nmr random coil index & protein dynamics

NMR Random Coil Index

& Protein Dynamics

Mark Berjanskii Edmonton, Alberta

November 19th, 2013

Outline

• RCI background

• Backbone RCI development

• Backbone RCI application

• Side-chain RCI

• Side-chain RCI application

RCI background

• What is protein dynamics?

• Why study protein dynamics?

• Methods of studying fast

equilibrium motions

• RCI history and basics

Proteins in textbooks are

shown as static structures

Proteins are dynamic

Prion protein

Dynamic pictures Static picture

Stevens DJ, Walter ED, Rodríguez A, Draper D, et al. (2009)

PLoS Pathog 5(4): http://www.rcsb.org/pdb/explore.do?structureId=2k1d

Equilibrium and non-equilibrium

dynamics

• Equilibium dynamics - thermal motions, happens in thermal

equilibrium state

• Non-equilibium dynamics - happens when protein is displaced

from its equilibrium state by an event (e.g. ligand binding, etc)

http://www.ch.cam.ac.uk/person/pjb91

Papoian G A PNAS 2008;105:14237-14238

Equilibrium motions are involved in protein

function

Borrowed from http://www.itqb.unl.pt/labs/protein-modelling/activities/upseknas

Ligand binding by serine protease subtilisin

http://www.itqb.unl.pt/labs/protein-modelling/activities/upseknas



Non-equilibrium motions are important too

Borrowed from http://www.itqb.unl.pt/labs/protein-modelling/activities/abc-transporters

The ATP-binding cassette (ABC) transporters change

conformations upon ATP binding and hydrolysis

http://www.itqb.unl.pt/labs/protein-modelling/activities/abc-transporters





Time-scales of protein motions

Functional aspects of protein flexibility. Teilum K, Olsen JG, Kragelund BB.

Cell Mol Life Sci. 2009 Jul;66(14):2231-47

Today we discuss only

fast equilibrium motions

Role of fast motions in thermodynamics

of protein-ligand interactions

Association and dissociation

constants:

Standard Gibbs free energy:

Molecular modelling,

rational drug design

Largest contributions Random Coil Index

What do we want to know about

a motion of a protein group?

• How fast? (time-scale, frequency)

• How far? (amplitude)

• Where does it move? (direction)

Methods of studying fast

equilibrium dynamics

X-Ray B-factor

Sources of B-factor uncertainty

- multiple conformations in different unit cells (internal static disorder),

- protein model errors,

- the contribution of more than one atom to a particular electron density,

- intermolecular crystal packing contacts,

- low temperature of X-ray experiments,

- non-equilibrium conformations

- the degree to which the electron density is spread out,

- uncertainty for each atom position

High uncertainty

Low uncertainty

mean squared displacement

B-factor info content:

Ampliutude: YES

Time-scale: No

Direction: No

Disorder in NMR ensembles (per residue RMSD)

• Number of restraints (harder to get for mobile regions)

• Model convergence (depends on length of TAD and CD steps)

• Number of structures in NMR ensemble

• Bias in structure-based assignment of ambiguous NOEs

• Unrealistic force-field (no water, no electrostatic term,

simplified Van der Waals interactions)

• Inclusion of unrecognized spin-diffusion NOEs

• Requires full structure determination

NMR RMSD info

content:

Ampliutude: YES

Time-scale: No

Direction: No

Protein motions can be visualized by Molecular Dynamics

simulations

Correlated changes in hydrogen bonds:

Comparison of MD conformations

with active and inactive

conformations of J domain

MD info content:

Ampliutude: YES

Time-scale: YES

Direction: YES

MD disadvantages:

1) Theoretical method

2) Requires a 3D structure

3) Depends on the quality of a force-field and starting structure

4) Time-consuming (weeks – months)

5) Does not fully explore conformational space

6) Can characterize only fast motions (<100-1000 ns)

MD solves Newton's equation of motion:

Fi=miai

Fast protein dynamics by NMR relaxation 15N relaxation experiments Amplitude and time-scale of motions

Model-free analysis

Disadvantages:

1) Requires a 3D structure

2) Poor separation of rates of overall tumbling and internal

dynamics for ns-motions

3) Sensitive to NMR signal intensity

4) Opposing effects arising from motions on different time-

scales for both transverse and longitudinal rates.

