ABSTRACT Summary: Non-Ribosomal Peptide Synthetases (NRPSs) are multimodular enzymes which biosynthesize many important peptide compounds produced by bacteria and fungi. Some studies have revealed that an individual domain within the NRPSs shows significant substrate selectivity. The discovery and characterisation of nonribosomal peptides are of great interest for the biotechnological industries. We have applied computational mining methods in order to build a database of NRPSs modules which bind to specific substrates. We have used this database to build an HMM predictor of substrates which bind to a given NRPS. Availability: The database and the predictor are freely available on an easy-to-use website at www.nrpssp.com

C Prieto

García-Estrada C

Lorenzana D

Martín Jf

CiteSeerX

1 
NRPSsp: Non-Ribosomal Peptide Synthase substrate predictor 
Prieto C
 1,*
, García-Estrada C
 1
, Lorenzana D
 1
 and Martín JF
 1
 
1
 Institute of Biotechnology of Leon, INBIOTEC, Parque Científico de León, Av. Real, 1, 24006 León, Spain. 
 
 
ABSTRACT 
Summary: Non-Ribosomal Peptide Synthetases (NRPSs) are multi-
modular enzymes which biosynthesize many important peptide 
compounds produced by bacteria and fungi. Some studies have 
revealed that an individual domain within the NRPSs shows signifi-
cant substrate selectivity. The discovery and characterisation of non-
ribosomal peptides are of great interest for the biotechnological 
industries. We have applied computational mining methods in order 
to build a database of NRPSs modules which bind to specific sub-
strates. We have used this database to build an HMM predictor of 
substrates which bind to a given NRPS.  
Availability: The database and the predictor are freely available on 
an easy-to-use website at www.nrpssp.com. 
Contact: carlos.prieto@unileon.es 
Supplementary Information: Supplementary data is available at 
Bioinformatics online. 
1 INTRODUCTION  
Nonribosomal peptide synthetases (NRPSs) are multi-modular 
enzymes involved in the biosynthesis of natural products.  A min-
imal NRPS module contains specific functional domains which are 
able to catalyse several activities, such as amino acid adenylation 
(A-activation), thioesterification (T- thiolation or acyl carrier do-
main) and peptide-bond formation (C-condensation domain), al-
lowing elongation of the nascent peptide (Schwarzer and Marahiel 
2001). The primary composition of the final product is determined 
by the sequential order of the A-domains along the synthetase, 
because each A-domain recruits a particular type of substrate. The 
crystal structure of the peptide synthetase GrsA, which was solved 
with a bound, a phenylalanine substrate molecule, has enabled the 
identification of 10 key residues in the A-domain which are im-
portant for the substrate binding (Conti et al. 1997). Accordingly, 
these residues can be determinant in the substrate specificity of A-
domains, and their extraction from characterised A-domains has 
archived a collection of key residues signatures and general rules 
for deducing substrate specificity of non-characterized A-domains 
(Challis et al. 2000; Stachelhaus et al. 1999). Moreover machine 
learning techniques have been applied to build a classifier based on 
20 key residues and on the physico-chemical properties of amino 
acids to gain prediction power (Rausch et al. 2005; Röttig et al. 
2011). Consequently, software tools and databases has been devel-
oped to collect NRPS products, such as NORINE (Caboche et al. 
2008) and NRPS-PKS(Anand et al. 2010), and to predict the bind-
ing substrates of a NRPS such as NRPS-PKS (Ansari et al. 2004)), 
  
