AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This study focuses on two multidomain proteins, LicT from Bacillus subtilis and P1 from the Rice Yellow Mottle Virus, that have long resisted structural characterization.
It is found that, while AF3 can perform well in favourable settings, this performance is uneven across applications and its predictions and use of confidence metrics will depend strongly on the specific application area and must be interpreted with respect to training-set overlap.
This mini review traces the evolution of AI-driven methods in protein research, from early residue-contact prediction using coevolutionary information to transformative breakthroughs, the rise of protein language models (PLMs), and the emerging era of generative design and functional modeling.
It is shown that accurate ΔTm prediction does not require structural input features, but can achieve state-of-the-art results with a careful training design for large sequence-based protein language models, and choices such as loss function, pooling strategy, auxiliary supervision, and finetuning regime materially affe...
While AI-based tools perform exceptionally on natural sequences, they do not reliably encode the physico-chemical principles governing ionizable residue placement, and it is proposed to include brief molecular dynamics simulations as a vital validation step for AI-generated structures.
Addressing and predicting ligand-binding sites in protein structures, as well as the prediction of reliable structures of proteins interacting with other proteins, will be pivotal for fully details of structural mechanisms and dynamics.
Modelling sequences both “dry” and in the presence of explicit potassium cations are suggested as a simple, practical way to sample alternative conformations and to expose disordered regions that current predictors tend to over-fold.
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