From 2013.igem.org

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Latest revision as of 03:57, 29 October 2013

NRPS-Designer. Designing custom peptides.

This tool allows you to design any customized peptide you can build using the most common NRPS domains. This gives you the oppurtunity to include non-proteinogenic amino acids and secondary modifications without going through chemical synthesis or tRNA reprogramming. On top of providing you with a target pathway sequence the tool is also integrated with the parts registry to include any further BioBricks and with Gibthon to directly go from NRP design to Gibson cloning strategy within 10 minutes.

You should also read our documentation and our RFC100!

Week 12

In this week we implemented a first draft of the NRPS-Designer algorithm in C++ and designed a databse structure, as well as some scripts for entry making

Week 13

While revising the database structure and the input interface we integrated pfam for semi-automated domain prediction.

Week 14

Since we noticed, that writing the whole interface ourselves will be very nasty and time-consuming we put our whole concept into the framework Django. Meanwhile our database design evolved further an the pfam integration was improved.

Week 15

Besides the regular changes to the database design, we integrated the graphical user interface in Django and also included OpenBable for displaying chemical structures. Furthermore we visualised our concept this week.

Week 16

This week we mainly made changes to the NRP-Design interface and integrated ajax.

Week 17

Besides fixing the relations in our database and improving th interface, we integrated Gibthon - the tool for Gibson primer design developed by the 2011 Cambridge team.

Week 18

This week we started filling our database, using the NRPS-PKS database. Besides that we introduced session data and updated the designer algorithm.

Week 19

As we noticed, that different prediction tools give different domain borders, we further evaluated them and finally integrated antiSMASH

Week 20

This week we started the manual sequence annotation and database curation and integrated the multiple sequence alignment in the database input interface.

Week 21

During this week we integrated our tool with the parts regisrty, did more manual database curation, added more hidden Markov-Models to antiSMASH and improved Gibthon.

Week 22

In order to do request management we introduced celery to our system. Furthermore we optimised the multiple sequence alignment and did lots of manual database curation.

Week 23

In the final week we added several linkouts, including SBOL and GenBank files and fixed lots of bugs. After the Regional Jamboree in Lyon, we fixed several bugs and most important we introduced a library function. This allows for construction of many similar NRPs at a time, which could for example be useful for medical compund screening.

Initial setup

Initial database setup

First of all we began to design a database - which we modified, edited and changed all-over again numerous times - which ought to be capable of saving the data concerning NRPS origins, pathways, modules, specificity and lots of more information on them we needed for a later prediction of domains capable of producing a certain NRP. We focused on saving the data in a non-redundantly and easy to rerieve manner. Therefore we first set up a GitHub repository which contained the SQL-file of our first database-design. For it's implementation we used phpmyadmin, which spared us writing the whole thing in SQL. Creating forms to "easily" fill our newly implemented database we started with - what later turned out to be a quite naive idea - writing php scripts which connected to our MySQL-database using nasty index-files containing root-username and passwort of different localhosts.

Draft of command-line application

We thought of a common XML format for NRPs. Additionally a first command line application was written in C++ for extracting data about an NRP from those XML files.

Design algorithm
Miscellaneous

Pathway finding implemented in C++

The basic C++ code needed for MySQL requests depending on a defined input was written. It fetches all the domains, that fit a certain substrate and calculates a pathway, which would theoretically produce the designed NRP.

Database redesign

The database was redesigned in order to better represent the natural composition of NRPS.

Extensions of user interface

The php-scripts for filling our database were adapted to the new database-structure and customized so that different tables in the database could be filled with them.

Nils finally uses a real operating system

After weeks of agony and trouble with his MacBook, Nils finally decided to follow the advice of master Ilia and made him install aptosid on his computer. Of course, because aptosid (based on Debian Sid) is not as user-friendly as Ubuntu, Nils and Ilia spent quite some time in order to change the background image from Fred the crab (the mascot of aptosid) to something else. Anyway, at least Nils has now a Linux distribution and maybe we will be able to convince our advisor Tim to also get rid of his Mac operating system.

Miscellaneous

Database redesign

The domains are now saved all in one table and they each have a domain type specified. Due to this setup the database can now also cover modification domains.

GUI extension

The php scripts were extended to allow for entries to the new database. Additionally an XML export funciton was added to the design interface.

Draft of PFAM querying

In the experimental parts, we determined a first rough estimate of NRPS domain boundaries based on Pfam. Thus we also wanted our software to use the Pfam predictions to make it easier to semi-automatically add new domains into our database. A first test Python script was written which queried the Pfam API using the Requests library according to the online documentation and then parsed the XML output. The Biopython library also proved to be of great help.

