False Hits Research Articles

Toward optimal clearance: A universal affinity-based mass spectrometry approach for comprehensive ELISA reagent coverage evaluation and HCP hitchhiker analysis.

In the control strategy for process related impurities in biopharmaceuticals, the enzyme linked immunosorbent assay (ELISA) is the method of choice for the quantification of host cell proteins (HCPs). Besides two dimensional-western blots (2D-WB), the coverage of ELISA antibodies is increasingly evaluated by affinity purification-based liquid chromatography-tandem mass spectrometry (AP-MS) methods. However, all these methods face the problem of unspecific binding issues between antibodies and the matrix, involving the application of arbitrarily defined thresholds during data evaluation. To solve this, a new approach (optimized AP-MS) was developed in this study, for which a cleavable linker was conjugated to the ELISA antibodies enabling the subsequent isolation of specifically interacting HCPs. By comparing both approaches in terms of method variability and the number of false positive or negative hits, we could demonstrate that the optimized AP-MS method is very reproducible and superior in the identification of antibody detection gaps, while previously described strategies suffered from over- or underestimating the coverage. As only antibody associated HCPs were identified, we demonstrated that the method is beneficial for hitchhiker analysis. Overall, the method described herein has proven as a powerful tool for reliable coverage determination of ELISA antibodies, without the need to arbitrarily exclude HCPs during the coverage evaluation.

Resolving isobaric interferences in direct infusion tandem mass spectrometry.

RationaleThe co‐fragmentation of precursors in direct infusion (DI) tandem high‐resolution mass spectrometry (HRMS) can complicate the fragment spectra and consequently lead to false hits during compound identification.MethodsThe method herein described, termed IQAROS (incremental quadrupole acquisition to resolve overlapping spectra), modulates the intensities of precursors and fragments by stepwise movement of the quadrupole isolation window over the mass‐to‐charge (m/z) range of the precursors. The modulated signals are then deconvoluted by a linear regression model to reconstruct the fragment spectra with less interference. The hardware to demonstrate the use of IQAROS was an orbitrap with electrospray ionization (ESI) or secondary electrospray ionization (SESI), although the method can also be applied to other ionization techniques or mass analyzers.ResultsAssessing the performance of IQAROS with isobaric standards revealed that the reconstructed fragment spectra match with spectra acquired from the pure standards and that more compounds were correctly identified compared with the classical approach with the quadrupole centered at the m/z value of the precursor of interest. Moreover, the strength of IQAROS is exemplified by the identification of two isobaric biomarkers directly from a breath sample with SESI‐HRMS.ConclusionsWith IQAROS, cleaner fragment spectra of co‐fragmenting isobars during DI‐HRMS analysis can be obtained. IQAROS can easily be set up by the standard graphical user interface of the instrument. Therefore, it facilitates the characterization of features of interest in samples analyzed by DI‐HRMS, for example, in high‐throughput or real‐time metabolomics.

An Enhanced Hybrid Screening Approach to Identify Potent Inhibitors for the SARS-CoV-2 Main Protease From the NCI Compound Library.

The emergence and rapid spread of SARS-CoV-2, the pathogen of COVID-19, have caused a worldwide public health crisis. The SARS-CoV-2 main protease (Mpro) is an essential enzyme for the virus and therefore an appealing target for the development of antivirals to treat COVID-19 patients. Recently, many in silico screenings have been performed against the main protease to discover novel hits. However, the actual hit rate of virtual screening is often low, and most of the predicted compounds are false positive hits. In this study, we developed a refined virtual screening strategy that incorporated molecular docking and post-docking filtering based on parameters including molecular weight and surface area, aiming to achieve predictions with fewer false positive hits. We applied this strategy to the NCI library containing 284,176 compounds against Mpro. In vitro potency analyses validated several potent inhibitors and thus confirmed the feasibility of our virtual screening strategy. Overall, The study resulted in several potent hit Mpro inhibitors, in which two inhibitors have IC50 values below 1 μM, that are worth being further optimized and explored. Meanwhile, the refined virtual screen strategy is also applicable to improve general in silico screening hit rates and is useful to accelerate drug discovery for treating COVID-19 and other viral infections.

