New Method to Quickly Determine Folded Structure of Proteins

Date

10/01/15

Searching for the precise, complexly folded three-dimensional structure of a protein can be like hacking through a jungle without a map: a long, intensive process with uncertain direction. University of Illinois researchers developed a new approach, dubbed COMPASS, that points directly to a protein’s likely structure using a combination of advanced molecular spectroscopy techniques, predictive protein-folding algorithms and image recognition software.

Led by U. of I. chemistry professor Chad Rienstra, the team published its results in the journal Structure.

“We’ve taken a process that would take months and brought it down to hours,” said Joseph Courtney, an Illinois graduate student and first author of the paper. “We expect this to not only accelerate the rate at which we can study proteins, but also increase its repeatability and the reliability of the results.”

Proteins carry out functions within the cell, and those functions are determined by the proteins’ precise structures – the way they fold and twist into an intricate three-dimensional shape.

“Many diseases are caused by a protein that’s not acting correctly, or there is too much of it. If you can understand what the proteins look like, you can study how they work, and you can help design drugs and treatments for those diseases,” Courtney said. “A major benefit is that if you can design a drug to perfectly fit a single protein, that cuts down on side effects, because it won’t interact with other molecules.”

One key method for determining a protein’s share is a technique called X-ray crystallography. However, many medically interesting proteins – for example, the fibrils that characterize Parkinson’s disease – do not form crystals, so researchers have turned to more advanced spectroscopic techniques. Those techniques require months to years of intensive data collection and analysis, taking numerous readings and measurements of the protein’s spectrum.

The Illinois team saw an opportunity to take advantage of recent advances in structure prediction algorithms, computational models that generate numerous possible ways a protein could fold based on its sequence.

“The major shortcoming of those modeling approaches is that they never know if they’re right,” Rienstra said. “It’s great to have models, but it still leaves thousands of possibilities. We need some type of experimental data to determine which is the right one.”

For COMPASS, the researchers rely on a single spectrum measurement using a spectroscopic technique called nuclear magnetic resonance, which gives a molecular “fingerprint” – no two protein structures have the same spectrum.

The COMPASS platform looks at the possible structures generated by the predictive models, projects a spectrum for each one, and uses advanced image-recognition software to compare each projected spectrum with the spectrum collected from the experimental sample.

“We call it COMPASS because we’re using a magnetic field to hopefully point us in the right direction of which protein structure is the right one out of all these options,“ Rienstra said.

The researchers compared COMPASS results of 15 proteins to the structure information determined from traditional methods, and found that COMPASS was successful in correctly determining the proteins’ structures.

The researchers hope that other chemists will adopt the COMPASS method. One advantage, Rienstra said, is that a chemist does not have to be an expert to use COMPASS, as the results from the algorithms are automatic, objective and repeatable.

Rienstra’s group plans to use COMPASS in biomedical applications, hoping to study proteins that have thus far eluded researchers because of structural complexity and scarcity of samples.

“We already have collaborators sending us samples to compare,” Rienstra said. “We’re working to compare the samples of a protein from Parkinson’s disease patients with the sample we study in the lab, to see if it’s the same in their brains as it is when we make it in the lab. That’s a very important question to address. The samples are very small and the signals are weak, but we can get one spectrum and see if the structures match. This would be impossible with traditional approaches because we would need brain samples a hundred times larger, and you just can’t do that with human patients.”

“The normal bottleneck of collecting and analyzing the data is now completely gone,” Courtney said. “What would be an entire thesis project for a graduate student can now be reduced to a day. And as the prediction algorithms get better, COMPASS will be able to take advantage of those advances to help find even more difficult protein structures.”

The National Institutes of Health supported this work.

