The Limited Predictive Power of the Pauling Rules.
Ontology highlight
ABSTRACT:
SUBMITTER: George J
PROVIDER: S-EPMC7217010 | biostudies-literature | 2020 May
REPOSITORIES: biostudies-literature
Publications
The Limited Predictive Power of the Pauling Rules.
George Janine J Waroquiers David D Di Stefano Davide D Petretto Guido G Rignanese Gian-Marco GM Hautier Geoffroy G
Angewandte Chemie (International ed. in English) 20200320 19
The Pauling rules have been used for decades to rationalise the crystal structures of ionic compounds. Despite their importance, there has been no statistical assessment of the performances of these five empirical rules so far. Here, we rigorously and automatically test all five Pauling rules for a large data set of around 5000 known oxides. We discuss each Pauling rule separately, stressing their limits and range of application in terms of chemistries and structures. We conclude that only 13 % ...[more]
Similar Datasets
Project description:Understanding how species assemble into communities is a key goal in ecology. However, assembly rules are rarely tested experimentally, and their ability to shape real communities is poorly known. We surveyed a diverse community of epiphyte-dwelling ants and found that similar-sized species co-occurred less often than expected. Laboratory experiments demonstrated that invasion was discouraged by the presence of similarly sized resident species. The size difference for which invasion was less likely was the same as that for which wild species exhibited reduced co-occurrence. Finally we explored whether our experimentally derived assembly rules could simulate realistic communities. Communities simulated using size-based species assembly exhibited diversities closer to wild communities than those simulated using size-independent assembly, with results being sensitive to the combination of rules employed. Hence, species segregation in the wild can be driven by competitive species assembly, and this process is sufficient to generate observed species abundance distributions for tropical epiphyte-dwelling ants.
| S-EPMC4342770 | biostudies-literature
Project description:Most small molecules are unable to rapidly traverse the outer membrane of Gram-negative bacteria and accumulate inside these cells, making the discovery of much-needed drugs against these pathogens challenging. Current understanding of the physicochemical properties that dictate small-molecule accumulation in Gram-negative bacteria is largely based on retrospective analyses of antibacterial agents, which suggest that polarity and molecular weight are key factors. Here we assess the ability of over 180 diverse compounds to accumulate in Escherichia coli. Computational analysis of the results reveals major differences from the retrospective studies, namely that the small molecules that are most likely to accumulate contain an amine, are amphiphilic and rigid, and have low globularity. These guidelines were then applied to convert deoxynybomycin, a natural product that is active only against Gram-positive organisms, into an antibiotic with activity against a diverse panel of multi-drug-resistant Gram-negative pathogens. We anticipate that these findings will aid in the discovery and development of antibiotics against Gram-negative bacteria.
| S-EPMC5737020 | biostudies-literature
Project description:BackgroundMicroarray data have been used for gene signature selection to predict clinical outcomes. Many studies have attempted to identify factors that affect models' performance with only little success. Fine-tuning of model parameters and optimizing each step of the modeling process often results in over-fitting problems without improving performance.ResultsWe propose a quantitative measurement, termed consistency degree, to detect the correlation between disease endpoint and gene expression profile. Different endpoints were shown to have different consistency degrees to gene expression profiles. The validity of this measurement to estimate the consistency was tested with significance at a p-value less than 2.2e-16 for all of the studied endpoints. According to the consistency degree score, overall survival milestone outcome of multiple myeloma was proposed to extend from 730 days to 1561 days, which is more consistent with gene expression profile.ConclusionFor various clinical endpoints, the maximum predictive powers of different microarray-based models are limited by the correlation between endpoint and gene expression profile of disease samples as indicated by the consistency degree score. In addition, previous defined clinical outcomes can also be reassessed and refined more coherent according to related disease gene expression profile. Our findings point to an entirely new direction for assessing the microarray-based predictive models and provide important information to gene signature based clinical applications.
