Peptide-based drug design: here and now

CancerPPD2: an updated repository of anticancer peptides and proteins

CancerPPD2 (http://webs.iiitd.edu.in/raghava/cancerppd2/) is an updated version of CancerPPD, developed to maintain comprehensive information about anticancer peptides and proteins. It contains 6521 entries, each entry provides detailed information about an anticancer peptide/protein that include origin of the peptide, cancer cell line, type of cancer, peptide sequence, and structure. These anticancer peptides have been tested against 392 types of cancer cell lines and 28 types of cancer-associated tissues. In addition to natural anticancer peptides, CancerPPD2 contains 781 entries for chemically modified and 3018 entries for N-/C- terminus modified anticancer peptides. Few entries are also linked with 47 clinical studies and have provided the cross reference to Uniprot, DrugBank, and ThPDB2. The possible entries also linked with clinical trials. On average, CancerPPD2 contains around 85% more information than its previous version, CancerPPD. The structures of these anticancer peptides and proteins were either obtained from the Protein Data Bank (PDB) or predicted using PEPstrMOD, I-TASSER, and AlphaFold. A wide range of tools have been integrated into CancerPPD2 for data retrieval and similarity searches. Additionally, we integrated a REST API into this repository to facilitate automatic data retrieval via program. Database URL: https://webs.iiitd.edu.in/raghava/cancerppd2/api/rest.html.

Algal proteins and bioactive peptides: Sustainable nutrition for human health

Animal proteins are the primary global protein source, but their production is environmentally challenging and has low conversion efficiency. This highlights the need to diversify dietary protein sources. Algal proteins provide a sustainable alternative, outperforming traditional plant and animal proteins in protein content, quality, and digestibility. Furthermore, bioactive peptides (BAPs) derived from algal proteins exhibit significant health benefits, including antihypertensive, antioxidant, antimicrobial, anticancer, and antidiabetic activities. This review comprehensively explores the nutritional benefits of algal proteins and provides an innovative summary of the production techniques for algal bioactive peptides. It also highlights the synergistic application methods of these technologies. By integrating pretreatment methods such as ultrasound-assisted extraction, pulsed electric field, and high hydrostatic pressure with enzymatic-assisted extraction, these techniques demonstrate a synergistic effect in improving protein hydrolysis efficiency while also increasing the yield of BAPs. Meanwhile, database resources related to algal proteins are integrated and the application of computer technology in the development of algal proteins is analyzed. It aims to provide new insights to optimize the development and utilization of algal proteins to help them become a sustainable source of nutrition to meet the needs of a growing global population.

Peptidic Compound as DNA Binding Agent: In Silico Fragment-based Design, Machine Learning, Molecular Modeling, Synthesis, and DNA Binding Evaluation

BACKGROUND

Cancer remains a global burden, with increasing mortality rates. Current cancer treatments involve controlling the transcription of malignant DNA genes, either directly or indirectly. DNA exhibits various structural forms, including the G-quadruplex (G4), a secondary structure in guanine-rich regions. G4 plays a crucial role in cellular processes by regulating gene expression and telomerase function. Researchers have recently identified G4-stabilizing binding agents as promising anti-cancer compounds. Additionally, peptides have emerged as effective anticancer pharmaceuticals due to their ability to form multiple hydrogen bonds, electrostatic interactions, and van der Waals forces. These properties enable peptides to bind to specific areas of DNA chains selectively. However, despite these advancements, designing G4-binding peptides remains challenging due to a lack of comprehensive information.

OBJECTIVE

In our present study, we employed an in silico fragment-based approach to design G4- binding peptides. This innovative method combines machine learning classification, molecular docking, and dynamics simulation.

METHODS

AutoDock Vina and Gromacs performed molecular docking and MD simulation, respectively. The machine learning algorithm was implemented by Scikit-learn. Peptide synthesis was performed using the SPPS method. The DNA binding affinity was measured by applying spectrophotometric titration.

RESULTS

As a result of this approach, we identified a high-scoring peptide (p10; sequence: YWRWR). The association constant (Ka) between p10 and the ctDNA double helix chain was 4.45 × 105 M-1. Molecular modeling studies revealed that p10 could form a stable complex with the G4 surface.

CONCLUSION

The obtained Ka value of 4.45 × 105 M-1 indicates favorable interactions. Our findings highlight the role of machine learning and molecular modeling approaches in designing new G4-binding peptides. Further research in this field could lead to targeted treatments that exploit the unique properties of G4 structures.

Recent advances and applications of peptide-agent conjugates for targeting tumor cells

BACKGROUND

Cancer, being a complex disease, presents a major challenge for the scientific and medical communities. Peptide therapeutics have played a significant role in different medical practices, including cancer treatment.

METHOD

This review provides an overview of the current situation and potential development prospects of anticancer peptides (ACPs), with a particular focus on peptide vaccines and peptide-drug conjugates for cancer treatment.

RESULTS

ACPs can be used directly as cytotoxic agents (molecularly targeted peptides) or can act as carriers (guiding missile) of chemotherapeutic agents and radionuclides by specifically targeting cancer cells. More than 60 natural and synthetic cationic peptides are approved in the USA and other major markets for the treatment of cancer and other diseases. Compared to traditional cancer treatments, peptides exhibit anticancer activity with high specificity and the ability to rapidly kill target cancer cells. ACP's target and kill cancer cells via different mechanisms, including membrane disruption, pore formation, induction of apoptosis, necrosis, autophagy, and regulation of the immune system. Modified peptides have been developed as carriers for drugs, vaccines, and peptide-drug conjugates, which have been evaluated in various phases of clinical trials for the treatment of different types of solid and leukemia cancer.

CONCLUSIONS

This review highlights the potential of ACPs as a promising therapeutic option for cancer treatment, particularly through the use of peptide vaccines and peptide-drug conjugates. Despite the limitations of peptides, such as poor metabolic stability and low bioavailability, modified peptides show promise in addressing these challenges. Various mechanism of action of anticancer peptides. Modes of action against cancer cells including: inducing apoptosis by cytochrome c release, direct cell membrane lysis (necrosis), inhibiting angiogenesis, inducing autophagy-mediated cell death and immune cell regulation.

