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Pepsin-like aspartic proteases (PAPs) as model systems for combining biomolecular simulation with biophysical
1Division of Biophysical Chemistry, Center for Molecular Protein Science, Department of Chemistry, Lund University P. O. Box 124 SE-22100 Lund Sweden soumendranath.bhakat@bpc.lu.se bhakatsoumendranath@gmail.com +46-769608418.
RSC Advances
|April 15, 2022
Summary
Pepsin-like aspartic proteases (PAPs) are crucial drug targets. This review explores order parameters for understanding their flap dynamics and molecular recognition, highlighting machine learning and biophysical experiments for atomistic insights.
Area of Science:
- Biochemistry and Structural Biology
- Computational Biophysics
- Pharmacology
Background:
- Pepsin-like aspartic proteases (PAPs) are structurally similar to human pepsin and feature a critical flap region.
- The flap's conformational dynamics are essential for PAPs' biological function, including substrate binding.
- PAPs are significant drug targets for diseases like malaria and Alzheimer's disease.
Purpose of the Study:
- To review order parameters for characterizing conformational dynamics in PAPs.
- To explore the application of machine learning methods as order parameters for PAPs.
- To propose future directions for computational and experimental studies on PAPs' molecular recognition.
Main Methods:
- Literature review of order parameters for conformational dynamics.
- Discussion of machine learning applications in analyzing PAP dynamics.
- Integration of biophysical experimental techniques (NMR, FRET) with molecular simulations.
Main Results:
- Identified key order parameters influencing flap dynamics in apo PAPs.
- Highlighted the potential of machine learning to model complex dynamics.
- Emphasized the necessity of combining computational and experimental approaches for detailed insights.
Conclusions:
- Understanding PAP conformational dynamics is vital for drug development.
- Advanced computational methods and machine learning offer powerful tools for analysis.
- Integrated biophysical and simulation strategies are crucial for complete atomistic understanding.
