Discovery of thiophen-2-ylmethylene bis-dimedone derivatives as novel WRN inhibitors for treating cancers with microsatellite instability.

Microsatellite instability (MSI) is a hypermutable condition caused by DNA mismatch repair system defects, contributing to the development of various cancer types. Recent research has identified Werner syndrome ATP-dependent helicase (WRN) as a promising synthetic lethal target for MSI cancers. Herein, we report the first discovery of thiophen-2-ylmethylene bis-dimedone derivatives as novel WRN inhibitors for MSI cancer therapy. Initial computational analysis and biological evaluation identified a new scaffold for a WRN inhibitor. Subsequent SAR study led to the discovery of a highly potent WRN inhibitor. Furthermore, we demonstrated that the optimal compound induced DNA damage and apoptotic cell death in MSI cancer cells by inhibiting WRN. This study provides a new pharmacophore for WRN inhibitors, emphasizing their therapeutic potential for MSI cancers.

Cytosolic microRNA-inducible nuclear translocation of Cas9 protein for disease-specific genome modification.

MicroRNA-dependent mRNA decay plays an important role in gene silencing by facilitating posttranscriptional and translational repression. Inspired by this intrinsic nature of microRNA-mediated mRNA cleavage, here, we describe a microRNA-targeting mRNA as a switch platform called mRNA bridge mimetics to regulate the translocation of proteins. We applied the mRNA bridge mimetics platform to Cas9 protein to confer it the ability to translocate into the nucleus via cleavage of the nuclear export signal. This system performed programmed gene editing in vitro and in vivo. Combinatorial treatment with cisplatin and miR-21-EZH2 axis-targeting CRISPR Self Check-In improved sensitivity to chemotherapeutic drugs in vivo. Using the endogenous microRNA-mediated mRNA decay mechanism, our platform is able to remodel a cell's natural biology to allow the entry of precise drugs into the nucleus, devoid of non-specific translocation. The mRNA bridge mimetics strategy is promising for applications in which the reaction must be controlled via intracellular stimuli and modulates Cas9 proteins to ensure safe genome modification in diseased conditions.

Characterization of the role of Kunitz-type protease inhibitor domain in dimerization of amyloid precursor protein.

A major difference between amyloid precursor protein (APP) isoforms (APP695 and APP751) is the existence of a Kunitz type protease inhibitor (KPI) domain which has a significant impact on the homo- and hetero-dimerization of APP isoforms. However, the exact molecular mechanisms of dimer formation remain elusive. To characterize the role of the KPI domain in APP dimerization, we performed a single molecule pull down (SiMPull) assay where homo-dimerization between tethered APP molecules and soluble APP molecules was highly preferred regardless of the type of APP isoforms, while hetero-dimerization between tethered APP751 molecules and soluble APP695 molecules was limited. We further investigated the domain level APP-APP interactions using coarse-grained models with the Martini force field. Though the model initial ternary complexes (KPI-E1, KPI-KPI, KPI-E2, E1-E1, E2-E2, and E1-E2) generated using HADDOCK (HD) and AlphaFold2 (AF2), the binding free energy profiles and the binding affinities of the domain combinations were investigated via the umbrella sampling with Martini force field. Additionally, membrane-bound microenvironments at the domain level were modeled. As a result, it was revealed that the KPI domain has a stronger attractive interaction with itself than the E1 and E2 domains, as reported elsewhere. Thus, the KPI domain of APP751 may form additional attractive interactions with E1, E2 and the KPI domain itself, whereas it is absent in APP695. In conclusion, we found that the APP751 homo-dimer formation is predominant than the homodimerization in APP695, which is facilitated by the presence of the KPI domain.

A-Prot: protein structure modeling using MSA transformer.