Advancement and application of novel cell-penetrating peptide in cancer management

Cell-penetrating peptides (CPPs) are small amino acid sequences with the potential to enter cell membranes. Along with nucleic acids, large proteins, and other chemical compounds, they can deliver several bioactive cargos inside cells. Numerous CPPs have been extracted from natural or synthetic materials since the discovery of the first CPP. In the past few decades, a significant variety of studies have shown the potential of CPPs to cure different diseases. The low toxicity in peptide compared to other drug delivery carriers is a significant benefit of CPP-based therapy, in addition to the high efficacy brought about by swift and effective delivery. A significant tendency for intracellular DNA delivery may also be observed when nanoparticles and the cell penetration peptide are combined. CPPs are frequently used to increase intracellular absorption of nucleic acid, and other therapeutic agents inside the cell. Due to long-term side effects and possible toxicity, its implementation is restricted. The use of cell-permeating peptides is a commonly used technique to increase their intracellular absorption. Additionally, CPPs have lately been sought for application in vivo, following their success in cellular studies. This review will go through the numerous CPPs, the chemical modifications that improve their cellular uptake, the various means for getting them across cell membranes, and the biological activity they acquire after being conjugate with specific chemicals.

Cell-free Biosynthesis of Peptidomimetics

A wide variety of peptidomimetics (peptide analogs) possessing innovative biological functions have been brought forth as therapeutic candidates through cell-free protein synthesis (CFPS) systems. A key feature of these peptidomimetic drugs is the use of non-canonical amino acid building blocks with diverse biochemical properties that expand functional diversity. Here, we summarize recent technologies leveraging CFPS platforms to expand the reach of peptidomimetics drugs. We also offer perspectives on engineering the translational machinery that may open new opportunities for expanding genetically encoded chemistry to transform drug discovery practice beyond traditional boundaries.

Efficacy Evaluation of SDF-1α-Based Polypeptides in an Acute Myocardial Infarction Model Using Structure-Based Drug Design

Stromal cell-derived factor-1 alpha (SDF-1α, CXCL12) mediates the migration of circulating cells to desired sites for tissue development, homeostasis, and regeneration and can be used to promote cardiac regeneration by recruiting stem cells. However, the use of SDF-1α in the injured heart necessitates not only higher binding affinity to its receptor, CXCR4+, but also better robustness against enzymatic degradation than other SDF-1 isoforms. Here, we conduct a screening of SDF-1α analog peptides that were designed by structure-based drug design (SBDD), a type of computer-aided drug design (CADD). We have developed in vitro and in vivo methods that enable us to estimate the effect of peptides on the migration of human mesenchymal stem cells (hMSCs) and cardiac regeneration in acute myocardial infarction (AMI)-induced animals, respectively. We demonstrate that one type of SDF-1α analog peptide, SDP-4, among the four analog peptides preselected by SBDD, is more potent than native SDF-1α for cardiac regeneration in myocardial infarction. It is interesting to note that the migratory effects of SDP-4 determined by a wound healing assay, a Transwell assay, and a 2D migration assay are comparable to those of SDF-1α. These results suggest that in vivo, as well as in vitro, screening of peptides developed by SBDD is a quintessential process to the development of a novel therapeutic compound for cardiac regeneration. Our finding also has an implication that the SDP-4 peptide is an excellent candidate for use in the regeneration of an AMI heart.

Peptide-based targeted cancer therapeutics: design, synthesis and biological evaluation

Cancer is the leading cause for human mortality together with cardiovascular diseases. Abl (Abelson) tyrosine kinases play a fundamental role in transducing various signals that control proliferation, survival, migration and invasion in several cancers such as Chronic Myeloid Leukemia (CML), breast cancer and brain cancer. For these reasons Abl tyrosine kinases are considered important biological targets in drug discovery. In this study a series of lysine-based oligopeptides with expected Abl inhibitory activity were designed resembling the binding of FDA-approved drugs (i.e. of Imatinib and Nilotinib), synthesized, purified by High Performance Liquid Chromatography (HPLC), analyzed by mass spectrometry (MS) and biologically tested in vitro in CML (AR-230 and K-562), breast cancers (MDA-MB 231 and MDA-MB 468) and glioblastoma cell lines (U87 and U118). The solid-phase peptide synthesis (SPPS) by Fmoc (9-fluorenylmethoxycarbonyl) chemistry was used to synthesize target compounds. AutoDock Vina was applied for simulation binding to Abl. The biological activities were measured evaluating cytotoxic effect, induction of apoptosis and inhibition of cancer cells migration. The new peptides exhibited different concentration-dependent antiproliferative effect against the tumor cell lines after 72 h treatment. The most promising results were obtained with the U87 glioblastoma cell line where a significant reduction of the migration ability was detected with one compound (H-Lys¹-Lys²-Lys³-NH₂).

Biologically Synthesized Peptides Show Remarkable Inhibition Activity against Angiotensin-Converting Enzyme: A Promising Approach for Peptide Development against Autoimmune Diseases

Angiotensin-converting enzyme (ACE) regulates several biological functions besides its vital role in immune functions. ACE is elevated in immune cells in inflammatory diseases including atherosclerosis, granuloma, chronic kidney disease, and also autoimmune diseases, like multiple sclerosis, rheumatoid arthritis, and type I diabetes. No significant information prevails in the literature regarding the isolation, identification, and profiling of potential ACE inhibitory peptides. In the present study, indigenous crop varieties like seeds (peanut, corn, oat, sunflower, chickpea, parsley, cottonseed, papaya, sesame, and flaxseed) were used to evaluate their ACE inhibition activity. Variables including hydrolysis time, enzyme-to-substrate ratio (E/S), pH, and temperature were standardized to acquire the most suitable and optimum ACE inhibition activity. Seeds of cotton, chickpea, and peanuts displayed remarkably maximum ACE inhibition activity than other plants. The study disclosed that maximum ACE inhibitory activity (86%) was evaluated from cottonseed at pH 8.0, temperature of 45°C, hydrolysis time of 2 hrs, and enzyme to the substrate (E/S) ratio of 1 : 5 followed by peanuts (76%) and chickpea (55%). SDS-PAGE confirmed that vicilin protein is present in cottonseed and peanut seed while cruciferin and napin proteins are present in chickpeas. LC-MS/MS analysis disclosed potential novel peptides in hydrolyzed cottonseed that can be ascribed as potential ACE inhibitors which have never been reported and studied earlier. The current study further showed that cottonseed peptides due to their promising ACE inhibitory activity can be a valuable source in the field of ACE inhibitor development.

