Center for Structures of Membrane Proteins © 2006 Optimizing x-ray structure determination James Holton LBNL/UCSF April 6, 2006

Beamline staff Acknowledgments George Meigs Jane Tanamachi

UCSF UC Berkeley Plexxikon MD Anderson Alberta Synchrotron Institute PRT Members Funding

Optimizing structure determination

How many are we solving?

Optimizing structure determination How many are we solving? What is the limit?

Optimizing structure determination How many are we solving? What is the limit? Are we there yet?

Optimizing structure determination How many are we solving? What is the limit? Are we there yet? Why not?

Optimizing structure determination How many are we solving? What is the limit? Are we there yet? Why not? What are the biggest problems?

Optimizing structure determination How many are we solving? What is the limit? Are we there yet? Why not? What are the biggest problems?

How many are we solving?

How many are we solving? Jiang & R.M. Sweet (2004)

How many are we solving?

How many are we solving?

How many are we solving?

Breaking it down

$$ → photons Breaking it down

$$ → photons photons → data Breaking it down

$$ → photons photons → data data → models Breaking it down

$$ → photons photons → data data → models models → results Breaking it down

$$ → photons photons → data data → models models → results results → $$ Breaking it down

$$ → photons photons → data data → models models → results results → $$ Breaking it down

$$ → photons 2x10 11 photons/s ÷ $600,000/year 6x10 12 photons/dollar Breaking it down

$$ → photons photons → data data → models models → results results → $$ Breaking it down

$$ → photons photons → data data → models models → results results → $$ Breaking it down

Operational Efficiency “representative” user

SecondsDescriptionPercent Assigned to user- Operational Efficiency “representative” user

SecondsDescriptionPercent Assigned to user Light available Operational Efficiency “representative” user

SecondsDescriptionPercent Assigned to user Light available91% Operational Efficiency “representative” user

SecondsDescriptionPercent Assigned and available91% Operational Efficiency “representative” user

SecondsDescriptionPercent Assigned and available91% Shutter open Operational Efficiency “representative” user

SecondsDescriptionPercent Assigned and available91% Shutter open40% Operational Efficiency “representative” user

SecondsDescriptionPercent Assigned and available91% Shutter open40% Collecting (3026 images) Operational Efficiency “representative” user

SecondsDescriptionPercent Assigned and available91% Shutter open40% Collecting (3026 images)50% Operational Efficiency “representative” user

SecondsDescriptionPercent Assigned and available91% Shutter open40% Collecting (3026 images)50% Something else Operational Efficiency “representative” user

SecondsDescriptionPercent Assigned and available91% Shutter open40% Collecting (3026 images)50% Something else50% Operational Efficiency “representative” user

SecondsDescriptionPercent Something else50% Operational Efficiency “representative” user

SecondsDescriptionPercent Something else100%  45 Mounting Operational Efficiency “representative” user

SecondsDescriptionPercent Something else100% 247s  45 Mounting22% Operational Efficiency “representative” user

SecondsDescriptionPercent Something else100% 247s  45 Mounting22%  37 Centering Operational Efficiency “representative” user

SecondsDescriptionPercent Something else100% 247s  45 Mounting22% 229s  37 Centering16% Operational Efficiency “representative” user

SecondsDescriptionPercent Something else100% 247s  45 Mounting22% 229s  37 Centering16%  109 Strategizing Operational Efficiency “representative” user

SecondsDescriptionPercent Something else100% 247s  45 Mounting22% 229s  37 Centering16% 179s  109 Strategizing38% Operational Efficiency “representative” user

SecondsDescriptionPercent Something else100% 247s  45 Mounting22% 229s  37 Centering16% 179s  109 Strategizing38%  37 Prepping Operational Efficiency “representative” user

SecondsDescriptionPercent Something else100% 247s  45 Mounting22% 229s  37 Centering16% 179s  109 Strategizing38% 309s  37 Prepping24% Operational Efficiency “representative” user

SecondsDescriptionPercent Something else32% 10s  45 Mounting1% 30s  37 Centering2% 140s  109 Strategizing29% 0s  37 Prepping0% Operational Efficiency “expert” user

SecondsDescriptionPercent Something else100% 10s  45 Mounting3% 30s  37 Centering7% 140s  109 Strategizing90% 0s  37 Prepping0% Operational Efficiency “expert” user

$$ → photons photons → data data → models models → results results → $$ Breaking it down

$$ → photons photons → data data → models models → results results → $$ Breaking it down

Turning data into models

NumberDescriptionPercent Images in 2003 Turning data into models

NumberDescriptionPercent Images (~7 TB)33% in 2003 Turning data into models

NumberDescriptionPercent Images (~7 TB)33% Data sets in 2003 Turning data into models

