A classification algorithm based on dynamic ensemble selection to predict mutational patterns of the envelope protein...

Area of Science:

• Virology
• Computational Biology
• Structural Biology

Background:

• Human immunodeficiency virus type 1 (HIV-1) envelope (Env) protein therapeutics are effective but challenged by emergent, therapy-resistant Env variants due to unpredictable mutations.
• Understanding the spatial and temporal patterns of Env mutations is crucial for developing effective and durable HIV-1 therapies.

Purpose of the Study:

• To develop and evaluate a novel algorithm for predicting the mutational state of HIV-1 Env protein positions.
• To leverage the structural context of adjacent amino acid positions for more accurate variability prediction.

Main Methods:

• Developed a dynamic ensemble selection algorithm (k-best classifiers) to predict the variability state of Env positions.
• Applied the algorithm to Env sequences from 300 HIV-1-infected individuals, mapping variability onto the protein's 3D structure.
• Estimated the variability state of each position based on the variability of adjacent positions.

Main Results:

• The proposed algorithm demonstrated superior performance compared to base learners and other classification algorithms.
• Accurate prediction of mutational states in key Env regions, including the high-mannose patch and CD4-binding site, targeted by therapeutics.
• The algorithm excelled in predicting variability at multi-position footprints of therapeutics on the Env protein.

Conclusions:

• The developed algorithm offers a high-performance dynamic ensemble selection technique for predicting Env variability.
• Improved prediction of spatiotemporal mutation patterns can inform the development of personalized HIV-1 treatment strategies.
• The approach provides a foundation for more targeted and effective therapies against evolving HIV-1 Env variants.