# HomoSAR: An Integrated Approach Using  Homology Modeling and Quantitative

Authors: Raghuvir R. S. Pissurlenkar; Evans C. Coutinho
Journal: Scholarly Research Exchange
 (2008)

---

## Abstract

3D-QSAR of peptides is a daunting task. The difficulty in peptide QSAR arises due to the sheer number of conformational degrees of freedom for peptides that makes alignment in a 3D grid an overwhelming task. In this paper, we propose a method of QSAR where the alignment of peptides is shifted from 3D space to 1D space, making the alignment of peptides a very simple proposition. The method calledHomoSAR, is based on an integrated approach that uses the principles of homology modeling in conjunction with the QSAR formalism to predict and design new peptide sequences.  The peptides to be studied are subjected to a multiple sequence alignment which is followed by scoring every position in the peptide sequence against a reference peptide in the alignment, through calculation of similarity indices. The
similarity indices obtained for each position (amino acid residue) in the peptide form the “descriptor” values (independent variables) which are then correlated to the biological activity of the peptide by G/PLS techniques.  As an application, the methodology has been illustrated for the dataset of nonamer peptides that bind to the Class I major histocompatibility complex (MHC) molecule HLA-A∗0201 as this dataset has been extensively studied.  The models generated have statistically significant correlation coefficients and predictive r2. The cross validated coefficients (q2) are in an acceptable range. The HomoSAR approach identifies amino acids and properties that are preferred or detrimental at every position in the peptide sequence. The approach is simple to use and is able to extract all information contained in the dataset to explain the underlying structure activity relationships. The approach is applicable to peptide sequences which are not all of uniform length.


---

## Body

## 1. Introduction

Peptides  and proteins form  one of the important
components of  all biological systems. Peptides are nature's choice to maintain
homeostasis and combat disease conditions and endowed them with high potency
(low dose), specificity, and selectivity (reduced side effects). The peptide
backbone is highly flexible, and the side chains of amino acids have the ability
to adopt a conformation complementary to the active site of the receptor so as to
match the bulk, hydrophobic, and electrostatic forces. For example, in the case of alpha-conotoxins,
which are a class of nicotinic acetylcholine receptor (nAChR) antagonists, a single amino acid substitution in
alpha-conotoxin PnIA shows a shift in the selectivity for the mammalian
neuronal nicotinic acetylcholine receptor subtypes [1]. The flexibility of the backbone and
the side chains also setup difficulties in the rational design of peptide drugs.
The process of experimental lead optimization of peptides becomes an exponentially
cumbersome procedure as the peptide length increases. For example, the design
of a dipeptide would require an experimental scan through 20×20 combinations
of amino acids to unravel the entire SAR of the dipeptide. Thus, the rational
design of peptides is still a daunting task.The
QSAR techniques have the power and ability to quickly optimize a peptide
sequence given a dataset of peptides with known biological activity. However,
this is mostly restricted to the 2D-QSAR methods. There are very few examples
in the literature that deal with the design and SAR of peptides by 3D-QSAR
approaches, for example, the
3D-QSAR studies of DPP-IV dipeptides [2], MHC binding peptides [3–7], and recently,
studies on the δopioid peptides—rubiscolins [8]. The
3D-QSAR techniques are not without their inherent problems. The difficulty of
obtaining a unique alignment of peptides for 3D-QSAR analysis makes the
application of CoMFA or CoMSIA techniques a daunting proposition. However, CoMFA and CoMSIA are the preferred
methods for small molecule 3D-QSAR. The alignment procedure becomes more complex
and uncertain as the degrees of freedom increase with increasing number of
rotatable bonds. However, receptor-based
alignment methods may provide a way out albeit at a high computational expense. It is for these reasons that 2D-QSAR
techniques being much simpler and quicker than the 3D techniques are often used
for studying peptides [9–24]. The power of 2D-QSAR results is dictated by the property spaced spanned.In view of these short comings, we propose a
method for QSAR of peptides where the central element of alignment is
translated from 3D space to 1D space, which can be executed easily and
accurately, while preserving the information content. The approach is calledHomoSAR and is distantly related to the
kernel-based approach of Salomon and Flower [23]. There are three basic steps in HomoSAR.
The first step in HomoSAR is the sequence
alignment of the peptides in the training and test sets separately as
followed in the homology modeling or comparative protein modeling procedures. The
multiple sequence alignment is the method of choice since it takes into account
all the peptides in the dataset. The second step of the approach scores all the
peptides of the dataset against a reference peptide in the alignment using similarity
indices. The similarity indices calculated for each and every
position in the peptide sequence is related to the binding activity in the third
step by a suitable statistical algorithm. The three individual steps in HomoSAR
are discussed in some details below.The central step in this approach is the
so-calledsequence alignment.
Sequence alignment is used for the detection of correspondences between amino
acids of a reference peptide/protein and those of the query peptide/protein and
can be related to the structure and activity of the peptides. The alignment of
amino acid sequences is a crucial step in homology modeling due to which many
different methods and programs have been published and are still being
developed. The earliest attempt to clarify the structural similarity between protein
sequences was by Needleman and Wunsch [25]. Variants of this algorithm have been
developed independently by others and applied in many fields. ALIGN, BESTFIT, and GAP [26] are some of the
computer-based programs which are being widely used for sequence alignment. The
original Needleman and Wunsch algorithm was written to handle only a pair of
sequences, whereas several other programs have been developed to handle multiple
sequence alignment. Recent ones in this category are CLUSTALW [27], MAXHOM [28],
and so forth. HomoSAR uses the multiple sequence
alignment over the pairwise alignment due to the ability of the algorithm to
handle multiple sequences and thus reduce the bias of a single reference.The second step following sequence alignment is
scoring or weighting the aligned sequences.
In homology modeling, this is provided by the so-calledhomology matrices which makes use of the most
probable amino acid substitutions according to the physical, chemical, or statistical
properties. From the various available matrices [29–35], the following
ones are frequently applied: identity
matrix, codon substitution matrix, mutation
matrix (Dayhoff or PAM 250 matrix), and physical
property matrices. HomoSAR uses one of the above-mentioned
scoring matrices for the multiple sequence alignment; however, for the QSAR
analysis, similarity indices calculated from specific amino acid properties are used. These indices are calculated for every amino
acid in the peptide sequence in relation to the amino acid in that position in
a reference peptide, as identified by the alignment procedure.The third step involves relating the
similarity indices for the amino acids in the sequences with the biological
activity through the use of a robust statistical method which is efficient enough
to identify relationships with statistical significance. The G/PLS algorithm is the statistical method
used in the third step ofHomoSAR,
which through its evolutionary nature is able to pick out descriptor variables
that have the closest relation to the biological activity.Homology modeling helps in identifying the
similarity between different peptide/protein sequences on the basis of
mutation, identity, or hydrophobic pattern of the sequences. This means that similar/related sequences will
have similar structures and in turn similar function. It is well accepted that activity is related
to structure, therefore variation in peptide sequences can be related to the
variation in their activity distribution.  Thus, the procedure of sequence alignment of
peptides/proteins does establish a relationship between the activities and the sequences/structures,
but is unable to quantify this relationship. On the other hand, QSAR which
deals with the relationship of structure with activity establishes this
relationship in a mathematical formulation.HomoSAR attempts to draw the strengths of homology modeling also called homology
modeling, to overcome some of the limitations inherent in peptide QSAR approaches.
Thus, a union of the principles of homology modeling and the QSAR formalism can
establish a novel means of understanding in a quantitative fashion the
variation in peptide sequence with activity. HomoSAR could also be used to address the difficulties of correlating
both sequence diversity and variation in length with activity. The relationship
between the length of the peptide chain and activity is not very obvious. An
increase or decrease in peptide length often has a variable effect on the
activity. For every biological effect,
there is an optimum length of the peptide for which the activity is the highest,
and deviation from this optimal length reflects directly on the activity. Thus
identifying the optimum length for peptides is not always easy, though
recognition of residues for affinity and activity may be somewhat simple.If all the peptides binding to a given
receptor are of uniform length, then the overlay of the peptides is straight
forward if the active site permits a snug fit of the peptides. However, if the
active site encloses a large space, then peptides with varying length could be translated in relation
to each other so as to attain a tighter binding in the active site. In such
cases, a simple overlay of peptides cannot be used to impose the condition that
the peptides share the same binding mode, but a good understanding of the
binding mode can be gained through the sequence alignment technique inHomoSAR.


