NERVE: New Enhanced Reverse Vaccinology Environment
© Vivona et al; licensee BioMed Central Ltd. 2006
Received: 27 March 2006
Accepted: 18 July 2006
Published: 18 July 2006
Since a milestone work on Neisseria meningitidis B, Reverse Vaccinology has strongly enhanced the identification of vaccine candidates by replacing several experimental tasks using in silico prediction steps. These steps have allowed scientists to face the selection of antigens from the predicted proteome of pathogens, for which cell culture is difficult or impossible, saving time and money. However, this good example of bioinformatics-driven immunology can be further developed by improving in silico steps and implementing biologist-friendly tools.
We introduce NERVE (New Enhanced Reverse Vaccinology Environment), an user-friendly software environment for the in silico identification of the best vaccine candidates from whole proteomes of bacterial pathogens. The software integrates multiple robust and well-known algorithms for protein analysis and comparison. Vaccine candidates are ranked and presented in a html table showing relevant information and links to corresponding primary data. Information concerning all proteins of the analyzed proteome is not deleted along selection steps but rather flows into an SQL database for further mining and analyses.
After learning from recent years' works in this field and analysing a large dataset, NERVE has been implemented and tuned as the first available tool able to rank a restricted pool (~8–9% of the whole proteome) of vaccine candidates and to show high recall (~75–80%) of known protective antigens. These vaccine candidates are required to be "safe" (taking into account autoimmunity risk) and "easy" for further experimental, high-throughput screening (avoiding possibly not soluble antigens). NERVE is expected to help save time and money in vaccine design and is available as an additional file with this manuscript; updated versions will be available at http://www.bio.unipd.it/molbinfo.
Reverse Vaccinology (RV) is one of the best examples of how Bioinformatics can boost Molecular Immunology. The conventional approach to design vaccines requires pathogen's cultivation and dissection of its main components before testing their ability to elicit protective immunity. RV's novelty consists in starting the search for immunogenic antigens from in silico analyses of the pathogen's genome instead of culturing the microorganism . This allows scientists to save time and money while facing pathogens for which cell culture is difficult or impossible. RV potentially permits researchers to select, in addition to most in vivo expressed antigens (the easiest ones to purify), any protein encoded by the genome of a pathogen. RV is less helpful with eucaryotes due to complexities of cellular and tissue organization, and it is more effective with prokaryotes, extra- and intracellular. Indeed, the production of specific antibodies can boost immunity not only against extracellular pathogens, usually controlled by Th2-polarized responses, but also against either obligate or facultative intracellular ones, usually controlled by Th1-polarized responses. Even these latter pathogens are susceptible to humoral immunity during the extracellular phases of their infectious cycle and are made vulnerable by antibody cross-linking that modifies the intracellular milieu through signaling [2, 3].
The extracellular pathogen, Neisseria meningitidis serogroup B, stands as a milestone for RV. From its genome, Pizza and coworkers selected 570 out of 2158 predicted ORFs for protein expression as those meant to be new, surface-exposed antigens . 350 ORFs were successfully cloned, expressed in Escherichia coli and tested in a variety of assays. In the end, five proteins were found to satisfy criteria of surface exposure, conservation/expression in all strains as well as a significant titer in serum bactericidal assays. This work induced further research of other pathogens in the same way: Porphyromonas gingivalis , Streptococcus pneumoniae , Chlamydia pneumoniae , Bacillus anthracis  and group B streptococci . Enhancing such a genome-based approach with a trascriptome-based one – through the use of DNA microarrays – shows whether, when and to what extent antigens are expressed, thus identifying those highly expressed during infection processes . Finally, proteomics, although expensive and laborious, can verify in silico prediction of membrane composition .
We focused on the selection of the in silico vaccine candidates (VCs) from the list of predicted proteins to address two issues: the possibility (i) of automatizing the process with new criteria of analyses and (ii) of reducing the percentage of VCs to less than the 20–30% reported until now . Indeed, the real goal of in silico selection is to choose the minimal number of VCs sufficient to find protective antigens (PAs) during experimental phases, thus designing a vaccine that conserves time and money. Therefore, it has to be stressed that obtaining a high recall of PAs in a very small number of selected VCs is more convenient than trying to find all possible PAs. According to this approach it is more productive to select the PAs that will most likely be easily expressed. This reduces the risk of experimental troubles. The main cause of failed cloning and expression of 250 out of 600 VCs from Neisseria meningitidis B was the presence of more than one transmembrane spanning region (TM) . Thus, we decided to have no more than two predicted TMs as an a priori requirement. Of course, viable VCs can be missed because of such a filter; the extracellular loops of multispanning membrane proteins can actually be significant targets especially if reasonably large. Hence, one may focus on fragments likely to be exposed and accessible to antibodies rather than the entire protein in order to overcome eventual cloning and expression problems. Although such a "local" approach is reasonable, we designed the software to select entire antigens for experimental challenges rather than hazarding fine predictions that may not correspond to in vivo conditions.