| S-EPMC3287499 | biostudies-literature
Project description:Antibiotic-resistant bacteria rapidly spread in clinical and natural environments and challenge our modern lifestyle. A major component of defense against antibiotics in Gram-negative bacteria is a drug permeation barrier created by active efflux across the outer membrane. We identified molecular determinants defining the propensity of small peptidomimetic molecules to avoid and inhibit efflux pumps in Pseudomonas aeruginosa, a human pathogen notorious for its antibiotic resistance. Combining experimental and computational protocols, we mapped the fate of the compounds from structure-activity relationships through their dynamic behavior in solution, permeation across both the inner and outer membranes, and interaction with MexB, the major efflux transporter of P. aeruginosa We identified predictors of efflux avoidance and inhibition and demonstrated their power by using a library of traditional antibiotics and compound series and by generating new inhibitors of MexB. The identified predictors will enable the discovery and optimization of antibacterial agents suitable for treatment of P. aeruginosa infections.IMPORTANCE Efflux pump avoidance and inhibition are desired properties for the optimization of antibacterial activities against Gram-negative bacteria. However, molecular and physicochemical interactions defining the interface between compounds and efflux pumps remain poorly understood. We identified properties that correlate with efflux avoidance and inhibition, are predictive of similar features in structurally diverse compounds, and allow researchers to distinguish between efflux substrates, inhibitors, and avoiders in P. aeruginosa The developed predictive models are based on the descriptors representative of different clusters comprising a physically intuitive combination of properties. Molecular shape (represented by acylindricity), amphiphilicity (anisotropic polarizability), aromaticity (number of aromatic rings), and the partition coefficient (LogD) are physicochemical predictors of efflux inhibitors, whereas interactions with Pro668 and Leu674 residues of MexB distinguish between inhibitors/substrates and efflux avoiders. The predictive models and efflux rules are applicable to compounds with unrelated chemical scaffolds and pave the way for development of compounds with the desired efflux interface properties.
| S-EPMC7845643 | biostudies-literature
Project description:This study describes a method to estimate the likelihood of success in determining a macromolecular structure by X-ray crystallography and experimental single-wavelength anomalous dispersion (SAD) or multiple-wavelength anomalous dispersion (MAD) phasing based on initial data-processing statistics and sample crystal properties. Such a predictive tool can rapidly assess the usefulness of data and guide the collection of an optimal data set. The increase in data rates from modern macromolecular crystallography beamlines, together with a demand from users for real-time feedback, has led to pressure on computational resources and a need for smarter data handling. Statistical and machine-learning methods have been applied to construct a classifier that displays 95% accuracy for training and testing data sets compiled from 440 solved structures. Applying this classifier to new data achieved 79% accuracy. These scores already provide clear guidance as to the effective use of computing resources and offer a starting point for a personalized data-collection assistant.
| S-EPMC7055369 | biostudies-literature
Project description:Recent functional genomic screens-such as CRISPR-Cas9 or RNAi screening-have fostered a new wave of targeted treatments based on the concept of synthetic lethality. These approaches identified LEthal Dependencies (LEDs) by estimating the effect of genetic events on cell viability. The multiple-hypothesis problem is related to a large number of gene knockouts limiting the statistical power of these studies. Here, we show that predictions of LEDs from functional screens can be dramatically improved by incorporating the "HUb effect in Genetic Essentiality" (HUGE) of gene alterations. We analyze three recent genome-wide loss-of-function screens-Project Score, CERES score and DEMETER score-identifying LEDs with 75 times larger statistical power than using state-of-the-art methods. Using acute myeloid leukemia, breast cancer, lung adenocarcinoma and colon adenocarcinoma as disease models, we validate that our predictions are enriched in a recent harmonized knowledge base of clinical interpretations of somatic genomic variants in cancer (AUROC > 0.87). Our approach is effective even in tumors with large genetic heterogeneity such as acute myeloid leukemia, where we identified LEDs not recalled by previous pipelines, including FLT3-mutant genotypes sensitive to FLT3 inhibitors. Interestingly, in-vitro validations confirm lethal dependencies of either NRAS or PTPN11 depending on the NRAS mutational status. HUGE will hopefully help discover novel genetic dependencies amenable for precision-targeted therapies in cancer. All the graphs showing lethal dependencies for the 19 tumor types analyzed can be visualized in an interactive tool.