Django
Miscellaneous

Concept transferred to django framework

Having distributed diverse tasks e.g. to write php scripts, we figured out that we would be constantly be reinventing the wheel and doing web-development like it was done a decade ago. Thus, we started looking at different web frameworks, which would make a lot of standardized tasks such as user login easy. We then decided to use Django, a Python framework, mainly because of the language. Some of us already knew Python, it is in general a language that is very easy to pick up and the Biopython project is pretty comprehensive. Additionally, a previously very successful iGEM Project (Gibthon) also used Django. Compared to most other choices (except Ruby on Rails, which has the disadvantage of being written in Ruby) it also seemed to be the most mature choice. We also thought of using a Perl or a Haskell framework, such as Yesod, but some team members disliked those languages (Haskellers disliked Perl and vice versa; the fact that the first Perl6 compiler was written in Haskell could not convince the Perl people either).

We then started reading the Django tutorials and the (great!) documentation and started porting our previous MySQL database designs to Django models. This was made a lot easier by an automated Django utility which converts a database into a draft models file, which we could then adapt to our needs. The rest of the team was impressed after seeing the admin site, which again was very easy to get up and running with Django. This week our official lab-youtube-song was this one.

Database redesign

Working with Django models rather than MySQL tables made it a lot easier to get a better grasp of the structure of our database and possible shortcomings. In particular, we noticed that our original idea of an "Origin" representing a large DNA sequence was problematic, as it would life a lot harder in regards to automated prediction: For example, coding sequences would have to be extracted out of the DNA sequence using a tool like Glimmer or by running through all 6 reading frames as Pfam does. Also it was not immediately obvious how big a DNA sequence in the database could or should be (for example, a biosynthetic cluster for NRPS might be split into two or more "Origins" depending on who added the entries).

Thus, we decided to change the models (which was very easy with Django) as follows: The "Origin" table now represented an actual source of DNA, out of which a domain could be amplified by PCR, such as a particular species or a biobrick. The DNA sequence were now saved in the new "Cds" (Coding sequence) table. The foreign key of the domain table entries also pointed at "Cds" entries rather than "Origins".

PFAM integration

Having learned a lot about one of the core concepts of Django, namely the models, we also started experimenting with the views and template systems, which basically are responsible for the rendering of the actual web pages. Thus, we tried to integrate the Pfam scripts we had previously written into Django views. Pfam actually includes a very nice help page (with gems such as: " IE, being IE, needs extra help [..] ")for its domain graphics, which basically beautifully render JSON documents using Prototype.js and other JS libraries. The JSON representation of the different domains is also calculated by the Pfam server after the domain prediction. Therefore, we adapted the original Pfam script in order to actually extract this JSON file and then it was passed on to the Pfam domain graphics javascript. This had to be done asynchronously (user enters DNA sequence, presses button, then the sequence is passed to the server, the Django function which queries Pfam API is executed and finally the Pfam JSON File is returned to the user's browser). Here, the extensibility of Django via reusable apps came in handy, because the Dajaxice app made the AJAX requests a matter of a few lines of code.

Database entries

In order to fill the database with the standard monomers, the L- and D-conformation of the 20 proteinogenic amino acids, as well as those of ornithine were taken from pubchem and entered into the database via the standard admin page. To allow for displaying the structures in the user interface the 3D-SDF formatted text files were also copied to the database.

Overview
Miscellaneous

Concept visualisation

For presentation purposes and in order to have a visual representation of all the different aspects of our project we drew a huge graphic on the blackboard. After two days of revision we redrew the whole thing using inkscape. The result is displayed in figure 16.1.

Figure 16.1: Whole concept of the NRPS-Designer and all its components. Please note that part of this overview has further developed or changed. For an up to date overview please see the documentation.

Graphical user interface and Django-registration

This week we started trying to get the basics necessary for a decent user experience up and running. The pages for the Pfam input and the selection of NRP monomers were created. For this we used jQuery UI and we very especially fond of its tabs. After playing around with some html and css, we soonly decided to integrate bootstrap 2for styling our interface. As our Django models and forms would deal a lot with many-to-many relationships and as the default multichoice html box is extremely user-unfriendly, we searched for JS libraries capable of improving this experience. We initially decided to use chosen.js, but then we quickly switched to select2, which provides many more functionalities.

Also in order to enable the smooth handling of user registration, login etc., the django-registration was roughly integrated to the rest of the software.

Use of OpenBable for structure display

OpenBable was included in the tool in order to display the currently designed NRP. The peptide bonds are formed automatically based on the SDF representations saved for each substrate in the database. One of the challenges faced was the correct conformation and the aesthetic display of the peptide chain.

Miscellaneous

Monomer selection page aesthetic improvements

This week we focused on improving the aesthetics of the NRP selection page. Most of the page layout was shuffled around, so that everything important (Monomer selection, buttons, peptide graphic) fit onto a laptop screen and no scrolling around is necessary. For this we also removed the help text, which added a lot of bloat to the page. We'll try to find a fancier way to document things and help the user later on. We also used a nice feature of sselect2, which basically allows you to determine how the dropdown selection options are styled. More specifically, each monomer is displayed together with its 2d structure as generated by the OpenBabel script we had already written.

