Random Peptide Library for Ligand and Drug Discovery

  • Tai KuboEmail author
Living reference work entry
Part of the Toxinology book series (TOXI)


In ancient times, natural extracts that derived mainly from plants and animals were utilized for medical or religious purposes. Even after starting the drug discovery and development in the realm of modern pharmacological sciences, the traditional medicines are still reasonably valued. Moreover, the modern approaches for drug discovery are appreciably supported by the resources and the accumulated knowledge of naturally occurring ligands and the offense- and defense-related substances. Among them, bioactive peptides, peptide toxins, and antibodies have been successfully applied in therapy due to their superior target selectivity. However, such biotherapeutics have middle to large molecular weights (>500 Da) in contrast to small molecular weight drugs. Size minimization is especially a concern for membrane permeability, stability in blood, antigenicity, and production costs. Biotherapeutic molecules have been engineered and further improved in terms of their affinity, stability and humanization for more than a decade to make them suitable for biotherapeutics. In this chapter, two approaches currently being used to improve biotherapeutics are overviewed: the size reduction of immunoglobulin-based biotherapeutics and the utilization of nonimmunoglobulin scaffolds. In both approaches, random peptide libraries are constructed based on each scaffold and screened for specific binders (polypeptides) with bioactivities of interest. Natural scaffolds that have been refined during long-term evolution are focused on. Some genes encoding offense/defense or reproduction-related proteins are known to undergo unusually rapid amino acid substitutions in the mature protein-coding regions, whereas the cysteine framework and α/β structures are conserved to maintain the molecular scaffold. This mode of evolution (called accelerated evolution) inspired the utilization of the gene as a template to construct a random peptide library for use for directed evolution in vitro. Screening and in vitro evolution technologies, including phage display, ribosome/mRNA/cDNA displays, the new technology “PERISS,” and in vitro compartmentalization, are also discussed.


In vitro evolution Peptide library Three finger (or 3-F) Inhibitor cystine knot (or ICK) Accelerated evolution Protein-protein interaction (or PPI) 


In the living system, receptors and ion channels play essential roles to receive or to percept environmental changes/stimuli. They transduce and propagate the signals to downstream networks to respond appropriately for maintaining homeostasis or to initiate higher-order biological functions such as immune responses, cellular development, learning, and memory. Molecular recognition and selective interactions are the molecular basis of such complex but highly controlled and harmonized performances of the living system. A malfunction of the system initiates an impairment of health and leads to diseases. Receptors and ion channels are now a major target for drug discovery. Ligands, the molecules that specifically bind to and modulate the function of the receptors and ion channels, are utilized for drug development and used as a molecular tool for scientific research as well (Imming et al. 2006). The binders/modulators could be a variety of materials; organic or inorganic chemical compounds, peptides, DNAs/RNAs, carbohydrates or proteins (Taussing et al. 2007). In Table 1, different types of ligands and therapeutic drugs are classified based on molecular sizes; small-, medium-, and high-molecular weight. Characteristic features of each type of the drugs and pros/cons in medical usages and in drug development/production are also compared. Here in this chapter, random peptide libraries inspired by naturally occurring toxin/bioactive peptides are focused on, because of its great advantages as screening resources and in physicochemical and pharmacological features.
Table 1

Comparison of synthetic drugs and biotherapeutics


Synthetic Drugs



Low Molecular Weight (<500 Da)

Mid-size Molecular Weight (500~10k Da)

High Molecular Weight (>10k Da)


• Chemical compounds






Quinine etc.

• Antibiotics and derivatives (Cyclosporine A, FK506, Rapamycin, etc.)

• Peptide hormones (ANP, CNP, Adrenomedulin, Ghrelin, etc.)

• Peptide toxins and derivatives (ω-Conotoxin MVIIA (Ziconotide), Eptifibatide (Integrilin), Tirofiban, etc.)

• Interferons

• Interleukins

• Vaccines

• Antibody medicines (Rituximab (anti-CD20), Bevacizumab (anti-VEGF), Trastuzumab (anti-HER2), Nivolumab (anti-PD1), etc.)

• Enzymes (Amylase, Pronase, Lysozyme, Pancreatin, etc.)

• Toxins (Botulinum toxins A and B)


Natural compounds (collection/library) Chemical libraries (chemical synthesis and synthetic deployment)

Natural resources or mimetics

Random peptide libraries with natural/designed scaffold

Biomolecules including immunoglobulin and immune-related proteins, and functional proteins

Screening, Identification and Drug Seeds Optimization

• High-throughput screening

• Experience-based development

• In scilico design based on MD simulation

• Peptide library: Molecular diversity is huge, and in vitro evolution technology is applicable; flexible in selection/assay criteria

• Biological/clinical information driven

• Antibody medicines: Antigen and host dependent

• Humanization, PEG, Potelligent, Compligent

Membrane Permeability (Oral Administration)



Permeable after modification or with DDS




Targeting Cytoplasm or Nucleus


Possible after modification or with DDS


Target Specificity (Off-target Problem)

Generally low

(serious in case)


(generally low)


(generally low)

PPI Control

Difficult (partial)




Generally low


Serious in case

Commercial Production

Chemical plant

Chemical/biochemical production

Bioprocessing plant

Quality Control



Higher control is required

Production Cost




Pharmaceutical Innovations

Mega-pharma (synthetic deployment/optimization, etc.)

Academia/institution lab or venture in seeds development and pharmaceutical companies at the later stage (production/clinical trials/approval)

Venture (optimization) and mega-pharma (production/clinical trials/approval)

Chemically synthesized drugs (“Low Molecular Weight” in Table 1) have a long history of development and are widely utilized for therapeutic and diagnostic purposes. However, they are not necessarily specific to the target but instead interact with other molecule(s) unexpectedly and usually unfavorably. This phenomenon sometimes leads to serious side effects and off-target problems (Muller and Milton 2012). In contrast, biopharmaceuticals (e.g., antibody medicines; “High Molecular Weight” in Table 1) are characterized by their selectivity and specificity for the target. Multivalent intermolecular interactions make it possible to securely recognize and interact with the target molecule and to commit to the specific protein-protein interactions (PPIs) of interest (Kintzing et al. 2016).

A typical example of PPIs is specific interactions between the antibody and antigen. Due to their specificity, antibodies have been successfully utilized in therapies as “antibody medicine” and in diagnosis as probes to detect biomarkers, which are used for critical decisions concerning medical treatment and drug evaluations. Despite of their obvious advantages in specific medication, bottlenecks underlying their production process must be overcome to achieve low cost and safe medication due to particular characteristics of immunoglobulins, such as their large molecular weight, glycosylation, and humanization. Size minimization and membrane permeability are both concerns. When membrane permeability is achieved, the drug can target cytoplasmic molecules by oral administration. Besides the PPIs by antigen and antibody, selective recognition and interactions are also found in natural biological systems; e.g., protease and substrate/inhibitor, ion channel and blocker, receptor and ligand, and transcription factor and nucleotide binding sequence. Inspired by these specific PPIs observed in nature, various types of nonimmunoglobulin binders have been developed in the past decade (Nygren and Skerra 2004; Binz et al. 2005; Wurch et al. 2012).

As compared in Table 1, biopharmaceuticals of medium size have broad advantages over small synthetic drugs or large protein medicines. Supported by recent progress in technologies and availability of high-performance computers, it is getting more precise to analyze the structure and function of drug candidates of mid-size biotherapeutics, and getting rapid to assess the druggability. Thus, the feasibility to challenge the drug discovery and development of the mid-size biotherapeutics is revolutionary increased. In this chapter, several different immunoglobulin/nonimmunoglobulin scaffolds that have been developed to construct random peptide libraries for the screening are overviewed, and recent progress in directed evolution technologies to identify specific binders from the libraries is described.