5) Insensitive to motions that do not change orientation of N-H

vector with respect to external magnetic field.

NMR relaxation info content:

Ampliutude: YES

Time-scale: YES

Direction: No

Random Coil Index

NMR signals from groups in protein random coils

are located in particular rigions of NMR spectra

http://www.biochem.ucl.ac.uk/~rharris/BCSB/NMR/hsqc.html

Folded protein Unfolded "random coil" protein

Random Coil

Chemical Shifts

What are random coil chemical

shifts? -> Statistical coil chemical shifts

Originate from an energy-weighted ensemble of conformations, in which a single residue

(1) is not involved in non-local interactions with other residues and

(2) can occupy any of the regions of the Ramachandran plot with a certain probability specified by the Boltzmann distribution

Unblocked statistical-coil tetrapeptides and pentapeptides in aqueous

solution: theoretical study. Jorge A. Vila, Daniel R.Ripoll, Hector A. Baldoni & Harold A. Scheraga. Journal of Biomolecular NMR, 24: 245–262, 2002.

BIG old idea

• Random coils are highly flexible

• If a residue has chemical shifts close to

random coil chemical shifts (=small

secondary shifts), it should be very

flexible too.

What are secondary chemical

shifts?

Difference between experimental chemical

shifts and random coil chemical shifts

Your protein

chemical shifts

Reference random coil

chemical shifts

- = Secondary

Chemical

Shifts

Random Coil Index

Random coil chemical

shifts

Secondary chemical shifts

Measured

chemical shifts

Random Coil Index

Random Coil Index definition

Random Coil Index (RCI) – evaluates the proximity of residue structural

and dynamic properties to the properties of flexible random coil rigions

from NMR chemical shifts

Outline

• RCI background



• Side-chain RCI


Not as easy as it

sounds

The first program "Dynamr" has

failed

Sequence corrected random coil

Ca chemical shifts Experimental Ca chemical shifts

|DdCa|

Double smoothing via three-

point moving average

K-DdCa

CSI-based scaling of (K-DdCa ) for helices and

b-strands

Dynamr worked for helical proteins

and failed to distinguish a b-strand from coil

Why did Dynamr fail?

• Ca secondary chemical shifts are small in b-

strands.

S. Spera and A. Bax: An empirical correlation between protein backbone conformation

and Ca and Cb chemical shifts.

J. Am. Chem. Soc. 113, 5490-5492 (1991).

Problem with false positives

PyJ

Possible solution? • use more than one chemical shift

• find a better mathematical expression

• optimize the way of combining

chemical shifts into a single parameter

RCI development

Testing various

empirical equations

Generating reference

flexibility profiles

by MD

Optimization of

empirical equations Method testing

and validation

Eight empirical equations have been

tested ADdCa+BDdCb+CDdHa+DDdCO+EDdHN+FDdN

A(DdCa)-1+B(DdCb)-1+C(DdHa )

-1+D(DdCO )-1+E(DdHN )

-1 +F(DdN )

-1

(DdCa)A+(DdCb)B+(DdHa )

C+(DdCO )D+(DdHN )

E +(DdN )

F

(DdCa)A (DdCb)B (DdHa )

C (DdCO )D (DdHN )

E (DdN )F

e(DdCa)A e(DdCb)B e (DdHa )C e (DdCO )D e (DdHN )E e (DdN )F

e(DdCa)A + e(DdCb)B + e (DdHa )C + e (DdCO )D + e (DdHN )E + e (DdN )F

Ae(DdCa) + Be(DdCb) +Ce (DdHa ) +De (DdCO ) +Ee (DdHN ) +Fe (DdN )

|A(DdCa)+B(DdCb)+C(DdHa )+D(DdCO )+E(DdHN )+F(DdN )|-1

Generation of reference

flexibility profiles by MD

Too many proteins, too little time

• os.system('xmgrace %s -hdevice PostScript -hardcopy -printfile %s.ps' % (data,data))

