Crystallographic landscape of SHP2 provides molecular insights for SHP2 targeted drug discovery.

The protein tyrosine phosphatase SHP2 encoded by PTPN11 is a promising therapeutic target for cancer therapy. The dynamic change of SHP2 between closed and open conformations under either physiological or pathological conditions provides opportunities to design SHP2 inhibitors for treating SHP2-related diseases. To date, several SHP2 allosteric inhibitors have advanced into clinical trials as mono- or combined therapy of cancers. In this review, we provide an overview on the structural landscape of SHP2 under physiological and pathological conditions and also comprehensively analyze the binding models of SHP2/inhibitor complexes. Structural features of SHP2 under pathological conditions and co-crystal structures of SHP2/inhibitor complexes will definitely facilitate structure-guided design of SHP2 inhibitors. Finally, proteolysis targeting chimeric (PROTAC) based SHP2 degraders have shown therapeutic promise for cancer therapy and are also briefly discussed. We hope this review could provide crystallographic landscape for SHP2 targeted drug discovery.

A method to assess the robustness of complex mathematical models used for quantitative interpretation of experimental data by nonlinear regression analysis: application to high-affinity binding models.

Nonlinear regression is widely used to fit experimental data to a specific mathematical model to extract numerical values for parameters representing the studied process. However, assessing the degree of precision that can be expected from such an analysis for a given parameter can be quite challenging for complex mathematical models. To address this issue, we propose here a method based on the analysis of a large number of data sets generated in such a way to mimic specific experimental conditions. Applying this methodology to high-affinity binding models, we report here a quantitative analysis of the robustness of such models, and how the precision on the fitting parameters can be expected to vary based on, e.g., the initial experimental conditions, the number or distribution of experimental points, or the experimental variability. We also show that these models, although widely used, are intrinsically limited by the fact that the two main fitting parameters, one representing the concentration of the studied species and the other its affinity, are inversely correlated, but demonstrate that this limitation can be overcome by global analysis of multiple data series generated at different concentrations of the titrated species.

On the relationship between inhibition and receptor occupancy by nondepolarizing neuromuscular blocking drugs.

BACKGROUND: Nondepolarizing neuromuscular blocking drugs (NDNBs) are clinically used to produce muscle relaxation during general anesthesia. To better understand clinical properties of NDNBs, comparative in vitro pharmacologic studies have been performed. In these studies, a receptor binding model, which relies on the assumption that the inhibition, i.e., the effect of an NDNB, is proportional to the receptor occupancy by the drug, has been effectively used to describe obtained experimental data. However, it has not been studied in literature under which conditions the above assumption can be justified nor the assumption still holds in vivo. The purpose of this study is to explore the in vivo relationship between the inhibition and the receptor occupancy by an NDNB and to draw implications on how in vitro experimental results can be used to discuss the in vivo properties of NDNBs. METHODS: An ordinary differential equation model is employed to simulate physiologic processes of the activation of receptors by acetylcholine (ACh) as well as inhibition by an NDNB. With this model, the degree of inhibition is quantified by the fractional amount of receptors that are not activated by ACh due to the presence of an NDNB. The results are visualized by plotting the fractional amounts of the activated receptors as a function of the receptor occupancy. RESULTS: Numerical investigations reflecting in vivo conditions show that the degree of inhibition is not proportional to the receptor occupancy, i.e., there is a nonlinear relationship between the inhibition and the receptor occupancy. However, under a setting of high concentration of ACh reflecting a typical situation of in vitro experiments, the relationship between the inhibition and the receptor occupancy becomes linear, suggesting the validity of the receptor binding model. Also, it is found that the extent of nonlinearity depends on the selectivity of NDNBs for the two binding sites of the receptors. CONCLUSIONS: While the receptor binding model may be effective for estimating affinity of an NDNB through in vitro experiments, these models do not directly describe in vivo properties of NDNBs, because the nonlinearity between the inhibition and the receptor occupancy causes the modulation of the resultant concentration-effect relationships of NDNBs.