Peptide Ligands as Medium-Sized Drug Candidates: Specific Binders to Target Molecules

Medium-sized biomedicines are expected to be promising for next-generation drug development. Their advantages over small molecule chemical drugs and high molecular weight biomedicines are target specificity and selectivity, the potential multivalency of the function, a long serum half-life, low GMP (good manufacturing practice ) and practical production costs (Table 1). In this section, two different approaches to develop medium-sized biomedicines are described; the first is downsizing of the immunoglobulin molecule (or its scaffold; minimized antibody) engineered without the loss of its target recognition ability, and the second is the utilization of naturally occurring nonimmunoglobulin scaffolds to create specific binders (nonimmunoglobulin binders) (Table 2).
Table 2

Immunoglobulin and nonimmunoglobulin scaffolds used to construct random peptide libraries

Scaffold name

Protein and its structure features

Example of target proteins


1. Immunoglobulin scaffold


Human bivalent scFv-Fc

Open image in new window


Human scFv-CH3


Human IgG4

Fab, (Fab’)2

Human Fab fragment(s)


Conjugate of human Ig VH-CH1 and VL-CL


Human Ig VH and VL


Human Ig single chain VH linked with VL


Two/three sets of scFv linked in tandem


Two different scFvs linked; bispecific diabody

CEA + CD3, CD19 + CD3, EpCAM + CD3



VHH domain of camel Ig

IL-6R, TNFα, vMF, etc.


dAbs, TandAb

Single variable region of VH or VL

TNFα, CD30 + CD16a


2. Non-immunoglobulin scaffold

2.1 Protein scaffold


Transferrin; α/β




α-Amylase inhibitor; β-sandwich

mAb, integrins



10th FN3 (fibronectin); β-sandwich

TNFα, ubiquitin, integrin, SH3, estrogen receptor domains, VEGFR-2

[6, 7]


Lipcalins; β-barrel; 0-3 S-S

Hemoglobin, CTLA-4 streptavidin, VEGF-A (digoxigenin, fluorescein, benzyl butyl phthalate)

[8, 9]


T-cell receptor; β-sandwich

Peptide/MHC complexes



Z domain of protein A; a member of the coagulation cascade; 3α

Taq DNA polymerase, insulin, apolipoprotein A1, protein Z, lgA, Factor Vlll, RSV protein G fragment, CD28, HER-2/neu



Ankirin repeat (AR); 2α/2β repeated



Zinc finger

α/β (Zn2+)




A-domain/Low density lipoprotein receptor




Tetranectin; C-type lectin domain family 3; trivalency




High cysteine containing mini-protein/non-human origin; multi-valency



Thioredoxin; α/β; 1 S-S

mAbs, CDK2, Mdm-2, E2F, E6



Cellulose binding domain; 3β; 2 S-S

Alkaline phosphatase, α-amylase

(Ni-NTA beads)

[19, 20]

2.2 Scaffold refined through accelerated evolution

Kunitz domain



Bovine pancreatic trypsin inhibitor; Kunitz domain; α/β; 3 S-S

Human neutrophil elastase



Serine protease inhibitor in the peroplasm of Gram-negative bacteria; 3β; β-sandwich

uPA, trypsin, plasma kallikrein, MT-SP1, FXllA



1st domain/human lipoprotein-associated coagulation inhibitor; α/β

Plasmin, thrombin, plasma kallikrein


ICK/Knottin motif


Scorpion toxins

Charybdotoxin; 1α/3β

Acetylcholine receptor, gp120, mAbs

[24, 25]

Spider toxin

GTx1-15; 1α/3β; 3 S-S

mAChR m2



Ecballium elaterium (squash) trypsin inhibitor II; Coiled, 3 S-S

Elastases, chymotrypsin, trypsin, carboxypeptidase, antibody epitopes



Insect defensin A peptide; 1α/2-3β; 3 S-S



Three Finger

MicTx3/three-finger neurotoxin; 4β; 4 S-S

IL-6 R, trypsin, AChBP, VEGF

[29, 30, 31]

WAP motif

BufTx1; 2β; 4 S-S



2.3 Rationally designed scaffold


Designed based on EETI II (squash trypsin inhibitor); 3β; 2 S-S (CSB, cystine-stabilized β-sheet motif)

mAbs, AMA-1, Tom70, HIV-1 Nef



Helix-loop-helix/Cys-helix-loop-helix-Cys; leucine-zipper stabilized helices

GCSF-R, Aurora-A, GM1, VEGF



Tryptophan-zipper stabilized scaffold



Cys-constrained 27 mer

Cys appearance in 12 % probability (3-4 Cys/27 aa)

NH2 group



Cysconstrained bicyclic peptide two variable regions flanked by Cys residues (3 Cys/two loops); bicyclic peptide formation by halomethylarene



Inset figure, schematic structures of engineered immunoglobulins, was from

Some scaffolds listed in “non-immunoglobulin scaffold” were adapted and modified from a detailed review by Binz et al. 2005

References in Table 2

[1] Baeuerle, al. (2008) Drugs Future 33:137–147

[2] Van Bockstaele, F. et al. (2009) Curr opinion in investigational drugs 10:1212–1224

[3] Holt, L.J. et al. (2003) Trends Biotechnol 21:484–490

[4] Prior, C.C. (2004) Patent WO2004044011

[5] McConnell, S.J. et al. (1995) J Mol Biol 250:460–470

[6] Xu, L. et al. (2002) Chemistry & biology 9:933–942

[7] Koide, A. et al. (1998) J Mol Biol 284:1141–1151

[8] Schlehuber, S. et al. (2005) Drug discovery today 10:23–33

[9] Beste, G. et al. (1999) Proc Natl Acad Sci U S A 96:1898–1903

[10] Holler, P.D. et al. (2000) Proc Natl Acad Sci U S A 97:5387–5392

[11] Shusta, E.V. et al. (2000) Nat Biotechnol 18:754–759

[12] Li, Y. et al. (2005) Nat Biotechnol 23:349–354

[13] Nord, K. et al. (1997) Nat Biotechnol 15:772–777

[14] Binz, H.K. et al. (2004) Nat Biotechnol 22:575–582

[15] Bianchi, E. et al. (1995) J Mol Biol 247:154–160

[16] Kolkman, et al. (2004) Patent WO2004044011

[17] Graversen, J.H. et al. (2000) J Biol Chem 275:37390–37396

[18] Colas, P. et al. (1996) Nature 380:548–550

[19] Smith, G.P. et al. (1998) J Mol Biol 277:317–332

[20] Lehtio, J. et al. (2000) Proteins 41:316–322

[21] Roberts, B.L. et al. (1992) Proc Natl Acad Sci U S A 89:2429–2433

[22] Stoop, A.A. et al. (2003) Nat Biotechnol 21:1063–1068

[23] Ley, A.C. et al. (1996) Molecular diversity 2:119–124

[24] Vita, C. et al. (1995) Proc Natl Acad Sci U S A 92:6404–6408

[25] Martin, L. et al. (2003) Nat Biotechnol 21:71–76.

[26] Kubo, T. et al. (2012) Toxicon 60:113

[27] Christmann, A. et al. (1999) Protein Eng 12:797–806

[28] Zhao, A. et al. (2004) Peptides 25:629–635

[29] Naimuddin, M. et al. (2011) Mol Brain 4:2

[30] Cai, W. et al. (2014) J Recept Signal Transduct Res 34:154–161

[31] Naimuddin, M. et al. (2016) ACS Comb Sci 18:117–129

[32] Yang, X. et al. mamuscript in preparation

[33] Souriau, C. et al. (2005) Biochemistry 44:7143–7155

[34] Fujiwara, D. et al. (2013) Curr Protoc Chem Biol 5:171–194

[35] Kim, S. et al. (2012) Angewandte Chemie International Edition, 51:1890–1894

[36] Mochizuki, Y. et al. (2014) Chem Common (Camb) 50:5608–5610

[37] Baeriswyl, V. et al. (2012) ChemMedChem 7:1173–1176

Antibody Medicine: Immunoglobulin to Minimized Antibody

Prior to the development of monoclonal antibody technology (Kohler and Milstein 1975), antibody production and quality were totally dependent on host animals for immunization. The hybridoma and large-scale mammalian cell culture technologies have accelerated broad applications of antibodies to medicine and moved them almost within our reach. Supported by recent progress in genetic engineering, immunoglobulin molecules can be manipulated by processes such as point mutations, deletions, and replacement to modify target selectivity and affinity, posttranslational modification, and even effector coupling. Due to their elaborate target selectivity and minimal side effects, monoclonal antibodies are greatly valued as biotherapeutics (antibody medicines). However, as mentioned above, biotherapeutics also have drawbacks (e.g., they are expensive to manufacture, are limited by route of administration (injection), and are unable to target cytosolic biomolecules).