AJAX functionality

In order to facilitate the communication of the web interface with the C++ code, we started testing the AJAX functionality. One thing we noticed was that the (otherwise great) Dajaxice library, can actually cause problems due to relative URLs, as it does not correctly utilize the wsgi settings. This was especially problematic, as the development and production environments differed, so that a very ugly hack would have to be written for this to work. Instead, we decided to use a more barebones/standard AJAX implementation by actually using jQuery. A test was written with the monomer selection being passed via AJAX and then a popup alert (yay!) which produced an XML out of this selection. This XML could be passed into the C++ function.

Interface
Gibthon

Designer interface - modifications

A selection box for the modifications a substrate can have was added to the designer interface. This will also be adopted for the chemical structure and the domains will be included in the NRPS design.

Integration of Gibthon

The Gibthon software by Cambridge 2010 was integrated in our software to provide a cloning strategy for the user. Some features (such as the primer folding analysis) were excluded at first, in order to prevent possible bugs and to runtime when testing the funtionality. During this process of thesting, we also ran into bugs in Gibthon and fixed them in order to improve both tools.

Database
Frontend

Database updates

As many organisms have several NRPS pathways and some NRPS pathways are present in different organisms (e.g. different bacterial strains) the database was changed in order to separate origin and product. The coding sequences now function as many to many connection between origin and products.

Integrating NRPS-PKS

We manually put all the organisms and pathways annotated in NRPS-PKS in our database. This basis was then used to write simple scraping script.

Session data

The user can now have his own personalised data kept in the database. This contains peptides designed and the results of the domains and primer predictions. An additional interface for the organisation of the NRPs was created.

antiSMASH
Miscellaneous

Tool selection for domain prediciton

We tried to determine, which tool would be most appropriate for automated determination of domains. Thus we used the first CDS of the teicoplanin NRPS, as curated in the NRPS-PKS (SBSPKS) database. We then compared the automated prediction with the tool of Maryland, as well as antiSMASH2.

	A domain		T domain		C domain		A domain		T domain		E domain
	Start	End	Start	End	Start	End	Start	End	Start	End	Start	End
NRPS-PKS	1	491	505	564	604	1021	1015	1511	1526	1584	1608	2053
Maryland	18	503	504	567	599	1012	1008	1524	1524	1587	1604	2063
antiSMASH	35	426	502	567	598	891	1056	1447	1522	1589	1602	1896
pfam	15	478	504	566	598	891	1036	1498	1524	1586	1620 (as C)	1896 (as C)

Note that Pfam can't differentiate between C and E domains. Pfam also does not return any predictions in regard to A-domain specificity. antiSMASH is able to predict the A-domain specificities as annotated in NRPS-PKS, while Maryland's tool can only predict the second A-domain (Tyrosine), while the first is incorrectly predicted to be specific for Leucine (curated: HpG, 4-hydroxyphenyl glycine).

The same analysis was also repeated for thaxtomin.

	A domain		NM domain		T domain		C domain
	Start	End	Start	End	Start	End	Start	End
NRPS-PKS	1	506	509	712	944	1005	1029	1458
Maryland	5	521	468	879	942	1006	1028	1455
antiSMASH	42	445	511	732	940	1007	1025	1325
pfam	22	486	538	638	942	1006	1023	1326

In this case, both tools correctly predict the L-Phenylalanine specificity of the A-domain.

Analysis of tycC from the tyrocidine-cluster.

	C domain		A domain		T domain		C domain		A domain		T domain		C domain		A domain		T domain		C domain		A domain		T domain		C domain		A domain		T domain		C domain		A domain		T domain		TE domain
	Start	End	Start	End	Start	End	Start	End	Start	End	Start	End	Start	End	Start	End	Start	End	Start	End	Start	End	Start	End	Start	End	Start	End	Start	End	Start	End	Start	End	Start	End	Start	End
Marahiel (C-T), Ilia (A-T), Philipp (C-A)				953	963	1033	1050	1345		1990	2000	2070	2087			3028	3038	3108	3125		3571	4063	4073	4143	4160			5107	5117	5187	5204			6150	6160	6230	6247
NRPS-PKS	13	447	442	957	972	1036	1058	1483	1478	1994	2009	2073	2095	2520	2515	3032	3048	3111	3133	3558	3553	4066	4082	4146	4168	4593	4588	5111	5126	5190	5212	5639	5636	6151	6169	6233	6256	6466
Maryland	8	438	441	964	972	1036	1053	1474	1477	2001	2009	2073	2090	2511	2516	3039	3047	3111	3128	3549	3552	4074	4082	4146	4163	4584	4587	5118	5126	5190	5207	5630	5637	6160	6169	6233	6254	6482
antiSMASH	8	305	490	887	970	1039	1054	1339	1526	1924	2007	2075	2090	2376	2563	2962	3045	3113	3128	3413	3601	3997	4080	4149	4165	4449	4636	5041	5124	5193	5208	5494	5682	6083	6167	6236	6253	6482
pfam	7	306	470	944	972	1036	1052	1341	1506	1981	2009	2073	2089	2378	2543	3019	3047	3111	3127	3416	3581	4054	4082	4146	4162	4450	4616	5098	5126	5190	5206	5495	5662	6141	6169	6233	6254	6482