BACKGROUND: The accuracy of protein 3D structure prediction has been dramatically improved with the help of advances in deep learning. In the recent CASP14, Deepmind demonstrated that their new version of AlphaFold (AF) produces highly accurate 3D models almost close to experimental structures. The success of AF shows that the multiple sequence alignment of a sequence contains rich evolutionary information, leading to accurate 3D models. Despite the success of AF, only the prediction code is open, and training a similar model requires a vast amount of computational resources. Thus, developing a lighter prediction model is still necessary. RESULTS: In this study, we propose a new protein 3D structure modeling method, A-Prot, using MSA Transformer, one of the state-of-the-art protein language models. An MSA feature tensor and row attention maps are extracted and converted into 2D residue-residue distance and dihedral angle predictions for a given MSA. We demonstrated that A-Prot predicts long-range contacts better than the existing methods. Additionally, we modeled the 3D structures of the free modeling and hard template-based modeling targets of CASP14. The assessment shows that the A-Prot models are more accurate than most top server groups of CASP14. CONCLUSION: These results imply that A-Prot accurately captures the evolutionary and structural information of proteins with relatively low computational cost. Thus, A-Prot can provide a clue for the development of other protein property prediction methods.

CRFalign: A Sequence-Structure Alignment of Proteins Based on a Combination of HMM-HMM Comparison and Conditional Random Fields.

Sequence-structure alignment for protein sequences is an important task for the template-based modeling of 3D structures of proteins. Building a reliable sequence-structure alignment is a challenging problem, especially for remote homologue target proteins. We built a method of sequence-structure alignment called CRFalign, which improves upon a base alignment model based on HMM-HMM comparison by employing pairwise conditional random fields in combination with nonlinear scoring functions of structural and sequence features. Nonlinear scoring part is implemented by a set of gradient boosted regression trees. In addition to sequence profile features, various position-dependent structural features are employed including secondary structures and solvent accessibilities. Training is performed on reference alignments at superfamily levels or twilight zone chosen from the SABmark benchmark set. We found that CRFalign method produces relative improvement in terms of average alignment accuracies for validation sets of SABmark benchmark. We also tested CRFalign on 51 sequence-structure pairs involving 15 FM target domains of CASP14, where we could see that CRFalign leads to an improvement in average modeling accuracies in these hard targets (TM-CRFalign ≃42.94%) compared with that of HHalign (TM-HHalign ≃39.05%) and also that of MRFalign (TM-MRFalign ≃36.93%). CRFalign was incorporated to our template search framework called CRFpred and was tested for a random target set of 300 target proteins consisting of Easy, Medium and Hard sets which showed a reasonable template search performance.

Readout Integrated Circuit for Small-Sized and Low-Power Gas Sensor Based on HEMT Device.

This paper presents a small-sized, low-power gas sensor system combining a high-electron-mobility transistor (HEMT) device and readout integrated circuit (ROIC). Using a semiconductor-based HEMT as a gas-sensing device, it is possible to secure high sensitivity, reduced complexity, low power, and small size of the ROIC sensor system. Unlike existing gas sensors comprising only HEMT elements, the proposed sensor system has both an ROIC and a digital controller and can control sensor operation through a simple calibration process with digital signal processing while maintaining constant performance despite variations. The ROIC mainly consists of a transimpedance amplifier (TIA), a negative-voltage generator, and an analog-to-digital converter (ADC) and is designed to match a minimum target detection unit of 1 ppm for hydrogen. The prototype ROIC for the HEMT presented herein was implemented in a 0.18 µm complementary metal-oxide-semiconductor (CMOS) process. The total measured power consumption and detection unit of the proposed ROIC for hydrogen gas were 3.1 mW and 2.6 ppm, respectively.

V-Dock: Fast Generation of Novel Drug-like Molecules Using Machine-Learning-Based Docking Score and Molecular Optimization.

