 24 August 15, 2021 (Mirdita M et al., bioRxiv 2021)
- 26. Future Directions for Structural Bioinformatics works after Alphafold2 ◉ Reuse the AF2 code ○ Docking, interaction ○ Data-assisted protein structure modeling ○ Protein design ○ Structure modeling of other molecules ◉ Application of AF2 structure models ○ Drug screening ○ Function (binding pocket classification ..) ◉ Explore new topics ○ Cryo-ET ○ Dynamics 26
- 27. Alphafold2 as BLAST ◉ Use Alphafold2 as the base-line tool, just like BLAST in sequence analysis ◉ Use it in a pipeline ◉ Modify the code, concepts, techniques 27
- 28. Kihara Lab Research Team 28 https://kiharalab.org @kiharalab @d_kihara Sai Raghavendra Maddhuri Venkata Subramaniya Zicong Zhang Special Thanks to Sai and Zicong for discussion