NumberDescriptionPercent Images (~7 TB)33% 2346 Data sets47% in 2003 Turning data into models

NumberDescriptionPercent Images (~7 TB)33% 2346 Data sets47% MAD/SAD in 2003 Turning data into models

NumberDescriptionPercent Images (~7 TB)33% 2346 Data sets47% 449 MAD/SAD (1:2)19% in 2003 Turning data into models

NumberDescriptionPercent Images (~7 TB)33% 2346 Data sets47% 449 MAD/SAD (1:2)19% Published in 2003 Turning data into models

NumberDescriptionPercent Images (~7 TB)33% 2346 Data sets47% 449 MAD/SAD (1:2)19% 48 Published2% in 2003 Turning data into models

Top producing beamlines of the world Structures credited

Top producing beamlines of the world x10 6 unique HKLs

Top producing beamlines of the world Structures/10 20 photons

Optimizing structure determination How many are we solving? What is the limit? Are we there yet? Why not? What are the biggest problems?

Optimizing structure determination How many are we solving? What is the limit? Are we there yet? Why not? What are the biggest problems?

What is the limit?

28 operating US beamlines What is the limit?

28 operating US beamlines 2x10 13 ph/s What is the limit?

28 operating US beamlines ~10 11 ph/μm 2 exposure limit 2x10 13 ph/s Henderson et al (1990) What is the limit?

28 operating US beamlines ~10 11 ph/μm 2 exposure limit 2x10 9 ph/μm 2 /s What is the limit?

28 operating US beamlines ~10 11 ph/μm 2 exposure limit ÷ 2x10 9 ph/μm 2 /s = 400,000 datasets/year What is the limit?

28 operating US beamlines ~10 11 ph/μm 2 exposure limit ÷ 2x10 9 ph/μm 2 /s ~ 200,000 datasets/year What is the limit?

28 operating US beamlines ~10 11 ph/μm 2 exposure limit ÷ 2x10 9 ph/μm 2 /s ~ 100,000 datasets/year What is the limit?

28 operating US beamlines ~10 11 ph/μm 2 exposure limit ÷ 2x10 9 ph/μm 2 /s ~ 100,000 datasets/year ÷ 1324 str in 2003 Jiang & R.M. Sweet (2004) What is the limit?

28 operating US beamlines ~10 11 ph/μm 2 exposure limit ÷ 2x10 9 ph/μm 2 /s ~ 100,000 datasets/year ÷ 1324 str in 2003 ~ 2% efficient What is the limit?

NumberDescriptionPercent Images (~7 TB)33% 2346 Data sets47% 449 MAD/SAD (1:2)19% 48 Published2% in 2003 Turning data into models

Optimizing structure determination How many are we solving? What is the limit? Are we there yet? Why not? What are the biggest problems?

Optimizing structure determination How many are we solving? What is the limit? Are we there yet? Why not? What are the biggest problems?

Optimizing structure determination How many are we solving? What is the limit? Are we there yet? Why not? What are the biggest problems?

DVD data archive

Breaking it down $$ → photons photons → data data → models models → results results → $$

Elves examine images and set-up data processing Elves run… mosflm scala solve mlphare dm arp/warp Elven Automation

Elves examine images and set-up data processing Elves run… mosflm scala solve mlphare dm arp/warp

Elven Automation Elves examine images and set-up data processing Elves run… mosflm scala solve mlphare dm arp/warp

How often does it really work? Elven Automation

Apr 6 – 24 at ALS Elven Automation How often does it really work?

Apr 6 – 24 at ALS Elven Automation 27,686images collected

Apr 6 – 24 at ALS Elven Automation 27,686images collected 148datasets (15 MAD)

Apr 6 – 24 at ALS Elven Automation 27,686images collected 148datasets (15 MAD) 31investigators

Apr 6 – 24 at ALS Elven Automation 27,686images collected 148datasets (15 MAD) 31investigators 56unique cells

Apr 6 – 24 at ALS Elven Automation 27,686images collected 148datasets (15 MAD) 31investigators 56unique cells 5 KDa – 23 MDaasymmetric unit

Apr 6 – 24 at ALS Elven Automation 27,686images collected 148datasets (15 MAD) 31investigators 56unique cells 5 KDa – 23 MDaasymmetric unit 0.94 – 32 Åresolution (3.2 Å)

Apr 6 – 24 at ALS Elven Automation 148datasets

Apr 6 – 24 at ALS Elven Automation 148datasets 117succeded

Apr 6 – 24 at ALS Elven Automation 148datasets 117succeded ~3.5 (0.1-75)hours

Apr 6 – 24 at ALS Elven Automation 148datasets 117succeded ~3.5 (0.1-75)hours 31failed