One solution proposed to overcome the problems is to downsize the antibody from the whole molecule (~150 kDa) to the minimal elements required for function. Several innovative antibody engineering approaches have been proposed based on immunoglobulin genes (e.g., Knappik et al. 2000; Fab combinatorial library approach, Mao et al. 2010). Moreover, proteins and domains essential for the recognition of and binding to the target(s) were extracted and reconstituted to generate functional polypeptides for this purpose (see “1. Immunoglobulin scaffolds” in Table 2).

A typical example is a “single-chain variable fragment (scFv).” In scFv, antigen-binding variable regions of the immunoglobulin H- and L-chains are linked by a flexible linker (Hanes et al. 2000; Soderlind et al. 2000). The size (~30 kDa) is 1/5 of that of the immunoglobulin. When two or three sets of scFvs are assembled or tandemly linked, they are called “Diabody”/“Tri(a)body” or “tandem di-scFv”/“tandem tri-scFv,” respectively (Table 2). This combination of multiple scFvs can be used to enhance affinity and to recognize different targets simultaneously. A BiTE (bispecific T cell engager ) consists of two different scFvs; the first scFv functions as an antitumor antibody, and the second recognizes CD3 on the T cells. For example, a BiTE generated to target CD19 and CD3 showed very potent regression activity against lymph node tumors (Bargou et al. 2008). The BiTE can support the assembly of helper T cells around target tumors and at the same time trigger ADCC (antibody-dependent cellular cytotoxicity )-like activity by activating T cells as effectors. Because of its rational design and broad applicability, BiTE technology is expected to drive next-generation antibody medicine.

A domain antibody (dAb) or single-domain antibody (sdAb) contains a single variable region (VH or VL) harboring three complementarity-determining regions (CDRs 1, 2, and 3), which generate diversity in the antibody and thus are responsible for the selectivity and affinity for target molecules (Table 2, and see review Holt et al. 2003). Presentation of a variable region from a single chain is naturally found in camels, llamas (nanobody), and sharks (IgNA, new antigen receptor). These regions might be the minimum extremes of the minimized antibody for target recognition (molecular weight, 12–15 kDa, 1/10 of the immunoglobulin). Because sdAbs are short and soluble polypeptides, they possess the following advantages as biotherapeutics: (1) convenient expression in E. coli and subsequent purification, (2) high cellular infiltration efficiency, and (3) broad application by combination with therapeutic payloads (i.e., radionuclides, toxins, enzymes, liposomes, and viruses) (Holliger and Hudson 2005).

Nonimmunoglobulin Binders

As mentioned above (section “Antibody Medicine: Immunoglobulin to Minimized Antibody”), there are advantages in the use of engineered antibodies. However, these concepts and technologies are covered by strategic patents, which are associated with higher production costs for biotherapeutics. Similar to the CDRs in immunoglobulin molecules, spatial presentation of the sequences responsible for target recognition and binding could also be achieved using a natural nonimmunoglobulin scaffold. Inspired by natural proteins, scientists have used many proteins to construct libraries that are then screened for selective binders. Some protein scaffolds are listed in Table 2 (the conventional or product name is in parenthesis) including fibronectin (adnectin/monobody), Staphylococcus aureus protein A (affibody), lipocalin (anticalin), LDL (low density lipoprotein) receptor A-domain (avimer), ankyrin (DARPin), thioredoxin (Flitrx), T cell receptor (mTCR), α-amylase inhibitor (tendamistat), transferrin (transbody), microprotein domain (versabodies), and variable lymphocyte receptor (“2. Non-immunoglobulin scaffold” in Table 2, Binz et al. 2005). These proteins are stabilized by their secondary structures (α-helices and/or β-structures). To prepare a random peptide library, randomized sequences are incorporated into the specified position(s) in the scaffold (e.g., positions in loops (fibronectin), flat surfaces (protein A), loop and helix combination (ankyrin), or a cavity (lipocalin)). All of these proteins are soluble and thermostable, and their yield is high when they are recombinantry expressed. Similar to minimized antibodies, the primarily selected proteins are usually further tuned up to improve their properties, such as affinity and specificity, by engineering called “maturation.”

An ankyrin repeat (AR) is an example. Pluckthun and colleagues focused on AR, which has 33 amino acid residues arranged in a unit composed of two antiparallel α-helices and a β-turn. The authors prepared peptide libraries by introducing random sequences into the regions exposed to the surface of the AR molecule (Binz et al. 2004). They isolated specific binders to Her2 (human epidermal growth factor 2) from the AR library and then successfully improved the affinity to K d = 90 pM by affinity maturation (error-prone PCR and amino acid substitutions) (Zahnd et al. 2007b). Interestingly, when two ARs were conjugated by a linker, each AR had an independent epitope against FcεR1α (K d = 10 nM each) that showed bispecificity and improved affinity (K d = 10 pM). Furthermore, the AR conjugate was found to inhibit the degranulation of basophilic leukocytes in a cell-based assay with a potency comparable to the known antibody medicine omalizumab (Xolair) (Eggel et al. 2009).

Some nonimmunoglobulin binders including AR have a characteristic feature of repetitive units. To design an effective nonimmunoglobulin scaffold, we can refer to peptides/proteins containing repetitive surface units (Forrer et al. 2004). Inspired by the antifreeze protein, which belongs to the β-solenoid proteins, an artificial scaffold with self-assembled flat surfaces was designed and characterized (Peralta et al. 2015). Although this type of flat surface assembly was originally proposed for use in nanoscale laminate construction (e.g., photovoltaics and scaffolding for tissue engineering), it may have potential applications in biotherapeutics upon introduction of random sequences for library construction or through attachment to functional entities.

Random Peptide Libraries Designed from Naturally Occurring Peptide Toxin Scaffolds

Disulfide bonds together with secondary structure elements (α-helix and β-structure) are important for the correct formation and maintenance of 3-dimentional structures of protein and contribute to its function. Disulfide-containing proteins often play important roles in defense and/or offense and range from prokaryotic toxins to immunoglobulins (Gruber et al. 2007; Tsetlin 1999; Kini and Doley 2010). There has been growing interest in plant and animal toxins for pharmacological and drug template uses (Kolmar 2008; Miljanich 2004). For example, an FDA (Food and Drug Administration )-approved drug “Ziconotide” is a derivative of one of the conotoxins (the ω-conotoxin MVIIA) that is a peptide of 25 amino acid residues long with three disulfide bonds. Ziconotide was characterized as a potent and selective blocker of N-type calcium channels. Peptide toxins from venomous animals (such as cone snail, spider, scorpion, toad, snake) are small (10–80 residues), compact, resistant to proteolysis, and lowly immunogenic, and some of them are stabilized by a knotted structure by three to five disulfide linkages that imparts thermal stability. These properties also make them more suitable for drug development due to their increased bioavailability (Kolmar 2008). An interesting and potentially intriguing aspect of these proteins is their diversity within the same species; for example, snake venom contains hundreds of toxins that have diverse functions (Tsetlin 1999; Kini and Doley 2010; Jeyaseelan et al. 2003; Fry et al. 2003). The diversity is thought to be generated from the accelerated evolution of the binding or surface loops of these proteins to adapt to diverse conditions, such as changes in defense/offense targets due to dynamic changes in the environment. During evolution, several principal scaffolds with potency against multiple targets might have been selected and further refined.