• os.system('xmgrace %s -autoscale none -hdevice PostScript -hardcopy -printfile %s.ps' % (data,data))

• os.system('xmgrace %s -autoscale y -world 0 0 %s 1 -hdevice PostScript -hardcopy -printfile %s.ps' % (data,L_last_residue,data))

• os.system('g_analyze -f %s -b %s -e %s > %s/%s' % (output,L_analysis_start,L_analysis_end,RMSF_folder,"logfile"))

• os.system('rm %s/logfile' % RMSF_folder)

• os.system('rm %s' % (N_index))

• os.system('rm %s' % (output))

• os.system('g_analyze -f %s -b %s -e %s > %s/%s/%s' % (output,L_analysis_start,L_analysis_end,RMSF_folder,G_MSD_folder,"logfile"))

• os.system('mv %s/%s/logfile logfile.bak' % (RMSF_folder,G_MSD_folder))

• os.system('rm %s' % (N_index))

• os.system('rm %s' % (output))

• os.system('rm %s' % NH_index)

• os.system('mv %s %s' % (energy_log,new_name_for_log))

• os.system('mv %s %s' % (output,new_name_for_plot))

• os.system('g_chi -s %s -f %s -corr %s/%s/%s_order.xvg -psi -phi -o %s/%s_order.xvg -p %s/%s_order.pdb -g %s/%s -shift -b %s -e %s' % (L_topology_file,L_traj_name,L_output_directory,L_output_directory_order,L_output_file,L_output_directory,L_output_file,L_output_directory,L_output_file,L_output_directory,L_output_file,L_start,L_end))

• os.system('rm *"#"*')

• os.system('rm -r %s' % RMSD_dir)

• os.system('mkdir %s' % RMSD_dir)

• os.system('rm -r %s' % Energy_dir)

• os.system('mkdir %s' % Energy_dir)

• os.system('rm -r %s' % RGYR_dir)

• os.system('mkdir %s' % RGYR_dir)

• os.system('rm -r %s' % NH_order_dir)

• os.system('mkdir %s' % NH_order_dir)

• os.system('rm -r %s' % DIH_dir)

• os.system('mkdir %s' % DIH_dir)

• os.system('g_rama -f %s -s %s -b %s -e %s -o %s/%s' % (traj_name,topology_file,analysis_start,DIH_analysis_end,DIH_dir,traj_name))

• os.system('g_filter -f %s -s %s -nf %s -oh %s_high_pass_%s.xtc -ol %s_low_pass_%s.xtc' % (traj_fit_name,topology_file,filter_value,Traj_name_no_ext,filter_value,Traj_name_no_ext,filter_value))

• os.system('rm -r %s/%s' % (RMSF_dir,G_MSD_dir))

• os.system('rm -r %s' % RMSF_dir)

• os.system('mkdir %s' % RMSF_dir)

• os.system('mkdir %s/%s' % (RMSF_dir,G_MSD_dir))

• os.system('rm -r %s' % NH_order_dir_filter_off)

• os.system('mkdir %s' % NH_order_dir_filter_off)

• os.system('rm -r %s' % NH_order_dir_filter_on)

• os.system('mkdir %s' % NH_order_dir_filter_on)

• os.system('rm -r %s/%s' % (G_CHI_dir_filter_off,DIH_order_dir))

• os.system('rm -r %s' % G_CHI_dir_filter_off)

• os.system('rm -r %s' % G_CHI_dir)

• os.system('mkdir %s' % G_CHI_dir)

• os.system('mkdir %s' % G_CHI_dir_filter_off)

• os.system('mkdir %s/%s' % (G_CHI_dir_filter_off,DIH_order_dir))

• os.system('rm -r %s/Filter_%s' % (Shift_dir,filter_value))

• os.system('rm -r %s' % (Shift_dir))

