Background: Determining a protein's quaternary state, i.e. the number of monomers in a functional unit, is a critical step in protein characterization.
View Article and Find Full Text PDFPeptide docking can be perceived as a subproblem of protein–protein docking. However, due to the short length and flexible nature of peptides, many do not adopt one defined conformation prior to binding. Therefore, to tackle a peptide docking problem, not only the relative orientation, but also the bound conformation of the peptide needs to be modeled.
View Article and Find Full Text PDFHighly accurate protein structure predictions by deep neural networks such as AlphaFold2 and RoseTTAFold have tremendous impact on structural biology and beyond. Here, we show that, although these deep learning approaches have originally been developed for the in silico folding of protein monomers, AlphaFold2 also enables quick and accurate modeling of peptide-protein interactions. Our simple implementation of AlphaFold2 generates peptide-protein complex models without requiring multiple sequence alignment information for the peptide partner, and can handle binding-induced conformational changes of the receptor.
View Article and Find Full Text PDFEach year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met.
View Article and Find Full Text PDFAt resolutions worse than 3.5 Å, the electron density is weak or nonexistent at the locations of the side chains. Consequently, the assignment of the protein sequences to their correct positions along the backbone is a difficult problem.
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