Three-Finger Peptide Library from Snake Neurotoxin Scaffold

Three-finger (3-F) proteins are found in a variety of organisms, such as the elapidae snake and mammals including humans. Hundreds of 3-F proteins/toxins have been reported to date, including α-bungarotoxin, erabutoxin A, α-cobratoxin, muscarinic toxins, and lynx 1 (reviewed in another chapter of this handbook and by Endo and Tamiya 1987; Tsetlin 1999; Miwa et al. 1999; Fry et al. 2003). 3-F proteins are small proteins (Mw 7–8 kDa) with 4–5 disulfide bonds, 2–5 β-structures, and 3 protruding loops that provide the topological basis for the three-finger structure (Endo and Tamiya 1987). The striking features of the 3-F protein family are the strict conservation of the cysteine framework and high sequence diversity in the loop (corresponding to the finger) regions. These features may reasonably provide 3-F toxins with a broad spectrum of target molecules in nature, such as ion channels, receptors, and proteases (Fry et al. 2003).

Based on target diversity and structure-function studies of various types of 3-F toxins, peptides with 3-F scaffolds were expected to be utilized in drug discovery (Kini and Doley 2010). The loop regions were subjected to various amendments (e.g., point mutations, replacement, or grafting) to install a new property (i.e., target selectivity, affinity, or physicochemical property) to the original structure. New ligands for G protein-coupled receptors (GPCRs) with novel functions were generated by grafting the loop regions of the 3-F muscarinic toxins (Fruchart-Gaillard et al. 2012).

When random sequences are introduced into the three fingers simultaneously, the resulting random peptide library may have profound potential to select binders to various target molecules. The pluripotency of the 3-F random peptide library was shown by Kubo and his colleagues by utilizing the library for one of the in vitro evolution technologies (“cDNA display”) (Fig. 1, see section 5). These authors successfully isolated 3-F peptides targeting the interleukin-6 (IL-6) receptor (Naimuddin et al. 2011), trypsin (Cai et al. 2014), acetylcholine binding protein (AChBP), and vascular endothelial growth factor (VEGF) (Naimuddin and Kubo 2016). To prepare a random 3-F peptide library, a cDNA encoding the snake α-neurotoxin MicTx3 from the South American coral snake Micrurus corallinus was used as a template. MicTx3 is composed of 61 amino acids and belongs to the short α-neurotoxin family (see multiple alignment with the family; Fig. 1a). Based on the alignment and computer modeling, three finger tips containing the amino acid residues Thr5–Pro10 in loop I (six residues), Lys25–Val34 in loop II (ten residues), and Ala46–His52 in loop III (seven residues) of MicTx3 were randomized by introducing (NNS)6, (NNS)10, and (NNS)7, respectively, into the corresponding positions of the cDNA (Fig. 1c). The resulting theoretical diversity of the library is 2023, although the practical diversity in a test tube may reach 1012–1013.
Fig. 1

Sequence and structure of MicTx3 and a three-finger peptide library constructed using the MicTx3 as a template. (a) Sequence alignment of MicTx3 and representative three-finger proteins. Mature forms of MicTx3, erabutoxin A (Ebtx_A; UniProt Acc#P60775), α-cobratoxin (α-Cbtx; P01391), α-bungarotoxin (α-Bgtx, P60615), and mouse lynx1 (mLynx_1, Q9WVC2) were aligned by the Clustal W method using the software Lasergene ver.7 (DNASTAR, Madison, WI, USA). Conserved cysteine residues among the toxins are highlighted in green, and the disulfide bonds expected based on the analysis of α-Bgtx are shown by lines. The number of amino acid residues is shown on the right. (b) Superimposition of three-dimensional structures of the MicTx3 model and the α-Bgtx experimental structure. The three-dimensional model of MicTx3 was generated by the software Internal Coordinate Mechanics (Molsoft, La Jolla, CA, USA) using the α-Bgtx structure (PDBID: 1ik8) as a modeling template. MicTx3 is shown in red, and α-Bgtx is in blue. The numbers in Roman numerals represent the loop numbers. (c) Schematic of the library based on the 3-F scaffold. The antiparallel β-sheets are depicted with blue arrows, and the randomized loop residues are indicated with red crosses. Numbers such as Y24 and P11 indicate residues adjacent to the randomized residues (also see a). N and C indicate the amino- and carboxyl-terminals, respectively. (d) Binding assay assessment of the quality of the libraries. The MicTx3-based 3-F library was subjected to screening for binders to the interleulkin-6 receptor (IL-6R). The initial library (R0) and round 10 library (R10) were prepared by the cDNA display method. The R10 library was reduced with dithiothreitol (DTT) to obtain the R10-D library. All three forms were incubated with noncoated beads, IL-6R-coated beads, or AChBP-coated beads and washed and detected with Penta His HRP (horseradish peroxide) (Adapted from Naimuddin et al. 2011)

As previously mentioned (Nygren and Skerra 2004), libraries with relatively large numbers (e.g., 10–24) of randomized residues (regarded as “broad and shallow”) might be helpful for finding first generation binders. These libraries can be further upgraded or fine-tuned by affinity maturation via screening of “narrow and deep” libraries (Gunneriusson et al. 1999; Schlehuber and Skerra 2002). In addition to affinity maturation, selected binders should be able to perform surveillance of the region(s) responsible for affinity and selectivity. Interestingly, the 3-F binders to the IL-6 receptor, which showed antagonist- or agonist-like activity in the IL-6-dependent cell-based assay, succeeded in size minimization to one finger (24 amino acid residues) without a significant loss of activity (Naimuddin et al. 2011).

Another example of the 3-F random peptide library was a bucandin (Bungarus candidus)-based library. Similar to MiTx3, each fingertip was randomized, and the library was successfully used to screen binders to survivin (Nemoto et al., personal communication), which is an inhibitor of the apoptosis protein family and a valuable biomarker for lung and colon cancers.

Inhibitor Cystine Knot Library from Spider Neurotoxin Scaffold

A protein motif called inhibitor cystine knot (ICK) is also used to prepare random peptide libraries . The ICK toxins have been identified in arachnids (e.g., spiders, scorpions, ticks, and mites), mollusks, and plants. Grammotoxin, hainantoxin, hanatoxin, huwentoxin, and vanillotoxin are examples, and their major targets are ion channels, including K+, Na+, and Ca2+channels; mechanosensitive and stretch-activated channels; and TRP (transient receptor potential ) channels (see other chapter of this handbook series and Kimura et al. 2012). ICK toxins are compact and globular proteins (35–40 amino acid residues) with an antiparallel triple-stranded β-sheet and either a short or incomplete α-helix that forms a knot-like fold with three disulfide bridges. Similar to 3-F toxins, ICK motif toxins commonly maintain the principal scaffold but show sequence variations in the outer loop or surface regions that may lead to target diversity. Based on a comparison of ICK family sequences, it is obvious that the ICK family was generated by accelerated gene evolution.