Marine Cyclic Peptides: Antimicrobial Activity and Synthetic Strategies

Oceans are a rich source of structurally unique bioactive compounds from the perspective of potential therapeutic agents. Marine peptides are a particularly interesting group of secondary metabolites because of their chemistry and wide range of biological activities. Among them, cyclic peptides exhibit a broad spectrum of antimicrobial activities, including against bacteria, protozoa, fungi, and viruses. Moreover, there are several examples of marine cyclic peptides revealing interesting antimicrobial activities against numerous drug-resistant bacteria and fungi, making these compounds a very promising resource in the search for novel antimicrobial agents to revert multidrug-resistance. This review summarizes 174 marine cyclic peptides with antibacterial, antifungal, antiparasitic, or antiviral properties. These natural products were categorized according to their sources—sponges, mollusks, crustaceans, crabs, marine bacteria, and fungi—and chemical structure—cyclic peptides and depsipeptides. The antimicrobial activities, including against drug-resistant microorganisms, unusual structural characteristics, and hits more advanced in (pre)clinical studies, are highlighted. Nocathiacins I–III (91–93), unnarmicins A (114) and C (115), sclerotides A (160) and B (161), and plitidepsin (174) can be highlighted considering not only their high antimicrobial potency in vitro, but also for their promising in vivo results. Marine cyclic peptides are also interesting models for molecular modifications and/or total synthesis to obtain more potent compounds, with improved properties and in higher quantity. Solid-phase Fmoc- and Boc-protection chemistry is the major synthetic strategy to obtain marine cyclic peptides with antimicrobial properties, and key examples are presented guiding microbiologist and medicinal chemists to the discovery of new antimicrobial drug candidates from marine sources.

A Treatment to Cure Diabetes Using Plant-Based Drug Discovery

The field of peptides and proteins has opened up new doors for plant-based medication development because of analytical breakthroughs. Enzymatic breakdown of plant-specific proteins yields bioactive peptides. These plant-based proteins and peptides, in addition to their in vitro and vivo outcomes for diabetes treatment, are discussed in this study. The secondary metabolites of vegetation can interfere with the extraction, separation, characterization, and commercialization of plant proteins through the pharmaceutical industry. Glucose-lowering diabetic peptides are a hot commodity. For a wide range of illnesses, bioactive peptides from flora can offer up new avenues for the development of cost-effective therapy options.

ACP-MHCNN: an accurate multi-headed deep-convolutional neural network to predict anticancer peptides

Although advancing the therapeutic alternatives for treating deadly cancers has gained much attention globally, still the primary methods such as chemotherapy have significant downsides and low specificity. Most recently, Anticancer peptides (ACPs) have emerged as a potential alternative to therapeutic alternatives with much fewer negative side-effects. However, the identification of ACPs through wet-lab experiments is expensive and time-consuming. Hence, computational methods have emerged as viable alternatives. During the past few years, several computational ACP identification techniques using hand-engineered features have been proposed to solve this problem. In this study, we propose a new multi headed deep convolutional neural network model called ACP-MHCNN, for extracting and combining discriminative features from different information sources in an interactive way. Our model extracts sequence, physicochemical, and evolutionary based features for ACP identification using different numerical peptide representations while restraining parameter overhead. It is evident through rigorous experiments using cross-validation and independent-dataset that ACP-MHCNN outperforms other models for anticancer peptide identification by a substantial margin on our employed benchmarks. ACP-MHCNN outperforms state-of-the-art model by 6.3%, 8.6%, 3.7%, 4.0%, and 0.20 in terms of accuracy, sensitivity, specificity, precision, and MCC respectively. ACP-MHCNN and its relevant codes and datasets are publicly available at: https://github.com/mrzResearchArena/Anticancer-Peptides-CNN . ACP-MHCNN is also publicly available as an online predictor at: https://anticancer.pythonanywhere.com/ .

APPTEST is a novel protocol for the automatic prediction of peptide tertiary structures

Good knowledge of a peptide's tertiary structure is important for understanding its function and its interactions with its biological targets. APPTEST is a novel computational protocol that employs a neural network architecture and simulated annealing methods for the prediction of peptide tertiary structure from the primary sequence. APPTEST works for both linear and cyclic peptides of 5-40 natural amino acids. APPTEST is computationally efficient, returning predicted structures within a number of minutes. APPTEST performance was evaluated on a set of 356 test peptides; the best structure predicted for each peptide deviated by an average of 1.9Å from its experimentally determined backbone conformation, and a native or near-native structure was predicted for 97% of the target sequences. A comparison of APPTEST performance with PEP-FOLD, PEPstrMOD and PepLook across benchmark datasets of short, long and cyclic peptides shows that on average APPTEST produces structures more native than the existing methods in all three categories. This innovative, cutting-edge peptide structure prediction method is available as an online web server at https://research.timmons.eu/apptest, facilitating in silico study and design of peptides by the wider research community.

xDeep-AcPEP: Deep Learning Method for Anticancer Peptide Activity Prediction Based on Convolutional Neural Network and Multitask Learning

Cancer is one of the leading causes of death worldwide. Conventional cancer treatment relies on radiotherapy and chemotherapy, but both methods bring severe side effects to patients, as these therapies not only attack cancer cells but also damage normal cells. Anticancer peptides (ACPs) are a promising alternative as therapeutic agents that are efficient and selective against tumor cells. Here, we propose a deep learning method based on convolutional neural networks to predict biological activity (EC50, LC50, IC50, and LD50) against six tumor cells, including breast, colon, cervix, lung, skin, and prostate. We show that models derived with multitask learning achieve better performance than conventional single-task models. In repeated 5-fold cross validation using the CancerPPD data set, the best models with the applicability domain defined obtain an average mean squared error of 0.1758, Pearson's correlation coefficient of 0.8086, and Kendall's correlation coefficient of 0.6156. As a step toward model interpretability, we infer the contribution of each residue in the sequence to the predicted activity by means of feature importance weights derived from the convolutional layers of the model. The present method, referred to as xDeep-AcPEP, will help to identify effective ACPs in rational peptide design for therapeutic purposes. The data, script files for reproducing the experiments, and the final prediction models can be downloaded from http://github.com/chen709847237/xDeep-AcPEP. The web server to directly access this prediction method is at https://app.cbbio.online/acpep/home.

