Photocontrol of the GTPase activity of the small G protein K-Ras by using an azobenzene derivative

AbstractThe small G protein Ras is a central regulator of cellular signal transduction processes, functioning as a molecular switch. Switch mechanisms utilizing conformational changes in nucleotide-binding motifs have been well studied at the molecular level. Azobenzene is a photochromic molecule that undergoes rapid and reversible isomerization between the cis and trans forms upon exposure to ultraviolet and visible light irradiation, respectively. Here, we introduced the sulfhydryl-reactive azobenzene derivative 4-phenylazophenyl maleimide (PAM) into the nucleotide-binding motif of Ras to regulate the GTPase activity by photoirradiation. We prepared four Ras mutants (G12C, Y32C, I36C, and Y64C) that have a single reactive cysteine residue in the nucleotide-binding motif. PAM was stoichiometrically incorporated into the cysteine residue of the mutants. The PAM-modified mutants exhibited reversible alterations in GTPase activity, nucleotide exchange rate, and interaction between guanine nucleotide exchange factor and Ras, accompanied by photoisomerization upon exposure to ultraviolet and visible light irradiation. The results suggest that incorporation of photochromic molecules into its nucleotide-binding motif enables photoreversible control of the function of the small G protein Ras

A second consensus sequence of ATP-requiring proteins resides in the 21-kDa C-terminal segment of myosin subfragment 1

AbstractPrevious comparisons of sequence homologies of ATP-requiring enzymes have defined three consensus sequences which appear to be involved in the binding of the nucleotide. One of these was identified in the N-terminal 27-kDa segment of the myosin heavy chain but the other two sequences have not hitherto been located in myosin. The present paper proposes that one of these other two consensus sequences is in the 21-kDa C-terminal portion of S1 and that it may contribute to the ATP binding domain

Time-resolved detection of SDS-induced conformational changes in α-synuclein by a micro-stopped-flow system

An intrinsically disordered protein, α-synuclein (αSyn), binds to negatively charged phospholipid membranes and adopts an α-helical structure. This conformational change is also induced by interaction with sodium dodecyl sulfate (SDS), which is an anionic surfactant used in previous studies to mimic membrane binding. However, while the structure of the αSyn and SDS complex has been studied widely by various static measurements, the process of structural change from the denatured state to the folded state remains unclear. In this study, the interaction dynamics between αSyn and SDS micelles was investigated using time-resolved measurements with a micro-stopped-flow system, which has been recently developed. In particular, the time-resolved diffusion based on the transient grating technique in combination with a micro-stopped-flow system revealed the gradual change in diffusion triggered by the presence of SDS micelles. This change is induced not only by binding to SDS micelles, but also by an intramolecular conformational change. It was interesting to find that the diffusion coefficient decreased in an intermediate state and then increased to the final state in the binding reaction. We also carried out stopped-flow-kinetic measurements of circular dichroism and intramolecular fluorescence resonance energy transfer, and the D change was assigned to the formation of a compact structure derived from the helix bending on the micelle

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery