Challenges in computational discovery of bioactive peptides in ’omics data

Abstract

Peptides have a plethora of activities in biological systems that can potentially be exploited biotechnologically. Several peptides are used clinically, as well as in industry and agriculture. The increase in available ’omics data has recently provided a large opportunity for mining novel enzymes, biosynthetic gene clusters, and molecules. While these data primarily consist of DNA sequences, other types of data provide important complementary information. Due to their size, the approaches proven successful at discovering novel proteins of canonical size cannot be naïvely applied to the discovery of peptides. Peptides can be encoded directly in the genome as short open reading frames (smORFs), or they can be derived from larger proteins by proteolysis. Both of these peptide classes pose challenges as simple methods for their prediction result in large numbers of false positives. Similarly, functional annotation of larger proteins, traditionally based on sequence similarity to infer orthology and then transferring functions between characterized proteins and uncharacterized ones, cannot be applied for short sequences. The use of these techniques is much more limited and alternative approaches based on machine learning are used instead. Here, we review the limitations of traditional methods as well as the alternative methods that have recently been developed for discovering novel bioactive peptides with a focus on prokaryotic genomes and metagenomes.

Current Trends and Technological Advancements in the Study of Honey Bee-Derived Peptides with an Emphasis on State-of-the-Art Approaches: A Review

“…Tables 6 and 7 present several significant challenges and promising research opportunities. In a nutshell, the study of honey bee peptides offers challenges and research opportunities that span across various fields including biochemistry, biotechnology, pharmacology, and biomedical science [71,77,90,96,105,131,149]. Through understanding these challenges and the exploration of these opportunities, researchers can tap into the therapeutic, nutritional, and biotechnological possibilities of honey bee peptides, ultimately enhancing human health and well-being [11,26,71,77,87,90,158].…”

“…Through the integration of various data sources, AI algorithms can identify peptides that show promise in terms of their bioactivities and therapeutic relevance. [120,129,135,[145][146][147][148][149][150] Table 5. Cont.…”