ACP-ESM: A novel framework for classification of anticancer peptides using protein-oriented transformer approach

Anticancer peptides (ACPs) are a class of molecules that have gained significant attention in the field of cancer research and therapy. ACPs are short chains of amino acids, the building blocks of proteins, and they possess the ability to selectively target and kill cancer cells. One of the key advantages of ACPs is their ability to selectively target cancer cells while sparing healthy cells to a greater extent. This selectivity is often attributed to differences in the surface properties of cancer cells compared to normal cells. That is why ACPs are being investigated as potential candidates for cancer therapy. ACPs may be used alone or in combination with other treatment modalities like chemotherapy and radiation therapy. While ACPs hold promise as a novel approach to cancer treatment, there are challenges to overcome, including optimizing their stability, improving selectivity, and enhancing their delivery to cancer cells, continuous increasing in number of peptide sequences, developing a reliable and precise prediction model. In this work, we propose an efficient transformer-based framework to identify ACPs for by performing accurate a reliable and precise prediction model. For this purpose, four different transformer models, namely ESM, ProtBERT, BioBERT, and SciBERT are employed to detect ACPs from amino acid sequences. To demonstrate the contribution of the proposed framework, extensive experiments are carried on widely-used datasets in the literature, two versions of AntiCp2, cACP-DeepGram, ACP-740. Experiment results show the usage of proposed model enhances classification accuracy when compared to the literature studies. The proposed framework, ESM, exhibits 96.45% of accuracy for AntiCp2 dataset, 97.66% of accuracy for cACP-DeepGram dataset, and 88.51% of accuracy for ACP-740 dataset, thence determining new state-of-the-art. The code of proposed framework is publicly available at github (https://github.com/mstf-yalcin/acp-esm).

Revolutionizing agroindustry: Towards the industrial application of antimicrobial peptides against pathogens and pests

Antibiotics are essential chemicals for medicine and agritech. However, all antibiotics are small molecules that pathogens evolve antimicrobial resistance (AMR). Alternatively, antimicrobial peptides (AMPs) offer potential to overcome or evade AMR. AMPs provide broad-spectrum activity, favourable biosafety profiles, and a rapid and efficient mechanism of action with low resistance incidence. These properties have driven innovative applications, positioning AMPs as promising contributors to advancements in various industrial sectors. This review evaluates the multifaceted nature of AMPs and their biotechnological applications in underexplored sectors. In the food industry, the application of AMPs helps to suppress the growth of microorganisms, thereby decreasing foodborne illnesses, minimizing food waste, and prolonging the shelf life of products. In animal husbandry and aquaculture, incorporating AMPs into the diet reduces the load of pathogenic microorganisms and enhances growth performance and survival rates. In agriculture, AMPs provide an alternative to decrease the use of chemical pesticides and antibiotics. We also review current methods for obtaining AMPs, including chemical synthesis, recombinant DNA technology, cell-free protein synthesis, and molecular farming, are also reviewed. Finally, we look to the peptide market to assess its status, progress, and transition from the discovery stage to benefits for society and high-quality products. Overall, our review exemplifies the other side of the coin of AMPs and how these molecules provide similar benefits to conventional antibiotics and pesticides in the agritech sector.