Multiscale protein-protein interactions – Disorder & aggregation – Biophysical question #1

View Github Repository Open presentation in a new window

thoppe

See all presentation from thoppe

Multiscale protein-protein interactions – Disorder & aggregation – Biophysical question #1

1 2

Presentation_Research_IDP

Research presentation on Intrinsically Disordered Proteins (IDP's)

On Github thoppe / Presentation_Research_IDP

Multiscale protein-protein interactions

Disorder & aggregation

Travis Hoppe

CSULA Seminar: February 3, 2015

National Institutes of Health (NIH)National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)Laboratory of Chemical Physics (LCP), Theoretical Biophysical Chemistry (TBC)

Biophysical question #1

How do we predict phase separations of protein solutions?

Biophysical question #2

How do we make predictions about intrinsically disordered proteinsgiven their large conformational landscape?

Acknowledgments:

Laboratory of Chemical Physics

Robert Best Wenwei Zheng

Laboratory of Biochemistry and Genetics

Allen Minton Di Wu

*Support provided by the Intramural Research Division of the NIDDK, NIH.

Protein Structure

Primary structure (sequence)

GSIGAASMEF CFDVFKELKV HHANENIFYC PIAIMSALAM VYLGAKDSTR TQINKVVRFD KLPGFGDEIE AQCGTSVNVH 
SSLRDILNQI TKPNDVYSFS LASRLYAEER YPILPEYLQC VKELYRGGLE PINFQTAADQ ARELINSWVE SQTNGIIRNV 
LQPSSVDSQT AMVLVNAIVF KGLWEKAFKD EDTQAMPFRV TEQESKPVQM MYQIGLFRVA SMASEKMKIL ELPFASGTMS 
MLVLLPDEVS GLEQLESIIN FEKLTEWTSS NVMEERKIKV YLPRMKMEEK YNLTSVLMAM GITDVFSSSA NLSGISSAES 
LKISQAVHAA HAEINEAGRE VVGGAEAGVD AASVSEEFRA DHPFLFCIKH IATNAVLFFG RCVSP
Secondary structurehelices [red], sheets [blue Tertiary structure3D structure Higher-order structurecomplexes, aggregation

Ovalbumin, Egg white protein PDB:1OVA, Crystal Structure, Carrell et al., J. Mol. Biol. (1991) SEM Aggregate structure, Zabik et al., J. Poul. Sci. (1980)

Primary Structure

Twenty residue "alphabet" forms polypeptide chain

Chemical Structure of the Twenty Common Amino Acids, Compound Interest

Protein folding problem

Predict structure from sequence

Sequence Structure Function

Native structure, folding pathways, ...

Energy Landscape, Wolynes, Phil. Trans. A (2004)MD simulation of WW-domain, Best and Mittal, J. Phys. Chem. B (2010)

Scientific Philosophy

Theoreticians need to keep in close contact with experimentalists.Imagination must be constrained by reality.

Models must as simple as possible (but no simpler).

The Treachery of Images by René Magritte

Part 1: Aggregation

How do we predict phase separations of protein solutions?

Higher order structure

Phase separations lead to sudden changes in liquid structure.

Leibler, Nature 2004 Tanaka, Phys. Rev. E 2005

How do we model many protein-protein interactions?Can we predict aggregates from experimental structure?

Human serum albuminPDB:1AO6 OvalbuminPDB:1OVA LysozymePDB:1W6Z Bovine Serum AlbuminPDB:3V03

Protein-Protein interactions

Important terms:

Volume exclusion, Electrostatics, solvent effects,Non-specific interactions (London/dispersion forces)

Second-order effects?

Non spherical geometries, polarization,internal conformational energies, ...

Need a way of validating model.

Experimental Measurements

Second virial coefficient, , measurementusing light scattering at different pH.

Dotted-line: Hard sphere potential. Good enough for sickle cell hemoglobin!

Virial Coefficients

An equation of state expanded in powers of density

is the pairwise interaction of two molecules is the interaction of three molecules, ...Negative values of often correlate with aggregation.

For rotationally invariant molecules*

Goal: Develop a realistic pair potential for virial calculation.
*Rotationally dependent calculation integrates over all orientations. For hard spheres .

The Process

Start with the crystallized PDB Structuree.g. Human Serum Albumin PDB:1A06

Electrostatics: Poisson-Boltzmann

Solve for with the Adaptive Poisson-Boltzmann Solver (APBS),

Typically (in the absence of ions), and .

APBS by Baker et al. Proc. Natl. Acad. Sci. 2001, Bjerrum length

Macrocharge fitting

Best fit macrocharges to approximate the field.

Decompose the field, determine a region of excluded volume +spherical harmonic decomposition for large distances.

Matching experiments

Theoretical predictions of the second virial coefficient considering only excluded volume and reduced electrostatics.

