Transcript Post-translational modifications - Swiss

First: a big welcome to the speakers
• We have a wonderful cast of speakers coming
from all over the world;
• Many of them have played, directly or indirectly,
an important role in the history of Swiss-Prot;
• In the name of the organizing committee I thank
all of them for having accepted to participate to
this anniversary meeting.
Then: a big welcome to the UniProters
• Since 2000, every two years, the members of the
Swiss-Prot groups at SIB and EBI have attended
a «retreat» to discuss various aspects of their
collaborations;
• This meeting doubles up as a very special retreat.
It is an opportunity for attendees and speakers to
tell us what they think we should be doing!
Antibes 2004
Welcome and thank you to all attendees
• For those coming from all over the world: we
know it was not easy to come to Brazil;
• We hope that this meeting will be an opportunity
for you to listen to interesting talks;
• But more importantly: meetings are essential to
network and to start or pursue collaborations;
• So please enjoy and make the most of these four
days that we will spend together.
And finally, but no the least!
a big thank you to all the sponsors
A few important last minute informations
and reminders
• In the conference bag external pocket you
will find many important things including:
– The pocket guide (the program at a glance);
– The instructions to access the Wifi Internet
wireless service;
– The vote bulletin for the best poster award;
– Information about a survey concerning
UniProtKB/Swiss-Prot.
The Swiss-Prot survey
• The Swiss-Prot annotators that are carrying out
the survey have a small red sticker on their
name badge;
• The persons that will have answered the
survey will receive a small yellow sticker to
put on their name badge so that they do not get
asked to participate over and over again!
Protein Spotlight book
• In your bag you will find a copy of «Tales
from a small world»;
• It is a book containing all the Protein
Spotlight articles published since 2000;
• We can offer a copy of this book to all of
you thanks to Current Biodata who fully
sponsored the cost of its printing.
Program changes
• Due to flight problems (Varig!) we “lost” 3 speakers:
Terri Attwood, Philipp Bucher and Minoru Kanehisa;
• We will use 2 of the 3 slots for different tutorials on
Swiss-Prot, the third slot will be used to get to the
beach an hour earlier on tuesday!;
• Nasri Nahas talk will be given by Ron Appel as Nasri
is busy trying to get his family out of Lebanon (last
minute: they are safely back in Geneva);
• Vitek Tracz talk will be given by Matthew Cockerill
who has overall responsibility for BioMed central;
• Really last minute: we just learned Gunnar fell sick on
the way here and has turned back to Sweden.
Speakers
• Try to end your talk 5 minutes before the alloted
time slot so as to leave the opportunity for a few
questions;
• There is for each day of the conference a Swiss-Prot
team member who is responsible for making sure we
are on time and to moderate the question “session”;
• You will have in front of you a digital timer that will
show you how much time is left;
• Once your time is over, it will ring and the
moderator will make his best efforts to expel you
from the podium!
Speakers - 2
• You can use the podium microphones and this
will ensure that your image is captured
correctly on the camera, but you can also use a
wireless lapel mike;
• Please use the mouse to point on objects in
your presentation instead of the laser pointer.
The SwissProt Song
Genome annotators,
with your big machines
If you didn't have Swiss-Prot,
You wouldn't find a thing
- with your big machines It you didn't have Swiss-Prot
you would not find a thing
Ain't no good the software
the grid and the middleware
If it'not for Swiss-Prot
You wouldn't get nowhere
- with your middleware If it's not for Swiss-Prot
You would not get nowhere
"Plus ça change, plus
c'est la même chose…”
the next 20 years
This will not be a talk on
the (pre)-history of Swiss-Prot
The universe in which Swiss-Prot
evolves
1953: 1st sequence (bovine insulin)
1986: 4’000 sequences
2006: 3.5 million sequences
Where will it stop?