• os.system('mkdir %s' % Shift_dir)

• os.system('mkdir %s/Filter_%s' % (Shift_dir,filter_value))

• os.system('mv corrpsi*.xvg %s/%s/.' % (G_CHI_dir_filter_off,DIH_order_dir))

• os.system('mv corrphi*.xvg %s/%s/.' % (G_CHI_dir_filter_off,DIH_order_dir))

• os.system('mv histo-psi*.xvg %s/%s/.' % (G_CHI_dir_filter_off,DIH_order_dir))

• os.system('mv histo-phi*.xvg %s/%s/.' % (G_CHI_dir_filter_off,DIH_order_dir))

• os.system('rm -r %s/%s' % (G_CHI_dir_filter_on,DIH_order_dir))

• os.system('rm -r %s' % (G_CHI_dir_filter_on))

• os.system('mkdir %s' % (G_CHI_dir_filter_on))

• os.system('mkdir %s/%s' % (G_CHI_dir_filter_on,DIH_order_dir))

• os.system('rm -r %s/Filter_%s' % (Shift_dir,filter_value))

• os.system('mkdir %s/Filter_%s' % (Shift_dir,filter_value))

• os.system('mv corrpsi*.xvg %s/%s/.' % (G_CHI_dir_filter_on,DIH_order_dir))

• os.system('mv corrphi*.xvg %s/%s/.' % (G_CHI_dir_filter_on,DIH_order_dir))

• os.system('mv histo-psi*.xvg %s/%s/.' % (G_CHI_dir_filter_on,DIH_order_dir))

• os.system('mv histo-phi*.xvg %s/%s/.' % (G_CHI_dir_filter_on,DIH_order_dir))

• os.system('rm *"#"*')

os.system('xmgrace %s -hdevice PostScript -hardcopy -printfile %s.ps' % (data,data))

os.system('xmgrace %s -autoscale none -hdevice PostScript -hardcopy -printfile %s.ps' %

(data,data))

os.system('xmgrace %s -autoscale y -world 0 0 %s 1 -hdevice PostScript -hardcopy -printfile

%s.ps' % (data,L_last_residue,data))

os.system('g_analyze -f %s -b %s -e %s > %s/%s' %

(output,L_analysis_start,L_analysis_end,RMSF_folder,"logfile"))

os.system('rm %s/logfile' % RMSF_folder)

os.system('rm %s' % (N_index))

os.system('rm %s' % (output))

os.system('g_analyze -f %s -b %s -e %s > %s/%s/%s' %

(output,L_analysis_start,L_analysis_end,RMSF_folder,G_MSD_folder,"logfile"))

os.system('mv %s/%s/logfile logfile.bak' %

(RMSF_folder,G_MSD_folder))

os.system('rm %s' % (N_index))

os.system('rm %s' % (output))

os.system('rm %s' % NH_index)

os.system('mv %s %s' % (energy_log,new_name_for_log))

os.system('mv %s %s' % (output,new_name_for_plot))

os.system('g_chi -s %s -f %s -corr %s/%s/%s_order.xvg -psi -phi -o %s/%s_order.xvg -p

%s/%s_order.pdb -g %s/%s -shift -b %s -e %s' %

(L_topology_file,L_traj_name,L_output_directory,L_output_directory_order,L_output_file,L_output_directory,L_output_file,L_output_dir

ectory,L_output_file,L_output_directory,L_output_file,L_start,L_end))

os.system('rm *"#"*')

os.system('rm -r %s' % RMSD_dir)

os.system('mkdir %s' % RMSD_dir)

os.system('rm -r %s' % Energy_dir)

os.system('mkdir %s' % Energy_dir)

os.system('rm -r %s' % RGYR_dir)

os.system('mkdir %s' % RGYR_dir)

os.system('rm -r %s' % NH_order_dir)

os.system('mkdir %s' % NH_order_dir)

os.system('rm -r %s' % DIH_dir)

os.system('mkdir %s' % DIH_dir)

os.system('g_rama -f %s -s %s -b %s -e %s -o %s/%s' %

(traj_name,topology_file,analysis_start,DIH_analysis_end,DIH_dir,traj_name))

os.system('g_filter -f %s -s %s -nf %s -oh

%s_high_pass_%s.xtc -ol %s_low_pass_%s.xtc' %

(traj_fit_name,topology_file,filter_value,Traj_name_no_ext,filter_value,Traj_name_no_ext,filter_value))

os.system('rm -r %s/%s'

