Unraveling the bioactivity of anticancer peptides as deduced from machine learning

Advancing Peptide-Based Cancer Therapy with AI: In-Depth Analysis of State-of-the-Art AI Models

Anticancer peptides (ACPs) play a vital role in selectively targeting and eliminating cancer cells. Evaluating and comparing predictions from various machine learning (ML) and deep learning (DL) techniques is challenging but crucial for anticancer drug research. We conducted a comprehensive analysis of 15 ML and 10 DL models, including the models released after 2022, and found that support vector machines (SVMs) with feature combination and selection significantly enhance overall performance. DL models, especially convolutional neural networks (CNNs) with light gradient boosting machine (LGBM) based feature selection approaches, demonstrate improved characterization. Assessment using a new test data set (ACP10) identifies ACPred, MLACP 2.0, AI4ACP, mACPred, and AntiCP2.0_AAC as successive optimal predictors, showcasing robust performance. Our review underscores current prediction tool limitations and advocates for an omnidirectional ACP prediction framework to propel ongoing research.

Integrating Traditional Machine Learning and Deep Learning for Precision Screening of Anticancer Peptides: A Novel Approach for Efficient Drug Discovery

The rapid and effective identification of anticancer peptides (ACPs) by computer technology provides a new perspective for cancer treatment. In the identification process of ACPs, accurate sequence encoding and effective classification models are crucial for predicting their biological activity. Traditional machine learning methods have been widely applied in sequence analysis, but deep learning provides a new approach to capture sequence complexity. In this study, a two-stage ACPs classification model was innovatively proposed. Three novel coding strategies were explored; two mainstream Natural Language Processing (NLP) models and 11 machine learning models were fused to identify ACPs, which significantly improved the prediction accuracy of ACPs. We analyzed the correlation between peptide chain amino acids and evaluated the relevant performance of the model by the ROC curve and t-SNE dimensionality reduction technique. The results indicated that the deep learning and machine learning fusion models of M3E-base and KNeighborsDist models, especially when considering the semantic information on amino acid sequences, achieved the highest average accuracy (AvgAcc) of 0.939, with an AUC value as high as 0.97. Then, in vitro cell experiments were used to verify that the two ACPs predicted by the model had antitumor efficacy. This study provides a convenient and effective method for screening ACPs. With further optimization and testing, these strategies have the potential to play an important role in drug discovery and design.

Peptidome Profiling of Bubalus bubalis Urine and Assessment of Its Antimicrobial Activity against Mastitis-Causing Pathogens

Urinary proteins have been studied quite exhaustively in the past, however, the small sized peptides have remained neglected for a long time in dairy cattle. These peptides are the products of systemic protein turnover, which are excreted out of the body and hence can serve as an important biomarker for various pathophysiologies. These peptides in other species of bovine have been reported to possess several bioactive properties. To investigate the urinary peptides in buffalo and simultaneously their bioactivities, we generated a peptidome profile from the urine of Murrah Buffaloes (n = 10). Urine samples were processed using <10 kDa MWCO filter and filtrate obtained was used for peptide extraction using Solid Phase Extraction (SPE). The nLC-MS/MS of the aqueous phase from ten animals resulted in the identification of 8165 peptides originating from 6041 parent proteins. We further analyzed these peptide sequences to identify bioactive peptides and classify them into anti-cancerous, anti-hypertensive, anti-microbial, and anti-inflammatory groups with a special emphasis on antimicrobial properties. With this in mind, we simultaneously conducted experiments to evaluate the antimicrobial properties of urinary aqueous extract on three pathogenic bacterial strains: S. aureus, E. coli, and S. agalactiae. The urinary peptides observed in the study are the result of the activity of possibly 76 proteases. The GO of these proteases showed the significant enrichment of the antibacterial peptide production. The total urinary peptide showed antimicrobial activity against the aforementioned pathogenic bacterial strains with no significant inhibitory effects against a buffalo mammary epithelial cell line. Just like our previous study in cows, the present study suggests the prime role of the antimicrobial peptides in the maintenance of the sterility of the urinary tract in buffalo by virtue of their amino acid composition.

Bionanofactory for green synthesis of collagen nanoparticles, characterization, optimization, in-vitro and in-vivo anticancer activities

Collagen nanoparticles (collagen-NPs) are promising biological polymer nanoparticles due to their exceptional biodegradability and biocompatibility. Collagen-NPs were bio-fabricated from pure marine collagen using the cell-free supernatant of a newly isolated strain, Streptomyces sp. strain NEAA-3. Streptomyces sp. strain NEAA-3 was identified as Streptomyces plicatus strain NEAA-3 based on its cultural, morphological, physiological properties and 16S rRNA sequence analysis. The sequence data has been deposited under accession number OR501412.1 in the GenBank database. The face-centered central composite design (FCCD) was used to improve collagen-NPs biosynthesis. The maximum yield of collagen-NPs was 9.33 mg/mL with a collagen concentration of 10 mg/mL, an initial pH of 7, an incubation time of 72 h, and a temperature of 35 °C. Using the desirability function approach, the collagen-NPs biosynthesis obtained after FCCD optimization (9.53 mg/mL) was 3.92 times more than the collagen-NPs biosynthesis obtained before optimization process (2.43 mg/mL). The TEM analysis of collagen-NPs revealed hollow sphere nanoscale particles with an average diameter of 33.15 ± 10.02 nm. FTIR spectra confirmed the functional groups of the collagen, collagen-NPs and the cell-free supernatant that are essential for the efficient capping of collagen-NPs. The biosynthesized collagen-NPs exhibited antioxidant activity and anticancer activity against HeP-G2, MCF-7 and HCT116 cell lines. Collagen-NPs assessed as an effective drug loading carrier with methotrexate (MTX), a chemotherapeutic agent. The TEM analysis revealed that the average size of MTX-loaded collagen-NPs was 35.4 ± 8.9 nm. The percentages of drug loading (DL%) and encapsulation efficiency (EE%) were respectively 22.67 and 45.81%.

Physical-Chemical Approach to Designing Drugs with Multiple Targets

Many people simultaneously exhibit multiple diseases, which complicates efficient medical treatments. For example, patients with cancer are frequently susceptible to infections. However, developing drugs that could simultaneously target several diseases is challenging. We present a novel theoretical method to assist in selecting compounds with multiple therapeutic targets. The idea is to find correlations between the physical and chemical properties of drug molecules and their abilities to work against multiple targets. As a first step, we investigated potential drugs against cancer and viral infections. Specifically, we investigated antimicrobial peptides (AMPs), which are short positively charged biomolecules produced by living systems as a part of their immune defense. AMPs show anticancer and antiviral activity. We use chemoinformatics and correlation analysis as a part of the machine-learning method to identify the specific properties that distinguish AMPs with dual anticancer and antiviral activities. Physical-chemical arguments to explain these observations are presented.

Exploratory data analysis of physicochemical parameters of natural antimicrobial and anticancer peptides: Unraveling the patterns and trends for the rational design of novel peptides

Introduction

Peptide-based research has attained new avenues in the antibiotics and cancer drug resistance era. The basis of peptide design research lies in playing with or altering physicochemical parameters. Here in this work, we have done exploratory data analysis (EDA) of physicochemical parameters of antimicrobial peptides (AMPs) and anticancer peptides (ACPs), two promising therapeutics for microbial and cancer drug resistance to deduce patterns and trends.

Methods

Briefly, we have captured the natural AMPs and ACPs data from the APD3 database. After cleaning the data manually and by CD-HIT web server, further data analysis has been done using Python-based packages, modlAMP and Pandas. We have extracted the descriptive statistics of 10 physicochemical parameters of AMPs and ACPs to build a comprehensive dataset containing all major parameters. The global analysis of datasets has been done using modlAMP to find the initial patterns in global data. The subsets of AMPs and ACPs were curated based on the length of the peptides and were analyzed by Pandas package to deduce the graphical profile of AMPs and ACPs.

