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Slide 1-

Intelligent Systems and Molecular Biology Richard H. Lathrop Dept. of Computer Science rickl@uci.edu Donald Bren Hall 4224 949-824-4021
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Intelligent Systems and Molecular Biology Artificial Intelligence for Biology and Medicine Biology is data-rich and knowledge-hungry AI is well suited to biomedical problems Examples Machine learning -- drug discovery Rule-based systems – drug-resistant HIV Heuristic search -- protein structure prediction Constraints – design of large synthetic genes Current Project Machine learning and p53 cancer rescue mutants Goal of talk: The power of information science to influence molecular science and technology
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“Computers are to Biology as Mathematics is to Physics.”--- Harold Morowitz(spiritual father of BioMatrix, and Intelligent Systems for Molecular Biology Conference) Goal of talk: The power of information science to influence molecular science and technology
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Biology has become Data Rich Massively Parallel Data Generation Genome-scale sequencing High-throughput drug screening Micro-array “gene chips” Combinatorial chemical synthesis “Shotgun” mutagenesis Directed protein evolution Two-hybrid protocols for protein interaction Half a million biomedical articles per year
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“Data Rich”Genomic sequence data
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“Data Rich”Protein 3D structure data Protein Databank Content Growth
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“Data Rich”Biomedical literature
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“Data Rich”10-100K data points per gene chip
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Characteristics of Biomedical Data Noise!! => need robust analysis methods Little or no theory. => need statistics, probability Multiple scales, tightly linked. => need cross-scale data integration Specialized (“boutique”) databases => need heterogeneous data integration
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Intelligent Systems are well suited to biology and medicine Robust in the face of inherent complexity Extract trends and regularities from data Provide models for complex processes Cope with uncertainty and ambiguity Content-based retrieval from literature Ontologies for heterogeneous databases Machine learning and data mining Intelligent systems handle complexity with grace
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Intelligent Systems and Molecular Biology Artificial Intelligence for Biology and Medicine Biology is data-rich and knowledge-hungry AI is well suited to biomedical problems Examples Machine learning -- drug discovery Rule-based systems – drug-resistant HIV Heuristic search -- protein structure prediction Constraints – design of large synthetic genes Current Project Machine learning and p53 cancer rescue mutants Goal of talk: The power of information science to influence molecular science and technology
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Drug Discovery Background Cost and time, per drug, start to finish US$500 million to US$1 billion 6 to 12 years High failure rate “Blockbuster” drugs Revenues > US$1 billion/year Profits > US$1 million/day Blood pressure, ulcers, …. A risky business….
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Positive Examples | Negative Examples * * *
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Digital 3D Shape Representation
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The Power of a Good Representation
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Learning the “Multiple Instance” Problem “Solving the multiple instance problem with axis-parallel rectangles” Dietterich, Lathrop, Lozano-Perez, Artificial Intelligence 89(1997) 31-71