ACP-DA: Improving the Prediction of Anticancer Peptides Using Data Augmentation

Anticancer peptides (ACPs) have provided a promising perspective for cancer treatment, and the prediction of ACPs is very important for the discovery of new cancer treatment drugs. It is time consuming and expensive to use experimental methods to identify ACPs, so computational methods for ACP identification are urgently needed. There have been many effective computational methods, especially machine learning-based methods, proposed for such predictions. Most of the current machine learning methods try to find suitable features or design effective feature learning techniques to accurately represent ACPs. However, the performance of these methods can be further improved for cases with insufficient numbers of samples. In this article, we propose an ACP prediction model called ACP-DA (Data Augmentation), which uses data augmentation for insufficient samples to improve the prediction performance. In our method, to better exploit the information of peptide sequences, peptide sequences are represented by integrating binary profile features and AAindex features, and then the samples in the training set are augmented in the feature space. After data augmentation, the samples are used to train the machine learning model, which is used to predict ACPs. The performance of ACP-DA exceeds that of existing methods, and ACP-DA achieves better performance in the prediction of ACPs compared with a method without data augmentation. The proposed method is available at http://github.com/chenxgscuec/ACPDA.

Tumor vasculature-targeted 10B delivery by an Annexin A1-binding peptide boosts effects of boron neutron capture therapy

Background

p-Boronophenylalanine (¹⁰BPA) is a powerful ¹⁰B drug used in current clinical trials of BNCT. For BNCT to be successful, a high (500 mg/kg) dose of ¹⁰BPA must be administered over a few hours. Here, we report BNCT efficacy after rapid, ultralow-dose administration of either tumor vasculature-specific annexin A1-targeting IFLLWQR (IF7)-conjugated ¹⁰BPA or borocaptate sodium (¹⁰BSH).

Methods

(1) IF7 conjugates of either ¹⁰B drugs intravenously injected into MBT2 bladder tumor-bearing mice and biodistribution of ¹⁰B in tumors and normal organs analyzed by prompt gamma-ray analysis. (2) Therapeutic effect of IF7-¹⁰B drug-mediated BNCT was assessed by either MBT2 bladder tumor bearing C3H/He mice and YTS-1 tumor bearing nude mice.

Results

Intravenous injection of IF7C conjugates of either ¹⁰B drugs into MBT2 bladder tumor-bearing mice promoted rapid ¹⁰B accumulation in tumor and suppressed tumor growth. Moreover, multiple treatments at ultralow (10–20 mg/kg) doses of IF7-¹⁰B drug-mediated BNCT significantly suppressed tumor growth in a mouse model of human YTS-1 bladder cancer, with increased Anxa1 expression in tumors and infiltration by CD8-positive lymphocytes.

Conclusions

We conclude that IF7 serves as an efficient ¹⁰B delivery vehicle by targeting tumor tissues via the tumor vasculature and could serve as a relevant vehicle for BNCT drugs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-020-07760-x.

Anticancer peptide: Physicochemical property, functional aspect and trend in clinical application (Review)

Cancer is currently ineffectively treated using therapeutic drugs, and is also able to resist drug action, resulting in increased side effects following drug treatment. A novel therapeutic strategy against cancer cells is the use of anticancer peptides (ACPs). The physicochemical properties, amino acid composition and the addition of chemical groups on the ACP sequence influences their conformation, net charge and orientation of the secondary structure, leading to an effect on targeting specificity and ACP-cell interaction, as well as peptide penetrating capability, stability and efficacy. ACPs have been developed from both naturally occurring and modified peptides by substituting neutral or anionic amino acid residues with cationic amino acid residues, or by adding a chemical group. The modified peptides lead to an increase in the effectiveness of cancer therapy. Due to this effectiveness, ACPs have recently been improved to form drugs and vaccines, which have sequentially been evaluated in various phases of clinical trials. The development of the ACPs remains focused on generating newly modified ACPs for clinical application in order to decrease the incidence of new cancer cases and decrease the mortality rate. The present review could further facilitate the design of ACPs and increase efficacious ACP therapy in the near future.

Photo-induced synthesis of Axinastatin 3 analogs, the secondary structures and their in vitro antitumor activities

Cyclic peptides combine several favorable properties such as good binding affinity, target selectivity and low toxicity that make them an attractive modality for drug development. In an effort to identify what conformation could be accounting for the bioactive disparity of natural and synthetic cyclic peptides, some structurally-constrained analogs of cyclopeptide Axinastatin 3 were prepared by photo-induced single electron transfer (SET) reaction. Detailed stereochemistry study was performed by experimental electronic circular dichroism combined with theoretical calculations. Our study suggested that the cyclopeptide 1 with βI-turn presented stronger antitumor activity comparing with those without such secondary structures. Moreover, a rare 'π helix unit' (compound 3) was realized because of the constrained cyclic structure, which could be considered an important research object for future study of unique helix secondary structures.

Plant-Derived Bioactive Peptides: A Treatment to Cure Diabetes

ABSTRACT

Recent advances in analytical techniques have opened new opportunities for plant-based drug discovery in the field of peptide and proteins. Enzymatic hydrolysis of plant parent proteins forms bioactive peptides which are explored in the treatment of various diseases. In this review, we will discuss the identified plant-based bioactive proteins and peptides and the in vitro, in vivo results for the treatment of diabetes. Extraction, isolation, characterization and commercial utilization of plant proteins is a challenge for the pharmaceutical industry as plants contain several interfering secondary metabolites. The market of peptide drugs for the treatment of diabetes is growing at a fast rate. Plant-based bioactive peptides might open up new opportunities to discover economic lead for the management of various diseases.