Apr 6 – 24 at ALS Elven Automation 148datasets 117succeded ~3.5 (0.1-75)hours 31failed ~61 (0-231)hours

Apr 6 – 24 at ALS Elven Automation 148datasets 117succeded ~3.5 (0.1-75)hours 31failed ~61 (0-231)hours 2 / 15MAD structures

Apr 6 – 24 at ALS Elven Automation 148datasets 117succeded ~3.5 (0.1-75)hours 31failed ~61 (0-231)hours 2 / 15MAD structures

NumberDescriptionPercent Images (~7 TB)33% 2346 Data sets47% 449 MAD/SAD (1:2)19% 48 Published2% in 2003 Turning data into models

Optimizing structure determination How many are we solving? What is the limit? Are we there yet? Why not? What are the biggest problems?

Optimizing structure determination How many are we solving? What is the limit? Are we there yet? Why not? What are the biggest problems?

Why do structures fail?

Overlaps Why do structures fail?

Overlaps Signal to noise Why do structures fail?

Overlaps Signal to noise Radiation Damage Why do structures fail?

Overlaps Signal to noise Radiation Damage Why do structures fail?

Apr 6 – 24 at ALS Elven Automation 148datasets 117succeded ~3.5 (0.1-75)hours 31failed ~61 (0-231)hours 2 / 15MAD structures

Apr 6 – 24 at ALS Elven Automation 148datasets 117succeded ~3.5 (0.1-75)hours 31 failed ~61 (0-231)hours 2 / 15MAD structures

unavoidable overlaps

detector

unavoidable overlaps phi detector

unavoidable overlaps mosaicity phi detector

unavoidable overlaps mosaicity phi detector c*

unavoidable overlaps mosaicity phi detector c* Ewald sphere

unavoidable overlaps mosaicity phi detector c* Ewald sphere

unavoidable overlaps mosaicity phi detector c* Ewald sphere

unavoidable overlaps mosaicity phi detector c* Ewald sphere

unavoidable overlaps mosaicity phi detector c* Ewald sphere

unavoidable overlaps mosaicity phi detector c* Ewald sphere

unavoidable overlaps mosaicity phi detector c* Ewald sphere

unavoidable overlaps mosaicity phi detector c* b c a

unavoidable overlaps mosaicity phi detector c* b c a

unavoidable overlaps mosaicity phi detector c* b c a

unavoidable overlaps mosaicity phi detector c* b c a

unavoidable overlaps mosaicity phi detector c* b c a Ewald sphere

unavoidable overlaps mosaicity phi detector c* b c a Ewald sphere

unavoidable overlaps mosaicity phi detector c* b c a Ewald sphere

unavoidable overlaps mosaicity phi detector c* b c a Ewald sphere

unavoidable overlaps mosaicity phi detector c* b c a Ewald sphere

Overlaps Signal to noise Radiation Damage Why do structures fail?

Overlaps Signal to noise Radiation Damage Why do structures fail?

Apr 6 – 24 at ALS Elven Automation 148datasets 117succeded ~3.5 (0.1-75)hours 31failed ~61 (0-231)hours 2 / 15MAD structures

Apr 6 – 24 at ALS Elven Automation 148datasets 117succeded ~3.5 (0.1-75)hours 31failed ~61 (0-231)hours 2 / 15MAD structures

“What is a good exposure time?”

“How much signal do I need?”

MAD phasing simulation Anomalous signal to noise ratio Correlation coefficient to correct model mlphare results

SAD phasing simulation Anomalous signal to noise ratio Correlation coefficient to correct model mlphare results

Minimum required signal (MAD/SAD)

SAD phasing experiment Anomalous signal to noise ratio Correlation coefficient to published model

MR simulation Signal to noise ratio Correlation coefficient to correct density corrupted data

MR simulation Signal to noise ratio Correlation coefficient to correct density corrupted data

MR simulation Rmsd from perfect search model ( Å ) Correlation coefficient to correct density corrupted model

MR simulation Fraction of full search model Correlation coefficient to correct density trimmed model

Is it real, or is it MLFSOM ?

Background scattering Resolution (Ǻ) Electron equivalents The form-factor of the cryostream measured theoretical

Background scattering Resolution (Ǻ) Photons/s/pixel Se edge with detector at 100 mm 

“We really need those high-resolution spots”

Incremental strategy incremental_strategy.com merged.mtz auto.mat

Incremental strategy incremental_strategy.com merged.mtz auto.mat

“We have a problem with non-isomorphism”

Proteins move

Overlaps Signal to noise Radiation Damage Why do structures fail?

