This webinar is by the ELIXIR 3D-BioInfo Community, chaired by Dr. Neeladri Sen of Uinversity College, London
Modeling all 437 catalytic typical protein kinases in the human proteome in active form
Prof. Roland Dunbrack
Institute for Cancer Research, Fox Chase Cancer Center
Humans have 437 catalytically competent protein kinase domains with the typical kinase fold, similar to PKA. From bioinformatics analysis of structures of 40 unique ATP+substrate-bound kinases, we derived criteria for the active form of protein kinases including the conformation of the DFG motif (in dihedral angles) and the N-terminal domain salt bridge, required for binding ATP and magnesium. There are also novel requirements on the position of the N and C terminal portions of the activation loop, which lead to the formation of a substrate binding cleft. With these criteria, only 130 of 437 kinase domains (30%) are present in the PDB in complete active
form. We used extensive sampling with AlphaFold2 with these active-state structures as templates and shallow multiple sequence alignments of orthologues to make active-conformation models of all 437 human kinases. We show that the pLDDT of the activation loop is correlated with low model RMSD to the 130 benchmark PDB structures. Models of all 437
human kinases in the active form are available at http://dunbrack.fccc.edu/kincore/active.
They are suitable for interpreting mutations leading to constitutive catalytic activity in cancer as
well as for templates for modeling substrate-kinase complexes and inhibitors which bind to the
active state.
The topological properties of the protein universe
Prof. Michael Stumpf
The University of Melbourne, Australia
Deep learning methods have revolutionised our ability to predict protein structures, allowing us a glimpse into the entire protein universe. As a result, our understanding of how protein structure drives function is now lagging behind our ability to determine and predict protein structure. Here, we describe how topology, the branch of mathematics concerned with qualitative properties of spatial structures, provides a lens through which we can identify fundamental organising features across the known protein universe. We identify topological determinants that capture global features of the protein universe, such as domain architecture and binding sites. Additionally, our analysis also identified highly specific properties, so-called topological generators, that can be used to provide deeper insights into protein structure-function and evolutionary relationships. We used our approach to determine structural, functional and disease consequences of mutations, explain differences in properties of proteins in mesophiles and thermophiles, and the likely structural and functional consequences of polymorphisms in a protein. Overall, we present a practical methodology for mapping the topology of the known protein universe at scale
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