In systems biology, qualitative data are often generated, but rarely used to parameterize models. We demonstrate an approach in which qualitative and quantitative data can be combined for parameter identification. In this approach, qualitative data are converted into inequality constraints imposed on the outputs of the model. These inequalities are used along with quantitative data points to construct a single scalar objective function that accounts for both datasets. To illustrate the approach, we estimate parameters for a simple model describing Raf activation. We then apply the technique to a more elaborate model characterizing cell cycle regulation in yeast. We incorporate both quantitative time courses (561 data points) and qualitative phenotypes of 119 mutant yeast strains (1647 inequalities) to perform automated identification of 153 model parameters. We quantify parameter uncertainty using a profile likelihood approach. Our results indicate the value of combining qualitative and quantitative data to parameterize systems biology models.

Interactions between proteins and other molecules play essential roles in all biological processes. Although it is widely held that a protein's ligand specificity is determined primarily by its three‐dimensional structure, the general principles by which structure determines ligand binding remain poorly understood. Here we use statistical analyses of a large number of protein−ligand complexes with associated binding‐affinity measurements to quantitatively characterize how combinations of atomic interactions contribute to ligand affinity. We find that there are significant differences in how atomic interactions determine ligand affinity for proteins that bind small chemical ligands, those that bind DNA/RNA and those that interact with other proteins. Although protein‐small molecule and protein‐DNA/RNA binding affinities can be accurately predicted from structural data, models predicting one type of interaction perform poorly on the others. Additionally, the particular combinations of atomic interactions required to predict binding affinity differed between small‐molecule and DNA/RNA data sets, consistent with the conclusion that the structural bases determining ligand affinity differ among interaction types. In contrast to what we observed for small‐molecule and DNA/RNA interactions, no statistical models were capable of predicting protein−protein affinity with >60% correlation. We demonstrate the potential usefulness of protein‐DNA/RNA binding prediction as a possible tool for high‐throughput virtual screening to guide laboratory investigations, suggesting that quantitative characterization of diverse molecular interactions may have practical applications as well as fundamentally advancing our understanding of how molecular structure translates into function. Proteins 2015; 83:2100–2114. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.

One goal of structural biology is to understand how a protein’s 3-dimensional conformation determines its capacity to interact with potential ligands. In the case of small chemical ligands, deconstructing a static protein-ligand complex into its constituent atom-atom interactions is typically sufficient to rapidly predict ligand affinity with high accuracy (>70% correlation between predicted and experimentally-determined affinity), a fact that is exploited to support structure-based drug design. We recently found that protein-DNA/RNA affinity can also be predicted with high accuracy using extensions of existing techniques, but protein-protein affinity could not be predicted with >60% correlation, even when the protein-protein complex was available. X-ray and NMR structures of protein-protein complexes, their associated binding affinities and experimental conditions were obtained from different binding affinity and structural databases. Statistical models were implemented using a generalized linear model framework, including the experimental conditions as new model features. We evaluated the potential for new features to improve affinity prediction models by calculating the Pearson correlation between predicted and experimental binding affinities on the training and test data after model fitting and after cross-validation. Differences in accuracy were assessed using two-sample t test and nonparametric Mann–Whitney U test. Here we evaluate a range of potential factors that may interfere with accurate protein-protein affinity prediction. We find that X-ray crystal resolution has the strongest single effect on protein-protein affinity prediction. Limiting our analyses to only high-resolution complexes (≤2.5 Å) increased the correlation between predicted and experimental affinity from 54 to 68% (p = 4.32x10−3). In addition, incorporating information on the experimental conditions under which affinities were measured (pH, temperature and binding assay) had significant effects on prediction accuracy. We also highlight a number of potential errors in large structure-affinity databases, which could affect both model training and accuracy assessment. The results suggest that the accuracy of statistical models for protein-protein affinity prediction may be limited by the information present in databases used to train new models. Improving our capacity to integrate large-scale structural and functional information may be required to substantively advance our understanding of the general principles by which a protein’s structure determines its function.

Objective: The present study investigated the epidemic of tinnitus in college-aged young adults. Our first objective was to identify health conditions associated with tinnitus in young adults. The second objective was to evaluate the predictive utility of some known risk factors. Study design: A cross-sectional design was used to investigate the prevalence and risk factors for tinnitus. Setting: A questionnaire was distributed, reaching out to a large college-aged population. A total of 2258 young adults aged 18–30 years were recruited from April 2021 to February 2022. Interventions: A questionnaire was administered to investigate the epidemiology of tinnitus in a population of college-aged young adults. Results: About 17.7% of young adults reported bothersome tinnitus perception lasting for ≥5 min in the last 12 months. The prevalence of chronic tinnitus (bothersome tinnitus for ≥1 year) and acute tinnitus (bothersome tinnitus for <1 year) was 10.6% and 7.1%, respectively. About 19% of the study sample reported at least one health condition. Individuals reporting head injury, hypertension, heart disease, scarlet fever, and malaria showed significantly higher odds of reporting chronic tinnitus. Meningitis and self-reported hearing loss showed significant associations with bothersome tinnitus. The prevalence of chronic tinnitus was significantly higher in males reporting high noise exposure, a positive history of reoccurring ear infections, European ethnic background, and a positive health history. Risk modeling showed that noise exposure was the most important risk factor for chronic tinnitus, followed by sex, reoccurring ear infections, and a history of any health condition. A positive history of COVID-19 and self-reported severity showed no association with tinnitus. Individuals reporting reoccurring ear infections showed a significantly higher prevalence of COVID-19. Conclusions: While young adults with health conditions are at a higher risk of reporting tinnitus, the predictive utility of a positive health history remains relatively low, possibly due to weak associations between health conditions and tinnitus. Noise, male sex, reoccurring ear infections, European ethnicity, and a positive health history revealed higher odds of reporting chronic tinnitus than their counterparts. These risk factors collectively explained about 16% variability in chronic tinnitus, which highlights the need for identifying other risk factors for chronic tinnitus in young adults.