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|id=Vol-1146/paper11
|storemode=property
|title=ASSP; the Antibody Secondary Structure Profile search tool
|pdfUrl=https://ceur-ws.org/Vol-1146/paper11.pdf
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==ASSP; the Antibody Secondary Structure Profile search tool==
https://ceur-ws.org/Vol-1146/paper11.pdf
ASSP; the Antibody Secondary Structure
Profile search tool
Dimitrios Vlachakis† , Alexandros Armaos† , Kasampalidis I, Arianna
Filntisi, Sophia Kossida⇤
Bioinformatics & Medical Informatics Team, Biomedical Research Foundation
Academy of Athens, Soranou Efessiou 4, Athens 11527, Greece
⇤
skossida@bioacademy.gr
Abstract
Antibodies constitute the first line of defense against harmful invaders. In the
post genomics era the sheer size of antibody related NGS information is a major
bottleneck in the quest of understanding and tackling complex genetic diseases
and immunological disorders. Bioinformatics is becoming hugely involved in the
processing of this data with the development of new, more accurate and effi-
cient algorithms. However, one of the major drawbacks of modern bioinformatics
is the fact that protein similarity and blast searches are still based on primary
amino acid sequence rather than structural data. Primary sequence searches are
inadequate, as they fail to provide a realistic fingerprint for the query protein.
Antibody function is much more related to its 3D structure and physicochemical
profile rather than its primary amino acid sequence. After all, structure is much
more conserved than sequence in nature. In this direction, a novel platform has
been developed, which is capable of performing a customized hydropathy blast
using traditional sequence blast filtering and an integrated fast similarity search
algorithm that uses protein secondary structure information. The Antibody Sec-
ondary Structure Profile (ASSP) tool will use secondary structural information
from the PDB database when available, whereas if the query antibody is not in-
dexed in the RCSB PDB database, it will automatically determine the secondary
elements of the given antibody by performing an “on the fly” secondary struc-
ture prediction. All query antibodies are then blasted against the RCSB PDB
secondary elements database. Hits are scored, ranked and returned to the user
via a well-organized and user friendly graphical interface.
Copyright c by the paper’s authors. Copying permitted only for private and academic purposes.
In: Costas S. Iliopoulos, Alessio Langiu (eds.): Proceedings of the 2nd International Conference
on Algorithms for Big Data, Palermo, Italy, 7-9 April 2014, published at http://ceur-ws.org/
† These authors have contributed equally to this study.
⇤ Corresponding author: Sophia Kossida, Bioinformatics & Medical Informatics Team, Biomedi-
cal Research Foundation, Academy of Athens, Soranou Efessiou 4, Athens 11527, Greece
Tel: + 30 210 6597 199, Fax: +30 210 6597 545 E-mail: skossida@bioacademy.gr
69
ASSP; the Antibody Secondary Structure Profile search tool
1 Introduction
The hydrophobic e↵ect is the tendency of non-polar substances to avoid contact
with water. The hydropathy of an amino acid, which is derived from the physico-
chemical properties of its side chains, determines in part the orientation of its side
chains in the three-dimensional protein structure. In particular, when a protein
folds into a three-dimensional structure, the majority of the hydrophobic side-
chains cluster together within the core of the protein. This removal of the hy-
drophobic side-chains out of contact with water generates sufficient free energy to
maintain the folded structure of the protein. The determination of the hydrophobic
or hydrophilic inclinations of a given amino acid side-chain has been approached
in a number of ways. Measuring the partition coefficient of a given amino acid
side-chain between water and a non-interacting, isotropic phase as well as calcu-
lating a transfer free energy from that coefficient is one such approach. Another
way to calculate the hydropathy of a given side-chain is the tabulation of residue
accessibilities from the atomic co-ordinates of twelve globular proteins, taking into
consideration that the ensemble average of the actual locations of a side-chain
should be a direct evaluation of its hydropathy. An additional approach combined
those previously mentioned methods, resulting in the construction of a hydropathy
scale, according to which each amino acid has been given a value reflective of its
relative hydrophilicity and hydrophobicity [4, 17].
