Creating Efficient Protein Folding Workflows for Accelerated Drug Discovery using Amazon SageMaker
Introduction:
Drug development is a complex and costly process that involves evaluating thousands of drug candidates to find effective treatments with minimal harm to patients. Machine learning (ML) methods, such as protein structure prediction, can help streamline the drug discovery process and save billions in development costs. Predicting protein folding, which determines how proteins interact with drug compounds, is a challenging task. Deep learning algorithms like AlphaFold2 and OpenFold have shown promise in accurately predicting protein structures. However, these models are computationally expensive and difficult to compare at scale. This article presents a solution using Amazon SageMaker, a fully managed ML service, to simplify protein folding structure prediction workflows. Scientists can launch experiments, analyze structures, monitor job progress, and track experiments in SageMaker Studio. The solution architecture involves genetic databases, multiple sequence alignment, protein structure prediction, visualization, and evaluation. Additionally, the article outlines the steps to run protein folding jobs on SageMaker using container images and SageMaker estimators.
Full Article: Creating Efficient Protein Folding Workflows for Accelerated Drug Discovery using Amazon SageMaker
Streamlining Protein Folding Structure Prediction with Amazon SageMaker
Drug development is a complex and expensive process that involves screening thousands of drug candidates. Traditional methods for evaluating leads, such as X-ray crystallography and NMR spectroscopy, are time-consuming and costly. However, recent advances in deep learning methods offer a more efficient solution. Machine learning (ML) methods can help identify suitable compounds at each stage of the drug discovery process, resulting in more streamlined drug prioritization and testing.
Proteins are the building blocks of life and understanding their 3D structure is crucial for drug development. Predicting how proteins fold into their 3D structure is a difficult problem, but deep learning methods have shown promise in achieving accurate predictions. Algorithms like AlphaFold2, ESMFold, OpenFold, and RoseTTAFold can quickly build accurate models of protein structures.
However, running these models can be computationally expensive and comparing results at a large scale can be cumbersome. To address these challenges, researchers and commercial R&D teams can leverage Amazon SageMaker, a fully managed service for machine learning. SageMaker provides a range of capabilities for building, training, and deploying ML models without the need to manage infrastructure, data, or scalability.
In this post, we introduce a fully managed ML solution with SageMaker that simplifies protein folding structure prediction workflows. Scientists can use SageMaker to launch protein folding experiments, analyze 3D structures, monitor job progress, and track experiments in a user-friendly environment. The solution utilizes key components like FASTA target sequences, genetic databases, multiple sequence alignment (MSA), folding algorithms, visualization and metrics.
The workflow begins with scientists using the web-based SageMaker ML IDE to explore the code base and build protein sequence analysis workflows. Genetic and structure databases required by the folding algorithms are downloaded using SageMaker Processing, which provides ephemeral compute for ML data processing. An Amazon FSx for Lustre file system is set up to store the databases, allowing for high throughput file retrieval.
SageMaker Pipelines orchestrate multiple runs of protein folding algorithms, such as AlphaFold and OpenFold. These computationally heavy algorithms can utilize the FSx for Lustre file system for efficient database search. The workflow is divided into an MSA construction step using a CPU instance, and a structure prediction step using a GPU instance. Job output is saved in an S3 location for analysis and comparison.
The protein folding prediction runs are automatically tracked by Amazon SageMaker Experiments, which facilitates further analysis. Job logs are stored in Amazon CloudWatch for monitoring purposes.
To run protein folding on SageMaker, you can leverage the platform’s fully managed capabilities, which eliminate the need for extensive infrastructure management. SageMaker allows you to start ephemeral jobs on-demand with container images, without the need for self-managing compute infrastructure. The flexibility of SageMaker estimators enables you to choose the container image, run script, and instance configuration that best suit your needs.
By utilizing Amazon SageMaker, researchers and commercial R&D teams can streamline the protein folding structure prediction process, saving time and resources. The user-friendly interface, along with the fully managed environment, enables easy experimentation, analysis, and collaboration. With the power of deep learning and the convenience of SageMaker, the drug development process can be accelerated and optimized.
Summary: Creating Efficient Protein Folding Workflows for Accelerated Drug Discovery using Amazon SageMaker
Drug development is a complex and lengthy process that involves screening thousands of potential drug candidates. Machine learning (ML) methods can help streamline this process by identifying suitable compounds and saving billions in development costs. Protein structure prediction is a critical aspect of drug discovery, but traditional methods can be time-consuming and expensive. However, recent advances in deep learning have shown promise in accurately predicting protein folding. Amazon SageMaker is a fully managed service that simplifies the operation of protein folding structure prediction workflows. Scientists can launch experiments, analyze structures, monitor progress, and track experiments in SageMaker Studio. This solution utilizes genetic databases, multiple sequence alignment, folding algorithms, visualization, and metrics to facilitate protein structure prediction. SageMaker Processing, Amazon FSx for Lustre, and SageMaker Pipelines are used to handle data processing, storage, and job orchestration. The solution also provides the ability to compare structure predictions with Amazon SageMaker Experiments. By using SageMaker, researchers and R&D teams can incorporate the latest advances in protein structure prediction in a scalable and efficient manner.
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