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AI algorithms 'unveil' protein structures and predict protein interactions
Release time:
2024-12-27
Proteins are the core components of life systems, and their structure is the decisive factor in their functionality. Generally, proteins do not function independently; instead, they carry out tasks through interactions with each other, known as protein-protein interactions (PPI). With the rapid advancement of artificial intelligence, deep learning and machine learning have been widely applied in protein structure prediction and interaction prediction.
The applications of AI in proteomics can be broadly categorized into three areas: protein structure prediction, protein interaction prediction, and protein interaction confidence analysis.
Protein Structure Prediction
Protein structure prediction (PSP) is the method of deducing a protein's three-dimensional structure based on its amino acid sequence.
Amino acids combine to form the primary structure of proteins, which then spontaneously fold into tertiary structures. Protein functions are determined by their structures, making an understanding of protein structure crucial for studying their biological roles. Experimental efforts have identified approximately 100,000 unique protein structures, but this represents only a small fraction of the billions of protein sequences. If protein structures could be predicted directly from amino acid sequences, it would significantly accelerate scientific research.
Currently, the main deep learning modeling approaches used for protein structure prediction include AlphaFold2, RoseTTAFold, and ESMFold.
Among these, AlphaFold2 (AF2), developed by DeepMind, is an AI-based deep learning method for protein structure prediction. By leveraging deep learning algorithms, AF2 uses a neural network architecture called Evoformer to predict protein structures with atomic-level accuracy from amino acid sequences. It achieves this by utilizing information from homologous proteins and multiple sequence alignment (MSA).
Protein Interaction Prediction
In general, proteins do not function independently but accomplish tasks through interactions with each other, known as protein-protein interactions (PPI).
With the rapid advancement of computational technology, deep learning (DL) methods have been applied to the study of PPI. These methods extract structural information from protein sequences and identify interaction-related features, which are then used as feature vectors. By combining these feature vectors with existing classifier models, the probability of interaction between two proteins can be evaluated.
A research team from the Macau University of Science and Technology and Beijing University of Posts and Telecommunications has jointly developed an AI-based framework for fundamental research on protein-protein interactions (PPI).
Researchers from Professor Ji Zhiwei's team at the Nanjing Agricultural University, drew inspiration from natural language processing techniques to develop a lightweight protein language model called HNSPPI for predicting protein-protein interactions.
Protein Interaction Confidence Analysis
Protein interaction confidence refers to a metric used in biological research to assess the reliability of interactions between proteins.
Confidence analysis of protein interactions is conducted based on experimental data or known protein interaction databases. This research effectively evaluates the reliability of protein-protein interactions, playing a crucial role in understanding the structure and function of protein networks.
Professor Huang Shengyou's team at the Institute of Biophysics, Huazhong University of Science and Technology, has developed an innovative online platform for protein complex structure prediction called HDOCK. This platform enables the prediction of protein complex structures and provides accuracy scores for the predicted models.
Pronet Biotech Product Information
1. AI-PPI
What is AI-PPI?
AI-PPI is an advanced deep learning model for protein-protein interaction prediction, developed by Nanjing Pronet Biotech. Building on traditional protein interaction methods, the model has been independently optimized and modernized to stay at the forefront of innovation.
What is the data foundation of AI-PPI?
The AI-PPI model leverages the BioGRID and STRING databases as its primary data sources for deep learning.
What are the functions of AI-PPI?
AI-PPI integrates traditional protein interaction approaches with artificial intelligence-based deep learning. Using information from the databases, it constructs species-specific 3D structure libraries and predicts the probability of protein-protein interactions based on these predicted structures. The interaction probability predictions can then guide further experimental validations, significantly improving efficiency. This technology has broad applications in biological research processes, including yeast genome editing.
What is required to use AI-PPI?
You only need to provide the research species and the bait protein sequence to complete the analysis.
2. Digital Simulation Library
What is a Digital Library?
Nanjing Pronet Biotech has developed several comprehensive digital simulation libraries that cover transcription factors (TF) from different species, suitable for digital screening technologies.
What are the advantages of the Digital Simulation Library?
Zero experimental operation and reagent costs: Fully automated service that eliminates the need for complex experimental procedures and expensive reagents and consumables.
Fast candidate gene screening: Obtain yeast hybridization results within 14 days, eliminating the 3-4 months of waiting time typical in traditional methods.
Reliable results: AI screening results have been validated experimentally, and the AI predictions reliably correspond with experimental outcomes.
Low cost: The price is only about one-third of that of traditional experiments.
Reliable confidence analysis: Significantly narrows down the scope of protein or gene validation, preventing unnecessary work.
Journal-ready images: High-resolution 3D protein structure analysis images provided, meeting the formatting requirements of various journals.
Hassle-free follow-up experiments: Option to provide yeast one-on-one verification services for further validation and quantitative analysis of screening results, improving reliability and accuracy.
What is required to construct a Digital Simulation Library?
Amino acid sequence or full-length CDS sequence.
3. Protein Interaction Confidence Analysis (Y2H-AOS)
What is Protein Interaction Confidence Analysis?
Nanjing Pronet Biotech has independently developed an intelligent system for protein interaction confidence scoring, which is used to measure the reliability of interactions between proteins.
What are the application scenarios for Protein Interaction Confidence Analysis?
This analysis is used to assess the confidence of interactions between DNA-protein or protein-protein interactions and provides a confidence score to serve as a reference for subsequent experiments.
What are the advantages of Protein Interaction Confidence Analysis?
Zero experimental operation and reagent costs: Fully automated service that eliminates the need for complex experimental procedures and expensive reagents and consumables.
Fast candidate gene screening: Quickly obtain protein interaction maps, eliminating long waiting times.
Reliable confidence analysis: This greatly narrows down the scope for protein or gene validation, helping you avoid unnecessary work.
Journal-ready images: High-resolution 3D protein structure analysis images provided, meeting the formatting requirements of various journals.
Hassle-free follow-up experiments: Option to provide yeast one-on-one verification services for further validation and quantitative analysis of screening results, improving reliability and accuracy.
What can be delivered using Digital Simulation Library construction and screening?
DNA-Protein Interaction:
Protein-Protein Interaction:
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