The twenty amino acids found in nature have been categorized in hydropathy
classes based on the previously mentioned amino acid hydropathy index developed
by Kyte and Doolittle (1982). Specifically, the amino acids with a hydropathy index
equal to or more than 1.8 were defined as hydrophobic. The amino acids with a
hydropathy index equal to or less than 3.3 were defined as hydrophilic, while the
amino acids with a hydropathy index less than 1.8 and more than -3.3 were defined
as neutral. Three classes were thus defined: the hydrophobic class (I, V, L, F, C, M,
A, W), the neutral class (G, T, S, Y, P, H) and the hydrophilic class (D, N, E, Q, K,
R). Tryptophan (W) was included in the hydrophobic class, its hydropathy index
varying from 0.9 to 1.9, depending on the study. As a general observation, amino
acids with large, nonpolar or largely nonpolar side-chains tend to be hydrophobic,
while the least hydrophobic amino acids are the ones that are charged and largely
polar, such as asparagine. Statistical analysis, in particular correspondence analysis
(COA) and hierarchic classification (CAH), has been conducted according to those
three hydropathy classes upon 2474 sequences of antibody variable regions, which
were extracted from human productively rearranged sequences [17, 25, 8]. There
is a significant variation among the hydrophobicities of the amino acids. Some
are strongly hydrophobic, others are strongly hydrophilic, while others include
both hydrophobic and hydrophilic parts and are called amphiphilic. For such
amphiphilic molecules it is sometimes useful to define a hydrophobic moment,
which is analogous to a dipole moment. For a single amino acid, the hydrophobic
moment can be defined as a line that points from the Ca atom to the middle of
the side-chain, and whose length is proportional to the hydrophobicity of the side-
chain. The dipole moment of a protein, or a part of it, is obtained by summing the
individual vectors (in magnitude and direction) corresponding to the amino acids
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ASSP; the Antibody Secondary Structure Profile search tool
the protein is composed of. For example, an ↵ helix located on the surface of a
protein will have one side of the helix exposed to solvent and the other side facing
the interior of the protein. The amino acids that comprise the buried side of the ↵
helix will, in general, be much more hydrophobic than those on the solvent-exposed
side of the helix. This asymmetry results in the ↵ helix having a large hydrophobic
moment directed towards the center of the protein [21].
The three-dimensional structure of a protein is determined by the balance be-
tween a number of destabilizing and stabilizing forces, such as conformational en-
tropy, electrostatic interactions, hydrogen bonds, van der Waals interactions and
hydrophobic interactions. However, hydropathy is considered to be the most promi-
nent driving force responsible for the folding of proteins. Protein folding occurs
in the presence of water, the properties of which are dominated by its inclination
to form hydrogen bonds. Polar compounds can share hydrogen bonds with water
and, for this reason, are readily soluble. In contrast, when a hydrophobic nonpolar
surface is introduced into an aqueous environment, it prevents hydrogen bonding
from occurring, which forces the water molecules to adopt alternative arrangements
that permit hydrogen bonding to other water molecules. This inflicted restriction
on the alignment of the water molecules has an energetic cost and is the physical
basis of the hydrophobic e↵ect. It has been calculated that when a protein folds,
81% of the nonpolar side-chains (Ala, Val, Ile, Leu, Met, Phe, Trp, Cys), 70% of
the peptide groups, 63% of the polar side chains (Asn, Gln, Ser, Thr, Tyr) and
54% of the charged side chains (Arg, Lys, His, Asp, Glu) are buried in the interior
of the protein, out of contact with water [21, 23].
2 Description of ASSP
Hydropathy is a physicochemical property known to be well conserved among an-
tibodies, which can be explained to a large extent by the significant contribution
of the hydrophobic residues to the folding of antibodies. Numerous studies on
proteins and antibodies have demonstrated that the information necessary to pro-
duce a given three dimensional protein structure can be encoded by many di↵erent
amino acids. In contrast, it has been demonstrated that the periodicity of po-
lar and nonpolar amino acids is the major determinant of secondary structure in
self-assembling oligomeric peptides. In fact, the choice between ↵-helical and -
sheet secondary structure is influenced by the sequence periodicity of polar and
nonpolar amino acids. Even though amino acid residues may di↵er in their intrin-
sic preferences for one secondary structure versus another, these preferences can
be overwhelmed by the drive to form amphiphilic structures capable of burying
hydrophobic surface area. It can be observed that structural similarity among an-
tibodies is reflected on the distribution of hydropathicity along their amino acid
sequences, since the hydrophobicity patterns of residues match the periodicity of
secondary structures.
