Lecture 10 CS566 Fall Structural Bioinformatics Motivation Concepts Structure Solving Structure Comparison Structure Prediction Modeling Structural Interaction

Lecture 10 CS566 Fall Motivation Holy Grail: Mapping between sequence and structure. Structure = F(Sequence) Structure allows the detailed understanding of function Science-Fiction: Simulate life as a movie of molecular interactions

Lecture 10 CS566 Fall Concepts Protein and DNA structure of maximum interest, though growing interest in carbohydrates Central Dogma: Primary structure => Secondary structure, Tertiary structure => Function Primary structure: –The actual permutation, i.e., the sequence per se –20 ~L possibilities, but only about families (“equivalence” classes) –High spatial locality Secondary structure: –Helix, Sheet, Loop, Coil –Intermediate spatial locality: Local organization Tertiary structure: –Only so many folds Evolutionary bias? Only so many stable structures? –Low spatial locality: The actual 3D structure

Lecture 10 CS566 Fall Structural Bioinformatics Macromolecular complex > Protein > (No. of species * 10 4 ) Fold > (10 3 ) Domain > ( ) Module > Motif > Residue > Atom Level of abstraction

Lecture 10 CS566 Fall Primary structure Determined –Experimentally Sequencing –Computationally Proteome prediction from genome Finite number of real-world families based on sequence similarity Significance: Sine qua none Databases: Swiss-Prot, PIR, Genpept

Lecture 10 CS566 Fall Secondary structure Determined –Experimentally Circular dichroism, NMR, Raman spectroscopy – Computationally (“Next semester!”) Sliding window context analysis Periodicity analysis Significance –Higher order building block –Mechanistic significance in protein folding

Lecture 10 CS566 Fall Tertiary structure Determined –Experimentally X-ray crystallography, NMR –Computationally Based on similarity to known structures (Homology modeling) “a priori” Significance –Level of abstraction highly indicative of function Databases: PDB, SCOP, CATH, FSSP

Lecture 10 CS566 Fall Structural representation Cartesian coordinates (x,y,z) for each atom in structure: One vector for each atom Internal coordinate representation (edges, angles): Set of inter-atom vectors Visualized in graphical programs as –Surface representation –All atoms or just backbone (N-C-C) atoms –Cartoons: Helix, Sheet, Loop shorthand

Lecture 10 CS566 Fall Structure Solving – X-ray crystallography (“With apologies to crystallographers”) Given primary structure, find the tertiary structure experimentally X-ray crystallography –Based on property of electrons to scatter X-rays –Make big beautiful protein crystal (“Like sugar crystals; Slow desiccation of protein soup”) –Shoot X-rays through crystal and record diffraction pattern (“Ripples from different sources forming interference pattern; Light waves too gross for measurement of small interatomic distances”) –Provided phase known, structure can be reconstructed using Fourier transforms

Lecture 10 CS566 Fall Structure Solving – X-ray crystallography (“With apologies to crystallographers”) Phase information deduced by –Incorporation of ‘heavy metals’ into proteins (“Crystals from immersion of protein in ‘heavy metal’ soup”) –Use of known structures of similar sequences – homology based structure solving Iterative structure refinement based on matching simulated data from model and experimental diffraction pattern obtained Limitation –No crystals, no structure! –Structure is an average over prolonged period of time

Lecture 10 CS566 Fall Structure Solving – Nuclear Magnetic Resonance Spectroscopy (“With apologies to NMR spectroscopists”) Basis –Based on magnetic properties of spinning protons and neutrons –Different atoms in different (structural) contexts react differently to magnetic fields Key elements –Specially prepared (different forms of N and C) protein (“soup”) solution subjected to strong electromagnetic field to obtain spectroscopic data –Spectroscopic data interpreted as constraints on distances between atoms –Simulated annealing used to find best match between constraints and model

Lecture 10 CS566 Fall Structure Solving – Nuclear Magnetic Resonance Spectroscopy (“With apologies to NMR spectroscopists”) Limitations –Set of structures (“not unique single solution”) determined –On average, higher error than crystallography –Solves smaller structures than crystallography Bright side of structure-solving –Algorithms and software well developed –More powerful sources of X-rays (can work with smaller crystals and in smaller time periods) and electromagnets becoming available –Optimized centers exist that can do a structure a week! Challenges – Higher resolution (“The elusive but all- powerful hydrogen bond”), Structures of complexes (“Life is molecular interaction. Period”)