Mechanism of RNA recognition by a Musashi RNA-binding protein

Molecular Mechanism of Phosphorylation-Mediated Impacts on the Conformation Dynamics of GTP-Bound KRAS Probed by GaMD Trajectory-Based Deep Learning

The phosphorylation of different sites produces a significant effect on the conformational dynamics of KRAS. Gaussian accelerated molecular dynamics (GaMD) simulations were combined with deep learning (DL) to explore the molecular mechanism of the phosphorylation-mediated effect on conformational dynamics of the GTP-bound KRAS. The DL finds that the switch domains are involved in obvious differences in conformation contacts and suggests that the switch domains play a key role in the function of KRAS. The analyses of free energy landscapes (FELs) reveal that the phosphorylation of pY32, pY64, and pY137 leads to more disordered states of the switch domains than the wild-type (WT) KRAS and induces conformational transformations between the closed and open states. The results from principal component analysis (PCA) indicate that principal motions PC1 and PC2 are responsible for the closed and open states of the phosphorylated KRAS. Interaction networks were analyzed and the results verify that the phosphorylation alters interactions of GTP and magnesium ion Mg2+ with the switch domains. It is concluded that the phosphorylation pY32, pY64, and pY137 tune the activity of KRAS through changing conformational dynamics and interactions of the switch domains. We anticipated that this work could provide theoretical aids for deeply understanding the function of KRAS.

The switch states of the GDP-bound HRAS affected by point mutations: a study from Gaussian accelerated molecular dynamics simulations and free energy landscapes

Point mutations play a vital role in the conformational transformation of HRAS. In this work, Gaussian accelerated molecular dynamics (GaMD) simulations followed by constructions of free energy landscapes (FELs) were adopted to explore the effect of mutations D33K, A59T and L120A on conformation states of the GDP-bound HRAS. The results from the post-processing analyses on GaMD trajectories suggest that mutations alter the flexibility and motion modes of the switch domains from HRAS. The analyses from FELs show that mutations induce more disordered states of the switch domains and affect interactions of GDP with HRAS, implying that mutations yield a vital effect on the binding of HRAS to effectors. The GDP-residue interaction network revealed by our current work indicates that salt bridges and hydrogen bonding interactions (HBIs) play key roles in the binding of GDP to HRAS. Furthermore, instability in the interactions of magnesium ions and GDP with the switch SI leads to the extreme disorder of the switch domains. This study is expected to provide the energetic basis and molecular mechanism for further understanding the function of HRAS.Communicated by Ramaswamy H. Sarma.

The Musashi RNA binding proteins direct the translational activation of key pituitary mRNAs

The pituitary functions as a master endocrine gland that secretes hormones critical for regulation of a wide variety of physiological processes including reproduction, growth, metabolism and stress responses. The distinct hormone-producing cell lineages within the pituitary display remarkable levels of cell plasticity that allow remodeling of the relative proportions of each hormone-producing cell population to meet organismal demands. The molecular mechanisms governing pituitary cell plasticity have not been fully elucidated. Our recent studies have implicated a role for the Musashi family of sequence-specific mRNA binding proteins in the control of pituitary hormone production, pituitary responses to hypothalamic stimulation and modulation of pituitary transcription factor expression in response to leptin signaling. To date, these actions of Musashi in the pituitary appear to be mediated through translational repression of the target mRNAs. Here, we report Musashi1 directs the translational activation, rather than repression, of the Prop1, Gata2 and Nr5a1 mRNAs which encode key pituitary lineage specification factors. We observe that Musashi1 further directs the translational activation of the mRNA encoding the glycolipid Neuronatin (Nnat) as determined both in mRNA reporter assays as well as in vivo. Our findings suggest a complex bifunctional role for Musashi1 in the control of pituitary cell function.