Choosing such a conservative way to face vaccine design inevitably implies missing some PAs, but this is a small price to reach a valuable compromise. Even selecting only surface antigens may imply missing non-surface PAs. Yet discarding non-surface-exposed predicted antigens, thereby forwarding further bioinformatic analyses on selected ones, proved successful. However, to mimimize the risk of loosing good VCs, we stored up information concerning analytical steps for each sequence without distinction: the final output presents only selected VCs, but information concerning all other proteins is retained. VCs are presented with corresponding integrated analyses. In order to "rationalize" selection step, we took into account the importance of avoiding antigens that can potentially cause autoimmunity in man . Since Major Hystocompatibility Complex (MHC) ligands can be really short (as few as eight residues); this problem may rise also from antigens sharing weak global similarity with host proteins. Addressing such a question on a proteomic scale would make manual management of bioinformatic analyses impossible.
We report here on a new, fully automated RV system, developed to predict best VCs from bacterial proteomes (inferred by completely sequenced genomes and publicly available) and to manage and show data by user-friendly output.
Results and discussion
The system we created predicts the best VCs starting from the flatfile proteome of a prokaryotic pathogen. It forwards six proteome-wide analyses that mine and save data in text files and in an automatically generated MySQL table. The table will contain as many records of extracted information as the number of sequences in thirteen fields. The best VCs are finally selected and are shown in a user-friendly html table reporting seven out of the thirteen fields, linked to the textfile-information that they summarize.
The first proteome scannings assign each sequence three predictions: (i) subcellular localization, (ii) adhesin probability and (iii) topology, using the algorithms PSORTb 2.0 , SPAAN  and HMMTOP  respectively. Indeed, surface-exposed proteins such as outer membrane proteins and especially adhesins, are ideal targets for vaccine development [4, 17]. At the same time, the presence of more than one TM in many of these VCs, proved problematic in cloning and expression phases of RV (see introduction) . This clearly shows how predicting number of TMs is potentially a crucial step in saving time and money for subsequent experimental tests. The fourth step addresses the problem of sharing similarity regions between pathogen and host proteins. It is well known that in vaccine development this can either cause low immune response/tolerance or autoimmunity . We have chosen the algorithm BLAST to compare each pathogen's sequence as a query against the human proteome . Local alignment is suitable for this task because potential MHC ligands – the ones we search for to help predict potential interferences (tolerance or autoimmunity) with the immune system – can be very short (~9 residues) . Thus, the script scans alignments, extracting each sequence fragment (shared peptide, SP) that shows no more than three "positives" (compatible substitutions) and one "mismatch" (not compatible substitution) per nine-residues (minimal length required) window. In this way we not only take into account MHC II, commonly involved in presentation of exogenous antigens, but also MHC I, involved in cross-presentation [20, 21]. These settings can be changed at the start of the manager programme according to the user's preferences. Once an SP is found, corresponding bacterial and human sequences are reported with their position in the protein and the available description for the human entry in the MySQL table and the text file. Once this fourth step is complete, each pathogen's sequence is assigned to a file, named as its accession, and sent to four new fields in the MySQL database. The first two of these fields show query length and number of mined SPs. The third one reports four features for each SP: position, amino acid sequence, occurrence and "MHC ligand". This last feature is expressed as either "positive" or "negative" depending on the results from a screening of human MHC ligands derived from the database MHCPEP . These MHC ligands do not necessarily correspond to T-epitopes. It would be more advantageous to use a T-epitope prediction tool, but as of now there is no such tool available for standalone use. In addition, since the number of unknown MHC ligands is possibly high, "positive" is a "necessary-but-not-sufficient" condition, whereas "negative" stands as an absence of evidence rather than an evidence of absence. The fourth field reports the aggregate number of amino acid residues from all peptides.
Tuning NERVE settings on a known dataset including 10 proteomes.
