Peptide Drug Discovery Research Articles

Abstract 6025: Discovery of novel macrocyclic peptide radioligands for tumor therapy by mRNA display

Abstract Radioligand therapeutics (RLT) aim to selectively deliver radioisotopes to cancer tissues to eradicate tumor cells while limiting the damage to surrounding tissues. They are composed of a tumor-targeting ligand and a radioactive payload. The therapeutic potential of RLT strongly depends on their biostability and residence time in tumor and healthy tissue after injection. Macrocyclic peptides are effective tumor-targeting ligands because their size and molecular characteristics confer optimal pharmacologic properties that can be fine-tuned during the development process. mRNA display is a powerful platform for macrocyclic peptide drug discovery that identifies high affinity peptide ligands through in vitro selection. Here, we describe the selection and initial characterization of macrocyclic peptides discovered by mRNA display that bind to the extracellular domain of a tumor target. Enrichment of the library for peptides that bound to the recombinant target protein was observed after four rounds of selection with no further enrichment in round five. The library of round 5 was sequenced by Illumina sequencing and the top 1,000 sequences (highest counts) were used to perform cluster analysis. We tested the top sequences of 43 different clusters in in vitro translation binding assays to validate the selection output and identify sequences for hit confirmation. Most of the sequences tested showed clear binding to the target protein and only a few peptides showed binding to a closely related protein from the same protein family. No non-specific binding of the discovered peptides to an irrelevant protein was observed showing the high specificity of the generated library. From this confirmatory selection data, we chose a subset of peptides for chemical synthesis. Binding affinity measurements were determined by surface plasmon resonance (SPR) and cellular binding of selected candidates to target-positive and -negative tumor cells was analyzed by flow cytometry. From this hit confirmation data, in vitro internalization and in vitro anti-tumor efficacy of radiolabeled peptide candidates for RLT are assessed. Our data highlights the potential of mRNA display platforms for fast and efficient discovery of highly specific macrocyclic peptides with optimal binding properties suitable for RLT. Citation Format: Anika Jaekel, Kathryn Sherry, Prachi Desai, David Fox, Tang Guo-Qing, Manuel Sturzbecher-Hoehne, Dennis Mewis, Kaori Noridomi, Liam Rayman, Ping Ye. Discovery of novel macrocyclic peptide radioligands for tumor therapy by mRNA display [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6025.

Peptide-encoding gene transfer to modulate intracellular protein-protein interactions

Peptide drug discovery has great potential, but the cell membrane is a major obstacle when the target is an intracellular protein‒protein interaction (PPI). It is difficult to target PPIs with small molecules; indeed, there are no intervention tools that can target any intracellular PPI. In this study, we developed a platform that enables the introduction of peptides into cells via mRNA-based gene delivery. Peptide-length nucleic acids do not enable stable ribosome binding and exhibit little to no translation into protein. In this study, a construct was created in which the sequence encoding dihydrofolate reductase (DHFR) was placed in front of the sequence encoding the target peptide, together with a translation skipping sequence, as a sequence that meets the requirements of promoting ribosome binding and rapid decay of the translated protein. This enabled efficient translation from the mRNA encoding the target protein while preventing unnecessary protein residues. Using this construct, we showed that it can inhibit Drp1/Fis1 binding, one of the intracellular PPIs, which governs mitochondrial fission, an important aspect of mitochondrial dynamics. In addition, it was shown to inhibit pathological hyperfission, normalize mitochondrial dynamics and metabolism, and inhibit apoptosis of the mitochondrial pathway.

Automatic generation of functional peptides with desired bioactivity and membrane permeability using Bayesian optimization.

Peptides are potentially useful modalities of drugs; however, cell membrane permeability is an obstacle in peptide drug discovery. The identification of bioactive peptides for a therapeutic target is also challenging because of the huge amino acid sequence patterns of peptides. In this study, we propose a novel computational method, PEptide generation system using Neural network Trained on Amino acid sequence data and Gaussian process-based optimizatiON (PENTAGON), to automatically generate new peptides with desired bioactivity and cell membrane permeability. In the algorithm, we mapped peptide amino acid sequences onto the latent space constructed using a variational autoencoder and searched for peptides with desired bioactivity and cell membrane permeability using Bayesian optimization. We used our proposed method to generate peptides with cell membrane permeability and bioactivity for each of the nine therapeutic targets, such as the estrogen receptor (ER). Our proposed method outperformed a previously developed peptide generator in terms of similarity to known active peptide sequences and the length of generated peptide sequences.

