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Related Concept Videos

Tagging and Fusion Proteins01:24

Tagging and Fusion Proteins

Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
Conservation of Protein Domains Over Different Proteins02:26

Conservation of Protein Domains Over Different Proteins

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Conservation of Protein Domains02:26

Conservation of Protein Domains

Protein domains are small structurally independent units that are part of a single amino acid chain.  Although these domains are often structurally independent, they may rely on synergistic effects to perform their functions as part of a larger protein. Protein domains may be conserved within the same organism, as well as across different organisms.
A limited set of protein domains often duplicate and recombine during evolution. These domains can be organized in different combinations to form...

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Related Experiment Videos

Enhancing robustness in protein function prediction via missing modality imputation and adaptive multimodal fusion.

Yingwen Zhao1, Tianming Zhan1, Chao Zheng1

  • 1School of Computer Science, Nanjing Audit University, Nanjing, 211815, China.

Computational Biology and Chemistry
|July 7, 2026
PubMed
Summary

ProMIAF enhances protein function prediction by imputing missing data and adaptively fusing multimodal information. This robust framework improves accuracy, even with incomplete data, outperforming existing methods.

Keywords:
Gated attention mechanismMissing modality imputationNetwork propagationProtein function prediction

Related Experiment Videos

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Protein function prediction is crucial in genomics.
  • Current methods struggle with incomplete data and fusion strategies.
  • Integrating multiple biological data types shows promise.

Purpose of the Study:

  • To develop a novel framework, ProMIAF, for robust protein function prediction.
  • To address challenges of missing modalities and suboptimal fusion.
  • To improve the accuracy and robustness of predicting protein functions.

Main Methods:

  • ProMIAF uses Missing Modality Imputation and Adaptive Multimodal Fusion.
  • Advanced protein generation techniques recover missing structural and textual data.
  • Modality-specific encoders, gated attention, and network propagation are employed.

Main Results:

  • ProMIAF significantly outperforms state-of-the-art methods in predicting novel functional annotations.
  • Achieved improvements of 8.21% (AUPR), 8.90% (Smin), and 2.61% (Fmax) on the biological process branch.
  • Demonstrated robustness and accuracy even without structural and textual modalities.

Conclusions:

  • ProMIAF offers a robust solution for protein function prediction challenges.
  • The framework effectively leverages cross-modal complementarity.
  • ProMIAF advances the field by improving predictive performance with incomplete data.