GRAPHIC ABSTRACT

ACP-DL: A Deep Learning Long Short-Term Memory Model to Predict Anticancer Peptides Using High-Efficiency Feature Representation

Cancer is a well-known killer of human beings, which has led to countless deaths and misery. Anticancer peptides open a promising perspective for cancer treatment, and they have various attractive advantages. Conventional wet experiments are expensive and inefficient for finding and identifying novel anticancer peptides. There is an urgent need to develop a novel computational method to predict novel anticancer peptides. In this study, we propose a deep learning long short-term memory (LSTM) neural network model, ACP-DL, to effectively predict novel anticancer peptides. More specifically, to fully exploit peptide sequence information, we developed an efficient feature representation approach by integrating binary profile feature and k-mer sparse matrix of the reduced amino acid alphabet. Then we implemented a deep LSTM model to automatically learn how to identify anticancer peptides and non-anticancer peptides. To our knowledge, this is the first time that the deep LSTM model has been applied to predict anticancer peptides. It was demonstrated by cross-validation experiments that the proposed ACP-DL remarkably outperformed other comparison methods with high accuracy and satisfied specificity on benchmark datasets. In addition, we also contributed two new anticancer peptides benchmark datasets, ACP740 and ACP240, in this work. The source code and datasets are available at https://github.com/haichengyi/ACP-DL.

mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides

Anticancer peptides (ACPs) are promising therapeutic agents for targeting and killing cancer cells. The accurate prediction of ACPs from given peptide sequences remains as an open problem in the field of immunoinformatics. Recently, machine learning algorithms have emerged as a promising tool for helping experimental scientists predict ACPs. However, the performance of existing methods still needs to be improved. In this study, we present a novel approach for the accurate prediction of ACPs, which involves the following two steps: (i) We applied a two-step feature selection protocol on seven feature encodings that cover various aspects of sequence information (composition-based, physicochemical properties and profiles) and obtained their corresponding optimal feature-based models. The resultant predicted probabilities of ACPs were further utilized as feature vectors. (ii) The predicted probability feature vectors were in turn used as an input to support vector machine to develop the final prediction model called mACPpred. Cross-validation analysis showed that the proposed predictor performs significantly better than individual feature encodings. Furthermore, mACPpred significantly outperformed the existing methods compared in this study when objectively evaluated on an independent dataset.

Plant-derived mitochondria-targeting cysteine-rich peptide modulates cellular bioenergetics

Mitochondria are attractive therapeutic targets for developing agents to delay age-related frailty and diseases. However, few promising leads have been identified from natural products. Previously, we identified roseltide rT1, a hyperstable 27-residue cysteine-rich peptide from Hibiscus sabdariffa, as a knottin-type neutrophil elastase inhibitor. Here, we show that roseltide rT1 is also a cell-penetrating, mitochondria-targeting peptide that increases ATP production. Results from flow cytometry, live-cell imaging, pulldown assays, and genetically-modified cell lines supported that roseltide rT1 enters cells via glycosaminoglycan-dependent endocytosis, and enters the mitochondria through TOM20, a mitochondrial protein import receptor. We further showed that roseltide rT1 increases cellular ATP production via mitochondrial membrane hyperpolarization. Using biotinylated roseltide rT1 for target identification and proteomic analysis, we showed that human mitochondrial membrane ATP synthase subunit O is an intramitochondrial target. Collectively, these data support our discovery that roseltide rT1 is a first-in-class mitochondria-targeting, cysteine-rich peptide with potentials to be developed into tools to further our understanding of mitochrondria-related diseases.

Synthesis of Modified RGD-Based Peptides and Their in vitro Activity

Arg-Gly-Asp (RGD) peptides represent the most outstanding recognition motif involved in cell adhesion that binds to the α_v β₃ integrin, which has been targeted for cancer therapy. Various RGD-containing peptides and peptidomimetics have been designed and synthesized to selectively inhibit this integrin. In this study, the synthesis of RGD-based peptides through the incorporation of the short bioactive peptide Phe-Ala-Lys-Leu-Phe (FAKLF) at the C and N termini of RGD has been achieved by using a solid-phase peptide synthesis approach. The peptides were purified by means of preparative RP-HPLC and their structures were confirmed through HRMS (ESI). The MTT assay revealed that the RGD and FAKLF peptides inhibited the proliferation of human umbilical vein endothelial cells (HUVECs) in a dose-dependent manner, with IC₅₀ values of 3000 and 500 ng mL^-1 , respectively. Interestingly, a drastic improvement was observed in the antiproliferative activity of the combined structures of the FAKLFRGD and RGDFAKLF peptides, leading to IC₅₀ values of 200 and 136.7 ng mL^-1 , respectively. Meanwhile, based on apoptosis results, the potential of peptides to induce apoptosis, in accordance with their antiproliferative activity, indicated that the RGD and FAKLF peptides, and the peptides synthesized based on their combinations induced cell apoptosis in a dose-dependent manner followed by inhibition of the proliferation of endothelial cells. Moreover, the incorporation of d-leucine increased the induction of apoptosis by these peptides.

N-Methyl D-aspartate receptor subunit signaling in fear extinction

N-Methyl D-aspartate receptors (NMDAR) are central mediators of glutamate actions underlying learning and memory processes including those required for extinction of fear and fear-related behaviors. Consistent with this view, in animal models, antagonists of NMDAR typically impair fear extinction, whereas partial agonists have facilitating effects. Promoting NMDAR function has thus been recognized as a promising strategy towards reduction of fear symptoms in patients suffering from anxiety disorders and post-traumatic disorder (PTSD). Nevertheless, application of these drugs in clinical trials has proved of limited utility. Here we summarize recent advances in our knowledge of NMDAR pharmacology relevant for fear extinction, focusing on molecular, cellular, and circuit aspects of NMDAR function as they relate to fear extinction at the level of behavior and cognition. We also discuss how these advances from animal models might help to understand and overcome the limitations of existing approaches in human anxiety disorders and how novel, more specific, and personalized approaches might help advance future therapeutic strategies.

ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides

Motivation

Anti-cancer peptides (ACPs) have recently emerged as promising therapeutic agents for cancer treatment. Due to the avalanche of protein sequence data in the post-genomic era, there is an urgent need to develop automated computational methods to enable fast and accurate identification of novel ACPs within the vast number of candidate proteins and peptides.