Exploring conformation changes of Janus kinase 2 pseudokinase mediated by mutations through Gaussian accelerated molecular dynamics and principal component analysis

The pseudokinase domain (JH2) of the protein tyrosine kinase (Janus kinase 2, JAK2) regulates the activity of a tyrosine kinase domain (JH1) in JAK2, which is further affected by mutations in the JH2. In this work, Gaussian accelerated molecular dynamics (GaMD) simulations followed by construction of free energy landscapes (FELs) and principal component analysis (PCA) were performed to study effect of two mutations V617F and V617F/E596A on the conformations of the ATP-bound JH2. The dynamic analyses reveal that mutations affect the structural flexibility and correlated motions of the JH2, meanwhile also change the dynamics behavior of the P-loop and αC-helix of the JH2. The information from FELs unveils that mutations induce less energy states than the free JH2 and the WT one. The analyses of interaction networks uncover that mutations affect the salt bridge interactions of ATP with K581, K677 and R715 and alter hydrogen bonding interactions (HBIs) of ATP with the JH2. The changes in conformations of the JH2 and ATP-JH2 interaction networks caused by mutations in turn generate effect on the activity regulations of the JH2 on the JH1. This work is expected to provide significant theoretical helps for deeply understanding the function of the JH2 and drug design toward JAK2.Communicated by Ramaswamy H. Sarma.

Musashi Exerts Control of Gonadotrope Target mRNA Translation During the Mouse Estrous Cycle

The anterior pituitary controls key biological processes, including growth, metabolism, reproduction, and stress responses through distinct cell types that each secrete specific hormones. The anterior pituitary cells show a remarkable level of cell type plasticity that mediates the shifts in hormone-producing cell populations that are required to meet organismal needs. The molecular mechanisms underlying pituitary cell plasticity are not well understood. Recent work has implicated the pituitary stem cell populations and specifically, the mRNA binding proteins of the Musashi family in control of pituitary cell type identity. In this study we have identified the target mRNAs that mediate Musashi function in the adult mouse pituitary and demonstrate the requirement for Musashi function in vivo. Using Musashi RNA immunoprecipitation, we identify a cohort of 1184 mRNAs that show specific Musashi binding. Identified Musashi targets include the Gnrhr mRNA, which encodes the gonadotropin-releasing hormone receptor (GnRHR), and the Fshb mRNA, encoding follicle-stimulating hormone (FSH). Reporter assays reveal that Musashi functions to exert repression of translation of the Fshb mRNA, in addition to the previously observed repression of the Gnrhr mRNA. Importantly, mice engineered to lack Musashi in gonadotropes demonstrate a failure to repress translation of the endogenous Gnrhr and Fshb mRNAs during the estrous cycle and display a significant heterogeneity in litter sizes. The range of identified target mRNAs suggests that, in addition to these key gonadotrope proteins, Musashi may exert broad regulatory control over the pituitary proteome in a cell type-specific manner.

Monovalent Ionic Atmosphere Modulates the Selection of Suboptimal RNA Sequences by Splicing Factors' RNA Recognition Motifs

The U2AF2 splicing factor is involved in the RNA recognition of the pre-mRNA poly-pyrimidine signaling sequence. This protein contains two RRM domains connected by a flexible linker, which ensure the preferential selection of a poly-uridine sequence over a poly-cytosine one. In this work, all-atom simulations provide insights into the U2AF2 recognition mechanism and on the features underlying its selectivity. Our outcomes show that U2AF2's RNA recognition is driven by cooperative events modulated by RNA-protein and RNA-ion interactions. Stunningly, monovalent ions contribute to mediating the binding of the weakly binding polyC strand, thus contributing to the selection of suboptimal poly-pyrimidine tracts. This finding broadens our understanding of the diverse traits tuning splicing factors' selectivity and adaptability to precisely handle and process diverse pre-mRNA sequences.