Novel and Potential Small Molecule Scaffolds as DYRK1A Inhibitors by Integrated Molecular Docking-Based Virtual Screening and Dynamics Simulation Study.

The dual-specificity tyrosine phosphorylation-regulated kinase 1A (DYRK1A) is a novel, promising and emerging biological target for therapeutic intervention in neurodegenerative diseases, especially in Alzheimer’s disease (AD). The molMall database, comprising rare, diverse and unique compounds, was explored for molecular docking-based virtual screening against the DYRK1A protein, in order to find out potential inhibitors. Ligands exhibiting hydrogen bond interactions with key amino acid residues such as Ile165, Lys188 (catalytic), Glu239 (gk+1), Leu241 (gk+3), Ser242, Asn244, and Asp307, of the target protein, were considered potential ligands. Hydrogen bond interactions with Leu241 (gk+3) were considered key determinants for the selection. High scoring structures were also docked by Glide XP docking in the active sites of twelve DYRK1A related protein kinases, viz. DYRK1B, DYRK2, CDK5/p25, CK1, CLK1, CLK3, GSK3β, MAPK2, MAPK10, PIM1, PKA, and PKCα, in order to find selective DYRK1A inhibitors. MM/GBSA binding free energies of selected ligand–protein complexes were also calculated in order to remove false positive hits. Physicochemical and pharmacokinetic properties of the selected six hit ligands were also computed and related with the proposed limits for orally active CNS drugs. The computational toxicity webserver ProTox-II was used to predict the toxicity profile of selected six hits (molmall IDs 9539, 11352, 15938, 19037, 21830 and 21878). The selected six docked ligand–protein systems were exposed to 100 ns molecular dynamics (MD) simulations to validate their mechanism of interactions and stability in the ATP pocket of human DYRK1A kinase. All six ligands were found to be stable in the ATP binding pocket of DYRK1A kinase.

Static Code Analysis Alarms Filtering Reloaded: A New Real-World Dataset and its ML-Based Utilization

Even though Static Code Analysis (SCA) tools are integrated into many modern software building and testing pipelines, their practical impact is still seriously hindered by the excessive number of false positive warnings they usually produce. To cope with this problem, researchers have proposed several post-processing methods that aim to filter out false hits (or equivalently identify “actionable” warnings) after the SCA tool produced its results. However, we found that most of these approaches are targeted (i.e., deal with only a few SCA warning types) and evaluated on synthetic benchmarks or small-scale manually collected data sets (i.e., with typical sample sizes of several hundred). In this paper, we present a dataset containing 224,484 real-world warning samples fixed (true positives) or explicitly ignored (false positives) by the developers, which we collected from 9,958 different opensource Java projects from GitHub using a data mining approach. Additionally, we utilize this rich dataset to train a code embedding-based machine learning model for filtering false positive warnings produced by 160 different SonarQube rule checks, one of the most widely adopted SCA tools today. This is the most extensive real-world public dataset and study we know of in this area. Our method works with an accuracy of 91% (best F1-score of 81.3% and AUC of 95.3%) for the classification of SonarQube warnings.

ICRISEE: an integrative analysis of CRISPR screen by reducing false positive hits.

Clustered regularly interspaced short palindromic repeats associated protein 9 (CRISPR/Cas9) technology has become a popular tool for the study of genome function, and the use of this technology can achieve large-scale screening studies of specific phenotypes. Several analysis tools for CRISPR/Cas9 screening data have been developed, while high false positive rate remains a great challenge. To this end, we developed iCRISEE, an integrative analysis of CRISPR ScrEEn by reducing false positive hits. iCRISEE can dramatically reduce false positive hits and it is robust to different single guide RNA (sgRNA) library by introducing precise data filter and normalization, model selection and valid sgRNA number correction in data preprocessing, sgRNA ranking and gene ranking. Furthermore, a powerful web server has been presented to automatically complete the whole CRISPR/Cas9 screening analysis, where we integrated the main hypothesis in multiple algorithms as a full workflow, including quality control, sgRNA extracting, sgRNA alignment, sgRNA ranking, gene ranking and pathway enrichment. In addition, output of iCRISEE, including result mapping, sample clustering, sgRNA ranking and gene ranking, can be easily visualized and downloaded for publication. Taking together, iCRISEE presents to be the state-of-the-art and user-friendly tool for CRISPR screening data analysis. iCRISEE is available at https://www.icrisee.com.

