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SAGE: a Fast Computational Tool for Linear Epitope Grafting onto a Foreign Protein Scaffold Riccardo Capelli, Filippo Marchetti, Guido Tiana, and Giorgio Colombo J. Chem. Inf. Model., Just Accepted Manuscript • DOI: 10.1021/acs.jcim.6b00584 • Publication Date (Web): 30 Nov 2016 Downloaded from http://pubs.acs.org on December 19, 2016
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SAGE: a Fast Computational Tool for Linear
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Epitope Grafting onto a Foreign Protein Scaffold
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Riccardo Capelli1,2, Filippo Marchetti2, Guido Tiana1, Giorgio Colombo2,*
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1 Center for Complexity & Biosystems and Dipartimento di Fisica, Università degli Studi di
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Milano and INFN, via Celoria 16, 20133 Milan, Italy
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2 Istituto di Chimica del Riconoscimento Molecolare, Consiglio Nazionale delle Ricerche, via
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Mario Bianco 9, 20131 Milan, Italy
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Graphical TOC
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ABSTRACT
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Computational design is becoming a driving force of structural vaccinology, whereby protein
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antigens are engineered to generate new biomolecules with optimized immunological properties.
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In particular, the design of new proteins that contain multiple, different epitopes can potentially
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provide novel highly efficient vaccine candidates. In this context, epitope grafting, which entails
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the transplantation of an antibody recognition motif from one protein onto a different protein
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scaffold (possibly containing other immunoreactive sequences) holds great promise for the
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realization of superantigens.
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Herein, we present SAGE (Strategy for Alignment and Grafting of Epitopes), an automated
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computational tool for the implantation of immunogenic epitopes onto a given scaffold. It is
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based on the comparison between the expected secondary structures of the candidates to be
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grafted with all the secondary structures in the target scaffold. Evaluating the differences both in
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sequence and in structure between the epitope and the scaffold returns a ranking of most
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probable molecules containing the new antigenic sequence. We validate this approach
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identifying the grafting positions obtained in previous works by experimental and computational
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methods, proving an efficient, flexible and fast tool to perform the initial scanning for epitope
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grafting. This approach is fully general and may be applied to any target antigen and candidate
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epitopes with known 3D structures.
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Introduction
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The raise in the number of epidemics caused by new pathogens, combined with emerging drug-
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resistance, make the development of preventive and therapeutic vaccines an urgent necessity1,2.
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Vaccine discovery has been revolutionized by the impact of genome sequencing technologies.
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The genomic analysis of pathogens permitted the discovery of novel candidates for protein-based
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vaccines (antigens) directly from genomic information. This process was named “reverse
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vaccinology” (RV) to stress the fact that vaccine discovery originated only from sequence
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information3. Ideally, RV entails the identification of candidates at relatively late stages of the
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development process, in contrast to the case of live-attenuated organisms. Consequently, one
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expects a reduction of some of the bottlenecks in vaccine development, and thus a lower risk of
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failure.
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The reach of RV was recently expanded by the integration with structural information, which
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should expectedly provide the molecular information necessary to obtain more effective vaccine
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candidates. In fact, the structure-based design of antigens (termed Structural Vaccinology, SV)
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represents a new driving force in vaccinology1.
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Previously, we developed and tested a computational SV approach which was implemented as a
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rapid and inexpensive tool for epitope design2-7, and still guarantees top performance, as
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validated by comparison with in vivo and in vitro immunological tests. The overall goal of this
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approach was the identification of immunoreactive epitopes recognized by antibodies, starting
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from the knowledge of 3D structures of the immunogenic antigens. The structural information
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permits the use of an epitope prediction method called Matrix of Local Coupling Energies
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(MLCE)8, now implemented in a webserver (http://bioinf.uab.es/BEPPE)9. Using this approach,
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we successfully designed seroreactive peptides for several diseases, including melioidosis10 and
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cystic fibrosis11.
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A possible improvement to the direct use of peptidic epitopes consists in implanting them onto a
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scaffold of interest to improve their stability. Moreover, to maximize the efficacy and the
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durability of the immune response, multiple epitopes can be inserted into the starting structure.
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This procedure, termed Epitope grafting, has been widely used on virus-like particles (VLPs)
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since early 1980s12,13 and was pioneered for immunogenic proteins by the Schief group14-17.
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Epitope grafting involves the transplantation of a structural/functional motif onto a structurally
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homologous region of an unrelated protein target, possibly already hosting other reactive
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sequences. The antigen target protein must display one or more regions that are conformationally
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compatible with those of the epitope to be grafted. Once the epitope is transplanted, its
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conformation should be stable in the new context, to support optimal presentation for the binding
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of the antibody and for processing by the immune system. The availability of an automated tool
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for the design of multi-epitope antigens is expected to be a relevant support for vaccine
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development and for their testing in different contexts, from structural biology to immunology.
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In particular, it permits to rapidly select stable protein constructs containing a foreign antigen
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among an ensemble of alternative solutions, without resorting to complex and lengthy techniques
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of structural biology and modeling.