Results

EDA of AMPs and ACPs shows selectivity in the length and amino acid compositions. The distribution of physicochemical parameters in defined quartile ranges was observed in the descriptive statistical and graphical analysis. The preferred length range of AMPs and ACPs was found to be 21-30 amino acids, whereas few outliers in each parameter were evident after EDA analysis.

Conclusion

The derived patterns from natural AMPs and ACPs can be used for the rational design of novel peptides. The statistical and graphical data distribution findings will help in combining the different parameters for potent design of novel AMPs and ACPs.

Tuna trimmings (Thunnas albacares) hydrolysate alleviates immune stress and intestinal mucosal injury during chemotherapy on mice and identification of potentially active peptides

In this study, Tuna trimmings (Thunnas albacares) protein hydrolysate (TPA) was produced by alcalase. The anti-tumor synergistic effect and intestinal mucosa protective effect of TPA on S180 tumor-bearing mice treated with 5-fluorouracil (5-FU) chemotherapy were investigated. The results showed that TPA can enhance the anti-tumor effect of 5-FU chemotherapy, as evident by a significant reduction in tumor volume observed in the medium and high dose TPA+5-FU groups compared to the 5-FU group (p < 0.001). Moreover, TPA significantly elevated the content of total protein and albumin in all TPA dose groups (p < 0.01, p < 0.001), indicating its ability to regulate the nutritional status of the mice. Furthermore, histopathological studies revealed a significant increase in the height of small intestinal villi, crypt depth, mucosal thickness, and villi area in the TPA+5-FU groups compared to the 5-FU group (p < 0.05), suggesting that TPA has a protective effect on the intestinal mucosa. Amino acid analysis revealed that TPA had a total amino acid content of 66.30 g/100 g, with essential amino acids accounting for 30.36 g/100 g. Peptide molecular weight distribution analysis of TPA indicated that peptides ranging from 0.25 to 1 kDa constituted 64.54%. LC-MS/MS analysis identified 109 peptide sequences, which were predicted to possess anti-cancer and anti-inflammatory activities through database prediction. Therefore, TPA has the potential to enhance the antitumor effects of 5-FU, mitigate immune depression and intestinal mucosal damage induced by 5-FU. Thus, TPA could be serve as an adjuvant nutritional support for malnourished patients undergoing chemotherapy.

Proteome of monocled cobra (Naja kaouthia) venom and potent anti breast cancer peptide from trypsin hydrolyzate of the venom protein

Anticancer peptide is one of the target in the development of new anticancer drug. Bioactive peptide can be originated from isolated free peptide or produced by hydrolysis of protein. Protein is the main component of Naja kaouthia venom, and due to the toxicity of the venom, it can be assessed as the source of anticancer peptides. This study aims to characterize the venom protein and to identify peptides from the snake venom of N. kaouthia as anticancer. Proteome analysis was employed trypsin hydrolysis of N. kaouthia venom protein completed with HRMS analysis protein database query. Preparative tryptic hydrolysis of the protein followed by reverse-phased fractionation and anti breast cancer activity testing were performed to identify the potent anticancer from the hydrolysate. Proteomic analysis by high-resolution mass spectrometry revealed that there are 20 enzymatic and non-enzymatic proteins in N. kaouthia venom. The 25% methanol peptide fraction had the most active anticancer activity against MCF-7 breast cancer cells and showed promising selectivity (selectivity index = 12.87). Amino acid sequences of eight peptides were identified as potentially providing anticancer compounds. Molecular docking analysis showed that WWSDHR and IWDTIEK peptides gave specific interactions and better binding affinity energy with values of -9.3 kcal/mol and -8.4 kcal/mol, respectively. This study revealed peptides from the snake venom of N. kaouthia became a potent source of new anticancer agents.

Strategic Approaches to Improvise Peptide Drugs as Next Generation Therapeutics

In recent years, the occurrence of a wide variety of drug-resistant diseases has led to an increase in interest in alternate therapies. Peptide-based drugs as an alternate therapy hold researchers' attention in various therapeutic fields such as neurology, dermatology, oncology, metabolic diseases, etc. Previously, they had been overlooked by pharmaceutical companies due to certain limitations such as proteolytic degradation, poor membrane permeability, low oral bioavailability, shorter half-life, and poor target specificity. Over the last two decades, these limitations have been countered by introducing various modification strategies such as backbone and side-chain modifications, amino acid substitution, etc. which improve their functionality. This has led to a substantial interest of researchers and pharmaceutical companies, moving the next generation of these therapeutics from fundamental research to the market. Various chemical and computational approaches are aiding the production of more stable and long-lasting peptides guiding the formulation of novel and advanced therapeutic agents. However, there is not a single article that talks about various peptide design approaches i.e., in-silico and in-vitro along with their applications and strategies to improve their efficacy. In this review, we try to bring different aspects of peptide-based therapeutics under one article with a clear focus to cover the missing links in the literature. This review draws emphasis on various in-silico approaches and modification-based peptide design strategies. It also highlights the recent progress made in peptide delivery methods important for their enhanced clinical efficacy. The article would provide a bird's-eye view to researchers aiming to develop peptides with therapeutic applications.

Graphical Abstract

Changes in Length and Positive Charge of Palindromic Sequence RWQWRWQWR Enhance Cytotoxic Activity against Breast Cancer Cell Lines

Breast cancer is one of the main causes of premature death in women; current treatments have low selectivity, generating strong physical and psychological sequelae. The palindromic peptide R-1-R (RWQWRWQWR) has cytotoxic activity against different cell lines derived from cancer and selectivity against noncancerous cells. To determine if changes in the charge/length of this peptide increase its activity, six peptides were obtained by SPPS, three of them with addition of Arg at the N, C-terminal or both and three with deletion of Arg at the N, C-terminal or both. The cytotoxic and selective activities were evaluated against MCF-7, MDA-MB-231, and MCF-12 cell lines and fibroblast primary cell culture, evidencing that the RR-1-R peptide with the inclusion of Arg in the N-terminal end maintained selectivity and increased cytotoxicity against lines derived from breast cancer. The effect of this addition regarding the type of induced cell death was evaluated by flow cytometry, showing very low rates of necrosis and a significant majority of apoptotic events with activation of both Caspase 8 and Caspase 9. This work allowed us to find a modification that generates a peptide with greater cytotoxic effects and can be considered a promising molecule for other approaches to improve anticancer peptides.

A Concise Review on the Role of Natural and Synthetically Derived Peptides in Colorectal Cancer

Colorectal cancer being the second leading cause of cancer-associated deaths has become a significant health concern around the globe. Though there are various cancer treatment approaches, many of them show adverse effects and some compromise the health of cancer patients. Hence, significant efforts are being made for the evolution of a novel biological therapeutic approach with better efficacy and minimal side effects. Current research suggests that the application of peptides in colorectal cancer therapeutics holds the possibility of the emergence of an anticancer reagent. The primary beneficial factors of peptides are their comparatively rapid and easy process of synthesis and the enormous potential for chemical alterations that can be evaluated for designing novel peptides and enhancing the delivery capacity of peptides. Peptides might be utilized as agents with cytotoxic activities or as a carrier of a specific drug or as cytotoxic agents that can efficiently target the tumor cells. Further, peptides can also be used as a tool for diagnostic purposes. The recent analysis aims at developing peptides that have the potential to efficiently target the tumor moieties without harming the nearby normal cells. Additionally, decreasing the adverse effects, and unfolding the other therapeutic properties of potential peptides, are also the subject matter of in-depth analysis. This review provides a concise summary of the function of both natural and synthetically derived peptides in colorectal cancer therapeutics that are recently being evaluated and their potent applications in the clinical field.

