Patch Up with Peptides: The Machine Learning Magic Behind Wound Care

Chronic wounds, such as diabetic foot ulcers and pressure sores, remain a significant global health challenge. These wounds are often characterized by persistent infections and prolonged inflammation leading to immense physical and financial burdens on patients and healthcare systems alike. Traditional treatments like antibiotics and non-steroidal anti-inflammatory drugs (NSAIDs) pose certain limitations. Antibiotics are effective in managing infections but have fuelled the alarming rise in antimicrobial resistance. NSAIDs, on the other hand, may reduce inflammation but have been shown to hinder healing by increasing the risk of bacterial attachment. Recognizing these gaps, researchers from the Department of Chemical Engineering and Pharmacy at BITS Pilani, Hyderabad Campus, have devised an innovative solution- their machine-learning framework, WHP-Pred. It offers a faster and more efficient approach to identifying therapeutic peptides capable of addressing both infection and inflammation in chronic wounds.

Therapeutic peptides represent a promising alternative in wound care due to their natural ability to combat bacteria and modulate immune responses. Antimicrobial peptides (AMPs) target bacteria effectively without fostering resistance, while anti-inflammatory peptides (AIPs) regulate cytokines to mitigate inflammation. A peptide combining these properties could revolutionize chronic wound treatment. However, identifying such multifunctional peptides through experimental methods is resource-intensive and time-consuming, requiring the exploration of vast combinatorial possibilities. The research team’s machine-learning-assisted screening framework aims to overcome these challenges by significantly streamlining the discovery process.

The WHP-Pred framework utilizes the XGBoost algorithm to classify peptides based on their antimicrobial and anti-inflammatory properties. XGBoost, short for Extreme Gradient Boosting, is a machine-learning algorithm that builds many small decision trees, each learning from the mistakes of the previous ones. This iterative approach makes it both fast and highly accurate, ideal for analyzing complex peptide datasets in WHP-Pred. The process involves two levels of classification. First, the algorithm predicts whether a peptide possesses antimicrobial activity. If successful, the peptide is then assessed for its anti-inflammatory potential. A peptide that meets both criteria is classified as a wound-healing peptide (WHP). This computational approach minimizes the need for exhaustive laboratory testing and accelerates the identification of therapeutic candidates.

To achieve this, the researchers curated datasets from publicly available repositories such as DRAMP (Data Repository of Antimicrobial Peptides), UniProt, and IEDB (Immune Epitope Database). They employed advanced feature representation methods to analyze peptide sequences comprehensively, focusing on their amino acid composition, hydrophobicity-guided triads, structural properties, and physicochemical attributes. Combining these methods, the framework achieved impressive predictive accuracies of 93.3% for antimicrobial properties and 76.2% for anti-inflammatory activity, demonstrating its robustness in identifying peptides with dual functionality.

In a bid to make this tool accessible to researchers worldwide, the team developed a web application, WHP-Pred. This user-friendly platform allows scientists to input peptide sequences and evaluate their potential as wound-healing agents. The tool is freely available online and is designed to handle single or batch sequence submissions, setting it up to be an invaluable resource for those engaged in peptide-based drug discovery.

This research, supported by a CDRF grant at BITS Pilani-Hyderabad, highlights the transformative potential of interdisciplinary collaboration. The study was led by Dr. Arnab Dutta, alongside Dr. Debirupa Mitra and Dr. Swati Biswas, combining expertise in Chemical Engineering, Pharmacy, and Computational Biology. By addressing the dual challenges of infection and inflammation in chronic wounds, this work not only paves the way for innovative therapeutics but also demonstrates the power of integrating computational techniques into biomedical research. As peptide therapeutics gain traction as alternatives to conventional drugs, this research sets a benchmark for integrating computational and experimental methods in healthcare innovation.

With its practical and scalable framework, WHP-Pred has the potential to accelerate discoveries in therapeutic peptides beyond wound care, making strides in addressing broader healthcare challenges. As antimicrobial resistance and inflammatory disorders continue to pose significant hurdles, solutions like this one exemplify how technology can transform the future of medicine.