*To whom correspondence should be addressed.  
NP.Searcher (Li et al. 2009), PKS/NRPS Analysis (Bachmann and 
Ravel 2009) and NRPSPredictor2 (Röttig et al. 2011). These pre-
diction Web sites are mainly based on the generally accepted rule 
of analysing the active site within the A-domains. However, it is 
known that this approach has difficulties analysing certain types of 
synthetases, especially those which belong to fungi species. Prob-
lems can be caused because the GrsA crystal seems to be an inade-
quate model for them (Jenke-Kodama and Dittmann 2009), or 
because the large number of sequence variants in the active center 
does not allow a correct extraction of the key residues for predic-
tion. This observation suggests the interest of developing new pre-
diction methods supported by other approaches. One of these ap-
proaches could be the use of HMM as Khurana et al. (2010) have 
applied to functionally classify the acyl:CoA synthetase super-
family members. This work suggests that the application of HMM 
profiles to classify this superfamily outperforms the predictions 
based on a limited number of active site residues (Khurana et al. 
2010).  The methods stated above can also be applied to a more 
ambitious goal, such as the determination of the substrate which 
binds to an adenylation domain. 
The current omics era has enabled the exponential growth of the 
sequenced NRPS.  This implies that a tool which could predict the 
specificity of their A domains is of increasing interest, and its 
training could be beneficial with the new annotated NRPS.  These 
facts, the previous experience of our group in the area, and the 
cited publications, have enabled the presented work, whose ulti-
mate goal is to develop a new bioinformatic tool in order to 
achieve the collection, annotation, storage and prediction of sub-
strates which bind to adenylation domains in NRPSs.  This open 
software tool applies a new approach in the areas of, the prediction 
based on HMM, enlarged training sets applying mining techniques, 
the regular update of its database and its design for the functional 
analysis of incoming NGS data.   
2 METHODS 
This work has been developed in three phases: (i) Construction of a data-
base with adenylation domains, which bind a known substrate; (ii) Build, 
train and test a computational predictor; (iii) Development of a web tool.  
The global work flow is represented in the additional figure 1 and a de-
tailed description of the methods is in the supplementary material.  In order 
to construct the database, a semi-automatic annotation protocol was imple-
mented and will be applied regularly in order to update the database (see 
supplementary material for a detailed description of the methods).  Regard-
ing the predictor, strategies based on position specific scoring matrices 
(PSSM) and hidden Markov models (HMM) were tested to build the classi-
fier.  HMM approaches obtain better results due to the sequence heteroge-
neity of the A-domains and consequently the difficulty of its alignment.  
That is why the classification method was developed with HMM, although 
Associate Editor: Prof. Alfonso Valencia
© The Author (2011). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com
 Bioinformatics Advance Access published November 29, 2011
 at Pennsylvania State U
niversity on O
ctober 5, 2016
http://bioinform
atics.oxfordjournals.org/
D
ow
nloaded from
 