## 2. Computational Details

We demonstrate
theHomoSAR methodology on a dataset of 128 nonameric peptides belonging
to the HLA-0201A* series. This dataset was chosen simply because it is one of
the established peptide dataset in terms of structural diversity, wide
distribution of activity and has been well characterized both by theoretical
and experimental studies [3–7, 16, 17, 18, 19, 20, 21, 22, 23]. It is the
best test bed for the validation of the HomoSAR methodology. The dataset was divided
into a training set (87 molecules) and a test set (41 molecules) randomly on
the basis of the activity values as shown in Table 1. For the present QSAR
studies, the binding affinities of the peptides in the dataset were compiled from the literature
[36–48] and
transformed as pIC50(−log⁡IC50)
values in terms of the molar concentration.Table 1
HLA-A*0201 dataset (used
for studying QSAR by the HomoSAR approach) with the experimental [pIC50]
and predicted affinity [pIC50].
(a)
Training setLow affinitySr. no.PeptideExptpIC50PredpIC50Sr. no.PeptideExptpIC50PredpIC501VALVGLFVL5.155.239SLHVGTQCA5.846.102VCMTVDSLV5.155.3111SLNFMGYVI5.886.263HLESLFTAV5.305.9212NLQSLTNLL6.005.764GTLVALVGL5.345.4513FVTWHRYHL6.036.715LLSCLGCKI5.455.5315QVMSLHNLV6.176.546LQTTIHDII5.505.3018ALAKAAAAI6.216.517TLLVVMGTL5.586.06Intermediate affinity19GLGQVPLIV6.305.9939TLGIVCPIC6.826.5920MLDLQPETT6.346.5240CLTSTVQLV6.836.7221LLSSNLSWL6.346.4642FLCKQYLNL6.887.2322GLACHQLCA6.386.2043FAFRDLCIV6.896.5523LIGNESFAL6.426.1244FLEPGPVTA6.907.3024ALAKAAAAV6.426.2445ALAKAAAAA6.956.4825LLAVGATKV6.486.7946LMAVVLASL6.957.0126KLPQLCTEL6.486.6547YVITTQHWL6.987.5227ALAKAAAAL6.516.5448LLCLIFLLV7.007.0328WILRGTSFV6.566.7249HLAVIGALL7.006.2829IISCTCPTV6.586.7350ITAQVPFSV7.027.5230FLGGTPVCL6.626.8051YLEPGPVTL7.067.5431ALIHHNTHL6.627.0252YTDQVPFSV7.077.2032NLSWLSLDV6.646.6253NLYVSLLLL7.117.4033YMIMVKCWM6.666.7254ILHNGAYSL7.137.4334VLQAGFFLL6.686.8155SIISAVVGI7.167.3935GTLGIVCPI6.716.2856VVMGTLVAL7.177.7536VILGVLLLI6.797.1657YLEPGPVTI7.196.9537VTWHRYHLL6.796.7858GLSRYVARL7.256.9638PLLPIFFCL6.806.74High affinity60VLLDYQGML7.337.1082MLGTHTMEV7.857.2761YLEPGPVTV7.347.2583LLFGYPVYV7.898.0263YLSPGPVTA7.387.2284ILKEPVHGV7.927.5165IIDQVPFSV7.407.4585YLMPGPVTV7.938.0466SVYDFFVWL7.447.5386WLDQVPFSV7.947.4967ITWQVPFSV7.468.0187KTWGQYWQV7.967.9168ITYQVPFSV7.488.1989YLAPGPVTA8.037.8269GLYSSTVPV7.487.3490YLYPGPVTV8.057.6171LLLCLIFLL7.597.2491LLMGTLGIV8.107.2573VLIQRNPQL7.647.4992YLWPGPVTV8.137.5074SLYADSPSV7.667.6693FLLTRILTI8.158.2475RLLQETELV7.687.2494GLLGWSPQA8.248.2176ILSQVPFSV7.707.5795ILYQVPFSV8.318.2277IMDQVPFSV7.727.6096GILTVILGV8.358.0678QLFEDNYAL7.767.3199YLFPGPVTA8.508.0579ALMDKSLHV7.777.19101ILFQVPFSV8.708.3580YAIDLPVSV7.807.85102ILWQVPFSV8.778.6281FVWLHYYSV7.828.00(b)
Test setLow affinitySr. no.PeptideExptpIC50PredpIC50Sr. no.PeptideExptpIC50PredpIC50106LTVILGVLL5.585.91110TVILGVLLL6.076.33107HLLVGSSGL5.795.77111WTDQVPFSV6.156.66108LLVVMGTLV5.875.74112AIAKAAAAV6.186.45109GIGILTVIL6.005.61Intermediate affinity113VLHSFTDAI6.386.18122TLHEYMLDL6.737.56114AAAKAAAAV6.406.18123TLDSQVMSL6.796.99115ILTVILGVL6.426.20124HLYQGCQVV6.836.53116MLLAVLYCL6.486.83125QLFHLCLII6.896.32117AVAKAAAAV6.506.66126ITDQVPFSV6.957.31119ILDEAYVMA6.626.12127ALCRWGLLL7.006.83120YLEPGPVTA6.677.40129HLYSHPIIL7.136.53121LLWFHISCL6.686.56131FTDQVPFSV7.217.57High affinity132YMLDLQPET7.317.49142YLAPGPVTV7.827.47133RLMKQDFSV7.348.09143VVLGVVFGI7.857.5134KLHLYSHPI7.358.22144MMWYWGPSL7.927.25135ITMQVPFSV7.407.71145ILAQVPFSV7.947.49137KIFGSLAFL7.487.18147FLLSLGIHL8.057.41138ALVGLFVLL7.596.69148ILMQVPFSV8.137.80139YLSPGPVTV7.646.67149YLFPGPVTV8.248.09140GLYSSTVPV7.706.97150YLMPGPVTA8.377.96141YLYPGPVTA7.777.77151YLWPGPVTA8.507.97


