Ab initio modelling methods Al Kikhney EMBL Hamburg Ab initio shape - - PowerPoint PPT Presentation

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Ab initio modelling methods Al Kikhney EMBL Hamburg Ab initio shape - - PowerPoint PPT Presentation

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Small angle scattering Ab initio modelling methods Al Kikhney EMBL Hamburg Ab initio shape reconstruction Log I(s) Experimental data s Ab initio shape reconstruction Log I(s) Experimental data Model s Ab initio shape reconstruction Log

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Small angle scattering

Ab initio modelling methods

Al Kikhney EMBL Hamburg

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Log I(s) s

Ab initio shape reconstruction

Experimental data

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Log I(s) s

Ab initio shape reconstruction

Experimental data Model

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Log I(s)

Ab initio shape reconstruction

Experimental data Model

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Log I(s) s

Ab initio shape reconstruction

Experimental data Model

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Feigin, L.A. and Svergun, D.I. Structure Analysis by Small-Angle X-Ray and Neutron Scattering. Plenum Press 1987

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BODIE IES

  • ellipsoid (semiaxes a, b, c)
  • ellipsoid of revolution (semiaxes a, c)
  • cylinder (radius r, height h)
  • elliptic cylinder (radius semiaxes a, b, height h)
  • hollow cylinder (outer radius R, inner radius r, height h)
  • rectangular prism (sides a, b, c)
  • hollow sphere (outer radius ro, inner radius ri)
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nm-1 log I(s) experimental SAXS pattern experimental SAXS pattern

Ab initio shape reconstruction

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nm-1 log I(s) experimental SAXS pattern experimental SAXS pattern calculated from model

Ab initio shape reconstruction: dummy atom modelling

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nm-1 log I(s) experimental SAXS pattern experimental SAXS pattern calculated from model

Ab initio shape reconstruction: dummy atom modelling

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nm-1 log I(s) experimental SAXS pattern

Rg = 3.4 nm smax = 8/Rg 2.35

Ab initio shape reconstruction: dummy atom modelling

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nm-1 log I(s) experimental SAXS pattern fit by p(r)

r, nm p(r) smax = 8/Rg

Ab initio shape reconstruction: dummy atom modelling

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nm-1 log I(s) fit by p(r) – target curve

r, nm p(r)

Ab initio shape reconstruction: dummy atom modelling

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log I(s) nm-1 target curve

≈2000–10000 “dummy atoms” 2–10 Å

Franke, D. and Svergun, D.I. (2009) J Appl Cryst 42, 342–346.

DAMMIF

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log I(s) nm-1 target curve calculated from the model

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log I(s) nm-1 target curve calculated from the model

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log I(s) nm-1 target curve calculated from the model

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log I(s) nm-1 target curve calculated from the model

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DA DAMMIN

  • Variable number of “dummy atoms” on a fixed grid
  • Scattering is computed using spherical harmonics
  • Monte-Carlo type search
  • Fixed search space (defined by Dmax)
  • Provides volume/molecular mass estimate
  • Idea first published by P. Chacón et al. (1998) Biophys J 74

Svergun, D.I. (1999) Biophys J 76

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DA DAMMIF

  • Variable number of “dummy atoms” on a fixed grid
  • Scattering is computed using spherical harmonics
  • Monte-Carlo type search
  • Expandable search space
  • Provides volume/molecular mass estimate
  • 40 time faster than DAMMIN

Franke, D. and Svergun, D.I. (2009) J Appl Cryst 42, 342–346

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DAMMIF

Expandable search space Particle

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https://www.embl-hamburg.de/biosaxs/atsas-online/dammif.php

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Single phase shape determination Fit one data set

Ab initio shape reconstruction: multi-phase dummy atom modelling

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Fit data from several subunits

Ab initio shape reconstruction: multi-phase dummy atom modelling

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https://www.embl-hamburg.de/biosaxs/atsas-online/monsa.php

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nm-1 log I(s) experimental SAXS pattern fit by p(r) up to wider angles

r, nm p(r)

Ab initio reconstruction: dummy residue modelling

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Svergun, D.I., Petoukhov, M.V, Koch, M.H.J. (2001) Biophys J 80, 2946–2953.

GASBOR 3.8 Å Dmax

Ab initio reconstruction: dummy residue modelling

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log10I(q) q, nm-1

Ab initio reconstruction: dummy residue modelling

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log10I(q) q, nm-1

Ab initio reconstruction: dummy residue modelling

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log10I(q) q, nm-1

Ab initio reconstruction: dummy residue modelling

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GASBOR

  • Fixed number of “dummy residues”
  • Distances to neighbor “residues” like in proteins

Svergun, D.I., Petoukhov, M.V, Koch, M.H.J. (2001) Biophys J 80, 2946–2953

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GASBOR

  • Fixed number of “dummy residues”
  • Distances to neighbor “residues” like in proteins
  • Fixed search space
  • Scattering is computed using Debye formula
  • Higher angles used (up to 12 nm-1)
  • Only for proteins smaller than 660 kDa

Svergun, D.I., Petoukhov, M.V, Koch, M.H.J. (2001) Biophys J 80, 2946–2953

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Ambiguity

I(s) s I(s) s

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I(s) s I(s) s

Ambiguity

First formulated by R. Kirste in 1964

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I(s) s I(s) s

Ambiguity

From a study by M. Petoukhov, 2015

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AMBI BIMETE TER

Petoukhov, M.V. and Svergun, D.I. (2015) Acta Cryst D71, 1051–1058

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AMBI BIMETE TER

Petoukhov, M.V. and Svergun, D.I. (2015) Acta Cryst D71, 1051–1058 Curves from all 14 112 possible shapes represented by one to seven interconnected beads

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Ab initio model validity

First validate your sample and input data! Check for:

– monodispersity; – radiation damage; – aggregation; – concentration effects; – overall parameters; – signal-to-noise level.

Make sure your model fits the data. Repeat multiple times.

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Ab initio model validity

Original body Typical solution with P5 symmetry Typical solution with no symmetry

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Ab initio model validity

Funari et al. (2000) J. Biol. Chem. 275, 31283-31288

Shape determination of 5S RNA: six DAMMIN models yielding identical fits

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Ab initio model validity

  • Superimpose models by

minimizing the Normalized Spatial Discrepancy (NSD)

  • Steps
  • Principle axes alignment
  • Gradient minimization
  • Local grid search

SUPCOMB SUPALM

  • Aligns models in Fourier space using spherical

harmonics representation

  • For MDa size particles – about 10 times faster than

SUPCOMB

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Ab initio model validity

  • Superimpose and find the “most probable” model

and outliers (DAMSEL)

  • Average all models and make a filtered model

(DAMFILT) → may not fit the experimental data! DAMAVER DAMCLUST

  • Clusters similar models
  • A single cluster (plus outliers) – less ambiguous

reconstruction

  • More than one cluster – more ambigous
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www.saxier.org/forum www.sasbdb.org

ATSAS online

www.embl-hamburg.de/biosaxs/atsas-online/

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Structural and biophysical methods for biological macromolecules in solution

EMBO Global Exchange Lecture Course 14 – 20 October 2019 | Santiago, Chile