We propose a computational workflow to design novel drug-like molecules by combining the global optimization of molecular properties and protein-ligand docking with machine learning. However, most existing methods depend heavily on experimental data, and many targets do not have sufficient data to train reliable activity prediction models. To overcome this limitation, protein-ligand docking calculations must be performed using the limited data available. Such docking calculations during molecular generation require considerable computational time, preventing extensive exploration of the chemical space. To address this problem, we trained a machine-learning-based model that predicted the docking energy using SMILES to accelerate the molecular generation process. Docking scores could be accurately predicted using only a SMILES string. We combined this docking score prediction model with the global molecular property optimization approach, MolFinder, to find novel molecules exhibiting the desired properties with high values of predicted docking scores. We named this design approach V-dock. Using V-dock, we efficiently generated many novel molecules with high docking scores for a target protein, a similarity to the reference molecule, and desirable drug-like and bespoke properties, such as QED. The predicted docking scores of the generated molecules were verified by correlating them with the actual docking scores.

Prediction of Molecular Electronic Transitions Using Random Forests.

Fluorescent molecules, fluorophores or dyes, play essential roles in bioimaging. Effective bioimaging requires fluorophores with diverse colors and high quantum yields for better resolution. An essential computational component to design novel dye molecules is an accurate model that predicts the electronic properties of molecules. Here, we present statistical machines that predict the excitation energies and associated oscillator strengths of a given molecule using the random forest algorithm. The excitation energies and oscillator strengths of a molecule are closely related to the emission spectrum and the quantum yields of fluorophores, respectively. In this study, we identified specific molecular substructures that induce high oscillator strengths of molecules. The results of our study are expected to serve as new design principles for designing novel fluorophores.

Low Power CMOS-Based Hall Sensor with Simple Structure Using Double-Sampling Delta-Sigma ADC.

A CMOS (Complementary metal-oxide-semiconductor) Hall sensor with low power consumption and simple structure is introduced. The tiny magnetic signal from Hall device could be detected by a high-resolution delta-sigma ADC in presence of offset and flickering noise. Also, the offset as well as the flickering noise are effectively suppressed by the current spinning technique combined with double sampling switches of the ADC. The double sampling scheme of the ADC reduces the operating frequency and helps to reduce the power consumption. The prototype Hall sensor is fabricated in a 0.18-µm CMOS process, and the measurement shows detection range of ±150 mT and sensitivity of 110 µV/mT. The size of active area is 0.7 mm2, and the total power consumption is 4.9 mW. The proposed system is advantageous not only for low power consumption, but also for small sensor size due to its simplicity.

AK-Score: Accurate Protein-Ligand Binding Affinity Prediction Using an Ensemble of 3D-Convolutional Neural Networks.

Accurate prediction of the binding affinity of a protein-ligand complex is essential for efficient and successful rational drug design. Therefore, many binding affinity prediction methods have been developed. In recent years, since deep learning technology has become powerful, it is also implemented to predict affinity. In this work, a new neural network model that predicts the binding affinity of a protein-ligand complex structure is developed. Our model predicts the binding affinity of a complex using the ensemble of multiple independently trained networks that consist of multiple channels of 3-D convolutional neural network layers. Our model was trained using the 3772 protein-ligand complexes from the refined set of the PDBbind-2016 database and tested using the core set of 285 complexes. The benchmark results show that the Pearson correlation coefficient between the predicted binding affinities by our model and the experimental data is 0.827, which is higher than the state-of-the-art binding affinity prediction scoring functions. Additionally, our method ranks the relative binding affinities of possible multiple binders of a protein quite accurately, comparable to the other scoring functions. Last, we measured which structural information is critical for predicting binding affinity and found that the complementarity between the protein and ligand is most important.

Novel approach to coronary artery perforation repair.

Coronary artery perforation (CAP) during percutaneous coronary intervention is a rare but serious complication. Treatment options of CAP include prolonged balloon inflation, covered stent, and coil embolization. Although most cases of CAP can be treated with prolonged balloon inflation, some cases, especially Ellis grade III CAP require covered stents or coiling. Covered stents may require a large bore guide catheter and have a high rate of restenosis, which can be a limiting factor in patients with severe peripheral arterial disease. Coil embolization is generally used in distal CAP because coiling in the proximal vessels results in a large territory of infarction. We present a case of an Ellis grade III CAP during rotational atherectomy successfully treated with a novel coiling technique whereby the thrombogenic coil extends through the perforation outside of the vessel, and the intraarterial portion of the coil is excluded from the lumen by drug-eluting stent placement over the proximal portion of the coil.