179'000'025'042 (179 billion)
179'000'025'042
1st estimate: ~30 million species (1.5 million named)
2nd estimate:
20
million bacteria/archea
x
4'000 genes
5
million protists
x
6'000 genes
3
million insects
x
14'000 genes
1
million fungi
x
6'000 genes
0.6 million plants
x
20'000 genes
0.2 million molluscs, worms, arachnids, etc.
x
20'000 genes
0.2 million vertebrates
x
25'000 genes
The calculation:
2x107x4000+5x106x6000+3x106x14000+106x6000+6x105x20000+2x
105x20000+2x105x25000+25000(Craig Venter)+42(Douglas Adam)
Caveat: this is an estimate of the number of potential sequence entries,
but not that of the number of distinct protein entities in the biosphere.
When will UniProtKB be complete?
• Swiss-Prot:
–
–
–
–
In July 2009: 500’000 entries;
In 2013: 1 million entries;
In 2026 (40th anniversary): 10 million entries;
In 2036 (50th anniversary): 100 million entries.
• TrEMBL:
– In May 2080 TrEMBL will have reached 10 billion entries;
– We can’t compute with Excel when we will reach 179 billion
entries;
– But we are confident these dates are worthless as new
sequencing techniques will have made all of these projections
a very futile exercise!
Sequences…
• The bread of Swiss-Prot. And yes: annotations
are the butter!;
• >99% of the protein sequences originate from
translation of mRNA or genomic sequences;
• Do we still need manual intervention to cater for
sequences or can we just build smart filters to
obtain those we want from TrEMBL?
So what is the current status?
• A snapshot of the situation:
–
–
–
–
28’200 entries with 82’000 sequence conflicts;
2’600 entries with corrected frameshifts;
15’100 entries with corrected initiation sites;
4’300 entries with other sequence ‘problems’.
• At least 43’000 entries (19% of Swiss-Prot)
required a minimal amount of curation effort so
as to obtain the “correct” sequence.
Quality of protein information from genome projects
• Lets look at proteins originating from 3 different
genome projects:
– Drosophila: the example of what a curated (thanks to
FlyBase) genome effort should look like: only 1.8% of
the gene models conflict with what we have in SwissProt;
– Arabidopsis: a typical example of a genome where lots
of work was spent to annotate it at the time where it was
sequenced, but where nothing as been done since (at
least in the public view): 19.5% of the gene models are
erroneous;
– Tetraodon nigroviridis: the typical example of a quick
and dirty automatic run through a genome with no
manual intervention: >90% of the gene models produce
incorrect proteins.
Human sequence entries as an example
• We have about 14’500 human entries in
Swiss-Prot:
– 4’300 entries contain information about 8’000
splice variants;
– 4’600 entries contain information about 27’000
sequence variants;
– 7’500 entries contain information about 22’000
sequence conflicts;
– In average each human entry is produced by
merging together sequence information from 6.2
different nucleotide sequence entries.
Take home message
• Producing a clean set of sequences is not a
trivial task;
• It is not getting easier as more and more
type of sequence data gets submitted;
• It is important to pursue our efforts in making
sure we provide to our users the most
correct set of sequences for a given
organism.
Post-translational modifications (PTMs)
• If sequences are important, their are generally not fully
representative of the final ‘biological entity’: most proteins
are the target of PTMs;
• PTMs are important at various levels, including the 3D
structure, interactions, subcellular location and also the
function;
• The story of the integration of PTMs in Swiss-Prot consists
of 3 distinct parts;
• 1st part: a long time ago in a distant proteogalaxy:
FT
FT
FT
MOD_RES
MOD_RES
CARBOHYD
86
110
203
86
110
203
GAMMA-CARBOXYGLUTAMIC ACID.
HYDROXYLATION.
POSSIBLE.
nd
The 2 phase: 2000 to 2005
• Complete overhaul and significant extension of a
controlled vocabulary for PTMs;
• Creation of a PTM annotation program within
the Swiss-Prot groups at SIB and EBI;
• Development of new tools (Sulfinator, DGPI) for
the prediction of some PTMs;
• Massive clean up and re-annotation of many
classes of PTMs.
