ECM-LSE: Prediction of Extracellular Matrix Proteins Using Deep Latent Space Encoding of k-Spaced Amino Acid Pairs

• Published in: Frontiers in bioengineering and biotechnology 2021-10, Vol.9, p.752658-752658
• Main Authors: Al-Saggaf, Ubaid M, Usman, Muhammad, Naseem, Imran, Moinuddin, Muhammad, Jiman, Ahmad A, Alsaggaf, Mohammed U, Alshoubaki, Hitham K, Khan, Shujaat
• Format: Article
• Language: English
• Subjects: auto-encoder; Bioengineering and Biotechnology; classification; composition of k-spaced amino acid pair (CKSAAP); extracellular matrix (ECM); latent space learning; neural network
• ISSN: 2296-4185; 2296-4185
• DOI: 10.3389/fbioe.2021.752658

Extracelluar matrix (ECM) proteins create complex networks of macromolecules which fill-in the extracellular spaces of living tissues. They provide structural support and play an important role in maintaining cellular functions. Identification of ECM proteins can play a vital role in studying various types of diseases. Conventional wet lab-based methods are reliable; however, they are expensive and time consuming and are, therefore, not scalable. In this research, we propose a sequence-based novel machine learning approach for the prediction of ECM proteins. In the proposed method, composition of k-spaced amino acid pair (CKSAAP) features are encoded into a classifiable latent space (LS) with the help of deep latent space encoding (LSE). A comprehensive ablation analysis is conducted for performance evaluation of the proposed method. Results are compared with other state-of-the-art methods on the benchmark dataset, and the proposed ECM-LSE approach has shown to comprehensively outperform the contemporary methods.