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“Compass: A shape-based machine learning tool for drug design,” Jain, Dietterich, Lathrop, Chapman, Critchlow, Bauer, Webster, Lozano-Perez, J. Of Computer-Aided Molecular Design, 8(1994) 635-652
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New Start-up Company:Arris Pharmaceutical Corp. Started a Venture Pharmaceutical Company Apply machine learning to drug discovery Became a Publicly Traded Company (1993) Value of $60 Million, 59 Full-time Employees Merged with Sequana Pharmaceuticals (1998) Merger result became Axys Pharmaceuticals Finally bought by Celera Genomics (2001) Purchase price $188 Million Became Celera Therapeutics
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Intelligent Systems and Molecular Biology Artificial Intelligence for Biology and Medicine Biology is data-rich and knowledge-hungry AI is well suited to biomedical problems Examples Machine learning -- drug discovery Rule-based systems – drug-resistant HIV Heuristic search -- protein structure prediction Constraints – design of large synthetic genes Current Project Machine learning and p53 cancer rescue mutants Goal of talk: The power of information science to influence molecular science and technology
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Knowledge-based avoidance of drug-resistant HIV mutants Physician’s advisor for drug-resistant HIV Rules link HIV mutations & drug resistance Rules extracted from literature manually Patient’s HIV is sequenced Rules identify patient-specific resistance Rank approved combination treatments
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Input/Output Behavior INPUT = HIV SEQUENCES FROM PATIENT: 5 HIV clones (clone = RT + PRO) = 5 RT + 5 PRO (RT = 1,299; PRO = 297) = 7,980 letters of HIV genome OUTPUT = RECOMMENDED TREATMENTS: 12 = 11 approved drugs + 1 humanitarian use Some drugs should not be used together 407 possible approved treatments
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Example Patient Sequence ofHIV Reverse Transcriptase (RT) CCA GTA AAA TTA AAG CCA GGA ATG GAT GGC CCA AAA GTT AAA CAA TGG CCA CCC ATT AGC CCT ATT GAG ACT GTA TTG ACA GAA GAA AAA ATA AAA GCA TTA GTA GAA ATT TGT ACA GAG ATG GAA AAG GAA GGG *AA ATT TCA AAA ATT GGG CCT GAA AAT CCA TAC AAT ACT CCA GTA TTT GCC ATA AAG AAA AAA GAC AGT ACT AAA TGG AGA AAA TTA GTA GAT TTC AGA GAA CTT AAT AAG AGA ACT CAA GAC TTC TGG GAA GTT CAA TTA GGA ATA CCA CAT CCC GCA GGG TAA AAA AAG AAA AAA TCA GTA ACA GTA CTG GAT GTG GGT GAT GCA TAT TTT TCA GTT CCC TTA GAT GAA GAC TTC AGG AAG TAT ACT GCA TTT ACC ATA CCT AGT ATA AAC AAT GAG ACA CCA GGG ATT AGA TAT CAG TAC AAT GTG CTT CCA [CAG] GGA TGG AAA GGA TCA CCA GCA ATA TTC CAA AGT AGC ATG ACA AAA ATC TTA GAG CCT TTT AGA AAA CAA AAT CCA GAC ATA GTT ATC TAT CAA TAC ATG GAT GAT TTG TAT GTA GGA TCT GAC TTA GAA ATA GGG GAG CAT AGA ACA AAA ATA GAG GAG CTG AGA CAA CAT CTG TTG AGG TGG GGA CTT ACC ACA CCA GAC AAA AAA CAT CAG AAA GAA CCT CCA TTC CTT TGG ATG GGT TAT GAA CTC CAT CCT GAT AAA TGG ACA GTA CAG CCT ATA GTG CTG CCA GAA AAA GAC AGC TGG ACT GTC AAT GAC ATA CAG AAG TTA GTG GGG AAA TTG AAT TGG GCA AGT CAG ATT TAC CCA GGG ATT AAA GTA AGG CAA TTA TGT AAA CTC CTT AGA GGA ACC AAA GCA CTA ACA GAA GTA ATA CCA CTA ACA GAA GAA GCA GAG CTA GAA CTG GCA GAA AAC AGA GAG ATT CTA TAA GAA CAA GTA CAT GGA GTG TAT TAT GAC CCA TCA AAA GAC TTA ATA GCA GAA ATA CAG AAG CAG GGG CAA GGC CAA TGG ACA TAT CAA ATT TAT CAA GAG CCA TTT AAA AAT CTG AAA ACA GGA AAA TAT GCA AGA ATG AGG GGT GCC CAC ACT AAT GAT GTA AAA CAA ATA ACA GAG GCA GTG CAA AAA ATA ACC ACA GAA AGC ATA GTA ATA TGG TGA AAG ACT CCT AAA TTT AAA CTG CCC ATA CAA AAG GAA ACA TGG GAA ACA TGG TGG ACA GAG TAT TGG CAA GCC ACC TGG ATT CCT GAG TGG GAG TTT GTT AAT ACC CCT CCC ATA GTG AAA TTA TGG TAC CAG TTA GAG AAA GAA CCC The bracketed codon [CAG] causes strong resistance to AZT.
