Automated Protein Function Prediction Using Machine Learning Techniques

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Table of content

  1. Abstract
  2. Related works
  3. Methodology
  4. Experiment Setup and Implementation
  5. Results and Analysis
  6. Conclusion
  7. Publications
  8. Links

Abstract

Protein function prediction is one of the significant tasks of bioinformatics that can help in a wide range of biological problems such as understanding disease mechanisms or finding drug targets.

The concept of protein function is highly context-sensitive and not very well-defined hence it acts as an umbrella term for all types of activities that a protein is involved in, be it cellular, molecular or physiological. Accordingly, several functional schemes have been proposed that were expected to have wide coverage, standardized format, hierarchical structure, disjoint categories, multiple functions, dynamic nature etc.

There are few such biomedical ontologies describing protein function, and the representations developed by the Gene Ontology (GO) Consortium is widely popular by now. The Gene Ontology (GO) provides a controlled vocabulary to classify the attributes of proteins based upon representative terms, referred to as “GO terms”. The GO system divides protein attributes into three main categories: molecular function (MF), biological process (BP) and cellular component (CC). Each GO term represents a unique functional attribute and all terms are associated to each other in a directed acyclic graph (DAG) structure based on inheritance relationships.Automated protein function prediction is critical for the annotation of uncharacterized protein sequences, where accurate prediction methods are still required.

The early techniques for predicting protein function have been experimental. Normally, they focused on a specific target gene or protein. Experimental procedures for protein function prediction are very low throughput and are thus unable to annotate a significant fraction of proteins that are becoming available due to rapid advances in genome sequencing technology. This has motivated the development of computational techniques that utilize a variety of high-throughput experimental data for protein function prediction, such as protein and genome sequences, gene expression data, protein interaction networks, and phylogenetic profiles.

The main target of the project was to create an accurate prediction model that assigns the best sub-graph of the gene ontology to each new protein and output a prediction score for this sub-graph and/or each predicted term. In this project two types of data are used to training the ML model, such as protein-protein interaction networks data and the protein sequence data with their GO terms. The experimental models in the project were created using two main DNN models, CNN and RNN.

Several different Deep learning models were created by combining these two components(CNN and RNN) several ways, and tested each of their performance. The comparison of their performance and the methodology followed are described below.

Methodology

Data set preparation

The machine learning models were trained with the protein sequence data and the Protein-Protein interactions data.Below is the methodology followed for preparation of these data.

Preparation of Sequence Data

Propagation of GO terms

The ancestors of each GO term were propagated using go-basic.obo file, and added to the GO term list in the dataset. The obsolete GO terms were removed.

Preparation of Protein-Protein interactions data(PPI)

Combining CNN and RNN

Overview

Through our literature review, we observed that CNN is often used for AFP and has shown relatively high performance in approaches that use CNN. CNNs are deep learning models that are widely used for image processing tasks. They have also been applied to protein sequence analysis, where they have been shown to outperform other methods in terms of accuracy and computational efficiency.

RNN, on the other hand, is also suitable for solving this problem. RNNs are a type of neural network designed to process sequential data by maintaining an internal memory of previous inputs. They have been widely used for tasks such as protein sequence analysis, where the sequence of amino acids in a protein plays a critical role in determining its function.

To understand the effect of using CNN and RNN separately for solving the AFP problem for proteins, an analysis can be performed. This analysis will help identify the strengths and weaknesses of each method and which approach is better suited for the problem at hand.

In addition, an analysis can also be conducted to understand the effect of combining CNN and RNN. The analysis can be based on the method used to combine the CNN and RNN components of the model. There are various methods for combining CNN and RNN, such as concatenation, addition, and attention. Each method has its strengths and weaknesses, and the performance of the model can vary depending on the method used.

In conclusion, an analysis of combining CNN and RNN can provide valuable insights into the strengths and weaknesses of each method and which approach is better suited for the AFP problem for proteins. It can also help identify the best method for combining CNN and RNN based on the problem at hand, leading to the development of more accurate and efficient models for Automatic Function Prediction in proteins. The application of these methods can improve our understanding of proteins and lead to the development of new drugs and treatments for various diseases. The analysis of CNN and RNN can make significant contributions to the field of biotechnology, and it will continue to be an area of active research for years to come.

Combining methods

Combining multiple models is a common approach used to improve the accuracy and reliability of predictions in machine learning. In some cases, a single model may not be able to capture all of the complexities of a problem, and combining several models can provide a more accurate and robust solution. In this article, we will discuss the two methods for combining models that we experimented on: ensemble methods and concatenating feature vectors.

Ensemble methods are a type of machine learning technique that involve combining multiple models to generate a final prediction. One of the simplest and most commonly used ensemble methods is the averaging method. In this method, the predictions of multiple models are averaged together to generate a final prediction. This method works well when the models being combined have similar accuracy and perform well on different parts of the data.

Another commonly used ensemble method is the weighted average method. In this method, each model’s predictions are weighted based on their relative performance. Models that perform better on the data receive higher weights, while models that perform poorly receive lower weights. This method can lead to better performance when the models have different strengths and weaknesses.

Concatenating feature vectors is another method for combining models. In this method, the features generated by multiple models are concatenated into a single vector, and this vector is used to generate the final prediction. This method is often used when the different models generate features that are complementary to each other. For example, one model may focus on identifying visual features in an image, while another model may focus on identifying textual features in the same image. By combining the features generated by these models, the final model can capture more of the relevant information.

