Protein Structure Prediction Matthew Betts Russell Group, University of Heidelberg, Germany
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Transcript of Protein Structure Prediction Matthew Betts Russell Group, University of Heidelberg, Germany
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Protein Structure Prediction
Matthew BettsRussell Group, University of Heidelberg, Germany
www.russelllab.org
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Sequence
Active/inactive?Binds/does not bind?Substrate specificity?
Function
Structure
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• What we do to find out what a protein might be doing
• Looking at sequences, with a particular emphasis on finding out something about the protein structure
• Some background for practical work
What is this about?
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• Functional domains (Pfam, SMART, COGS, CDD, etc.)• Intrinsic features
– Signal peptide, transit peptides (signalP)– Transmembrane segments (TMpred, etc)– Coiled-coils (coils server)– Low complexity regions, disorder (e.g. SEG, disembl)
• Hints about structure?
Given a sequence, what should you look for?
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“Low sequence complexity”(Linker regions? Flexible? Junk?
Signal peptide(secreted or membrane attached)
Transmembrane segment(crosses the membrane)
Tyrosine kinase (phosphorylates Tyr)
Immunoglobulin domains(bind ligands?)
SMART domain ‘bubblegram’ for human fibroblast growth factor (FGF) receptor 1(type P11362 into web site: smart.embl.de)
Given a sequence, what should you look for?
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• Intrinsic features general mean trouble for structure determination, so they are usually skipped• Knock on effect is that structures for large, flexible multi-domain proteins are rare• Structure determination/prediction therefore typically restricted to parts (with exceptions obviously)
3D 3D 3D
What about structure?
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algorithm
Sequence Structure
Structure prediction
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• Is your sequence homologous to a known structure?
• If yes, then often very good models of structure can be constructed.
• This is what we will do in the practical
Best predictions are by homology
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+
algorithm
Homology Modelling
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• Identify a homologue of known structure
• Get the best alignment of your sequence to the structure
• Model building– Side-chain replacement– Loop building– Optimisation/relaxation/minimisation
Homology Modelling Steps
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Two subtilisin-like serine proteases
Problems with loops
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Sanchez et al, Nature Struct. Biol. (Suppl), 7, 986-990, 2001
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Based on Sander & Schneider, Proteins, 9, 56, 1991
Sander & Schneider (EMBL, ca. 1990)
1. Compared all known structures to each other using sequence comparison.
2. For each fragment of a particular length & sequence identity, simply asked the question: is the structure similar or different.
3. The line to the right is where one can be 90% confident that an alignment of a particular length & sequence identity
4. Below the line, structures can be either similar or different: the twilight zone.
(Basis for much of the sequence alignment statistics that are now in use today)
The Twilight Zone
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…can we find these similarities without known structures if sequence searches fail?
Russell et al, J.Mol. Biol., 1997
sequence identity: 80% 8.8% 4.4%
Similar structures within the twilight zone
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Does the sequence“fit” on any of a library of known3D structures?
? ? ? ? ?
>C562_RHOSHTQEPGYTRLQITLHWAIAGL…
Fold Recognition (‘Threading’)
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Jones, Taylor, Thornton,Nature, 358, 86-89, 1992.
Fold Recognition (‘Threading’)
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Asp
ArgAsp
Phe
Phe
PheGOOD
BAD
Residue pair potentials
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• Works some of the time• Probably best at identifying distant
homologues, where sequence identity is in the twilight zone
• Useful sites:– 3D-PSSM, FUGUE, (Gen)-Threader
• Meta predictions are the best - combine all and get a consensus – E.g. bioinfo.pl/meta
Fold RecognitionExecutive Summary
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• Is your sequence homologous to a known structure?