### 2.1. Multiple
Sequence Alignment of the Peptides

The first step inHomoSAR involves an alignment of all the peptides in the dataset,
shown in Figure 1. The alignment was executed using the DNASIS Max [49] sequence alignment software running on a Windows
platform. The peptides sequences in both the training and test sets were
aligned separately, aligned by the multiple sequence alignment strategy. The peptide 102 in the dataset was
chosen as the reference peptide for scoring (vide infra) following the alignment step. The eight nonapeptides cocrystallized
with the MHC protein and whose structures have been solved by X-ray
crystallography (PDB codes are 1AKJ, 1DUZ, 1HHG, 1HHJ, 1OGA, 1QEW, 1QSE, and
1QSF) were also included in the peptide alignment, as a check against alignment
results obtained by the multiple sequence alignment protocol.Figure 1
A
picture of the alignment of
HLA-A*0201 peptides along with positionwise similarity indices calculated by (1). SISAiAB similarity indices for the query peptide [B]
aligned against the reference peptide [A] for positions P−4 to P+2,
the similarity indices have been calculated by (1) using the property—isotropic surface
area (ISA).


### 2.2. Similarity Indices

Following alignment of the peptides in the
dataset, the second step inHomoSAR involves calculating a similarity index for every amino acid position in the peptide
sequence against the amino acid in the same position in the reference peptide (see
Figure 1), as established by the alignment rule.The similarity index (S) between peptide A
(the reference peptide) and peptide B for “ith” position in the sequences, for a given physicochemical property, is given by (1)S[P]iAB=2×PiA×PiB(PiA)2+(PiB)2, where S[P]iAB is the similarity between peptides A and B at
the “ith” position in the peptide
sequences for the physicochemical property P;PiA and PiB are the physicochemical property of the amino acid in the respective
peptide sequences A and B at the “ith” position. The denominator is a normalizing factor.


### 2.3. Physicochemical
Properties [P] for Computing Similarity Indices

The properties[P] used to calculate the
similarity indices (S) (1) are the properties of amino acids such as isotropic surface area (ISA), electronic
charge index (ECI), hydrophobicity (HS), molar refractivity (MR), total dipole moment
(TDM), and total lipole moment (TLM). The similarity values for the
peptides (1) are used as the X-variables (descriptors) for derivation of the QSAR
models. These properties were selected as they describe the steric, electronic,
and hydrophobic nature of the amino acids that are key descriptors of the
binding process. The significance of these properties used to calculate the
similarity indices are discussed below.


#### 2.3.1. Isotropic Surface Area (ISA)

Isotropic surface area (ISA) is the
portion of the solute molecule which is accessible for nonspecific interactions
with water. The nonspecific interactions are those between water and solute
molecules other than hydrogen bond interactions. The ISA is calculated as the sum of the surfaces over the side chain
atoms accessible to nonspecific solvent interactions. Surfaces which interface the
waters of hydration and the solute are excluded from the ISA [13]. Thus ISA provides a means to quantify
hydrophobic nature of the solute molecules.


#### 2.3.2. Electronic Charge Index (ECI)

Electronic charge
index (ECI) is the sum of the absolute value of the CNDO/2 charges of the
side-chain atoms [13]. It is a measure of the local polarity at the amino acid
side chain. A significant contribution of ECI to activity may indicate the
presence of dipolar interactions of the side chain with the receptor site. It
is calculated by the following formula: (2)ECI=∑|qi|, where  qi is the atomic charge
of the ith atom in the amino acid
side chain.


#### 2.3.3. Valence  Relative Chirality Index(RCIv)

Valence relative chirality index allows
distinction between  the R and S chiral isomers which the regular
physicochemical properties cannot distinguish as reflected in the activity of
the molecule. In the relative chirality indices [50]
calculation, the three groups in descending priority attached to the chiral
center are viewed from a reference point to calculate the new chirality metric.
The groups/atoms a,b,c and d are then assigned valence delta value (δv) according to the method of Hall and Kier. The
group delta value for any group (δiv) attached to a chiral carbon is calculated as (3)δiv=δn1v+(δn2v2)+(δn3v4)+(δn4v8)+⋯+(δnNv2(N−1)), where n1 is the atom attached directly to the chiral center
(nearest neighbor), n2 is attached to n1,n3, to n2, and so on.The relative chirality indices(RCIvs)
for a pair of enantiomers are calculated as (4)RCIvR=δav+(δav+δavδbv)+(δav+δavδbv+δavδbvδcv)+δavδbvδcvδdv,RCIvS=δav+(δav+δavδcv)+(δav+δavδcv+δavδbvδcv)+δavδbvδcvδdv.


#### 2.3.4. Hydrophobicity Scale (HS)

The estimated hydrophobic effects [51] (kcal/mol) are
values based on the contribution of the hydrophobic effect to the burial of
each type of amino acid residue and side chain, obtained by analyzing the
multitude of hydrophobicity scales. The scale estimates the free energy for transferring
a residue from water to a nonaqueous solvent, that is, the affinity of a residue for the solvent. The
hydrophobic scale for the amino acid side chains is calculated as the
difference between the estimated hydrophobic effect for the individual amino
acid burial and that for glycine residue. It describes the thermodynamics of
the partitioning of nonpolar compounds between water and a nonaqueous phase.
This scale has been calculated to overcome the flaws of a set of previous
hydrophobic scales which account for the partitioning of the amino acid residue
between aqueous and organic solvents.