Pseudanabaena galeata CCNP1313—Biological Activity and Peptides Production

Even cyanobacteria from ecosystems of low biodiversity, such as the Baltic Sea, can constitute a rich source of bioactive metabolites. Potent toxins, enzyme inhibitors, and anticancer and antifungal agents were detected in both bloom-forming species and less commonly occurring cyanobacteria. In previous work on the Baltic Pseudanabaena galeata CCNP1313, the induction of apoptosis in the breast cancer cell line MCF-7 was documented. Here, the activity of the strain was further explored using human dermal fibroblasts, African green monkey kidney, cancer cell lines (T47D, HCT-8, and A549ACE2/TMPRSS2) and viruses (SARS-CoV-2, HCoV-OC43, and WNV). In the tests, extracts, chromatographic fractions, and the main components of the P. galeata CCNP1313 fractions were used. The LC-MS/MS analyses of the tested samples led to the detection of forty-five peptides. For fourteen of the new peptides, putative structures were proposed based on MS/MS spectra. Although the complex samples (i.e., extracts and chromatographic fractions) showed potent cytotoxic and antiviral activities, the effects of the isolated compounds were minor. The study confirmed the significance of P. galeata CCNP1313 as a source of metabolites with potent activity. It also illustrated the difficulties in assigning the observed biological effects to specific metabolites, especially when they are produced in minute amounts.

A Novel Network Science and Similarity-Searching-Based Approach for Discovering Potential Tumor-Homing Peptides from Antimicrobials

Peptide-based drugs are promising anticancer candidates due to their biocompatibility and low toxicity. In particular, tumor-homing peptides (THPs) have the ability to bind specifically to cancer cell receptors and tumor vasculature. Despite their potential to develop antitumor drugs, there are few available prediction tools to assist the discovery of new THPs. Two webservers based on machine learning models are currently active, the TumorHPD and the THPep, and more recently the SCMTHP. Herein, a novel method based on network science and similarity searching implemented in the starPep toolbox is presented for THP discovery. The approach leverages from exploring the structural space of THPs with Chemical Space Networks (CSNs) and from applying centrality measures to identify the most relevant and non-redundant THP sequences within the CSN. Such THPs were considered as queries (Qs) for multi-query similarity searches that apply a group fusion (MAX-SIM rule) model. The resulting multi-query similarity searching models (SSMs) were validated with three benchmarking datasets of THPs/non-THPs. The predictions achieved accuracies that ranged from 92.64 to 99.18% and Matthews Correlation Coefficients between 0.894–0.98, outperforming state-of-the-art predictors. The best model was applied to repurpose AMPs from the starPep database as THPs, which were subsequently optimized for the TH activity. Finally, 54 promising THP leads were discovered, and their sequences were analyzed to encounter novel motifs. These results demonstrate the potential of CSNs and multi-query similarity searching for the rapid and accurate identification of THPs.

SCORPION is a stacking-based ensemble learning framework for accurate prediction of phage virion proteins

Fast and accurate identification of phage virion proteins (PVPs) would greatly aid facilitation of antibacterial drug discovery and development. Although, several research efforts based on machine learning (ML) methods have been made for in silico identification of PVPs, these methods have certain limitations. Therefore, in this study, we propose a new computational approach, termed SCORPION, (StaCking-based Predictior fOR Phage VIrion PrOteiNs), to accurately identify PVPs using only protein primary sequences. Specifically, we explored comprehensive 13 different feature descriptors from different aspects (i.e., compositional information, composition-transition-distribution information, position-specific information and physicochemical properties) with 10 popular ML algorithms to construct a pool of optimal baseline models. These optimal baseline models were then used to generate probabilistic features (PFs) and considered as a new feature vector. Finally, we utilized a two-step feature selection strategy to determine the optimal PF feature vector and used this feature vector to develop a stacked model (SCORPION). Both tenfold cross-validation and independent test results indicate that SCORPION achieves superior predictive performance than its constitute baseline models and existing methods. We anticipate SCORPION will serve as a useful tool for the cost-effective and large-scale screening of new PVPs. The source codes and datasets for this work are available for downloading in the GitHub repository (https://github.com/saeed344/SCORPION).

Anticancer peptide Q7 suppresses the growth and migration of human endometrial cancer by inhibiting DHCR24 expression and modulating the AKT-mediated pathway

Endometrial cancer is one of the most common malignancy affecting women in developed countries. Resection uterus or lesion area is usually the first option for a simple and efficient therapy. Therefore, it is necessary to find a new therapeutic drug to reduce surgery areas to preserve fertility. Anticancer peptides (ACP) are bioactive amino acids with lower toxicity and higher specificity than chemical drugs. This study is to address an ACP, herein named Q7, which could downregulate 24-Dehydrocholesterol Reductase (DHCR24) to disrupt lipid rafts formation, and sequentially affect the AKT signal pathway of HEC-1-A cells to suppress their tumorigenicity such as proliferation and migration. Moreover, lipo-PEI-PEG-complex (LPPC) was used to enhance Q7 anticancer activity in vitro and efficiently show its effects on HEC-1-A cells. Furthermore, LPPC-Q7 exhibited a synergistic effect in combination with doxorubicin or paclitaxel. To summarize, Q7 was firstly proved to exhibit an anticancer effect on endometrial cancer cells and combined with LPPC efficiently improved the cytotoxicity of Q7.

Computational study, synthesis and evaluation of active peptides derived from Parasporin-2 and spike protein from Alphacoronavirus against colorectal cancer cells

Parasporin-2Aa1 (PS2Aa1) is a toxic protein of 37 KDa (30 kDa, activated form produced by proteolysis) that was shown to be cytotoxic against specific human cancer cells, although its mechanism of action has not been elucidated yet. In order to study the role of some native peptide fragments of proteins on anticancer activity, here we investigated the cytotoxic effect of peptide fragments from domain-1 of PS2Aa1 and one of the loops present in the binding region of the virus spike protein from Alphacoronavirus (HCoV-229E), the latter according to scientific reports, who showed interaction with the human APN (h-APN) receptor, evidence corroborated through computational simulations, and thus being possible active against colon cancer cells. Peptides namely P264-G274, Loop1-PS2Aa, and Loop2-PS2Aa were synthesized using the Fmoc solid-phase synthesis and characterized by mass spectrometry (MS). Additionally, one region from loop 1 of HCoV-229E, Loop1-HCoV-229E, was also synthesized and characterized. The A4W-GGN5 anticancer peptide and 5-fluorouracil (5-FU) were taken as a control in all experiments. Circular dichroism revealed an α-helix structure for the peptides derived from PS2Aa1 (P264-G274, Loop1-PS2Aa, and Loop2-PS2Aa) and β-laminar structure for the peptide derived from Alphacoronavirus spike protein Loop1-HCoV-229E. Peptides showed a hemolysis percentage of less than 20% at 100 µM concentration. Besides, peptides exhibited stronger anticancer activity against SW480 and SW620 cells after exposure for 48 h. Likewise, these compounds showed significantly lower toxicity against normal cells CHO-K1. The results suggest that native peptide fragments from Ps2Aa1 may be optimized as a novel potential cancer-therapeutic agents.

A novel sequence-based predictor for identifying and characterizing thermophilic proteins using estimated propensity scores of dipeptides

Owing to their ability to maintain a thermodynamically stable fold at extremely high temperatures, thermophilic proteins (TTPs) play a critical role in basic research and a variety of applications in the food industry. As a result, the development of computation models for rapidly and accurately identifying novel TTPs from a large number of uncharacterized protein sequences is desirable. In spite of existing computational models that have already been developed for characterizing thermophilic proteins, their performance and interpretability remain unsatisfactory. We present a novel sequence-based thermophilic protein predictor, termed SCMTPP, for improving model predictability and interpretability. First, an up-to-date and high-quality dataset consisting of 1853 TPPs and 3233 non-TPPs was compiled from published literature. Second, the SCMTPP predictor was created by combining the scoring card method (SCM) with estimated propensity scores of g-gap dipeptides. Benchmarking experiments revealed that SCMTPP had a cross-validation accuracy of 0.883, which was comparable to that of a support vector machine-based predictor (0.906-0.910) and 2-17% higher than that of commonly used machine learning models. Furthermore, SCMTPP outperformed the state-of-the-art approach (ThermoPred) on the independent test dataset, with accuracy and MCC of 0.865 and 0.731, respectively. Finally, the SCMTPP-derived propensity scores were used to elucidate the critical physicochemical properties for protein thermostability enhancement. In terms of interpretability and generalizability, comparative results showed that SCMTPP was effective for identifying and characterizing TPPs. We had implemented the proposed predictor as a user-friendly online web server at http://pmlabstack.pythonanywhere.com/SCMTPP in order to allow easy access to the model. SCMTPP is expected to be a powerful tool for facilitating community-wide efforts to identify TPPs on a large scale and guiding experimental characterization of TPPs.

