Structural signature of the MYPT1-PP1 interaction

Muscle relaxation is triggered by the dephosphorylation of Ser19 in the myosin regulatory light chain. This reaction is catalyzed by the holoenzyme myosin phosphatase (MP), which includes the catalytic subunit protein phosphatase 1 (PP1) and the regulatory targeting subunit (MYPT). MYPT1 (myosin phosphatase targeting subunit 1) is responsible for both targeting the holoenzyme to subcellular compartments in the muscle and directing PP1 specificity towards myosin. In order to understand the molecular events leading to the MYPT1:PP1 holoenzyme formation, we used NMR spectroscopy to determine the structural and dynamic characteristics of unbound MYPT1. This allowed the conformations of MYPT1 in the free, unbound state to be directly compared to the PP1-bound state. Our results show that MYPT1(1-98) behaves like a two-domain protein in solution. The first 40 residues of MYPT1(1-98), the disordered region, are intrinsically disordered and highly dynamic, whereas residues 41–98, the folded ankyrin-repeat region, are well-structured and rigid. Furthermore, the integrated use of NMR and biophysical data enabled us to calculate an ensemble model for MYPT1(1-98). The most prominent structural feature of the MYPT1(1-98) ensemble is a 25% populated transient α-helix in the disordered region of MYPT1(1-98). This α-helix becomes fully populated when bound to PP1 and, as we show, likely plays a central role in the formation of the MYPT1:PP1 holoenzyme complex. Finally, this combined analysis shows that the structural and dynamic behaviors exhibited by MYPT1 for PP1 are distinct from those of any other previously analyzed PP1 regulatory protein. Collectively, these data enable us to present a new model of the molecular events that drive MYPT1:PP1 holoenzyme formation and demonstrate that there are structural differences in unbound PP1 regulators that have not been previously observed. Thus this work adds significant insights to the currently limited data for molecular structures and dynamics of PP1 regulators

Learning to Evolve Structural Ensembles of Unfolded and Disordered Proteins Using Experimental Solution Data

We have developed a Generative Recurrent Neural Networks (GRNN) that learns the probability of the next residue torsions $X_{i+1}=\ [\phi_{i+1},\psi_{i+1},\omega _{i+1}, \chi_{i+1}]$from the previous residue in the sequence$X_i$ to generate new IDP conformations. In addition, we couple the GRNN with a Bayesian model, X-EISD, in a reinforcement learning step that biases the probability distributions of torsions to take advantage of experimental data types such as J-couplingss, NOEs and PREs. We show that updating the generative model parameters according to the reward feedback on the basis of the agreement between structures and data improves upon existing approaches that simply reweight static structural pools for disordered proteins. Instead the GRNN "DynamICE" model learns to physically change the conformations of the underlying pool to those that better agree with experiment

Finding Our Way in the Dark Proteome

The traditional structure-function paradigm has provided significant insights for well-folded proteins in which structures can be easily and rapidly revealed by X-ray crystallography beamlines. However approximately one third of the human proteome are comprised of intrinsically disordered proteins and regions (IDPs/IDRs) that do not adopt a dominant well-folded structure, and therefore remain “unseen” by traditional structural biology methods. This Perspective article considers the challenges raised by the “Dark Proteome”, in which determining the diverse conformational substates of IDPs in their free states, in encounter complexes of bound states, and in complexes retaining significant disorder, requires an unprecedented level of integration of multiple and complementary solution-based experiments that are analyzed with state-of-the art molecular simulation, Bayesian probabilistic models, and high throughput computation. We envision how these diverse experimental and computational tools can work together through formation of a “computational beamline” that will allow key functional features to be identified in IDP structural ensembles

Low potency toxins reveal dense interaction networks in metabolism

Background The chemicals of metabolism are constructed of a small set of atoms and bonds. This may be because chemical structures outside the chemical space in which life operates are incompatible with biochemistry, or because mechanisms to make or utilize such excluded structures has not evolved. In this paper I address the extent to which biochemistry is restricted to a small fraction of the chemical space of possible chemicals, a restricted subset that I call Biochemical Space. I explore evidence that this restriction is at least in part due to selection again specific structures, and suggest a mechanism by which this occurs. Results Chemicals that contain structures that our outside Biochemical Space (UnBiological groups) are more likely to be toxic to a wide range of organisms, even though they have no specifically toxic groups and no obvious mechanism of toxicity. This correlation of UnBiological with toxicity is stronger for low potency (millimolar) toxins. I relate this to the observation that most chemicals interact with many biological structures at low millimolar toxicity. I hypothesise that life has to select its components not only to have a specific set of functions but also to avoid interactions with all the other components of life that might degrade their function. Conclusions The chemistry of life has to form a dense, self-consistent network of chemical structures, and cannot easily be arbitrarily extended. The toxicity of arbitrary chemicals is a reflection of the disruption to that network occasioned by trying to insert a chemical into it without also selecting all the other components to tolerate that chemical. This suggests new ways to test for the toxicity of chemicals, and that engineering organisms to make high concentrations of materials such as chemical precursors or fuels may require more substantial engineering than just of the synthetic pathways involved

Structural diversity in free and bound states of intrinsically disordered protein phosphatase 1 regulators

Complete folding is not a prerequisite for protein function, as disordered and partially folded states of proteins frequently perform essential biological functions. In order to understand their functions at the molecular level, we utilized diverse experimental measurements to calculate ensemble models of three non-homologous, intrinsically disordered proteins: I-2, spinophilin and DARPP-32, which bind to and regulate protein phosphatase 1 (PP1). The models demonstrate that these proteins have dissimilar propensities for secondary and tertiary structure in their unbound forms. Direct comparison of these ensemble models with recently determined PP1 complex structures suggests a significant role for transient, pre-formed structure in the interactions of these proteins with PP1. Finally, we generated an ensemble model of partially disordered I-2 bound to PP1 that provides insight into the relationship between flexibility and biological function in this dynamic complex