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343
15.2.5.9 Peptides Modification by Genetic Code Expansion
Proteins are naturally synthesized from 20 natural or canonical amino acids. The amino acid
sequences in biosynthesized protein molecules are strictly conserved, thereby constraining their
structural and functional diversity. Of note, evolution in genetic code expansion has played a vital
role in overcoming such restriction [170]. During the translation of proteins, noncanonical amino
acids with specific physiological properties can be selectively incorporated into a growing poly-
peptide by implementing genetic code expansion [171]. In the process, an orthogonal aminoacyl-
tRNA synthetase charges a noncanonical amino acid to the orthogonal tRNA that meanwhile
suppresses the unique stop codon and expresses the amber or quadruplet codon (UAG).
Consequently, the noncanonical amino acids are site specifically incorporated into the growing
peptide molecule [172].
A plethora of noncanonical amino acids with specific functional modules have been genetically
encoded into viruses, prokaryotes, and eukaryotes that evidently provided precious tools for pro-
tein engineering [173–175]. Numerous vaccines have been developed through genetic code expan-
sion [176]. Site-specific insertion of noncanonical immunogenic amino acids like pNO2F in
m-TNFα and EGF as well posttranslational modification of Tyr residues at desired locations in IgG
have broken immunological tolerances and further developed therapeutic vaccines Noncanonical
amino acids with phenylalanine backbone were successfully inserted in Mycobacterium tuberculo-
sis, Mycobacterium smegmatis, and Bacillus Calmette Guerin for the development of tuberculosis
vaccines Genetic code expansion was also implemented for the development of live attenuated
HIV-1 vaccine by incorporation of phenyl alanine analogues into the protein envelope of HIV virus
by controlling its replication cycles.
Table 15.5 rDNA-derived therapeutic peptides.
Therapeutic
peptide
Amino
acid
residues Brand name Manufacturer Indication
Human insulin 51 Humulin, Novolin Eli lily, Novo Nordisk Types 1 and 2 diabetes
Insulin detemir 50 Levemir Novo Nordisk Types 1 and 2 diabetes
Glucagon 28 GlucaGen Eli lily, Novo Nordisk,
Zymo Genetics
Hypoglycemia
Teduglutide 36 Revestive Nycomed Short bowel syndrome
Liraglutide 37 Victoza, Saxenda Novo Nordisk Type 2 diabetes, obesity
Albiglutide 2 × 30 Tanzeum GlaxoSmithkine Type 2 diabetes
Dulaglutide 2 × 30 Trulicity Eli lily Type 2 diabetes
Semaglutide 31 Ozempic Novo Nordisk Type 2 diabetes, obesity
Carperitide 28 HANP injection Daiichi Pharmaceuticals Acute heart failure
Neseritide 32 Natrecor Scios Congestive heart failure
Calcitonin 32 Acticalcin TRB Pharma Postmenopausal
osteoporosis
Teriparatide 34 Forteo Eli lily Osteoporosis
Mecasemin 70 Increlex Ipsen Insulin-like growth factor-1
deficiency
344
15.2.5.10 PEGylation of Peptides and Proteins
PEGylation improves renal clearance of therapeutic peptides by enhancing their effective molecular
weight [177]. PEGylation has been the method of choice for increasing the half-life and metabolic
stability of peptide therapeutics in the last two decades [178, 179]. Consequently, PEGylation has
evolved as a prime strategy for the modification of therapeutic peptides. Currently, there are >10
FDA-approved therapeutic peptides in the market and several other potential candidates under
clinical investigations [180].
The majority of the approved therapeutic peptides have been synthesized by nonsite-specific
PEGylation, specially at Lys or Cys residues [181]. The nonsite-specific conjugation strategy
although leads to the accumulation of heterogenous products if more than one reactive Lys or Cys
residue is present on the target protein and is quite difficult to separate [182]. Moreover, the activ-
ity of the protein may be lost due to PEGylation at the active site [183], entanglement in peptide
chains by PEG residues [184] or conformational changes in protein [185]. Under such circum-
stances, the site specific PEGylation becomes an effective alternative approach where selective
conjugation and positional control is achieved.
