Ioerger Lab – Bioinformatics Research

1. Ioerger Lab – Bioinformatics Research • Pattern recognition/machine learning • issues of representation • effect of feature extraction, weighting, and interaction on performance of induction algorithm • Applications in Structural Biology • molecular basis of biology: protein structures • predicting structures • tools for solving structures (X-ray crystallography, NMR) • stability, folding, packing, motions • drug design (small-molecule inhibitors) • large datasets exist – exploit them – find the patterns

2. TEXTAL - Automated Crystallographic Protein Structure Determination Using Pattern Recognition Principal Investigators: Thomas Ioerger (Dept. Computer Science) James Sacchettini (Dept. Biochem/Biophys) Other contributors: Tod D. Romo, Kreshna Gopal, Erik McKee, Lalji Kanbi, Reetal Pai & Jacob Smith Funding: National Institutes of Health Texas A&M University

3. X-ray crystallography • Most widely used method for protein modeling • Steps: • Grow crystal • Collect diffraction data • Generate electron density map (Fourier transform) • Interpret map i.e. infer atomic coordinates • Refine structure • Model-building • Currently: crystallographers • Challenges: noise, resolution • Goal: automation

4. X-ray crystallography • Most widely used method for protein modeling • Steps: • Grow crystal • Collect diffraction data • Generate electron density map (Fourier transform) • Interpret map i.e. infer atomic coordinates • Refine structure • Model-building • Currently: crystallographers • Challenges: noise, resolution • Goal: automation

5. Overview of TEXTAL • Automated model-building program • Can we automate the kind of visual processing of patterns that crystallographers use? • Intelligent methods to interpret density, despite noise • Exploit knowledge about typical protein structure • Focus on medium-resolution maps • optimized for 2.8A (actually, 2.6-3.2A is fine) • typical for MAD data (useful for high-throughput) • other programs exist for higher-res data (ARP/wARP) Electron density map (or structure factors) Protein model (may need refinement) TEXTAL

6. Crystal Collect data Electron density map Diffraction data LOOKUP: model side chains CAPRA: models backbone SCALE MAP TRACE MAP CALCULATE FEATURES PREDICT Cα’s BUILD CHAINS PATCH & STITCH CHAINS REFINE CHAINS Model of backbone Model of backbone & side chains POST-PROCESSING SEQUENCE ALIGNMENT REAL SPACE REFINEMENT Corrected & refined model

7. F=<1.72,-0.39,1.04,1.55...> F=<1.58,0.18,1.09,-0.25...> F=<0.90,0.65,-1.40,0.87...> F=<1.79,-0.43,0.88,1.52...>

8. Examples of Numeric Density Features • Distance from center-of-sphere to center-of-mass • Moments of inertia - relative dispersion along orthogonal axes • Geometric features like “Spoke angles” • Local variance and other statistics Features are designed to be rotation-invariant, i.e. same values for region in any orientation/frame-of-reference. TEXTAL uses 19 distinct numeric features to represent the pattern of density in a region, each calculated over 4 different radii, for a total of 76 features.

9. The LOOKUP Process Find optimal rotation Database of known maps Two-step filter: 1) by features 2) by density correlation “2-norm”: weighted Euclidean distance metric for retrieving matches: Region in map to be interpreted

10. SLIDER: Feature-weighting algorithm • Euclidean distance metric used for retrieval: • relevant features – good, irrelevant features – bad • Goal: find optimal weight vector w the generates highest probability of hits (matches) in top K candidates from database • Concept of Slider: • adjust features so the most matches are ranked higher than mismatches Slider Algorithm(w,F,{Ri},matches,mismatches) choose feature fF at random for each <Ri,Rj,Rk>, Rjmatches(Ri),Rkmismatches(Ri) compute cross-over point li where: dist’(Ri,Rj)=dist’(Ri,Rk) dist’(X,Y)= l(Xf-Yf)2+(1-l)dist\f(X,Y) pick l that is best compromise among li ranks most matches above mismatches update weight vector: w’update(w,f,l), wf’=l repeat until convergence

11. Quality of TEXTAL models • Typically builds >80% of the protein atoms • Accuracy of coordinates: ~1Å error (RMSD) • Depends on resolution and quality of map

12. Closeup of b-strand (TEXTAL model in green)

13. Deployment • September 2004: Linux and OSX distributions • Can be downloaded from http://textal.tamu.edu • 40 trial licenses granted so far • June 2002: WebTex (http://textal.tamu.edu) • Till May 2005: TB Structural Genomics Consortium members only • Recently open to the public • users upload data; processed on server; can download results • 120 users from 70 institutions in 20 countries • July 2003: Model building component of PHENIX • Python-based Hierarchical ENvironment for Integrated Xtallography • Consortium members: • Lawrence Berkeley National Lab • University of Cambridge • Los Alamos National Lab • Texas A&M University

14. Intelligent Methods for Drug Design • structure-based: • given protein structure, predict ligands that might bind active site • other methods: • QSAR, high-throughput/combi-chem, manual design using 3D • Virtual Screening • docking algorithm + large library of chemical structures • sort compounds by interaction energy • purchase top-ranked hits and assay in lab • looking for mM inhibitors (leads that can be refined) • goal: enrichment to ~5% hit rate

15. Virtual Screening • diversity • ZINC database: ~2.6 million compounds • purchasable; satisfy Lipinski’s rules • docking algorithms: • FlexX, DOCK, GOLD, AutoDock, ICM... • search for position and conformation of ligand • scoring function • electrostatic + steric + desolvation • entropy effects? • major open issues: • active site flexibility, charge state, waters, co-factors • works best with co-crystal structures (already bound)

16. Grid at Texas A&M gridmaster.tamu.edu DOCK binaries + receptor files + 20 ligands at a time West Campus Library typical configuration: 2.8 GHz dual-core Pentium CPUs running Windows XP Blocker Zachary ~1600 computers in student labs on TAMU campus (Open-Access Labs) GridMP software by United Devices (Austin, TX)

17. Data Mining of Results • promiscuous binders • clusters of related compounds • patterns of contacts within active site • hydrogen-bonding interactions • adjust weights of scoring function for unique properties of each site • open/closed, hydrophobic/charged... • ideas for active site variations • development of pharmacophore search patterns

18. Current Screens in Sacchettini Lab • proteins related to tuberculosis (Mycobacterium) • focus on unique pathways involved in dormancy/starvation • glyoxylate shunt – slow-growth metabolic pathway • cell-wall biosynthesis (unique mycolic acid layer in tb.) • biosynthesis of amino acids/co-factors that humans get from diet • isocitrate lyase • malate synthase • PcaA: mycolic acid cyclopropane synthase • ACPS: acyl-carrier protein synthase • InhA: enoyl-acyl reductase (target of isoniazid) • KasB: fatty-acid synthase • BioA: biotin (co-factor) synthase • PGDH: phospho-glycerol dehydrogenase (serine biosynthesis) • Related proteins in malaria, SARS, shigella

23. Conclusions • Many opportunities for research in Structural Bioinformatics • large datasets • significant problems • Provides challenges for machine learning • drives development of novel methods, especially for dealing with noise, sampling biases, extraction of features... • Requires inherently interdisciplinary approach • training in biochemistry; knowledge of molecular interactions • understanding chemical intuition; use of visualization tools • insights about strengths and limitations of existing methods • Requires collaboration to construct appropriate representations to enable learning algorithms to find patterns • translate expectations about what is relevant, dependencies, smoothing, sources of noise...