In regards to prediction of A-domain specificity, antiSMASH predictions and the curated NRPS-PKS amino acids were the same. On the other hand, Maryland predicted did not get any hit for A6 (Leu) and for A1/A3 respectively it predicted to possible amino acids (Asn+Asp compared to Asn / Tyr + Trp compared to Tyr in antiSMASH and NRPS-PKS).

TycB3 C-A domain

Clustal Omega MSA of different C-A domain borders of Tyrocidine cluster with annotation (prediction) of the start of the A domain according to different tools.

Clustal Omega MSA of different C-A domain borders of Tyrocidine cluster with annotation (prediction) of the end of the C domain according to different tools.

	C domain		A domain
	Start	End	Start	End
paper primer position	2520 (MLTAA..)
NRPS-PKS	2100	2527	2540	3029
Maryland	2095	2518	2521	3039
antiSMASH	2096	2381	2570	2961
pfam	2094	2383	2550	3019

== Inegration of antiSMASH ==

Domain visualisation using pfam

We added the domain visualisation as it is in pfam to the domain types in the database in order to display the resulting NRPS structure.

Misc

Misc

Misc

SBOL and GenBank

Proper output of the designed NRPS as well as the primers, was created both for GenBank and for SBOL. Moreover the tool internal communication was shifted to SBOL.

Database Curation

The final state of the database after a major portion of SBSPKS was curated is displayed in the table below.