The Relationship Between Indecisiveness and Network Integration in Scalp Resting-State EEG

Fragment-based drug design (FBDD) is a structure-based approach of ligand designing used in drug discovery. It involves the identification of low molecular weight fragments as hit and then their binding mode is determined using X-ray crystallography and NMR spectroscopy. The binding information retrieved facilitates the optimization of fragments as hits which are later on converted into drug-like molecules. The identified fragments bind efficiently and form high-quality binding interactions. X-ray protein crystallography is considered as one of the most reliable and sensitive biophysical methods used for screening that reduce the chances to come up with false positive hits. It also provides insight into the structural details of the protein–fragment complex at the atomic level. Therefore, the fragment-based screening by using X-ray crystallography can be considered as an efficient method to identify the binding hotspots on proteins that can be further explored by chemists and biologists for the discovery of new drugs.

PKT: fast color-based spatial model for human skin detection

We present a new detection method for color-based object detection, which can improve the performance of learning procedures in terms of speed, accuracy, and efficiency, using spatial inference, and algorithm. We applied the model to human skin detection from an image; however, the method can also work for other machine learning tasks involving image pixels. We propose (1) an improved RGB/HSL human skin color threshold to tackle darker human skin color detection problem. (2), we also present a new rule-based fast algorithm (packed k-dimensional tree --- PKT) that depends on an improved spatial structure for human skin/face detection from colored 2D images. We also implemented a novel packed quad-tree (PQT) to speed up the quad-tree performance in terms of indexing. We compared the proposed system to traditional pixel-by-pixel (PBP)/pixel-wise (PW) operation, and quadtree based procedures. The results show that our proposed spatial structure performs better (with a very low false hit rate, very high precision, and accuracy rate) than most state-of-the-art models.

Large-scale computational discovery and analysis of virus-derived microbial nanocompartments

Encapsulins are a class of microbial protein compartments defined by the viral HK97-fold of their capsid protein, self-assembly into icosahedral shells, and dedicated cargo loading mechanism for sequestering specific enzymes. Encapsulins are often misannotated and traditional sequence-based searches yield many false positive hits in the form of phage capsids. Here, we develop an integrated search strategy to carry out a large-scale computational analysis of prokaryotic genomes with the goal of discovering an exhaustive and curated set of all HK97-fold encapsulin-like systems. We find over 6,000 encapsulin-like systems in 31 bacterial and four archaeal phyla, including two novel encapsulin families. We formulate hypotheses about their potential biological functions and biomedical relevance, which range from natural product biosynthesis and stress resistance to carbon metabolism and anaerobic hydrogen production. An evolutionary analysis of encapsulins and related HK97-type virus families shows that they share a common ancestor, and we conclude that encapsulins likely evolved from HK97-type bacteriophages.

Recurring Real-Time Monitoring of Inflammations in Living Mice with a Chemiluminescent Probe for Hypochlorous Acid

Recurring Real-Time Monitoring of Inflammations in Living Mice with a Chemiluminescent Probe for Hypochlorous Acid

Discovery of the First Non-cGMP Mimetic Small Molecule Activators of cGMP-Dependent Protein Kinase 1 α (PKG1α).

PKG1α is a central node in cGMP signaling. Current therapeutics that look to activate this pathway rely on elevation of cGMP levels and subsequent activation of PKG1α. Direct activation of PKG1α could potentially drive additional efficacy without associated side effects of blanket cGMP elevation. We undertook a high-throughput screen to identify novel activators. After triaging through numerous false positive hits, attributed to compound mediated oxidation and activation of PKG1α, a piperidine series of compounds was validated. The hit 1 was a weak activator with EC50 = 47 μM. The activity could be improved to single digit micromolar, as seen in compounds 21 and 25 (7.0 and 3.7 μM, respectively). Several compounds were tested in a pVASP cell-based assay, and for compounds with moderate permeability, good agreement was observed between the biochemical and functional assays. These compounds will function as efficient tools to further interrogate PKG1α biology.