2 
the idea of identifying key residues in input sequences was abandoned.  A 
cross validation of the predictor was done applying a Leave One Out test 
(LOO) and an ROC curve of these results was plotted with the R package 
ROCR (see supplementary figure.2). The input sequences of the LOO test 
were also analysed with NRPSpredictor2 (one class classifier) and 
PKS/NRPS Analysis in order to compare different approaches.  The predic-
tor is available online through the website www.nrpssp.com.  It was devel-
oped with LAMP architecture (Linux, Apache, MySQL, PHP).  
3 RESULTS 
The large increase of sequenced proteins which has occurred in 
recent years has enabled the collection of more data than in previ-
ous studies.  37,126 proteins were initially worked with, which 
were annotated as NRPSs or had at least one A-domain.  However, 
only a small subset of these proteins are fully annotated (only 721 
proteins are in the Swissprot subset), and a small fraction of this 
subset was useful for building the database.  The automatic annota-
tion of substrates with its corresponding A-domain obtained 1,490 
entries.  Then, a data curation was manually done correcting exist-
ing data errors and deleting the doubtful entries (mainly because (i) 
they do not belong to an NRPS module (ii) the lack of knowledge 
of the exact correspondence with a substrate).  This process results 
in a database with 1,598 domains which have a known binding 
substrate.  From these, 1,578 sequences were used for training the 
classifier because the rest binds to substrates which have less than 
4 annotated sequences.  Although the size is not too large, it is the 
biggest database which has been used to train a method which 
predicts NRPS substrates.  Rausch et al. (2005) used a database 
with 394 entries, of which 300 were used to train the SVM classi-
fier and the recent update of this method used a database with 557 
entries (the number of entries to train the classifier was not de-
scribed) (Rausch et al. 2005; Röttig et al. 2011).  This means that 
our database has more than triple the size of previous ones.  It is 
available online and the semi-automatic methods which have been 
developed allow its regular update.  We expect that this resource 
will be a reference set for future research in this area. 
This database was used to construct the classifier by means of 
the application of HMM profiles.  The reliability of the classifier 
was measured using all data as a train and test set and by a LOO 
method as well (see Methods section).  The error rate was 13.6% 
and 4.96% for LOO and whole training data respectively.  If low 
score results (highlighted in red in the web application) are consid-
ered as not available, the error rate decreases to 5.77% and 3.76% 
respectively.  The LOO sequences were analysed with NRPSpre-
dictor2 and PKS/NRPS Analysis in order to estimate their error 
rate in similar terms.  The NRPSpredictor2 test obtained an error 
rate of 22.6% taking into account all the predictions and 8.29% 
excluding null and unavailable predictions.  Similarly, PKS/NRPS 
Analysis obtained error rates of 73.7% and 27.35% respectively 
(see supplementary table.1).  This is a very promising result, indi-
cating that the use of more comprehensive training data and HMM 
achieves a more reliable predictor.  In addition, the results obtained 
by classifying fungal proteins were studied separately.  The error 
rate excluding low score results were 4.35% for LOO and 2.17% 
for whole training data, and if the low score results are not exclud-
ed, 35% for LOO and 4.10% for whole training data.  An increase 
of the error rate is noticeable when low score results are not fil-
tered.  This increase is induced by the wide variety of fungi A-
domains and their small number in the training set.  Similar results 
have been obtained with NRPSpredictor2 and PKS/NRPS Analy-
sis, whose predictions had a low coverage (around 30%) and an 
error rate of 36% and 9.1% respectively (excluding null and non-
available predictions, see supplementary table.2).  However, cov-
erage problems are expected to disappear as the number of fungi 
A-domains in the test set is increased and this is a major objective 
which NRPSsp attempts to address with frequent updates. 
NRPSsp is available via the website www.nrpssp.com.  This 
website easily allows the analysis of a set of sequences which are 
passed as parameters in a FASTA format (additional Figure 3 
shows an example).  In addition, the website has a download sec-
tion which contains the updated database that has been used to 
train the current classifier and the HMM profiles which have been 
built.  It enables future studies in the area and the execution of the 
classifier in a stand-alone mode.  In this way, the application is 
designed for use with NGS data, which is becoming common in 
biotechnological research, and allows a quick functional annotation 
of NRPS proteins and knowledge of the substrate specificity of 
their A domains. 
ACKNOWLEDGEMENTS 
Funding: This work was supported by a grant of the Agencia de 
Inversiones y Servicios de Castilla y León (record 
CCTT/10/LE/0001). Carlos Prieto was supported by a Juan de la 
Cierva programme (JCI-2009-05444) of the Ministry of Science 
and Innovation (Spain). 
REFERENCES 
Anand, S., et al. (2010), 'SBSPKS: structure based sequence analysis of polyketide 
synthases', Nucleic Acids Res, 38 (Web Server issue), W487-96. 
Ansari, M. Z., et al. (2004), 'NRPS-PKS: a knowledge-based resource for analysis of 
NRPS/PKS megasynthases', Nucleic Acids Res, 32 (Web Server issue), W405-13. 
Bachmann, B. O. and Ravel, J. (2009), 'Chapter 8. Methods for in silico prediction of 
microbial polyketide and nonribosomal peptide biosynthetic pathways from DNA 
sequence data', Methods Enzymol, 458, 181-217. 
Caboche, S., et al. (2008), 'NORINE: a database of nonribosomal peptides', Nucleic 
Acids Res, 36 (Database issue), D326-31. 
Conti, E., et al. (1997), 'Structural basis for the activation of phenylalanine in the non-
ribosomal biosynthesis of gramicidin S', EMBO J, 16 (14), 4174-83. 
Challis, G. L., Ravel, J., and Townsend, C. A. (2000), 'Predictive, structure-based 
model of amino acid recognition by nonribosomal peptide synthetase adenylation 
domains', Chem Biol, 7 (3), 211-24. 
Jenke-Kodama, H. and Dittmann, E. (2009), 'Bioinformatic perspectives on 
NRPS/PKS megasynthases: advances and challenges', Nat Prod Rep, 26 (7), 874-
83. 
Khurana, P., Gokhale, R. S., and Mohanty, D. (2010), 'Genome scale prediction of 
substrate specificity for acyl adenylate superfamily of enzymes based on active 
site residue profiles', BMC Bioinformatics, 11, 57. 
Li, M. H., et al. (2009), 'Automated genome mining for natural products', BMC Bioin-
formatics, 10, 185. 
Rausch, C., et al. (2005), 'Specificity prediction of adenylation domains in nonriboso-
mal peptide synthetases (NRPS) using transductive support vector machines 
(TSVMs)', Nucleic Acids Res, 33 (18), 5799-808. 
Röttig, Marc, et al. (2011), 'NRPSpredictor2—a web server for predicting NRPS 
adenylation domain specificity', Nucleic Acids Research. 
Schwarzer, D. and Marahiel, M. A. (2001), 'Multimodular biocatalysts for natural 
product assembly', Naturwissenschaften, 88 (3), 93-101. 
Stachelhaus, T., Mootz, H. D., and Marahiel, M. A. (1999), 'The specificity-conferring 
code of adenylation domains in nonribosomal peptide synthetases', Chem Biol, 6 
(8), 493-505. 
 at Pennsylvania State U
niversity on O
ctober 5, 2016
http://bioinform
atics.oxfordjournals.org/
D
ow
nloaded from
 


NRPSsp: non-ribosomal peptide synthase substrate predictor

http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=A2371F5CFE75E37854B111A6759D8ABD?doi=10.1.1.1035.2481&rep=rep1&type=pdf

NRPSsp: non-ribosomal peptide synthase substrate predictor

Abstract

Similar works

Full text

Available Versions

CiteSeerX