The expanding world of PTMs
• We now have 283 different PTM descriptions
(excluding processing, disulfide bonds and
glycosylation events).
The new document listing post-translational modifications
Contains many information items and is available in html format
or by ftp in tab-delimited format.
Finally LSEs for PTMs!
• Finally «Proteoman» has arrived! And PTM
information can now be obtained from results of
proteomics large scale experiments (LSE);
• In the past 12 months we have added about 6’000
experimental PTMs using data originating from
some of these projects.
But LSEs are not so easy to deal with
• Issues mundane to the incorporation of LSE PTM data:
– Quality:
• Trying to assess whether the methodology really allows the detection of invivo modifications;
• How many false positives are expected (often absent or very well hidden!);
– Accessing the data:
• Often in supplementary material tables and in a variety of formats (HTML
tables, excel spreadsheets, etc.);
• With a variety of identifiers (UniPRotKB, NCBI gi, pID, etc.);
– Sanity checking:
• Making sure that the right sequence position is modified;
• Does it make sense in the biological context;
– Propagating the information to orthologs.
• So the big issue is how will we be able to scale up and deal
with the expected increase in the number of such projects!
Cross-references: then
• The ‘DR’ lines were introduced in release 4 in
April 1987; they first linked Swiss-Prot to
EMBL, PDB and PIR;
• They were instrumental in the development of
SRS by Thure Etzold in the early 90’s;
• And also for ExPASy, the first web server in the
life sciences in 1993.
Organism-specific gene
databases
AGD
DictyBase
EchoBASE
EcoGene
FlyBase
GeneDB_Spombe
GeneFarm
Gramene
HGNC
H-InvDB
HIV
LegioList
Leproma
ListiList
MaizeDB
MGI
MIM
MypuList
PhotoList
RGD
SagaList
SGD
StyGene
SubtiList
TAIR
TubercuList
WormBase
WormPep
ZFIN
Genome annotation
databases
Ensembl
GenomeReviews
TIGR
Sequence databases
EMBL
PIR
UniGene
Enzyme and pathway
databases
Family and domain
databases
BioCyc
Reactome
Gene3D
HAMAP
InterPro
PANTHER
PIRSF
Pfam
PRINTS
ProDom
PROSITE
SMART
TIGRFAMs
UniProtKB/Swiss-Prot
explicit links
3D structure
databases
HSSP
PDB
SMR
Miscellaneous
dbSNP
GO
IntAct
LinkHub
RZPD-ProtExp
2D-gel databases
ANU-2DPAGE
Aarhus/Ghent-2DPAGE
COMPLUYEAST-2DPAGE
ECO2DBASE
HSC-2DPAGE
OGP
PHCI-2DPAGE
PMMA-2DPAGE
Rat-heart-2DPAGE
Siena-2DPAGE
SWISS-2DPAGE
Protein family/group
databases
PTM databases
GlycoSuiteDB
PhosSite
PptaseDB
GermOnline
MEROPS
REBASE
TRANSFAC
Cross-references: now
• There are now cross-references from Swiss-Prot to
74 different databases (6 more are in the pipeline);
• Almost 3 million DR lines: an average of 12 per
entry;
• Many other links to external resources are also
available through the OX (NCBI taxonomy), RX
(PubMed, DOI), CC («Web resource» topic) and
FT lines (dbSNP);
• Cross-references are not only a mean to help
navigate between resources, they sometimes add
information to the entries.
Examples of cross-references that provide information
• The cross-references to the Gene Ontology (GO):
DR
DR
DR
GO; GO:0005634; C:nucleus; ISS.
GO; GO:0005515; F:protein binding; IPI.
GO; GO:0007165; P:signal transduction; TAS.
• The PDB cross-references include information on the
mapping of the structure on the sequence:
DR
PDB; 1QQG; X-ray; A/B=4-267.
• The cross-references to domain databases include
information on the name/acronyms of the domains and the
number of occurrences of these domains:
DR
DR
DR
PROSITE; PS50026; EGF_3; 2.
PROSITE; PS50092; TSP1; 3.
