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Lysosomal Hydrolases

Lysosomal Hydrolases

Lysosomes are the site for the degradation of macromolecules and biological polymers released during membrane trafficking events such as secretory, endocytic, autophagic, and phagocytic pathways. The membrane-enclosed area of the lysosome, called the lumen, contains hydrolytic enzymes active in an acidic environment. These acid hydrolases are functional at a pH between 4.5 and 5 and are involved in cellular processes such as cell signaling, energy metabolism, restoration of the plasma membrane,...

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Characterization of Neuronal Lysosome Interactome with Proximity Labeling Proteomics

Characterization of Neuronal Lysosome Interactome with Proximity Labeling Proteomics

Published on: June 23, 2022

Discriminating lysosomal membrane protein types using dynamic neural network.

Vijay Tripathi¹, Dwijendra Kumar Gupta

¹a Genome Diversity Center, Institute of Evolution, University of Haifa , Haifa , Israel .

Journal of Biomolecular Structure & Dynamics

|August 24, 2013

Summary

This study introduces a dynamic artificial neural network to classify protein types using only their sequences. The Layer Recurrent Network (LRN) achieved 93.2% accuracy in distinguishing lysosomal membrane proteins from others.

Keywords:

dynamic neural network generalized regression neural network layer recurrent network lysosomal membrane proteins probabilistic neural network support vector machine

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Area of Science:

Bioinformatics
Computational Biology
Machine Learning in Proteomics

Background:

Accurate classification of membrane proteins is crucial for understanding cellular functions.
Existing methods often require complex experimental data, limiting large-scale analysis.
Distinguishing lysosomal membrane proteins from other membrane protein classes remains a challenge.

Purpose of the Study:

To develop a novel dynamic artificial neural network methodology for protein classification based solely on amino acid sequences.
To propose a neural network-based system for predicting lysosomal-associated membrane protein types.
To evaluate the effectiveness of different protein sequence representations and dimensionality reduction techniques.

Main Methods:

Feature extraction from protein sequences using seven distinct sets, including amino acid composition and dipeptide composition.
Application of Principal Component Analysis (PCA) for dimensionality reduction of feature vectors.
Comparison of classifiers: Probabilistic Neural Network, Generalized Regression Neural Network, and Elman Recurrent Neural Network (RNN) against Layer Recurrent Network (LRN).

Main Results:

The Layer Recurrent Network (LRN), a dynamic network with memory, demonstrated the highest accuracy among the tested artificial neural networks.
The proposed methodology achieved an overall accuracy of 93.2% using jackknife cross-validation.
Protein sequence representations were found to be more effective in capturing core membrane protein features than traditional amino acid composition.

Conclusions:

The dynamic artificial neural network approach, particularly LRN, can effectively discriminate lysosomal associated membrane proteins from other membrane and globular proteins.
The method offers a computationally efficient way to classify proteins based on sequence data alone.
This work highlights the potential of advanced machine learning techniques in advancing proteomic analysis and protein function prediction.