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Rules represent knowledge about HIV drug resistance IF THEN [weight] (reference). IF RT codon 151 is ATG THEN do not use AZT, ddI, d4T, or ddc. [weight=1.0] (Iversen et al. 1996) The weight is the degree of resistance, NOT a confidence or probability.
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“Knowledge-based avoidance of drug-resistant HIV mutants” Lathrop, Steffen, Raphael, Deeds-Rubin, Pazzani Innovative Applications of Artificial Intelligence Conf. Madison, WI, USA, 1998
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“Knowledge-based avoidance of drug-resistant HIV mutants” Lathrop, Steffen, Raphael, Deeds-Rubin, Pazzani, Cimoch, See, Tilles AI Magazine 20(1999)13-25
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Intelligent Systems and Molecular Biology Artificial Intelligence for Biology and Medicine Biology is data-rich and knowledge-hungry AI is well suited to biomedical problems Examples Machine learning -- drug discovery Rule-based systems – drug-resistant HIV Heuristic search -- protein structure prediction Constraints – design of large synthetic genes Current Project Machine learning and p53 cancer rescue mutants Goal of talk: The power of information science to influence molecular science and technology
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Protein structure prediction
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“Protein Threading”
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“Global Optimum Protein Threading with Gapped Alignment and Empirical Pair Score Functions” Lathrop and Smith J. Mol. Biol. 255(1996)641-665
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Multi-queue Branch-and-Bound Originally developed for protein structure prediction by “protein threading” Abstracted to a general-purpose method Have also run on SAT, Traveling Salesman Small molecule conformation search, DNA motif discovery, DNA nanotechnology, synthetic gene design and assembly Difficult applications inspire new techniques. New techniques enable other applications.
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Optimal Exponent < Proof Exponent X axis = log10(search space size), Y axis = log10(time in seconds) “x” = time to best result, “o” = time to optimal, “*“ = search abandoned Small organic molecule conformation search Protein-DNA binding motif search
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“A multi-queue branch-and-bound algorithm for anytime optimal search with biological applications” Lathrop, Sazhin, Sun, Steffen, Irani Genome Informatics 12(2001)73-82 GIW’2001, Tokyo, Japan
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Intelligent Systems and Molecular Biology Artificial Intelligence for Biology and Medicine Biology is data-rich and knowledge-hungry AI is well suited to biomedical problems Examples Machine learning -- drug discovery Rule-based systems – drug-resistant HIV Heuristic search -- protein structure prediction Constraints – design of large synthetic genes Current Project Machine learning and p53 cancer rescue mutants Goal of talk: The power of information science to influence molecular science and technology
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Current Application:Synthetic gene design Constraint-based search through sequence space Constraints guarantee correct self-assembly and other desirable properties Underlying domain-specific computation done on a 64-node cluster of 3GHz Xeon dual processors
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Assembly of Integrase Gene (1640 bp)
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Problem: Melting Temperatures of correct and incorrect hybridization assemblies overlap solid: correct overlap of oligos dashdot: correct overlap of intermediate fragments dashed: incorrect overlap of small oligos dotted: incorrect overlap of intermediate fragments - CODA Result: Intermediate assemblies contain multiple fragments and error products (smears above)
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Solution: Fragments designed with non-overlapping melting temps + CODA solid: correct overlap of oligos dashdot: correct overlap of intermediate fragments dashed: incorrect overlap of small oligos dotted: incorrect overlap of intermediate fragments Result: Single products assembled correctly.