Overlaps Signal to noise Radiation Damage Why do structures fail?

thaw Radiation Damage

Distention of cryo with dose

before

Distention of cryo with dose after

Water ring shift saturated sucrose in 250mM WO4 0 MGy

Water ring shift saturated sucrose in 250mM WO4 37 MGy

Water ring shift saturated sucrose in 250mM WO4 80 MGy

Water ring shift saturated sucrose in 250mM WO4 184 MGy

Water ring shift Resolution (Ǻ) Photons/s/pixel  saturated sucrose in 250mM WO4

Water ring shift Resolution (Ǻ) Photons/s/pixel  saturated sucrose in 250mM WO4

Water ring shift Resolution (Ǻ) Photons/s/pixel  saturated sucrose in 250mM WO4

Water ring shift Resolution (Ǻ) Photons/s/pixel  saturated sucrose in 250mM WO4

Water ring shift Resolution (Ǻ) Photons/s/pixel  saturated sucrose in 250mM WO4

Water ring shift Absorbed dose (MGy) Water ring position (Ǻ) saturated sucrose in 250mM WO4

Protein crystal background

Water ring shift Absorbed dose (MGy) Water ring position (Ǻ) GCN4-p1-N16A trigonal crystal

Water ring shift Absorbed dose (MGy) Water ring position (Ǻ) GCN4-p1-N16A trigonal crystal crystal background saturated sucrose

Water ring shift

Water ring shift

Water ring shift bubbles? Richard D. Leapman, Songquan Sun, Ultramicroscopy (1995)

Water ring shift Hydrogen bubbles? Richard D. Leapman, Songquan Sun, Ultramicroscopy (1995)

Water ring shift Hydrogen bubbles? “The hydrogen atom reacts with organic compounds by abstracting H from saturated molecules and by adding to centers of unsaturation, for example,

Water ring shift Hydrogen bubbles? “The hydrogen atom reacts with organic compounds by abstracting H from saturated molecules and by adding to centers of unsaturation, for example,

Damage model system

67 consecutive data sets

Data quality vs exposure Exposure time (min) Correlation coefficient

Data quality vs exposure Exposure time (min)  

Data quality vs exposure Exposure time (min)  

Data quality vs exposure Exposure time (min) Resolution limit

Data quality vs exposure Exposure time (min) R sym

Experimentally-phased map

Data quality vs phasing quality Exposure time (min) Correlation coefficient

Specific Radiolysis of Selenomethionine

67 consecutive data sets

Individual atoms decay at different rates Exposure time (min) Correlation coefficient to observed data

Damage changes fluorescence spectrum Photon energy (eV) counts

Damage changes fluorescence spectrum Photon energy (eV) counts

Damage changes fluorescence spectrum Photon energy (eV) counts

Damage changes fluorescence spectrum fluence (10 3 photons/mm 2 ) Fraction unconverted 25mM SeMet in 25% glycerol Exposing at eV

Damage changes fluorescence spectrum fluence (10 3 photons/mm 2 ) Fraction unconverted 25mM SeMet in 25% glycerol Exposing at eV Se cross-section at eV

Damage changes fluorescence spectrum Absorbed dose (MGy) Fraction unconverted 25mM SeMet in 25% glycerol Half-dose = 10.6 MGy Exposing at eV

fluorescence probe for damage Absorbed Dose (MGy) Fraction unconverted Wide range of decay rates seen Half-dose = 41.7 ± 4 MGy “GCN4” in crystal Half-dose = 5.5 ± 0.6 MGy 8 mM SeMet in NaOH Protection factor: 660% ± 94%

“Can we do more with what we’ve got?”

SecondsDescriptionPercent Something else100% 247s  45 Mounting22% 229s  37 Centering16% 179s  109 Strategizing38% 309s  37 Prepping24% Beamline Efficiency “representative” user

SecondsDescriptionPercent Something else32% 10s  45 Mounting1% 30s  37 Centering2% 140s  109 Strategizing29% 0s  37 Prepping0% Beamline Efficiency “expert” user

SecondsDescriptionPercent Something else100% 10s  45 Mounting3% 30s  37 Centering7% 140s  109 Strategizing90% 0s  37 Prepping0% Beamline Efficiency “expert” user

Interleaved Scheduling experiment queuebeamline Minor 30s Choe 120s Alberta 60s Choe 30s Minor 30s

cool hand luke

Hampton Pin

Syrrx Pin

plastic Pin

Yale Pin

what we have here is… failure to communicate

SuperPin

SuperTong

Hampton PinSuper Tong

Syrrx PinSuper Tong

plastic PinSuper Tong

Yale PinSuper Tong

“infinite capacity” sample carousel

6-foot conveyor

Carousel open

Carousel cold

CHL idlepos

Beamline staff Acknowledgments George Meigs Jane Tanamachi

Is it real, or is it MLFSOM ?

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