Pathway	Gene	Module	SBSPKS domains	HMM domains	Specificity (Chirality C, TE)	Specificity (CA Substrate)	SBSPKS	SVM (0)	Stachelhaus (0)	Minowa (1)	Literature	Modifications	E-domain contained	Literature references	Comment
Actinomycin	acmA	1	A	A	-	4-MHA	4-MHA	hydrophobic/alphatic	PIP	4-MHA	4-MHA	-		http://www.ncbi.nlm.nih.gov/pubmed/9573200	"T-domain after A is definitely missing C-starter can go with or without 4-MHA"
	acmB	2	C-A-T	CSt-A-T	-/4-MHA	Thr	Thr	Thr	Thr	Thr	Thr	-
		3	C-A-T-E	C-A-T-E-com	L	Val	D-Val	Val	Val	Val	Val	-	X
	acmC	4	C-A-T	com-C-A-T	D	Pro	Pro	Pro	Pro	Pro	Pro	-
		5	C-A-M-T	C-A-NM-T	L	Gly	Gly	Gly	Gly	Gly	Gly	N-Methylierung
		6	C-A-M-T-TE	C-A-NM-T-TE	L, L	Val	Val	Val	Val	Val	Val	N-Methylierung
ACV	acvA	1	A-T	A-T	-	Aad	Aad	Aad	Aad	Aad	Aad	-		"A: http://www.ncbi.nlm.nih.gov/pubmed/9266851 E: http://www.ncbi.nlm.nih.gov/pubmed/21889568 TE: http://www.ncbi.nlm.nih.gov/pubmed/10715209 "
		2	C-A-T	C-A-T	D (pred) / L (sbspks, lit)	Cys	Cys	Cys	Cys	Cys	Cys	-
		3	C-A-T-E-TE	C-A-T-E-TE	L, D	Val	Val-D	Val	Val	Val	Val	-	X
A47934	staA	1	A-T	A-T		HpG	DHpG	HpG	HpG	HpG	HpG			http://www.ncbi.nlm.nih.gov/pubmed/12060705	"- staB E-domain is nonfunctional because of His-Pro mutation - CX in the end is either non-functional or L-selective"
		2	C-A-T-E	Cglyc-A-T-E	D	Tyr	Tyr	Tyr	Tyr	Tyr	Tyr	C-glyc linking hydroxy	X
	staB	3	C-A-T-E	Cglyc-A-T-E*-com	D	DHpG	HpG	DHpG	DHpG	DHpG	DHpG	C-glyc linking hydroxy	X
	staC	4	C-A-T-E	com-Cglyc-A-T-E	L	HpG	HpG2Cl	HpG	HpG	HpG	HpG	C-glyc linking hydroxy	X
		5	C-A-T-E	Cglyc-A-T-E	D	HpG	HpG	HpG	HpG	HpG	HpG	C-glyc linking hydroxy	X
		6	C-A-T	Cglyc-A-T	D	bht	Tyrb-O	bht	bht	bht	bht	C-glyc linking hydroxy
	staD	7	C-A-T-E-TE	com-Cglyc-A-T-CX-TE	L, L	DHpG	DHpG	DHpG	DHpG	DHpG	DHpG	C-glyc linking hydroxy
Arthrofactin	arfA	1	C-A-T	C-A-T		Leu	Leu	Leu	Leu	Leu	Leu			http://www.ncbi.nlm.nih.gov/pubmed/14522057	Paper claims that there are epimerisation domains, but the dual domains sound more reasonable.
		2	C-A-T	CDual-A-T		Asp	Asp	Asp	Asp	Asp	Asp		X
	arfB	3	C-A-T	CDual-A-T		Thr	Thr	Thr	Thr	Thr	Thr		X
		4	C-A-T	CDual-A-T		Leu	Leu	Leu	Leu	Leu	Leu		X
		5	C-A-T	CDual-A-T		Leu	Leu	Leu	Leu	Leu	Leu		X
		6	C-A-T	CDual-A-T		Ser	Ser	Ser	Ser	Ser	Ser		X
	arfC	7	C-A-T	CDual-A-T		Leu	Leu	Leu	Leu	Leu	Leu		X
		8	C-A-T	C-A-T	L	Ser	Ser	Ser	Ser	Ser	Ser
		9	C-A-T	CDual-A-T		Ile	Ile	Ile	Ile	Ile	Ile		X
		10	C-A-T	C-A-T	L	Ile	Ile	Ile	Ile	Ile	Ile
		11	C-A-T-TE-TE	C-A-T-TE-TE	L,L	Asp	Asp	Asp	Asp	Asp	Asp
Bacitracin	bacA	1	A-T	A-T		Ile	Ile	Ile	Ile	Ile	Ile			http://www.ncbi.nlm.nih.gov/pubmed/9427658
		2	C-A-T	Cy-A-T	L	Cys	Cys	Cys	Cys	Cys	Cys
		3	C-A-T	C-A-T	L	Leu	Leu	Leu	Leu	Leu	Leu
		4	C-A-T-E	C-A-T-E	L	Glu	Glu	Glu	Glu	Glu	D-Glu
		5	C-A-T	C-A-T	D	Ile	Ile	Ile	Ile	Ile	Ile
	bacB	6	C-A-T	C-A-T	L	Lys	Lys	Lys	Lys	Lys	Lys
		7	C-A-T-E	C-A-T-E	L	Orn	Orn	Orn	Orn	Orn	D-Orn
	bacC	8	C-A-T	com-C-A-T	D	Ile	Ile	Ile	Ile	Ile	Ile
		9	C-A-T-E	C-A-T-E	L	Phe	Phe	Phe	Phe	Phe	D-Phe
		10	C-A-T	C-A-T	D	His	His	Tyr	His	His	His
		11	C-A-T-E	C-A-T-E	L	Asp	Asp	Asp	Asp	Asp	D-Asp
		12	C-A-T-TE	C-A-T-TE	D, L	Asn	Asn	Asn	Asn	Asn	Asn
CDA	cdaPSI	1	C-A-T	CSt-A-T		Ser	Ser	Ser	Ser	Ser	Ser			http://www.ncbi.nlm.nih.gov/pubmed/12445768	Not sure why Cglyc instead of C since there is no modification here.
		2	C-A-T	C-A-T	L	Thr	Thr	Thr	Thr	Thr	Thr
		3	C-A-E	C-A-T-E	L	Trp	Trp	Trp	Trp	Trp	Trp		X