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In vivo Expression of Gag protein in E. coli Confirmed by Western blot and sequencing
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Intelligent Systems and Molecular Biology Artificial Intelligence for Biology and Medicine Biology is data-rich and knowledge-hungry AI is well suited to biomedical problems Examples Machine learning -- drug discovery Rule-based systems – drug-resistant HIV Heuristic search -- protein structure prediction Constraints – design of large synthetic genes Current Project Machine learning and p53 cancer rescue mutants Goal of talk: The power of information science to influence molecular science and technology
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Cho, Y., Gorina, S., Jeffrey, P.D., Pavletich, N.P. Crystal structure of a p53 tumor suppressor-DNA complex: understanding tumorigenic mutations. Science v265 pp.346-355, 1994 p53 is a central tumor suppressor protein “The guardian of the genome” Controls apoptosis (programmed cell death) and cell cycle arrest Monitors cellular distress The most-mutated gene in human cancers All cancers must disable the p53 apoptosis pathway. p53 core domain bound to DNA Image generated with UCSF Chimera p53 and Human Cancers
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Consequences of p53 mutations Cho et al., Science 265, 346-355 (1994) Loss of DNA contact Disruption of local structure Denaturation of entire core domain ~250,000 US deaths/year Over 1/3 of all human cancers express full-length p53 with only one a.a. change
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N C Core domain for DNA binding Tetramerization 102-292 324-355 Restore integrity of p53 core domain Transactivation 1-42 A Long-held Goal of Anti-cancer Therapy Restore apoptosis (cell death) in tumor cells
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Suppressor Mutations Several second-site mutations restore functionality to some p53 cancer mutants in vivo.
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Cancer Mutant RescuedCancer Mutant PREDICT
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N C Machine Learning: Predict phenotype from genotype G245S A functional census of p53 “cancer rescue” mutants => Catalog *all* Actives
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Will not grow. Will grow. INACTIVE (-) ACTIVE (+) Baroni, T.E., et al., 2004 Class Labels: Active/+ or Inactive/- p53 Transcription Assay Human p53 consensus (S) = Strong (W) = Weak (N) = Negative Danziger, S.D., et al., 2009 Baronio, R., et al., 2010 URA− First measurement Firefly luciferase p53 dependent Second measurement Renilla luciferase p53 independent Initial: Yeast Growth Selection, Sequencing Confirm: Human 1299 Cell-based Luciferase
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Classifier & Feature Overview p53 Wild type Crystal p53 Mutant Structure Amber™ Homology Modeler Mutant Residue (s) Features Classifier Mutant Functionality
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Multi-dimensional Features 2D Surface Map 3D Structural Changes 4D Unfolding Trajectory Danziger et al., Functional Census of Mutation Sequence Spaces, IEEE/ACM Trans Comput Biol Bioinform (2006)
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Theory Find New Cancer Rescue Mutants Knowledge Experiment Active Machine Learning for Biological Discovery
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How Big is The Problem? Spiral Galaxy M101 http://hubblesite.org/ ~10^9 stars. Known Mutants: 16,722 Known Actives: 143 Assuming up to 5 mutations in 200 residuesHow Many Mutants are There?: ~10^11
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Computational Active Learning Pick the Best (= Most Informative) Unknown Examples to Label
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Example of Active Learning:Minimum Marginal Hyperplane Should unknown Mutant 1 or Mutant 2 be added to the training set? Select Mutant 2 ACTIVE INACTIVE Known Active Known Inactive 1 2
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Example of Active Learning: Maximum Curiosity Should Mutant 1 or Mutant 2 be added to the training set? Cross-validator Training Set + Mutant 1 (Active) Training Set + Mutant 1 (Inactive) .0411 .0276 -.6014 .0309 Training Set Change in correlation coefficient Cross-validator Cross-validator Cross-validator Training Set + Mutant 2 (Active) Training Set + Mutant 2 (Inactive) Select Mutant 1
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Which is the Best Active Learning Method? TYPE I: Select mutants that most improve the classifier if correctly predicted. Maximum Curiosity Composite Classifier Improved Composite Classifier TYPE II: Select mutants that most improve the classifier. Additive Curiosity Additive Bayesian Surprise TYPE III: Common methods taken from the literature. Minimum Marginal Hyperplane Maximum Entropy TYPE IV: Variations on methods from the literature. Maximum Marginal Hyperplane (negative control) Minimum Entropy (negative control) Entropic Tradeoff TYPE C: Controls Non-iterated Prediction Predict All Inactive Random (30 trials)
