Explained: Computational Predictions of Protein Structures Associated with COVID-19

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Protein Structures Associated with COVID-19 : AlphaFold system by releasing structure predictions of several under-studied proteins associated with SARS-CoV-2, the virus that causes COVID-19.

New Delhi (ABC Live India): In the era of COVID-19 pandemic, Immune system of human body has become the most public discussed subject for past two years worldwide. The ABC Research team working and researching on human body referred a scientific terminology for our readers so that our readers can better understand how immune system of our body works.

The term “Protein folding” is the most complex subject in  field of the human body science till today, as science is able to know about some protein structures via which protein performs various works at cell levels but still many secrets of functioning of human body is out of reach of human’s mind.

Why Protein folding is Important for us?

Protein folding occurs in a cellular compartment called the endoplasmic reticulum. This is a vital cellular process because proteins must be correctly folded into specific, three-dimensional shapes in order to function correctly. Unfolded or misfolded proteins contribute to the pathology of many diseases.

What is the process of protein folding?

Protein folding is a process by which a polypeptide chain folds to become a biologically active protein in its native 3D structure. ... The amino acids in the chain eventually interact with each other to form a well-defined, folded protein. The amino acid sequence of a protein determines its 3D structure.

How Protein folding Start?

Protein folding is a spontaneous process that is mainly guided by hydrophobic interactions, formation of intermolecular hydrogen bonds, van der Waals forces, and it is opposed by conformational entropy

How Many Types of Protein folding?

As human body science there are four structures through which protein performs various works in human body at cell levels including immunity against COVID-19, Primary structure, Secondary structure, Tertiary structure and Quaternary structure.

Know the Computational predictions of protein structures associated with COVID-19

The DeepMind open source  research  published the following article on its official website. 

The scientific community has galvanised in response to the recent COVID-19 outbreak, building on decades of basic research characterising this virus family. Labs at the forefront of the outbreak response shared genomes of the virus in open access databases, which enabled researchers to rapidly develop tests for this novel pathogen. Other labs have shared experimentally-determined and computationally-predicted structures of some of the viral proteins, and still others have shared epidemiological data. We hope to contribute to the scientific effort using the latest version of our AlphaFold system by releasing structure predictions of several under-studied proteins associated with SARS-CoV-2, the virus that causes COVID-19. We emphasise that these structure predictions have not been experimentally verified, but hope they may contribute to the scientific community’s interrogation of how the virus functions, and serve as a hypothesis generation platform for future experimental work in developing therapeutics. We’re indebted to the work of many other labs: this work wouldn’t be possible without the efforts of researchers across the globe who have responded to the COVID-19 outbreak with incredible agility.

Knowing a protein’s structure provides an important resource for understanding how it functions, but experiments to determine the structure can take months or longer, and some prove to be intractable. For this reason, researchers have been developing computational methods to predict protein structure from the amino acid sequence.  In cases where the structure of a similar protein has already been experimentally determined, algorithms based on “template modelling” are able to provide accurate predictions of the protein structure. AlphaFold, our recently published deep learning system, focuses on predicting protein structure accurately when no structures of similar proteins are available, called “free modelling”.  We’ve continued to improve these methods since that publication and want to provide the most useful predictions, so we’re sharing predicted structures for some of the proteins in SARS-CoV-2 generated using our newly-developed methods.

It’s important to note that our structure prediction system is still in development and we can’t be certain of the accuracy of the structures we are providing, although we are confident that the system is more accurate than our earlier CASP13 system. We confirmed that our system provided an accurate prediction for the experimentally determined SARS-CoV-2 spike protein structure shared in the Protein Data Bank, and this gave us confidence that our model predictions on other proteins may be useful. We recently shared our results with several colleagues at the Francis Crick Institute in the UK, including structural biologists and virologists, who encouraged us to release our structures to the general scientific community now. Our models include per-residue confidence scores to help indicate which parts of the structure are more likely to be correct. We have only provided predictions for proteins which lack suitable templates or are otherwise difficult for template modeling.  While these understudied proteins are not the main focus of current therapeutic efforts, they may add to researchers’ understanding of SARS-CoV-2.  

Normally we’d wait to publish this work until it had been peer-reviewed for an academic journal. However, given the seriousness and time-sensitivity of the situation, we’re releasing the predicted structures as we have them now, under an open license so that anyone can make use of them.  

Interested researchers can read more technical details about these predictions in a document included with the data. The protein structure predictions we're releasing are for SARS-CoV-2 membrane protein, protein 3a, Nsp2, Nsp4, Nsp6, and Papain-like proteinase (C terminal domain). To emphasise, these are predicted structures which have not been experimentally verified. Work on the system continues for us, and we hope to share more about it in due course.

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