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/ .

The Synthesis of Conjugated Peptides Containing Triazole and Quinolone-3-Carboxamide Moieties Designed as Anticancer Agents

Background

Cancer is a major health concern in human populations worldwide, and due to its causes being multi-factorial, it is not easily curable. Many attempts have been made to tackle this disease in hopes of finding effective anticancer agents which are not harmful to healthy tissues. Peptides with several medicinal activities have been shown to be good candidates as anticancer agents to replace common classic anticancer drugs. Peptides in conjugation with either biologically active heterocyclic compounds or anticancer drugs may result in new molecules compiling the biological benefits of both individual compounds within a unit structure.

Objective

In this study some triazole-peptide conjugates as well as ciprofloxacin-peptide conjugates were designed, synthesized, and their anticancer activities evaluated. A normal skin cell line, NIH3, was also employed to determine the safety profiles of these conjugates.

Materials and Methods

Two peptides; YIGSR and LSGNK were synthesized by the solid phase peptide synthesis (SPPS) method using Wang resin. Cell viability was examined by employing the MTT assay. To determine the cytotoxicity of the triazole and ciprofloxacin conjugates, two human cancer cell lines were employed; HepG2 (human liver cancer cell line) and LNCaP (human prostatic carcinoma cell line). A human skin fibroblast cell line was also included for comparison.

Results

MTT results showed that all the compounds could inhibit the viability of cancerous cells in a concentration- dependent manner.

Conclusions

The results showed that these peptide conjugates are toxic against the aforementioned cancerous cells and thus may raise a hope for finding new anticancer agents made by such strategy in the near future.

Virtual Screening for Biomimetic Anti-Cancer Peptides from Cordyceps militaris Putative Pepsinized Peptidome and Validation on Colon Cancer Cell Line

Colorectal cancer is one of the leading causes of cancer-related death in Thailand and many other countries. The standard practice for curing this cancer is surgery with an adjuvant chemotherapy treatment. However, the unfavorable side effects of chemotherapeutic drugs are undeniable. Recently, protein hydrolysates and anticancer peptides have become popular alternative options for colon cancer treatment. Therefore, we aimed to screen and select the anticancer peptide candidates from the in silico pepsin hydrolysate of a Cordyceps militaris (CM) proteome using machine-learning-based prediction servers for anticancer prediction, i.e., AntiCP, iACP, and MLACP. The selected CM-anticancer peptide candidates could be an alternative treatment or co-treatment agent for colorectal cancer, reducing the use of chemotherapeutic drugs. To ensure the anticancer properties, an in vitro assay was performed with "CM-biomimetic peptides" on the non-metastatic colon cancer cell line (HT-29). According to the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay results from peptide candidate treatments at 0-400 µM, the IC₅₀ doses of the CM-biomimetic peptide with no toxic and cancer-cell-penetrating ability, original C. militaris biomimetic peptide (C-ori), against the HT-29 cell line were 114.9 µM at 72 hours. The effects of C-ori compared to the doxorubicin, a conventional chemotherapeutic drug for colon cancer treatment, and the combination effects of both the CM-anticancer peptide and doxorubicin were observed. The results showed that C-ori increased the overall efficiency in the combination treatment with doxorubicin. According to the acridine orange/propidium iodine (AO/PI) staining assay, C-ori can induce apoptosis in HT-29 cells significantly, confirmed by chromatin condensation, membrane blebbing, apoptotic bodies, and late apoptosis which were observed under a fluorescence microscope.

Elucidating the anticancer activities of guanidinium-functionalized amphiphilic random copolymers by varying the structure and composition in the hydrophobic monomer

Rationale: Use of traditional anticancer chemotherapeutics has been hindered by the multifactorial nature of multi-drug resistance (MDR) development and metastasis. Recently, cationic polycarbonates were reported as novel unconventional anticancer agents that mitigated MDR and inhibited metastasis. The aim of this study is to explore structure-anticancer activity relationship. Specifically, a series of cationic guanidinium-based random copolymers of varying hydrophobicity was synthesized with a narrow polydispersity (Ð = 1.12-1.27) via organocatalytic ring-opening polymerization (OROP) of functional cyclic carbonate monomers, and evaluated for anticancer activity, killing kinetics, degradability and functional mechanism. Methods: Linear, branched and aromatic hydrophobic side chain units, such as ethyl, benzyl, butyl, isobutyl and hexyl moieties were explored as comonomer units for modulating anticancer activity. As hydrophobicity/hydrophilicity balance of the polymers determines their anticancer efficacy, the feed ratio between the two monomers was varied to tune their hydrophobicity. Results: Notably, incorporating the hexyl moiety greatly enhanced anticancer efficiency and killing kinetics on cancer cells. Degradation studies showed that the polymers degraded completely within 4-6 days. Flow cytometry and lactate dehydrogenase (LDH) release analyses demonstrated that anticancer mechanism of the copolymers containing a hydrophobic co-monomer was concentration dependent, apoptosis at IC50, and both apoptosis and necrosis at 2 × IC50. In contrast, the homopolymer without a hydrophobic comonomer killed cancer cells predominantly via apoptotic mechanism. Conclusion: The hydrophobicity of the polymers played an important role in anticancer efficacy, killing kinetics and anticancer mechanism. This study provides valuable insights into designing novel anticancer agents utilizing polymers.

Identification of cyclin protein using gradient boost decision tree algorithm

Cyclin proteins are capable to regulate the cell cycle by forming a complex with cyclin-dependent kinases to activate cell cycle. Correct recognition of cyclin proteins could provide key clues for studying their functions. However, their sequences share low similarity, which results in poor prediction for sequence similarity-based methods. Thus, it is urgent to construct a machine learning model to identify cyclin proteins. This study aimed to develop a computational model to discriminate cyclin proteins from non-cyclin proteins. In our model, protein sequences were encoded by seven kinds of features that are amino acid composition, composition of k-spaced amino acid pairs, tri peptide composition, pseudo amino acid composition, geary correlation, normalized moreau-broto autocorrelation and composition/transition/distribution. Afterward, these features were optimized by using analysis of variance (ANOVA) and minimum redundancy maximum relevance (mRMR) with incremental feature selection (IFS) technique. A gradient boost decision tree (GBDT) classifier was trained on the optimal features. Five-fold cross-validated results showed that our model would identify cyclins with an accuracy of 93.06% and AUC value of 0.971, which are higher than the two recent studies on the same data.

Identification of subtypes of anticancer peptides based on sequential features and physicochemical properties

Anticancer peptides (ACPs) are a kind of bioactive peptides which could be used as a novel type of anticancer drug that has several advantages over chemistry-based drug, including high specificity, strong tumor penetration capacity, and low toxicity to normal cells. As the number of experimentally verified bioactive peptides has increased significantly, various of in silico approaches are imperative for investigating the characteristics of ACPs. However, the lack of methods for investigating the differences in physicochemical properties of ACPs. In this study, we compared the N- and C-terminal amino acid composition for each peptide, there are three major subtypes of ACPs that are defined based on the distribution of positively charged residues. For the first time, we were motivated to develop a two-step machine learning model for identification of the subtypes of ACPs, which classify the input data into the corresponding group before applying the classifier. Further, to improve the predictive power, the hybrid feature sets were considered for prediction. Evaluation by five-fold cross-validation showed that the two-step model trained with sequence-based features and physicochemical properties was most effective in discriminating between ACPs and non-ACPs. The two-step model trained with the hybrid features performed well, with a sensitivity of 86.75%, a specificity of 85.75%, an accuracy of 86.08%, and a Matthews Correlation Coefficient value of 0.703. Furthermore, the model also consistently provides the effective performance in independent testing set, with sensitivity of 77.6%, specificity of 94.74%, accuracy of 88.99% and the MCC value reached 0.75. Finally, the two-step model has been implemented as a web-based tool, namely iDACP, which is now freely available at http://mer.hc.mmh.org.tw/iDACP/ .

