# 1st Place Solution for the CAFA 6 Protein Function Prediction

A. MODEL SUMMARY
A.1 Background on you/your team
Competition Name:
CAFA6 Protein Function Prediction
Private Leaderboard Score:
0.52430
Private Leaderboard Place:
1
Name:
Hancheng Liu
Location:
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
Email:
[lhcheng0407@gmail.com](mailto:lhcheng0407@gmail.com)
Name:
Tianyang Huang
Location:
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
Email:
[tyhuang22@m.fudan.edu.cn](mailto:tyhuang22@m.fudan.edu.cn)
Name:
Huiying Yan
Location:
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
Email:
[yanhuiyingwangyi@163.com](mailto:yanhuiyingwangyi@163.com)
A2. Background on you/your team
Our team consists of three PhD students from ZhuLab at Fudan University, which is led by Professor Shanfeng Zhu. Our research focuses on automated protein function prediction, and biomedical text mining. Our lab has actively participated in the CAFA challenges and achieved top performance in CAFA3, CAFA4, and CAFA5. For CAFA6, our team developed GOAlpha, a Learning-to-Rank framework that integrates diverse sources of biological evidence, including protein sequence, network, structure, and textual information, for large-scale protein function prediction. H. Liu was responsible for collecting data, building the framework, and training the model. T. Huang and H. Yan were mainly engaged in biomedical text mining for protein annotation and result analysis.
A3. Summary
As illustrated in Figure 1, our CAFA6 solution, GOAlpha, integrates multiple sources of biological evidence, including protein sequence, interaction network, structure, domain, and biomedical literature information. The prediction scores generated by individual component methods are subsequently combined through a Learning-to-Rank Intergration to produce the final protein function predictions.
![](assets/dfdf1bec4819f186e8fda76c.png)
Figure 1. Overview of GOAlpha.
There are seven major component methods in GOAlpha:
BLAST-KNN:
A K-nearest-neighbor method based on sequence similarity. BLAST is used to identify homologous proteins in the annotated training set, and GO terms are transferred by weighted voting of the nearest homologs [1].
Net-KNN:
Similar to BLAST-KNN, it predicts protein functions from neighboring proteins in the protein-protein interaction (PPI) network [3].
FoldSeek-KNN:
Similar to BLAST-KNN, it transfers GO annotations from structurally similar proteins identified by FoldSeek.
SVM-ESM2:
It predicts GO terms using protein representations generated by the ESM2 protein language model. For each GO term, an SVM classifier is trained to estimate the association between proteins and GO terms [6].
LR-InterPro:
Logistic regression classifiers are trained on a 118,677-dimensional binary feature vector generated from protein families, domains, and motifs identified by InterProScan. Each dimension indicates the presence or absence of a specific InterPro feature in the target protein [1].
GOXML:
It is a biomedical text mining framework for large-scale GO function prediction. It formulates GO annotation as an extreme multi-label classification problem over protein–literature evidence units.
GORetriever:
It first retrieves candidate GO terms from proteins with similar descriptions and literature evidence, and subsequently reranks them according to the semantic relevance between GO concepts and protein-associated textual evidence [7].
In addition to the component methods described above, GOAlpha further incorporates GO term frequency, and a 21-dimensional species encoding. These features, together with the prediction scores from all component methods, are integrated using an XGBoost-based Learning-to-Rank framework [1] to generate the final predictions.
A4. Features Selections / Engineering
To evaluate the contribution of different biological evidence sources, we first constructed a validation benchmark that mimics the species distribution of the CAFA6 test set. Specifically, after obtaining the species composition of the CAFA6 target proteins, we randomly sampled 1,000 experimentally annotated proteins according to the same species distribution. These proteins were excluded from model training and used exclusively for validation and model selection.
The component methods of GOAlpha can be grouped into five categories according to the information source they utilize:
Sequence-based methods:
BLAST-KNN, and SVM-ESM2.
