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Novozymes Enzyme Stability Prediction
- 1. Novozymes Enzyme Stability Prediction Help identify the thermostable mutations in enzymes 組員：11莊O禾、18郭O智、19林O泰、20張O豪
- 2. About Presentation Content About This Project ! Execute How To Do This Project ? Motivation Why Are We Doing This Project ?
- 3. Content About This Project ! Competition Enzyme
- 4. Novozymes Enzyme Stability Prediction Kaggle is biggest data science competition web The goal of this competition is to predict the thermostability of enzyme variants (Tm) Total 1331 teams 4
- 5. 5 What is Protein A A U P P U C I Primary structure Secondary structure Tertiary structure Quaternary structure
- 6. A large group of proteins Can accelerating chemical reactions 6 What is Enzyme Can be a Biologic Therapy pH and Tm is crucial factors
- 7. Motivation Why Are We Doing This Project ? Market Demand
- 8. Market The global biologics therapy market was estimated at US$ 366.43 billion in 2021 and it is expected to hit over US$ 719.84 billion by 2030 with a noteworthy CAGR of 7.8% from 2022 to 2030. 資料來源:precedenceresearch/Biologics Market Biologic Therapy Global Market 2021-2030 2030 2021 Market growth will ACCELERATE at a CAGR of Growth Contributed by NORTH AMERICA Incremental growth ($B) Growth for 2021 7.8% 57% 719.84 196.2%
- 9. Regulatory Approval Clinical Trials Pre-clinical Drug discovery 3-5 years Demand 1-2 years 6-7 years 1-2 years least over 10 years Regulatory Approval Clinical Trials Pre-clinical Drug discovery 1-2 years 2-3 years Traditional development AI AI development AI 5 years
- 10. Execute How To Do This Project ? Process Scoring
- 11. Process Input Trained Output Protein sequence X protein melting point VPVNPEPDATSVENVALKTGS GDSQSDPIKADLEVKGQSALPF DVDCWAILCKGAPN... Models ŷTm
- 12. Models 12 XG Boost Baese on ML, we use different feature which form protein sequence Py Rosetta Compare 3D structural between wild type and mutant type The protein sequence sees as “Natural Language” Protein BERT Analysis protein 3D structural to extract protein’s features 3D-CNN
- 13. XG Boost Process Training data Kaggle Training Data cleaning Outcome Protein sequence X y Tm Testing data 1. Drop pH > 9 & PH < 5.5 2. embedding: length entropy aaindex1 atc aac Model ŷTm protein melting point Data cleaning
- 14. Length Sequence length in amino acids Entropy Shannon entropy for each sequence in the dataset atc sum of atomic and bond compositions for each amino acid sequence pH Drop pH > 10 & PH < 6 aaindex1 is a set of 20 numerical values representing 566 physicochemical and biological properties of amino acids aac the frequency of amino acids for each sequence in the dataset Data Cleaning
- 15. Py Rosetta Process Training data Training Outcome Protein sequence X Testing data 1. Input data 2. Import model 3. Predict scores
- 16. PyRosetta Test data Single-Point Mutation pdb Energy Score Function scores Wild type sequence: VPVNPEPDATSVENVALKTGSGDSQSDPIKADL EVKGQSALPFDVDCWAILCKGAPN... Mutant sequence: VPVNPEPDATSVENVALKTGSGDSASDPIKADL EVKGQSALPFDVDCWAILCKGAPN...
