bioexcel.eu
Partners Funding
Robust solutions for cryoEM fitting and visualisation of interaction space
Presenters: Gydo van Zundertwith Mikael Trellet and Jörg Schaarschmidt
Host: Adam Carter
BioExcel Webinar Series
15 February, 2017
bioexcel.eu
Thiswebinarisbeingrecorded
bioexcel.eu
BioExcel Overview• Excellence in Biomolecular Software
- Improve the performance, efficiency and scalability of key codes
• Excellence in Usability- Devise efficient workflow environments
with associated data integration
• Excellence in Consultancy and Training- Promote best practices and train end users
DMI Monitor
DMI Enactor
DMI Executor
DMI Enactor
Data Delivery Point
Data Source
Monitoring flow
Data flow
Service Invocation
DMI Optimiser
DMI Planner
DMIValidator
DMI Gateway
DMI Gateway
DMI Gateway
DMI Enactor
Portal / Workbench
DMI Request
DADC Engineer
DMI Expert
Repository
Registry
DMI Expert
Domain Expert
bioexcel.eu
Interest Groups
• Integrative Modeling IG• Free Energy Calculations IG• Hybrid methods for biomolecular systems IG• Biomolecular simulations entry level users IG• Practical applications for industry IG• Training• Workflows
Support platformshttp://bioexcel.eu/contact
Forums Code Repositories Chat channel Video Channel
bioexcel.eu
Audience Q&A session
Please use the Questionsfunction in GoToWebinar
application
Any other questions or points to discuss after the live
webinar? Join the discussion the discussion at
http://ask.bioexcel.eu.
bioexcel.eu
Today’s PresenterGydo van Zundert studied Chemistry (BSc) and Nanomaterials (MSc) at Utrecht University, the Netherlands and obtained his PhD at the Computational Structural Biology group under the supervision of Prof. Alexandre Bonvin in 2015. His research focused on new methods and protocols for integrative modeling, such as cryo-electron microscopy integration in the HADDOCK macromolecular docking package. As of October 2016, he joined Schrodinger Inc. (NY) as a Postdoctoral Associate working on room-temperature crystallography modeling in collaboration with Stanford University and UCSF (CA).
6
Gydo van ZundertSchrodinger - SLAC Stanford – UCSF
Computational Structural Biology, Utrecht Universitywww.bonvinlab.org/softwarewww.github.com/haddocking
PowerFit DisVis
Robust solutions for cryoEM ,tting and visualisation of interaction space
PowerFit
G.C.P. van Zundert and A.M.J.J. Bonvin
AIMS Biophysics 2, 73-87 (2015).
G.C.P. van Zundert and A.M.J.J. Bonvin
J. Struct. Biol. 195, 252-258 (2016).
www.github.com/haddocking/power0t
h1p://milou.science.uu.nl/services/power0t/
Cryo-electron microscopy:
The rising star in structural biology
High resolution modelingwith cryo-EM data
Combine high-resolution structures with cryo-EM data
High resolution modelingwith cryo-EM data
Combine high-resolution structures with cryo-EM data
Rigid body ,t structure in density
High resolution modelingwith cryo-EM data
Combine high-resolution structures with cryo-EM data
Rigid body ,t structure in density
Perform real space (manual) re,nement
z
x
y
● Sensitivity (scoring)● Speed (sampling)
Cross-correlation based rigid body,tting
6D exhaustive search3 translational degrees of freedom3 rotational degrees of freedom
Laplace ,lter: enhances edges
Increasing sensitivity:Laplace pre-,lter
Laplace ,lter: enhances edges
Increasing sensitivity:Laplace pre-,lter
Increasing sensitivity:Overlapping neighboring densities
Up-weight voxels close at the core
Increasing sensitivity:Core-weighted cross-correlation
Fast Fourier Transform for fasttransla�onal scans
GPU accelera�on
Op�mized rota�on sets
Resampling and trimming target
z
x
y
Speeding up the search
Some successful examples:GroEL-GroES
23Å 8.9Å
13.3Å ribosome + KsgA
Some successful examples:Ribosome
9.8Å ribosome + RsgA
5 high-resolu�on ribosome densi�es (5.5Å – 7Å) with -.ed structures: 379 subunits total
Exploring the limits of rigid body,tting
Fitting success rate
Fisher z-transformation
Con,dence intervals
Fisher (1921), Volkmann (2009)
z=1
2ln(1+CC1−CC
)
σz=√
1
N−3
Detecting successful ,ts:Correlation con,dence intervals
Detecting successful ,ts:Reliability measure of ,t
DisVis
G.C.P. Van Zundert and A.M.J.J. Bonvin
Bioinforma5cs 31, 3222-3224 (2015)
.
G.C.P. van Zundert et al.
J. Mol. Biol., Advanced Online Publica5on.
www.github.com/haddocking/disvis
h1p://milou.science.uu.nl/services/disvis
8 cross-links
A modeling problem: how to dealwith distance restraints/constraints?
Given 2 interacting structures and a set ofdistance constraints between them, are there
any solutions that satisfy N constraints?
8 cross-links
On the existence of consistency
core regioninteraction region
receptor
ligand core region
Systematic 6-dimensional search
of conformations
Explorative modeling
Explorative data-consistency
Systematically sample billions of complexes using a 6D search
Explorative data-consistency
Systematically sample billions of complexes using a 6D search
Count for each complex how many constraints are satisfied/consistent
Exploring data-consistency:the accessible interaction space
Exploring data-consistency:Detecting false-positive constraints
Systematically sample billions of complexes using a 6D search
Count for each complex how many constraints are satisfied/consistent
Exploring data-consistency:Detecting false-positive constraints
Systematically sample billions of complexes using a 6D search
Count for each complex how many constraints are satisfied/consistent
For each complex consistent with N constraintscount how often a specific constraint is violated
Exploring data-consistency:Detecting false-positive constraints
Systematically sample billions of complexes using a 6D search
Count for each complex how many constraints are satisfied/consistent
For each complex consistent with N constraintscount how often a specific constraint is violated
Normalize over all complexesconsistent with N restraints
Exploring data-consistency:Detecting false-positive constraints
Exploring data-consistency:Detecting false-positive constraints
Exploring data-consistency:Detecting false-positive constraints
Exploring data-consistency:Detecting false-positive constraints
Exploring data-consistency:Detecting false-positive constraints
Exploring data-consistency:Detecting false-positive constraints
Exploring highly accessed residues
Systematically sample billions of complexes using a 6D search
Count for each complex how many constraints are satisfied/consistent
Exploring highly accessed residues
Systematically sample billions of complexes using a 6D search
Count for each complex how many constraints are satisfied/consistent
Count for each complex how oftena specific residue interacts
Normalize over all counted complexes:Average interactions per complex
Exploring highly accessed residues
Visualizing theaccessible interaction space
Where can the ligand be found for complexes consistent with N restraints?
Accessible interac5on spaceconsistent with all 6 restraints
Visualizing theaccessible interaction space
Visualizing theaccessible interaction space
What space does the ligand most likely occupy for complexes with N restraints?
25% iso-contour 10% iso-contour25% iso-contour
Visualizing theligand space occupancy
25% iso-contour 10% iso-contour25% iso-contour
Visualizing theligand space occupancy
PowerFit: combining speed, sensi�vity, andreliability
DisVis: Explora�ve modeling for determining the accessible interac�on space
Conclusions
bioexcel.eu
Audience Q&A session
Please use the Questionsfunction in GoToWebinar
application
Any other questions or points to discuss after the live
webinar? Join the discussion the discussion at
http://ask.bioexcel.eu.