Results

To address this, we propose a novel predictor named Anti-Cancer peptide Predictor with Feature representation Learning (ACPred-FL) for accurate prediction of ACPs based on sequence information. More specifically, we develop an effective feature representation learning model, with which we can extract and learn a set of informative features from a pool of support vector machine-based models trained using sequence-based feature descriptors. By doing so, the class label information of data samples is fully utilized. To improve the feature representation, we further employ a two-step feature selection technique, resulting in a most informative five-dimensional feature vector for the final peptide representation. Experimental results show that such five features provide the most discriminative power for identifying ACPs than currently available feature descriptors, highlighting the effectiveness of the proposed feature representation learning approach. The developed ACPred-FL method significantly outperforms state-of-the-art methods.

Availability and implementation

The web-server of ACPred-FL is available at http://server.malab.cn/ACPred-FL.

Supplementary information

Supplementary data are available at Bioinformatics online.

Current situation and future usage of anticancer drug databases

Cancer is a deadly disease with increasing incidence and mortality rates and affects the life quality of millions of people per year. The past 15 years have witnessed the rapid development of targeted therapy for cancer treatment, with numerous anticancer drugs, drug targets and related gene mutations been identified. The demand for better anticancer drugs and the advances in database technologies have propelled the development of databases related to anticancer drugs. These databases provide systematic collections of integrative information either directly on anticancer drugs or on a specific type of anticancer drugs with their own emphases on different aspects, such as drug-target interactions, the relationship between mutations in drug targets and drug resistance/sensitivity, drug-drug interactions, natural products with anticancer activity, anticancer peptides, synthetic lethality pairs and histone deacetylase inhibitors. We focus on a holistic view of the current situation and future usage of databases related to anticancer drugs and further discuss their strengths and weaknesses, in the hope of facilitating the discovery of new anticancer drugs with better clinical outcomes.

Potential role of bioactive peptides in prevention and treatment of chronic diseases: a narrative review

In the past few years, increasing interest has been directed to bioactive peptides of animal and plant origin: in particular, researchers have focused their attention on their mechanisms of action and potential role in the prevention and treatment of cancer, cardiovascular and infective diseases. We have developed a search strategy to identify these studies in PubMed (January 1980 to May 2016); particularly those papers presenting comprehensive reviews or meta-analyses, plus in vitro and in vivo studies and clinical trials on those bioactive peptides that affect cardiovascular diseases, immunity or cancer, or have antioxidant, anti-inflammatory and antimicrobial effects. In this review we have mostly focused on evidence-based healthy properties of bioactive peptides from different sources. Bioactive peptides derived from fish, milk, meat and plants have demonstrated significant antihypertensive and lipid-lowering activity in clinical trials. Many bioactive peptides show selective cytotoxic activity against a wide range of cancer cell lines in vitro and in vivo, whereas others have immunomodulatory and antimicrobial effects. Furthermore, some peptides exert anti-inflammatory and antioxidant activity, which could aid in the prevention of chronic diseases. However, clinical evidence is at an early stage, and there is a need for solid pharmacokinetic data and for standardized extraction procedures. Further studies on animals and randomized clinical trials are required to confirm these effects, and enable these peptides to be used as preventive or therapeutic treatments.

LINKED ARTICLES

This article is part of a themed section on Principles of Pharmacological Research of Nutraceuticals. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v174.11/issuetoc.

Effect of tacticity-derived topological constraints in bactericidal peptides

Topology is a key element in structure-activity relationship estimation while designing physiologically-active molecular constructs. Peptides may be a preferred choice for therapeutics, principally due to their biocompatibility, low toxicity and predictable metabolism. Peptide design only guarantees functional group constitution by opting specific amino acid sequence, and not their spatial orientation to bind and incite physiological response on chosen targets. This is principally because peptide conformation is subject to external flux, due to the isotactic stereochemistry of the peptide chain. Stereochemical engineering of the peptide main chain offers the possibility of multiplying the structural space of a typical sequence to many orders of magnitude, and limiting the otherwise fluxional non-specific functional group dispensation in space by offering greater conformational rigidity. We put to test, this conceptual possibility already established in theoretical models, by designing amphipathic peptide systems and experimenting with them on Gram-positive, Gram-negative and antibiotic-resistant bacteria. The unusual conformational rigidity and stability of syndiotactic peptides enable them to retain the designed electrostatic environment, while they encounter the membrane surface. All the six designed systems exhibited bactericidal activity, pointing to the utility and specificity of stereo-engineered peptide systems for therapeutic applications. Overall, we hope that this work provides important insights and useful directives in designing novel peptide systems with antimicrobial activity, by expanding the design space, incorporating D-amino acid as an additional design variable.

Information-Driven, Ensemble Flexible Peptide Docking Using HADDOCK

Modeling protein-peptide interactions remains a significant challenge for docking programs due to the inherent highly flexible nature of peptides, which often adopt different conformations whether in their free or bound forms. We present here a protocol consisting of a hybrid approach, combining the most frequently found peptide conformations in complexes with representative conformations taken from molecular dynamics simulations of the free peptide. This approach intends to broaden the range of conformations sampled during docking. The resulting ensemble of conformations is used as a starting point for information-driven flexible docking with HADDOCK. We demonstrate the performance of this protocol on six cases of increasing difficulty, taken from a protein-peptide benchmark set. In each case, we use knowledge of the binding site on the receptor to drive the docking process. In the majority of cases where MD conformations are added to the starting ensemble for docking, we observe an improvement in the quality of the resulting models.

Development of peptide inhibitors of HIV transmission

Treatment of HIV has long faced the challenge of high mutation rates leading to rapid development of resistance, with ongoing need to develop new methods to effectively fight the infection. Traditionally, early HIV medications were designed to inhibit RNA replication and protein production through small molecular drugs. Peptide based therapeutics are a versatile, promising field in HIV therapy, which continues to develop as we expand our understanding of key protein-protein interactions that occur in HIV replication and infection. This review begins with an introduction to HIV, followed by the biological basis of disease, current clinical management of the disease, therapeutics on the market, and finally potential avenues for improved drug development.

