Protein W3

BIOS5003 Biochemistry of Cell Functions

Techniques for Studying Protein Structure

  • Determination of 3D Structure

    • Methods Used:

    • NMR spectroscopy on solution proteins

    • X-ray crystallography

    • Electron microscopy

    • Computer prediction from sequence

    • Additional Techniques:

    • Start with approximate structure by comparison with related known structures; refine by energy minimization

    • Ab initio prediction

Electron Microscopy

  • Development:

    • Five super-imposed images of E. coli glutamine synthase (referenced from Voet & Voet, Fig. 7.60)

    • Previously characterized by low resolution.

  • Cryo-Electron Microscopy (Cryo-EM):

    • Described as a massive game-changer in structural biology

    • Reference: Chung, Jae-Hee & Kim, Homin, High-Resolution Cryo-Electron Microscopy, Applied Microscopy. 47. 218-222.

    • Nobel Prize in Chemistry awarded in 2017 for developments in cryo-electron microscopy to:

    • Jacques Dubochet

    • Joachim Frank

    • Richard Henderson

  • Recent Improvements in Cryo-EM:

    • Contributing Factors:

    • New cameras

    • Better sample preparation

    • New data processing techniques

    • Resulting in a massive increase in resolution (i.e., detail discernment).

    • Drug design capability: Knowing structures allows for designing drugs to influence protein functions.

Insulin Receptor Structures

  • Type:

    • The insulin receptor is classified as a Tyrosine Kinase Receptor (TKR).

  • Structural Characteristics:

    • Uniquely covalently linked as a multimer of type (ab)2.

    • Upon insulin binding, the beta (b) domains align, activating tyrosine kinase activity.

    • Reference: Gutmann et al, 2018 J Cell Biol.

  • Functionality:

    • Single particle high-resolution EM demonstrates receptor conformational changes:

    • Converts from U form to T form in response to increasing insulin concentration.

    • Receptor proteins can be conjugated to either 1 (1U or 1T) or 2 (2U or 2T) nanodiscs.

    • Analysis of 10,000 particles for categorization.

  • Structural Comparisons:

    • U form derived from x-ray crystallography; T form obtained from cryo-EM.

Computational Protein Structure Prediction

  • Question: What if a protein sequence exists in the database but hasn’t had a resolved structure?

  • Concept Explanation:

    • Protein Folding Challenge:

    • For a protein with 150 amino acids in sequence:

      • Assuming 3 configurations per peptide bond (referencing one of the 3 main regions on the Ramachandran plot).

      • Folding time computed as:

      • Folding takes 1 picosecond (10^{-12} s) to convert between configurations.

      • It could take $10^{48}$ years to explore all possible conformations of $3^{150}$ (approximately = $10^{68}$ configurations).

    • Typical folding time for proteins is between 0.1 to 1000 seconds.

    • This calculation illustrates why comprehensive computation of native structures is impractical.

  • Historical Context:

    • Christian Anfinsen’s experiment (1950s):

    • Demonstrates that protein structure is encoded in its primary sequence.

    • When denaturants are removed, a denatured protein can refold into its active state.

    • This phenomenon confirms the sequence coding for structure despite potential refolding resulting randomly in the presence of denaturants.

    • Bonds (a-S-S-) can reform in 10^5 possible ways but weak interactions dictate structure.

  • Emerging Techniques:

    • Ab initio computer prediction for protein structures is rapidly improving.

    • Notable event: CASP14 (November-December 2020).

    • Resources:

    • Video on AlphaFold: https://youtu.be/nGVFbPKrRWQ

    • Lasker Foundation recognition of AlphaFold: https://laskerfoundation.org/winners/alphafold-a-technology-for-predicting-protein-structures/

Machine Learning in Protein Design

  • Subfields:

    • Artificial Intelligence: Machine Learning and Deep Learning subset.

    • Applications in managing metabolic diseases and their complications, monitoring dietary habits, and delivering dietary interventions through e-coaching.

CASP15 Overview (2022)

  • Statistical Summary of Protein Evaluation:

    • GDT_TS scores and RMSD evaluations tracked.

    • Over time from CASP1 to CASP15, trends suggest:

    • Significant changes in the percentage of targets under specified Ca RMSD cut-off (in Ångstroms).

    • Highlight progress in improvement metrics over several CASP events.

  • AlphaFold’s Performance:

    • Note: AlphaFold unaided is not the highest-performing model.

    • Domain and Multimer evaluation, including TMscore comparisons, showcased AlphaFold's capabilities relative to other approaches.

AlphaFold’s Synergy with Structural Biology

  • Impact on Structure Determination:

    • Number of protein entries increased substantially by AlphaFold predictions.

    • Prediction capabilities led to a transformative increase in known structures, detailing:

    • 200 million total proteins,

    • 350,000 for human-related proteins and model organisms.

    • Collaboration between DeepMind and EMBL-EBI expanded the AlphaFold database significantly over recent years.

Upcoming CASP16 (December 2024)

  • Event Summary:

    • Thirteenth Community Wide Experiment on Critical Assessment of Techniques for Protein Structure Prediction.

  • Modeling Categories:

    • Consistent categories including single proteins and domains, protein complexes, nucleic acid structures, and macromolecular binding interactions.

  • Progress and Challenges:

    • Emphasis on model reliability, improved rankings, and better methods for complex structures especially antibody targets.

Addressing Protein Design Challenges

  • Current Limitations:

    • While primary sequences are predictable from codon sequences, the direct relation to 3D structure functionality remains unclear.

    • Ability to predict function relies extensively on comparisons to resolved structures.

  • Example in Mechanisms:

    • The MCT (Monocarboxylate Transporters) family exemplifies nuanced understanding of transport roles:

    • MCTs 1, 2, 3, & 4 transport lactate.

    • MCT10 carries Trp, Tyr, and Phe; MCT8 facilitates T3 and T4 transport; MCT9 is responsible for carnitine transport.

  • Final Note:

    • Experimental science in protein structure determination remains essential in resolving outstanding questions and achieving practical applications for drug design and functional predictions.