Highly Accurate Structure-Based Prediction of HIV-1 Coreceptor Usage Suggests Intermolecular Interactions Driving Tropism

HIV-1 entry into host cells is mediated by interactions between the V3-loop of viral glycoprotein gp120 and chemokine receptor CCR5 or CXCR4, collectively known as HIV-1 coreceptors. Accurate genotypic prediction of coreceptor usage is of significant clinical interest and determination of the factors driving tropism has been the focus of extensive study. We have developed a method based on nonlinear support vector machines to elucidate the interacting residue pairs driving coreceptor usage and provide highly accurate coreceptor usage predictions. Our models utilize centroid-centroid interaction energies from computationally derived structures of the V3-loop:coreceptor complexes as primary features, while additional features based on established rules regarding V3-loop sequences are also investigated. We tested our method on 2455 V3-loop sequences of various lengths and subtypes, and produce a median area under the receiver operator curve of 0.977 based on 500 runs of 10-fold cross validation. Our study is the first to elucidate a small set of specific interacting residue pairs between the V3-loop and coreceptors capable of predicting coreceptor usage with high accuracy across major HIV-1 subtypes. The developed method has been implemented as a web tool named CRUSH, CoReceptor USage prediction for HIV-1, which is available at http://ares.tamu.edu/CRUSH/

An analysis and evaluation of the WeFold collaborative for protein structure prediction and its pipelines in CASP11 and CASP12

Every two years groups worldwide participate in the Critical Assessment of Protein Structure Prediction (CASP) experiment to blindly test the strengths and weaknesses of their computational methods. CASP has significantly advanced the field but many hurdles still remain, which may require new ideas and collaborations. In 2012 a web-based effort called WeFold, was initiated to promote collaboration within the CASP community and attract researchers from other fields to contribute new ideas to CASP. Members of the WeFold coopetition (cooperation and competition) participated in CASP as individual teams, but also shared components of their methods to create hybrid pipelines and actively contributed to this effort. We assert that the scale and diversity of integrative prediction pipelines could not have been achieved by any individual lab or even by any collaboration among a few partners. The models contributed by the participating groups and generated by the pipelines are publicly available at the WeFold website providing a wealth of data that remains to be tapped. Here, we analyze the results of the 2014 and 2016 pipelines showing improvements according to the CASP assessment as well as areas that require further adjustments and research

Advances in Big Data Analytics for Modeling, Optimization and Control: Applications in Process Systems Engineering

The advancement in technology and computational power has enabled large amounts of data collection in real time, which has initiated the "Big Data" era. Big data analytics is playing an essential role in academy, business as well as government, providing assistance in decision-making in numerous fields. In this work, selected challenges in process systems engineering are addressed through advances of and applications in big data analytics. First, challenges in chemical process monitoring, such as fault detection and diagnosis, are addressed by exploiting the industrial data abundance. Data-driven process monitoring has become one of the key approaches in industry to maintain a safe and robust operation while increasing process efficiency to ensure high standards in product quality. In this work, a novel fault detection and diagnosis framework based on nonlinear Support Vector Machine-based feature selection and modeling algorithm is developed for the simultaneous fault detection and diagnosis of chemical processes (s-FDD framework) in both continuous and batch modes. The major advantage of the s-FDD framework is its ability to identify the optimal number of process variables diagnosing the fault while providing highly accurate models for fault detection. The s-FDD framework is further improved with the integration of (i) maintenance optimization strategies, and (ii) multi-parametric model predictive control (mp-MPC) in order to maximize the process profitability and resilience while minimizing process downtime. A novel "parametric fault-tolerant control" concept has been developed for chemical/biochemical processes that serves as an active fault tolerant strategy. This work can serve as an online decision support tool during process operations to enable (i) early detection and diagnosis of process faults, and (ii) rapid actions to adapt altering process or controller conditions to achieve smarter operation. Secondly, we address challenges in understanding the environmental health impact of complex substance/mixture exposures during environmental emergency-related contamination events (i.e. hurricanes). A data-driven framework is developed to group complex substances with known chemicals by analyzing high dimensional analytical chemistry data, and predict their impact on the environmental health. This facilitates the communication of substance characteristics and decision-making via read-across in order to mitigate the adverse environmental health effects

Effect of the rules and V3-loop:coreceptor interactions included in SVM models on prediction accuracy.