% (RMSF_dir,G_MSD_dir))

os.system('rm -r %s' % RMSF_dir)

os.system('mkdir %s' % RMSF_dir)

os.system('mkdir %s/%s' % (RMSF_dir,G_MSD_dir))

os.system('rm -r %s' % NH_order_dir_filter_off)

os.system('mkdir %s' % NH_order_dir_filter_off)

os.system('rm -r %s' % NH_order_dir_filter_on)

os.system('mkdir %s' % NH_order_dir_filter_on)

os.system('rm -r %s/%s' % (G_CHI_dir_filter_off,DIH_order_dir))

os.system('rm -r %s' % G_CHI_dir_filter_off)

os.system('rm -r %s' % G_CHI_dir)

os.system('mkdir %s' % G_CHI_dir)

os.system('mkdir %s' % G_CHI_dir_filter_off)

os.system('mkdir %s/%s' % (G_CHI_dir_filter_off,DIH_order_dir))

os.system('rm -r

%s/Filter_%s' % (Shift_dir,filter_value))

os.system('rm -r %s' %

(Shift_dir))

os.system('mkdir %s' % Shift_dir)

os.system('mkdir %s/Filter_%s' % (Shift_dir,filter_value))

os.system('mv corrpsi*.xvg %s/%s/.' % (G_CHI_dir_filter_off,DIH_order_dir))

os.system('mv corrphi*.xvg %s/%s/.' % (G_CHI_dir_filter_off,DIH_order_dir))

os.system('mv histo-psi*.xvg %s/%s/.' % (G_CHI_dir_filter_off,DIH_order_dir))

os.system('mv histo-phi*.xvg %s/%s/.' % (G_CHI_dir_filter_off,DIH_order_dir))

os.system('rm -r %s/%s' %

(G_CHI_dir_filter_on,DIH_order_dir))

os.system('rm -r %s' % (G_CHI_dir_filter_on))

os.system('mkdir %s' % (G_CHI_dir_filter_on))

os.system('mkdir %s/%s' %


os.system('rm -r %s/Filter_%s' % (Shift_dir,filter_value))

os.system('mkdir

%s/Filter_%s' % (Shift_dir,filter_value))

os.system('mv corrpsi*.xvg %s/%s/.' %


os.system('mv corrphi*.xvg %s/%s/.' %


os.system('mv histo-psi*.xvg %s/%s/.' %


os.system('mv histo-phi*.xvg %s/%s/.' %


os.system('rm *"#"*')

MD set-up MD analysis

GromPy A: a Python wrapper for Gromacs.

Generation of trajectories.

Protein structure (PDB format) Secondary structure info (GromPy format)

Simulation set-up. (1) Temperature (2) Force-field (3) Water type (4) Water box

(5) Flags and length of MD simulation steps (6) Time-step, etc.

Simulation:

- Conversion of pdb file to Gromacs format and setting up restraints

- Identification of total protein charge

- Protein solvation

- Minimization of protein and water

- Setting the type and number of ions and adding them to the system

- Restrained minimization with decreasing strength of protein restraints

- MD equilibration of water and ions

- MD equilibration of water, ions and protein side-chains

- MD equilibration of water, ions, protein side-chains and flexible backbone

- MD production run

Trajectories, energy file, RMSD for every step, secondary structure file

GromPy B: a Python wrapper for Gromacs. Analysis and

conversion of trajectories.

GromPy A output files

Analysis and conversion set-up:

(1) Part of trajectory to analyze, (2) Names of output directories, (3) Flags for

trajectory conversion and analysis steps, etc

Trajectory conversion:

Compact trajectory, PDB files, water removal

Analysis:

Stability of simulation: Time series for (1) Energies, (2) RMSD, (3)

Radius of gyration.

