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

Heterologous expression of frog antimicrobial peptide Odorranain-C1 in Pichia pastoris: Biological characteristics and its application in food preservation

To reduce food spoilage and deterioration caused by microbial contamination, antimicrobial peptides (AMPs) have gradually gained attention as a biological preservative. Odorranain-C1 is an α-helical cationic antimicrobial peptide extracted from the skin of frogs with broad-spectrum antimicrobial activity. In this study, we achieved the expression of Odorranain-C1 in Pichia pastoris (P. pastoris) (also known as Komagataella phaffii) by employing DNA recombination technology. The recombinant Odorranain-C1 showed broad-spectrum antibacterial activity and displayed a minimum inhibitory concentration within the range of 8-12 μg.mL-1. Meanwhile, Odorranain-C1 exhibited superior stability and lower hemolytic activity. Mechanistically, Odorranain-C1 disrupted the bacterial membrane's integrity, ultimately causing membrane rupture and subsequent cell death. In tilapia fillets preservation, Odorranain-C1 inhibited the total colony growth and pH variations, while also reducing the production of total volatile basic nitrogen (TVB-N) and thiobarbituric acid (TBA). In conclusion, these studies demonstrated the efficient recombinant expression of Odorranain-C1 in P. pastoris, highlighting its promising utilization in food preservation.

In silico identification and in vitro assessment of a potential anti-breast cancer activity of antimicrobial peptide retrieved from the ATMP1 Anabas testudineus fish peptide

A previous study has shown that synthetic antimicrobial peptides (AMPs) derived from Anabas testudineus (ATMP1) could in-vitro inhibit the progression of breast cancer cell lines. In this study, we are interested in studying altered versions of previous synthetic AMPs to gain some insight into the peptides functions. The AMPs were altered and subjected to bioinformatics prediction using four databases (ADP3, CAMP-R3, AMPfun, and ANTICP) to select the highest anticancer activity. The bioinformatics in silico analysis led to the selection of two AMPs, which are ATMP5 (THPPTTTTTTTTTTTYTAAPATTT) and ATMP6 (THPPTTTTTTTTTTTTTAAPARTT). The in silico analysis predicted that ATMP5 and ATMP6 have anticancer activity and lead to cell death. The ATMP5 and ATMP6 were submitted to deep learning databases (ToxIBTL and ToxinPred2) to predict the toxicity of the peptides and to (AllerTOP & AllergenFP) check the allergenicity. The results of databases indicated that AMPs are non-toxic to normal human cells and allergic to human immunoglobulin. The bioinformatics findings led to select the highest active peptide ATMP5, which was synthesised and applied for in-vitro experiments using cytotoxicity assay MTT Assay, apoptosis detection using the Annexin V FTIC-A assay, and gene expression using Apoptosis PCR Array to evaluate the AMP's anticancer activity. The antimicrobial activity is approved by the disc diffusion method. The in-vitro experiments analysis showed that ATMP5 had the activity to inhibit the growth of the breast cancer cell line (MDA-MB-231) after 48 h and managed to arrest the cell cycle of the MDA-MB-231, apoptosis induction, and overexpression of the p53 by interaction with the related apoptotic genes. This research opened up new opportunities for developing potential and selective anticancer agents relying on antimicrobial peptide properties.

LGBM-ACp: an ensemble model for anticancer peptide prediction and in silico screening with potential drug targets

