Pin1 is a peptidyl-prolyl isomerase consisting of a WW domain and a catalytic isomerase (PPIase) domain connected by a flexible linker. Pin1 recognizes phospho-Ser/Thr-Pro motifs in cell-signaling proteins, and is both a cancer and an Alzheimer's disease target. Here, we provide novel insight into the functional motions underlying Pin1 substrate interaction using nuclear magnetic resonance deuterium ((2)D) and carbon ((13)C) spin relaxation. Specifically, we compare Pin1 side-chain motions in the presence and absence of a known phosphopeptide substrate derived from the mitotic phosphatase Cdc25. Substrate interaction alters Pin1 side-chain motions on both the microsecond-millisecond (mus-ms) and picosecond-nanosecond (ps-ns) timescales. Alterations include loss of ps-ns flexibility along an internal conduit of hydrophobic residues connecting the catalytic site with the interdomain interface. These residues are conserved among Pin1 homologs; hence, their dynamics are likely important for the Pin1 mechanism.

The current canon attributes the binding specificity of protein-recognition motifs to distinctive chemical moieties in their constituent amino acid sequences. However, we show for a WW domain that the sequence crucial for specificity is an intrinsically flexible loop that partially rigidifies upon ligand docking. A single-residue deletion in this loop simultaneously reduces loop flexibility and ligand binding affinity. These results suggest that sequences of recognition motifs may reflect natural selection of not only chemical properties but also dynamic modes that augment specificity.

Molecular dynamics (MD) simulations have been actively used in the study of protein structure and function. However, extensive sampling in the protein conformational space requires large computational resources and takes a prohibitive amount of time. In this study, we demonstrated that variational autoencoders (VAEs), a type of deep learning model, can be employed to explore the conformational space of a protein through MD simulations. VAEs are shown to be superior to autoencoders (AEs) through a benchmark study, with low deviation between the training and decoded conformations. Moreover, we show that the learned latent space in the VAE can be used to generate unsampled protein conformations. Additional simulations starting from these generated conformations accelerated the sampling process and explored hidden spaces in the conformational landscape.

No abstract

TEM family of enzymes is one of the most commonly encountered β-lactamases groups with different catalytic capabilities against various antibiotics. Despite the studies investigating the catalytic mechanism of TEM β-lactamases, the binding modes of these enzymes against ligands in different functional catalytic states have been largely overlooked. But the binding modes may play a critical role in the function and even the evolution of these proteins. In this work, a newly developed machine learning analysis approach to the recognition of protein dynamics states was applied to compare the binding modes of TEM-1 β-lactamase with regard to penicillin in different catalytic states. While conventional analysis methods, including principal components analysis (PCA), could not differentiate TEM-1 in different binding modes, the application of a machine learning method led to excellent classification models differentiating these states. It was also revealed that both reactant/product states and apo/product states are more differentiable than the apo/reactant states. The feature importance generated by the training procedure of the machine learning model was utilized to evaluate the contribution from residues at active sites and in different secondary structures. Key active site residues, Ser70 and Ser130, play a critical role in differentiating reactant/product states, while other active site residues are more important for differentiating apo/product states. Overall, this study provides new insights into the different dynamical function states of TEM-1 and may open a new venue for β-lactamases functional and evolutional studies in general.