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Assessment of the quality of energy functions for protein folding by using a criterion derived with the help of the noisy go model.

ABSTRACT: We propose a simple criterion based on the Z-scoreto assess the quality of energy functions for protein folding: one should obtain Z>-10 for the equilibrium ensembleat about native conditions. We derive this criterionby studying a Go model with random errors added to the native interactions. The dependence of the Z-score on the thermodynamic parameters,including the noise, can be precisely obtained in this case,as the ground state of the model is known exactly.We apply this criterion to rapidly rule out two otherwise promisingpairwise energy approximations.The advantage of adopting the present criterionis that it is not necessary to know the ground state of an energy function to assess its quality. It is sufficient to compute the Z-scorefrom a single equilibrium simulation at around the folding temperature.

SUBMITTER: Vendruscolo M

PROVIDER: S-EPMC3456590 | biostudies-other | 2001 Jun

REPOSITORIES: biostudies-other

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Assessment of the quality of energy functions for protein folding by using a criterion derived with the help of the noisy go model.

Vendruscolo M M

Journal of biological physics 20010601 2-3

We propose a simple criterion based on the Z-scoreto assess the quality of energy functions for protein folding: one should obtain Z>-10 for the equilibrium ensembleat about native conditions. We derive this criterionby studying a Go model with random errors added to the native interactions. The dependence of the Z-score on the thermodynamic parameters,including the noise, can be precisely obtained in this case,as the ground state of the model is known exactly.We apply this criterion to rapidly ...[more]

PMID: 23345744

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Project description:Gene Ontology (GO) is a community effort to represent functional features of gene products. GO annotations (GOA) provide functional associations between GO terms and gene products. Due to resources limitation, only a small portion of annotations are manually checked by curators, and the others are electronically inferred. Although quality control techniques have been applied to ensure the quality of annotations, the community consistently report that there are still considerable noisy (or incorrect) annotations. Given the wide application of annotations, however, how to identify noisy annotations is an important but yet seldom studied open problem.We introduce a novel approach called NoGOA to predict noisy annotations. NoGOA applies sparse representation on the gene-term association matrix to reduce the impact of noisy annotations, and takes advantage of sparse representation coefficients to measure the semantic similarity between genes. Secondly, it preliminarily predicts noisy annotations of a gene based on aggregated votes from semantic neighborhood genes of that gene. Next, NoGOA estimates the ratio of noisy annotations for each evidence code based on direct annotations in GOA files archived on different periods, and then weights entries of the association matrix via estimated ratios and propagates weights to ancestors of direct annotations using GO hierarchy. Finally, it integrates evidence-weighted association matrix and aggregated votes to predict noisy annotations. Experiments on archived GOA files of six model species (H. sapiens, A. thaliana, S. cerevisiae, G. gallus, B. Taurus and M. musculus) demonstrate that NoGOA achieves significantly better results than other related methods and removing noisy annotations improves the performance of gene function prediction.The comparative study justifies the effectiveness of integrating evidence codes with sparse representation for predicting noisy GO annotations. Codes and datasets are available at http://mlda.swu.edu.cn/codes.php?name=NoGOA .

Project description:ImportancePrevious studies have found a mixed association between Patient-Centered Medical Home (PCMH) designation and improvements in primary care quality indicators, including avoidable pediatric emergency department (ED) encounters. Whether these associations persist after accounting for the geographic locations of providers relative to where patients reside is unknown.ObjectiveTo examine the association between geographic proximity to primary care providers versus hospitals and risk of avoidable and potentially avoidable ED visits among children with pre-existing diagnosis of attention-deficit/hyperactivity disorder or asthma.MethodsRetrospective cohort study of a panel of pediatric Medicaid claims data from the South Carolina from 2016-2018 for 2,959 beneficiaries having a pre-existing diagnosis of attention-deficit/hyperactivity disorder (ADD, ages 6-12) and 6,390 beneficiaries with asthma (MMA, ages 5-18), as defined using Healthcare Effectiveness Data and Information Set (HEDIS) performance measures. We calculated differences in avoidable and potentially avoidable ED visits by the beneficiary's PCMH attribution type and in relation to differences in proximity to their primary care providers versus hospitals. Outcomes were defined using the New York University Emergency Department Algorithm (NYU-EDA). Differences in ED visit risk were assessed using generalized estimation equations and compared using marginal effects models.ResultsThe 2.4 percentage point reduction in risk of avoidable ED visits among children in the ADD cohort who attended a PCMH versus those who did not increased to 3.9 to 7.2 percentage points as relative proximity to primary care providers versus hospitals improved (p < 0.01). Children in the ADD and MMA cohorts that were enrolled in a medical home, but did not attend one for primary care services exhibited a 5.4 and 3.0 percentage point increase in avoidable ED visit compared to children who were unenrolled and did not attend medical homes (p < 0.05), but these differences were only observed when geographic proximity to hospitals was more convenient than primary care providers. Mixed findings were observed for potentially avoidable visits.ConclusionsIn several health care performance evaluations, patient-centered medical homes have not been found to reduce differences in hospital utilization for conditions that are treatable in primary care settings among children with chronic illnesses. Analytical approaches that also consider geographic proximity to health care services can identify performance benefits of medical homes. Expanding risk-adjustment models to also include geographic data would benefit ongoing quality improvement initiatives.

Dataset Information

Assessment of the quality of energy functions for protein folding by using a criterion derived with the help of the noisy go model.

Publications

Assessment of the quality of energy functions for protein folding by using a criterion derived with the help of the noisy go model.

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