AI-Powered Protein Structure Prediction System
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Private
Technology Title
AI-Powered Quality Control and Inspection in Manufacturing
AI-Powered Quality Control and Inspection in Manufacturing
Project Title
AI-Powered Protein Structure Prediction System
AI-Powered Protein Structure Prediction System
Category
Computer Science
Computer Science
Short Description
A machine learning platform designed to predict 3D protein structures from amino acid sequences.
A machine learning platform designed to predict 3D protein structures from amino acid sequences.
Long Description
The machine learning platform, termed 'Proteus', is designed to predict 3D protein structures from amino acid sequences. It leverages a deep learning architecture, combining elements of natural language processing (NLP) and computer vision to tackle the complex task of protein structure prediction.At its core, Proteus utilizes a transformer-based encoder-decoder framework. The encoder takes in an amino acid sequence and outputs a set of vectors representing the sequence. These vectors are then passed through a series of layers, including a multiple sequence alignment (MSA) transformer and a structure transformer. The MSA transformer generates a set of features that capture the evolutionary relationships between the input sequence and its homologs. The structure transformer uses these features to predict the 3D coordinates of the protein's atoms.The platform's architecture is built around the AlphaFold model, with several key modifications. Proteus incorporates a more advanced MSA generation pipeline, which leverages a combination of HHblits and JackHMMER to generate a more comprehensive set of homologs. Additionally, the platform utilizes a novel attention mechanism, termed 'Evoformer', which allows the model to more effectively capture long-range dependencies in the protein sequence.Proteus is trained on a large dataset of protein structures, including those from the Protein Data Bank (PDB) and AlphaFold's training set. The platform's performance is evaluated using a range of metrics, including the Global Distance Test (GDT) and the Root Mean Square Deviation (RMSD). Proteus has demonstrated state-of-the-art performance on several benchmark datasets, including the Critical Assessment of protein Structure Prediction (CASP) and the Protein Structure Prediction (PSP) dataset.The platform's software architecture is built around a microservices-based framework, with separate services for data ingestion, model training, and prediction. Proteus utilizes a combination of open-source libraries, including PyTorch and TensorFlow, to build and train its models. The platform's API is designed to be highly scalable and flexible, allowing users to easily integrate Proteus into their existing workflows.In terms of technical specifications, Proteus is built on a high-performance computing (HPC) cluster, comprising multiple nodes with NVIDIA V100 and A100 GPUs. The platform's training dataset is stored on a distributed file system, allowing for fast and efficient access to the data. Proteus's prediction pipeline is optimized for performance, using a combination of parallel processing and GPU acceleration to minimize prediction times.Overall, Proteus represents a significant advancement in the field of protein structure prediction, offering a highly accurate and efficient platform for predicting 3D protein structures from amino acid sequences. Its advanced architecture and state-of-the-art performance make it an attractive solution for researchers and developers working in the fields of structural biology and protein engineering.
The machine learning platform, termed 'Proteus', is designed to predict 3D protein structures from amino acid sequences. It leverages a deep learning architecture, combining elements of natural language processing (NLP) and computer vision to tackle the complex task of protein structure prediction.At its core, Proteus utilizes a transformer-based encoder-decoder framework. The encoder takes in an amino acid sequence and outputs a set of vectors representing the sequence. These vectors are then passed through a series of layers, including a multiple sequence alignment (MSA) transformer and a structure transformer. The MSA transformer generates a set of features that capture the evolutionary relationships between the input sequence and its homologs. The structure transformer uses these features to predict the 3D coordinates of the protein's atoms.The platform's architecture is built around the AlphaFold model, with several key modifications. Proteus incorporates a more advanced MSA generation pipeline, which leverages a combination of HHblits and JackHMMER to generate a more comprehensive set of homologs. Additionally, the platform utilizes a novel attention mechanism, termed 'Evoformer', which allows the model to more effectively capture long-range dependencies in the protein sequence.Proteus is trained on a large dataset of protein structures, including those from the Protein Data Bank (PDB) and AlphaFold's training set. The platform's performance is evaluated using a range of metrics, including the Global Distance Test (GDT) and the Root Mean Square Deviation (RMSD). Proteus has demonstrated state-of-the-art performance on several benchmark datasets, including the Critical Assessment of protein Structure Prediction (CASP) and the Protein Structure Prediction (PSP) dataset.The platform's software architecture is built around a microservices-based framework, with separate services for data ingestion, model training, and prediction. Proteus utilizes a combination of open-source libraries, including PyTorch and TensorFlow, to build and train its models. The platform's API is designed to be highly scalable and flexible, allowing users to easily integrate Proteus into their existing workflows.In terms of technical specifications, Proteus is built on a high-performance computing (HPC) cluster, comprising multiple nodes with NVIDIA V100 and A100 GPUs. The platform's training dataset is stored on a distributed file system, allowing for fast and efficient access to the data. Proteus's prediction pipeline is optimized for performance, using a combination of parallel processing and GPU acceleration to minimize prediction times.Overall, Proteus represents a significant advancement in the field of protein structure prediction, offering a highly accurate and efficient platform for predicting 3D protein structures from amino acid sequences. Its advanced architecture and state-of-the-art performance make it an attractive solution for researchers and developers working in the fields of structural biology and protein engineering.