Copper-binding anticancer peptides from the piscidin family: an expanded mechanism that encompasses physical and chemical bilayer disruption

In the search for novel broad-spectrum therapeutics to fight chronic infections, inflammation, and cancer, host defense peptides (HDPs) have garnered increasing interest. Characterizing their biologically-active conformations and minimum motifs for function represents a requisite step to developing them into efficacious and safe therapeutics. Here, we demonstrate that metallating HDPs with Cu²⁺ is an effective chemical strategy to improve their cytotoxicity on cancer cells. Mechanistically, we find that prepared as Cu²⁺-complexes, the peptides not only physically but also chemically damage lipid membranes. Our testing ground features piscidins 1 and 3 (P1/3), two amphipathic, histidine-rich, membrane-interacting, and cell-penetrating HDPs that are α-helical bound to membranes. To investigate their membrane location, permeabilization effects, and lipid-oxidation capability, we employ neutron reflectometry, impedance spectroscopy, neutron diffraction, and UV spectroscopy. While P1-apo is more potent than P3-apo, metallation boosts their cytotoxicities by up to two- and seven-fold, respectively. Remarkably, P3-Cu²⁺ is particularly effective at inserting in bilayers, causing water crevices in the hydrocarbon region and placing Cu²⁺ near the double bonds of the acyl chains, as needed to oxidize them. This study points at a new paradigm where complexing HDPs with Cu²⁺ to expand their mechanistic reach could be explored to design more potent peptide-based anticancer therapeutics.

Peptide profiling in cow urine reveals molecular signature of physiology-driven pathways and in-silico predicted bioactive properties

Peptidomics allows the identification of peptides that are derived from proteins. Urinary peptidomics has revolutionized the field of diagnostics as the samples represent complete systemic changes happening in the body. Moreover, it can be collected in a non-invasive manner. We profiled the peptides in urine collected from different physiological states (heifer, pregnancy, and lactation) of Sahiwal cows. Endogenous peptides were extracted from 30 individual cows belonging to three groups, each group comprising of ten animals (biological replicates n = 10). Nano Liquid chromatography Mass spectrometry (nLC-MS/MS) experiments revealed 5239, 4774, and 5466 peptides in the heifer, pregnant and lactating animals respectively. Urinary peptides of <10 kDa size were considered for the study. Peptides were extracted by 10 kDa MWCO filter. Sequences were identified by scanning the MS spectra ranging from 200 to 2200 m/z. The peptides exhibited diversity in sequences across different physiological states and in-silico experiments were conducted to classify the bioactive peptides into anti-microbial, anti-inflammatory, anti-hypertensive, and anti-cancerous groups. We have validated the antimicrobial effect of urinary peptides on Staphylococcus aureus and Escherichia coli under an in-vitro experimental set up. The origin of these peptides was traced back to certain proteases viz. MMPs, KLKs, CASPs, ADAMs etc. which were found responsible for the physiology-specific peptide signature of urine. Proteins involved in extracellular matrix structural constituent (GO:0005201) were found significant during pregnancy and lactation in which tissue remodeling is extensive. Collagen trimers were prominent molecules under cellular component category during lactation. Homophilic cell adhesion was found to be an important biological process involved in embryo attachment during pregnancy. The in-silico study also highlighted the enrichment of progenitor proteins on specific chromosomes and their relative expression in context to specific physiology. The urinary peptides, precursor proteins, and proteases identified in the study offers a base line information in healthy cows which can be utilized in biomarker discovery research for several pathophysiological studies.

Prediction of Anticancer Peptides with High Efficacy and Low Toxicity by Hybrid Model Based on 3D Structure of Peptides

Recently, anticancer peptides (ACPs) have emerged as unique and promising therapeutic agents for cancer treatment compared with antibody and small molecule drugs. In addition to experimental methods of ACPs discovery, it is also necessary to develop accurate machine learning models for ACP prediction. In this study, features were extracted from the three-dimensional (3D) structure of peptides to develop the model, compared to most of the previous computational models, which are based on sequence information. In order to develop ACPs with more potency, more selectivity and less toxicity, the model for predicting ACPs, hemolytic peptides and toxic peptides were established by peptides 3D structure separately. Multiple datasets were collected according to whether the peptide sequence was chemically modified. After feature extraction and screening, diverse algorithms were used to build the model. Twelve models with excellent performance (Acc > 90%) in the ACPs mixed datasets were used to form a hybrid model to predict the candidate ACPs, and then the optimal model of hemolytic peptides (Acc = 73.68%) and toxic peptides (Acc = 85.5%) was used for safety prediction. Novel ACPs were found by using those models, and five peptides were randomly selected to determine their anticancer activity and toxic side effects in vitro experiments.

Amino Acid Reduction Can Help to Improve the Identification of Antimicrobial Peptides and Their Functional Activities

Antimicrobial peptides (AMPs) are considered as potential substitutes of antibiotics in the field of new anti-infective drug design. There have been several machine learning algorithms and web servers in identifying AMPs and their functional activities. However, there is still room for improvement in prediction algorithms and feature extraction methods. The reduced amino acid (RAA) alphabet effectively solved the problems of simplifying protein complexity and recognizing the structure conservative region. This article goes into details about evaluating the performances of more than 5,000 amino acid reduced descriptors generated from 74 types of amino acid reduced alphabet in the first stage and the second stage to construct an excellent two-stage classifier, Identification of Antimicrobial Peptides by Reduced Amino Acid Cluster (iAMP-RAAC), for identifying AMPs and their functional activities, respectively. The results show that the first stage AMP classifier is able to achieve the accuracy of 97.21 and 97.11% for the training data set and independent test dataset. In the second stage, our classifier still shows good performance. At least three of the four metrics, sensitivity (SN), specificity (SP), accuracy (ACC), and Matthews correlation coefficient (MCC), exceed the calculation results in the literature. Further, the ANOVA with incremental feature selection (IFS) is used for feature selection to further improve prediction performance. The prediction performance is further improved after the feature selection of each stage. At last, a user-friendly web server, iAMP-RAAC, is established at http://bioinfor.imu.edu. cn/iampraac.

Improved prediction and characterization of anticancer activities of peptides using a novel flexible scoring card method

As anticancer peptides (ACPs) have attracted great interest for cancer treatment, several approaches based on machine learning have been proposed for ACP identification. Although existing methods have afforded high prediction accuracies, however such models are using a large number of descriptors together with complex ensemble approaches that consequently leads to low interpretability and thus poses a challenge for biologists and biochemists. Therefore, it is desirable to develop a simple, interpretable and efficient predictor for accurate ACP identification as well as providing the means for the rational design of new anticancer peptides with promising potential for clinical application. Herein, we propose a novel flexible scoring card method (FSCM) making use of propensity scores of local and global sequential information for the development of a sequence-based ACP predictor (named iACP-FSCM) for improving the prediction accuracy and model interpretability. To the best of our knowledge, iACP-FSCM represents the first sequence-based ACP predictor for rationalizing an in-depth understanding into the molecular basis for the enhancement of anticancer activities of peptides via the use of FSCM-derived propensity scores. The independent testing results showed that the iACP-FSCM provided accuracies of 0.825 and 0.910 as evaluated on the main and alternative datasets, respectively. Results from comparative benchmarking demonstrated that iACP-FSCM could outperform seven other existing ACP predictors with marked improvements of 7% and 17% for accuracy and MCC, respectively, on the main dataset. Furthermore, the iACP-FSCM (0.910) achieved very comparable results to that of the state-of-the-art ensemble model AntiCP2.0 (0.920) as evaluated on the alternative dataset. Comparative results demonstrated that iACP-FSCM was the most suitable choice for ACP identification and characterization considering its simplicity, interpretability and generalizability. It is highly anticipated that the iACP-FSCM may be a robust tool for the rapid screening and identification of promising ACPs for clinical use.