Protein Loop Structure Prediction Using Conformational Space Annealing.

We have developed a protein loop structure prediction method by combining a new energy function, which we call EPLM (energy for protein loop modeling), with the conformational space annealing (CSA) global optimization algorithm. The energy function includes stereochemistry, dynamic fragment assembly, distance-scaled finite ideal gas reference (DFIRE), and generalized orientation- and distance-dependent terms. For the conformational search of loop structures, we used the CSA algorithm, which has been quite successful in dealing with various hard global optimization problems. We assessed the performance of EPLM with two widely used loop-decoy sets, Jacobson and RAPPER, and compared the results against the DFIRE potential. The accuracy of model selection from a pool of loop decoys as well as de novo loop modeling starting from randomly generated structures was examined separately. For the selection of a nativelike structure from a decoy set, EPLM was more accurate than DFIRE in the case of the Jacobson set and had similar accuracy in the case of the RAPPER set. In terms of sampling more nativelike loop structures, EPLM outperformed EDFIRE for both decoy sets. This new approach equipped with EPLM and CSA can serve as the state-of-the-art de novo loop modeling method.

Absolute binding free energies for octa-acids and guests in SAMPL5 : Evaluating binding free energies for octa-acid and guest complexes in the SAMPL5 blind challenge.

As part of the SAMPL5 blind prediction challenge, we calculate the absolute binding free energies of six guest molecules to an octa-acid (OAH) and to a methylated octa-acid (OAMe). We use the double decoupling method via thermodynamic integration (TI) or Hamiltonian replica exchange in connection with the Bennett acceptance ratio (HREM-BAR). We produce the binding poses either through manual docking or by using GalaxyDock-HG, a docking software developed specifically for this study. The root mean square deviations for our most accurate predictions are 1.4 kcal mol-1 for OAH with TI and 1.9 kcal mol-1 for OAMe with HREM-BAR. Our best results for OAMe were obtained for systems with ionic concentrations corresponding to the ionic strength of the experimental solution. The most problematic system contains a halogenated guest. Our attempt to model the σ-hole of the bromine using a constrained off-site point charge, does not improve results. We use results from molecular dynamics simulations to argue that the distinct binding affinities of this guest to OAH and OAMe are due to a difference in the flexibility of the host. We believe that the results of this extensive analysis of host-guest complexes will help improve the protocol used in predicting binding affinities for larger systems, such as protein-substrate compounds.

Absolute binding free energy calculations of CBClip host-guest systems in the SAMPL5 blind challenge.

Herein, we report the absolute binding free energy calculations of CBClip complexes in the SAMPL5 blind challenge. Initial conformations of CBClip complexes were obtained using docking and molecular dynamics simulations. Free energy calculations were performed using thermodynamic integration (TI) with soft-core potentials and Bennett's acceptance ratio (BAR) method based on a serial insertion scheme. We compared the results obtained with TI simulations with soft-core potentials and Hamiltonian replica exchange simulations with the serial insertion method combined with the BAR method. The results show that the difference between the two methods can be mainly attributed to the van der Waals free energies, suggesting that either the simulations used for TI or the simulations used for BAR, or both are not fully converged and the two sets of simulations may have sampled difference phase space regions. The penalty scores of force field parameters of the 10 guest molecules provided by CHARMM Generalized Force Field can be an indicator of the accuracy of binding free energy calculations. Among our submissions, the combination of docking and TI performed best, which yielded the root mean square deviation of 2.94 kcal/mol and an average unsigned error of 3.41 kcal/mol for the ten guest molecules. These values were best overall among all participants. However, our submissions had little correlation with experiments.

Template based protein structure modeling by global optimization in CASP11.