Excerpted from UIUC's News Bureau original article, author Liz Ahlberg

Photo by L. Brian Stauffer

Related People

Directory

scheelinAlexander
Scheeline
bjmccallBenjamin
McCall
cmartn10Calgary
Martin
r-gennisRobert
Gennis
j-gerltJohn
Gerlt
sgranickSteve
Granick
mgruebelMartin
Gruebele
hergenroPaul
Hergenrother
huangRaven
Huang
mlkraftMary
Kraft
leckbandDeborah
Leckband
yi-luYi
Lu
martinisSusan
Martinis
snairSatish
Nair
eoldfielEric
Oldfield
rienstraChad
Rienstra
cmsCharles
Schroeder
zanZaida
Luthey-Schulten
sksScott
Silverman
s-sligarStephen
Sligar
zhao5Huimin
Zhao
mselfba2Michelle
Self-Ballard
pbraunPaul
Braun
mdburkeMartin
Burke
jeffchanJefferson
Chan
sdenmarkScott
Denmark
dlottDana
Dlott
foutAlison
Fout
agewirthAndrew
Gewirth
ggirolamGregory
Girolami
shs3Sharon
Hammes-Schiffer
sohirataSo
Hirata
kamihullKami
Hull
jainPrashant
Jain
jkatzeneJohn
Katzenellenbogen
nmakriNancy
Makri
douglasmDouglas
Mitchell
jsmooreJeffrey
Moore
murphycjCatherine
Murphy
r-nuzzoRalph
Nuzzo
dimerPhilip
Phillips
rauchfuzThomas
Rauchfuss
joaquinrJoaquín
Rodríguez-López
jrogersJohn
Rogers
sarlahDavid
Sarlah
kschweizKenneth
Schweizer
jsweedleJonathan
Sweedler
vddonkWilfred
van der Donk
renskeRenske
van der Veen
vuraweisJosh
Vura-Weis
mcwhite7M.
White
sczimmerSteven
Zimmerman
beakPeter
Beak
wklemperWalter
Klemperer
jdmcdonaJ.
McDonald
mvp11Michael
Pak
pogoreloTaras
Pogorelov
mshen233Mei
Shen
dewoonDavid
Woon
wboulangWilliam
Boulanger
rxbRohit
Bhargava
qchen20Qian
Chen
jianjuncJianjun
Cheng
hy66Hong
Yang
andinomaJosé
Andino Martinez
decosteDonald
DeCoste
thhuangTina
Huang
tjhummelThomas
Hummel
dkellDavid
Kell
doctorkMichael
Koerner
marvilleKelly
Marville
crrayChristian
Ray
tlbrownTheodore
Brown
rmcoatesRobert
Coates
thdjrThom
Dunning,
dykstraClifford
Dykstra
j-jonasJiri
Jonas
j-lisyJames
Lisy
shapleyJohn
Shapley
pshapleyPatricia
Shapley
awieckowAndrzej
Wieckowski
zumdahl2Steven
Zumdahl
ksuslickKenneth
Suslick
jlbearJodi
Bear
jcoxJenny
Cox
ealthausEllen
Althaus
staciryStaci
Ryan
sqdSean
Drummond
dmillsDouglas
Mills
sheeleySarah
Sheeley
jsmaddenJoseph
Madden
cknight4Connie
Knight
schulzeHeather
Schulze
slangleySamantha
Langley
ssmurrayStar
Murray
kbaumgarKeena
Finney
adkssnBeatrice
Adkisson
bmylerBeth
Myler
trabari1Katie
Trabaris
kewatsonKaren
Watson
strussTheresa
Struss
metclfKara
Metcalf
ljohnso2Lori
Johnson
jlwJamison
Lowe
jenruslJennifer
Russell
lchenoweLeslie
Chenoweth
jcfJonathan
Freiman
wdedoWolali
Dedo
ebielserElaina
Kutz
spinnerDavid
Spinner
plblumPatricia
Simpson
stevens2Chad
Stevens
lsagekarLori
Sage-Karlson
bertholdDeborah
Berthold
kecarlsoKathryn
Carlson
tlchen4Timothy
Chen
sdesmondSerenity
Desmond
angelaecAngela
Crawford
hsahmed3Hajira
Ahmed
kakinsKenye
Akins
asali3Arzeena
Ali
axelson2Jordan
Axelson
bai11Yugang
Bai
scbakerStephanie
Baker
duffin2Kevin
Duffin
duttadDebapriya
Dutta
pflotschPriscila
Falagan Lotsch
iflemingIan
Fleming
dgrayDanielle
Gray
thennes2Thomas
Hennessey
mhettingMary Jo
Hettinger
holdaNancy
Holda
holler2Jordan
Holler
aibarrAlejandro
Ibarra
kimshSung Hoon
Kim
kocherg2Nikolai
Kocherginsky
philipk2Philip
Kocheril
dlee106David
Lee
legare2Stephanie
Legare
alewandoAgnieszka
Lewandowska
qianliliQianli
Li
bdmccallBirgit
McCall
smccombiStuart
McCombie
jdm5Justin
McGlauchlen
egmooreEdwin
Moore
myerscouKathleen
Myerscough
snalla2Siva
Nalla
oraham2Aaron
Oraham
lah5LeeAnn
Pannebaker
poonawa2Maria
Poonawalla
rrollerR.
Roller
romanovaElena
Romanova
roubakhiStanislav
Rubakhin
vsfVictoria
Shepherd-Fortner
shvedalxAlexander
Shved
asoudaAlexander
Soudakov
ktsween2Kalee
Sweeney
sktarterSamantha
Tarter
aathoma2Andy
Thomas
kwilhelKaren
Wilhelmsen
wilkeyRandy
Wilkey
luxu3Lu
Xu
yuanyao4Yuan
Yao
silongSilong
Zhang
schlembaMary
Schlembach
emccarr2Elise
McCarren
cmercierChristen
Mercier
atimpermAaron
Timperman
niesShuming
Nie
hshanHee-Sun
Han
mmgMutha
Gunasekera