Toward an Understanding of Pan-Assay Interference Compounds and Promiscuity: A Structural Perspective on Binding Modes.

Pan-assay interference compounds (PAINS) are promiscuous compound classes that produce false positive hits in high-throughput screenings. Yet, the mechanisms of PAINS activity are poorly understood. Although PAINS are often associated with protein reactivity, several recent studies have shown that they also mediate noncovalent interactions. Aiming at a deep understanding of PAINS promiscuity, we performed an analysis of the Protein Data Bank to characterize the binding modes of PAINS. We explored the binding mode conservation of 34 PAINS classes present in 871 ligands and among 517 protein targets. The two major findings of this work are the following: First, different PAINS classes exhibit different levels of binding mode conservation. Our novel classification of PAINS based on binding mode similarity enables a rational assessment of PAINS from a structural perspective. Second, PAINS classes with variable binding modes can bind with high affinity. The evaluation of noncovalent binding modes of PAINS-like compounds sheds light on the mechanisms of promiscuous binding. Our findings could facilitate the decisions on how to deal with PAINS and help scientists to understand why PAINS produce hits in their screenings.

Measuring precipitation in Eastern Himalaya: Ground validation of eleven satellite, model and gauge interpolated gridded products

Precipitation plays a key role in shaping land surface processes in Himalaya. It is also the most challenging meteorological variable to model in climate change studies due to inadequate ground data. Gridded Precipitation Products (GPPs) are useful alternatives to ground data but require validation, especially in topographically complex and wet Eastern Himalaya. This study presents a fine-scaled ground validation of eleven GPPs, including five satellite-based (GPM-IMERGV06, TRMM-3B42V7, TRMM-3B42V7RT, CHIRPS-2.0 and PERSIANN-CCS), four reanalysis model-based (ERA5, ERA5-Land, AgERA5, and WRF) and two gauge-interpolated (IMD-0.25° and APHRODITE-2V18) GPPs in Eastern Himalaya. Hourly precipitation data from 27 rain gauges (Gauges) from Sikkim, representing the Eastern Himalayan climatology, is used to statistically validate the GPPs and assess their ability to capture diurnal and seasonal patterns, and extreme events.Overall, GPM-IMERG, WRF, and IMD-0.25° outperformed the other satellite, model, and interpolated GPPs, respectively, in comparison to Gauges. Near real-time TRMM-3B42RTV7 performed better than TRMM-3B42V7 for high-intensity precipitation and large storms, but not in overall performance. CHIRPS-2.0 and PERSIANN-CCS showed the least detectability and highest errors; however, PERSIANN-CCS was better at reproducing known spatial patterns in ground precipitation. The GPPs underestimated (overestimated) precipitation frequencies and volumes below (above) 3000 masl, and high (low) intensities. The highest detectability and lowest errors were observed at mid-elevation (1000-2000 masl), and in monsoon (JJASO) and summer (MAM), whereas winter (NDJF) precipitation was overestimated with high false hits. Strong diurnal cycles in precipitation were observed in Gauges with peaks around late-night-early morning (2300–0300 h) in monsoon and afternoon-evening (1600–1900 h) in summer. Satellite GPPs captured the diurnal cycle in Gauges, albeit with subdued amplitudes, whereas model GPPs failed. Owing to their superior performance and fine spatiotemporal resolutions, GPM-IMERG and WRF are recommended for hydrological studies in Eastern Himalaya. The three ERA5-based products (ERA5, ERA5-Land, AGERA5), along with TRMM-3B42RTV7 and IMD-0.25°, showed the lowest bias in tracking large storms (longer than 5 days) and are advocated for understanding extreme events and in geohazard applications. IMD-0.25° is recommended for precipitation trend and variability analysis, albeit with suitable bias-correction to address the negative biase at high elevations and the positive bias in trans-Himalayan regions. The study improves our understanding of remotely-sensed and spatially-gridded precipitation in the world's highest mountain range, where ground observations are likely to be inadequate for years to come.