Among the ICK toxins, charybdotoxin from the scorpion was the first to be engineered by introducing a metal-chelating sequence (nine amino acids) to generate carbonic anhydrase-like activity (Vita et al. 1995). Another peptide library with an ICK motif was constructed based on the spider toxin GTx1-15. The peptide GTx1-15 was originally isolated from the venom of the tarantula Grammostola rosea and characterized as a T-type calcium channel modulator (Ono et al. 2011). Mature GTx1-15 is 34 amino acid residues long and contains 12 randomized amino acid residues in loop I (three residues), loop II (four residues), loop IV (three residues), and the C-terminus (two residues) (Fig. 2a). The randomized regions were determined based on the multiple alignment of the family and a 3-D structural model, which was constructed using the closely related ICK peptide HnTx4 (hainantoxin-4, PDB 1njy) as a template. This ICK-based library was subjected to screening of binders to the m2 subtype of the muscarinic acetylcholine receptor (mAChR) (Kubo et al. 2012; Ono et al. manuscript in preparation). To target the membrane protein, a novel semi in vitro evolution system named “PERISS” (intra periplasm secretion and selection) was developed; the target membrane protein was expressed in the inner membrane, and the peptide was secreted into the periplasm (see details in section “PERISS Method”). After six rounds of selection, sequence convergences were observed in the peptides selected from the ICK-based library. One of the peptides had moderate affinity (K i app ~300 nM) and subtype selectivity for the m2 receptor.
Fig. 2

Random peptide library of ICK motif peptides and the new screening system “PERISS ”. (a) 3D model of the GTx1-15 T-type Ca2+ channel modulator isolated from the venom of the tarantula Grammostola rosea. The amino acid sequence of the mature peptide is shown below. Cysteine residues and disulfide bridges are colored in green. Randomized regions are shown in red. (b) The designed plasmid vector pGRII-Tx harboring cDNAs encoding a target protein and a peptide library in tandem. Each polypeptide is expressed in a fusion protein so that the target proteins are expressed in the inner membrane and the peptides are expressed in the periplasm. (c) Schematic diagram of the PERISS method. 1: Transformation of Gram-negative bacteria with the target- and peptide-coding cDNAs constructed in the vector pGRII-Tx. 2: Expression of the target membrane proteins in the inner membrane and secretion and refolding of the peptides in the periplasmic space. 3: Interaction of the peptide and the target membrane protein in the periplasm. 4: After disruption of the outer membrane, the complex [peptide-target protein-spheroplast] is selected by magnetic beads. 5: Amplification of the peptide cDNAs from the selected complexes. The amplified cDNA proceed to the next selection cycle (1), or the peptide sequence information will be decoded from the plasmid. (Adapted from JPN Patents 5717143 and 5787298; Figures in the courtesy of Seigo Ono)

Other Peptide/Protein Scaffolds Refined Through Accelerated Evolution

From the preceding study of the 3-F and ICK peptide libraries, other toxin/bioactive peptides that evolved by accelerated evolution were identified that could also be used to construct alternative libraries to screen binders. Taking the size, number of disulfide bonds, and conformation into consideration, conotoxin and Kunitz-type protease inhibitors were identified. Conotoxins are a remarkably diverse family with unique potency and selectivity profiles (see other chapters of this handbook series and a recent review by Robinson and Norton 2014). Because the conotoxins bind to ion channels and receptors with high potency and selectivity, it will be advantageous to use the library to find the key residues for the target pharmacophore, which can lead to the production of peptidomimetics (Brady et al. 2013). A high degree of polymorphism was observed in a narrow region surrounding the reactive site in a family of serine protease inhibitors (serpins) (Borriello and Krauter 1991). The Kunitz-type protease inhibitor family including BPTI (bovine pancreatic trypsin inhibitor), PSTI (pancreatic secretory trypsin inhibitor), and Kunitz domain inhibitor of factor VIIa were used as templates to prepare random peptide libraries, of which a relatively small portion (5–8 amino acid residues) was randomized and used in phage display to generate new binders, such as elastase inhibitors (Roberts et al. 1992; Rottgen and Collins 1995; Dennis and Lazarus 1994).

Random Peptide Libraries: Rationally Designed and Cys-Constrained

Artificial Scaffold Stabilized by Leucine or Tryptophan Zipper

A peptide scaffold with a helix-loop-helix conformation, in which two α-helices each consisting of 14 amino acid residues linked by a flexible linker of 7 amino acid residues, was designed de novo (Fig. 3a). Leucine residues were placed into the heptad repeat positions to dimerize the α-helices by hydrophobic interactions; then, the solvent-exposed outside residues were randomized to generate a library (microantibody; Fujiwara and Fujii 2013). In the recent design, cysteine residues were incorporated in both the N- and C-termini and linked by disulfide bridges for stabilization. The libraries were screened by phage display, and effective binders to a cytokine receptor (granulocyte colony-stimulating factor receptor), protein kinase (Aurora-A), ganglioside (GM1), and VEGF (Fujii et al., JPN patent pending 2014-047156) were generated from the designed peptide library.
Fig. 3

De novo designed peptide libraries. (a) “Microantibody” with two α-helices. Two α-helices are firmly aligned by hydrophobic interactions generated by leucine side chain moieties. Five residues (positions 24, 25, 28, 31, and 32) located outside of the C-terminal side helix are exposed to the space to efficiently interact with target molecules. (Adapted from Fujiwara and Fujii 2013) (b) Schematic representation of “Aptide”. The aptide consists of two randomized regions (6 amino acid residues long each) and a scaffold of 12 amino acids β-hairpin, which is stabilized by two pairs of tryptophan (W)-tryptophan (W) interaction (aromatic ring stacking). The scaffold was named tryptophan zipper or trpzip following to “leucine zipper”. (Adapted from Kim et al. 2012) (c) Random peptide libraries were generated from 27 mer with nine codon triplets XYZ, in which each nucleotide composition at X, Y, and Z is controlled so that cysteine residues appear with 12 % probability. A linear peptide (reduced form) and three possible S-S configurations [CP1(2SS)-α, CP1(2SS)-β, and CP1(2SS)-γ] were examined for target recognition and selectivity (Adapted from Mochizuki et al. 2014) (d) Construction of a bicyclic peptide library. A linear peptide library with C-(X)6-C-(X)6-C sequence fused with the P3 phage coat protein (upper); and a library with two cyclic loops (a bicyclic peptide library) after chemical conjugation of Cys-SH by TBMB, tris (bromomethyl) benzene (lower). (Adapted from Heinis et al. 2009)

Aptide is an artificial aptamer-like peptide, which consists of a highly stable tryptophan zipper or “trpzip” in β­-hairpin structure (12 amino acids) and two linear polypeptides of six randomized amino acids at each end of the trpzip scaffold (Fig. 3b; Kim et al. 2012). Two random peptide regions in the aptide library are spatially supported by a leucine­zipper-­like structure and are enabled to recognize and bind target molecule(s), like a tweezers. Using this aptide library in phage display screening, a VEGF­-binding peptide with an affinity of approximately 30 nM was reported (Kim et al. 2012).

Cys-constrained Library

Small natural polypeptides with high target selectivity and affinity are primarily constrained by disulfide bridge(s) (e.g., venom peptides including conotoxins and polycyclic peptides). Nemoto and colleagues constructed a peptide library in which 27 amino acid residues were randomized but under some constraint of amino acid composition so that cysteine residues appeared with a frequency of 12 % (i.e., 3–4 cysteines per 27 residues) (Fig. 3c; Mochizuki et al. 2014). The library was screened by the cDNA display method to find binders to a small chemical moiety amino group. After four rounds of selection, the selected peptides showed a consensus sequence containing four cysteine residues (C1 to C4). As shown in Fig. 3c three disulfide-bridge frameworks are possible: (C1-C2) + (C3-C4), (C1-C3) + (C2-C4), or (C1-C4) + (C2-C3). Among them, they found only the peptide with the S-S configuration (C1-C2) + (C3-C4) specifically bound to the amino group.

A new approach to constructing a constrained peptide library was introduced by combining chemistry and biochemistry. Heinis, Winter and colleagues used halomethylarene, tris (bromomethyl) benzene (TBMB), to chemically link the SH groups of cysteine residues allocated in a random peptide sequence (Fig. 3d; clamping three cysteine residues in a random peptide sequence by TBMB; Heinis et al. 2009). The resulting peptide complexes are expected to form a constrained scaffold and spatially independent two cyclic loops (a bicyclic peptide). When affinity selections against the serum kallikrein were performed using this bicyclic peptide library, inhibitors with IC50s between 20 and 100 nM at initial rounds of selection (later achieved 1.7 nM by affinity maturation) were selected.

