3 research outputs found
Predictive and experimental approaches for elucidating protein–protein interactions and quaternary structures
The elucidation of protein–protein interactions is vital for determining the function and action of quaternary protein structures. Here, we discuss the difficulty and importance of establishing protein quaternary structure and review in vitro and in silico methods for doing so. Determining the interacting partner proteins of predicted protein structures is very time-consuming when using in vitro methods, this can be somewhat alleviated by use of predictive methods. However, developing reliably accurate predictive tools has proved to be difficult. We review the current state of the art in predictive protein interaction software and discuss the problem of scoring and therefore ranking predictions. Current community-based predictive exercises are discussed in relation to the growth of protein interaction prediction as an area within these exercises. We suggest a fusion of experimental and predictive methods that make use of sparse experimental data to determine higher resolution predicted protein interactions as being necessary to drive forward development
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IntFOLD: an integrated web resource for high performance protein structure and function prediction
The IntFOLD server provides a unified resource
for the automated prediction of: protein tertiary
structures with built-in estimates of model accuracy (EMA), protein structural domain boundaries,
natively unstructured or disordered regions in proteins, and protein–ligand interactions. The component methods have been independently evaluated via
the successive blind CASP experiments and the continual CAMEO benchmarking project. The IntFOLD
server has established its ranking as one of the best
performing publicly available servers, based on independent official evaluation metrics. Here, we describe significant updates to the server back end,
where we have focused on performance improvements in tertiary structure predictions, in terms of
global 3D model quality and accuracy self-estimates
(ASE), which we achieve using our newly improved
ModFOLD7 rank algorithm. We also report on various upgrades to the front end including: a streamlined submission process, enhanced visualization of
models, new confidence scores for ranking, and links
for accessing all annotated model data. Furthermore,
we now include an option for users to submit selected models for further refinement via convenient
push buttons