Domain-based methods:
LR-InterPro.
Structure-based methods:
FoldSeek-KNN.
Network-based methods:
Net-KNN.
Literature-based methods:
GOXML and GORetriever.
![](assets/78d368046f439529d1d9682c.png)
Figure 2. Performance on the validation benchmark.
Figure 2 shows the performance of GOAlpha and its component methods on the validation benchmark. Among individual predictors, sequence-based methods achieved the strongest standalone performance, highlighting the importance of evolutionary and language-model-derived sequence representations. In particular, BLAST-KNN and SVM-ESM2 consistently ranked among the best-performing component methods. FoldSeek-KNN demonstrated that structural similarity provides valuable functional signals beyond sequence homology. Although its standalone performance was slightly lower than the best sequence-based methods, it contributed complementary information during ensemble learning. Although the literature-based methods GOXML and GORetriever achieved only moderate standalone performance, they provided unique biological evidence that was often unavailable from sequence, structure, or network information alone. Overall, no single information source dominated across all proteins and GO categories. The final GOAlpha model therefore combines heterogeneous biological evidence through Learning-to-Rank, allowing the strengths of different component methods to be effectively integrated.
A5. Training Method(s)
Similar to our previous studies [6], we collected functional annotation information from three databases, SwissProt, GOA, and GO, according to the CAFA6 evidence codes. For model selection, we constructed a validation benchmark consisting of 1,000 experimentally annotated proteins sampled according to the species distribution of the CAFA6 target proteins. Finally, the prediction scores from all component methods were integrated using a Learning-to-Rank framework to generate the final GOAlpha predictions.
A6. Interesting findings
![](assets/717aad3d42d26d47ba8fd8fd.png)
Figure 3. Public and private leaderboard scores differ across components.
One interesting observation is that the relative performance of component methods differs between the public and private leaderboard. As shown in Figure 3, most component methods achieved higher scores on the private leaderboard than on the public leaderboard, although the magnitude of improvement varied considerably. In particular, BLAST-KNN, FoldSeek-KNN, and SVM-ESM2 showed noticeable gains on the private leaderboard, suggesting that sequence- and structure-based signals generalize well to unseen proteins. In contrast, the literature-based methods, GOXML and GORetriever achieved the strongest performance on the public leaderboard but experienced a decrease on the private leaderboard. These results indicate that leaderboard rankings may depend on the specific composition of the evaluation proteins. Consequently, strong public leaderboard performance does not necessarily guarantee the best final ranking. This was one of the main reasons why we relied on Learning-to-Rank to combine heterogeneous predictors instead of selecting a single best-performing component. More broadly, we believe that public leaderboard evaluation alone may not fully reflect a method's ability to generalize to future protein annotations. In practical applications, different evidence sources including protein domain/family databases, interaction networks, GO annotations databases, and literature resources are continuously updated over time. Therefore, when constructing evaluation benchmarks, it is important to ensure that all evidence used by a method is restricted to information available before the test protein experimental annotations were obtained [1,3,4,6].
A7. Simple Features and Methods
Among individual component methods, protein language model-based methods exhibit the most robust standalone performance. Nevertheless, integrating literature-based evidence and structural information consistently improves the final ensemble. We estimate that a combination of homology search and protein language models can recover a substantial fraction of the final performance, while additional information sources mainly contribute complementary signals for difficult proteins.
A8. Model Execution Time
Most component methods can be trained independently and in parallel. Generating protein embeddings, InterPro annotations, structural similarity searches, and literature-derived features requires the majority of computational resources. Training all component methods typically takes less than one day on modern GPU servers. Inference for CAFA6-scale datasets can be completed within one day, while the final Learning-to-Rank integration requires only a few hours.
A9. Acknowledgement
We would like to thank Prof. Shanfeng Zhu and Dr. Shaojun Wang for their valuable technical support, insightful discussions, and helpful suggestions throughout this work.
A10. References
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