Identifying Antitubercular Peptides via Deep Forest Architecture with Effective Feature Representation.

Tuberculosis (TB) is a severe disease caused by Mycobacterium tuberculosis that poses a significant threat to human health. The emergence of drug-resistant strains has made the global fight against TB even more challenging. Antituberculosis peptides (ATPs) have shown promising results as a potential treatment for TB. However, conventional wet lab-based approaches to ATP discovery are time-consuming and costly and often fail to discover peptides with desired properties. To address these challenges, we propose a novel machine learning-based framework called ATPfinder that can significantly accelerate the discovery of ATP. Our approach integrates various efficient peptide descriptors and utilizes the deep forest algorithm to construct the model. This neural network-like cascading structure can effectively process and mine features without complex hyperparameter tuning. Our experimental results show that ATPfinder outperforms existing ATP prediction tools, achieving state-of-the-art performance with an accuracy of 89.3% and an MCC of 0.70. Moreover, our framework exhibits better robustness than baseline algorithms commonly used for other sequence analysis tasks. Additionally, the excellent interpretability of our model can assist researchers in understanding the critical features of ATP. Finally, we developed a downloadable desktop application to simplify the use of our framework for researchers. Therefore, ATPfinder can facilitate the discovery of peptide drugs and provide potential solutions for TB treatment. Our framework is freely available at https://github.com/lantianyao/ATPfinder/ (data sets and code) and https://awi.cuhk.edu.cn/dbAMP/ATPfinder.html (software).

De Novo Discovery of Cysteine Frameworks for Developing Multicyclic Peptide Libraries for Ligand Discovery.

Conserved cysteine frameworks are essential components of disulfide-rich peptides (DRPs), which dominantly define the structural diversity of both naturally occurring and de novo-designed DRPs. However, there are only very limited numbers of conserved cysteine frameworks, and general methods enabling de novo discovery of cysteine frameworks with robust foldability are still not available. Here, we devised a "touchstone"-based strategy that relies on chasing oxidative foldability between two individual disulfide-rich folds on the phage surface to discover new cysteine frameworks from random sequences. Unique cysteine frameworks with a high degree of compatibility with phage display systems and broad sequence tolerance were successfully identified, which were subsequently exploited for the development of multicyclic DRP libraries, enabling the rapid discovery of new peptide ligands with low-nanomolar and picomolar binding affinity. This study provides an unprecedented method for exploring and exploiting the sequence and structure space of DRPs that is not readily accessible by existing strategies, holding the potential to revolutionize the study of DRPs and significantly advance the design and discovery of multicyclic peptide ligands and drugs.

Back Cover: Integration of organoids in peptide drug discovery: Rise of the high‐throughput screening (View 6/2023)

Back Cover: Integration of organoids in peptide drug discovery: Rise of the high‐throughput screening (View 6/2023)

Side-Selective Solid-Phase Metallaphotoredox N(in)-Arylation of Peptides.

Postsynthetic diversification of peptides through selective modification of endogenous amino acid side chains has enabled significant advances in peptide drug discovery while expanding the biological and medical chemistry space. However, current tools have been focused on the modification of reactive polar and ionizable side chains, whereas the decoration of aromatic systems (e.g., the N(in) of the tryptophan) has been a long-standing challenge. Here, we introduce metallaphotocatalysis in solid-phase peptide synthesis for the on-resin orthogonal N-arylation of relevant tryptophan-containing peptides. The protocol allows the chemoselective introduction of a new C(sp2)-N bond at the N(in) of tryptophan in biologically active protected peptide sequences in the presence of native redox-sensitive side chains. The fusion of metallaphotocatalysis with solid-phase peptide synthesis opens new perspectives in diversifying native amino acid side chains.

