fDETECT webserver: fast predictor of propensity for protein production, purification, and crystallization

Background: Development of predictors of propensity of protein sequences for successful crystallization has been actively pursued for over a decade. A few novel methods that expanded the scope of these predictions to address additional steps of protein production and structure determination pipelines were released in recent years. The predictive performance of the current methods is modest. This is because the only input that they use is the protein sequence and since the experimental annotations of these data might be inconsistent given that they were collected across many laboratories and centers. However, even these modest levels of predictive quality are still practical compared to the reported low success rates of crystallization, which are below 10%. We focus on another important aspect related to a high computational cost of running the predictors that offer the expanded scope. Results: We introduce a novel fDETECT webserver that provides very fast and modestly accurate predictions of the success of protein production, purification, crystallization, and structure determination. Empirical tests on two datasets demonstrate that fDETECT is more accurate than the only other similarly fast method, and similarly accurate and three orders of magnitude faster than the currently most accurate predictors. Our method predicts a single protein in about 120 milliseconds and needs less than an hour to generate the four predictions for an entire human proteome. Moreover, we empirically show that fDETECT secures similar levels of predictive performance when compared with four representative methods that only predict success of crystallization, while it also provides the other three predictions. A webserver that implements fDETECT is available at http://biomine.cs.vcu.edu/servers/ fDETECT/. Conclusions: fDETECT is a computational tool that supports target selection for protein production and X-ray crystallography-based structure determination. It offers predictive quality that matches or exceeds other state-ofthe-art tools and is especially suitable for the analysis of large protein sets

Role of intangible assets and their assessment in the modern economy of Ukraine

Possession of intangible assets is an important competitive advantage, which is not always taken into account by the Board of Directors and management of the company when developing its development strategy

SYSTEM ORGANIZATION OF LOGISTICS IN CONSTRUCTION

Topical issues of logistics in the system of organization of construction are considered. The analysis of the problems arising in the development and application of existing organizational technologies for the construction of facilities with complex infrastructure is carried out, organizational changes are identified and characterized, directions for optimizing construction production are identified. Logistic concepts are used to increase the organization (efficiency) of organizations based on synchronization, optimization and integration of flows in construction processes in order to increase work efficiency. Keywords: вuilding complex, logistics, information flow, logistic system, financial flows, integrated management

MissForest - nonparametric missing value imputation for mixed-type data

Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set. Missing value imputation offers a solution to this problem. However, the majority of available imputation methods are restricted to one type of variable only: continuous or categorical. For mixed-type data the different types are usually handled separately. Therefore, these methods ignore possible relations between variable types. We propose a nonparametric method which can cope with different types of variables simultaneously. We compare several state of the art methods for the imputation of missing values. We propose and evaluate an iterative imputation method (missForest) based on a random forest. By averaging over many unpruned classification or regression trees random forest intrinsically constitutes a multiple imputation scheme. Using the built-in out-of-bag error estimates of random forest we are able to estimate the imputation error without the need of a test set. Evaluation is performed on multiple data sets coming from a diverse selection of biological fields with artificially introduced missing values ranging from 10% to 30%. We show that missForest can successfully handle missing values, particularly in data sets including different types of variables. In our comparative study missForest outperforms other methods of imputation especially in data settings where complex interactions and nonlinear relations are suspected. The out-of-bag imputation error estimates of missForest prove to be adequate in all settings. Additionally, missForest exhibits attractive computational efficiency and can cope with high-dimensional data.Comment: Submitted to Oxford Journal's Bioinformatics on 3rd of May 201

SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences

<p>Abstract</p> <p>Background</p> <p>Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction.</p> <p>Results</p> <p>SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier's input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight-zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors.</p> <p>Conclusion</p> <p>The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is attributed to the design of the features, which are capable of separating the structural classes in spite of their low dimensionality. We also demonstrate that the SCPRED's predictions can be successfully used as a post-processing filter to improve performance of modern fold classification methods.</p

Method of Heat Treating Aluminum-Lithium Alloy to Improve Formability

A method is provided for heat treating aluminum-lithium alloys to improve their formability. The alloy is heated to a first temperature, maintained at the first temperature for a first time period, heated at the conclusion of the first time period to a second temperature, maintained at the second temperature for a second time period, actively cooled at the conclusion of the second time period to a third temperature, maintained at the third temperature for a third time period, and then passively cooled at the conclusion of the third time period to room temperature

Prediction of flexible/rigid regions from protein sequences using k-spaced amino acid pairs

