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Amino Acid Biosynthetic Pathways01:29

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Amino acid biosynthesis is essential for cell growth, protein synthesis, and metabolic regulation. Cells generate essential and non-essential amino acids from metabolic intermediates to sustain vital biological functions. These intermediates originate from key metabolic pathways: glycolysis, the tricarboxylic acid (TCA) cycle, and the pentose phosphate pathway. Important precursors include α-ketoglutarate, pyruvate, oxaloacetate, phosphoenolpyruvate, and erythrose-4-phosphate, which...
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Cellular respiration is a fundamental metabolic process that enables organisms to generate energy from organic molecules. One of its central pathways is the tricarboxylic acid (TCA) cycle, also known as the Krebs cycle, which plays a crucial role in energy production and biosynthetic processes.Conversion of Pyruvate to Acetyl-CoAThe pyruvate generated from glycolysis undergoes oxidative decarboxylation by the pyruvate dehydrogenase complex, producing acetyl-CoA, one molecule of NADH, and one...
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Metabolism encompasses all biochemical reactions in a living organism, facilitating both the breakdown and synthesis of biomolecules. These metabolic processes are categorized into catabolic and anabolic pathways, which operate in a coordinated manner to ensure energy balance and cellular function.Catabolic Pathways and Energy ReleaseCatabolic pathways involve the breakdown of complex macromolecules such as carbohydrates, lipids, and proteins into smaller structures like monosaccharides, fatty...
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MVML-MPI: Multi-View Multi-Label Learning for Metabolic Pathway Inference.

Xiaoyi Liu1, Hongpeng Yang1, Chengwei Ai2

  • 1Computer Science and Engineering, University of South Carolina, Columbia 29208, USA.

Briefings in Bioinformatics
|November 6, 2023
PubMed
Summary
This summary is machine-generated.

We developed a novel Multi-View Multi-Label Learning for Metabolic Pathway Inference (MVML-MPI) framework to accurately predict compound participation in metabolic pathways. This approach enhances drug discovery and genome-scale metabolic model development.

Keywords:
feature fusionmetabolic pathwaymolecular representationmulti-label predictionmulti-view learning

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Area of Science:

  • Computational biology
  • Cheminformatics
  • Systems biology

Background:

  • Accurate identification of compound participation in metabolic pathways is crucial for drug discovery and constructing genome-scale metabolic models (GEMs).
  • Existing machine learning methods often fail to capture the complex, multifaceted nature of compounds, leading to inaccurate pathway predictions.
  • There is a need for advanced computational frameworks to improve metabolic pathway inference.

Purpose of the Study:

  • To introduce a novel framework, MVML-MPI (Multi-View Multi-Label Learning for Metabolic Pathway Inference), for accurate metabolic pathway prediction.
  • To address the limitations of current methods by effectively representing compound features and their relationships with metabolic pathways.
  • To enhance strategies for synthesizing new compounds, drug targeting, and developing GEMs.

Main Methods:

  • MVML-MPI employs parallel compound encoders to learn distinct representations and extract comprehensive features.
  • An attention-based fusion module integrates multi-view compound representations, capturing complex interdependencies.
  • The framework utilizes multi-label learning to predict multiple pathways a compound may participate in.

Main Results:

  • MVML-MPI demonstrated superior performance compared to state-of-the-art methods on the Kyoto Encyclopedia of Genes and Genomes pathways dataset.
  • The framework accurately represents compounds and effectively captures the intricate relationships between compounds and metabolic pathways.
  • Experimental results validate the efficacy of MVML-MPI in improving metabolic pathway inference.

Conclusions:

  • MVML-MPI offers a robust and effective solution for metabolic pathway inference, outperforming existing approaches.
  • The framework has significant potential to advance metabolic pathway design, aiding in the optimization of drug-like compounds.
  • MVML-MPI facilitates the development of more accurate and comprehensive GEMs, supporting various biological research applications.