Dynamics parameters: (1) NH order parameters, (2) y and f order

parameters, (3) RMSF per residue, (4) Fluctuations of y and f

Pictures: Postscript and Grace formats

Extracting weighting coefficients

Combination number

Co

rrela

tio

n c

oeff

icie

nt

Co

rre

lati

on

co

eff

icie

nt

0.007

Current RCI expression RCI = Q * |A(DdCa)+B(DdCb)+C(DdHa )+D(DdCO )+E(DdHN )+F(DdN )|

-1

Q – is a scaling coefficient that makes the sum of

weighting coefficients identical for all cases of

incomplete assignments.

RCI protocol Experimental shifts: Ca, Cb, CO, N, HN, Ha Random coil values of chemical shifts

Reference correction by RefCor Neighboring residue correction, i+1, i-1, i+2, i-2

Calculation of secondary chemical shifts for Ca, Cb, CO, N, HN, Ha

Combining secondary chemical shifts using the RCI equation.

Random Coil Index

Filling gaps by averaging Dd of residues i+1, i-1 or i+2, i-2

Smoothing by three-point moving averaging

Hertz, Extreme & End-Effect corrections

Smoothing by three-point moving averaging

Before and after the RCI treatment

PyJ

2 3

1

Occurrence of several random

coil shifts in loops is higher

PyJ

2 3

1

1 2 3

Nuclei used: CA, CB, CO, N, NH, HA

RCI performance

RCI info content:

Ampliutude: YES

Time-scale: No

Direction: No

Who cares?

Outline

• RCI background



• Side-chain RCI


RCI applications

RCI validation of NMR

RMSD

RCI correlation with NMR RMSF of

1D3Z – 0.60

1XQQ – 0.75

1ILF

2JHB

CORE BINDING FACTOR BETA

2JHB 1ILF

1D3Z 1XQQ

Ubiquitin

RCI can be used to detect over- and under-

restrained regions in NMR ensembles

RCI validation of MD

simulations

MD validation by RCI Chicken prion

RCI vs. Model-Free order paramter

How can RCI supplement Model-Free S2 ?

- Unfolded proteins: structure is needed for model-free

- Large proteins with overlapped signals: the error in peak position is smaller than in peak intensity

- Proteins at high pH: NHs are weak, needed for MF, not needed for RCI

- Proteins with intermediate exchange: peaks are too weak to measure relaxation, but not shifts

- Multi-domain proteins: overall motion is too complex for MF, not for RCI

- Poor separation between overall and internal motions on ns time-scale

Unlike MF, RCI is not sensitive to the overall motions

RCI can assess mobility where NMR relaxation S2 can't

(Nuclease)

RCI agrees with MD RMSF for the intermediate

exchangeregin in nuclease

RCI of large multi-domain proteins

32kD HIV-1 Gag (283 aa)

HIV-1 Gag

Correlation coefficient - 0.74

RCI of partially unfolded proteins

Octa-peptide

repeats

RCI webserver and stand-

alone program

RCI web. server

http://wishart.biology.ualberta.ca/rci

http://www.randomcoilindex.com

RCI web. server

RCI stand-alone version http://wishart.biology.ualberta.ca/download/rci/

RCI impact on protein NMR

community

Backbone RCI publications

Impact factor: 11

Citations: 125

Impact factor: 3

Citations: 43

Impact factor: 8

Citations: 33

Impact factor: 8

Citations: 39

Total number of citations is by 2013 is 240

RCI implementation in other programs

Ad Bax's group

NIH

Bethesda, MD

US

RCI of apomyoglobin molten

globule

(B) Secondary structure and RCI-S2 parameter for the transient MG state and

the N state. The secondary structure of the MG state was predicted by TALOS+

(30) from An external file that holds a picture, illustration, etc.