Development of an engineered peptide antagonist against periostin to overcome doxorubicin resistance in breast cancer

Background

Chemoresistance is one of the main problems in treatment of cancer. Periostin (PN) is a stromal protein which is mostly secreted from cancer associated fibroblasts in the tumor microenvironment and can promote cancer progression including cell survival, metastasis, and chemoresistance. The main objective of this study was to develop an anti-PN peptide from the bacteriophage library to overcome PN effects in breast cancer (BCA) cells.

Methods

A twelve amino acids bacteriophage display library was used for biopanning against the PN active site. A selected clone was sequenced and analyzed for peptide primary structure. A peptide was synthesized and tested for the binding affinity to PN. PN effects including a proliferation, migration and a drug sensitivity test were performed using PN overexpression BCA cells or PN treatment and inhibited by an anti-PN peptide. An intracellular signaling mechanism of inhibition was studied by western blot analysis. Lastly, PN expressions in BCA patients were analyzed along with clinical data.

Results

The results showed that a candidate anti-PN peptide was synthesized and showed affinity binding to PN. PN could increase proliferation and migration of BCA cells and these effects could be inhibited by an anti-PN peptide. There was significant resistance to doxorubicin in PN-overexpressed BCA cells and this effect could be reversed by an anti-PN peptide in associations with phosphorylation of AKT and expression of survivin. In BCA patients, serum PN showed a correlation with tissue PN expression but there was no significant correlation with clinical data.

Conclusions

This finding supports that anti-PN peptide is expected to be used in the development of peptide therapy to reduce PN-induced chemoresistance in BCA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-020-07761-w.

Cancer Treatment by Caryophyllaceae-Type Cyclopeptides

Cancer is one of the leading diseases, which, in the most cases, ends with death and, thus, continues to be a major concern in human beings worldwide. The conventional anticancer agents used in the clinic often face resistance among many cancer diseases. Moreover, heavy financial costs preclude patients from continuing treatment. Bioactive peptides, active in several diverse areas against man's health problems, such as infection, pain, hypertension, and so on, show the potential to be effective in cancer treatment and may offer promise as better candidates for combating cancer. Cyclopeptides, of natural or synthetic origin, have several advantages over other drug molecules with low toxicity and low immunogenicity, and they are easily amenable to several changes in their sequences. Given their many demanded homologues, they have created new hope of discovering better compounds with desired properties in the field of challenging cancer diseases. Caryophyllaceae-type cyclopeptides show several biological activities, including cancer cytotoxicity. These cyclopeptides have been discovered in several plant families but mainly are from the Caryophyllaceae family. In this review, a summary of biological activities found for these cyclopeptides is given; the focus is on the anticancer findings of these peptides. Among these cyclopeptides, information about Dianthins (including Longicalycinin A), isolated from different species of Caryophyllaceae, as well as their synthetic analogues is detailed. Finally, by comparing their structures and cytotoxic activities, finding the common figures of these kinds of cyclopeptides as well as their possible future place in the clinic for cancer treatment is put forward.

Caprine Bactenecins as Promising Tools for Developing New Antimicrobial and Antitumor Drugs

Proline-rich antimicrobial peptides (PR-AMPs) having a potent antimicrobial activity predominantly toward Gram-negative bacteria and negligible toxicity toward host cells, are attracting attention as new templates for developing antibiotic drugs. We have previously isolated and characterized several bactenecins that are promising in this respect, from the leukocytes of the domestic goat Capra hircus: ChBac5, miniChBac7.5N-α, and -β, as well as ChBac3.4. Unlike the others, ChBac3.4 shows a somewhat unusual pattern of activities for a mammalian PR-AMP: it is more active against bacterial membranes as well as tumor and, to the lesser extent, normal cells. Here we describe a SAR study of ChBac3.4 (RFRLPFRRPPIRIHPPPFYPPFRRFL-NH2) which elucidates its peculiarities and evaluates its potential as a lead for antimicrobial or anticancer drugs based on this peptide. A set of designed structural analogues of ChBac3.4 was explored for antibacterial activity toward drug-resistant clinical isolates and antitumor properties. The N-terminal region was found to be important for the antimicrobial action, but not responsible for the toxicity toward mammalian cells. A shortened variant with the best selectivity index toward bacteria demonstrated a pronounced synergy in combination with antibiotics against Gram-negative strains, albeit with a somewhat reduced ability to inhibit biofilm formation compared to native peptide. C-terminal amidation was examined for some analogues, which did not affect antimicrobial activity, but somewhat altered the cytotoxicity toward host cells. Interestingly, non-amidated peptides showed a slight delay in their impact on bacterial membrane integrity. Peptides with enhanced hydrophobicity showed increased toxicity, but in most cases their selectivity toward tumor cells also improved. While most analogues lacked hemolytic properties, a ChBac3.4 variant with two additional tryptophan residues demonstrated an appreciable activity toward human erythrocytes. The variant demonstrating the best tumor/nontumor cell selectivity was found to more actively initiate apoptosis in target cells, though its action was slower than that of the native ChBac3.4. Its antitumor effectiveness was successfully verified in vivo in a murine Ehrlich ascites carcinoma model. The obtained results demonstrate the potential of structural modification to manage caprine bactenecins’ selectivity and activity spectrum and confirm that they are promising prototypes for antimicrobial and anticancer drugs design.

Machine learning-guided discovery and design of non-hemolytic peptides

Reducing hurdles to clinical trials without compromising the therapeutic promises of peptide candidates becomes an essential step in peptide-based drug design. Machine-learning models are cost-effective and time-saving strategies used to predict biological activities from primary sequences. Their limitations lie in the diversity of peptide sequences and biological information within these models. Additional outlier detection methods are needed to set the boundaries for reliable predictions; the applicability domain. Antimicrobial peptides (AMPs) constitute an extensive library of peptides offering promising avenues against antibiotic-resistant infections. Most AMPs present in clinical trials are administrated topically due to their hemolytic toxicity. Here we developed machine learning models and outlier detection methods that ensure robust predictions for the discovery of AMPs and the design of novel peptides with reduced hemolytic activity. Our best models, gradient boosting classifiers, predicted the hemolytic nature from any peptide sequence with 95–97% accuracy. Nearly 70% of AMPs were predicted as hemolytic peptides. Applying multivariate outlier detection models, we found that 273 AMPs (~ 9%) could not be predicted reliably. Our combined approach led to the discovery of 34 high-confidence non-hemolytic natural AMPs, the de novo design of 507 non-hemolytic peptides, and the guidelines for non-hemolytic peptide design.

Peptides with Dual Antimicrobial-Anticancer Activity: Strategies to Overcome Peptide Limitations and Rational Design of Anticancer Peptides

Peptides are naturally produced by all organisms and exhibit a wide range of physiological, immunomodulatory, and wound healing functions. Furthermore, they can provide with protection against microorganisms and tumor cells. Their multifaceted performance, high selectivity, and reduced toxicity have positioned them as effective therapeutic agents, representing a positive economic impact for pharmaceutical companies. Currently, efforts have been made to invest in the development of new peptides with antimicrobial and anticancer properties, but the poor stability of these molecules in physiological environments has triggered a bottleneck. Therefore, some tools, such as nanotechnology and in silico approaches can be applied as alternatives to try to overcome these obstacles. In silico studies provide a priori knowledge that can lead to the development of new anticancer peptides with enhanced biological activity and improved stability. This review focuses on the current status of research in peptides with dual antimicrobial-anticancer activity, including advances in computational biology using in silico analyses as a powerful tool for the study and rational design of these types of peptides.

