Exploring New Frontiers in Peptide Informatics

Peptide informatics is a rapidly growing field that blends bioinformatics, chemistry, and biology. Peptides, short chains of amino acids, are crucial in various biological processes like protein folding, signaling, and immunity. Peptide informatics uses computer techniques to study peptides, examining their sequence, structure, function, and interactions.

Recent advancements in peptide informatics have uncovered exciting discoveries and practical applications. For instance, novel techniques now allow us to predict peptide structures, enabling the development of new drugs and therapies. Additionally, we've introduced methods to identify how peptides interact with proteins, shedding light on the underlying causes of diseases.

Impact of this research topic:

Our research topic garnered significant attention from the scientific community, resulting in the publication of just 8 manuscripts here.

Here is a snapshot of the impact the research topic has generated.

The subject matter has attracted considerable online engagement, with a noteworthy total of over 20,000 views by a diverse audience. Furthermore, it has been downloaded approximately 4,000 times, indicating a substantial interest and uptake in our research findings.

The views and engagement with our research topic transcend geographic constraints, forming a truly global interest that knows no boundaries, as visually represented in the accompanying figure. This widespread global interest emphasizes the broad and far-reaching impact of our work on an international scale, resonating with individuals and communities worldwide.

Here is the list of manuscripts included:

1. EPIphany Platform: Cates et al. created EPIphany for easy peptide immunoarray data analysis.
2. Cross-Reactive Proteins: Moody et al. identified human proteins with potential cross-reactivity to SARS-CoV-2.
3. AbCPE Algorithm: Kadam et al. introduced AbCPE for predicting antibody class binding to B cell epitopes.
4. CAT-CPI Model: Qian et al. developed CAT-CPI, a deep learning tool for compound-protein interaction prediction.
5. Macrocyclic Peptides in Cancer: Yang et al. explored the use of macrocyclic peptides in cancer treatment.
6. Peptide Structure Prediction: Tufféry and Derreumaux discussed a method for predicting peptide structures.
7. Protein Structure Prediction: Bertoline et al. reviewed advances in predicting protein structures, including AlphaFold2.

Concluding remark

Peptide informatics, while presenting its share of challenges, offers a wealth of promise, brimming with numerous potential applications. As computational methods tailored for peptide research continue to evolve, they will pave the way for groundbreaking discoveries and fresh opportunities within the realms of biology, chemistry, and medicine. We warmly invite readers to delve into our comprehensive Research Topic, which explores a wide range of peptide informatics applications, and uncover the thrilling prospects it holds.