- 17. Protein BERT Process Training data Training Outcome Protein sequence X 1. Input data 2. Import model ŷTm protein melting point 4. Predict protein melting point seq_id tm 0 75.7 1 50.5 2 40.5 3 47.2 4 49.5 5 48.4 6 45.7 7 55.9 8 48.1
- 18. ◍ Protein Sequence vs NLP ○ Meaningful sentence ○ Bio-language ◍ BERT, stronger NLP model pretrained on two tasks ○ language modeling ○ next sentence prediction ◍ Use Transfer Learning ○ Less training time ○ Significantly improve the efficiency of reinforcement learning NLP Model Transfer Learning
- 19. ◍ BERT vs Protein ○ protein sequence not have “chunk” ○ protein have 3D structural are more complex than sentences ◍ Import Gene Ontology (GO) ○ Define protein’s space distance in organism ○ Separate global and local, then process each Protein BERT Input protein BERT module carry out Finetune ranking test data Input pretrained module Build Finetune module Setting Finetune Parameter 參考：https://github.com/nadavbra/protein_bert/blob/master/ProteinBERT demo.ipynb Protein BERT
- 20. 3D-CNN Process Training data Training Outcome Protein sequence X 2. 3D structural 3D-CNN 1. Input data 3. Import model 4. PredictΔΔG seq_id ΔΔG 31390 1.404995 31391 1.343793 31392 0.241666 31393 0.534203 31394 0.134588 31395 0.697623 31396 1.346896 31397 1.001297 31398 1.083319
- 22. 資料來源: Predicting changes in protein thermodynamic stability upon point mutation with deep 3D convolutional neural networks Bian Li,Yucheng T. Yang,John A. Capra ,Mark B. Gerstein 3D Convolutional Neural Networks
- 23. 資料來源: https://www.youtube.com/watch?v=f0t-OCG79-U Convolutional Neural Networks (CNN)
- 24. Convolutional Neural Networks (CNN) 資料來源: https://www.louisbouchard.ai/densenet-explained/ 資料來源: https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53
- 25. 3D Convolutional Neural Networks (CNN) Architecture
- 26. 3D Convolutional Neural Networks (CNN) Architecture
- 28. 0 0.1 0.2 0.3 0.4 0.5 0.6 XGBoost ProteinBERT Rosetta 3D-CNN Score 0.494 0.471 0.168 Table of ranking Score 0.292
- 29. Process Training Outcome Ranking table of ranking Sorting Protein BERT 3D-CNN Training model Ranking Output Data X Data PR Data PB Data CN Ensemble 𝛼1 𝛼2 𝛼3 𝛼4
- 30. Table of ranking Score 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 XGBoost ProteinBERT Rosetta 3D-CNN Ensemble Score 0.592 0.494 0.471 0.292 0.168 Table of ranking Score
- 31. References Kaggle: https://www.kaggle.com/code/cdeotte/protein-bert-finetune-lb-0-30 https://www.kaggle.com/code/dschettler8845/novo-esp-residue-depth-and-more-w-biopython https://www.kaggle.com/code/vslaykovsky/nesp-thermonet https://www.kaggle.com/code/lucasmorin/nesp-changes-eda-and-baseline/notebook#Submission ESM: https://www.pnas.org/doi/full/10.1073/pnas.2016239118 EVE: https://www.nature.com/articles/s41586-021-04043-8 Alphafold2: https://github.com/deepmind/alphafold RoseTTAFold: https://github.com/RosettaCommons/RoseTTAFold
- 32. THE END
- 33. What is Enzyme 酵素是一類大分子生物催化劑，大部分的酵 素是屬於蛋白質。能加快化學反應的速度， 在現代工業中，酵素可以取代化學品作為重 要的生產催化劑， 以少量的資源，創造更多 的產品，同時節省能源，並減少浪費、加速 生產過程。 然而，酵素作用的溫度有著十分嚴苛的條件， 加熱時或與化學變性劑接觸時，酵素結構會 發生去摺疊（即變性），原有的結構被打亂， 活性也往往隨之喪失。這限制酵素場景使用 的廣泛程度。 蛋白質的分子結構可劃分為四 級，以描述其不同的方面： 胺基酸 3-字母 1-字母 丙氨酸（Alanine） Ala A 精氨酸（Arginine） Arg R 天冬醯胺（Asparagine） Asn N 天冬氨酸（Aspartate） Asp D 半胱氨酸（Cysteine） Cys C 穀氨酸（Glutamic acid） Glu E 穀氨醯胺（Glutamine） Gln Q 甘氨酸（Glycine） Gly G 組氨酸（Histidine） His H 異亮氨酸（Isoleucine） Ile I 亮氨酸（Leucine） Leu L 賴氨酸（Lysine） Lys K 甲硫氨酸（Methionine） Met M 苯丙氨酸（Phenylalanine） Phe F 脯氨酸（Proline） Pro P 絲氨酸（Serine） Ser S 蘇氨酸（Threonine） Thr T 色氨酸（Tryptophan） Trp W 酪氨酸（Tyrosine） Tyr Y 纈氨酸（Valine） Val V
- 35. 36
Notas del editor
- 先在大型設備進行pretraing建立模型，再由已建立的模型在自己的資料集執行finetue 早期NLP只做文字的的單向說明，會造成詞不達意。BERT會考量上下文，再決定持定文字所要表達的意義 將已經在一個特定資料集上訓練好的模型，在其他的資料集進行訓練
- 蛋白質的長度比句子的變化更大，會有超過數萬個氨基酸 由於它們的 3D 結構，所以字母前後結構的影響，比一般字句還長 定義全蛋白質在所有生物體的蛋白質空間上的內容 將全局(Global)和局部(Local)分開處理
- '疏水'，'芳香'，'h鍵受體'，'h鍵供體'，'正電離'，'負電離'，'佔用'
- 資料來源: https://www.louisbouchard.ai/densenet-explained/ 資料來源: https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53
- 資料來源: https://journals.plos.org/ploscompbiol/article/figure?id=10.1371/journal.pcbi.1006514.g001 https://www.youtube.com/watch?v=_v-7gZUWjmQ
- https://pubs.acs.org/doi/10.1021/ja100744h