There are several benefits to using a combination of models. One of the main benefits is increased accuracy. By combining multiple models, the final model is able to capture a broader range of features and information, leading to more accurate predictions. In addition, combining models can also improve the robustness of the final model. By using multiple models, the final model is less likely to be affected by noise or errors in the individual models.

However, there are also some challenges associated with combining models. One challenge is the potential for overfitting. If the models used for combination are highly correlated, the final model may simply be combining noise rather than useful information. Another challenge is the computational cost. Combining multiple models can require significant computing resources, which may be a limitation in some applications.

In conclusion, combining multiple models is a powerful approach for improving the accuracy and robustness of machine learning models. Ensemble methods and concatenating feature vectors are two popular methods for combining models, each with their own strengths and weaknesses. While there are some challenges associated with combining models, the benefits of increased accuracy and robustness make it a worthwhile technique to consider in many machine learning applications.

Integrating Protein Sequence data and Protein-Protein Interaction data

Overview

While many machine learning algorithms have been developed to predict protein function, the accuracy of these models can be limited by the quality and quantity of available data.

One way to improve the performance of AFP models is to integrate additional data sources, such as protein-protein interaction (PPI) networks. PPI networks provide valuable information about the relationships between different proteins, which can be used to infer the function of unannotated proteins.

Several studies have shown that integrating PPI networks can significantly improve the accuracy of AFP models. For example, studies has demonstrated that integrating PPI data with sequence-based features improved the performance of protein function prediction models. Similarly, some studies have shown that combining sequence and PPI data resulted in higher accuracy than using either data source alone.

Integrating PPI networks can also help address some of the challenges associated with AFP, such as the limited availability of annotated proteins. Previous studies have shown that incorporating PPI data improved the performance of AFP models even when the training data was limited.

In addition, integrating PPI networks into automated protein function prediction models can improve the accuracy of predicting protein functions related to the Biological Process Ontology (BPO) in gene ontology. PPI data provides valuable information about the interactions between different proteins, which can be used to infer the biological processes in which these proteins participate.

Integrating PPI data with sequence-based features has been shown to increase the accuracy of AFP models in predicting BPO terms. This is because PPI data can help identify the functional context of a protein, such as the biological pathways in which it is involved, which can be difficult to infer from sequence data alone. By incorporating information about the interactions between proteins, the performance of AFP models can be improved for predicting BPO terms.

Overall, integrating PPI networks with sequence-based features is a promising approach for improving the accuracy of AFP models in predicting BPO terms. By combining information from multiple data sources, such as PPI networks and sequence data, researchers can improve their understanding of the complex relationships between proteins and biological processes.

However, integrating PPI networks can also introduce new challenges. For example, PPI data can be noisy, incomplete, or biased towards certain types of proteins or interactions. To address these challenges, several methods have been proposed for integrating PPI data with sequence-based features, such as network-based methods and kernel-based methods.

In conclusion, integrating PPI networks with sequence-based features is a promising approach for improving the accuracy of AFP models. While there are some challenges associated with integrating PPI data, the benefits of higher accuracy and improved robustness make it a worthwhile strategy to consider in AFP research.

Experiment Setup and Implementation

Accordingly we developed multiple models as follows and experimented on them with Yeast data.

  1. CNN baseline
  2. Seq-CNN
  3. Seq-RNN
  4. Ensemble Seq (average of Seq-CNN and Seq-RNN)
  5. Seq-CNN-RNN-vectConcat (concatenated feature vector)
  6. Seq-CNN-RNN-layerMixed (Mixed CNN RNN layers)
  7. PPI-RNN
  8. Integrated model version 1

The table below contains the essential details of the above models.

Model Input/Feature Architecture Combining methods
CNN baseline Protein Sequence CNN -
Seq-CNN Protein Sequence CNN -
Seq-RNN Protein Sequence RNN -
Ensemble Seq (average of Seq-CNN and Seq-RNN) Protein Sequence CNN + RNN Average Ensembling
Seq-CNN-RNN-vectConcat (concatenated feature vector) Protein Sequence CNN + RNN Concatenating feature vectors
Seq-CNN-RNN-layerConcat (concatenated CNN RNN layers) Protein Sequence CNN + RNN Mixing layers of CNN and RNN
PPI-RNN PPI RNN -
Fully Integrated model(version 1) Protein Sequence + PPI CNN + RNN Average Ensembling (in sequence component),Concatenating feature vectors

Results and Analysis

When comparing sequence-only methods combined CNN and RNN methods have outperformed the Seq-CNN, Seq-RNN and CNN baseline. It is observed that there is a significant performance improvement in protein function prediction when CNN and RNN components are combined than the prediction with standalone components for sequence data.

Compared to the sequence-only methods that have combined CNN and RNN, ‘Integrated model version 1’ has shown better performance. And also ‘Integrated model version 1’ shows a significant performance(Fmax) improvement for CCO ontology when trained using the HUMAN species data. It has not shown much performance difference between ontologies when trained with the YEAST species data. Therefore by training with a higher number of protein data, the PPI integration can make a much higher improvement in the performance.

The PPI network dataset has missing association scores in it. Therefore in future work, we expect to handle the missing accuracy scores to improve the prediction. And also we expect to follow more preprocessing steps on sequence data for further improvements.

Conclusion

Publications