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Nov 30, 2016 - motif from one protein onto a different protein scaffold (possibly containing ... Herein, we present SAGE (strategy for alignment and g...
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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 (