# Automated Confirmation of Protein Annotation Using NLP and the UniProtKB Database
## 4 Conclusions and Future Work  
In this paper, we presented a novel smart program to confirm protein annotations. Our smart program uses ensemble learning together with word embedding to effectively classify whether the title of a journal publication provides the information needed to show that experimental evidence of protein function for a given protein annotation is presented in the publication. Our ensemble learning method achieves 71.26% accuracy, 91.25% recall, 65.19% precision, and an F1 score of 76.05%. When a publication is classified as positive, its title together with the accession number for the protein entry will be sent to a human curator for manual inspection. This ensures a precision of 100%. After the curator has confirmed that the publication or publications do verify protein function experimentally, they indicate that the protein annotation has been confirmed. We have described our smart program for verifying protein annotation. It will be part of a system we are currently developing, which will have a query-able website as a front end to a query-able database. The database will consist of homologous clusters of protein sequences obtained using the pClust software [5]. pClust will be used to simultaneously cluster proteomes deduced from the genomes of bacteria, archaea, eukarya, and viruses. The website will show the cluster most closely matching the protein sequence query, and each sequence in the cluster will include the FASTA header for the sequence, which gives the accession number, annotation, and organism. Protein sequences that have been marked by the human curator as having been verified will be identified by colored FASTA text. As discussed previously, the smart program will automatically query the UniProtKB database periodically so that annotations are updated on a regular basis. Because homology does not guarantee the same molecular function, users are free to choose an annotation based on their understanding of the relationship of their organism to those in the cluster or else to use a program we will provide that will create a sequence similarity network (SSN) of the protein sequences they have chosen (including any that have been verified). The SSN will indicate the sequences most closely related to their query sequence. A sequence that has been confirmed experimentally will appear in color in the SSN, and this will allow a user to make an informed decision about whether or not to use the annotation for their sequence. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Protein Annotation** & **Entry Identifier** & **Prediction** & **Database \({}^{*}\)** \\ \hline Glucarate dehydratase & P0 AES2 & Positive & SP \\ Methylaspartate ammonia-lyase & Q05514 & Positive & SP \\
60S ribosomal protein L5-2 & Q8L4L4 & Negative & SP \\ Protein PsiE homolog & Q81 XB6 & Negative & SP \\ MEOX2 protein & Q6 FFIY5 & Positive & T \\ Beta catenin 1 & B1 MV73 & Positive & T \\ Maillard deglycase & K7G4K8 & Negative & T \\ Catenin (Cadherin-associated protein), beta 1, 88 kDa & H2R2U1 & Negative & T \\ \hline \hline \end{tabular} \({}^{*}\) SP: Swiss-Prot, T: TEMBL.  
\end{table} 
Table 2: Results using our ensemble learning model for several different protein annotations. **Author Contributions:** [PERSON] collected the data, pre-processed them, trained the models, designed and performed the experiments, analyzed the results, and prepared the initial manuscript. [PERSON] and [PERSON] analyzed the collected data, approved the method, guided the experiments, edited the manuscript, and further interpreted the experimental results. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported in part by the Carl M. Hansen Foundation.