A new era in digital biology.. "Alphafold" a revolutionary database to predict the shape of 200 million proteins

 Understanding how proteins necessary for life are formed has been considered one of the "great challenges" in biology, and scientists have spent decades trying to understand how they are formed.

Starting today, determining the three-dimensional shape of any protein known to science will be simple, as a new tool has identified the structures of about 200 million proteins, after the company "Deep Mind" unveiled a revolutionary artificial intelligence network to predict the structures of three-dimensional proteins, covering almost all proteins known to all living organisms, and this will enable scientists to immediately access in-depth information about the basic building blocks of life.


"Alphafold" and "Deep Mind"

According to the British newspaper "Daily Mail", before the artificial intelligence program known as "alpha Fold", scientists used to spend months or years understanding the structure of proteins, and researchers often used tools such as X-rays, but the program "alphaFold" developed by the company "Deep Mind" of the company "Google" is able to accomplish deep learning in order to predict the structure of proteins.

The first version of this program was published in 2018, and the second version was published in late 2020 and is available with open source software for searching within databases on the total proteome of species and organisms.
More than 500 thousand researchers in the world from 190 countries have used the database "alphafold" to display more than two million protein structures, and this complex information is now available at the same search speed on Google, and the program now predicts the structure of almost all proteins known to science, whether in animals, plants, humans, bacteria or other living organisms.


And" Deep Mind " is a British artificial intelligence company, founded in 2010, and renamed after it was acquired by Google in 2014. The company has created a "neural network software" that can learn how to play video games in a similar way to a human, and the neural network may be able to access external memory, so make the computer able to simulate the short-term memory of the human brain.

An important resource for scientists
The ability to quickly see the structure of a protein in three dimensions is valuable for scientists seeking to treat diseases and researchers wanting immediate access to in-depth information about the building blocks of life.

Since its launch in 2020, researchers have already used "alphaFold" to understand proteins that affect the health of honey bees and to develop an effective malaria vaccine. The expanded database could serve as an important resource for scientists to better understand diseases, and it could also accelerate innovation in drug discovery and biology.

The database allows researchers to search for three-dimensional structures of proteins "as easily as doing a Google search with keywords," says DeepMind founder and CEO Demis Hassabis.

He explained in an article on the company's website that the latest version of the data gives a strong boost to the database, and the update includes structures for "plants, bacteria, animals and many other living organisms", and this opens up huge opportunities for the alphafold program to influence important issues such as "sustainability, fuel, food insecurity, neglected diseases".

Just a starting point
"AlphaFold is perhaps the largest contribution of the AI community to the scientific community," said Jianping, a professor of computer science at the University of Illinois-Champaign and a specialist in computational biology for MIT Technology Review. "It can also help scientists reevaluate previous research to better understand how diseases occur," he added.


"Predicting protein structures takes a long time, and having a tool with 200 million protein structures readily available will save researchers a lot of time," Mohammed Al-Quraishi, a systems biologist at Columbia University, who is not involved in the DeepMind research, told MIT Technology Review. However, regarding many proteins "we are interested in understanding how their structure changes through mutations and normal allelic diversity, and it would not be taking that up through this database".

Others use predictions of protein structures to develop vaccines and investigate basic biology questions, such as examining the evolution of proteins when life first evolved. However, the researchers warned in an article published in the journal Science that the launch of the database is only a starting point, "and it is clear that there is still a lot of biology, a lot of chemistry, to be done".

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