For the template-based modeling (TBM) of CASP11 targets, we have developed three new protein modeling protocols (nns for server prediction and LEE and LEER for human prediction) by improving upon our previous CASP protocols (CASP7 through CASP10). We applied the powerful global optimization method of conformational space annealing to three stages of optimization, including multiple sequence-structure alignment, three-dimensional (3D) chain building, and side-chain remodeling. For more successful fold recognition, a new alignment method called CRFalign was developed. It can incorporate sensitive positional and environmental dependence in alignment scores as well as strong nonlinear correlations among various features. Modifications and adjustments were made to the form of the energy function and weight parameters pertaining to the chain building procedure. For the side-chain remodeling step, residue-type dependence was introduced to the cutoff value that determines the entry of a rotamer to the side-chain modeling library. The improved performance of the nns server method is attributed to successful fold recognition achieved by combining several methods including CRFalign and to the current modeling formulation that can incorporate native-like structural aspects present in multiple templates. The LEE protocol is identical to the nns one except that CASP11-released server models are used as templates. The success of LEE in utilizing CASP11 server models indicates that proper template screening and template clustering assisted by appropriate cluster ranking promises a new direction to enhance protein 3D modeling. Proteins 2016; 84(Suppl 1):221-232. © 2015 Wiley Periodicals, Inc.

Transulnar versus transradial access for coronary angiography or percutaneous coronary intervention: A meta-analysis of randomized controlled trials.

BACKGROUND: Although transfemoral access (TFA) remains the standard of care for patients undergoing coronary angiography (CA) or percutaneous coronary intervention (PCI) in the USA, TRA is being increasingly used over TFA due to lower bleeding and mortality rates on the basis of meta-analyses and recently published MATRIX trial. In patients with unsuccessful ipsilateral radial access, TUA has been used as an alternative approach. The randomized controlled trials (RCTs) comparing TUA and TRA have reached mixed conclusions regarding the use of transulnar approach for coronary procedures. OBJECTIVES: To systematically review and perform a meta-analysis of published RCTs comparing the safety and efficacy of transulnar access (TUA) vs. transradial access (TRA) in patients undergoing CA or PCI. METHODS: PubMed, EMBASE, and CENTRAL databases were searched for RCTs since inception through December, 2014. Meta-analysis was performed using random-effects model. RESULTS: Five RCTs involving 2,744 total patients were included in the meta-analysis. TUA compared with TRA had similar risks of MACE [risk ratio (RR): 0.87; 95% confidence interval (CI): 0.56-1.36; P = 0.54] and access-related complications [RR: 0.92 (0.67-1.27); P = 0.62]. Higher rates of access cross-over [RR: 2.31 (1.07-4.98); P = 0.003] and number of punctures [1.57 vs. 1.4; mean difference (MD): 0.17; 95% CI: 0.08-0.26; P = 0.0002] were noted with TUA. There was no difference in arterial access time [12.8 vs. 10.9 min; MD: 1.86 (-1.35-5.7); P = 0.26], fluoroscopy time [7.6 vs. 7.2 min; MD: 0.37 (-0.39 - 1.13); P = 0.34] and contrast volume [151 vs. 153.7 ml; MD: -2.74 (-17.21 - 11.73); P = 0.71]. CONCLUSION: For patients requiring CA or PCI, TUA compared with TRA has similar efficacy and safety except for higher puncture rates and access cross-over.

Blind prediction of distribution in the SAMPL5 challenge with QM based protomer and pK a corrections.

The computation of distribution coefficients between polar and apolar phases requires both an accurate characterization of transfer free energies between phases and proper accounting of ionization and protomerization. We present a protocol for accurately predicting partition coefficients between two immiscible phases, and then apply it to 53 drug-like molecules in the SAMPL5 blind prediction challenge. Our results combine implicit solvent QM calculations with classical MD simulations using the non-Boltzmann Bennett free energy estimator. The OLYP/DZP/SMD method yields predictions that have a small deviation from experiment (RMSD = 2.3 [Formula: see text] D units), relative to other participants in the challenge. Our free energy corrections based on QM protomer and [Formula: see text] calculations increase the correlation between predicted and experimental distribution coefficients, for all methods used. Unfortunately, these corrections are overly hydrophilic, and fail to account for additional effects such as aggregation, water dragging and the presence of polar impurities in the apolar phase. We show that, although expensive, QM-NBB free energy calculations offer an accurate and robust method that is superior to standard MM and QM techniques alone.