Progress with peptide scanning to study structure-activity relationships: the implications for drug discovery

INTRODUCTION

Peptides have gained renewed interest as candidate therapeutics. However, to bring them to a broader clinical use, challenges such as the rational optimization of their pharmacological properties remain. Peptide scanning techniques offer a systematic framework to gain information on the functional role of individual amino acids of a peptide. Due to progress in mastering new chemical synthesis routes targeting amino acid backbone, they are currently diversified. Structure-activity relationship (SAR) analyses such as alanine- or enantioneric- scanning can now be supplemented by N-substitution, lactam cyclisation- or aza-amino scanning procedures addressing not only SAR considerations but also the peptide pharmacological properties.

AREAS COVERED

This review highlights the different scanning techniques currently available and illustrates how they can impact drug discovery.

EXPERT OPINION

Progress in peptide scanning techniques opens new perspectives for peptide drug development. It comes with the promise of a paradigm change in peptide drug design in which peptide drugs will be closer to the parent peptides. However, scanning still remains assimilable to a trial and error strategy that could benefit from being combined with specific in silico approaches that start reaching maturity.

SATPdb: a database of structurally annotated therapeutic peptides

SATPdb (http://crdd.osdd.net/raghava/satpdb/) is a database of structurally annotated therapeutic peptides, curated from 22 public domain peptide databases/datasets including 9 of our own. The current version holds 19192 unique experimentally validated therapeutic peptide sequences having length between 2 and 50 amino acids. It covers peptides having natural, non-natural and modified residues. These peptides were systematically grouped into 10 categories based on their major function or therapeutic property like 1099 anticancer, 10585 antimicrobial, 1642 drug delivery and 1698 antihypertensive peptides. We assigned or annotated structure of these therapeutic peptides using structural databases (Protein Data Bank) and state-of-the-art structure prediction methods like I-TASSER, HHsearch and PEPstrMOD. In addition, SATPdb facilitates users in performing various tasks that include: (i) structure and sequence similarity search, (ii) peptide browsing based on their function and properties, (iii) identification of moonlighting peptides and (iv) searching of peptides having desired structure and therapeutic activities. We hope this database will be useful for researchers working in the field of peptide-based therapeutics.

PEPstrMOD: structure prediction of peptides containing natural, non-natural and modified residues

Background

In the past, many methods have been developed for peptide tertiary structure prediction but they are limited to peptides having natural amino acids. This study describes a method PEPstrMOD, which is an updated version of PEPstr, developed specifically for predicting the structure of peptides containing natural and non-natural/modified residues.

Results

PEPstrMOD integrates Forcefield_NCAA and Forcefield_PTM force field libraries to handle 147 non-natural residues and 32 types of post-translational modifications respectively by performing molecular dynamics using AMBER. AMBER was also used to handle other modifications like peptide cyclization, use of D-amino acids and capping of terminal residues. In addition, GROMACS was used to implement 210 non-natural side-chains in peptides using SwissSideChain force field library. We evaluated the performance of PEPstrMOD on three datasets generated from Protein Data Bank; i) ModPep dataset contains 501 non-natural peptides, ii) ModPep16, a subset of ModPep, and iii) CyclicPep contains 34 cyclic peptides. We achieved backbone Root Mean Square Deviation between the actual and predicted structure of peptides in the range of 3.81–4.05 Å.

Conclusions

In summary, the method PEPstrMOD has been developed that predicts the structure of modified peptide from the sequence/structure given as input. We validated the PEPstrMOD application using a dataset of peptides having non-natural/modified residues. PEPstrMOD offers unique advantages that allow the users to predict the structures of peptides having i) natural residues, ii) non-naturally modified residues, iii) terminal modifications, iv) post-translational modifications, v) D-amino acids, and also allows extended simulation of predicted peptides. This will help the researchers to have prior structural information of modified peptides to further design the peptides for desired therapeutic property. PEPstrMOD is freely available at http://osddlinux.osdd.net/raghava/pepstrmod/.

Reviewers

This article was reviewed by Prof Michael Gromiha, Dr. Bojan Zagrovic and Dr. Zoltan Gaspari.

Electronic supplementary material

The online version of this article (doi:10.1186/s13062-015-0103-4) contains supplementary material, which is available to authorized users.

Regulation of fear extinction versus other affective behaviors by discrete cortical scaffolding complexes associated with NR2B and PKA signaling

In patients suffering from post-traumatic stress disorder (PTSD), fear evoked by trauma-related memories lasts long past the traumatic event and it is often complicated by general anxiety and depressed mood. This poses a treatment challenge, as drugs beneficial for some symptoms might exacerbate others. For example, in preclinical studies, antagonists of the NR2B subunit of N-methyl-d-aspartate receptors and activators of cAMP-dependent protein kinase (PKA) act as potent antidepressants and anxiolytics, but they block fear extinction. Using mice, we attempted to overcome this problem by interfering with individual NR2B and PKA signaling complexes organized by scaffolding proteins. We infused cell-permeable Tat peptides that displaced either NR2B from receptor for activated C kinase 1 (RACK1), or PKA from A-kinase anchor proteins (AKAPs) or microtubule-associated proteins (MAPs). The infusions were targeted to the retrosplenial cortex, an area involved in both fear extinction of remotely acquired memories and in mood regulation. Tat-RACK1 and Tat-AKAP enhanced fear extinction, all peptides reduced anxiety and none affected baseline depression-like behavior. However, disruption of PKA complexes distinctively interfered with the rapid antidepressant actions of the N-methyl-D-aspartate receptors antagonist MK-801 in that Tat-MAP2 blocked, whereas Tat-AKAP completely inverted the effect of MK-801 from antidepressant to depressant. These effects were unrelated to the MK-801-induced changes of brain-derived neurotrophic factor messenger RNA levels. Together, the findings suggest that NR2B-RACK1 complexes specifically contribute to fear extinction, and may provide a target for the treatment of PTSD. AKAP-PKA, on the other hand, appears to modulate fear extinction and antidepressant responses in opposite directions.