Physiochemical properties of the amino acid residues play a vital role in the selective modifica-
tion of peptides. Differences in the pK
a
values between amino groups of lysine, arginine, and
N-terminal amino acid residue provide a good platform for the N-terminal modification of pep-
tides [186]. N-terminal conjugation can be achieved by the use of oxidized agents, chemoselective
utilization of catechol, pH-controlled acylation and alkylation, transamination, and reductive
alkylation [187]. ATRP and RAFT methods (graft from) polymerization methods have been devel-
oped to grow PEG from the surface of protein [188–190]. Genetic code expansion has also emerged
a promising tool for PEG conjugation. In this approach, a noncanonical amino acid is genetically
encoded with bio-orthogonal chemical handle at required position into the target protein and
further conjugated to PEG. IFN-a2b [191], hGH variants [192], and GLP-1 [193] have been modi-
fied by genetic code expansion coupled with site-specific PEGylation to improve pharmacokinetic
and pharmacodynamic profiles. Currently, enzymes are also employed for site-specific ligation of
amino acids with PEG [194].
15.3 New Technologies for Peptide-Based Drug Discovery
Several cutting-edge methods for finding therapeutic peptides have emerged in recent years. The
development of novel peptide medications is being facilitated by these cutting-edge methods at a
breakneck pace. Peptide-based drug discovery is benefiting from the development of new display
technologies that have several advantages over more conventional approaches. Peptide libraries
can now be made that are larger, manufactured more quickly, and at a lower cost than ever before
because of the availability of these technologies. In this chapter, we look at some of the cutting-
edge tools for peptide-based drug development
15.3.1 Phage Display
Phage display systems have improved, allowing this technology to be employed in immunology,
biomedicine, material development, and more. The 2018 Nobel Prize “for the phage display of
peptides and antibodies” recognized the importance of phage display systems [195]. The pro-
cess of designing the library is considered to be one of the most significant aspects of the phage
display experiment. This stage involves the incorporation of millions, if not more, of DNA
345
clones that contain target sequences that encode peptide or antibody fragments. Following that,
these fragments are capable of being duplicated, copied, translated, and shown on the surface
of the phage [196]. The subsequent step is to incorporate the library into the genome of the
phage by means of either phagemid replication or standard vector replication. Utilizing biopan-
ning (Figure 15.5) which is the process of selecting a specific phage particle [197], it is feasible
to accomplish identification in a very basic manner. Following the verification of the functional
expression of a peptide or protein, mutagenesis makes it possible to generate a large number of
variations.
Several FDA-approved peptides have been designed by phage display technology, like
peginesatide, romiplostim, and ecallantide. Many peptides have been identified by phage
display as leads for antibiotic synthesis, antifreeze agents, and protease inhibitors of the SARS
coronavirus [7].
15.3.2 mRNA Display
mRNA display represents a cutting-edge molecular technology that holds great promise in the
realms of protein engineering, drug discovery, and functional genomics. This innovative technique
involves the covalent linkage of a coding mRNA molecule to its encoded protein, creating a stable
mRNA–protein complex. This linkage allows for the direct phenotypic screening of vast combina-
torial libraries, enabling the rapid identification and optimization of peptides and proteins with
desired functions or binding properties. Unlike traditional display methods, mRNA display
uniquely captures the genotype–phenotype relationship in a single entity, facilitating the explora-
tion of diverse sequence spaces. With applications ranging from the selection of therapeutic anti-
bodies to the engineering of enzymes and the study of protein–protein interactions, mRNA display
emerges as a powerful tool at the intersection of molecular biology and protein engineering, poised
to redefine the landscape of functional genomics and accelerate the pace of drug discovery.
Phage addition to immobilized antigen
Phage library
Phage amplification
Biopanning
Peptide sequence
analysis after 3
rounds of panning
Antigen specific phages inoculated to bacteria
Unbound
phages
Washing of unbound phages
Figure 15.5 Biopanning and phage display method for peptide expression.
346
Phase II/III peptides like zilucoplan and phase III peptides like pegcetacoplan were found using
mRNA display technology; a number of lead peptides have been identified as strong inhibitors of
the SARS-CoV-2 spike protein [198], E6AP ubiquitin ligase [199], and influenza virus [200].