#### 2.3.5. Total Dipole Moment (TDM)

It
is a partial charge-dependent parameter calculated on the basis of the center
of charge over the substitution as the origin [52–55]. Tsar3.3 [56] uses an empirical procedure
called Charge-2 for the rapid
evaluation of partial atomic charges, which utilizes two fundamental chemical
concepts; the inductive effect in saturated molecules and Hückel molecular
orbital calculations for π systems. The total dipole moment along the amino
acid side chain describes the electrostatic interaction at the receptor site.
It is calculated as follows: (5)μ→=e∑iri→qi, where ri→ is the distance of the “ith” atom from the origin and “qi” is the atomic charge of the “ith” atom.


#### 2.3.6. Total Lipole Moment (TLM)

The
lipole of a molecule is a measure of the lipophilic distribution [57]. It is
calculated from the sum of atomic log⁡P values. This property has been
calculated for the amino acid side chains using Tsar3.3 [55]. It is calculated using (6)L→=∑iri→li, where ri→ is the distance of an “ith” atom from the origin and “li” is the atomic log⁡P of the “ith” atom.


#### 2.3.7. Molar Refractivity (MR)

The
molar refractive index of a molecule is a combined measure of its size and
polarizability [57]. This fragment constant thermodynamic descriptor relates
the effect of substituents on a reaction center from one type of process to
another. The basic idea behind the use of such a descriptor is that similar
changes in structure are likely to produce similar changes in reactivity,
ionization, and binding. It can be experimentally determined or theoretically
calculated using empirical rules. This property has been calculated using the
method described by Vishwanadhan et
al. as implemented in Tsar3.3 [56]. It is calculated as (7)MR=((n2−1)(n2+2))(MW)d, where, “n” is the refractive index, “MW” molecular weight, and “d” is the density of the substituent
group.A few other similarity indices were derived
from the above described properties.
These new similarity indices were derived for “dipeptide” pairs, that
is, neighboring amino acids (i,i+1) and denoted as S[P]ijAB;
for “tripeptide” segments, that
is, amino acids in a 1–3 relationship (i,i+1,i+2) and denoted as S[P]ijkAB,
using one of the above mentioned properties [P].The similarity indices for peptides “A” and “B” for “dipeptide” pairs, that is, neighboring residues “i” and “i+1” are
given by (8)S[P]ijAB=S[P]iAB×S[P]jAB, where j=i+1 and S[P]iAB,S[P]jAB, are similarity indices calculated using (1)
for positions “i” and “j,” using property [P].Likewise, the similarity between peptides “A” and “B” computed for“tripeptide” segments, that is, three successive amino acids “i,” “j,” and “k” is (9)S[P]ijkAB=S[P]iAB×S[P]jAB×S[P]kAB, where j=i+1 and k=i+2 and S[P]iAB,S[P]jAB,S[P]kAB are the similarity indices calculated using (1)
for positions “i,”  “j,” and “k” using property [P].Likewise, three other variables were calculated.The total similarity between peptides A and B is given by(10)SS[P]AB=∑iS[P]iAB, where S[P]iAB is the similarity index for position “i” in the two sequences according to (1). Likewise, (11)DS[P]AB=∑ijS[P]ijAB is the sum of the similarity indices for all dipeptides in
the sequences A and B, as defined by (8).Moreover,(12)TS[P]AB=∑ijkS[P]ijkAB is the sum of the similarity indices for all tripeptides motifs
in the sequences A and B, as defined by (9).Every amino acid in the query sequence is
assigned a similarity index (1) on the basis of a particular amino acid
property [P] against the amino acid at that particular position in the
reference peptide (see Figure 1), as defined by the sequence alignment rule.
When there is a gap in the alignment, that
is, no amino acid can be matched in the query sequence, the position in
the query is assigned a zero (0) value for the similarity index (see Figure 1),
while in the situation where a gap occurs in the alignment, because no amino
acid match occurs in the reference sequence but an amino acid is found in the
query sequence, then this position in the query sequence is penalized with a
negative value of its similarity index calculated against glycine. The matrix
containing the similarity indices calculated for a particular property [P] for
all sequences in the training set forms the X-variables in the QSAR table which
is correlated with the biological activity (Y-variable). During the multiple
sequence alignment, there are peptide sequences which translate to the right or
left of the reference peptide. The amino acids in the query peptides which are aligned
to the right of the first amino acid in the reference peptide are marked by additional
position numbers with a negative sign while the amino acids in the query
peptide  which are  aligned to the left of the last amino acid in the reference
peptide are marked with positive position numbers, as seen in Figure 1.


### 2.4. QSAR Models and Statistics

The regression procedure, the third step in
theHomoSAR, was carried out with the
program—Cerius2 (v4.11
Accelrys Inc., San Diego, Calif, USA) [58] running on a RedHat Linux Enterprise WS 4.0 workstation
and on an SGI Fuel workstation (Silicon Graphics Inc., Calif, USA). Other modeling
and computations were carried out using InsightII (v2005L Accelrys Inc., USA)
[58] running on
a RedHat Linux Enterprise WS4.0 workstation. All QSAR equations were generated with
the genetic function approximation/partial least squares (G/PLS) method [59, 60]
as implemented in Cerius2, with 10 000 generations, a population size of 500,
a smoothness value (d) of 1.0, 6 PLS components, and no scaling of descriptors.
The models were generated with equation lengths varying from 7 to 11. The rest
of the parameters were set at their default values. The QSAR models were generated for similarity indices calculated for all properties described in Sections  2.3.1 to 2.3.7 collectively.
The total X-variables (the similarity indices) numbered 315.


## 2.1. Multiple
Sequence Alignment of the Peptides

The first step inHomoSAR involves an alignment of all the peptides in the dataset,
shown in Figure 1. The alignment was executed using the DNASIS Max [49] sequence alignment software running on a Windows
platform. The peptides sequences in both the training and test sets were
aligned separately, aligned by the multiple sequence alignment strategy. The peptide 102 in the dataset was
chosen as the reference peptide for scoring (vide infra) following the alignment step. The eight nonapeptides cocrystallized
with the MHC protein and whose structures have been solved by X-ray
crystallography (PDB codes are 1AKJ, 1DUZ, 1HHG, 1HHJ, 1OGA, 1QEW, 1QSE, and
1QSF) were also included in the peptide alignment, as a check against alignment
results obtained by the multiple sequence alignment protocol.Figure 1
A
picture of the alignment of
HLA-A*0201 peptides along with positionwise similarity indices calculated by (1). SISAiAB similarity indices for the query peptide [B]
aligned against the reference peptide [A] for positions P−4 to P+2,
the similarity indices have been calculated by (1) using the property—isotropic surface
area (ISA).