		4	C-A-T	C-A-T	D	Asp	Asp	Asp	Asp	Asp	Asp
		5	C-A-T	C-A-T	L	Asp	Asp	Asp	Asp	Asp	Asp
		6	C-A-E	C-A-T-E	L	HpG	HpG	HpG	HpG	HpG	HpG		X
	cdaPS2	7	C-A-T	Cglyc-A-T	D	Asp	Asp	Asp	Asp	Asp	Asp	no glycosylation here
		8	C-A-T	C-A-T	L	Gly	Gly	Gly	Gly	Gly	Gly
		9	C-A-E	C-A-T-E	L	Asn	Asn	Asn	Asn	Asn	Asn		X
	cdaPS3	10	C-A-T	com-C-A-T	D	Glu/Glu3Me	GluMe3	Asp/Asn	Glu	Asn	GluMe3
		11	C-A-T-TE	C-A-T-TE	L, L	Trp	Trp	Trp	Trp	Trp	Trp
Cyclosporin	simA	1	C-A-T	C-A-T	???	D-Ala	D-Ala	Pro	D-Ala	Ala	D-Ala			"http://www.ncbi.nlm.nih.gov/pubmed/8376400 domain order: http://www.ncbi.nlm.nih.gov/pubmed/16895337 D-Ala:http://www.ncbi.nlm.nih.gov/pubmed/8175682"	final C-domain is never metioned anywhere, but since the product is cyclic it might have similar function as a thioesterase. Thus it is uncertain, how the stereochemistry of the first alanine is achieved.
		2	C-A-T	C-A-NM-T	L	Leu	Leu	val,leu,ile,abu,iva	Leu	Leu	Leu	N-Methylierung
		3	C-A-T	C-A-NM-T	L	Leu	Leu	val,leu,ile,abu,iva	Leu	Leu	Leu	N-Methylierung
		4	C-A-T	C-A-NM-T	L	Val	Val	val,leu,ile,abu,iva	Val	Val	Val	N-Methylierung
		5	C-A-T	C-A-NM-T	L	Bmt	Bmt	phe,trp,phg,tyr,bht	Bmt	Bmt	Bmt	N-Methylierung
		6	C-A-T	C-A-T	L	Abu	Abu	val,leu,ile,abu,iva	Abu	Abu
		7	C-A-T	C-A-NM-T	L	Gly (NM > Sar)	Glu	Pro	Gly	Sar	sarcosine	N-Methylierung
		8	C-A-T	C-A-NM-T	L	Leu	Leu	val,leu,ile,abu,iva	Leu	Leu	Leu	N-Methylierung
		9	C-A-T	C-A-T	L	Val	Val	Pro	Val	Val	Val
		10	C-A-T	C-A-NM-T	L	Leu	Leu	val,leu,ile,abu,iva	Leu	Leu	Leu	N-Methylierung
		11	C-A-T	C-A-T	L	Ala	Ala	Pro	Ala	Ala	Ala
		12	C	C	???
Gramicidin	gramicidin synthetase 1	1	A-T-E	A-T-E	D	Phe	D-Phe	Phe	Phe	Phe	Phe		X	http://www.ncbi.nlm.nih.gov/pubmed/1560782	"On the genomic sequence for first synthetase are an additional TE and an additional Com-C domain on separate gene products. Two primary peptides can be fused to one ring."
	grsB	2	C-A-T	com-C-A-T	L	Pro	Pro	Pro	Pro	Pro	Pro
		3	C-A-T	C-A-T	L	Val	Val	Val	Val	Val	Val
		4	C-A-T	C-A-T	L	Orn	Orn	Orn	Orn	Orn	Orn
		5	C-A-T-TE	C-A-T-TE	L, L	Leu	Leu	Leu	Leu	Leu	Leu
Lichenycin	licA	1	C-A-T	CSt-A-T		Gln	Gln	Gln	Gln	Gln	Gln			http://www.ncbi.nlm.nih.gov/pubmed/9864322	Separate TE domain has unknown function and can't be predicted. The first C-Domain and the Thioesterase put a beta-hydroxy fatty acid in the circular peptide
		2	C-A-T	C-A-T	L	Leu	Leu	Leu	Leu	Leu	Leu
		3	C-A-T-E	C-A-T-E	L	Leu	D-Leu	Leu	Leu	Leu	Leu		X
	licB	4	C-A-T	com-C-A-T	D	Val	Val	Val	Val	Val	Val
		5	C-A-T	C-A-T	L	Asp	Asp	Asp	Asp	Asp	Asp
		6	C-A-T-E	C-A-T-E	L	Leu	D-Leu	Leu	Leu	Leu	Leu		X
	licC	7	C-A-T-TE	com-C-A-T-TE	D, L	Ile	Ile	Ile	Ile	Ile	Ile/Val/Leu
	licTE (deleted)	TE	TE
Syringomycin	syrE	1	C-A-T	Cst-A-T		Ser	Ser	Ser	Ser	Ser	Ser			http://www.ncbi.nlm.nih.gov/pubmed/9830033	syrB1 is 9th module somehow attaching to C-TE after 8th module.
		2	C-A-T	Cdual-A-T	L	Ser	D-Ser	Ser	Ser	Ser	Ser		X
		3	C-A-T	Cdual-A-T	D	Dab	D-Dab	gly,ala,val,leu,ile,abu,iva	Dab	Dab	Dab		X
		4	C-A-T	Cdual-A-T	D	Dab	Dab	gly,ala,val,leu,ile,abu,iva	Dab	Dab	Dab		X
		5	C-A-T	C-A-T	L	Arg	Arg	Arg	Arg	Arg	Arg
		6	C-A-T	C-A-T	L	Phe	Phe	Phe	Phe	Phe	Phe
		7 (not curated)	C-A-T	C-A-T	L		DhBu-3OH	Thr	Thr	Thr		maybe Thr is recognised and Cdual is dehydrating instead of racemising
		8	C-A-T	Cdual-A-T	X	Asp	Asp-3OH	Asp	Asp	Ala	Asp		X
			C-T-TE	C-T-TE	L, L	only TE kept
	syrB1 (del)	9	A-T	A-T		Thr	Thr-4Cl				Thr