Revolutionizing peptide‐based drug discovery: Advances in the post‐AlphaFold era

AbstractPeptide‐based drugs offer high specificity, potency, and selectivity. However, their inherent flexibility and differences in conformational preferences between their free and bound states create unique challenges that have hindered progress in effective drug discovery pipelines. The emergence of AlphaFold (AF) and Artificial Intelligence (AI) presents new opportunities for enhancing peptide‐based drug discovery. We explore recent advancements that facilitate a successful peptide drug discovery pipeline, considering peptides' attractive therapeutic properties and strategies to enhance their stability and bioavailability. AF enables efficient and accurate prediction of peptide‐protein structures, addressing a critical requirement in computational drug discovery pipelines. In the post‐AF era, we are witnessing rapid progress with the potential to revolutionize peptide‐based drug discovery such as the ability to rank peptide binders or classify them as binders/non‐binders and the ability to design novel peptide sequences. However, AI‐based methods are struggling due to the lack of well‐curated datasets, for example to accommodate modified amino acids or unconventional cyclization. Thus, physics‐based methods, such as docking or molecular dynamics simulations, continue to hold a complementary role in peptide drug discovery pipelines. Moreover, MD‐based tools offer valuable insights into binding mechanisms, as well as the thermodynamic and kinetic properties of complexes. As we navigate this evolving landscape, a synergistic integration of AI and physics‐based methods holds the promise of reshaping the landscape of peptide‐based drug discovery.This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Molecular and Statistical Mechanics > Molecular Interactions Software > Molecular Modeling

Use of tree-based machine learning methods to screen affinitive peptides based on docking data.

Screening peptides with good affinity is an important step in peptide-drug discovery. Recent advancement in computer and data science have made machine learning a useful tool in accurately affinitive-peptide screening. In current study, four different tree-based algorithms, including Classification and regression trees (CART), C5.0 decision tree (C50), Bagged CART (BAG) and Random Forest (RF), were employed to explore the relationship between experimental peptide affinities and virtual docking data, and the performance of each model was also compared in parallel. All four algorithms showed better performances on dataset pre-scaled, -centered and -PCA than other pre-processed dataset. After model re-built and hyperparameter optimization, the optimal C50 model (C50O) showed the best performances in terms of Accuracy, Kappa, Sensitivity, Specificity, F1, MCC and AUC when validated on test data and an unknown PEDV datasets evaluation (Accuracy=80.4 %). BAG and RFO (the optimal RF), as two best models during training process, did not performed as expecting during in testing and unknown dataset validations. Furthermore, the high correlation of the predictions of RFO and BAG to C50O implied the high stability and robustness of their prediction. Whereas although the good performance on unknown dataset, the poor performance in test data validation and correlation analysis indicated CARTO could not be used for future data prediction. To accurately evaluate the peptide affinity, the current study firstly gave a tree-model competition on affinitive peptide prediction by using virtual docking data, which would expand the application of machine learning algorithms in studying PepPIs and benefit the development of peptide therapeutics.

Lipid-Based Nanoparticles as Oral Drug Delivery Systems: Overcoming Poor Gastrointestinal Absorption and Enhancing Bioavailability of Peptide and Protein Therapeutics.

Delivery and formulation of oral peptide and protein therapeutics have always been a challenge for the pharmaceutical industry. The oral bioavailability of peptide and protein therapeutics mainly relies on their gastrointestinal solubility and permeability which are affected by their poor membrane penetration, high molecular weight and proteolytic (chemical and enzymatic) degradation resulting in limited delivery and therapeutic efficacy. The present review article highlights the challenges and limitations of oral delivery of peptide and protein therapeutics focusing on the application, potential and importance of solid lipid nanoparticles (SLNs) and nanostructured lipid carriers (NLCs) as lipid-based drug delivery systems (LBDDSs) and their advantages and drawbacks. LBDDSs, due to their lipid-based matrix can encapsulate both lipophilic and hydrophilic drugs, and by reducing the first-pass effect and avoiding proteolytic degradation offer improved drug stability, dissolution rate, absorption, bioavailability and controlled drug release. Furthermore, their small size, high surface area and surface modification increase their mucosal adhesion, tissue-targeted distribution, physiological function and half-life. Properties such as simple preparation, high-scale manufacturing, biodegradability, biocompatibility, prolonged half-life, lower toxicity, lower adverse effects, lipid-based structure, higher drug encapsulation rate and various drug release profile compared to other similar carrier systems makes LBDDSs a promising drug delivery system (DDS). Nevertheless, undesired physicochemical features of peptide and protein drug development and discovery such as plasma stability, membrane permeability and circulation half-life remain a serious challenge which should be addressed in future.