Matching experiments

Theoretical predictions of the second virial coefficient considering only excluded volume and reduced electrostatics.

A Simplified Representation of Anisotropic Charge Distributions within Proteins, Hoppe, J. Chem. Phys. (2013)

Phase separations summary

Calculate the non-ideality of a protein molecule after includingboth the excluded volume and electrostatics.

Predict the second-virial coefficient as a function of pH values, protein concentrations, binary mixtures, and salt concentrations.

Ongoing research: Use the model in higher-order simulationsto predict phase behavior via Gibb's ensembles.

Part 2: Disorder

How do we make predictions about intrinsically disordered proteinsgiven their large conformational landscape?

Paradigm shift

Proteins were thought to adopt stable, folded conformations.Solving the structure was paramount for understanding the function.

Unexpected: disorder is abundant!

Grouping proteins in the yeast proteome, Gsponer, Science (2008)

Intrinsically disordered proteins

Structure

  • Lack tertiary structure (disorder!)
  • Still may form secondary structure
  • Different primary structure (residue propensity)
  • More charged, less hydrophobic and aromatic residues

Binding

Not disordered, Lock and KeyBarnase-Barstar complex Disorder-to-orderHif-1 α/CBP Always disorderedSIC1 binding to CDC4

Theory

  • What advantages do IDPs have over traditional proteins?
  • Recognition that the cellular environment is a crowded place.

Function

  • Often found in signaling pathways, centers of protein hubs
  • Linkers (entropic chains), Chaperones, HIV transcription (TAT)
  • Binding specificity, with lower affinity

Modeling

IDPs: Folding Sampling

Goal: Develop a model for IDP interactions.

Statistical Potentials

Residue-residue interactions, quasi-chemical lattice-gas

Potentials constructed from Top 8000 Protein Database, Richardson Group

Residue-residue interaction matrix, MJ

Other statistical potentials: Tanaka and Scheraga (1976), Spil (1990), Miyazawa and Jernigan (1996),Betancourt and Thirumalai (1999), Skolnick, Kolinski and Ortiz (2000)

MJ matrix reveals biophysical structure

H (hydrophobic), P (polar), C (charged)

MJ Contact energy, from structure

Mean-field (MF) energy, from sequence

MJ contact energy reproduces MF energy

Energy per residue shows good correlation as well.

MF Energy distributions: Physically reasonable

IDP Propensity, Coeytaux & Poupon, Bioinformatics (2005)Hydrophilicity index, Kyte & Doolittle, J. Mol. Biol. (1982)Amyloidogenic regions, Garbuzynskiy et. al. Bioinformatics (2010)

Protein Networks

  • Target protein interacts with a range of possible surfaces.
  • Measure average binding affinity of protein to surfaces.
  • Measure binding specificity of protein to surfaces.
Example network: Protein-protein interactions in yeast, S. cerevisiaeSchwikowski & Fields et al., Nature 2000.

Protein-complex energy

Pairwise decomposition of protein complex energy; Binding affinity

Contact matrix is not symmetric

Specificity score: Define "decoys" as weakly boundstructures in protein network.

Binding affinity

Binding specificity

MF IDP Summary:

  • MF models reproduce MJ contact energies. MF IDP's bound to native structures show increased specificity with lower affinity.

PDB:1B8A 1B0B 1BQ8 1DQP 1DOI 1C4Q 1ARB 1BXU 1CC8 1CCJ 1DFU 1DMG

What's next? Add structure to mean field calculations.Lattices may be optimal for IDP's, they can reproduce native-energies but quickly sample extended conformational space.

Active research projects & collaborations

Crowding, surface adsorption and protein fibrillation, Biophys (in press).Programmable Nanoscaffolds and Multivalent Effects, JACS.Integer sequence discovery from small graphs, Discrete Math. (submitted).Dependence of Internal Friction on Folding Mechanism, JACS (submitted).Quantification of plasma HIV RNA, Nature Comm.

Allen Minton Andrew Dix, Daniel Appella, et. al Anna Petrone Robert Best, Wenwei Zheng Zhao, Daniel Appella, et. al.

Future Research Projects

Phase separation calculations, aggregation.

Quantitative IDP models, disorder.

Benchmarks in sampling algorithms, BiSA.

Graph fingerprint and invariant database, EoFG.

Dependence of topology on sampling, WL topology.

RNA structure as multigraphs.

Theoretical liquid state calculations for simple potentials.

Entropic microscopes: free chain calculations of PNA.

Thanks, you.

0
Multiscale protein-protein interactionsDisorder & aggregation Travis Hoppehttps://github.com/thoppe/Presentation_Research_IDP CSULA Seminar: February 3, 2015 National Institutes of Health (NIH)National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)Laboratory of Chemical Physics (LCP), Theoretical Biophysical Chemistry (TBC)