Computational analysis of CLIP-seq data.

CLIP-seq experiments are currently the most important means for determining the binding sites of RNA binding proteins on a genome-wide level. The computational analysis can be divided into three steps. In the first pre-processing stage, raw reads have to be trimmed and mapped to the genome. This step has to be specifically adapted for each CLIP-seq protocol. The next step is peak calling, which is required to remove unspecific signals and to determine bona fide protein binding sites on target RNAs. Here, both protocol-specific approaches as well as generic peak callers are available. Despite some peak callers being more widely used, each peak caller has its specific assets and drawbacks, and it might be advantageous to compare the results of several methods. Although peak calling is often the final step in many CLIP-seq publications, an important follow-up task is the determination of binding models from CLIP-seq data. This is central because CLIP-seq experiments are highly dependent on the transcriptional state of the cell in which the experiment was performed. Thus, relying solely on binding sites determined by CLIP-seq from different cells or conditions can lead to a high false negative rate. This shortcoming can, however, be circumvented by applying models that predict additional putative binding sites.

Binding mechanism of full-length Aß40 peptide to a mixed lipid bilayer.

The destructive effect of Aß peptides on membranes is an important source of its cytotoxicity in the pathogenesis of Alzheimer's disease. We have investigated the binding mechanism between the Aß42 peptide and bilayer in our former work. However, as another abundant form of Aß peptides in the physiological environment, the binding mechanism between Aß40 peptide and the lipid bilayer still remains ambiguous. Hence, we performed all-atom simulations on the Aß40 peptides with the lipid bilayer herein using replica exchange with the solute tempering 2 method. We obtained four major binding models with the hydrophobic C-terminus as the most preferable binding region. Hydrophobic residues and positively charged residues are the principal residues involved in the peptide-bilayer interactions. Aß40 peptides in our simulation mainly adopt a ß-rich conformation in both bound and unbound states. Besides, we determined peptide-water interactions and found that bound peptides prefer forming hydrogen bonds with water molecules than unbound peptides. Our findings herein may provide new insights for the in-depth understanding of the membrane-destructive mechanism of Aß peptides.

A Reinvestigation of the Role of the Sorbic Acid Tail on the Antibacterial and Anti-Tuberculosis Properties of Moiramide B.

Due to worldwide increasing resistances, there is a considerable need for antibacterial compounds with modes of action not yet realized in commercial antibiotics. One such promising structure is the acetyl-CoA carboxylase (ACC) inhibitor moiramide B which shows strong antibacterial activity against gram-positive bacteria such as Bacillus subtilis and weaker activities against gram-negative bacteria. However, the narrow structure-activity relationship of the pseudopeptide unit of moiramide B represents a formidable challenge for any optimization strategy. In contrast, the lipophilic fatty acid tail is considered an unspecific vehicle responsible only for the transport of moiramide into the bacterial cell. Here we show that the sorbic acid unit, in fact, is highly relevant for ACC inhibition. A hitherto undescribed sub-pocket at the end of the sorbic acid channel binds strongly aromatic rings and allows the development of moiramide derivatives with altered antibacterial profiles including anti-tubercular activity.

Design and Analysis of Gallium Arsenide-Based Nanowire Using Coupled Non-Equilibrium Green Function for RF Hybrid Applications.

