Bacteria and archaea play key roles in global biogeochemical cycles and have extensive applications in biotechnology. Annotating protein sequences according to their biological functions is one of the key steps in understanding microbial diversity, metabolic potential, and evolutionary history. However, experimental methods to identify protein function are time-consuming and expensive, resulting in many proteins remaining uncharacterized to this day. It means that even the best-studied microorganisms are still not fully functionally characterized. The integration of big data and artificial intelligence has emerged as a promising approach to predict and facilitate protein functional discovery. At CD ComputaBio, our scientists train models using statistical algorithms to identify patterns in large data sets. These can integrate diverse data sources such as sequence homology, structural information, and functional annotations of related proteins.
Advantages of AI Methods in Functional Protein Annotation
| Classical Methods |
AI Methods |
| Content |
Features |
Content |
Features |
| Sequence Homology |
Classical methods often rely on comparing protein sequences to known sequences in databases using tools like BLAST or FASTA. |
Machine Learning (ML) |
AI-based ML methods use algorithms to train models on annotated protein data. ML algorithms can handle large datasets efficiently. |
| Protein Family Analysis |
This approach aims to identify conserved protein domains or regions within a protein sequence, which can provide insights into its function. |
Unsupervised Learning |
These methods cluster proteins based on similarities in their features, aiding in functional annotation. It identifies novel members and extracts meaningful information from protein sequence data without the need for prior labeling or supervision. |
| Structural Prediction |
Classical methods use algorithms like homology modeling or protein threading to predict the 3D structure of proteins. Structural information can aid in functional annotation. |
Supervised Learning |
Models are trained on labeled data, consisting of protein sequences and their known functions. They can learn non-linear relationships and identify hidden patterns in the data. |
| Gene Ontology (GO) Terms |
Protein functions can be annotated using controlled vocabularies provided by GO terms. |
Deep Learning (DL) |
Neural networks and deep learning architectures can integrate multiple types of data. DL offers improved accuracy, non-linear relationships capturing, automatic feature extraction, scalability, and transfer learning opportunities. |
Our Services
By harnessing the power of AI for functional protein annotation, we can unlock substantial opportunities for innovation, accelerate protein-based research, and ultimately contribute to the development of cutting-edge products and therapies in the protein industry.
- Data Integration and Mining
We leverage AI algorithms to integrate diverse biological data sources, including genomics, proteomics, and structural biology data, to supply a more holistic view for protein annotation.
- Functional Prediction
Using machine learning models, we predict and annotate functional attributes such as protein subcellular localization, enzymatic activity, and biological pathways.
- Structural Analysis Support
Our AI tools assist in predicting and modeling protein structures and identifying structural motifs critical for protein function.
- Customized Annotation Reports
We provide detailed reports summarizing the functional annotations, allowing clients to grasp and interpret the implications of the results for their specific research objectives.
Why Choose Us?
CD ComputaBio provides AI-based functional protein annotation services, prediction of enzyme property, trans-isomerase screening and design services, etc. We use machine learning, deep learning, neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and reinforcement learning (RL) methods to provide valuable insights for various biological research and drug development applications. If you are interested in our services or have any questions, please feel free to contact us.
Reference:
- Mazurenko S, Prokop Z, Damborsky J. Machine learning in enzyme engineering[J]. ACS Catalysis, 2019, 10(2): 1210-1223.
