Lessons in Modeling from 3-D Structural & Data Science Perspectives
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The document discusses how macromolecular structure and data science can inform modeling. It notes that 3D structure provides insights but knowledge is distributed across many papers, while data science allows a new appreciation of data and new methodologies. Combined, they enable multi-scale modeling, drug refinement and repurposing. Specific examples are given on signaling pathways, protein-ligand docking to predict drug efficacy, and integrating data to represent biological networks. The future involves overcoming cultural roadblocks to fully leverage multi-scale integration across levels from DNA to populations.
![ Value – assuring societal
benefit
Design - Communication
of the value of data
Systems – the means to
communicate and convey
benefit
Analytics – models and
methods
Practice – where
everything happens
02/04/20 Pune
[From Raf Alvarado]](https://image.slidesharecdn.com/bme83152020-200416105649/85/Lessons-in-Modeling-from-3-D-Structural-Data-Science-Perspectives-10-320.jpg)
![ Value + Design = Openness,
responsibility
Value + Analytics = Human
centered AI, algorithmic bias
Value + Systems =
sustainability, access,
environmental impact
Design + Analytics = literate
programming, visualization
Design + Systems =
dashboards, engineering
design
Analytics + Systems = ML
engineering
02/04/20 Pune
[From Raf Alvarado]
Thinking of data as a science unto itself is actually quite novel](https://image.slidesharecdn.com/bme83152020-200416105649/85/Lessons-in-Modeling-from-3-D-Structural-Data-Science-Perspectives-11-320.jpg)
Lessons in Modeling from 3-D Structural & Data Science Perspectives
- 1. Philip E. Bourne, PhD, FACMI peb6a@virginia.edu http://www.slideshare.net/pebourne April 16, 2020 BME 8315
- 3. What macromolecular structure brings to modeling What data science brings to modeling Taken together A new appreciation of data itself New methodologies A new translational emphasis Use cases Drug refinement and repurposing Multi-scale modeling The future – one person’s perspective
- 4. Structural Bioinformatics, Second Edition Edited by Jenny Gu and Philip E. Bourne
- 6. Sci Data. 2016 Mar 15;3:160018 Bioinformatics. 2000 Feb;16(2):159-68 The FAIR Principles Rigorous Ontological Representation
- 7. BMC Bioinformatics. 2005; 6: 21. • What you see is not what you get • Good software has real academic value • Good design transcends the transience of programming technologies
- 8. Chromatin restructuring RNA Splicing Signal transduction in kinases RNA interference (RNAi) pre-tRNA processing Genome integrity: RPA, TEBP Signal transduction (various pathways) Transcriptional regulation RNA processing and degradation What Lessons 3D Structure Brings to Modeling Same structural framework, lots of structural and functional variations Knowledge is spread over 1,000’s of papers BME 50th Anniversary 8Structure. 2019 Jan 2;27(1):6-26
- 9. Protein Sci. 2019 Dec; 28(12): 2119–2126 Urfold Ur – to combine
- 10. Value – assuring societal benefit Design - Communication of the value of data Systems – the means to communicate and convey benefit Analytics – models and methods Practice – where everything happens 02/04/20 Pune [From Raf Alvarado]
- 11. Value + Design = Openness, responsibility Value + Analytics = Human centered AI, algorithmic bias Value + Systems = sustainability, access, environmental impact Design + Analytics = literate programming, visualization Design + Systems = dashboards, engineering design Analytics + Systems = ML engineering 02/04/20 Pune [From Raf Alvarado] Thinking of data as a science unto itself is actually quite novel
- 12. 02/04/20 Pune
- 13. 02/04/20 Pune Mura, Draizen, Bourne Curr Op in Struc Bio 2018, 52:95–102
- 14. 02/04/20 Pune
- 17. What macromolecular structure brings to modeling What data science brings to modeling Taken together A new appreciation of data itself New methodologies A new translational emphasis Use cases Drug refinement and repurposing Multi-scale modeling The future – one person’s perspective
