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Predicting The Protein Structures Using AI

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USM BUSINESS SYSTEMS
Predicting The Protein Structures Using AI

Proteins are found in all organisms, and they are a sequence of amino acids arranged in a specific structure. For many decades, scientists have been able to quantify the order of amino acids accurately, but accurately estimating the form of a protein has always been a challenging task.

If we can decode the sequence of amino acids in a protein structure and determine the form of a protein accurately, it will provide many functions.

Accurate assessment of protein structure is beneficial in understanding the biological evolution of a particular protein and also helps to understand what kind of diseases it causes and what protection it can provide against other diseases.

In particular, an accurate assessment of the structure of a protein is crucial to understanding the function of proteins and cells and how they can function and cause disease, and this understanding is also beneficial for the development of treatment and vaccines.

Now, if you look at SARS-CoV-2, it is the spike protein structure of the virus that attaches to the AS2 receptor in our cells, causing infection. We were able to develop this understanding very quickly, mainly due to the progress we have made regarding our performance and assessment of protein structures.

First Discovery

See, this field began to develop many decades ago, and in 1972, Christian B. Unfinsen, a scientist, predicted that by accurately calculating the order of amino acids, we could reduce protein formation. 

So this discovery won him the Nobel Prize in Chemistry, and it laid the foundation for the analysis and evaluation of protein structures.

X-ray crystallography

About 60 years ago, a scientist named Max Perutz began to evaluate protein structures using experiments. He used X-ray crystallography to determine the exact form of myoglobin and haemoglobin, and this discovery helped us to understand the proper function of haemoglobin in the blood, which transports oxygen from the lungs to the tissues and cells of the lungs. This understanding of the structure of myoglobin and haemoglobin has helped us to understand how a change in a single amino acid can lead to diseases such as sickle cell anaemia.

Genome sequencing

Today, decoding the sequence of amino acids and completing the line more accurately is even more advanced, thanks to advances in the genetic series. 

Because at the end of the day, the protein structures in the amino acids are essentially a part of the gene and the rapid advances we have made in the gene sequence have helped us to calculate the order of the amino acids easily. However, when creating the 3D structure of a protein, these experimental methods, such as X-ray crystallography, nuclear magnetic resonance, etc. are very inefficient, and they are also very time consuming and expensive.

Contribution of India

When it comes to the field of protein structural work, India is also a leading country because GN Ramachandran, an Indian scientist who developed the Ramachandran plot, has become a model for scientists around the world today to understand the structure of proteins.

CASP technique

In 1994, a scientist named John Moult pioneered the CASP technique, a critical assessment for protein structure prediction, which provides a comparative model by which protein structures can be estimated.

The uniqueness of AI

However, tinging protein structures is a very complex and challenging exercise, primarily when you rely on traditional experimental methods such as X-ray crystallography and nuclear magnetic resonance. It was here that the emergence of artificial intelligence caused a significant disruption in the field of protein structure prediction. In 2018, a company called Deep Mind brought out artificial intelligence and in-depth learning with a predictive algorithm that today has become a breakthrough technology that can accurately predict protein structure.

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Conclusion:

These advances in protein structure production with the use of artificial intelligence and deep learning algorithms changed our understanding of SARS-CoV-2 and certainly accelerated the development of vaccines.

 
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