A Hybrid Approach Combining R*-Tree and k-d Trees to Improve Linked Open Data Query Performance

Semantic Web has recently gained traction with the use of Linked Open Data (LOD) on the Web. Although numerous state-of-the-art methodologies, standards, and technologies are applicable to the LOD cloud, many issues persist. Because the LOD cloud is based on graph-based resource description framework (RDF) triples and the SPARQL query language, we cannot directly adopt traditional techniques employed for database management systems or distributed computing systems. This paper addresses how the LOD cloud can be efficiently organized, retrieved, and evaluated. We propose a novel hybrid approach that combines the index and live exploration approaches for improved LOD join query performance. Using a two-step index structure combining a disk-based 3D R*-tree with the extended multidimensional histogram and flash memory-based k-d trees, we can efficiently discover interlinked data distributed across multiple resources. Because this method rapidly prunes numerous false hits, the performance of join query processing is remarkably improved. We also propose a hot-cold segment identification algorithm to identify regions of high interest. The proposed method is compared with existing popular methods on real RDF datasets. Results indicate that our method outperforms the existing methods because it can quickly obtain target results by reducing unnecessary data scanning and reduce the amount of main memory required to load filtering results.

Discovery of Novel Mammalian target of rapamycin (mTOR) Inhibitors by Support Vector Machine

Mammalian target of rapamycin (mTOR) is a protein serine/threonine kinase playing the central downstream role in multiple mitogenic signalling pathways. As a c entral regulator of cell growth, proliferation, differentiation and survival, mTOR has b een reported to modulate proliferation and angiogenesis in neoplastic processes. Curre ntly, sirolimus and its analogues the only five mTOR inhibitors approved for clinical u se, which shows a great capacity in anticancer therapy. However, endocrine resistance in cancer therapy has been observed in sirolimus analogues, and the unavailability of n ew mTOR inhibitor besides similar structure of sirolimus analogues makes the resistan ce even worse. It is urgent to discovery new mTOR inhibitors as candidates for develo pment of effective anticancer drugs. In this study, support vector machine (SVM) as a virtual screening strategy was proposed. SVM models of mTOR inhibitors were constr ucted by training data published before 2012, and the ones published after 2012 as test set were used to verify according to cross validation. The selected model performed thi n false hit rates of 0.12% and 0.46% by screening PubChem and MDDR chemical libr aries respectively. As results, 9 novel novel scaffolds for mTOR were identified, and 6 of them have been reported their anticancer-related therapeutic capacity. In summary, SVM performed its ability to identify novel mTOR inhibitors, which can supply some candidates for mTOR anticancer drugs, and supply effective method for anticancer dru g discovery in future.

Exploring RdRp–remdesivir interactions to screen RdRp inhibitors for the management of novel coronavirus 2019-nCoV

ABSTRACT A novel coronavirus recently identified in Wuhan, China (2019-nCoV) has resulted in an increasing number of patients globally, and has become a highly lethal pathogenic member of the coronavirus family affecting humans. 2019-nCoV has established itself as one of the most threatening pandemics that human beings have faced, and therefore analysis and evaluation of all possible responses against infection is required. One such strategy includes utilizing the knowledge gained from the SARS and MERS outbreaks regarding existing antivirals. Indicating a potential for success, one of the drugs, remdesivir, under repurposing studies, has shown positive results in initial clinical studies. Therefore, in the current work, the authors have attempted to utilize the remdesivir–RdRp complex – RdRp (RNA-dependent RNA polymerase) being the putative target for remdesivir – to screen a library of the already reported RdRp inhibitor database. Further clustering on the basis of structural features and scoring refinement was performed to filter out false positive hits. Finally, molecular dynamics simulation was carried out to validate the identification of hits as RdRp inhibitors against novel coronavirus 2019-nCoV. The results yielded two putative hits which can inhibit RdRp with better potency than remdesivir, subject to further biological evaluation.