DeepTPpred: A Deep Learning Approach With Matrix Factorization for Predicting Therapeutic Peptides by Integrating Length Information.

The abuse of traditional antibiotics has led to increased resistance of bacteria and viruses. Efficient therapeutic peptide prediction is critical for peptide drug discovery. However, most of the existing methods only make effective predictions for one class of therapeutic peptides. It is worth noting that currently no predictive method considers sequence length information as a distinct feature of therapeutic peptides. In this article, a novel deep learning approach with matrix factorization for predicting therapeutic peptides (DeepTPpred) by integrating length information are proposed. The matrix factorization layer can learn the potential features of the encoded sequence through the mechanism of first compression and then restoration. And the length features of the sequence of therapeutic peptides are embedded with encoded amino acid sequences. To automatically learn therapeutic peptide predictions, these latent features are input into the neural networks with self-attention mechanism. On eight therapeutic peptide datasets, DeepTPpred achieved excellent prediction results. Based on these datasets, we first integrated eight datasets to obtain a full therapeutic peptide integration dataset. Then, we obtained two functional integration datasets based on the functional similarity of the peptides. Finally, we also conduct experiments on the latest versions of the ACP and CPP datasets. Overall, the experimental results show that our work is effective for the identification of therapeutic peptides.

ACP-Dnnel: anti-coronavirus peptides' prediction based on deep neural network ensemble learning.

The ongoing COVID-19 pandemic has caused dramatic loss of human life. There is an urgent need for safe and efficient anti-coronavirus infection drugs. Anti-coronavirus peptides (ACovPs) can inhibit coronavirus infection. With high-efficiency, low-toxicity, and broad-spectrum inhibitory effects on coronaviruses, they are promising candidates to be developed into a new type of anti-coronavirus drug. Experiment is the traditional way of ACovPs' identification, which is less efficient and more expensive. With the accumulation of experimental data on ACovPs, computational prediction provides a cheaper and faster way to find anti-coronavirus peptides' candidates. In this study, we ensemble several state-of-the-art machine learning methodologies to build nine classification models for the prediction of ACovPs. These models were pre-trained using deep neural networks, and the performance of our ensemble model, ACP-Dnnel, was evaluated across three datasets and independent dataset. We followed Chou's 5-step rules. (1) we constructed the benchmark datasets data1, data2, and data3 for training and testing, and introduced the independent validation dataset ACVP-M; (2) we analyzed the peptides sequence composition feature of the benchmark dataset; (3) we constructed the ACP-Dnnel model with deep convolutional neural network (DCNN) merged the bi-directional long short-term memory (BiLSTM) as the base model for pre-training to extract the features embedded in the benchmark dataset, and then, nine classification algorithms were introduced to ensemble together for classification prediction and voting together; (4) tenfold cross-validation was introduced during the training process, and the final model performance was evaluated; (5) finally, we constructed a user-friendly web server accessible to the public at http://150.158.148.228:5000/ . The highest accuracy (ACC) of ACP-Dnnel reaches 97%, and the Matthew's correlation coefficient (MCC) value exceeds 0.9. On three different datasets, its average accuracy is 96.0%. After the latest independent dataset validation, ACP-Dnnel improved at MCC, SP, and ACC values 6.2%, 7.5% and 6.3% greater, respectively. It is suggested that ACP-Dnnel can be helpful for the laboratory identification of ACovPs, speeding up the anti-coronavirus peptide drug discovery and development. We constructed the web server of anti-coronavirus peptides' prediction and it is available at http://150.158.148.228:5000/ .