Known protective antigens (non cytoplasmic, ≤2 TMs)
Primary accession numbers (Uniprot database)
PA83, Sap, EA1, AhpC
P13423 P49051 P94217 Q81Y83
Caf1, LcrV, YscF
P26948 P21206 Q7ARI0
Streptococcus agalactiae V
Sip, GBS67, GBS80
Q8E2F6 Q8DYR5 Q8E0S9
Streptococcus agalactiae III
Sip, GBS67, GBS80, C5a-ase
Q8E7W4 Q8E4C3 Q8E6E4 Q8E4T9
Streptococcus agalactiae Ia
Sip, GBS67, GBS80, C5a-ase, C protein alpha-antigen, C protein beta-antigen
Q3K3Z5 Q3K246 Q3K250 Q3K0M1 Q02192 Q3K3P4
Neisseria meningitidis B
NspA, PorA, TbpA, TbpB, GNA33, GNA992, NadA, App, GNA1870
Q7DDM2 Q51240 Q9K0U9 Q9K0V0 Q7DDU3 Q7DDJ2 Q9JXK7 Q9JXL6 Q9JXV4
FimA, HagA, HagB, PG32, PG33
P59914 P59915 Q7MTI5 Q9S3R9 Q9S3R8
OspA, OspC, BBI16
P14013 Q07337 O50870
Chlamydia trachomatis D
MOMP, OMP3, OMP2
Q46409 P21355 P38006
Test of NERVE settings on another dataset including 6 proteomes.
Known protective antigens (non cytoplasmic, ≤2 TMs)
Primary accession numbers (Uniprot database)
Spa, PBP2A, ClfA, collagen-binding, fibronectin-binding, enterotoxin A, alpha-toxin
Q8NXT0 Q8NWP5 P0A0L1 Q8NUH0 Q8NUU7 Q8NX49 Q8NXJ1
PorB, TbpA, TbpB
Q5F5V7 Q5F6Q4 Q5F6Q3
Streptococcus pyogenes M1
Spy0128, Spy0130, Spy0843, Spy1357, Spy1274, Spy1390, Spy0416, Spy2010, Spy2018
P60811 Q7DAL7 Q99ZD7 Q9A0C0 Q9A180 Q9A1S0 Q9A1S2 P58099 Q99XV0
CagA, VacA, NAP
Q9ZKW5 Q9ZLT1 Q9ZMJ1
MOMP, Pmp2, ArtJ, HtrA, OmpH-like
P27455 Q9Z3A1 Q9Z869 Q9Z6T0 Q9Z8N7
RV stands as a turning stone in Vaccinology. It shows how powerful and useful Bioinformatics can be in the post-genomic era. Creating a tool specifically designed to automatize in silico steps not only makes RV really available but also time and cost efficient. Indeed, NERVE was conceived to combine automation with an exhaustive treatment of VCs selection task by implementing and integrating six different kinds of analyses. Its modular structure allows further development of new, additional steps as well as the refinement of existing modules. This would improve the compromise between VC selection restrictivness and PA recall. Another goal was to avoid loosing information, thereby giving the user the chance to recover all data mined by NERVE for further investigation. For instance, recovered data regarding shared similarity regions between VCs and human proteins may be of help when taking into account possible occurrences of either tolerance or autoimmunity.
Finally, the NERVE prediction system proved – on a large number of bacterial proteomes – to be reliably effective in selecting very restricted VC pools that are characterized by a high recall (75–80%) of PAs. In particular these results narrow VC pool restriction from the reported 20–30% to an improved 8–9% of proteome sequences. NERVE's attempt to save time and money is also mediated by selection criteria, meant to facilitate crucial experimental steps, the protein cloning and expression phases. Last but not least, NERVE's user-friendly format and easily interpretable output should further aid researchers in designing subunit vaccines against bacterial pathogens.
Availability and requirements
Project name: NERVE
Home page: http://www.bio.unipd.it/molbinfo
This software is also available as an additional file [See Additional file 1]
Operating system: Linux (tested distribution: Debian)
Programming language: Perl
Other requirements: Perl 5.8.7 or higher, PSORTb 2.0, SPAAN
License: GNU GPL
Any restrictions to use by non-academics: licence needed
= major hystocompatibility complex
= open reading frame
= protective antigen
= Reverse Vaccinology
= shared peptide
= transmembrane helix
= vaccine candidate.
This work was supported by grant "Ricerca Nazionale – ex 60%" to FF. We thank Gabor Tusnady and Istvan Simon for providing HMMTOP algorithm, Massimo Facchinetti for addressing and managing intellectual property issues, Aaron Riedel for reviewing and editing the English and Dmitri Trakovsky for manuscript proofreading.
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