Design of an electroactive peptide probe for sensing of a protein

We designed a new electroactive peptide probe that has a molecular recognition function for the sensing of a protein. Ovalbumin (OVA) was the model protein, and when RNRCKGTDVQAW interacted with OVA, it conjugated with a tyrosine-rich peptide (Y4C). This peptide is electroactive, has a high degree of biocompatibility, and offers the possibility of gene expression. To measure the effect of a number of the tyrosine residues, voltammetric measurements were conducted using a series of tyrosine-rich peptides (YnC, n = 3-7) with sensitivities that ranged from 10(-9) to 10(-8) M. The electrode response of Y5C was the maximum value in the series. However, the peak current did not increase when the number of tyrosine residues was increased in a linear fashion. This may have been due to the micelles that are formed by a tyrosine-rich surfactant peptide. Thus, Y4C was suitable as an electroactive label for the construction of the peptide probe. The electrode response of Y4CRNRCKGTDVQAW obtained by a glassy carbon electrode was 100-fold that of tyrosine alone. The measurement of OVA via the peptide probe resulted in a detection on the order of 10(-12) M. In contrast, the sensitivity of OVA using RCKGTDVQAWY4C probe was at the 10(-11) M level, because the hydrophobic moiety gave it a molecular recognition function. The recoveries of the OVA using Y4CRNRCKGTDVQAW in a solution containing fetal bovine serum ranged between 98 and 101%. Consequently, the combination of a specific peptide and an electroactive element could be a powerful probe for the sensing of proteins.

Development of a peptide-functionalized imaging nanoprobe for the targeting of (FXYD2)γa as a highly specific biomarker of pancreatic beta cells

Diabetes is characterized by a progressive decline of the pancreatic beta cell mass (BCM), which is responsible for insufficient insulin secretion and hyperglycaemia. There are currently no reliable methods to measure non-invasively the BCM in diabetic patients. Our work describes a phage display-derived peptide (P88) that is highly specific to (FXYD2)γa expressed by human beta cells and is proposed as a molecular vector for the development of functionalized imaging probes. P88 does not bind to the exocrine pancreas and is able to detect down to ~156 human pancreatic islets/mm(3) in vitro after conjugation to ultra-small particles of iron oxide (USPIO), as proven by the R2 measured on MR images. For in vivo evaluation, MRI studies were carried out on nude mice bearing Capan-2 tumours that also express (FXYD2)γa. A strong negative contrast was obtained subsequent to the injection of USPIO-P88, but not in negative controls. On human histological sections, USPIO-P88 seems to be specific to pancreatic beta cells, but not to duodenum, stomach or kidney tissues. USPIO-P88 thus represents a novel and promising tool for monitoring pancreatic BCM in diabetic patients. The quantitative correlation between BCM and R2 remains to be demonstrated in vivo, but the T2 mapping and the black pixel estimation after USPIO-P88 injection could provide important information for the future pancreatic BCM evaluation by MRI.

Unusual reactivity of nitronates with an aryl alkyl carbonate: synthesis of α-amino esters

The monoanions of nitroalkanes are ambident nucleophiles that react with carbonate electrophiles through the oxygen atom. Products arising from reactivity at the carbon atom will yield α-nitro esters, which are precursors for α-amino esters. We demonstrate this in the reactions of nitroalkanes with benzyl phenyl carbonate and DABCO where α-nitro esters are obtained instead of nitrile oxides. The products are readily reduced to α-amino esters. This pathway could be a safe alternative to the Strecker reaction.

CancerPPD: a database of anticancer peptides and proteins

CancerPPD (http://crdd.osdd.net/raghava/cancerppd/) is a repository of experimentally verified anticancer peptides (ACPs) and anticancer proteins. Data were manually collected from published research articles, patents and from other databases. The current release of CancerPPD consists of 3491 ACP and 121 anticancer protein entries. Each entry provides comprehensive information related to a peptide like its source of origin, nature of the peptide, anticancer activity, N- and C-terminal modifications, conformation, etc. Additionally, CancerPPD provides the information of around 249 types of cancer cell lines and 16 different assays used for testing the ACPs. In addition to natural peptides, CancerPPD contains peptides having non-natural, chemically modified residues and D-amino acids. Besides this primary information, CancerPPD stores predicted tertiary structures as well as peptide sequences in SMILES format. Tertiary structures of peptides were predicted using the state-of-art method, PEPstr and secondary structural states were assigned using DSSP. In order to assist users, a number of web-based tools have been integrated, these include keyword search, data browsing, sequence and structural similarity search. We believe that CancerPPD will be very useful in designing peptide-based anticancer therapeutics.

Selection of peptide inhibitors for double-stranded RNA-dependent protein kinase PKR

Protein kinase inhibitors have been developed and applied as antitumor drugs. The majority of these inhibitors are derived from ATP analogs with limited specificity towards the kinase target. Here we present our proof-of-principle study on peptide inhibitors for kinases. Two peptides were selected by phage display against double-stranded RNA-dependent protein kinase (PKR). In vitro assay revealed that these peptides exhibit an inhibitory effect on PKR-catalyzed phosphorylation of the alpha subunit of eukaryotic initiation factor 2 (eIF2α). The peptides also interrupt PKR activity in cells infected by viruses, as PKR activation is one of the hallmarks of host response to viral infection. Kinetic study revealed that one of the peptides, named P1, is a competitive inhibitor for PKR, while the other, named P2, exhibits a more complicated pattern of inhibition on PKR activity. Fragment-based docking of the PKR-peptide complex suggests that P1 occupies the substrate pocket of PKR and thus inhibits the binding between PKR and eIF2α, whereas P2 sits near the substrate pocket. The computational model of PKR-peptide complex agrees with their kinetic behavior. We surmise that peptide inhibitors for kinases have higher specificity than ATP analogs, and that they provide promising leads for the optimization of kinase inhibitors.

Oncogenic protein interfaces: small molecules, big challenges

Historically, targeting protein-protein interactions with small molecules was not thought possible because the corresponding interfaces were considered mostly flat and featureless and therefore 'undruggable'. Instead, such interactions were targeted with larger molecules, such as peptides and antibodies. However, the past decade has seen encouraging breakthroughs through the refinement of existing techniques and the development of new ones, together with the identification and exploitation of unexpected aspects of protein-protein interaction surfaces. In this Review, we describe some of the latest techniques to discover modulators of protein-protein interactions and how current drug discovery approaches have been adapted to successfully target these interfaces.