• If no then actual models are less accurate, but structural insights still possible
• First, secondary structure prediction
If no homology…
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• Neural networks• Inductive logic programming• Spin-glass theory• Human intuition
algorithm
Secondary-structure prediction
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E.g. Chou & Fasman, 1974
Helix forming: Glu, Ala, LeuHelix breaking: Pro, GlyStrand forming: Met, Val, IleStrand breaking: Glu, Lys, Ser, His, AsnEtc.Numerical approach + simple protocol = prediction of secondary structure
Said “80%” accuracy. Reality: 50-60%Tested the method on the same proteins used to derive the parameters… big no-no.
Secondary-structure prediction
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SS pred
70% accuracy!
Homologous proteins adda lot of information
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• Can you simulate folding using physics to predict the structure of a protein
• No, not usually.
• However, advances have been made…
• David Baker, co-workers and subsequent followers: fragment based structure prediction. De novo not ab initio
What about de novo or ab initio prediction?
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Preferences learned from all stretches with a similar structure
Predicting Fragments
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Database of structures
Fragments matching the target sequence
Assembly of fragmentsSelection of best model
Assembling Fragments
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• General trend: increasing accuracy is more a function of data than algorithms
• In other words: as we know more structure, and indeed even sequence data, we get better at predicting
• Probably we will have a perfect algorithm for protein structure prediction when we know all of the answers
• Structural genomics & the generally increased pace of structure predictions means there aren’t many really “new” structures anymore
The Prediction Irony
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• Methods have mostly been developed for soluble, globular proteins or domains
• Problems with membrane proteins, low-complexity, etc.
• Many segments in proteins should be studied with other methods:– Signal peptides– TM regions– Coiled-coils– Intrinsic Disorder (e.g. http://dis.embl.de)
Things to Remember
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What we use this for…
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We aim to:
• Understand molecular interactions• Predict molecular interactions• Focus on those interactions of biomedical importance• Apply tools to large datasets• Use interaction networks predictively
– To predict new interactions– To predict other details like pathologies, toxicities
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Your favourite proteinN C
Your second favourite proteinN C
tRNA Synthetase
Histidyl adenylate
Templatesin contact?
Modelled Interaction
Match toknown structure
Match toknown structure
Modelling or predicting interactions by homology
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homology(e.g. blast)
homology
homology
homology
X-rayFive component complex
Two-hybrid network
+
Electron microscopy & Mass SpectometryRussell et al, Curr. Opin. Struct Biol. 2004Aloy & Russell, Nature Rev. Mol. Cell. Biol. 2006Taverner et al, Adv Chem. Res. 2008
Prediction of Structures of Complexes
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RGS-4
Adding Mechanisms to Interaction
Networks
RGS-3
G/i
G/q
Which piece from which protein?
What does the interaction look like?Who interacts with whom?
How strong? How fast?
PP
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Modelled complexes
Aloy & Russell, Nature Rev. Mol. Cell. Biol., 2006.
Bridging the information gap
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Kuehner et al, Science, 2010
From Proteomics to Cellular Anatomy?
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From Proteomics to Cellular Anatomy?
Kuehner et al, Science, 2010
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www.russelllab.org/aasGuide to the amino acids
www.russelllab.org/gtspGuide to Structure Prediction
meta.bioinfo.plMeta server (runs virtually all reliable prediction methods)
Some Links
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Sequence
In groups of two or more you will attempt to answer functional questions about a particular protein target
Active/inactive?Binds/does not bind?Substrate specificity?
Function
Structure
Structure Prediction Practicalwww.russelllab.org/wiki
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Acknowledgements
www.russelllab.orgCurrent group membersRob Russell (the boss), Matthew Betts, Leonardo Trabuco, Oliver Wichmann, Mathias Utz, Yvonne Lara
AlumniChad Davis, Olga Kalinina, Ricardo de la Vega, Victor Neduva, Evangelia Petsalaki Damien Devos
Complex modeling & interactions collaboratorsPatrick Aloy (IRB Barcelona)Anne-Claude Gavin (EMBL Heidelberg)Peer Bork (EMBL Heidelberg)Luis Serrano (CRG Barcelona)Achilleas Frangakis (Uni Frankfurt)Bettina Boettcher (Edinburgh)