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The ‘Giveaway Piggy Back Scam’ In Full Swing [2022]
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A fundamental understanding of protein structure is crucial in unraveling the mysteries of various biological processes. However, traditional experimental methods for determining protein structures, such as X-ray crystallography and NMR spectroscopy, are not only expensive but also time-consuming. In recent years, computational methods have emerged as a robust alternative, significantly accelerating the process of protein structure prediction. Breakthroughs like AlphaFold and the newly developed ESMFold have opened new doors in the field of computational biology.
ESMFold is a deep learning-based method that boasts high accuracy in predicting protein structures. Its backbone consists of a protein language model (pLM) that functions without the need for a lookup, MSA step, or external databases. The model is trained on millions of protein sequences from UniRef, an extensive database that provides a comprehensive representation of protein sequence diversity.
The secret sauce behind ESMFold’s high-speed predictions lies in its attention patterns, which represent the evolutionary interactions between the amino acids in a given sequence. As a result, ESMFold is capable of delivering prediction times up to 60 times faster than other computational methods.
The power of ESMFold can be easily harnessed through Amazon SageMaker, a robust platform that simplifies the process of deploying, scaling, and maintaining machine learning models. We demonstrate this by predicting the heavy chain structure of trastuzumab, a monoclonal antibody used for treating HER2-positive breast cancer. ESMFold’s speed in predicting trastuzumab’s structure holds immense potential for testing the effects of various sequence modifications, ultimately leading to improved patient outcomes and reduced side effects.
To run this example, it is recommended that you utilize an Amazon SageMaker Studio notebook with PyTorch 1.13 Python 3.9 CPU-optimized image on an ml.r5.xlarge instance type.
The procedure for predicting the protein structure using ESMFold consists of four main steps:
Visualize the experimental structure of trastuzumab:
Download the protein structure using the biopython library and a helper script from the RCSB Protein Data Bank. Then, visualize the 3D interactive structure using the py3Dmol library.
Prepare the sequence input for ESMFold:
Pre-process the trastuzumab sequence for ESMFold prediction using standard techniques.
Set up Amazon SageMaker, Hugging Face, and the ESMFold model:
Install the SageMaker and Hugging Face SDK, and import the necessary libraries. Load the pre-trained ESMFold model from Hugging Face, and generate the ESMFold prediction for trastuzumab’s structure.
Visualize and compare the predicted structure:
Visualize the predicted structure with the help of the py3Dmol library. Finally, compare the experimental and predicted protein structures to evaluate the efficacy of the ESMFold method.
ESMFold represents a groundbreaking advancement in computational protein structure prediction. Its deep-learning-based approach, combined with the use of protein language models, provides significantly faster prediction times without compromising on accuracy. Moreover, the end-to-end functionality of the ESMFold model allows for seamless integration with platforms like Amazon SageMaker. As demonstrated with the case of trastuzumab, this powerful tool optimizes the drug design process and ushers in a new era of personalized medicine, paving the way for improved patient outcomes and reduced side effects.
Up until working with Casey, we had only had poor to mediocre experiences outsourcing work to agencies. Casey & the team at CJ&CO are the exception to the rule.
Communication was beyond great, his understanding of our vision was phenomenal, and instead of needing babysitting like the other agencies we worked with, he was not only completely dependable but also gave us sound suggestions on how to get better results, at the risk of us not needing him for the initial job we requested (absolute gem).
This has truly been the first time we worked with someone outside of our business that quickly grasped our vision, and that I could completely forget about and would still deliver above expectations.
I honestly can't wait to work in many more projects together!
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*The information this blog provides is for general informational purposes only and is not intended as financial or professional advice. The information may not reflect current developments and may be changed or updated without notice. Any opinions expressed on this blog are the author’s own and do not necessarily reflect the views of the author’s employer or any other organization. You should not act or rely on any information contained in this blog without first seeking the advice of a professional. No representation or warranty, express or implied, is made as to the accuracy or completeness of the information contained in this blog. The author and affiliated parties assume no liability for any errors or omissions.