15.3.3 DNA-Encoded Libraries
In the dynamic landscape of drug discovery, the constant pursuit of innovative methodologies has
significantly shaped both the pharmaceutical industry and academic research. A notable advance-
ment in this arena is the transition from traditional approaches of accumulating compounds for
robotic high-throughput screening (HTS) to the revolutionary realm of DEL technology [201].
DELs represent combinatorial collections of drug-like molecules, each uniquely labeled with a
DNA barcode, offering a powerful platform for screening a vast array of compounds for their abil-
ity to interact with specific proteins.
The development of reactions compatible with DELs has been a formidable task, with stringent
requirements such as compatibility with water, achieving quantitative yield, working with dilute
reactants, and ensuring DNA orthogonality. These challenges underscore the complexities faced
by modern synthetic organic chemistry in the pursuit of effective DEL-compatible reactions. Click
reactions, with their ability to achieve diverse chemical functions with minimal reactions, align
perfectly with these requirements, facilitating the creation of high-quality DELs with optimal
structural diversity [202].
Recently, DELs have been successfully used in the design of respiratory syncytial virus protein
inhibitors [203] and PPI inhibitors for skp1/skp2 aggregation [204].
15.3.4 Cell-Penetrating Peptides
Cell-penetrating peptides (CPPs) are sequences of diverse amino acids that facilitate cellular
uptake through various mechanisms. The discovery of the transactivator of transcription (TAT)
protein from HIV-1 in 1988 marked the inception of CPPs, followed by the identification of “pen-
etratin” from the Drosophila Antennapedia-homeotic transcription factor in 1994. Subsequently,
numerous natural and synthetic CPPs have been explored and refined for their cell-penetrating
capabilities [205].
CPPs can be manipulated systematically to deliver cargo to specific cells or tissues, showcasing
their adaptability. In addition, CPPs can be combined with other delivery systems to enhance cell-
penetrating abilities [206].
15.3.5 Macrocyclic Peptides
Macrocyclic peptides, known for their ring structure with 12 or more members, emerge as a prom-
ising class for tackling complex therapeutic targets, especially protein–protein interactions. Their
capacity for substantial binding interfaces, resembling natural protein–protein interactions, cou-
pled with restricted conformational flexibility due to cyclization, imparts noteworthy selectivity
and affinity compared to their noncyclic counterparts. With over 40 macrocyclic peptides in clini-
cal use, the field sees the introduction of about one new macrocyclic peptide drug annually over
the past decade [207].
Two main discovery approaches for macrocyclic peptides exist: design-led and screening/
selection-led approaches. Design-led strategies leverage knowledge of the bound conformation
347
to engineer ring-forming modifications that stabilize the active conformation. In contrast,
screening/selection approaches rely on diverse candidate ligand pools or libraries without prior
target structure or binding conformation knowledge. Recent technologies like DELs, split intein
circular ligation of peptides and proteins (SICLOPPS), phage display, and mRNA display encode
library members with unique genetic tags, facilitating the selection of molecules with desired
traits using molecular biology and DNA sequencing. This differs from traditional screening,
which individually assesses each library member’s interaction with the target [208].
15.4 Computational Approaches in Peptide Drug Discovery
Computational methods enable an elaborate study of biomolecules in conjunction with their
receptors at molecular levels. The knowledge of molecular-level protein and peptide interactions
paves the way for rational peptide drug design and development by overcoming inherent short-
comings such as poor oral bioavailability and membrane permeability [209].
One of the strategies to improve oral bioavailability of peptide therapeutics is by enhancing their
water solubility. The aqueous solubility of the peptides can be optimized by identifying and replacing
undesired hydrophobic amino acid residues; however, the process is empirically large [210]. To
expedite such optimizations, several support vector machine (SVM) learning tools like ccSOL
omics [211] and PROSO II [212] were developed. The online tools facilitated in predicting peptide
solubility by identifying hydrophobic amino acid residues along with their positions in the protein
or peptide structure.
The CPPs can easily cross the plasma membranes owing to their hydrophobic nature. The cell
penetrating ability of the CPPs are well exploited for delivering biological cargos, including anti-
bodies, small molecule drugs, or other peptides which are otherwise impermeable across biologi-
cal lipid bilayers [213]. Various robust bioinformatic tools are currently applied for predicting and
optimizing peptide designing using the CPPs. Table 15.6 displays some online webservers available
for predicting peptide solubility and permeability of CPPs.