The rectangles depict the location of helical structure in each state; the

thickness of each rectangle is proportional to the population of helix. The

hatched lines indicate the small population of transient helical structure in the

C- and E-helix regions of MG.

(C) Changes in secondary structure accompanying the N[left arrow over right

arrow]MG transition, mapped to the structure of holoMb. Residues predicted

to be helical by TALOS+ are red. The population of helix in the MG ensemble is

indicated by the tube radius, with a larger radius indicating higher population.

Flexible regions with RCI S2 < 0.7 are blue, and coil regions with S2 > 0.7 are

green.

Measurement of protein unfolding/refolding kinetics and structural characterization of hidden intermediates by NMR

relaxation dispersion. Proc Natl Acad Sci U S A. 2011 May 31;108(22):9078-83. 2011 May 11.

Peter Wright

The Scripps Research Institute

La Jolla, CA

US

RCI of transiently formed state of

a T4 lysozyme mutant

Bouvignies G, Vallurupalli P, Hansen DF, Correia BE, Lange O, Bah A, Vernon RM, Dahlquist FW, Baker D, Kay LE

Nature. 2011 Aug 21;477(7362):111-4. doi: 10.1038/nature10349.

Solution structure of a minor and transiently formed state of a T4 lysozyme mutant.

University of Toronto

Toronto, Canada

DynaMine optimization to predict protein

flexibility from sequence

From protein sequence to dynamics and disorder with DynaMine.

Cilia E, Pancsa R, Tompa P, Lenaerts T, Vranken WF.

Nat Commun. 2013 Nov 14;4:2741

Outline

• RCI background



• Side-chain RCI


Side-chain RCI

Side-chain RCI is more challanging due

to variability in side-chain structure

Phe78

1 2

f y

3) Collective motion 1) Rotations around

side-chain torsion

angles

2) Rotation around local backbone torsion angles

What side-chain motions do

we want to predict?

MD RMSF of total side-chain

motions

Existing protocol for backbone RCI could

not be applied to side-chains

I will skip slides about side-

chain RCI development

Side-chain RCI expressions

Side-chain RCI performance

Localization of PyJ side-chains with low (<0.11, violet color) and

high (>0.15, green color) RCISC values. Rigid side-chains (low

RCISC) are located primarily in the PyJ core (A), whereas flexible

side-chains (high RCISC) are mostly solvent exposed (B).

Side-chain RCI correlates with accessible surface area

Side-chain RCI webserver and paper

http://www.randomcoilindex.ca

Outline

• RCI background



• Side-chain RCI


Application of side-chain RCI

RCI can help to identify mechanism

of total side-chain motions

Side-chain RCI detects dynamic changes

due to transient interactions Decrease in side-chain mobility due to

interactions with N-terminus

90°

Increase in

side-chain

mobility in PrP

"rigid" loop

Increase in side-

chain mobility on

the exposed side

of PrP helix 3

STAT1

TAZ2 domain

Side-chain RCI detects enthropy-entropy compensation

upon protein ligand interactions

Visualizing Side Chains of Invisible

Protein Conformers by Solution NMR

University of Toronto

Toronto, Canada

Side-chain mobility from 13C chemical shifts. RCI plots and for the native (a) and intermediate (b)

states of the L24A FF domain as a function of residue number. The first nine residues are omitted

as they are completely flexible. RCIBB and RCISC values are shown in blue and red, respectively,

along with the secondary structural elements in each of the states.

Bouvignies G, Vallurupalli P, Kay LE., J Mol Biol. 2013 Nov 8. pii: S0022-2836(13)00700-6

Summary • We have developed a new method, Random Coil

Index or RCI, to predict mobility of protein backbone

and side-chain groups from NMR chemical shifts

• The RCI method is not limited by the protein overall

tumbling

• The method does not require a 3D structure

• The RCI method does not require special

experiments beyond standard NMR assignment

experiments

• Side-chain RCI can be applied to all 19 side-chain

bearing residues

• Comparison of backbone and side-chain RCI can

identify the dominant mechanism of side-chain

motions

nmr random coil index & protein dynamics

Science