Sigmoid Accelerated Molecular Dynamics: An Efficient Enhanced Sampling Method for Biosystems

Gaussian accelerated molecular dynamics (GaMD) is recognized as a popular enhanced sampling method for tackling long-standing challenges in biomolecular simulations. Inspired by GaMD, Sigmoid accelerated molecular dynamics (SaMD) is proposed in this work by adding a Sigmoid boost potential to improve the balance between the highest acceleration and accurate reweighting. Compared with GaMD, SaMD extends the accessible time scale and improves the computational efficiency as tested in three tasks. In the alanine dipeptide task, SaMD can produce the free energy landscape with better accuracy and efficiency. In the chignolin folding task, the estimated Gibbs free energy difference can converge to the experimental value ∼30% faster. In the protein-ligand binding task, the bound conformations are closer to the crystal structure with a minimal ligand root-mean-square deviation of 1.7 Å. The binding of the ligand XK263 to the HIV protease is reproduced by SaMD in ∼60% less simulation time.

Binding modes of GDP, GTP and GNP to NRAS deciphered by using Gaussian accelerated molecular dynamics simulations

Probing binding modes of GDP, GTP and GNP to NRAS are of significance for understanding the regulation mechanism on the activity of RAS proteins. Four separate Gaussian accelerated molecular dynamics (GaMD) simulations were performed on the apo, GDP-, GTP- and GNP-bound NRAS. Dynamics analyses suggest that binding of three ligands highly affects conformational states of the switch domains from NRAS, which disturbs binding of NRAS to its effectors. The analyses of free energy landscapes (FELs) indicate that binding of GDP, GTP and GNP induces more energetic states of NRAS compared to the apo NRAS but the presence of GNP makes the switch domains more ordered than binding of GDP and GNP. The information of interaction networks of ligands with NRAS reveals that the π-π interaction of residue F28 and the salt bridge interactions of K16 and D119 with ligands stabilize binding of GDP, GTP and GNP to NRAS. Meanwhile magnesium ion plays a bridge role in interactions of ligands with NRAS, which is favourable for associations of GDP, GTP and GNP with NRAS. This work is expected to provide useful information for deeply understanding the function and activity of NRAS.

Decoding the Identification Mechanism of an SAM-III Riboswitch on Ligands through Multiple Independent Gaussian-Accelerated Molecular Dynamics Simulations

S-Adenosyl-l-methionine (SAM)-responsive riboswitches play a central role in the regulation of bacterial gene expression at the level of transcription attenuation or translation inhibition. In this study, multiple independent Gaussian-accelerated molecular dynamics simulations were performed to decipher the identification mechanisms of SAM-III (S_MK) on ligands SAM, SAH, and EEM. The results reveal that ligand binding highly affects the structural flexibility, internal dynamics, and conformational changes of SAM-III. The dynamic analysis shows that helices P3 and P4 as well as two junctions J23 and J24 of SAM-III are highly susceptible to ligand binding. Analyses of free energy landscapes suggest that ligand binding induces different free energy profiles of SAM-III, which leads to the difference in identification sites of SAM-III on ligands. The information on ligand-nucleotide interactions not only uncovers that the π-π, cation-π, and hydrogen bonding interactions drive identification of SAM-III on the three ligands but also reveals that different electrostatic properties of SAM, SAH, and EEM alter the active sites of SAM-III. Meanwhile, the results also verify that the adenine group of SAM, SAH, and EEM is well recognized by conserved nucleotides G7, A29, U37, A38, and G48. We expect that this study can provide useful information for understanding the applications of SAM-III in chemical, synthetic RNA biology, and biomedical fields.

Conformational Heterogeneity of RNA Stem-Loop Hairpins Bound to FUS-RNA Recognition Motif with Disordered RGG Tail Revealed by Unbiased Molecular Dynamics Simulations