Homologous antibodies and proteins within a antibody/protein family as well
as proteins with related structures appear to have similarities in their hydropathy
distributions, even when sequence similarities could not be detected [14, 2, 32, 24].
Since the hydropathy distribution along the antibody sequence has been recog-
71
ASSP; the Antibody Secondary Structure Profile search tool
nized as a feature useful for the characterization of protein structure in the form of
hydropathy profiles, a number of methods based on hydropathy have been devel-
oped in order to explain the folding and the structural features of antibodies. The
realization that protein sequence contains hydropathy patterns led to the develop-
ment of reduced amino acid alphabets based on hydropathy for the prediction of
secondary structure.
Hydropathy has also been utilized for the detection of analogous and distantly
related proteins and the classification of new protein sequence data. The use of
hydropathy profile analysis has made possible the identification of more distantly
related antibodies than could be done by sequence comparison. In addition, anti-
body sequence databases have been analyzed using hydropathy patterns with the
goal of identifying new members of functional classes [17, 24, 7, 26, 5, 33, 19, 20, 6].
Many homologous proteins share very low primary sequence identity and sim-
ilarity scores amongst them. The most characteristic example of such proteins is
viral enzymes. Helicases, proteases and polymerases are just few of the many ex-
amples of protein families that are structurally conserved but may share no more
than 10% sequence identity with each other. Consequently, looking for homologues
of a certain viral enzyme or even for a suitable template structure during homol-
ogy modelling using traditional amino acid based blast searches is futile. However,
careful structural analysis of any of the above enzymes reveals that those proteins
are actually highly conserved in their secondary, tertiary and quaternary struc-
tures. Moreover, all evolutionary protein relationships as well as protein function
analysis should also be based on searches that utilize structural information. Over-
all, it has been established that homologous proteins are much more conserved in
their structures than in their amino acid primary sequences [4, 17].
Herein, the ASSP tool takes advantage of the full RCSB PDB secondary struc-
ture database in order to perform blast-like searches in the secondary element level
amongst proteins. To date, even though long studies have been conducted in many
fields of structural biology and modern bioinformatics this problem not been yet
satisfactorily addressed [15, 1, 22]. This is a fact that necessitates the need to the
development of such a platform.
ASSPs main-window is a menu-driven interface as well as a tab step-by-step
layout. Initially the user has an option regarding the query input type that will
be used. ASSP will handle both primary amino acid sequence as well as DSSP-
formatted secondary element protein sequence [13]. The user can follow two main
routes for the ASSP run: Firstly, the user may input either raw primary amino
acid sequence for a conventional blast search or opt for a quick secondary struc-
ture prediction of the amino acid sequence using the built-in STRAP module [9].
STRAP will perform a very fast, over the internet secondary structure prediction,
which will eventually return the predicted secondary element composition of the
query protein [9]. Eventually a DSSP compatible secondary structure determi-
nation code will have been obtained for the actual secondary structure similarity
search [13]. Secondly, an existing DSSP compatible secondary structure determi-
nation code may be used as input from the user straightaway, which will then be
automatically blasted against the secondary structural index database of the RCSB
Protein Databank. If a secondary element antibody sequence description is used,
72
ASSP; the Antibody Secondary Structure Profile search tool
Figure 1: The graphical user interface for the ASSP platform provides an intuitive
pipeline for running similarity searches that eliminate the chance for human error,
while at the same time providing a graphical, easy to comprehent results output
window. The graphical user interface of the ASSP platform, consists of the follow-
ing main windows: 1. The blast type selection option. 2. The input query window.
3. Sequence name and number of hits selections 4. The result-output area. The
alignmed regions are color-coded in accordance to the IMGT coloring scheme for
hydropathy. The identical residues are colored green, the hydrophobic residues are
colored blue, the neutral residues are colored red and the hydrophilic residues are
colored yellow.
then ASSP will move swiftly to the actual similarity blast search.