- 19. PKA Phosphoinositide-3 Kinase (D) and Actin- Fragmin Kinase (E) ChaK (“Channel Kinase”) PKA Scheeff & Bourne 2005 PLOS Comp Biol 1(5):e49
- 20. • Tykerb – Breast cancer • Gleevac – Leukemia, GI cancers • Nexavar – Kidney and liver cancer • Staurosporine – natural product – alkaloid – uses many e.g., antifungal antihypertensive Collins and Workman 2006 Nature Chemical Biology 2 689-700 10/16/13 ACSSA 20
- 21. Can we predict drug efficacy and toxicity? Can we reuse old drugs? Can we design personalized medicines? ~200 drugs with identified effects http://www.fda.gov/Drugs/ScienceResearch/ResearchAreas/Pharmacogenetics/ucm083378.htm
- 23. Output: arrhythmia Xie et al 2015 PLOS Comp Biol 10(5):e1003554
- 25. Integrating chemical genomics and structural systems biology MD simulation Mj Q Refined interaction model Mj Q SMAP Protein-ligand docking Mj Q Mi 3D model of novel Target 3D model of annotated target Initial interaction model Query chemical Network modeling Experimental support Generalized Network Enrichment of Structure- Activity Relationships Xie & Bourne 2008 PNAS 105(14):5441-6 Xie et al 2012 Ann Rev Pharm & Tox 52:361-79 Xie et al 2017 Ann Rev Pharm & Tox 6;57:245-262
- 26. Similar binding sites may bind similar ligands A 3D object recognition problem • Globally different, but locally similar • Dynamic • Scalable SMAP – Determining Binding Site Similarity Across Protein Space
- 27. Why? Large search space Challenge: inherent flexibility and errors in predicted structures Representation of the protein structure - Ca atoms only - Delaunay tessellation - Graph representation Geometric Potential (GP) 0.2 0.1)cos( 0.1 i Di Pi PGP neighbors a100 0 Geometric Potential Scale 0 0.5 1 1.5 2 2.5 3 3.5 4 0 11 22 33 44 55 66 77 88 99 Geometric Potential binding site non-binding site Algorithm Xie & Bourne 2007 BMC Bioinformatics 4:S9
- 28. SMAP - Sequence-order Independent Profile-Profile Alignment (SOIPPA) L E R V K D L L E R V K D L Structure A Structure B S = 8 S = 4 Algorithm L E R V K D L S = 8 Xie & Bourne 2008 PNAS 105(14):5441-6
- 29. 0 0.01 0.02 0.03 0.04 0.05 0.06 0 0.1 0.2 0.3 0.4 True Positive RatioFalsePositiveRatio PSI-Blast CE SOIPPA 0 0.01 0.02 0.03 0.04 0.05 0.06 0 0.1 0.2 0.3 0.4 True Positive Ratio FalsePositiveRatio PSI-Blast CE SOIPPA Proteins with the same global shape Proteins with different global shape Xie & Bourne, PNAS, 105(2008):5441
- 31. Zhao et al 2016 J. Med. Chem. 12:59(9) 4326-41
- 32. Crizotinib (1o) ALK Kinase (wt) Ceritinib (2o) ALK Kinase (L1196M) ALK Kinase (wt) ALK Kinase (L1196M) X X
- 33. http://www.rcsb.org/pdb/pathway/pw.do Brunk et al 2016 BMC Sys Biol, 10:26
- 34. What macromolecular structure brings to modeling What data science brings to modeling Taken together A new appreciation of data itself New methodologies A new translational emphasis Use cases Drug refinement and repurposing Multi-scale modeling The future – one person’s perspective
- 35. https://en.wikipedia.org/wiki/Jim_Gray_(computer_scientist) https://www.microsoft.com/en-us/research/wp- content/uploads/2009/10/Fourth_Paradigm.pdf https://twitter.com/aip_publishing/status/856825353645559808 02/04/20 Pune Of course this was all predicted by smart people ..
- 36. Model Transportability Horizontal Integration Multi-scale Integration human mouse zebrafish DNA Gene/Protein Network Cell Tissue Organ Body Population CNV SNP methylation 3D structure Gene expression Proteomics Metabolomics MetabolicSignaling transduction Gene regulation Hepatic Myoepithelial Erythrocyte Epithelial Muscle Nervous Liver Kidney Pancreas Heart Physiologically based pharmacokinetics GWASPopulation dynamics Microbiota From Harnessing Big Data for Systems Pharmacology 2017 https://doi.org/10.1146/annurev-pharmtox-010716-104659 Current roadblocks are more cultural than technical 02/04/20 Pune
- 37. Gohlke et al. 201902/04/20 Pune EHR Animal Models Pathways
- 38. Thank You! Questions? peb6a@virginia.edu @pebourne 02/04/20 Pune .. And the ~160 who went before