This research work uses sp3d5s* tight-binding models to design and analyze the structural properties of group IV and III-V oriented, rectangular Silicon (Si) and Gallium Arsenide (GaAs) Nanowires (NWs). The electrical characteristics of the NWs, which are shielded with Lanthanum Oxide (La2O3) material and the orientation with z [001] using the Non-Equilibrium Green Function (NEGF) method, have been analyzed. The electrical characteristics and the parameters for the multi-gate nanowires have been realized. A nanowire comprises a heavily doped n+ donor source and drains doping and n-donor doping at the channel. The specified nanowire has a gate length and channel length of 15 nm each, a source-drain device length LSD = 35 nm, with La2O3 as 1 nm (gate dielectric oxide) each on the top and bottom of the core material (Si/GaAs). The Gate-All-Around (GAA) Si NW is superior with a high (ION/IOFF ratio) of 1.06 × 109, and a low leakage current, or OFF current (IOFF), of 3.84 × 10-14 A. The measured values of the mid-channel conduction band energy (Ec) and charge carrier density (ρ) at VG = VD = 0.5 V are -0.309 eV and 6.24 × 1023 C/cm3, respectively. The nanowires with hydrostatic strain have been determined by electrostatic integrity and increased mobility, making them a leading solution for upcoming technological nodes. The transverse dimensions of the rectangular nanowires with similar energy levels are realized and comparisons between Si and GaAs NWs have been performed.

Density-functional expansion methods: Grand challenges.

We discuss the source of errors in semiempirical density functional expansion (VE) methods. In particular, we show that VE methods are capable of well-reproducing their standard Kohn-Sham density functional method counterparts, but suffer from large errors upon using one or more of these approximations: the limited size of the atomic orbital basis, the Slater monopole auxiliary basis description of the response density, and the one- and two-body treatment of the core-Hamiltonian matrix elements. In the process of discussing these approximations and highlighting their symptoms, we introduce a new model that supplements the second-order density-functional tight-binding model with a self-consistent charge-dependent chemical potential equalization correction; we review our recently reported method for generalizing the auxiliary basis description of the atomic orbital response density; and we decompose the first-order potential into a summation of additive atomic components and many-body corrections, and from this examination, we provide new insights and preliminary results that motivate and inspire new approximate treatments of the core-Hamiltonian.

Coarse-grained tight-binding models.

Calculating the electronic structure of systems involving very different length scales presents a challenge. Empirical atomistic descriptions such as pseudopotentials or tight-binding models allow one to calculate the effects of atomic placements, but the computational burden increases rapidly with the size of the system, limiting the ability to treat weakly bound extended electronic states. Here we propose a new method to connect atomistic and quasi-continuous models, thus speeding up tight-binding calculations for large systems. We divide a structure into blocks consisting of several unit cells which we diagonalize individually. We then construct a tight-binding Hamiltonian for the full structure using a truncated basis for the blocks, ignoring states having large energy eigenvalues and retaining states with energies close to the band edge energies. A numerical test using a GaAs/AlAs quantum well shows the computation time can be decreased to less than 5% of the full calculation with errors of less than 1%. We give data for the trade-offs between computing time and loss of accuracy. We also tested calculations of the density of states for a GaAs/AlAs quantum well and find a ten times speedup without much loss in accuracy.

Modulation of miRISC-Mediated Gene Silencing in Eukaryotes.

Gene expression is regulated at multiple levels in eukaryotic cells. Regulation at the post-transcriptional level is modulated by various trans-acting factors that bind to specific sequences in the messenger RNA (mRNA). The binding of different trans factors influences various aspects of the mRNA such as degradation rate, translation efficiency, splicing, localization, etc. MicroRNAs (miRNAs) are short endogenous ncRNAs that combine with the Argonaute to form the microRNA-induced silencing complex (miRISC), which uses base-pair complementation to silence the target transcript. RNA-binding proteins (RBPs) contribute to post-transcriptional control by influencing the mRNA stability and translation upon binding to cis-elements within the mRNA transcript. RBPs have been shown to impact gene expression through influencing the miRISC biogenesis, composition, or miRISC-mRNA target interaction. While there is clear evidence that those interactions between RBPs, miRNAs, miRISC and target mRNAs influence the efficiency of miRISC-mediated gene silencing, the exact mechanism for most of them remains unclear. This review summarizes our current knowledge on gene expression regulation through interactions of miRNAs and RBPs.

Binding Pattern Reconstructions of FGF-FGFR Budding-Inducing Signaling in Reef-Building Corals.