## 2.2. Similarity Indices

Following alignment of the peptides in the
dataset, the second step inHomoSAR involves calculating a similarity index for every amino acid position in the peptide
sequence against the amino acid in the same position in the reference peptide (see
Figure 1), as established by the alignment rule.The similarity index (S) between peptide A
(the reference peptide) and peptide B for “ith” position in the sequences, for a given physicochemical property, is given by (1)S[P]iAB=2×PiA×PiB(PiA)2+(PiB)2, where S[P]iAB is the similarity between peptides A and B at
the “ith” position in the peptide
sequences for the physicochemical property P;PiA and PiB are the physicochemical property of the amino acid in the respective
peptide sequences A and B at the “ith” position. The denominator is a normalizing factor.


## 2.3. Physicochemical
Properties [P] for Computing Similarity Indices

The properties[P] used to calculate the
similarity indices (S) (1) are the properties of amino acids such as isotropic surface area (ISA), electronic
charge index (ECI), hydrophobicity (HS), molar refractivity (MR), total dipole moment
(TDM), and total lipole moment (TLM). The similarity values for the
peptides (1) are used as the X-variables (descriptors) for derivation of the QSAR
models. These properties were selected as they describe the steric, electronic,
and hydrophobic nature of the amino acids that are key descriptors of the
binding process. The significance of these properties used to calculate the
similarity indices are discussed below.


### 2.3.1. Isotropic Surface Area (ISA)

Isotropic surface area (ISA) is the
portion of the solute molecule which is accessible for nonspecific interactions
with water. The nonspecific interactions are those between water and solute
molecules other than hydrogen bond interactions. The ISA is calculated as the sum of the surfaces over the side chain
atoms accessible to nonspecific solvent interactions. Surfaces which interface the
waters of hydration and the solute are excluded from the ISA [13]. Thus ISA provides a means to quantify
hydrophobic nature of the solute molecules.


### 2.3.2. Electronic Charge Index (ECI)

Electronic charge
index (ECI) is the sum of the absolute value of the CNDO/2 charges of the
side-chain atoms [13]. It is a measure of the local polarity at the amino acid
side chain. A significant contribution of ECI to activity may indicate the
presence of dipolar interactions of the side chain with the receptor site. It
is calculated by the following formula: (2)ECI=∑|qi|, where  qi is the atomic charge
of the ith atom in the amino acid
side chain.


### 2.3.3. Valence  Relative Chirality Index(RCIv)

Valence relative chirality index allows
distinction between  the R and S chiral isomers which the regular
physicochemical properties cannot distinguish as reflected in the activity of
the molecule. In the relative chirality indices [50]
calculation, the three groups in descending priority attached to the chiral
center are viewed from a reference point to calculate the new chirality metric.
The groups/atoms a,b,c and d are then assigned valence delta value (δv) according to the method of Hall and Kier. The
group delta value for any group (δiv) attached to a chiral carbon is calculated as (3)δiv=δn1v+(δn2v2)+(δn3v4)+(δn4v8)+⋯+(δnNv2(N−1)), where n1 is the atom attached directly to the chiral center
(nearest neighbor), n2 is attached to n1,n3, to n2, and so on.The relative chirality indices(RCIvs)
for a pair of enantiomers are calculated as (4)RCIvR=δav+(δav+δavδbv)+(δav+δavδbv+δavδbvδcv)+δavδbvδcvδdv,RCIvS=δav+(δav+δavδcv)+(δav+δavδcv+δavδbvδcv)+δavδbvδcvδdv.


### 2.3.4. Hydrophobicity Scale (HS)

The estimated hydrophobic effects [51] (kcal/mol) are
values based on the contribution of the hydrophobic effect to the burial of
each type of amino acid residue and side chain, obtained by analyzing the
multitude of hydrophobicity scales. The scale estimates the free energy for transferring
a residue from water to a nonaqueous solvent, that is, the affinity of a residue for the solvent. The
hydrophobic scale for the amino acid side chains is calculated as the
difference between the estimated hydrophobic effect for the individual amino
acid burial and that for glycine residue. It describes the thermodynamics of
the partitioning of nonpolar compounds between water and a nonaqueous phase.
This scale has been calculated to overcome the flaws of a set of previous
hydrophobic scales which account for the partitioning of the amino acid residue
between aqueous and organic solvents.


### 2.3.5. Total Dipole Moment (TDM)

It
is a partial charge-dependent parameter calculated on the basis of the center
of charge over the substitution as the origin [52–55]. Tsar3.3 [56] uses an empirical procedure
called Charge-2 for the rapid
evaluation of partial atomic charges, which utilizes two fundamental chemical
concepts; the inductive effect in saturated molecules and Hückel molecular
orbital calculations for π systems. The total dipole moment along the amino
acid side chain describes the electrostatic interaction at the receptor site.
It is calculated as follows: (5)μ→=e∑iri→qi, where ri→ is the distance of the “ith” atom from the origin and “qi” is the atomic charge of the “ith” atom.


### 2.3.6. Total Lipole Moment (TLM)

The
lipole of a molecule is a measure of the lipophilic distribution [57]. It is
calculated from the sum of atomic log⁡P values. This property has been
calculated for the amino acid side chains using Tsar3.3 [55]. It is calculated using (6)L→=∑iri→li, where ri→ is the distance of an “ith” atom from the origin and “li” is the atomic log⁡P of the “ith” atom.


