12.1 Protein crystallography and structure determination

Protein crystallography is a powerful technique for determining the 3D structure of proteins at atomic resolution. It involves growing protein crystals, exposing them to X-rays, and analyzing diffraction patterns to reconstruct the structure.

This method is crucial in biology and pharmaceuticals, allowing scientists to understand protein function, design drugs, and study disease mechanisms. It combines physics, chemistry, and biology to unlock the secrets of life's molecular machinery.

Protein Crystallography Principles

Fundamentals of X-ray Crystallography

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• Protein crystallography determines three-dimensional protein structures at atomic resolution using X-ray diffraction
• Process involves growing protein crystals, exposing them to X-ray beams, and analyzing diffraction patterns to reconstruct structure
• Bragg's law ($nλ = 2d sinθ$) relates X-ray wavelength to crystal plane spacing and diffraction angle
• Phase information crucial for structure determination lost during data collection leads to "phase problem"
• Methods to solve phase problem include molecular replacement, isomorphous replacement, and anomalous dispersion techniques
  • Molecular replacement uses known structures of similar proteins
  • Isomorphous replacement introduces heavy atoms into the crystal
  • Anomalous dispersion exploits anomalous scattering of certain atoms

Advanced Techniques and Instrumentation

• Synchrotron radiation sources provide high-intensity X-ray beams
  • Allow faster data collection and higher resolution structures (up to ~1 Å)
  • Examples include Advanced Photon Source (USA) and Diamond Light Source (UK)
• Cryo-crystallography techniques reduce radiation damage and improve data quality
  • Involve flash-freezing crystals in liquid nitrogen (temperature ~100 K)
  • Cryoprotectants (glycerol, ethylene glycol) prevent ice formation
• Free-electron lasers enable serial femtosecond crystallography
  • Allow study of radiation-sensitive proteins and time-resolved experiments

Growing Protein Crystals

Protein Purification and Preparation

• High-purity protein crucial for crystal growth obtained through chromatography techniques
  • Examples include affinity chromatography, ion exchange, and size exclusion
• Assess protein purity using methods like SDS-PAGE and mass spectrometry
• Crystallization occurs when protein solution reaches supersaturated state
  • Achieved through controlled precipitation methods
  • Balance between protein-protein and protein-solvent interactions

Crystallization Techniques and Optimization

• Common crystallization techniques include vapor diffusion, batch crystallization, and dialysis
  • Vapor diffusion methods (hanging drop, sitting drop) most widely used
• Screen crystallization conditions by varying parameters
  • Protein concentration (typically 5-20 mg/mL)
  • pH (range 4-9)
  • Temperature (4°C, 20°C common)
  • Precipitants (PEG, ammonium sulfate, MPD)
• Seeding techniques promote crystal growth or improve quality
  • Microseeding introduces small crystal fragments
  • Macroseeding uses larger, pre-existing crystals
• Assess crystal quality based on size, shape, and diffraction properties
  • Larger, single crystals generally more suitable for X-ray diffraction
  • Ideal crystal size ranges from 0.1-0.5 mm
• Post-crystallization treatments improve diffraction quality or facilitate structure determination
  • Dehydration can tighten crystal packing
  • Soaking with heavy atoms aids phase determination
  • Ligand soaking allows study of protein-ligand complexes

Analyzing Diffraction Data

Data Collection and Processing

• Record diffraction patterns at various crystal orientations using rotating crystal method
• Integrate diffraction spots and scale intensities to obtain complete dataset of structure factor amplitudes
• Patterson function derived from squared structure factor amplitudes
  • Used in molecular replacement and heavy atom methods for phase determination
• Calculate electron density maps using amplitudes and phases of structure factors
  • Initial maps often improved through density modification techniques (solvent flattening, histogram matching)

Model Building and Refinement

• Fit amino acid residues into electron density map using specialized software (Coot, Phenix)
• Refine model iteratively by adjusting atomic positions
  • Improve agreement between observed and calculated structure factors
  • Minimize R-factor and R-free values
• Validate final model through multiple assessments
  • Stereochemistry (bond lengths, angles)
  • Ramachandran plot analysis for backbone conformations
  • Evaluation of fit to electron density (real-space correlation coefficient)

Structure and Function Relationship

Structural Hierarchy Analysis

• Primary structure (amino acid sequence) determines protein folding
• Analyze secondary structure elements (α-helices, β-sheets) for contribution to overall architecture
  • α-helices typically 3.6 residues per turn, stabilized by hydrogen bonds
  • β-sheets formed by hydrogen bonding between adjacent strands
• Examine tertiary structure for spatial arrangement of secondary elements
  • Analyze interactions stabilizing fold (hydrophobic core, salt bridges, disulfide bonds)
• Study quaternary structure to understand subunit interactions
  • Implications for protein function (allosteric regulation, cooperativity)

Functional Interpretation of Structure

• Identify and characterize active sites and binding pockets
  • Based on three-dimensional structure and chemical properties
  • Often found in clefts or cavities on protein surface
• Infer structure-function relationships by comparing to known functional motifs
  • Analyze conservation of structural features across homologous proteins
  • Examples include zinc finger motifs in DNA-binding proteins, catalytic triads in enzymes
• Use molecular dynamics simulations to predict protein flexibility
  • Reveal potential conformational changes relevant to function
  • Timescales range from picoseconds to microseconds
• Apply computational methods for function prediction
  • Docking simulations for protein-ligand interactions
  • Machine learning approaches for functional annotation