Combination Library

DkTx is a bivalent toxin from the “Earth Tiger” tarantula (Ornithoctonus huwena) and is a single polypeptide consisting of double head-to-tail ICK unit repeats separated by a short linker (Bohlen et al. 2010). Each ICK unit was found to activate the TRPV1 channel independently in a reversible manner. However, when combined, they synergized to produce a ligand of exceedingly high avidity that irreversibly activated TRPV1. This finding inspired us to generate a combination library by linking homo-/heteropeptide libraries of natural and/or artificial scaffolds in tandem that could be utilized to find binders of increased potency with multiple selectivities/functions toward targets.

Directed In Vitro Evolution: Utilization of Random Peptide Libraries for Screening of Ligands and Biotherapeutics

Phage Display and Other Cell Surface Display

In addition to the development of various types of peptide libraries with some constrained scaffold, selection technologies have also been developed. Phage display is a widely applied method to screen binders (polypeptides). The utilization of the filamentous phage genome as an expression vector to present an exogenous peptide sequence on the virion surface was first reported in 1985 (phage display, Smith 1985). Other display technologies such as yeast (Boder and Wittrup 1997; Feldhaus et al. 2003) and bacterial surface displays (Bessette et al. 2004) have also been established. The general concept behind these display technologies is that a peptide library with either a short stretch or embedded in some scaffold is secreted, refolded, and represented on the cell surface either in fusion with the surface protein or by anchoring.


A new expression and screening technology called the PERISS (intra periplasm secretion and selection) method is based on a new concept (Fig. 2; Kubo et al. 2012; Ono et al., manuscript in preparation). In this method, cDNAs encoding a target (membrane) protein and a peptide library are constructed in tandem in a plasmid vector (pGRII-Tx, Fig. 2b). After transformation of Gram-negative bacteria (e.g., E. coli), the target proteins are expressed in the inner membrane, and the peptides are secreted into the periplasm. The cellular machinery necessary for oxidative folding is present in the bacterial periplasm. Thus, the periplasmic space is utilized for the expression and interaction of the target protein and peptide. After disrupting the outer membrane, the target and binder complexes are retained on the surface of the spheroplast (Fig. 2c). Sequence information of the selected binders are encoded in the plasmid DNAs in the spheroplast. It will be amplified for the next round (generation) library for screening or decoded for identifying the binders. It is noteworthy that in this PERISS method the target may not be limited to membrane protein, but soluble proteins would also be targeted by expressing them in fusion with an anchoring protein in the inner membrane (Kubo et al., JPN patents 5717143 and 5787298).

Ribosome/mRNA/cDNA Display Methods

In the in vitro selection system, linkages between genetic information (genotype) and protein function (phenotype) are achieved by chemical/biochemical/physicochemical conjugations. Several different levels of display (ribosome, mRNA, or cDNA) were developed. In ribosome display, stop codons are intentionally removed from the mRNA, and thus the translation apparatus (e.g., rRNA) is stalled without release from the mRNA. The resulting complexes of mRNA, ribosome and protein are accumulated, and are used to screen binders (see reviews Binz et al. 2004; Zahnd et al. 2007a). In mRNA display, mRNA and its translation products are linked via a puromycin-conjugated linker (Nemoto et al. 1997; Xu et al. 2002). Puromycin is an analogue of aminoacyl tRNA and is incorporated during translation. Once incorporated, puromycin prevents further peptidyl chain elongation. cDNA display is principally the same as mRNA display except that the conversion of mRNA to cDNA by reverse transcriptase is performed (Ueno and Nemoto 2012). Several modifications of the mRNA/cDNA display methods and puromycin linkers have been reported (e.g., Mochizuki et al. 2011; Naimuddin and Kubo 2016).

In every cell-surface display method, the diversity of the random peptide library depends on the efficiency of the transformation/transfection of the host cells and ranges from 105–6. In contrast, the cell-free system or in vitro selection system has no cell-dependent process, and therefore the diversity can reach 1011–13 depending on the reaction scale.

In Vitro Compartmentalization

In addition to chemical conjugation, linkage between genotype and phenotype for in vitro evolution can be achieved by restricting the genetic information and the protein product into tiny compartments. The compartments were formed in W/O (water-in-oil) or W/O/W (water-in-oil-in-water) emulsions (Tawfik and Griffiths 1998; Miller et al. 2006). Principally, one molecule from a DNA library is entrapped in a droplet, and transcription and translation are completed in the same space. In the next step, several methods have been devised to expose the protein to the target molecule or substrate (e.g., Doi and Yanagawa 1999; Griffiths and Tawfik 2006). One of the advantages of this compartmentalization method is that the droplets are treated as cells and can be separated by a cell sorter by coupling the droplets with chromogenic/fluorogenic substrates (or antibodies).


Enormous efforts have been made to overcome the intrinsic problems of antibody medicines with the aim to generate better antibody alternatives or super-antibodies. The principal concern is size minimization without a loss of target diversity, selectivity, or affinity. On the basis of the accumulated knowledge of immunoglobulin structure-function relationships, minimal essential domains (e.g., scFv and dAb) and their combinations (dAb) were successfully innovated. The CDRs, which are the most variable regions in the immunoglobulin molecule, are responsible for diverged target recognition and affinity. Nonimmunoglobulin architectures similar to CDRs or architectures advantageous for the spatial presentation of random sequences were used to construct random peptide libraries. Peptide toxins have great potential for the application of these libraries due to their compact size, stability, diversity, and evolutionally refined scaffold. Together with the development of selection technologies for a specific target molecule (directed evolution), many druggable seeds for biotherapeutics, some of which have proceeded to preclinical studies or clinical trials, are being generated from nonimmunoglobulin libraries. These middle-sized biotherapeutics are expected to overcome the bottlenecks of both antibody medicine (specific but expensive) and chemical drugs (low cost but off-target problems). Due to the diversity in family and target molecules, peptide toxins are promising candidates for middle-size biotherapeutics and are ready for further innovative evolution. Recent remarkable progress in computation technologies including AI will make the de novo design of constrained peptide scaffold more rational and accurate (see e.g. Bhardwaj et al. 2016). It is also getting realized that the next generation sequencing (NGS) in cooperation with AI is contributing to replace time-consuming and laborious library screening with logical estimation of convergence direction(s) of the sequences; a tide toward in silico screening or in silico evolution.



