Protein Structure Prediction: Alphafold2

Description

This lecture delves into the revolutionary Alphafold2, a protein structure prediction tool developed by Google's DeepMind. It explores the history of protein structure prediction, from John Kendrew's musings in the '60s to the recent breakthroughs in CASP competitions. The lecture discusses the critical assessment of protein structure prediction, the global distance test, and the significance of achieving high accuracy in protein structure prediction. It also covers the impact of Alphafold2 on the field, its exceptional performance in CASP14, and the implications for protein folding biology and bioinformatics. By the end, the lecture highlights the exponential growth in the number of proteins solved by Alphafold2 since its launch in November 2020.

Official source

https://mediaspace.epfl.ch/media/0_49xsi2ht

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Protein secondary structure

Protein secondary structure is the local spatial conformation of the polypeptide backbone excluding the side chains. The two most common secondary structural elements are alpha helices and beta sheets, though beta turns and omega loops occur as well. Secondary structure elements typically spontaneously form as an intermediate before the protein folds into its three dimensional tertiary structure. Secondary structure is formally defined by the pattern of hydrogen bonds between the amino hydrogen and carboxyl oxygen atoms in the peptide backbone.

Protein primary structure

Protein primary structure is the linear sequence of amino acids in a peptide or protein. By convention, the primary structure of a protein is reported starting from the amino-terminal (N) end to the carboxyl-terminal (C) end. Protein biosynthesis is most commonly performed by ribosomes in cells. Peptides can also be synthesized in the laboratory. Protein primary structures can be directly sequenced, or inferred from DNA sequencess.

Protein structure prediction

Protein structure prediction is the inference of the three-dimensional structure of a protein from its amino acid sequence—that is, the prediction of its secondary and tertiary structure from primary structure. Structure prediction is different from the inverse problem of protein design. Protein structure prediction is one of the most important goals pursued by computational biology; and it is important in medicine (for example, in drug design) and biotechnology (for example, in the design of novel enzymes).

Threading (protein sequence)

In molecular biology, protein threading, also known as fold recognition, is a method of protein modeling which is used to model those proteins which have the same fold as proteins of known structures, but do not have homologous proteins with known structure. It differs from the homology modeling method of structure prediction as it (protein threading) is used for proteins which do not have their homologous protein structures deposited in the Protein Data Bank (PDB), whereas homology modeling is used for those proteins which do.

Intrinsically disordered proteins

In molecular biology, an intrinsically disordered protein (IDP) is a protein that lacks a fixed or ordered three-dimensional structure, typically in the absence of its macromolecular interaction partners, such as other proteins or RNA. IDPs range from fully unstructured to partially structured and include random coil, molten globule-like aggregates, or flexible linkers in large multi-domain proteins. They are sometimes considered as a separate class of proteins along with globular, fibrous and membrane proteins.