The screening process of ASSP is broken down in two steps. First a conventional
primary sequence-based blast is performed with a threshold value of 30% identity,
when temporary file is created with all sequences sharing more than 30% identity
with the query antibody sequence (using the blosum62 substitution matrix). Then
the custom made hydropathy substitution matrix is engaged and the previously
filtered entries are ranked according to their identity/similarity scores based on
their hydropathy profiles.
The hydropathy matrix has been created using the antibody hydropathy index
from IMGT [18]. Results in the form of alignments and similarity percentages are
calculated, scored, ranked and returned to the user through the same graphical
interface that has been specifically designed to simplify the task for the user and
to eliminate the possibility of a user-inflicted error (see Figure 1). The output
window is color-coded in accordance to the IMGT coloring scheme for hydropathy.
The identical residues are colored green, the hydrophobic residues (4, 5 to 0, 9)
are colored blue, the neutral residues ( 0, 4 to 3, 2) are colored red and the
hydrophilic residues ( 3, 5 to 4, 5) are colored yellow. The results are outputted
and saved in easy to manipulate text-based text/ascii files for future analysis.
The secondary description code that ASSP has adopted is the same with the
one DSSP has been using for many years now [13]. This was intentionally done
for ease of use and backward compatibility issues. More specifically an eight-letter
description code is used. Using just eight letters, instead of the traditional twenty
73
ASSP; the Antibody Secondary Structure Profile search tool
amino acid letters, makes similarity searches ever more efficient and faster than
ever before. The eight letter secondary element code comprises of the following
letters: H for ↵ helix conformation, B for residues in isolated beta-bridge, E for
extended strands that participate in beta ladders, G for 3/10 helices, I for pi helices,
T for hydrogen bonded turns and S for bends. C is used for the blank space in
the DSSP secondary structure, which represents a loop or an irregular element.
Other major suites, such as the PDBFINDER suite, also adopt this convention
with unstructured protein regions [11]. Same WHATIF uses C, as many times
leaving a blank may be confusing, misleading and inconvenient [31, 10, 12]. A
batch execultion mode has also been prepared for the ASSP suite. A simple text
file is required with as many sequences as the user wishes, each one stored in a
di↵erent line. The ASSP algorithm will then automatically read that file line by
line and execute the antibody similarity search for as many times as the lines of the
input batch file. This comes quite handy for those who wish to perform secondary
structure similarity searches on large databases of protein or peptide sequences
[28, 30, 29, 3, 27, 16]. Finally, an extensive manual and use-case based examples
for the use of ASSP, will pop-up through the Help button, using the operating
systems HTML browser application.
3 Conclusions
In conclusion, the ASSP toolkit provides a novel, quick and reliable tool for in
silico antibody similarity searches in one pipelined platform under a user friendly
graphical user interface. We therefore, propose that our structural similarities
application described here would yield results of great interest to many antibody-
related scientific disciplines. The ASSP platform is distributed as freeware under
a GNU license.
4 Availability
Availability: ASSP can be freely downloaded via our dedicated server system at
http://www.bioacademy.gr/bioinformatics/assp/index.html
ASSP is an open source, cross platform application available freely to all users
under a GNU license basis. The full package, including installation scripts, figures,
a full description, a detailed manual, complete tutorials as hands-on use cases,
software prerequisites and various examples can be downloaded at: http://www.
bioacademy.gr/bioinformatics/assp/. Prior to download; check the provided
information on the website about software prerequisites. Please email comments
and bug reports at dvlachakis@bioacademy.gr.
Acknowledgements
This work was partially supported by:
1. The BIOEXPLORE research project. BIOEXPLORE research project falls un-
der the Operational Program “Education and Lifelong Learning” and is co-financed
by the European Social Fund (ESF) and National Resources.
2. European Union (European Social Fund - ESF) and Greek national funds
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ASSP; the Antibody Secondary Structure Profile search tool
through the Operational Program “Education and Lifelong Learning” of the
National Strategic Reference Framework (NSRF) - Research Funding Program:
Thales. Investing in knowledge society through the European Social Fund.
The authors would like to thank Prof. Marie-Paule LeFranc and the IMGT
institute for their constructive comments and support.
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