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Alphafold2 - Protein Structural Bioinformatics After CASP14
The document discusses advancements in protein structural bioinformatics with a focus on AlphaFold2, highlighting its development after AlphaFold1 and its significant contributions to protein structure prediction. Key methodologies, such as sequence embedding and multi-sequence alignment processing, are detailed along with the model's applications in areas like protein docking and drug screening. Future directions for research following AlphaFold2 are also suggested, emphasizing ongoing developments in structural modeling and computational techniques.
Alphafold2 - Protein Structural Bioinformatics After CASP14
- 1. Alphafold2 - ProteinStructural 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
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- 5. Alphafold1 (CASP13) 5 (Jumper Jet al., Nature 2020) Jan. 15, 2020 Incomplete code release at Github
- 6. 6 (From CASP13 presentationsby 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 (JumperJ et al., Nature 2021) Input Sequence Embedding Structure building (Inference) code at Github & well-documented Suppl. Info. Released End-to-End
- 10. Input MSA ◉ DBSearch ○ 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
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- 12. 12 Evoformer Block (SequenceEmbedding) “tri-amino acid contact (potential)” i j k “MSA Transformer”
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- 14. 14 Structure Module (IPA) 8blocks (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 (BaekM et al., Science 2021) Preparing input of SE(3) “MSA transformer”
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- 19. Alphafold US Patent 19 June3, 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 CaseReports (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 (166papers 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 (MirditaM et al., bioRxiv 2021)
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- 26. Future Directions forStructural 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 ResearchTeam 28 https://kiharalab.org @kiharalab @d_kihara Sai Raghavendra Maddhuri Venkata Subramaniya Zicong Zhang Special Thanks to Sai and Zicong for discussion