  1. Bargou R, Leo E, Zugmaier G, Klinger M, Goebeler M, Knop S, Noppeney R, Viardot A, Hess G, Schuler M, Einsele H, Brandl C, Wolf A, Kirchinger P, Klappers P, Schmidt M, Riethmuller G, Reinhardt C, Baeuerle PA, Kufer P. Tumor regression in cancer patients by very low doses of a T cell-engaging antibody. Science. 2008;321(5891):974–7.CrossRefPubMedGoogle Scholar
  2. Bessette PH, Rice JJ, Daugherty PS. Rapid isolation of high-affinity protein binding peptides using bacterial display. Protein Eng Des Sel. 2004;17(10):731–9.CrossRefPubMedGoogle Scholar
  3. Bhardwaj G, et al. Accurate de novo design of hyperstable constrained peptides. Nature. 2016;538:329–35.CrossRefPubMedGoogle Scholar
  4. Binz HK, Amstutz P, Kohl A, Stumpp MT, Briand C, Forrer P, Grutter MG, Pluckthun A. High-affinity binders selected from designed ankyrin repeat protein libraries. Nat Biotechnol. 2004;22(5):575–82.CrossRefPubMedGoogle Scholar
  5. Binz HK, Amstutz P, Pluckthun A. Engineering novel binding proteins from nonimmunoglobulin domains. Nat Biotechnol. 2005;23(10):1257–68.CrossRefPubMedGoogle Scholar
  6. Boder ET, Wittrup KD. Yeast surface display for screening combinatorial polypeptide libraries. Nat Biotechnol. 1997;15(6):553–7.CrossRefPubMedGoogle Scholar
  7. Bohlen CJ, Priel A, Zhou S, King D, Siemens J, Julius D. A bivalent tarantula toxin activates the capsaicin receptor, TRPV1, by targeting the outer pore domain. Cell. 2010;141(5):834–45.CrossRefPubMedPubMedCentralGoogle Scholar
  8. Borriello F, Krauter KS. Multiple murine alpha 1-protease inhibitor genes show unusual evolutionary divergence. Proc Natl Acad Sci U S A. 1991;88(21):9417–21.CrossRefPubMedPubMedCentralGoogle Scholar
  9. Brady RM, Baell JB, Norton RS. Strategies for the development of conotoxins as new therapeutic leads. Mar Drugs. 2013;11(7):2293–313.CrossRefPubMedPubMedCentralGoogle Scholar
  10. Cai W, Naimuddin M, Inagaki H, Kameyama K, Ishida N, Kubo T. Directed evolution of three-finger toxin to produce serine protease inhibitors. J Recept Signal Transduct Res. 2014;34(3):154–61.CrossRefPubMedGoogle Scholar
  11. Dennis MS, Lazarus RA. Kunitz domain inhibitors of tissue factor-factor VIIa. I. Potent inhibitors selected from libraries by phage display. J Biol Chem. 1994;269(35):22129–36.PubMedGoogle Scholar
  12. Doi N, Yanagawa H. STABLE: protein-DNA fusion system for screening of combinatorial protein libraries in vitro. FEBS Lett. 1999;457(2):227–30.CrossRefPubMedGoogle Scholar
  13. Eggel A, Baumann MJ, Amstutz P, Stadler BM, Vogel M. DARPins as bispecific receptor antagonists analyzed for immunoglobulin E receptor blockage. J Mol Biol. 2009;393(3):598–607.CrossRefPubMedGoogle Scholar
  14. Endo T, Tamiya N. Current view on the structure-function relationship of postsynaptic neurotoxins from snake venoms. Pharmacol Ther. 1987;34(3):403–51.CrossRefPubMedGoogle Scholar
  15. Feldhaus MJ, Siegel RW, Opresko LK, Coleman JR, Feldhaus JM, Yeung YA, Cochran JR, Heinzelman P, Colby D, Swers J, Graff C, Wiley HS, Wittrup KD. Flow-cytometric isolation of human antibodies from a nonimmune Saccharomyces cerevisiae surface display library. Nat Biotechnol. 2003;21(2):163–70.CrossRefPubMedGoogle Scholar
  16. Forrer P, Binz HK, Stumpp MT, Pluckthun A. Consensus design of repeat proteins. Chembiochem. 2004;5(2):183–9.CrossRefPubMedGoogle Scholar
  17. Fruchart-Gaillard C, Mourier G, Blanchet G, Vera L, Gilles N, Menez R, Marcon E, Stura EA, Servent D. Engineering of three-finger fold toxins creates ligands with original pharmacological profiles for muscarinic and adrenergic receptors. PLoS One. 2012;7(6):e39166.CrossRefPubMedPubMedCentralGoogle Scholar
  18. Fry BG, Wuster W, Kini RM, Brusic V, Khan A, Venkataraman D, Rooney AP. Molecular evolution and phylogeny of elapid snake venom three-finger toxins. J Mol Evol. 2003;57(1):110–29.CrossRefPubMedGoogle Scholar
  19. Fujiwara D, Fujii I. Phage selection of peptide “microantibodies”. Curr Protoc Chem Biol. 2013;5(3):171–94.CrossRefPubMedGoogle Scholar
  20. Griffiths AD, Tawfik DS. Miniaturising the laboratory in emulsion droplets. Trends Biotechnol. 2006;24(9):395–402.CrossRefPubMedGoogle Scholar
  21. Gruber CW, Cemazar M, Anderson MA, Craik DJ. Insecticidal plant cyclotides and related cystine knot toxins. Toxicon. 2007;49(4):561–75.CrossRefPubMedGoogle Scholar
  22. Gunneriusson E, Nord K, Uhlen M, Nygren P. Affinity maturation of a Taq DNA polymerase specific affibody by helix shuffling. Protein Eng. 1999;12(10):873–8.CrossRefPubMedGoogle Scholar
  23. Hanes J, Schaffitzel C, Knappik A, Pluckthun A. Picomolar affinity antibodies from a fully synthetic naive library selected and evolved by ribosome display. Nat Biotechnol. 2000;18(12):1287–92.CrossRefPubMedGoogle Scholar
  24. Heinis C, Rutherford T, Freund S, Winter G. Phage-encoded combinatorial chemical libraries based on bicyclic peptides. Nat Chem Biol. 2009;5:502–7.CrossRefPubMedGoogle Scholar
  25. Holliger P, Hudson PJ. Engineered antibody fragments and the rise of single domains. Nat Biotechnol. 2005;23(9):1126–36.CrossRefPubMedGoogle Scholar
  26. Holt LJ, Herring C, Jespers LS, Woolven BP, Tomlinson IM. Domain antibodies: proteins for therapy. Trends Biotechnol. 2003;21(11):484–90.CrossRefPubMedGoogle Scholar
  27. Imming P, Sinning C, Meyer A. Drugs, their targets and the nature and number of drug targets. Nat Rev Drug Discov. 2006;5:821–34. doi:10.1038/nrd2132.CrossRefPubMedGoogle Scholar
  28. Jeyaseelan K, Poh SL, Nair R, Armugam A. Structurally conserved alpha-neurotoxin genes encode functionally diverse proteins in the venom of Naja sputatrix. FEBS Lett. 2003;553(3):333–41.CrossRefPubMedGoogle Scholar
  29. Kaczorowski GJ, McManus OB, Priest BT, Garcia ML. Ion channels as drug targets: the next GPCRs. J Gen Physiol. 2008;131(5):399–405.CrossRefPubMedPubMedCentralGoogle Scholar
  30. Kim S, Kim D, Jung HH, Lee IH, Kim JI, Suh JY, Jon S. Bio-inspired design and potential biomedical applications of a novel class of high-affinity peptides. Angew Chem Int Ed. 2012;51(8):1890–4.CrossRefGoogle Scholar
  31. Kimura T, Ono S, Kubo T. Molecular cloning and sequence analysis of the cDNAs encoding toxin-like peptides from the venom glands of tarantula grammostola rosea. Int J Pept. 2012;2012:731293.CrossRefPubMedPubMedCentralGoogle Scholar
  32. Kini RM, Doley R. Structure, function and evolution of three-finger toxins: mini proteins with multiple targets. Toxicon. 2010;56(6):855–67.CrossRefPubMedGoogle Scholar
  33. Kintzing JR, Filsinger Interrante MV, Cochran JR. Emerging strategies for developing next-generation protein therapeutics for cancer treatment. Trends Pharmacol Sci. 2016;37(12):993–1008.CrossRefPubMedGoogle Scholar
  34. Knappik A, Ge L, Honegger A, Pack P, Fischer M, Wellnhofer G, Hoess A, Wolle J, Pluckthun A, Virnekas B. Fully synthetic human combinatorial antibody libraries (HuCAL) based on modular consensus frameworks and CDRs randomized with trinucleotides. J Mol Biol. 2000;296(1):57–86.CrossRefPubMedGoogle Scholar