Table 15.6 Online tools for CPP designing.
Bioinformatic
tool Server Prediction
Machine learning
technique Input format
KELM-
CPPpred
http://sairam.people.iitgn.ac.in/
KELMCPPpred.html/
Permeability Kernel extreme
learning model
FAS TA
CellPPD http://crdd.osdd.net/raghava/cellppd/
multi_pep.php
Permeability Support vector
machine
FAS TA
CPPpred http://bioware.ucd.ie/cpppred Permeability Artificial neural
networks
FAS TA
CPPpred-RF http://server.malab.cn/CPPred-RF Permeability Random forest FASTA
PROSO II http://mbiljj45.bio.med.uni-muenchen.
de:8888/prosoII/prosoII.seam
Solubility Support vector
machine
FAS TA
ccSOL omics http://s.tartaglialab.com/static_files/
shared/tutorial_ccsol_omics.html
Solubility Support vector
machine
FAS TA
348
Computational methods are extremely beneficial in predicting protein structures, surface
charges, and interaction capacities. The in silico docking methods are cutting-edge tools for study-
ing the protein–protein interactions that usually define disease pathologies. The peptide–protein
docking methods can be classified as local, global, and template-based docking. The local docking
methods screen the best binding pose of a peptide at user defined target binding site. Global dock-
ing identifies the best peptide pose and peptide binding site on the target protein. Global docking
is the method of choice for analyzing protein–peptide interactions. Template docking generates
the best peptide–protein interaction models by abstracting amino acid sequences from query pep-
tide and binding site residue sequences of the target receptor [16]. The software tools used in spe-
cific peptide–protein docking are enlisted in Table 15.7.
Table 15.7 Molecular modeling tools for peptide–protein docking.
Software
Docking
type Accessibility Features Reference
PepCrawler Local
docking
http://bioinfo3d.cs.tau.ac.il/
PepCrawler
Uses rapidly exploring random
tree algorithm
Sampling based on motion
planning
Peptide structure is flexible
[214]
Rosetta
FlexPepDock
Local
docking
http://flexpepdock.furmanlab.
cs.huji.ac.il or http://www.
rosettacommons.org/software
Monte-Carlo-based algorithm
Modeling of hot spot residue
Receptor is flexible
Clustering and scoring are based
on Rosetta energy function
[215]
HADDOCK Local
docking
http://haddock.science.uu.nl/
services/HADDOCK2.2/
Homology modeling using
ensemble canonical structure
Docking at user defined residues
in binding pocket of target
protein
Uses a binding energy-based
scoring function
Flexible docking
[216]
DynaDock Local
docking
Commercial Performs soft core potential
optimized molecular dynamic
simulation
Faster sampling time
Flexible docking
[217]
Pepsite 2.0 Local
docking
http://pepsite2.russelllab.org Fastest identification of peptide
binding site
Generate low resolution peptide
model
Generate coarse grained peptide
orientation by using spatial
specific scoring matrix
[218]
ClusPro
Peptidock
Global
docking
https://peptidock.cluspro.org/ Fourier transform based docking
Motif based structure prediction
[219]
349
Molecular simulations are applied to investigate therapeutic peptide–excipient interactions,
assessing drug–conjugate interaction, study interactions of synthesized compounds with peptides
and proteins, screening of peptide-based enzyme inhibitors as well as a rational formulation design
tool. Molecular docking, in combination with genetic algorithms, expedites the virtual screening
of large peptide libraries [226]. The interaction of water-soluble metal complexes with human
serum albumin was well demonstrated through molecular docking analysis using HEX 6.1 soft-
ware by Tabassum and group [227]. In silico tools were instrumental in the design of N-octyl-N-
arginine chitosan as a permeation-enhancing and drug-loading polymer for the oral delivery of
insulin [228]. Molecular dynamic studies favored the glycosylation of peptides, which improved
their poor membrane permeability via sodium-dependent glucose transporters [229]. The stability
of peptide conjugates with polymers or other molecules can also be elucidated by molecular simu-
lations [230, 231]. Molecular docking guided formulation of bioinspired oral insulin delivery sys-
tem using yeast microcapsules is yet another groundbreaking application of computational
avenues in the development of therapeutic peptides [232].