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The Active Learning Tradeoff:How Fast Does It Learn? Danziger, et al., ISMB 2007 (also Bioinformatics 2007)
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Blind in vivo Trial of MIPOn Three p53 Regions MIP Positive Region: Predicted to be Informative & Many Positive Mutants MIP Negative Region: Predicted to be Informative & Few Positive Mutants Expert Region: Selected by a Human Cancer Biologist No previous single-a.a. rescue mutants in any region Danziger, et al. (2009)
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Positive Region: Predicted Active 96-105 (Green) Negative Region: Predicted Inactive 223-232 (Red) Expert Region: Predicted Active 114-123 (Blue) Visualization of Selected Regions Danziger, et al. (2009)
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p-Values are two-tailed, comparing Positive to Negative and Expert regions. Danziger, et al. (2009) Novel Single-a.a. Cancer Rescue Mutants No significant differences between the MIP Positive and Expert regions. Both were statistically significantly better than the MIP Negative region. The Positive region rescued for the first time the cancer mutant P152L. No previous single-a.a. rescue mutants in any region.
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Mutations Rescue Cancerous p53 Cancer Cancer Ultimate Goal
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The long road to a future anti-cancer drug drug Peter Kaiser Rommie Amaro Dick Chamberlin Melanie Cocco Hudel Luecke Wes Hatfield Chris Wassman Roberta Baronio Ozlem Demir Faezeh Salehi Edwin Vargas Da-Wei Lin
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Intelligent Systems and Molecular Biology Artificial Intelligence for Biology and Medicine Biology is data-rich and knowledge-hungry AI is well suited to biomedical problems Examples Machine learning -- drug discovery Rule-based systems – drug-resistant HIV Heuristic search -- protein structure prediction Constraints – design of large synthetic genes DNA nanotechnology and space-filling DNA tetrahedra Current Project Machine learning and p53 cancer rescue mutants Goal of talk: The power of information science to influence molecular science and technology
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N C 175 245 248 273 282 249 Core domain for DNA binding Tetramerization Transactivation IARC TP53 Mutation Database (R7, Sep. 2002): 17,689 somatic mutations 12,631 (71%) missense mutations affecting codons 100-300 974 different amino acid changes in core domain The spectrum of p53 mutations
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Intelligent Systems and Molecular Biology Artificial Intelligence for Biology and Medicine Biology is data-rich and knowledge-hungry AI is well suited to biomedical problems Examples Machine learning -- drug discovery Rule-based systems – drug-resistant HIV Heuristic search -- protein structure prediction Constraints – design of large synthetic genes DNA nanotechnology and space-filling DNA tetrahedra Current Project Machine learning and p53 cancer rescue mutants Goal of talk: The power of information science to influence molecular science and technology
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3D DNA Nanostructures Christopher D. Wassman UC Irvine Dept. of Computer Science
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Why DNA Nanotechnology DNA has an well understood 3D structure DNA is easily synthesized and manipulated DNA Feature Sizes: 3.6 nm per helical rise, 2 nm helical width Intel Feature Sizes: Current chips, 45nm feature size Research chips, 32nm feature size (Sept, 2008) Bio-Nanotechnology is a emerging field Lots to do, and lots of fun to be had!
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Tiling 3-Space A familiar concept Building blocks Cubes fill space Cylinders do not Other building blocks are possible We will focus on tetrahedral building blocks, constructed by “folding DNA”
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Irregular Tetrahedra… Can Tile 3-Space Completely!
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Full Tetrahedron
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A Closer Look
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Atomic Force Microscopy (AFM)
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Experimental AFM Image
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Simulated AFM Image X axis (nanometers) Y axis (nanometers)
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Experimental AFM Image Y axis (nanometers) X axis (nanometers)