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DeePNAP: A Deep Learning Method to Predict Protein-Nucleic Acid Binding Affinity from Their Sequences.

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DeePNAP predicts protein-nucleic acid interaction binding affinity and mutation-induced free energy changes using only sequence data. This machine learning model offers high precision and generalizability for diverse biological systems.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Molecular Interactions

Background:

  • Predicting protein-nucleic acid (PNA) binding affinity is crucial for understanding PNA interactions (PNAIs).
  • Existing models often require structural information and are limited to specific PNAIs, hindering generalizability due to scarce structural data.
  • Current tools typically predict single parameters, limiting their versatility.

Purpose of the Study:

  • To develop a versatile machine learning model, DeePNAP, for predicting PNA binding affinity and mutation effects solely from sequences.
  • To overcome limitations of existing methods reliant on structural data and limited PNAI scope.
  • To provide a tool for rapid and precise prediction of PNAI parameters.

Main Methods:

  • Utilized a large, heterogeneous dataset of 14,401 entries from the ProNAB database, including wild-type and mutant PNA complexes.
  • Developed DeePNAP, a machine learning model employing sequence-based features for prediction.
  • Validated the model's performance using correlation coefficients and root mean squared errors for K_D and ΔΔG predictions.

Main Results:

  • DeePNAP accurately predicts binding affinity (K_D) and free energy changes (ΔΔG) exclusively from PNA sequences.
  • The model demonstrates high correlation coefficients and low root mean squared errors, indicating strong predictive power and generalizability.
  • Achieved precise predictions for a wide range of PNAIs across eukaryotes and prokaryotes.

Conclusions:

  • DeePNAP offers a robust, sequence-based approach for predicting PNA binding affinity and mutation effects, overcoming structural data limitations.
  • The model's generalizability and versatility make it a valuable tool for PNAI research.
  • A web interface for DeePNAP is available, facilitating rapid prediction and deeper understanding of PNAIs in biological systems.