Conventional cancer therapies are highly expensive and have serious complications. An alternative approach now emphasizes on the development of small, biologically active peptides without acute toxicity. Experimental screening to find curative anticancer peptides (ACP) often gives rise to multiple obstacles and is time dependent. Consequently, developing an effective computational technique to identify promising ACP candidates prior to preclinical research is in high demand. This study proposed a machine-learning framework that used the light gradient-boosting machine as a classifier and two compositional and two binary profile features as input. The ensemble model displayed an accuracy, MCC, and AUROC of 97.52%, 0.91, and 0.98, respectively, which outclassed most of the existing sequence-based computational tools. A distinct dataset of non-mutagenic, non-toxic, and non-inhibitory Cytochrome P-450 peptides was used to validate the hybrid model. The most relevant ACP in the alternative dataset was compared with two standard ACPs, beta defensin 2, and cecropin-A. Molecular docking of the predicted peptide revealed that it has a strong binding affinity with twenty-five anticancer drug targets, most notably phosphoenolpyruvate carboxykinase (- 7.2 kcal/mol). Additionally, molecular dynamics simulation and principal component analysis supported the stability of the peptide-receptor complex. Overall, the present findings will take a step forward in rational drug design through rapid identification and screening of therapeutic peptides.

Role of Anti-Cancer Peptides as Immunomodulatory Agents: Potential and Design Strategy

The usage of peptide-based drugs to combat cancer is gaining significance in the pharmaceutical industry. The collateral damage caused to normal cells due to the use of chemotherapy, radiotherapy, etc. has given an impetus to the search for alternative methods of cancer treatment. For a long time, antimicrobial peptides (AMPs) have been shown to display anticancer activity. However, the immunomodulatory activity of anti-cancer peptides has not been researched very extensively. The interconnection of cancer and immune responses is well-known. Hence, a search and design of molecules that can show anti-cancer and immunomodulatory activity can be lead molecules in this field. A large number of anti-cancer peptides show good immunomodulatory activity by inhibiting the pro-inflammatory responses that assist cancer progression. Here, we thoroughly review both the naturally occurring and synthetic anti-cancer peptides that are reported to possess both anti-cancer and immunomodulatory activity. We also assess the structural and biophysical parameters that can be utilized to improve the activity. Both activities are mostly reported by different groups, however, we discuss them together to highlight their interconnection, which can be used in the future to design peptide drugs in the field of cancer therapeutics.

Emerging Computational Approaches for Antimicrobial Peptide Discovery

In the last two decades many reports have addressed the application of artificial intelligence (AI) in the search and design of antimicrobial peptides (AMPs). AI has been represented by machine learning (ML) algorithms that use sequence-based features for the discovery of new peptidic scaffolds with promising biological activity. From AI perspective, evolutionary algorithms have been also applied to the rational generation of peptide libraries aimed at the optimization/design of AMPs. However, the literature has scarcely dedicated to other emerging non-conventional in silico approaches for the search/design of such bioactive peptides. Thus, the first motivation here is to bring up some non-standard peptide features that have been used to build classical ML predictive models. Secondly, it is valuable to highlight emerging ML algorithms and alternative computational tools to predict/design AMPs as well as to explore their chemical space. Another point worthy of mention is the recent application of evolutionary algorithms that actually simulate sequence evolution to both the generation of diversity-oriented peptide libraries and the optimization of hit peptides. Last but not least, included here some new considerations in proteogenomic analyses currently incorporated into the computational workflow for unravelling AMPs in natural sources.

Development of Anticancer Peptides Using Artificial Intelligence and Combinational Therapy for Cancer Therapeutics

Cancer is a group of diseases causing abnormal cell growth, altering the genome, and invading or spreading to other parts of the body. Among therapeutic peptide drugs, anticancer peptides (ACPs) have been considered to target and kill cancer cells because cancer cells have unique characteristics such as a high negative charge and abundance of microvilli in the cell membrane when compared to a normal cell. ACPs have several advantages, such as high specificity, cost-effectiveness, low immunogenicity, minimal toxicity, and high tolerance under normal physiological conditions. However, the development and identification of ACPs are time-consuming and expensive in traditional wet-lab-based approaches. Thus, the application of artificial intelligence on the approaches can save time and reduce the cost to identify candidate ACPs. Recently, machine learning (ML), deep learning (DL), and hybrid learning (ML combined DL) have emerged into the development of ACPs without experimental analysis, owing to advances in computer power and big data from the power system. Additionally, we suggest that combination therapy with classical approaches and ACPs might be one of the impactful approaches to increase the efficiency of cancer therapy.