After the Regional Jamboree in Lyon, we fixed several bugs and most important we introduced a library function. This allows for construction of many similar NRPs at a time, which could for example be useful for medical compund screening.

Thanks to

@@ Line 15: / Line 15: @@
                          <h1><span style="font-size:180%;color:#074E0B;">NRPS-Designer.</span><span class="text-muted" style="font-family:Arial, sans-serif; font-size:100%"> Designing custom peptides.</span></h1>
                          <p>This tool allows you to design any customized peptide you can build using the most common NRPS domains. This gives you the oppurtunity to include non-proteinogenic amino acids and secondary modifications without going through chemical synthesis or tRNA reprogramming. On top of providing you with a target pathway sequence the tool is also integrated with the parts registry to include any further BioBricks and with Gibthon to directly go from NRP design to Gibson cloning strategy within 10 minutes.</p>
-                         <p>You should also read our <a href="https://2013.igem.org/Team:Heidelberg/Project_Software">documentation</a> and our <a href="http://hdl.handle.net/1721.1/81333"><b>RFC100</b></a>!
+                         <p>You should also read our <a href="https://2013.igem.org/Team:Heidelberg/Project_Software">documentation</a> and our <a href="https://2013.igem.org/Team:Heidelberg/RFCs"><b>RFC100</b></a>!
              </div>
              <div class="row">
@@ Line 90: / Line 90: @@
                                    <h1>Week 15</h1>
                                    <p style="font-size:10pt; text-align:justify; position:relative; margin-left:6%;">Besides the regular changes to the database design, we integrated the graphical user interface in Django and also included OpenBable for displaying chemical structures. Furthermore we visualised our concept this week.</p>
-                                   <p><a class="btn btn-large btn-primary" href="#">Learn more</a></p>
                                  </div>
                                </div>
@@ Line 99: / Line 99: @@
                                    <h1>Week 16</h1>
                                    <p style="font-size:10pt; text-align:justify; position:relative; margin-left:6%;">This week we mainly made changes to the NRP-Design interface and integrated ajax.</p>
-                                  <p><a class="btn btn-large btn-primary" href="#">Browse gallery</a></p>
                                  </div>
                                </div>
@@ Line 109: / Line 109: @@
                                    <h1>Week 17</h1>
                                    <p style="font-size:10pt; text-align:justify; position:relative; margin-left:6%;">Besides fixing the relations in our database and improving th interface, we integrated Gibthon - the tool for Gibson primer design developed by the 2011 Cambridge team.</p>
-                                   <p><a class="btn btn-large btn-primary" href="#">Browse gallery</a></p>
                                  </div>
                                </div>
@@ Line 119: / Line 119: @@
                                    <h1>Week 18</h1>
                                    <p style="font-size:10pt; text-align:justify; position:relative; margin-left:6%;">This week we started filling our database, using the NRPS-PKS database. Besides that we introduced session data and updated the designer algorithm.</p>
-                                  <p><a class="btn btn-large btn-primary" href="#">Browse gallery</a></p>
                                  </div>
                                </div>
@@ Line 129: / Line 129: @@
                                    <h1>Week 19</h1>
                                    <p style="font-size:10pt; text-align:justify; position:relative; margin-left:6%;">As we noticed, that different prediction tools give different domain borders, we further evaluated them and finally integrated antiSMASH</p>
-                                  <p><a class="btn btn-large btn-primary" href="#">Browse gallery</a></p>
                                  </div>
                                </div>
@@ Line 139: / Line 139: @@
                                    <h1>Week 20</h1>
                                    <p style="font-size:10pt; text-align:justify; position:relative; margin-left:6%;">This week we started the manual sequence annotation and database curation and integrated the multiple sequence alignment in the database input interface.</p>
-                                  <p><a class="btn btn-large btn-primary" href="#">Browse gallery</a></p>
                                  </div>
                                </div>
@@ Line 149: / Line 149: @@
                                    <h1>Week 21</h1>
                                    <p style="font-size:10pt; text-align:justify; position:relative; margin-left:6%;">During this week we integrated our tool with the parts regisrty, did more manual database curation, added more hidden Markov-Models to antiSMASH and improved Gibthon.</p>
-                                   <p><a class="btn btn-large btn-primary" href="#">Browse gallery</a></p>
                                  </div>
                                </div>
@@ Line 159: / Line 159: @@
                                    <h1>Week 22</h1>
                                    <p style="font-size:10pt; text-align:justify; position:relative; margin-left:6%;">In order to do request management we introduced celery to our system. Furthermore we optimised the multiple sequence alignment and did lots of manual database curation.</p>
-                                  <p><a class="btn btn-large btn-primary" href="#">Browse gallery</a></p>
                                  </div>
                                </div>
@@ Line 168: / Line 168: @@
                                  <div class="carousel-caption scrollContent2" data-spy="scroll" data-target="#navbarExample" data-offset="0">