PROSITE; PS01208; VWFC_1; 1.
From sequences to structures..and back!
• Efficient bidirectional links between UniProtKB
and PDB/MSD are very important;
• Currently 10’000 Swiss-Prot entries are linked to
30’200 PDB entries;
• These links are constantly updated and verified; the
converse is unfortunatly still not yet true;
• We have always made use of 3D structure
information to help in the annotation process;
• But we are only now starting to systematically mine
3D structures to extract various information such as
disulfide bonds, metal-binding sites, active sites,
etc.
So what is the future of cross-references?
• Will we really need hard-coded cross-references in the
future?
• Can we gradually replace some of them by computed
«on the fly» links using referenceable objects?
• Will we make more use of client-server systems such
as the distributed annotation system (DAS)?
• The answer is obviously dependent on
standardization;
• But the Life Sciences are still living in the
dark ages of the tower of Babel
CVs and ontologies
• Since the very beginning of Swiss-Prot we have been
building a growing sets of controlled vocabularies
(««ontologies»»);
• Species, strains, plasmids, journals, tissues, PTMs;
domain names and, of course, keywords are all
«under control» (see posters SP117 and SP120);
• We are very well advanced in the process of having a
CV for pathways (see the UniPathway poster;
SP140);
• We are now tackling the problems of protein and
gene names (see poster SP118). But this is of course
not very easy!
Do we need annotations?
• Annotators spend a big part of their time capturing and
synthesizing a huge amount of «functional» information;
• For example we populate Swiss-Prot with data relevant to
the:
–
–
–
–
–
Role and function of the proteins;
Subcellular location;
Interactions (binary and “complex”);
Tissue specificity, developmental stage;
Involvement in diseases.
• We have many «anecdotal» evidence that users find this
very important and that this is one of the important
hallmark of Swiss-Prot. Yet is this really true?
Do we need annotations? – part 2
• This is a time consuming process and we will
never be complete and up-to-date;
• Many users want quick and easy to «summarize»
answers, yet the more detailed an entry becomes
the less it is easy to transform it into a
summarizable entity;
• We are often the victims of the «fasta format
syndrome»: users expect everything important
about a protein to be available in the header of a
fasta format entry!;
• So should we continue?
Yes we need annotation!
• Because (among many other reasons):
– Automatized annotation is the only way to transfer
knowledge from a model organism to a less studied
one;
– To apply such techniques safely one needs template
entries that are representative of the state of the
knowledge;
– While literature mining tools could be conceived as a
way to automatically build a summary view of the
knowledge around a given protein, these techniques are
not yet powerful enough to create a coherent synthetic
view;
– Literature mining tools also require the existence of
well annotated (corpus) entries.
From pull to push..
• For now more than 20 years we have
been «pulling» information and
knowledge from various sources, but
mainly from literature;
• It is now time to make sure that the
next 20 years will be defined by the
fact that researchers «push» their
results and the interpretation of their
results in the knowledgebase.
• Attempt to try to get the community to directly
submit information on the proteins that they are
studying;
• Using a wikepedia-type model/interface;
• Will first be «field-tested» in the yeast community;
• We are hopeful, yet we are realist: only a small
percentage of life researchers will take the time and
are altruistic enough to fully participate in such a
scheme.
Grey grey matter
counts!
• Many life scientists with knowledge of the
molecular world and that are computerproficient are reaching retirement age;
• Some want to continue to play a role in the
advancement of research, yet they will not be
able to do lab work anymore;
• We should offer them the tools necessary for
them to contribute to the annotation process.
Anabelle and Asterix
• Two important tools could contribute to the
democratization of Swiss-Prot style annotation:
– Anabelle: a web based protein sequence analysis
platform;
– Asterix: the new Swiss-Prot editor.
Anabelle selection module
Viewer Layout:
Link to entry NiceProt view
Blast (full) entry
more links!...
Links…
Link to InterPro
Link to most similar Align most similar
entry NiceProt view entry with entry
Blast uncharted region
Link to domain
original database
And here is
what the
users gets
back
But what about the rest of the life
scientists?
