In recent developments, London-based company DeepMind shared news about the successful prediction of three-dimensional structures of over 200 million proteins through AlphaFold. So, what exactly is this buzzworthy tool?
About AlphaFold
AlphaFold is a groundbreaking Artificial Intelligence-assisted protein structure prediction tool. Drawing on the power of Deep Neural Networks, it functions akin to human brains by accepting protein sequences as inputs and producing their predicted 3D structures as outputs.
The magic happens within the ‘black box’ nestled between the input and output layers, known as the Hidden Networks. Much of the real work occurs here, setting AlphaFold distinctly apart in the realm of protein structure prediction.
Mechanism of AlphaFold
The mechanism behind AlphaFold is based on a systematic approach. Initially, the system undergoes training using 170,000 available protein structures from the Protein Data Bank (PDB). The tool then uses this training to predict the structural designs of proteins not present in PDB.
In the next step, these highly accurate predictions are utilized for further retraining and learning, ultimately leading to more precise outcomes. Leveraging this methodology, AlphaFold has successfully projected 3D structures for 214 million unique protein sequences submitted to the Universal Protein Resource (UniProt) database.
The Implication and Impact of AlphaFold
Understanding the structure and function of proteins is pivotal for comprehending human diseases. Traditional techniques like X-ray crystallography, nuclear magnetic resonance spectroscopy, and cryogenic electron microscopy are time-intensive and largely rely on trial-and-error methods.
AlphaFold heralds an evolution, greatly simplifying the time-consuming process of protein structuring. It’s regarded as a landmark achievement in science and structural biology. Since its public release, AlphaFold has facilitated rapid advancements for scientists around the globe in fields like vaccine and drug development.
Alternatives to AlphaFold
Despite its merits, AlphaFold is not without flaws. Moreover, it doesn’t hold a monopoly in AI-based protein structure prediction. Another tool named RoseTTaFold, designed by the University of Washington, Seattle, USA, also enables protein complex structure prediction. However, it’s less precise than AlphaFold.
The Role of AlphaFold in India
India should promptly harness the capabilities of the AlphaFold database to improve vaccine and drug design. Utilizing accurate structures of Covid-19 virus proteins will expedite vaccine and drug development against the virus.
Public-private partnerships in the sciences should also be prioritized. A cohesive collaboration between the private sector’s data science talent and academic institutions’ specialists will lay a strong foundation for future data science innovations.
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