Potential Applications
Drug discovery and development: The platform can be used to predict the 3D structure of proteins that are targets for drugs, allowing for more accurate identification of potential binding sites and design of more effective drugs.
Protein engineering: By predicting the 3D structure of proteins, the platform can help design new proteins with specific functions, such as enzymes with improved catalytic activity or proteins with enhanced stability.
Disease research and diagnosis: The platform can be used to study the 3D structure of proteins associated with various diseases, such as Alzheimer's or Parkinson's, to better understand the underlying mechanisms and identify potential therapeutic targets.
Biotechnology and bioindustry: The platform can be applied to predict the 3D structure of proteins used in industrial processes, such as enzymes for biofuel production or proteins for food processing, to improve their efficiency and stability.
Personalized medicine: By predicting the 3D structure of proteins specific to an individual's genetic profile, the platform can help tailor treatments to a patient's unique needs and genetic makeup.
Vaccine development: The platform can be used to predict the 3D structure of viral proteins, allowing for the design of more effective vaccines that target specific epitopes.
Protein-ligand interactions: The platform can be used to study the binding of small molecules to proteins, allowing for a better understanding of protein-ligand interactions and the design of more effective inhibitors or activators.
Synthetic biology: By predicting the 3D structure of designed proteins, the platform can help construct new biological pathways and circuits.
Protein degradation and recycling: The platform can be used to study the 3D structure of proteins involved in protein degradation and recycling, such as ubiquitin and proteasome.
Membrane protein structure prediction: The platform can be applied to predict the 3D structure of membrane proteins, which are difficult to study experimentally.
Drug discovery and development: The platform can be used to predict the 3D structure of proteins that are targets for drugs, allowing for more accurate identification of potential binding sites and design of more effective drugs.
Protein engineering: By predicting the 3D structure of proteins, the platform can help design new proteins with specific functions, such as enzymes with improved catalytic activity or proteins with enhanced stability.
Disease research and diagnosis: The platform can be used to study the 3D structure of proteins associated with various diseases, such as Alzheimer's or Parkinson's, to better understand the underlying mechanisms and identify potential therapeutic targets.
Biotechnology and bioindustry: The platform can be applied to predict the 3D structure of proteins used in industrial processes, such as enzymes for biofuel production or proteins for food processing, to improve their efficiency and stability.
Personalized medicine: By predicting the 3D structure of proteins specific to an individual's genetic profile, the platform can help tailor treatments to a patient's unique needs and genetic makeup.
Vaccine development: The platform can be used to predict the 3D structure of viral proteins, allowing for the design of more effective vaccines that target specific epitopes.
Protein-ligand interactions: The platform can be used to study the binding of small molecules to proteins, allowing for a better understanding of protein-ligand interactions and the design of more effective inhibitors or activators.
Synthetic biology: By predicting the 3D structure of designed proteins, the platform can help construct new biological pathways and circuits.
Protein degradation and recycling: The platform can be used to study the 3D structure of proteins involved in protein degradation and recycling, such as ubiquitin and proteasome.
Membrane protein structure prediction: The platform can be applied to predict the 3D structure of membrane proteins, which are difficult to study experimentally.
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Email
Anu@yopmail.com
Anu@yopmail.com