How AlphaFold Bridges the Sequence-Structure Gap Using Deep Learning

AI that predicts 3D shape of proteins using amino acids is efficacious.

The model improves biological research speed, and it solved complex protein folding problem. With the help of protein Data Bank, it is non arduous to get the global database of known protein 3D structures.

Lab experiment gives protein structure, which stores in PDB, that PDB data used in AI training to build prediction Models, Alpha fold.

With the help of alpha fold it's easy to predict the large biological molecules that build life, aka macromolecules, they are the core building block of cells,

Types include

1. Proteins
2. DNA/ RNA (Nucleic acids)
3. Lipids
4. Carbohydrate

Protein are macromolecules made of amino acids, DNA is a macromolecule too that stores genetic code, lipids form macromolecule-based cell membranes. carbohydrate are macromolecules used for energy.

Enzymes are protein macromolecules that speed up reactions.

Lipids

Lipids are simply fat based molecules that build membrane and store energy. its highly used in energy storage, cell membrane, insulation, hormone.

Alpha fold using something called Multiple sequence alignment, arranging many biological sequences to find similarities. to exemplify line up DNA/protein sequences to find pattern.

In the future with more polyethene terephthalate, its really parsimonious and environmentally friendly to degrade plastic waste especially PET bottles.

it is discovered in bacteria that evolved to eat plastic waste

The starting point of calculation is Structural Coordinates (e.g., x, y, z coordinates of atoms from experimental structures like PDB).

intra Residue Distance

calculating distance between 2 amino acids in a protein structure with the help of right PDF its non-arduous manifestly as well as measure of how a bond rotates in space.