Computational tools are valuable assets for providing insight into the activity of peptide antimi-
crobials and thereby exploiting their potential to design new analogs. The APD3 have played a
Table 15.7 (Continued)
Software
Docking
type Accessibility Features Reference
pepATTRACT Global
docking
http://bioserv.rpbs.
univparisdiderot.fr/services/
pepATTRACT/
Predicts peptide structure by
employing threading sequence
[220,
221]
HPEPDOCK Global
docking
http://huanglab.phys.hust.
edu.cn/hpepdock/
Ensemble protein structure
conformation
Uses hierarchical algorithm
[202]
GalaxyPepDock Template
docking
http://galaxy.seoklab.org/
pepdock
Motif similarity search
Model building using PeptiDB
database
Rigid docking
[222]
SPRINT-str Template
docking
http://sparks-lab.org/server/
SPRINT-Str
Predicts interacting residues on
peptide–protein binding
interface
Uses SVM algorithm
Ability to distinguish binding
site on macromolecules
[223]
PBRpredict-
suite
Template
docking
http://cs.uno.edu/~{}tamjid/
Software/PBRpredict/
pbrpredict-suite.zip
Using template sequences from
NCBI database the probable
interacting residues are predicted
Use integrated machine learning
algorithm
[224]
PepComposer Template
docking
https://cassandra.med.
uniroma1.it/pepcomposer/
webserver/pepcomposer.php
Uses monomeric protein
databases for similarity search of
binding site motifs
Monte-Carlo-based geometry
optimization
[225]
350
major role in the screening of large peptide libraries and the development of computational tools
for the prediction and design of novel peptide antibiotics [173]. Several existing web tools allow the
predictive design of AMPs with the aid of machine learning techniques (Table 15.8).
Computational approaches have been employed for the discovery of novel antimicrobial pep-
tides. Several novel peptides have been designed by performing database screening and ab initio
methods in APD for MRSA infections [233]. Capsid proteins from various enveloped and nonen-
veloped viruses were screened for antimicrobial potency using AMPA online tools [234]. A pleth-
ora of computational tools are also available for designing therapeutic peptides.
15.5 Conclusion
In recent years, peptides have become an increasingly important therapeutic class. Their use was
generally hampered by poor membrane impermeability, oral bioavailability, and poor stability
in vivo. However, peptides have been studied extensively to overcome such limitations. The amal-
gamation of existing traditional discovery approaches with new advances, such as peptide diversi-
fication through unnatural amino acids, C–H functionalization, recombinant synthesis, and DELs
and display technologies, has resulted in the rapid discovery and development of peptide therapeu-
tics. The advent of new chemical and biological synthetic processes allows large-scale production
of the peptides. Thus, more than 100 peptide drugs have entered the clinic, and a significant num-
ber are in clinical development. Peptide-based discovery approaches are emerging as valuable tools
for addressing several unresolved disease areas. Therefore, peptides have enormous therapeutic
potential and economic market value, and recent advances are expected to enable the rapid identi-
fication of drugs to combat various diseases, including cancer.
Table 15.8 Online tools for AMP designing.
Webtool URL Basic algorithm Application
APD3 aps.unmc.edu/AP/ Parameter space AMP prediction
CAMP www.bicnirrh.res.in/
antimicrobial
DA, SVM, RF AMP prediction
CS-AMPPred sourceforge.net/projects/
csamppred/
SVM Prediction of cytosine stabilized
AMPs
PeptideLocator bioware.ucd.ie BRNN Predicting bioactive peptides in
proteins
AVPpred crdd.osdd.net/servers/avppred SVM, BLASTP Prediction of antiviral peptides
AntiBP2 http://crdd.osdd.net/raghava/
antibp/
SVM Prediction of AMP domains in
protein sequences
CPPpred bioware.ucd.ie/cpppred NN Prediction of cell penetrating
peptides
C-P Amp bioserver2.bioacademy.gr/
Bioserver/CPAmP/
SVM Prediction of plant AMPs
Defensinpred www.defensinpred.cdac.in SVM Prediction of defensin peptides
BAGEL bioinformatics.biol.rug.nl/
websoftware/bage
ORF prediction
tools
Predicting bacteriocin ORFs in
DNA sequences
iAMP2L www.jci-bioinfo.cn/iAMP-2L FKNN Predicting AMP query sequences
References 351
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