Syllabus

GS-3: 

Science and Technology- developments and their applications and effects in everyday life; Awareness in the fields of IT, Space, Computers, robotics, nano-technology, bio-technology.

Context:

Recently, Microsoft launched BioEmu, an AI system that swiftly predicts how proteins move and change in the human body, work that generally takes years using traditional computer systems.

About BioEmu

• It is new deep learning system that predicts how the full range of shapes a protein naturally explores under biological conditions.
• It allows high-resolution protein flexibility modelling at scale, unlike slower, more classical approaches such as molecular dynamics (MD).
• BioEmu is a high-speed emulator of protein motion, capable of generating thousands of conformational states in just one Graphical Processing Unit.
• Developed by Microsoft and researchers at Rice University in the US and Freie Universität in Germany.

Working Mechanism of BioEmu

It can generate thousands of protein structures per hour on a single graphics processing unit.

Training: enabled merging 3 different types of datasets.

• millions of AlphaFold-predicted assemblies.
• 200 milliseconds of MD simulations spanning thousands of proteins.
• half a million mutant sequences from experimental stability measurements.

After training, it can generate thousands of plausible protein conformations from scratch.

It can generate diverse conformations that replicate the functional landscape of a protein, without the need to run new simulations for each case.

Benchmarks of BioEmu

• It captured large shape changes in enzymes, local unfolding that switches proteins on or off, and fleeting cryptic pockets, temporary crevices that can be used as drug docking sites, like in the cancer-linked protein Ras.
• It predicted 83% of large shifts and 70-81% of small changes accurately, together with open and closed forms of a vital enzyme called adenylate kinase.
• It also handled hard to predict proteins that don’t have a fixed 3D structure and how mutations affect protein stability.

Limitations of BioEmu:

• BioEmu shows the final shapes of protein but it lacks to stimulate step to step path a protein takes to reach those protein shapes, unlike Molecular Dynamics (MD) simulations.
• BioEmu’s static predictions can’t handle temperature shifts and membranes in contrast to MD and also can’t model cell walls, drug molecules, pH changes or show prediction reliability like AlphaFold.
• It is restricted to single chains and can’t model how proteins interact, a key part of most biological processes and drug targets.

Significance of BioEmu:

• Cost Effective: BioEmu reproduces MD equilibrium distributions accurately with a tiny fraction of the computational cost.
• Drug Discovery: BioEmu improves accuracy in drug delivery by revealing dynamic, cryptic binding pockets on target proteins that static models often fail to recognize.
• Complementing Tool: BioEmu can rapidly generate a range of plausible conformations, which MD can then explore in detail. This hybrid approach could significantly decrease simulation time while also conserving fidelity

Source:

https://www.thehindu.com/sci-tech/science/bioemu-reveals-protein-choreography-in-biological-conditions/article69810588.ece