Integration of organoids in peptide drug discovery: Rise of the high‐throughput screening

AbstractOrganoids are three‐dimensional cell aggregates with near‐physiologic cell behaviors and can undergo long‐term expansion in vitro. They are amenable to high‐throughput drug screening processes, which renders them a viable preclinical model for drug development. The procedure of organoid‐based high‐throughput screening has been extensively employed to discover small‐molecule drugs, encompassing the steps of generating organoids, examining efficient drugs in organoid cultures, and data assessment. Compared to small molecules, peptides are more straightforward to synthesize, can be modified chemically, and demonstrate high target specificity and low cytotoxicity. Therefore, they have emerged as promising carriers to deliver drugs to disease‐associated targets and could be efficient therapeutic drugs for various diseases. To date, organoids have been used to evaluate the efficacy of certain peptide agents; however, no organoid‐based high‐throughput screening of peptide drugs has been reported. Given the advantages of peptide drugs, there is an urgent need to establish organoid‐based peptide high‐throughput screening platforms. In this review, we discuss the typical approach of screening small‐molecular drugs with the use of organoid cultures, as well as provide an overview of the studies that have incorporated organoids in peptide research. Drawing on the knowledge from small molecular screens, we explore the difficulties and potential avenues for creating new platforms to identify peptide agents using organoid models.

Development of Bifunctional, Raman Active Diyne-Girder Stapled α-Helical Peptides.

Stapled peptides are a unique class of cyclic α-helical peptides that are conformationally constrained via their amino acid side-chains. They have been transformative to the field of chemical biology and peptide drug discovery through addressing many of the physicochemical limitations of linear peptides. However, there are several issues with current chemical strategies to produce stapled peptides. For example, two distinct unnatural amino acids are required to synthesize i, i+7 alkene stapled peptides, leading to high production costs. Furthermore, low purified yields are obtained due to cis/trans isomers produced during ring-closing metathesis macrocyclisation. Here we report the development of a new i, i+7 diyne-girder stapling strategy that addresses these issues. The asymmetric synthesis of nine unnatural Fmoc-protected alkyne-amino acids facilitated a systematic study to determine the optimal (S,S)-stereochemistry and 14-carbon diyne-girder bridge length. Diyne-girder stapled T-STAR peptide 29 was demonstrated to have excellent helicity, cell permeability and stability to protease degradation. Finally, we demonstrate that the diyne-girder constraint is a Raman chromophore with potential use in Raman cell microscopy. Development of this highly effective, bifunctional diyne-girder stapling strategy leads us to believe that it can be used to produce other stapled peptide probes and therapeutics.

Copper-catalyzed asymmetric C(sp3)-H cyanoalkylation of glycine derivatives and peptides

Alkylnitriles play important roles in many fields because of their unique electronic properties and structural characteristics. Incorporating cyanoalkyl with characteristic spectroscopy and reactivity properties into amino acids and peptides is of special interest for potential imaging and therapeutic purposes. Here, we report a copper-catalyzed asymmetric cyanoalkylation of C(sp3)-H. In the reactions, glycine derivatives can effectively couple with various cycloalkanone oxime esters with high enantioselectivities, and the reaction can be applied to the late-stage modification of peptides with good yields and excellent stereoselectivities, which is useful for modern peptide synthesis and drug discovery. The mechanistic studies show that the in situ formed copper complex by the coordination of glycine derivatives and chiral phosphine Cu catalyst can not only mediate the single electronic reduction of cycloalkanone oxime ester but also control the stereoselectivity of the cyanoalkylation reaction.

Lipidated variants of the antimicrobial peptide nisin produced via incorporation of methionine analogs for click chemistry show improved bioactivity

The increase in antibiotic resistance calls for accelerated molecular engineering strategies to diversify natural products for drug discovery. The incorporation of non-canonical amino acids (ncAAs) is an elegant strategy for this purpose, offering a diverse pool of building blocks to introduce desired properties into antimicrobial lanthipeptides. We here report an expression system using Lactococcus lactis as a host for non-canonical amino acid incorporation with high efficiency and yield. We show that incorporating the more hydrophobic analog ethionine (instead of methionine) into nisin improves its bioactivity against several Gram-positive strains we tested. New-to-nature variants were further created by click chemistry. By azidohomoalanine (Aha) incorporation and subsequent click chemistry, we obtained lipidated variants at different positions in nisin or in truncated nisin variants. Some of them show improved bioactivity and specificity against several pathogenic bacterial strains. These results highlight the ability of this methodology for lanthipeptide multi-site lipidation, to create new-to-nature antimicrobial products with diverse features, and extend the toolbox for (lanthi)peptide drug improvement and discovery.