### 2.3.7. Molar Refractivity (MR)

The
molar refractive index of a molecule is a combined measure of its size and
polarizability [57]. This fragment constant thermodynamic descriptor relates
the effect of substituents on a reaction center from one type of process to
another. The basic idea behind the use of such a descriptor is that similar
changes in structure are likely to produce similar changes in reactivity,
ionization, and binding. It can be experimentally determined or theoretically
calculated using empirical rules. This property has been calculated using the
method described by Vishwanadhan et
al. as implemented in Tsar3.3 [56]. It is calculated as (7)MR=((n2−1)(n2+2))(MW)d, where, “n” is the refractive index, “MW” molecular weight, and “d” is the density of the substituent
group.A few other similarity indices were derived
from the above described properties.
These new similarity indices were derived for “dipeptide” pairs, that
is, neighboring amino acids (i,i+1) and denoted as S[P]ijAB;
for “tripeptide” segments, that
is, amino acids in a 1–3 relationship (i,i+1,i+2) and denoted as S[P]ijkAB,
using one of the above mentioned properties [P].The similarity indices for peptides “A” and “B” for “dipeptide” pairs, that is, neighboring residues “i” and “i+1” are
given by (8)S[P]ijAB=S[P]iAB×S[P]jAB, where j=i+1 and S[P]iAB,S[P]jAB, are similarity indices calculated using (1)
for positions “i” and “j,” using property [P].Likewise, the similarity between peptides “A” and “B” computed for“tripeptide” segments, that is, three successive amino acids “i,” “j,” and “k” is (9)S[P]ijkAB=S[P]iAB×S[P]jAB×S[P]kAB, where j=i+1 and k=i+2 and S[P]iAB,S[P]jAB,S[P]kAB are the similarity indices calculated using (1)
for positions “i,”  “j,” and “k” using property [P].Likewise, three other variables were calculated.The total similarity between peptides A and B is given by(10)SS[P]AB=∑iS[P]iAB, where S[P]iAB is the similarity index for position “i” in the two sequences according to (1). Likewise, (11)DS[P]AB=∑ijS[P]ijAB is the sum of the similarity indices for all dipeptides in
the sequences A and B, as defined by (8).Moreover,(12)TS[P]AB=∑ijkS[P]ijkAB is the sum of the similarity indices for all tripeptides motifs
in the sequences A and B, as defined by (9).Every amino acid in the query sequence is
assigned a similarity index (1) on the basis of a particular amino acid
property [P] against the amino acid at that particular position in the
reference peptide (see Figure 1), as defined by the sequence alignment rule.
When there is a gap in the alignment, that
is, no amino acid can be matched in the query sequence, the position in
the query is assigned a zero (0) value for the similarity index (see Figure 1),
while in the situation where a gap occurs in the alignment, because no amino
acid match occurs in the reference sequence but an amino acid is found in the
query sequence, then this position in the query sequence is penalized with a
negative value of its similarity index calculated against glycine. The matrix
containing the similarity indices calculated for a particular property [P] for
all sequences in the training set forms the X-variables in the QSAR table which
is correlated with the biological activity (Y-variable). During the multiple
sequence alignment, there are peptide sequences which translate to the right or
left of the reference peptide. The amino acids in the query peptides which are aligned
to the right of the first amino acid in the reference peptide are marked by additional
position numbers with a negative sign while the amino acids in the query
peptide  which are  aligned to the left of the last amino acid in the reference
peptide are marked with positive position numbers, as seen in Figure 1.


## 2.3.1. Isotropic Surface Area (ISA)

Isotropic surface area (ISA) is the
portion of the solute molecule which is accessible for nonspecific interactions
with water. The nonspecific interactions are those between water and solute
molecules other than hydrogen bond interactions. The ISA is calculated as the sum of the surfaces over the side chain
atoms accessible to nonspecific solvent interactions. Surfaces which interface the
waters of hydration and the solute are excluded from the ISA [13]. Thus ISA provides a means to quantify
hydrophobic nature of the solute molecules.


## 2.3.2. Electronic Charge Index (ECI)

Electronic charge
index (ECI) is the sum of the absolute value of the CNDO/2 charges of the
side-chain atoms [13]. It is a measure of the local polarity at the amino acid
side chain. A significant contribution of ECI to activity may indicate the
presence of dipolar interactions of the side chain with the receptor site. It
is calculated by the following formula: (2)ECI=∑|qi|, where  qi is the atomic charge
of the ith atom in the amino acid
side chain.


## 2.3.3. Valence  Relative Chirality Index(RCIv)

Valence relative chirality index allows
distinction between  the R and S chiral isomers which the regular
physicochemical properties cannot distinguish as reflected in the activity of
the molecule. In the relative chirality indices [50]
calculation, the three groups in descending priority attached to the chiral
center are viewed from a reference point to calculate the new chirality metric.
The groups/atoms a,b,c and d are then assigned valence delta value (δv) according to the method of Hall and Kier. The
group delta value for any group (δiv) attached to a chiral carbon is calculated as (3)δiv=δn1v+(δn2v2)+(δn3v4)+(δn4v8)+⋯+(δnNv2(N−1)), where n1 is the atom attached directly to the chiral center
(nearest neighbor), n2 is attached to n1,n3, to n2, and so on.The relative chirality indices(RCIvs)
for a pair of enantiomers are calculated as (4)RCIvR=δav+(δav+δavδbv)+(δav+δavδbv+δavδbvδcv)+δavδbvδcvδdv,RCIvS=δav+(δav+δavδcv)+(δav+δavδcv+δavδbvδcv)+δavδbvδcvδdv.


## 2.3.4. Hydrophobicity Scale (HS)

The estimated hydrophobic effects [51] (kcal/mol) are
values based on the contribution of the hydrophobic effect to the burial of
each type of amino acid residue and side chain, obtained by analyzing the
multitude of hydrophobicity scales. The scale estimates the free energy for transferring
a residue from water to a nonaqueous solvent, that is, the affinity of a residue for the solvent. The
hydrophobic scale for the amino acid side chains is calculated as the
difference between the estimated hydrophobic effect for the individual amino
acid burial and that for glycine residue. It describes the thermodynamics of
the partitioning of nonpolar compounds between water and a nonaqueous phase.
This scale has been calculated to overcome the flaws of a set of previous
hydrophobic scales which account for the partitioning of the amino acid residue
between aqueous and organic solvents.


## 2.3.5. Total Dipole Moment (TDM)

It
is a partial charge-dependent parameter calculated on the basis of the center
of charge over the substitution as the origin [52–55]. Tsar3.3 [56] uses an empirical procedure
called Charge-2 for the rapid
evaluation of partial atomic charges, which utilizes two fundamental chemical
concepts; the inductive effect in saturated molecules and Hückel molecular
orbital calculations for π systems. The total dipole moment along the amino
acid side chain describes the electrostatic interaction at the receptor site.
It is calculated as follows: (5)μ→=e∑iri→qi, where ri→ is the distance of the “ith” atom from the origin and “qi” is the atomic charge of the “ith” atom.


## 2.3.6. Total Lipole Moment (TLM)

The
lipole of a molecule is a measure of the lipophilic distribution [57]. It is
calculated from the sum of atomic log⁡P values. This property has been
calculated for the amino acid side chains using Tsar3.3 [55]. It is calculated using (6)L→=∑iri→li, where ri→ is the distance of an “ith” atom from the origin and “li” is the atomic log⁡P of the “ith” atom.