• We saw how we could get parents (adopt a
protein) and grand parents (grey matter
count) involvements, but what about the
children…..;
• …the young researchers, those who are
active in producing new knowledge?
Two carrots, a stick and lots of
education!
• The carrots:
– Making sure that granting agencies see favorably
the involvement of researchers in the process of
submitting information to databases;
– The same criteria should be considered by any
hiring or promotion committee;
• The stick: getting journal editors to refuse to
accept to publish a paper if the results have
not been submitted to the relevant knowledge
resources;
Education!
• Everyone should feel concerned;
• Awareness of the content and usage of
knowledge resources is a pre-requisite to do any
type of « serious » research in the field of
molecular life sciences;
• Organizations such as EMBNet, EBI, SIB,
NCBI, NIG should continue and strenghten their
«outreach» efforts;
• We (databases providers) should do more in term
of providing tutorials (on-line and on-site).
An important issue…
• The process of developing a data resource for
the Life Sciences is akin to the work of middle
age copists, renaissance encyclopedists or the
19th century OED development : it is a very
tedious, manually intensive, long term job…
How to get funding for knowledge
infrastructures in the life sciences?
• Funding knowledge resources is difficult:
– It’s a very long term process;
– It’s not prestigious;
– and its not cheap!
And its not only databases that are
endangered!
Service groups are
also at risk
Proposition for a new tax
• Each grant proposal for a high throughput dataproducing project would be obliged to set aside a
predefined percentage of the grant money to help
cover the cost of storing and managing the
produced data;
• How this money would be redistributed is not
trivial to define and even less to implement;
• The priority would be to use this tax as a financial
tool to help fund the data repositories.
The tax for Biomolecular data archival
The 6 observations of a « databaser »
1. Your task will be much more complex and far bigger that
you ever thought it could be;
2. If your database is successful and useful to the user
community, then you will have to dedicate all your efforts to
develop it for a much longer period of time than you would
have thought possible;
3. You will always wonder why life scientists abhor complying
with nomenclature guidelines or standardization efforts that
would simplify your and their life;
4. You will have to continually fight to obtain a minimal amount
of funding;
5. As with any service efforts, you will be told far more what
you do wrong rather than what you do right;
6. But when you will see how useful your efforts are to your
users, all the above drawbacks will loose their importance!!
Aiala, Alain x4, Alan x4, Alastair, Alex x2, Alexander x2, Alexandre x2, Alice, Alistair, Allyson, Alvis, Amanda, Ana Tereza,
Anastasia, Andre x3, Andrea, Andreas, Andrew, Angela, Anne x4, Anne-Lise, Anthony, Antoine, Anulka, Arnaud x2, Arthur,
Astrid, Athel, Barbara x2, Barend, Baris, Barry, Bart, Bastien, Bengt, Bernard x2, Bernd, Bernhard x2, Bill, Bob, Brigitte, Bruno