BACKGROUND: Traditionally, it is believed that the native structure of a protein corresponds to a global minimum of its free energy. However, with the growing number of known tertiary (3D) protein structures, researchers have discovered that some proteins can alter their structures in response to a change in their surroundings or with the help of other proteins or ligands. Such structural shifts play a crucial role with respect to the protein function. To this end, we propose a machine learning method for the prediction of the flexible/rigid regions of proteins (referred to as FlexRP); the method is based on a novel sequence representation and feature selection. Knowledge of the flexible/rigid regions may provide insights into the protein folding process and the 3D structure prediction. RESULTS: The flexible/rigid regions were defined based on a dataset, which includes protein sequences that have multiple experimental structures, and which was previously used to study the structural conservation of proteins. Sequences drawn from this dataset were represented based on feature sets that were proposed in prior research, such as PSI-BLAST profiles, composition vector and binary sequence encoding, and a newly proposed representation based on frequencies of k-spaced amino acid pairs. These representations were processed by feature selection to reduce the dimensionality. Several machine learning methods for the prediction of flexible/rigid regions and two recently proposed methods for the prediction of conformational changes and unstructured regions were compared with the proposed method. The FlexRP method, which applies Logistic Regression and collocation-based representation with 95 features, obtained 79.5% accuracy. The two runner-up methods, which apply the same sequence representation and Support Vector Machines (SVM) and Naïve Bayes classifiers, obtained 79.2% and 78.4% accuracy, respectively. The remaining considered methods are characterized by accuracies below 70%. Finally, the Naïve Bayes method is shown to provide the highest sensitivity for the prediction of flexible regions, while FlexRP and SVM give the highest sensitivity for rigid regions. CONCLUSION: A new sequence representation that uses k-spaced amino acid pairs is shown to be the most efficient in the prediction of the flexible/rigid regions of protein sequences. The proposed FlexRP method provides the highest prediction accuracy of about 80%. The experimental tests show that the FlexRP and SVM methods achieved high overall accuracy and the highest sensitivity for rigid regions, while the best quality of the predictions for flexible regions is achieved by the Naïve Bayes method

A creature with a hundred waggly tails: intrinsically disordered proteins in the ribosome

This article is made available for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.Intrinsic disorder (i.e., lack of a unique 3-D structure) is a common phenomenon, and many biologically active proteins are disordered as a whole, or contain long disordered regions. These intrinsically disordered proteins/regions constitute a significant part of all proteomes, and their functional repertoire is complementary to functions of ordered proteins. In fact, intrinsic disorder represents an important driving force for many specific functions. An illustrative example of such disorder-centric functional class is RNA-binding proteins. In this study, we present the results of comprehensive bioinformatics analyses of the abundance and roles of intrinsic disorder in 3,411 ribosomal proteins from 32 species. We show that many ribosomal proteins are intrinsically disordered or hybrid proteins that contain ordered and disordered domains. Predicted globular domains of many ribosomal proteins contain noticeable regions of intrinsic disorder. We also show that disorder in ribosomal proteins has different characteristics compared to other proteins that interact with RNA and DNA including overall abundance, evolutionary conservation, and involvement in protein–protein interactions. Furthermore, intrinsic disorder is not only abundant in the ribosomal proteins, but we demonstrate that it is absolutely necessary for their various functions

Expedite Design of Variable-Topology Broadband Hybrid Couplers for Size Reduction using Surrogate-Based Optimization and Co-Simulation Coarse Models

In this paper, we discuss a computationally efficient approach to expedite design optimization of broadband hybrid couplers occupying a minimized substrate area. Structure size reduction is achieved here by decomposing an original coupler circuit into low- and high-impedance components and replacing them with electrically equivalent slow-wave lines with reduced physical dimensions. The main challenge is reliable design of computationally demanding low-impedance slow-wave structures that feature a quasi-periodic circuit topology for wideband operation. Our goal is to determine an adequate number of recurrent unit elements as well as to adjust their designable parameters so that the coupler footprint area is minimal. The proposed method involves using surrogate-based optimization with a reconfigurable co-simulation coarse model as the key component enabling design process acceleration. The latter model is composed in Keysight ADS circuit simulator from multiple EM-evaluated data blocks of the slow-wave unit element and theory-based feeding line models. The embedded optimization algorithm is a trust-region-based gradient search with coarse model Jacobian estimation. We exploit a penalty function approach to ensure that the electrical conditions for the slow-wave lines are accordingly satisfied, apart from explicitly minimizing the area of the coupler. The effectiveness of the proposed technique is demonstrated through a design example of two-section 3-dB branch-line coupler. For the given example, we obtain nine circuit design solutions that correspond to the compact couplers whose multi-element slow-wave lines are composed of unit cells ranging from two to ten

Physical training, inflammation and bone integrity in elite female rowers

This study examined whether fluctuations in training load during an Olympic year lead to changes in mineral properties and factors that regulate bone (sclerostin (SOST), osteoprotegerin (OPG)), and receptor activator of nuclear factor kappa-B ligand (RANKL)) and energy metabolism (insulin-like growth factor-1 (IGF-1) and leptin), and inflammation (tumor necrosis factor-α (TNF-α)) in elite heavyweight female rowers. Blood samples were drawn from female heavy-weight rowers (n=15) (27.0±0.8y, 80.9±1.3 kg, 179.4±1.4 cm) at baseline (T1 – 45 weeks pre-Olympic Games) and following 7, 9, 20, 25 and 42 weeks (T1-6, respectively). Serum was analyzed by Multiplex assays (EMD Millipore, Toronto, CAN). Total weekly training load was recorded over the weeks prior to each time point. Bone mineral density (BMD) was measured by dual energy X-ray absorptiometry at T1 and T6. Total BMD increased significantly pre- to post-training (+1.6%). OPG, IGF-1, and leptin were not different across all time points. OPG/RANKL was significantly higher at both T4 and 5 compared to T1 and 2. High training load (T5) was associated with the highest TNF-α levels (2.1 pg/ml), and a parallel increase in SOST (993.1 pg/ml), while low training load (T6 - recovery) was associated with significantly lower TNF-α (1.5 pg/ml) and a parallel decrease in SOST (741.0 pg/ml). Leptin was a significant determinant of bone-mineral properties in these athletes. These results suggest exercise training can lead to an increase in OPG/RANKL, and training load periodization can control the inflammatory response associated with intense training, and combined with adequate caloric intake can preserve bone mineral integrity in elite female athletes