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, A 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. LOOKUP: model side chains CAPRA: models backbone
Crystal
Collect data
Diffraction data
Electron density map
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, >
F=<1.58,0.18,1.09, >
F=<0.90,0.65,-1.40, >
F=<1.79,-0.43,0.88, >

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
40 trial licenses granted so far
June 2002: WebTex (
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 ~1600 computers in student labs on TAMU
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...