x2, Burkhard, Carl, Carola, Carolyn, Catherine x4, Cathy x2, Cecile x2, Cecilia, Cedric, Cesare, Chantal x3, Charles x2, Chris,
Chrissie, Christian x3, Christiane, Christine x2, Christoph, Christophe, Christopher, Christos, Claude, Claudia x2, Claudine,
Colin, Colombe, Corinne, Cristiano, Damien, Dan, Dana, Daniel x3, Daniela, Danielle, Darcy, Darren, Dave x2, David x5,
Delphine, Denis x2, Dennis, Des, Dietmar, Dolnide, Dominique, Doron, Dorothy, Doug, Duncan, Eddie, Edgar, Edouard, Eleanor,
Elisabeth x2, Elmar, Elvis, Emily, Emmanuel, Eric x3, Erik, Ernest, Ernst, Esther, Eugene x2, Eva, Eve, Evelyn, Evgenia, Evgeny,
Ewan, Fabrice, Fiona, Flavio, Florence x3, Fotis, Francis, Frank, François x3, Frederic, Frederique x2, Gabriel, Gabriella,
Ganesh, Gaston, Geoff, Gerry, Gert, Ghislaine, Gilbert, Gill, Goran, Gottfried, Graham x2, Greg, Gregoire, Guido, Guillaume,
Gunnar, Guy x2, Guy-Olivier, Hanah, Heidi, Henning, Hien, Hilde, Holger, Hongzhan, Howard, Hsing-Kuo, Ian, Iirit, Ilkka,
Ioannis, Irving, Isabelle x2, Ivan x2, Ivo, Jack, Jacques x2, Jaime, Janet x2, Jean-Charles, Jean-François, Jean-Jacques, JeanMichel, Jean-Pierre x2, Jeffrey, Jenny, Jerome, Jim, Jingchu, Joachim, Joanna, Joel, John x7, Jonas, Jonathan x2, Jorja, Jos,
Juan, Juergen, Julia, Julio, Julius, Kai, Karin, Karine, Kate, Kati, Katja, Katsumi, Kay, Keiichi, Keith x3, Ken x2, Kenta, Khaled,
Kirill, Kirsty, Kristian, Larry, Laure, Laurent x3, Lee, Leigh, Leon, Li, Lina, Lionel, Lisa x2, Livia, Lorenza, Lorenzo, Louise,
Luca, Luciane, Lucien, Luisa x2, Luiz, Lydie x2, Ma'ayan, Madelaine, Maggie, Mahesh, Manolo, Manuel x2, Manuela, Marc x6,
Marcia, Marco, Margaret x2, Mari Trini, Maria, Maria Esperanza, Maria-Jesus, Marie-Claude, Marilyn, Marisa, Mark x2,
Martin x2, Martine, Marvin, Mary, Massimo, Matteo, Matthew, Mauricio, Michael x7, Michel x3, Michele, Michelle, Miguel,
Mike x2, Minna, Minoru, Monica, Monika, Morido, Nabil, Nadeem, Nadine x2, Naruya, Nasri, Natalia, Nathalie, Neil x2, Nicky,
Nicola, Nicolas x3, Nicole x3, Nicoletta, Nicolle, Nikos, Nina, Oliver, Olivier x4, Orna, Owen, Paolo, Pascal, Pat, Patricia x6,
Patrick x5, Paul, Paula, Pavel, Pedro, Peer, Peter x7, Petra, Phil x2, Philip, Philippe x3, Pierre, Pierre-Alain, Pieter, Piotr,
Rachael, Raffaella, Rainer, Raja, Rasko, Raton laveur, Rebecca x2, Rein, Reinhard x2, Remi, Reto, Reynaldo, Rich, Richard,
Robert x2, Roberto, Robin, Rodger, Rodrigo, Roland, Ron, Rosita, Ross, Roy, Russ x2, Ruth x3, Saeid, Salvo, Samia, Samuel x2,
Sandor, Sandra x2, Sandrine, Sarah, Scott, Sebastien x2, Serenella, Sergio, Severine x2, Shigehaki, Shmuel, Shoko, Shoshana,
Shyamala, Silvia x2, Sineaid, Siv, Sona, Soren, Sorogini, Steffen x2, Steffi, Stephanie x2, Steve, Steven, Stuart x2, Stylianos,
Sunil, Sylvain, Sylvie x2, Takashi, Tamara, Tammera, Tania x2, Temple, Terri, Terry, Thomas x3, Thure, Tim x2, Timothy, Toby,
Tom, Toni, Torsten, Ujwal, Ulrich, Ursula, Valeria, Vassilios, Veronique, Vicente, Victor x2, Vincent, Vinnei, Violaine, Virginie x2,
Vitaliano, Vitek, Vivien x2, Vivienne, Wanessa, Wei mun, Weimin, William, Williams, Willy, Winona, Winston, Witek, Wolfgang,
Xavier x2, Yasmin, Yasuhiro, Yongxing, Yoshio, Youla, Young-Ki, Zeev, Zhang-Zhi.