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Our computational pipeline, called SAGE (Strategy for Alignment and Grafting of Epitopes),
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was tested analyzing the results of blind linear epitope grafting predictions, and benchmarking
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against known cases of successful design reported in the literature15-20. It is found that SAGE
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identifies successfully the best experimentally-validated candidates among its top scoring
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solutions without any preliminary knowledge-based input.
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Workflow
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The prediction of the grafting position requires the knowledge of the structure of the target, of
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the structure of the whole immunogenic cognate protein that contains the epitope to be
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transplanted and of the position of the epitope along the sequence. It consists of 4 phases: 1)
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Structural/sequence alignment, 2) Secondary structure prediction, 3) Structure scoring, 4)
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Exposed surface scoring. SAGE is written as a Python 2.7 package that performs the analysis by
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accessing external programs, such as PSIBLAST and Naccess, via the Internet.
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Alignment. The program takes the user-defined linear epitope from the original crystallographic
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structure of the cognate protein and performs 3 different types of alignment onto the target
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protein. For this step PyMOL21 scripts are used, namely a pure sequence alignment (Pymol script
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align), a structure-based alignment (Pymol script super) and hybrid alignment (Pymol script
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CEAlign22). In every alignment run, the script returns the 3 best candidates. Not to obtain a larger
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set of suboptimal candidates, the alignment is repeated for several cycles, constraining the search
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to segments of decreasing length which cover exhaustively the protein, as shown in Figure 1. At
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the end of this first part, SAGE generates a set of FASTA files containing the sequences of all
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the grafting candidates.
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--- Figure 1 here ---
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Secondary structure scoring. Since the secondary structure of the cognate fragment and of the
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scaffold can change upon grafting, we evaluated the secondary-structure propensities of the two
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based solely on their sequence, independently of the actual secondary structure they display. For
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this purpose we applied the s2D method23, which returns a per-residue α/β formation probability
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based on sequence information. Applying the prediction to a scaffold of N amino acids, the /
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propensities can be seen as two different points a, in a N-dimensional Euclidean space.
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After the alignment, we obtained M different sequences of N amino acids each. Thus, we can
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define a distance , , , , = ∑
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the difference in each of the two types of secondary structure between any two sequences. These
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distances are calculated between the scaffold and the sequences obtained from the alignment to
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evaluate how similar the grafting candidates are expected to be to the original structure. The
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scores on the two types of secondary structures are then merged in a score for secondary-
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structure scaffold compatibility, defined as
,
,
−
between such points that quantifies
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=
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To take into account the similarity between the epitope in its original protein and in the
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candidates, we also evaluated the similarity in secondary-structure propensity in the epitope-
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containing segment. If this is composed by L residues, we compare L-dimensional vector ,
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for the epitope in its cognate protein and two different L-dimensional vectors , for every
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candidate. Therefore, we define another score which reports the epitope secondary structure
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compatibility
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$ =
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Exposed surface scoring. The structural information available is used to evaluate how grafting
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affects the exposed surface of the protein. The exposed surface area is measured using Naccess24.
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For every candidate, SAGE, via the PyMOL mutagenesis tool, creates a new structure mutating
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the residues of the original scaffold with those of the new epitope using the most probable
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rotamer. The exposed surface is evaluated for all epitope residues in the cognate protein and for
,!
+
#
.
,!
+ ,! . ,! #
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the corresponding grafted residues on all the putative structures. We obtain a L-dimensional
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vector a with the reference exposure and, having M candidates, a collection of M L-dimensional
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vectors for the exposure of the grafted fragment. We can compute again an Euclidean distance
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, = ∑% − between the per-residue exposed area a measured on the epitope in
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the cognate protein and b measured on the grafted epitope, and an exposure score is defined as
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$&' =
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Selection of the candidates. To restrict the number of viable candidates, we need to define a
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single score. The main problem with the three scores defined above is that they are
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incommensurable, being defined on different scales. To make them comparable, every score is
.
,!
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normalized between 0 and 1 dividing it by the highest score found in the whole candidate pool.
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A reactive epitope in an immunogenic context has to be exposed to the solvent to be recognized
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by the immune system. To exploit the structural information given by the exposed surface
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analysis and to penalize the candidates with a hidden grafted epitope we use the normalized
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exposed surface score as a weight for the remaining scores (,$ =
$&' ,$ ∙ max! $&' max! ,$
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Finally, we generate the total score as the average of the scaffold and the epitope similarity
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reweighted scores defined above 1 ,-, = ( + ($ 2
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Validation
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To this end, we compared the grafting candidates obtained by SAGE with the structure shown in
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previous structural epitope grafting works. It is important to note here that a limited number of
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grafting reports have appeared in the literature, in particular with regards to cases where the final
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structure of the protein could be crystallized/obtained by homology modelling. We therefore
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retrieved the most possible available cases with structural information, running our analysis for
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viral sequences taken from HIV-1, RSV and snake toxin16,17,19,25, in which the authors grafted a
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relevant linear epitope onto a set of different scaffolds. We also tested SAGE performance for
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epitope grafting on virus-like particles18,20, although the graft length in one case18 does not
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correspond to the length of the scaffold deletion. The authors used fragments of different length,
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ranging from extremely short17,18,20 (