Peptide Fibrillar Assemblies Exhibit Membranolytic Effects and Antimetastatic Activity on Lung Cancer Cells

Cancer metastasis is a central oncology concern that worsens patient conditions and increases mortality in a short period of time. During metastatic events, mitochondria undergo specific physiological alterations that have emerged as notable therapeutic targets to counter cancer progression. In this study, we use drug-free, cationic peptide fibrillar assemblies (PFAs) formed by poly(L-Lysine)-block-poly(L-Threonine) (Lys-b-Thr) to target mitochondria. These PFAs interact with cellular and mitochondrial membranes via electrostatic interactions, resulting in membranolysis. Charge repulsion and hydrogen-bonding interactions exerted by Lys and Thr segments dictate the packing of the peptides and enable the PFAs to display enhanced membranolytic activity toward cancer cells. Cytochrome c (cyt c), endonuclease G, and apoptosis-inducing factor were released from mitochondria after treatment of lung cancer cells, subsequently inducing caspase-dependent and caspase-independent apoptotic pathways. A metastatic xenograft mouse model was used to show how the PFAs significantly suppressed lung metastasis and inhibited tumor growth, while avoiding significant body weight loss and mortality. Antimetastatic activities of PFAs are also demonstrated by in vitro inhibition of lung cancer cell migration and clonogenesis. Our results imply that the cationic PFAs achieved the intended and targeted mitochondrial damage, providing an efficient antimetastatic therapy.

To What Extent Do Fluorophores Bias the Biological Activity of Peptides? A Practical Approach Using Membrane-Active Peptides as Models

The characterization of biologically active peptides relies heavily on the study of their efficacy, toxicity, mechanism of action, cellular uptake, or intracellular location, using both in vitro and in vivo studies. These studies frequently depend on the use of fluorescence-based techniques. Since most peptides are not intrinsically fluorescent, they are conjugated to a fluorophore. The conjugation may interfere with peptide properties, thus biasing the results. The selection of the most suitable fluorophore is highly relevant. Here, a comprehensive study with blood-brain barrier (BBB) peptide shuttles (PepH3 and PepNeg) and antimicrobial peptides (AMPs) (vCPP2319 and Ctn[15-34]), tested as anticancer peptides (ACPs), having different fluorophores, namely 5(6)-carboxyfluorescein (CF), rhodamine B (RhB), quasar 570 (Q570), or tide fluor 3 (TF3) attached is presented. The goal is the evaluation of the impact of the selected fluorophores on peptide performance, applying routinely used techniques to assess cytotoxicity/toxicity, secondary structure, BBB translocation, and cellular internalization. Our results show that some fluorophores significantly modulate peptide activity when compared with unlabeled peptides, being more noticeable in hydrophobic and charged fluorophores. This study highlights the need for a careful experimental design for fluorescently labeled molecules, such as peptides.

The Spectrum of Design Solutions for Improving the Activity-Selectivity Product of Peptide Antibiotics against Multidrug-Resistant Bacteria and Prostate Cancer PC-3 Cells

The link between the antimicrobial and anticancer activity of peptides has long been studied, and the number of peptides identified with both activities has recently increased considerably. In this work, we hypothesized that designed peptides with a wide spectrum of selective antimicrobial activity will also have anticancer activity, and tested this hypothesis with newly designed peptides. The spectrum of peptides, used as partial or full design templates, ranged from cell-penetrating peptides and putative bacteriocin to those from the simplest animals (placozoans) and the Chordata phylum (anurans). We applied custom computational tools to predict amino acid substitutions, conferring the increased product of bacteriostatic activity and selectivity. Experiments confirmed that better overall performance was achieved with respect to that of initial templates. Nine of our synthesized helical peptides had excellent bactericidal activity against both standard and multidrug-resistant bacteria. These peptides were then compared to a known anticancer peptide polybia-MP1, for their ability to kill prostate cancer cells and dermal primary fibroblasts. The therapeutic index was higher for seven of our peptides, and anticancer activity stronger for all of them. In conclusion, the peptides that we designed for selective antimicrobial activity also have promising potential for anticancer applications.

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.

Recent Development of Machine Learning Methods in Microbial Phosphorylation Sites

A variety of protein post-translational modifications has been identified that control many cellular functions. Phosphorylation studies in mycobacterial organisms have shown critical importance in diverse biological processes, such as intercellular communication and cell division. Recent technical advances in high-precision mass spectrometry have determined a large number of microbial phosphorylated proteins and phosphorylation sites throughout the proteome analysis. Identification of phosphorylated proteins with specific modified residues through experimentation is often labor-intensive, costly and time-consuming. All these limitations could be overcome through the application of machine learning (ML) approaches. However, only a limited number of computational phosphorylation site prediction tools have been developed so far. This work aims to present a complete survey of the existing ML-predictors for microbial phosphorylation. We cover a variety of important aspects for developing a successful predictor, including operating ML algorithms, feature selection methods, window size, and software utility. Initially, we review the currently available phosphorylation site databases of the microbiome, the state-of-the-art ML approaches, working principles, and their performances. Lastly, we discuss the limitations and future directions of the computational ML methods for the prediction of phosphorylation.

PVPred-SCM: Improved Prediction and Analysis of Phage Virion Proteins Using a Scoring Card Method

Although, existing methods have been successful in predicting phage (or bacteriophage) virion proteins (PVPs) using various types of protein features and complex classifiers, such as support vector machine and naïve Bayes, these two methods do not allow interpretability. However, the characterization and analysis of PVPs might be of great significance to understanding the molecular mechanisms of bacteriophage genetics and the development of antibacterial drugs. Hence, we herein proposed a novel method (PVPred-SCM) based on the scoring card method (SCM) in conjunction with dipeptide composition to identify and characterize PVPs. In PVPred-SCM, the propensity scores of 400 dipeptides were calculated using the statistical discrimination approach. Rigorous independent validation test showed that PVPred-SCM utilizing only dipeptide composition yielded an accuracy of 77.56%, indicating that PVPred-SCM performed well relative to the state-of-the-art method utilizing a number of protein features. Furthermore, the propensity scores of dipeptides were used to provide insights into the biochemical and biophysical properties of PVPs. Upon comparison, it was found that PVPred-SCM was superior to the existing methods considering its simplicity, interpretability, and implementation. Finally, in an effort to facilitate high-throughput prediction of PVPs, we provided a user-friendly web-server for identifying the likelihood of whether or not these sequences are PVPs. It is anticipated that PVPred-SCM will become a useful tool or at least a complementary existing method for predicting and analyzing PVPs.

Toward insights on determining factors for high activity in antimicrobial peptides via machine learning

The continued and general rise of antibiotic resistance in pathogenic microbes is a well-recognized global threat. Host defense peptides (HDPs), a component of the innate immune system have demonstrated promising potential to become a next generation antibiotic effective against a plethora of pathogens. While the effectiveness of antimicrobial HDPs has been extensively demonstrated in experimental studies, theoretical insights on the mechanism by which these peptides function is comparably limited. In particular, experimental studies of AMP mechanisms are limited in the number of different peptides investigated and the type of peptide parameters considered. This study makes use of the random forest algorithm for classifying the antimicrobial activity as well for identifying molecular descriptors underpinning the antimicrobial activity of investigated peptides. Subsequent manual interpretation of the identified important descriptors revealed that polarity-solubility are necessary for the membrane lytic antimicrobial activity of HDPs.

iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou's 5-Steps Rule and Informative Physicochemical Properties

Understanding of quorum-sensing peptides (QSPs) in their functional mechanism plays an essential role in finding new opportunities to combat bacterial infections by designing drugs. With the avalanche of the newly available peptide sequences in the post-genomic age, it is highly desirable to develop a computational model for efficient, rapid and high-throughput QSP identification purely based on the peptide sequence information alone. Although, few methods have been developed for predicting QSPs, their prediction accuracy and interpretability still requires further improvements. Thus, in this work, we proposed an accurate sequence-based predictor (called iQSP) and a set of interpretable rules (called IR-QSP) for predicting and analyzing QSPs. In iQSP, we utilized a powerful support vector machine (SVM) cooperating with 18 informative features from physicochemical properties (PCPs). Rigorous independent validation test showed that iQSP achieved maximum accuracy and MCC of 93.00% and 0.86, respectively. Furthermore, a set of interpretable rules IR-QSP was extracted by using random forest model and the 18 informative PCPs. Finally, for the convenience of experimental scientists, the iQSP web server was established and made freely available online. It is anticipated that iQSP will become a useful tool or at least as a complementary existing method for predicting and analyzing QSPs.

