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Background <p>The annotation of protein functions constitutes a key connection between genetic sequences, molecular conformations, and biochemical roles, driving progress in biomedical studies. Traditional experimental methods are time-consuming and resource-intensive, making it difficult to meet the demand for functional annotation of a vast number of proteins in the post-genomic era. The development of high-throughput sequencing technology has generated a large amount of protein-protein interaction (PPI) data. Prediction methods based on network topology have attracted attention due to their high efficiency and interpretability. The FSWeight algorithm calculates functional similarity by evaluating the commonality of second-order neighbors of proteins. However, it has limitations in terms of insufficient local information and a limited global perspective.</p> Results <p>In this study, we propose the topology-aware functional similarity (TAFS) framework, which integrates local neighborhood information with global topological information. A distance-dependent functional attenuation factor <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12859_2025_6273_Article_IEq1.gif" Format="GIF" Height="12" Rendition="HTML" Resolution="72" Type="Linedraw" Width="13" /> </InlineMediaObject> <EquationSource Format="TEX">\(\gamma \)</EquationSource> </InlineEquation> is introduced to dynamically adjust the weights of distant nodes, and a bidirectional joint co-function probability model is constructed. Experiments show that TAFS outperforms traditional baseline methods in both single-species and cross-species evaluations.</p> Conclusion <p>TAFS significantly improves prediction accuracy and interpretability through refined topological modeling, providing new insights for functional inference in complex biological networks.</p>

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Topology-aware functional similarity: integrating extended neighborhoods via exponential attenuation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月21日

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  • Peng Wang

摘要

Background

The annotation of protein functions constitutes a key connection between genetic sequences, molecular conformations, and biochemical roles, driving progress in biomedical studies. Traditional experimental methods are time-consuming and resource-intensive, making it difficult to meet the demand for functional annotation of a vast number of proteins in the post-genomic era. The development of high-throughput sequencing technology has generated a large amount of protein-protein interaction (PPI) data. Prediction methods based on network topology have attracted attention due to their high efficiency and interpretability. The FSWeight algorithm calculates functional similarity by evaluating the commonality of second-order neighbors of proteins. However, it has limitations in terms of insufficient local information and a limited global perspective.

Results

In this study, we propose the topology-aware functional similarity (TAFS) framework, which integrates local neighborhood information with global topological information. A distance-dependent functional attenuation factor \(\gamma \) is introduced to dynamically adjust the weights of distant nodes, and a bidirectional joint co-function probability model is constructed. Experiments show that TAFS outperforms traditional baseline methods in both single-species and cross-species evaluations.

Conclusion

TAFS significantly improves prediction accuracy and interpretability through refined topological modeling, providing new insights for functional inference in complex biological networks.