Flood risk forecasting at weather to medium range incorporating weather model, topography, socio-economic information and land use exposure

Non-structural mitigation measures to the globally increasing flood events include forecast based alert generation. However, the extreme rainfall forecasts are associated with low hit rate, high false alarm, and spatiotemporal bias; which makes it difficult to rely on them. Further, the losses due to flood in a region not only depend on rainfall severity but also on topography, socioeconomic conditions and exposure of the region to floods. Here, we introduce a new concept of spatial flood risk mapping and forecasting at weather to medium range based on forecasted hazard, embedded with vulnerability (topographic and socioeconomic) and exposure information. We define hazard as the probability of extreme rainfall event during upcoming days given an available weather forecast for the same days. As hindcast is used for computation of probabilities, hazard contains prior information about the false alarm, hit rate and spatiotemporal bias of the forecast. Vulnerability is calculated by averaging the topographic and socioeconomic indicators, and exposure is calculated using a land use land cover map. Topographic vulnerability is computed with digital elevation model using Height Above the Nearest Drainage method, and Data Envelopment Analysis is performed to derive the socioeconomic vulnerability based on the demographic census data. For a specific region and a specific event, the relative flood risk maps are generated at an administrative level (e.g., district, subdistrict or village level for India) and the high-risk areas can be identified from those maps for mitigation. The methodology is demonstrated for a very recent extremely severe flood event that happened in Kerala, India in August 2018. It is evident from the results that the high-risk areas forecasted well in advance (as high lead time as 15 days) match fairly well with the areas, which suffered maximum losses because of direct flood.

Short k-mer abundance profiles yield robust machine learning features and accurate classifiers for RNA viruses.

High-throughput sequencing technologies have greatly enabled the study of genomics, transcriptomics and metagenomics. Automated annotation and classification of the vast amounts of generated sequence data has become paramount for facilitating biological sciences. Genomes of viruses can be radically different from all life, both in terms of molecular structure and primary sequence. Alignment-based and profile-based searches are commonly employed for characterization of assembled viral contigs from high-throughput sequencing data. Recent attempts have highlighted the use of machine learning models for the task, but these models rely entirely on DNA genomes and owing to the intrinsic genomic complexity of viruses, RNA viruses have gone completely overlooked. Here, we present a novel short k-mer based sequence scoring method that generates robust sequence information for training machine learning classifiers. We trained 18 classifiers for the task of distinguishing viral RNA from human transcripts. We challenged our models with very stringent testing protocols across different species and evaluated performance against BLASTn, BLASTx and HMMER3 searches. For clean sequence data retrieved from curated databases, our models display near perfect accuracy, outperforming all similar attempts previously reported. On de novo assemblies of raw RNA-Seq data from cells subjected to Ebola virus, the area under the ROC curve varied from 0.6 to 0.86 depending on the software used for assembly. Our classifier was able to properly classify the majority of the false hits generated by BLAST and HMMER3 searches on the same data. The outstanding performance metrics of our model lays the groundwork for robust machine learning methods for the automated annotation of sequence data.

Integrating bioassay data for improved prediction of drug-target interaction

Identifying drug targets is one of the major tasks in drug discovery. As experimental identification of targets is rather challenging, development of computational methods is necessary for efficient identification of drug-target interaction. Traditional computational method, such as docking, is based solely on the chemical structure, which is not available for most of the targets. On the other hand, bioassay data might contain information helpful for prediction of drug-target interaction. In this study, a feature enrichment method integrating bioassay and chemical structure data was developed to predict drug-target interaction. Using a large-scale benchmark on the datasets, we demonstrated that the model adopting integrated fingerprint outperformed the one using chemical fingerprint. Influence of the false positive hits in bioassays and algorithm-related factors on the model performance were also investigated. The results suggested that prediction by using integrated fingerprint was robust to false positive hits, the choice of classifiers, and different random splits of the datasets.

Applying a Multiple Comparison Control to IRT Item-fit Testing

ABSTRACT We used simulation techniques to assess the item-level and familywise Type I error control and power of an IRT item-fit statistic, the S-X2 . Previous research indicated that the S-X2 has good Type I error control and decent power, but no previous research examined familywise Type I error control. We varied percentage of misfitting items, sample size, and test length, and computed familywise Type I error with no correction, a Bonferroni correction, and a Benjamini-Hochberg correction. The S-X2 controlled item-level and familywise Type I errors when corrections were applied to conditions with no misfitting items. In the presence of misfitting items, the S-X2 exhibited inflated item-level and familywise false hit rates in many conditions, even with familywise Type I error corrections. Lastly, power was low and negatively impacted when either of the familywise Type I error corrections was applied. We suggest using the S-X2 with no familywise Type I error control in conjunction with other methods of assessing item fit (e.g., visual analysis).