                                    <h1>Week 23</h1>
-                                   <p style="font-size:10pt; text-align:justify; position:relative; margin-left:6%;">In the final week we added several linkouts, including SBOL and GenBank files and fixed lots of bugs.</p>
+                                   <p style="font-size:10pt; text-align:justify; position:relative; margin-left:6%;">In the final week we added several linkouts, including SBOL and GenBank files and fixed lots of bugs. After the Regional Jamboree in Lyon, we fixed several bugs and most important we introduced a library function. This allows for construction of many similar NRPs at a time, which could for example be useful for medical compund screening.</p>
-                                   <p><a class="btn btn-large btn-primary" href="#">Browse gallery</a></p>
                                  </div>
                                </div>
@@ Line 350: / Line 350: @@
                      <ul class="nav nav-tabs">
                        <li class="active"><a href="#a18" data-toggle="tab">Interface</a></li>
-                       <li><a href="#b18" data-toggle="tab">Database</a></li>
+                       <!--<li><a href="#b18" data-toggle="tab">Database</a></li>-->
                        <li><a href="#c18" data-toggle="tab">Gibthon</a></li>
                      </ul>
@@ Line 362: / Line 362: @@
                                  </html>{{:Team:Heidelberg/Templates/NRPS-W-18a}}<html>
                                  </p>
-                             </div>
+                             </div><!--
                              <div class="tab-pane" id="b18">
                                  <p style="font-size:12pt; text-align:justify;">
                                  </html>{{:Team:Heidelberg/Templates/NRPS-W-18b}}<html>
                                  </p>
-                             </div>
+                             </div>-->
                              <div class="tab-pane" id="c18">
                                  <p style="font-size:12pt; text-align:justify;">
@@ Line 382: / Line 382: @@
                  <div>
                      <ul class="nav nav-tabs">
-                       <li class="active"><a href="#a19" data-toggle="tab">Algorithm</a></li>
+                       <!--<li class="active"><a href="#a19" data-toggle="tab">Algorithm</a></li>-->
-                       <li><a href="#b19" data-toggle="tab">Database</a></li>
+                       <li class="active"><a href="#b19" data-toggle="tab">Database</a></li>
                        <li><a href="#c19" data-toggle="tab">Frontend</a></li>
                      </ul>
@@ Line 390: / Line 390: @@
                      <div class="scrollContent nav navbar" data-spy="scroll" data-target="#navbarExample" data-offset="0">
-                        <div class="tab-content">
+                        <div class="tab-content"><!--
                              <div class="tab-pane active" id="a19">
                                  <p style="font-size:12pt; text-align:justify;">
                                  </html>{{:Team:Heidelberg/Templates/NRPS-W-19a}}<html>
                                  </p>
-                             </div>
+                             </div>-->
                              <div class="tab-pane" id="b19">
                                  <p style="font-size:12pt; text-align:justify;">
@@ Line 401: / Line 401: @@
                                  </p>
                              </div>
-                             <div class="tab-pane" id="c19">
+                             <div class="tab-pane active" id="c19">
                                  <p style="font-size:12pt; text-align:justify;">
                                  </html>{{:Team:Heidelberg/Templates/NRPS-W-19c}}<html>
@@ Line 453: / Line 453: @@
                              <div class="tab-pane active" id="a21">
                                  <p style="font-size:12pt; text-align:justify;">
-                                 Please see our documentation for our final results.
+                                 </html>{{:Team:Heidelberg/Templates/NRPS-W-21a}}<html>
                                  </p>
                              </div>
@@ Line 497: / Line 497: @@
                              <div class="tab-pane active" id="a23">
                                  <p style="font-size:12pt; text-align:justify;">
-                               Please see our documentation for our final results.
+                               </html>{{:Team:Heidelberg/Templates/NRPS-W-23}}<html>
+After the Regional Jamboree in Lyon, we fixed several bugs and most important we introduced a library function. This allows for construction of many similar NRPs at a time, which could for example be useful for medical compund screening.
                                  </p>
                              </div>

Team:Heidelberg/NRPSDesigner

From 2013.igem.org

Latest revision as of 03:57, 29 October 2013

Team

Project

Notebook

Parts

Software

Human Practice

Safety

Modeling

NRPS-Designer. Designing custom peptides.

Week 12

Week 13

Week 14

Week 15

Week 16

Week 17

Week 18

Week 19

Week 20

Week 21

Week 22

Week 23

Methods:

Initial database setup

Draft of command-line application

Pathway finding implemented in C++

Database redesign

Extensions of user interface

Nils finally uses a real operating system

Database redesign

GUI extension

Draft of PFAM querying

Concept transferred to django framework

Database redesign

PFAM integration

Database entries

Concept visualisation

Graphical user interface and Django-registration

Use of OpenBable for structure display

Monomer selection page aesthetic improvements

AJAX functionality

Designer interface - modifications

Integration of Gibthon

Database updates

Integrating NRPS-PKS

Session data

Tool selection for domain prediciton

Domain visualisation using pfam

NRPS-PKS sequence search and manual curation

SBOL and GenBank

Database Curation