RNA-protein complexes use diverse binding strategies, ranging from structurally well-defined interfaces to completely disordered regions. Experimental characterization of flexible segments is challenging and can be aided by atomistic molecular dynamics (MD) simulations. Here, we used an extended set of microsecond-scale MD trajectories (400 μs in total) to study two FUS-RNA constructs previously characterized by nuclear magnetic resonance (NMR) spectroscopy. The FUS protein contains a well-structured RNA recognition motif domain followed by a presumably disordered RGG tail that binds RNA stem-loop hairpins. Our simulations not only provide several suggestions complementing the experiments but also reveal major methodological difficulties in studies of such complex RNA-protein interfaces. Despite efforts to stabilize the binding via system-specific force-field adjustments, we have observed progressive distortions of the RNA-protein interface inconsistent with experimental data. We propose that the dynamics is so rich that its converged description is not achievable even upon stabilizing the system. Still, after careful analysis of the trajectories, we have made several suggestions regarding the binding. We identify substates in the RNA loops, which can explain the NMR data. The RGG tail localized in the minor groove remains disordered, sampling countless transient interactions with the RNA. There are long-range couplings among the different elements contributing to the recognition, which can lead to allosteric communication throughout the system. Overall, the RNA-FUS systems form dynamical ensembles that cannot be fully represented by single static structures. Thus, albeit imperfect, MD simulations represent a viable tool to investigate dynamic RNA-protein complexes.

Deciphering Conformational Changes of the GDP-Bound NRAS Induced by Mutations G13D, Q61R, and C118S through Gaussian Accelerated Molecular Dynamic Simulations

The conformational changes in switch domains significantly affect the activity of NRAS. Gaussian-accelerated molecular dynamics (GaMD) simulations of three separate replicas were performed to decipher the effects of G13D, Q16R, and C118S on the conformational transformation of the GDP-bound NRAS. The analyses of root-mean-square fluctuations and dynamics cross-correlation maps indicated that the structural flexibility and motion modes of the switch domains involved in the binding of NRAS to effectors are highly altered by the G13D, Q61R, and C118Smutations. The free energy landscapes (FELs) suggested that mutations induce more energetic states in NRAS than the GDP-bound WT NRAS and lead to high disorder in the switch domains. The FELs also indicated that the different numbers of sodium ions entering the GDP binding regions compensate for the changes in electrostatic environments caused by mutations, especially for G13D. The GDP-residue interactions revealed that the disorder in the switch domains was attributable to the unstable hydrogen bonds between GDP and two residues, V29 and D30. This work is expected to provide information on the energetic basis and dynamics of conformational changes in switch domains that can aid in deeply understanding the target roles of NRAS in anticancer treatment.

Binding Analysis Using Accelerated Molecular Dynamics Simulations and Future Perspectives

Biomolecular recognition such as binding of small molecules, nucleic acids, peptides and proteins to their target receptors plays key roles in cellular function and has been targeted for therapeutic drug design. Molecular dynamics (MD) is a computational approach to analyze these binding processes at an atomistic level, which provides valuable understandings of the mechanisms of biomolecular recognition. However, the rather slow biomolecular binding events often present challenges for conventional MD (cMD), due to limited simulation timescales (typically over hundreds of nanoseconds to tens of microseconds). In this regard, enhanced sampling methods, particularly accelerated MD (aMD), have proven useful to bridge the gap and enable all-atom simulations of biomolecular binding events. Here, we will review the recent method developments of Gaussian aMD (GaMD), ligand GaMD (LiGaMD) and peptide GaMD (Pep-GaMD), which have greatly expanded our capabilities to simulate biomolecular binding processes. Spontaneous binding of various biomolecules to their receptors has been successfully simulated by GaMD. Microsecond LiGaMD and Pep-GaMD simulations have captured repetitive binding and dissociation of small-molecule ligands and highly flexible peptides, and thus enabled ligand/peptide binding thermodynamics and kinetics calculations. We will also present relevant application studies in simulations of important drug targets and future perspectives for rational computer-aided drug design.

Challenges and frontiers of computational modelling of biomolecular recognition

Biomolecular recognition including binding of small molecules, peptides and proteins to their target receptors plays a key role in cellular function and has been targeted for therapeutic drug design. However, the high flexibility of biomolecules and slow binding and dissociation processes have presented challenges for computational modelling. Here, we review the challenges and computational approaches developed to characterise biomolecular binding, including molecular docking, molecular dynamics simulations (especially enhanced sampling) and machine learning. Further improvements are still needed in order to accurately and efficiently characterise binding structures, mechanisms, thermodynamics and kinetics of biomolecules in the future.