  35. Kohler G, Milstein C. Continuous cultures of fused cells secreting antibody of predefined specificity. Nature. 1975;256(5517):495–7.CrossRefPubMedGoogle Scholar
  36. Kolmar H. Alternative binding proteins: biological activity and therapeutic potential of cystine-knot miniproteins. FEBS J. 2008;275(11):2684–90.CrossRefPubMedGoogle Scholar
  37. Kubo T, Ono S, Kimura T, Kobayashi S, Kondo T, Fukuda E, Haga T, Kameyama K. Random peptide library based on a spider neurotoxin, and utilization of the library in in vitro evolution directed to GPCR ligands. Toxicon. 2012;60(2):113.CrossRefGoogle Scholar
  38. Mao H, Graziano JJ, Chase TM, Bentley CA, Bazirgan OA, Reddy NP, Song BD, Smider VV. Spatially addressed combinatorial protein libraries for recombinant antibody discovery and optimization. Nat Biotechnol. 2010;28(11):1195–202.CrossRefPubMedGoogle Scholar
  39. Miljanich GP. Ziconotide: neuronal calcium channel blocker for treating severe chronic pain. Curr Med Chem. 2004;11(23):3029–40.CrossRefPubMedGoogle Scholar
  40. Miller OJ, Bernath K, Agresti JJ, Amitai G, Kelly BT, Mastrobattista E, Taly V, Magdassi S, Tawfik DS, Griffiths AD. Directed evolution by in vitro compartmentalization. Nat Methods. 2006;3(7):561–70.CrossRefPubMedGoogle Scholar
  41. Miwa JM, Ibanez-Tallon I, Crabtree GW, Sanchez R, Sali A, Role LW, Heintz N. lynx1, an endogenous toxin-like modulator of nicotinic acetylcholine receptors in the mammalian CNS. Neuron. 1999;23(1):105–14.CrossRefPubMedGoogle Scholar
  42. Mochizuki Y, Biyani M, Tsuji-Ueno S, Suzuki M, Nishigaki K, Husimi Y, Nemoto N. One-pot preparation of mRNA/cDNA display by a novel and versatile puromycin-linker DNA. ACS Comb Sci. 2011;13(5):478–85.CrossRefPubMedGoogle Scholar
  43. Mochizuki Y, Nishigaki K, Nemoto N. Amino group binding peptide aptamers with double disulphide-bridged loops selected by in vitro selection using cDNA display. Chem Commun (Camb). 2014;50(42):5608–10.CrossRefGoogle Scholar
  44. Muller PY, Milton MN. The determination and interpretation of the therapeutic index in drug development. Nat Rev Drug Discov. 2012;11:751–61.CrossRefPubMedGoogle Scholar
  45. Naimuddin M, Kobayashi S, Tsutsui C, Machida M, Nemoto N, Sakai T, Kubo T. Directed evolution of a three-finger neurotoxin by using cDNA display yields antagonists as well as agonists of interleukin-6 receptor signaling. Mol Brain. 2011;4:2.CrossRefPubMedPubMedCentralGoogle Scholar
  46. Naimuddin M, Kubo T. A high performance platform based on cDNA display for efficient synthesis of protein fusions and accelerated directed evolution. ACS Comb Sci. 2016;18(2):117–29.CrossRefPubMedGoogle Scholar
  47. Nemoto N, Miyamoto-Sato E, Husimi Y, Yanagawa H. In vitro virus: bonding of mRNA bearing puromycin at the 3′-terminal end to the C-terminal end of its encoded protein on the ribosome in vitro. FEBS Lett. 1997;414(2):405–8.CrossRefPubMedGoogle Scholar
  48. Nygren PA, Skerra A. Binding proteins from alternative scaffolds. J Immunol Methods. 2004;290(1–2):3–28.CrossRefPubMedGoogle Scholar
  49. Ono S, Kimura T, Kubo T. Characterization of voltage-dependent calcium channel blocking peptides from the venom of the tarantula Grammostola rosea. Toxicon. 2011;58(3):265–76.CrossRefPubMedGoogle Scholar
  50. Peralta MD, Karsai A, Ngo A, Sierra C, Fong KT, Hayre NR, Mirzaee N, Ravikumar KM, Kluber AJ, Chen X, Liu GY, Toney MD, Singh RR, Cox DL. Engineering amyloid fibrils from beta-solenoid proteins for biomaterials applications. ACS Nano. 2015;9(1):449–63.CrossRefPubMedGoogle Scholar
  51. Roberts BL, Markland W, Ley AC, Kent RB, White DW, Guterman SK, Ladner RC. Directed evolution of a protein: selection of potent neutrophil elastase inhibitors displayed on M13 fusion phage. Proc Natl Acad Sci U S A. 1992;89(6):2429–33.CrossRefPubMedPubMedCentralGoogle Scholar
  52. Robinson SD, Norton RS. Conotoxin gene superfamilies. Mar Drugs. 2014;12(12):6058–101.CrossRefPubMedPubMedCentralGoogle Scholar
  53. Rottgen P, Collins J. A human pancreatic secretory trypsin inhibitor presenting a hypervariable highly constrained epitope via monovalent phagemid display. Gene. 1995;164(2):243–50.CrossRefPubMedGoogle Scholar
  54. Schlehuber S, Skerra A. Tuning ligand affinity, specificity, and folding stability of an engineered lipocalin variant – a so-called ‘anticalin’ – using a molecular random approach. Biophys Chem. 2002;96(2–3):213–28.CrossRefPubMedGoogle Scholar
  55. Smith GP. Filamentous fusion phage: novel expression vectors that display cloned antigens on the virion surface. Science. 1985;228(4705):1315–7.CrossRefPubMedGoogle Scholar
  56. Soderlind E, Strandberg L, Jirholt P, Kobayashi N, Alexeiva V, Aberg AM, Nilsson A, Jansson B, Ohlin M, Wingren C, Danielsson L, Carlsson R, Borrebaeck CA. Recombining germline-derived CDR sequences for creating diverse single-framework antibody libraries. Nat Biotechnol. 2000;18(8):852–6.CrossRefPubMedGoogle Scholar
  57. Taussig M, et al. ProteomeBinders: planning a European resource of affinity reagents for analysis of the human proteome. Nat Methods. 2007;4(1):13–7.CrossRefPubMedGoogle Scholar
  58. Tawfik DS, Griffiths AD. Man-made cell-like compartments for molecular evolution. Nat Biotechnol. 1998;16(7):652–6.CrossRefPubMedGoogle Scholar
  59. Tsetlin V. Snake venom alpha-neurotoxins and other “three-finger” proteins. Eur J Biochem. 1999;264(2):281–6.CrossRefPubMedGoogle Scholar
  60. Ueno S, Nemoto N. cDNA display: rapid stabilization of mRNA display. Methods Mol Biol. 2012;805:113–35.CrossRefPubMedGoogle Scholar
  61. Vita C, Roumestand C, Toma F, Menez A. Scorpion toxins as natural scaffolds for protein engineering. Proc Natl Acad Sci U S A. 1995;92(14):6404–8.CrossRefPubMedPubMedCentralGoogle Scholar
  62. Wurch T, Pierre A, Depil S. Novel protein scaffolds as emerging therapeutic proteins: from discovery to clinical proof-of-concept. Trends Biotechnol. 2012;30(11):575–82.CrossRefPubMedGoogle Scholar
  63. Xu L, Aha P, Gu K, Kuimelis RG, Kurz M, Lam T, Lim AC, Liu H, Lohse PA, Sun L, Weng S, Wagner RW, Lipovsek D. Directed evolution of high-affinity antibody mimics using mRNA display. Chem Biol. 2002;9(8):933–42.CrossRefPubMedGoogle Scholar
  64. Zahnd C, Amstutz P, Pluckthun A. Ribosome display: selecting and evolving proteins in vitro that specifically bind to a target. Nat Methods. 2007a;4(3):269–79.CrossRefPubMedGoogle Scholar
  65. Zahnd C, Wyler E, Schwenk JM, Steiner D, Lawrence MC, McKern NM, Pecorari F, Ward CW, Joos TO, Pluckthun A. A designed ankyrin repeat protein evolved to picomolar affinity to Her2. J Mol Biol. 2007b;369(4):1015–28.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Molecular Profiling Research Center for Drug DiscoveryNational Institute of Advanced Industrial Science and Technology (AIST)Koto-ku, TokyoJapan

Personalised recommendations