## 2.3.7. Molar Refractivity (MR)

The
molar refractive index of a molecule is a combined measure of its size and
polarizability [57]. This fragment constant thermodynamic descriptor relates
the effect of substituents on a reaction center from one type of process to
another. The basic idea behind the use of such a descriptor is that similar
changes in structure are likely to produce similar changes in reactivity,
ionization, and binding. It can be experimentally determined or theoretically
calculated using empirical rules. This property has been calculated using the
method described by Vishwanadhan et
al. as implemented in Tsar3.3 [56]. It is calculated as (7)MR=((n2−1)(n2+2))(MW)d, where, “n” is the refractive index, “MW” molecular weight, and “d” is the density of the substituent
group.A few other similarity indices were derived
from the above described properties.
These new similarity indices were derived for “dipeptide” pairs, that
is, neighboring amino acids (i,i+1) and denoted as S[P]ijAB;
for “tripeptide” segments, that
is, amino acids in a 1–3 relationship (i,i+1,i+2) and denoted as S[P]ijkAB,
using one of the above mentioned properties [P].The similarity indices for peptides “A” and “B” for “dipeptide” pairs, that is, neighboring residues “i” and “i+1” are
given by (8)S[P]ijAB=S[P]iAB×S[P]jAB, where j=i+1 and S[P]iAB,S[P]jAB, are similarity indices calculated using (1)
for positions “i” and “j,” using property [P].Likewise, the similarity between peptides “A” and “B” computed for“tripeptide” segments, that is, three successive amino acids “i,” “j,” and “k” is (9)S[P]ijkAB=S[P]iAB×S[P]jAB×S[P]kAB, where j=i+1 and k=i+2 and S[P]iAB,S[P]jAB,S[P]kAB are the similarity indices calculated using (1)
for positions “i,”  “j,” and “k” using property [P].Likewise, three other variables were calculated.The total similarity between peptides A and B is given by(10)SS[P]AB=∑iS[P]iAB, where S[P]iAB is the similarity index for position “i” in the two sequences according to (1). Likewise, (11)DS[P]AB=∑ijS[P]ijAB is the sum of the similarity indices for all dipeptides in
the sequences A and B, as defined by (8).Moreover,(12)TS[P]AB=∑ijkS[P]ijkAB is the sum of the similarity indices for all tripeptides motifs
in the sequences A and B, as defined by (9).Every amino acid in the query sequence is
assigned a similarity index (1) on the basis of a particular amino acid
property [P] against the amino acid at that particular position in the
reference peptide (see Figure 1), as defined by the sequence alignment rule.
When there is a gap in the alignment, that
is, no amino acid can be matched in the query sequence, the position in
the query is assigned a zero (0) value for the similarity index (see Figure 1),
while in the situation where a gap occurs in the alignment, because no amino
acid match occurs in the reference sequence but an amino acid is found in the
query sequence, then this position in the query sequence is penalized with a
negative value of its similarity index calculated against glycine. The matrix
containing the similarity indices calculated for a particular property [P] for
all sequences in the training set forms the X-variables in the QSAR table which
is correlated with the biological activity (Y-variable). During the multiple
sequence alignment, there are peptide sequences which translate to the right or
left of the reference peptide. The amino acids in the query peptides which are aligned
to the right of the first amino acid in the reference peptide are marked by additional
position numbers with a negative sign while the amino acids in the query
peptide  which are  aligned to the left of the last amino acid in the reference
peptide are marked with positive position numbers, as seen in Figure 1.


## 2.4. QSAR Models and Statistics

The regression procedure, the third step in
theHomoSAR, was carried out with the
program—Cerius2 (v4.11
Accelrys Inc., San Diego, Calif, USA) [58] running on a RedHat Linux Enterprise WS 4.0 workstation
and on an SGI Fuel workstation (Silicon Graphics Inc., Calif, USA). Other modeling
and computations were carried out using InsightII (v2005L Accelrys Inc., USA)
[58] running on
a RedHat Linux Enterprise WS4.0 workstation. All QSAR equations were generated with
the genetic function approximation/partial least squares (G/PLS) method [59, 60]
as implemented in Cerius2, with 10 000 generations, a population size of 500,
a smoothness value (d) of 1.0, 6 PLS components, and no scaling of descriptors.
The models were generated with equation lengths varying from 7 to 11. The rest
of the parameters were set at their default values. The QSAR models were generated for similarity indices calculated for all properties described in Sections  2.3.1 to 2.3.7 collectively.
The total X-variables (the similarity indices) numbered 315.