Reef-building corals play an important role in marine ecosystems. However, owing to climate change, ocean acidification, and predation by invasive crown-of-thorns starfish, these corals are declining. As marine animals comprise polyps, reproduction by asexual budding is pivotal in scleractinian coral growth. The fibroblast growth factor (FGF) signaling pathway is essential in coral budding morphogenesis. Here, we sequenced the full-length transcriptomes of four common and frequently dominant reef-building corals and screened out the budding-related FGF and FGFR genes. Thereafter, three-dimensional (3D) models of FGF and FGFR proteins as well as FGF-FGFR binding models were reconstructed. Based on our findings, the FGF8-FGFR3 binding models in Pocillopora damicornis, Montipora capricornis, and Acropora muricata are typical receptor tyrosine kinase-signaling pathways that are similar to the Kringelchen (FGFR) in hydra. However, in P. verrucosa, FGF8 is not the FGFR3 ligand, which is found in other hydrozoan animals, and its FGFR3 must be activated by other tyrosine kinase-type ligands. Overall, this study provides background on the potentially budding propagation signaling pathway activated by the applications of biological agents in reef-building coral culture that could aid in the future restoration of coral reefs.

Quantum computation for predicting electron and phonon properties of solids.

Quantum chemistry is one of the most promising near-term applications of quantum computers. Quantum algorithms such as variational quantum eigen solver (VQE) and variational quantum deflation (VQD) algorithms have been mainly applied for molecular systems and there is a need to implement such methods for periodic solids. Using Wannier tight-binding Hamiltonian (WTBH) approaches, we demonstrate the application of VQE and VQD to accurately predict both electronic and phonon bandstructure properties of several elemental as well as multi-component solid-state materials. We apply VQE-VQD calculations for 307 spin-orbit coupling based electronic WTBHs and 933 finite-difference based phonon WTBHs. Also, we discuss a workflow for using VQD with lattice Green's function that can be used for solving dynamical mean-field theory problems. The WTBH model solvers can be used for testing other quantum algorithms and models also.

A Deep Hydrophobic Binding Cavity is the Main Interaction for Different Y2 R Antagonists.

The neuropeptide Y2 receptor (Y2 R) is involved in various pathophysiological processes such as epilepsy, mood disorders, angiogenesis, and tumor growth. Therefore, the Y2 R is an interesting target for drug development. A detailed understanding of the binding pocket could facilitate the development of highly selective antagonists to study the role of Y2 R in vitro and in vivo. In this study, several residues crucial to the interaction of BIIE0246 and SF-11 derivatives with Y2 R were investigated by signal transduction assays. Using the experimental results as constraints, the antagonists were docked into a comparative structural model of the Y2 R. Despite differences in size and structure, all three antagonists display a similar binding site, including a deep hydrophobic cavity formed by transmembrane helices (TM) 4, 5, and 6, as well as a hydrophobic patch at the top of TM2 and 7. Additionally, we suggest that the antagonists block Q3.32 , a position that has been shown to be crucial for binding of the amidated C terminus of NPY and thus for receptor activation.

Structural basis for the inhibition of AKR1B10 by caffeic acid phenethyl ester (CAPE).

Caffeic acid phenethyl ester (CAPE), the major bioactive component of honeybee propolis, is a potent selective inhibitor of aldo-keto reductase family member 1B10 (AKR1B10), and a number of derivatives hold promise as potential anticancer agents. However, sequence homology between AKR1B10 and other members of the superfamily, including critical phase I metabolizing enzymes, has resulted in a concern over the selectivity of any potential therapeutic agent. To elucidate the binding mode of CAPE with AKR1B10 and to provide a tool for future in silico efforts towards identifying selective inhibitors, the crystal structure of AKR1B10 in complex with CAPE was determined. The observed interactions provide an explanation for the selectivity exhibited by CAPE for AKR1B10, and could be used to guide further derivative design.