<p>(A) The effect of the number of V3-loop:coreceptor interactions on accuracy. Accuracy is represented by the median AUC for 500 runs of 10-fold cross validation for both panels A and B. The accuracy at zero interactions is the accuracy based on only the rules. (B) Contribution of rules to accuracy when used in addition to the top 11 interactions <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0148974#pone.0148974.g003" target="_blank">Fig 3A</a>. The naming scheme is as follows: Int Only–interactions only; Q–net charge; R – 11/24/25 rule; M–glycosylation motif; L–length. Dashed red line illustrates the accuracy when using all four rules and the top 11 interactions (QLMR, 0.977).</p

Diagrams of the top selected interactions for the cases of all rules + interactions and interactions only.

<p><b>(</b>A) Interaction map for the 11 interactions selected in combination with all rules. V3-loop is shown as an idealized loop with 35 amino acids where grey circles indicate positions for which no interactions were selected (inactive), while green circles indicate V3-loop positions with interactions selected (active). Red triangles represent residues of CCR5 and blue squares represent residues of CXCR4, with dashed lines representing interactions with V3-loop residues. Ordered lists of observed amino acids (based on occurrence with a minimum of 5%) in one-letter code for each active V3-loop residue are provided. Observed amino acids for CCR5 tropic sequences are in red and those observed for CXCR4 tropic sequences in blue. Bolded letters in the ordered list of observed amino acids indicate an amino acid that is observed in at least 50% of sequences at a given position. (B) Interaction map for the 18 interactions selected without rules. Color scheme and layout is the same as in (A). Faded triangles/squares indicate interactions that were also selected when including all rules. The crossed out interaction was selected when including rules, but not when using interactions only.</p

Flowchart of hybrid modeling approach.

<p>Flowchart of hybrid modeling approach.</p

Classification of estrogenic compounds by coupling high content analysis and machine learning algorithms.

Environmental toxicants affect human health in various ways. Of the thousands of chemicals present in the environment, those with adverse effects on the endocrine system are referred to as endocrine-disrupting chemicals (EDCs). Here, we focused on a subclass of EDCs that impacts the estrogen receptor (ER), a pivotal transcriptional regulator in health and disease. Estrogenic activity of compounds can be measured by many in vitro or cell-based high throughput assays that record various endpoints from large pools of cells, and increasingly at the single-cell level. To simultaneously capture multiple mechanistic ER endpoints in individual cells that are affected by EDCs, we previously developed a sensitive high throughput/high content imaging assay that is based upon a stable cell line harboring a visible multicopy ER responsive transcription unit and expressing a green fluorescent protein (GFP) fusion of ER. High content analysis generates voluminous multiplex data comprised of minable features that describe numerous mechanistic endpoints. In this study, we present a machine learning pipeline for rapid, accurate, and sensitive assessment of the endocrine-disrupting potential of benchmark chemicals based on data generated from high content analysis. The multidimensional imaging data was used to train a classification model to ultimately predict the impact of unknown compounds on the ER, either as agonists or antagonists. To this end, both linear logistic regression and nonlinear Random Forest classifiers were benchmarked and evaluated for predicting the estrogenic activity of unknown compounds. Furthermore, through feature selection, data visualization, and model discrimination, the most informative features were identified for the classification of ER agonists/antagonists. The results of this data-driven study showed that highly accurate and generalized classification models with a minimum number of features can be constructed without loss of generality, where these machine learning models serve as a means for rapid mechanistic/phenotypic evaluation of the estrogenic potential of many chemicals