## 3. Results and Discussion

In a previous paper, we had reported the
Hansch approach using specific properties of the amino acids as descriptors, and
the Free-Wilson method to understand the SAR of HLA-A*0201 nonamer peptides [24]. The approaches were able to throw light on
how the variation in amino acids at the nine positions influence the activity; but
could not shed light on why minor similarities or dissimilarities in the
peptide sequences cause large variation in the activity. The method also falls
short in explaining whether all the peptides have the same binding pose within
the MHC protein. It is not always true that peptides of the same length have
the same binding mode. There is a possibility that one sequence may glide or translate
in the binding pocket relative to the other amino acid sequence of the same
length, thus affecting the binding affinity. Thus simply overlaying peptides in
the active site (the atom-based alignment in CoMFA) may be insufficient in understanding
peptide QSAR. HomoSAR is a QSAR technique that is based on homology
modeling which is an efficient tool in identifying peptide/protein sequences
that have a strong underlying relationship in terms of structure and function
(activity). The method also uses similarity indices that are based on amino
acid properties that reflect important binding attributes (electrostatic,
steric, and hydrophobic) to score the peptide sequences aligned against the
reference sequence.The training set and the test sets were
separately aligned against the 8 peptides whose crystal structures have been
solved. The 8 peptides show perfect sequence alignment without any gaps; which is
in harmony with their identical binding modes as seen in the X-ray structures. This places
confidence in the alignment results for both the training and test set
peptides.The models derived byHomoSAR along
with their statistical data are presented in Table 2. All models constructed are
statistically significant. The models were internally validated using cross-validation
by the leave-one-out (LOO) and leave-group-out (LGO) protocols and by boot
strapping. The models were also tested for their predictive power on a test
set. The predictive r2(rpred2) for the models
is given in Table 2.
The plot of the experimental versus predicted binding affinities for the best model is given in Figure 2. The affinities
predicted by the best HomoSAR model are
given in Table 1. All the 500 equations were analyzed to identify the properties
associated with each position in the peptide sequence that best accounts for the
biological activity. The frequency of appearance of each property at the
different positions in the peptide sequence in the QSAR equation is shown by
the bar graph in Figure 3. The results of the QSAR models for the HLA-A*0201
dataset indicating the preferred nature and type of the amino acid at each
position in the sequence are
discussed below.Table 2
HomoSAR models with the statistical data.
Sr. no.HomoSAR modelsnNo. of termsPRESSr2rCV2 (LOO)rCV2 (LGO)BSr2rrandom2rpred2(1)pIC50=6.83+1.88*S[ISA]78AB−2.18*S[HS]89AB+0.90*S[TDM]678AB−0.615683*S[ECI]5AB+1.85*S[HS]23AB−0.42*S[TLM]12AB87715.370.800.750.790.780.310.42(2)pIC50=4.41+0.24*DS[HS]AB+1.91*S[TDM]9[+1][+2]AB+1.20*S[HS]23AB−2.36*S[HS]89AB+1.44*S[ISA]78AB+0.72*S[TDM]678AB−0.63*S[ECI]5AB87814.490.830.700.830.820.340.62(3)4.94−2.55*S[HS]89AB+1.49*S[HS]23AB+0.59*S[TDM]678AB+1.61*S[ISA]789AB+0.17*DS[HS]AB+1.83*S[TLM]9[+1][+2]AB−0.73*S[ECI]5AB+0.47*S[TDM]7AB87913.620.850.770.790.810.360.67(4)4.93−2.43*S[HS]89AB−0.67*S[ECI]5AB+0.77*S[TDM]678AB+1.49*S[HS]23AB+0.20*DS[HS]AB+1.93*S[TLM]9[+1][+2]AB+0.31*S[MR][−1]12AB+1.59*S[ISA]78AB−0.23*S[TLM]1AB871013.440.860.770.730.840.070.67(5)4.63+1.04*S[TLM]9[+1][+2]AB+1.75*S[ISA]789AB−0.45*S[ECI]456AB−0.35*S[TDM]5AB−2.39*S[HS]89AB−0.31*S[RCIv]123AB+0.22*DS[HS]AB+0.73*S[TDM]678AB+1.51*S[HS]234AB+0.80*S[MR]9[+1][+2]AB871112.780.850.790.830.840.310.67(a) Plot
of experimental versus predicted activity
for the training set. (b) Plot
of experimental versus predicted activity
for the test set.
(a)(b)Frequency of appearance of the physicochemical property associated at different
positions in the sequence in theHomoSAR models.
(a)(b)The termDS[HS]AB appears with high frequency in the QSAR
equations; it is the sum of the similarity indices for hydrophobicity of “dipeptide” pairs in the sequence, thus indicating
the prevalence of hydrophobic character over the entire length of the peptide
as a significant attribute for activity. This is perfectly in line with the
nature of the binding cavity of the MHC protein [61]. The models also emphasize
hydrophobic character for residues at the 2nd and the 3rd positions of the
nonamer peptide. This is in complete harmony with all QSAR studies reported on
this dataset [3, 19, 24]. Further, at position
4, a small increase in the hydrophobic nature is predicted to improve affinity
of the peptide.The models speak of the need to strike a
balance for amino acids at positions 7 and 8; these should be residues with sufficient
hydrophobic character as well as a capacity for dipolar interaction with the
receptor. This is supported by the X-ray crystal structures of the HLA-A*0201
complexes, which show residues like tyrosine, tryptophan, and phenylalanine at
these positions making dipolar contacts in the binding pocket.The termS[ECI]456AB—the electronic
similarity index for the “tripeptide” segment spanning positions 4, 5, and 6—emerges with a
negative frequency. This means that the electronic character of the amino acids
at the three positions 4, 5, and 6 needs to be lowered to an optimal level to
enhance binding; this is more so for the 5th position in the sequence. This
insight into the requirements for positions 4, 5, and 6 was not revealed in the
“descriptor-based QSAR” study [24],
but the observations are in line with earlier papers [3, 19].It is appealing to note from the terms appearing
in theHomoSAR models that there
needs to be a considerable increase in the electronic property of the amino
acid occupying positions 6 and 7, while maintaining sufficient hydrophobic
character at these positions. This requirement is in agreement with the “binary QSAR” approach [24].There is a titular appearance of the similarity
terms for the extended positions 10 and 11 (see Figure1) at the C-terminal end
of the peptide. These terms show that an
increase in the chain length at the C-terminal end is possible; however there can
be no extension at the N-terminal end. This is in accordance with the fact that
decapeptides do show decent levels of biological activity [61]. The standard
QSAR methods are unable to extract this information about the peptide length
and activity.The analysis of theHomoSAR models has led to the design of some new peptides with affinity
higher than the peptides listed in Table 1. The peptide sequences with their
predicted affinities are given in Table 3.Table 3
Some of
the newly designed peptides with their affinities for the HLA-A*0201 molecule
as predicted by the best HomoSAR model.
Sr. no.PeptidePredictedpIC50(1)RLWDRPPTV9.26(2)RLYRRASTV7.95(3)YLWDFPPEV8.46(4)RLMTFFPSV7.85(5)WIWQVPFRV9.19


## 4. Conclusions

The
complexity in peptide design by 3D-QSAR methods arises because of several
variables: first, the large number of degrees of freedom that makes secondary
structure determination difficult. Second, as the peptide length increases from
two to ten, the probability of arriving at the optimal alignment is very remote.
The problem aggravates when peptides of varying length have the same level of
activity. For this reason, while 2D/3D QSAR has been very successful in the
design and discovery of small molecules, the successful applications in
peptides are far
and few in between.
TheHomoSAR approach is an attempt to
solve the problem of peptide QSAR by primarily moving the crucial step of
alignment in 3D-QSAR from 3D space to the less complex 1D space. This has been achieved by adopting the
principles of homology modeling into the QSAR formalism. As an application to the MHC class of
peptides, the technique was able to extract all known SARs reported for their
class as well as reveal a few that were hitherto unknown. The HomoSAR approach is also able to give an idea of the relative binding mode the query
peptides can have in relation to the reference peptide. Thus, this technique
can be gainfully employed to understand and optimize the relationship between
activity and the position and nature of amino acids in any peptide sequence,
without resorting to the cumbersome 3D spatial analysis. In conclusion, HomoSAR as a union of homology modeling and QSAR principles is a useful tool in the medicinal
chemists’ armamentarium to design peptide ligands.



---

*Source: 360572-2008-09-14.xml*