Beyond 20 in the 21st Century: Prospects and Challenges of Non-canonical Amino Acids in Peptide Drug Discovery.

Life is constructed primarily using a toolbox of 20 canonical amino acids-relying upon these building blocks for the assembly of proteins and peptides that regulate nearly every cellular task, including cell structure, function, and maintenance. While Nature continues to be a source of inspiration for drug discovery, medicinal chemists are not beholden to only 20 canonical amino acids and have begun to explore non-canonical amino acids (ncAAs) for the construction of designer peptides with improved drug-like properties. However, as our toolbox of ncAAs expands, drug hunters are encountering new challenges in approaching the iterative peptide design-make-test-analyze cycle with a seemingly boundless set of building blocks. This Microperspective focuses on new technologies that are accelerating ncAA interrogation in peptide drug discovery (including HELM notation, late-stage functionalization, and biocatalysis) while shedding light on areas where further investment could not only accelerate the discovery of new medicines but also improve downstream development.

Synthesis and Applications of Peptides and Peptidomimetics in Drug Discovery

AbstractPeptides are described as naturally occurring short chains of amino acids and offer great potential as therapeutic agents because of their target selectivity, safe, and well tolerable. Hence, there is an increased interest in peptides in pharmaceutical research and development and approximately more than 170 peptide therapeutics are currently being evaluated in clinical trials and many are being used as therapeutic drugs for various diseases including COVID‐19. The present Review article focuses on recent progress in peptide drug discovery and advancements in synthetic methodologies to enhance their stability and physiological activity of peptides and as well peptidomimetics.

Synthesis and characterization of two potential impurities (des-ethyl-Ganirelix) generated in the Ganirelix manufacturing process.

Controlling certain diseases using peptide drugs has remarkably increased in the past two decades. In this regard, a generic formulation is an upfront solution to fulfill market demands. Ganirelix, a leading peptide active pharmaceutical ingredient (API) primarily used as a gonadotropin-releasing hormone antagonist (GnRH), has established a potential market value worldwide. But its generic formulation mandates detailed impurity profiles from a synthetic source and contemplates the sameness of a reference-listed drug (RLD). Post-chemical synthesis and processing of Ganirelix, some commercial sources have revealed two new potential impurities among many known, which show the deletion of an ethyl group from the hArg(Et)2 residue at the sixth and eighth positions, named des-ethyl-Ganirelix. These impurities are unprecedented in traditional peptide chemistry, and such monoethylated-hArg building blocks are not easily accessible commercially to synthesize these two impurities. Here, we have outlined the synthesis, purification, and enantiomeric purity characterization of the amino acids and their incorporation in the Ganirelix peptide sequence to synthesize these potential peptide impurities. This methodology will enable the convenient synthesis of side-chain substituted Arg and hArg derivatives in peptide drug discovery platforms.

CycPeptMPDB: A Comprehensive Database of Membrane Permeability of Cyclic Peptides.

Recently, cyclic peptides have been considered breakthrough drugs because they can interact with "undruggable" targets such as intracellular protein-protein interactions. Membrane permeability is an essential indicator of oral bioavailability and intracellular targeting, and the development of membrane-permeable peptides is a bottleneck in cyclic peptide drug discovery. Although many experimental data on membrane permeability of cyclic peptides have been reported, a comprehensive database is not yet available. A comprehensive membrane permeability database is essential for developing computational methods for cyclic peptide drug design. In this study, we constructed CycPeptMPDB, the first web-accessible database of cyclic peptide membrane permeability. We collected information on a total of 7334 cyclic peptides, including the structure and experimentally measured membrane permeability, from 45 published papers and 2 patents from pharmaceutical companies. To unambiguously represent cyclic peptides larger than small molecules, we used the hierarchical editing language for macromolecules notation to generate a uniform sequence representation of peptides. In addition to data storage, CycPeptMPDB provides several supporting functions such as online data visualization, data analysis, and downloading. CycPeptMPDB is expected to be a valuable platform to support membrane permeability research on cyclic peptides. CycPeptMPDB can be freely accessed at http://cycpeptmpdb.com.