Calculating distribution coefficients based on multi-scale free energy simulations: an evaluation of MM and QM/MM explicit solvent simulations of water-cyclohexane transfer in the SAMPL5 challenge.

One of the central aspects of biomolecular recognition is the hydrophobic effect, which is experimentally evaluated by measuring the distribution coefficients of compounds between polar and apolar phases. We use our predictions of the distribution coefficients between water and cyclohexane from the SAMPL5 challenge to estimate the hydrophobicity of different explicit solvent simulation techniques. Based on molecular dynamics trajectories with the CHARMM General Force Field, we compare pure molecular mechanics (MM) with quantum-mechanical (QM) calculations based on QM/MM schemes that treat the solvent at the MM level. We perform QM/MM with both density functional theory (BLYP) and semi-empirical methods (OM1, OM2, OM3, PM3). The calculations also serve to test the sensitivity of partition coefficients to solute polarizability as well as the interplay of the quantum-mechanical region with the fixed-charge molecular mechanics environment. Our results indicate that QM/MM with both BLYP and OM2 outperforms pure MM. However, this observation is limited to a subset of cases where convergence of the free energy can be achieved.

Innovation in Data Abstraction, Education, and Consistent Application of Best Practice Guidelines: Experience and Real-World Results from a University Hospital in a Cost-Centric Environment.

BACKGROUND: Adhering to core measures and consistent application of best practice guidelines in patients with acute coronary syndromes is challenging for hospitals. METHODS: A task force addressed gaps in care and adherence to guidelines, and included Emergency Medical Services (EMS) in the decision pathway. RESULTS: Previously, our institutional performance on most core metrics was in the lower tertile nationally. Task force recommendations and the recognition of EMS's role in care produced significant improvement. Seventy-four percent of our cardiac catheterization laboratory activations were prehospital activations, which resulted in expeditious revascularization. Our composite acute myocardial infarction (MI) performance in 2014 was 97.5% for Q1, 97.2% for Q2, 97.3% for Q3, and 97.3% for Q4. Compliance in most of the individual parameters was greater than 95%. CONCLUSION: Identification of systemic gaps, application of best practice guidelines, and partnering with EMS improved our core measures and patient outcomes without the need for additional resources.

Sigma-RF: prediction of the variability of spatial restraints in template-based modeling by random forest.

BACKGROUND: In template-based modeling when using a single template, inter-atomic distances of an unknown protein structure are assumed to be distributed by Gaussian probability density functions, whose center peaks are located at the distances between corresponding atoms in the template structure. The width of the Gaussian distribution, the variability of a spatial restraint, is closely related to the reliability of the restraint information extracted from a template, and it should be accurately estimated for successful template-based protein structure modeling. RESULTS: To predict the variability of the spatial restraints in template-based modeling, we have devised a prediction model, Sigma-RF, by using the random forest (RF) algorithm. The benchmark results on 22 CASP9 targets show that the variability values from Sigma-RF are of higher correlations with the true distance deviation than those from Modeller. We assessed the effect of new sigma values by performing the single-domain homology modeling of 22 CASP9 targets and 24 CASP10 targets. For most of the targets tested, we could obtain more accurate 3D models from the identical alignments by using the Sigma-RF results than by using Modeller ones. CONCLUSIONS: We find that the average alignment quality of residues located between and at two aligned residues, quasi-local information, is the most contributing factor, by investigating the importance of input features used in the RF machine learning. This average alignment quality is shown to be more important than the previously identified quantity of a local information: the product of alignment qualities at two aligned residues.