Meta-iAVP: A Sequence-Based Meta-Predictor for Improving the Prediction of Antiviral Peptides Using Effective Feature Representation

In spite of the large-scale production and widespread distribution of vaccines and antiviral drugs, viruses remain a prominent human disease. Recently, the discovery of antiviral peptides (AVPs) has become an influential antiviral agent due to their extraordinary advantages. With the avalanche of newly-found peptide sequences in the post-genomic era, there is a great demand to develop a sequence-based predictor for timely identifying AVPs as this information is very useful for both basic research and drug development. In this study, we propose a novel sequence-based meta-predictor with an effective feature representation, called Meta-iAVP, for the accurate prediction of AVPs from given peptide sequences. Herein, the effective feature representation was extracted from a set of prediction scores derived from various machine learning algorithms and types of features. To the best of our knowledge, the model proposed herein represents the first meta-based approach for the prediction of AVPs. An overall accuracy and Matthews correlation coefficient of 95.20% and 0.90, respectively, was achieved from the independent test set on an objective benchmark dataset. Comparative analysis suggested that Meta-iAVP was superior to that of existing methods and therefore represents a useful tool for AVP prediction. Finally, in an effort to facilitate high-throughput prediction of AVPs, the model was deployed as the Meta-iAVP web server and is made freely available online at http://codes.bio/meta-iavp/ where users can submit query peptide sequences for determining the likelihood of whether or not these peptides are AVPs.

DRAMP 2.0, an updated data repository of antimicrobial peptides

Data Repository of Antimicrobial Peptides (DRAMP, http://dramp.cpu-bioinfor.org/) is an open-access comprehensive database containing general, patent and clinical antimicrobial peptides (AMPs). Currently DRAMP has been updated to version 2.0, it contains a total of 19,899 entries (newly added 2,550 entries), including 5,084 general entries, 14,739 patent entries, and 76 clinical entries. The update covers new entries, structures, annotations, classifications and downloads. Compared with APD and CAMP, DRAMP contains 14,040 (70.56% in DRAMP) non-overlapping sequences. In order to facilitate users to trace original references, PubMed_ID of references have been contained in activity information. The data of DRAMP can be downloaded by dataset and activity, and the website source code is also available on dedicatedly designed download webpage. Although thousands of AMPs have been reported, only a few parts have entered clinical stage. In the paper, we described several AMPs in clinical trials, including their properties, indications and clinicaltrials.gov identifiers. Finally, we provide the applications of DRAMP in the development of AMPs.

Bacterial Proteinaceous Compounds With Multiple Activities Toward Cancers and Microbial Infection

In recent decades, cancer and multidrug resistance have become a worldwide problem, resulting in high morbidity and mortality. Some infectious agents like Streptococcus pneumoniae, Stomatococcus mucilaginous, Staphylococcus spp., E. coli. Klebsiella spp., Pseudomonas aeruginosa, Candida spp., Helicobacter pylori, hepatitis B and C, and human papillomaviruses (HPV) have been associated with the development of cancer. Chemotherapy, radiotherapy and antibiotics are the conventional treatment for cancer and infectious disease. This treatment causes damage in healthy cells and tissues, and usually triggers systemic side-effects, as well as drug resistance. Therefore, the search for new treatments is urgent, in order to improve efficacy and also reduce side-effects. Proteins and peptides originating from bacteria can thus be a promising alternative to conventional treatments used nowadays against cancer and infectious disease. These molecules have demonstrated specific activity against cancer cells and bacterial infection; indeed, proteins and peptides can be considered as future antimicrobial and anticancer drugs. In this context, this review will focus on the desirable characteristics of proteins and peptides from bacterial sources that demonstrated activity against microbial infections and cancer, as well as their efficacy in vitro and in vivo.

Antimicrobial Peptides: Potential Application in Liver Cancer

The physicochemical properties of antimicrobial peptides (AMPs) including size, net charge, amphipathic structure, hydrophobicity, and mode-of-action together determine their broad-spectrum activities against bacteria, fungi, protozoa, and viruses. Recent studies show that some AMPs have both antimicrobial and anticancer activities, suggesting a new strategy for cancer therapy. Hepatocellular carcinoma (HCC), the primary liver cancer, is a leading cause of cancer mortality worldwide, and lacks effective treatment. Anticancer peptides (ACPs) derived from AMPs or natural resources could be applied to combat HCC directly or as a synergistic treatment. However, the number of known ACPs is low compared to the number of antibacterial and antifungal peptides, and very few of them can be applied clinically for HCC treatment. In this review, we first summarize recent studies related to ACPs for HCC, followed by a description of potential modes-of-action including direct killing, anti-inflammation, immune modulation, and enhanced wound healing. We then describe the structures of AMPs and methods to design and modify these peptides to improve their anticancer efficacy. Finally, we explore the potential application of ACPs as vaccines or nanoparticles for HCC treatment. Overall, ACPs display several attractive properties as therapeutic agents, including broad-spectrum anticancer activity, ease-of-design and modification, and low production costs. As this is an emerging and novel area of cancer therapy, additional studies are needed to identify existing candidate AMPs with ACP activity, and assess their anticancer activity and specificity, and immunomodulatory effects, using in vitro, in silico, and in vivo approaches.

ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides

Anticancer peptides (ACPs) have emerged as a new class of therapeutic agent for cancer treatment due to their lower toxicity as well as greater efficacy, selectivity and specificity when compared to conventional small molecule drugs. However, the experimental identification of ACPs still remains a time-consuming and expensive endeavor. Therefore, it is desirable to develop and improve upon existing computational models for predicting and characterizing ACPs. In this study, we present a bioinformatics tool called the ACPred, which is an interpretable tool for the prediction and characterization of the anticancer activities of peptides. ACPred was developed by utilizing powerful machine learning models (support vector machine and random forest) and various classes of peptide features. It was observed by a jackknife cross-validation test that ACPred can achieve an overall accuracy of 95.61% in identifying ACPs. In addition, analysis revealed the following distinguishing characteristics that ACPs possess: (i) hydrophobic residue enhances the cationic properties of α-helical ACPs resulting in better cell penetration; (ii) the amphipathic nature of the α-helical structure plays a crucial role in its mechanism of cytotoxicity; and (iii) the formation of disulfide bridges on β-sheets is vital for structural maintenance which correlates with its ability to kill cancer cells. Finally, for the convenience of experimental scientists, the ACPred web server was established and made freely available online.

De novo design of anticancer peptides by ensemble artificial neural networks

Membranolytic anticancer peptides (ACPs) are drawing increasing attention as potential future therapeutics against cancer, due to their ability to hinder the development of cellular resistance and their potential to overcome common hurdles of chemotherapy, e.g., side effects and cytotoxicity. In this work, we present an ensemble machine learning model to design potent ACPs. Four counter-propagation artificial neural-networks were trained to identify peptides that kill breast and/or lung cancer cells. For prospective application of the ensemble model, we selected 14 peptides from a total of 1000 de novo designs, for synthesis and testing in vitro on breast cancer (MCF7) and lung cancer (A549) cell lines. Six de novo designs showed anticancer activity in vitro, five of which against both MCF7 and A549 cell lines. The novel active peptides populate uncharted regions of ACP sequence space.