| S-EPMC9264916 | biostudies-literature
Project description:Establishing brain-behavior associations that map brain organization to phenotypic measures and generalize to novel individuals remains a challenge in neuroimaging. Predictive modeling approaches that define and validate models with independent datasets offer a solution to this problem. While these methods can detect novel and generalizable brain-behavior associations, they can be daunting, which has limited their use by the wider connectivity community. Here, we offer practical advice and examples based on functional magnetic resonance imaging (fMRI) functional connectivity data for implementing these approaches. We hope these ten rules will increase the use of predictive models with neuroimaging data.
| S-EPMC6521850 | biostudies-literature
Project description:Mitochondrial transcription factor A (TFAM) plays an important role in mitochondrial DNA (mtDNA) transcription and replication. In some experimental settings, TFAM expression parallels parameters of mitochondrial biogenesis, which led to a widespread acceptance of TFAM as marker of mitochondrial biogenesis. We modulated TFAM expression in several experimental systems and observed that it fails to consistently parallel mtDNA copy number and expression of mtDNA-encoded polypeptides. We suggest that the use of TFAM as a marker of mitochondrial biogenesis should be avoided outside of systems in which its performance has been carefully validated.
| S-EPMC6885536 | biostudies-literature
Project description:Polygenic scores have recently been used to summarise genetic effects among an ensemble of markers that do not individually achieve significance in a large-scale association study. Markers are selected using an initial training sample and used to construct a score in an independent replication sample by forming the weighted sum of associated alleles within each subject. Association between a trait and this composite score implies that a genetic signal is present among the selected markers, and the score can then be used for prediction of individual trait values. This approach has been used to obtain evidence of a genetic effect when no single markers are significant, to establish a common genetic basis for related disorders, and to construct risk prediction models. In some cases, however, the desired association or prediction has not been achieved. Here, the power and predictive accuracy of a polygenic score are derived from a quantitative genetics model as a function of the sizes of the two samples, explained genetic variance, selection thresholds for including a marker in the score, and methods for weighting effect sizes in the score. Expressions are derived for quantitative and discrete traits, the latter allowing for case/control sampling. A novel approach to estimating the variance explained by a marker panel is also proposed. It is shown that published studies with significant association of polygenic scores have been well powered, whereas those with negative results can be explained by low sample size. It is also shown that useful levels of prediction may only be approached when predictors are estimated from very large samples, up to an order of magnitude greater than currently available. Therefore, polygenic scores currently have more utility for association testing than predicting complex traits, but prediction will become more feasible as sample sizes continue to grow.
| S-EPMC3605113 | biostudies-literature
Project description:BackgroundA study was undertaken to validate the modified American Thoracic Society (ATS) rule and two British Thoracic Society (BTS) rules for the prediction of ICU admission and mortality of community acquired pneumonia and to provide a validation of these predictions on the basis of the pneumonia severity index (PSI).MethodSix hundred and ninety six consecutive patients (457 men (66%), mean (SD) age 67.8 (17.1) years, range 18-101) admitted to a tertiary care hospital were studied prospectively. Of these, 116 (16.7%) were admitted to the ICU.ResultsThe modified ATS rule achieved a sensitivity of 69% (95% CI 50.7 to 77.2), specificity of 97% (95% CI 96.4 to 98.9), positive predictive value of 87% (95% CI 78.3 to 93.1), and negative predictive value of 94% (95% CI 91.8 to 95.8) in predicting admission to the ICU. The corresponding predictive indices for mortality were 94% (95% CI 82.5 to 98.7), 93% (95% CI 90.6 to 94.7), 49% (95% CI 38.2 to 59.7), and 99.5% (95% CI 98.5 to 99.9), respectively. These figures compared favourably with both the BTS rules. The BTS-CURB criteria achieved predictions of pneumonia severity and mortality comparable to the PSI.ConclusionsThis study confirms the power of the modified ATS rule to predict severe pneumonia in individual patients. It may be incorporated into current guidelines for the assessment of pneumonia severity. The CURB criteria may be used as an alternative tool to PSI for the detection of low risk patients.
| S-EPMC1747015 | biostudies-literature