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Updated: Nov 10, 2025

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

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P3CMQA: Single-Model Quality Assessment Using 3DCNN with Profile-Based Features.

Yuma Takei^1,2, Takashi Ishida¹

¹Department of Computer Science, School of Computing, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo 152-8550, Japan.

Bioengineering (Basel, Switzerland)

|April 3, 2021

Summary

This summary is machine-generated.

We developed P3CMQA, a new method for protein model quality assessment. Incorporating sequence profile features into 3DCNN significantly improved accuracy in predicting near-native protein structures.

Keywords:

3DCNN CASP deep learning estimation of model accuracy (EMA)machine learning model quality assessment (MQA)protein structure prediction

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Area of Science:

Computational Biology
Structural Bioinformatics
Machine Learning in Biology

Background:

Model quality assessment (MQA) is crucial for selecting accurate protein tertiary structures from predicted models.
Previous 3DCNN approaches for MQA showed limited performance due to reliance on atom-type features alone.

Purpose of the Study:

To enhance protein structure model quality assessment using a 3DCNN approach.
To improve MQA performance by integrating sequence profile-based features.

Main Methods:

Development of P3CMQA, a single-model MQA method utilizing 3DCNN.
Incorporation of sequence profile-based features alongside atom-type features as input for the 3DCNN.
Performance evaluation on the CASP13 dataset.

Main Results:

The inclusion of profile-based features significantly improved the assessment performance of the 3DCNN model.
P3CMQA demonstrated superior performance compared to existing single-model MQA methods.
The proposed method outperformed the previous 3DCNN-based MQA technique.

Conclusions:

Sequence profile-based features are vital for enhancing MQA accuracy in protein structure prediction.
P3CMQA represents a significant advancement in single-model MQA, offering improved reliability.
A user-friendly web interface was developed to facilitate the application of P3CMQA.