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

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

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

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

• 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)

Overview of TEXTAL

Electron density map(or structure factors) TEXTAL Protein model

(may need refinement)

SCALE MAP

TRACE MAP

CALCULATE FEATURES

PREDICT Cα’s

BUILD CHAINS

PATCH & STITCH CHAINS

REFINE CHAINS

LOOKUP: model side chains CAPRA: models backbone

POST-PROCESSING

SEQUENCE ALIGNMENT

REAL SPACE REFINEMENT

Crystal Collect data Diffraction data Electron density map

Model of backbone

Model of backbone & side chains

Corrected & refined model

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...>

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. samevalues 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.

Databaseof knownmaps

Region in map to be interpreted

The LOOKUP ProcessFind optimalrotation

i

iii RFRFwRRdist 22121 ))()((),(

“2-norm”: weighted Euclideandistance metric for retrieving matches:

Two-step filter: 1) by features 2) by density correlation

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

i

iii RFRFwRRdist 22121 ))()((),(

Slider Algorithm(w,F,{Ri},matches,mismatches) choose feature fF at random for each <Ri,Rj,Rk>, Rjmatches(Ri),Rkmismatches(Ri) compute cross-over point i where: dist’(Ri,Rj)=dist’(Ri,Rk) dist’(X,Y)= (Xf-Yf)2+(1-)dist\f(X,Y) pick that is best compromise among i

ranks most matches above mismatches update weight vector: w’update(w,f,), wf’= repeat until convergence

Quality of TEXTAL models

• Typically builds >80% of the protein atoms

• Accuracy of coordinates: ~1Å error (RMSD)– Depends on resolution and quality of map

Closeup of -strand (TEXTAL model in green)

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

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 M inhibitors (leads that can

be refined)– goal: enrichment to ~5% hit rate

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)

Grid at Texas A&M

~1600 computersin student labs on TAMU campus (Open-Access Labs)

Blocker Zachary

gridmaster.tamu.edu

GridMP softwareby United Devices(Austin, TX)

West CampusLibrary

DOCK binaries +receptor files +20 ligands at a time

typical configuration:2.8 GHz dual-core Pentium CPUsrunning Windows XP

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

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

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...