MOTIVATION: Glycans play important roles in protein folding and cell-cell interactions-and, furthermore, glycosylation of protein antigens can dramatically impact immune responses. While there have been attempts to quantify the glycan shielding or coverage of a protein surface, none of the publicly available tools analyzes glycan shielding computationally at an atomistic level. RESULTS: Here, we developed an in silico approach, GLYCO (GLYcan COverage), to quantify the glycan shielding of a protein surface. The software provides insights into glycan-dense/sparse regions of the entire protein surface or a subset of the protein surface. GLYCO calculates glycan shielding from a single coordinate file or from multiple coordinate files, for instance, as obtained from molecular dynamics simulations or by nuclear magnetic resonance spectroscopy structure determination, enabling analysis of glycan dynamics. Overall, GLYCO provides fundamental insights into the glycan shielding of glycosylated proteins. AVAILABILITY AND IMPLEMENTATION: GLYCO is freely available at GitHub (https://github.com/myungjinlee/GLYCO). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: X-ray crystallography has facilitated the majority of protein structures determined to date. Sequence-based predictors that can accurately estimate protein crystallization propensities would be highly beneficial to overcome the high expenditure, large attrition rate, and to reduce the trial-and-error settings required for crystallization. RESULTS: In this study, we present a novel model, BCrystal, which uses an optimized gradient boosting machine (XGBoost) on sequence, structural and physio-chemical features extracted from the proteins of interest. BCrystal also provides explanations, highlighting the most important features for the predicted crystallization propensity of an individual protein using the SHAP algorithm. On three independent test sets, BCrystal outperforms state-of-the-art sequence-based methods by more than 12.5% in accuracy, 18% in recall and 0.253 in Matthew's correlation coefficient, with an average accuracy of 93.7%, recall of 96.63% and Matthew's correlation coefficient of 0.868. For relative solvent accessibility of exposed residues, we observed higher values to associate positively with protein crystallizability and the number of disordered regions, fraction of coils and tripeptide stretches that contain multiple histidines associate negatively with crystallizability. The higher accuracy of BCrystal enables it to accurately screen for sequence variants with enhanced crystallizability. AVAILABILITY AND IMPLEMENTATION: Our BCrystal webserver is at https://machinelearning-protein.qcri.org/ and source code is available at https://github.com/raghvendra5688/BCrystal. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Biological experiments including proteomics and transcriptomics approaches often reveal sets of proteins that are most likely to be involved in a disease/disorder. To understand the functional nature of a set of proteins, it is important to capture the function of the proteins as a group, even in cases where function of individual proteins is not known. In this work, we propose a model that takes groups of proteins found to work together in a certain biological context, integrates them into functional relevance networks, and subsequently employs an iterative inference on graphical models to identify group functions of the proteins, which are then extended to predict function of individual proteins. RESULTS: The proposed algorithm, iterative group function prediction (iGFP), depicts proteins as a graph that represents functional relevance of proteins considering their known functional, proteomics and transcriptional features. Proteins in the graph will be clustered into groups by their mutual functional relevance, which is iteratively updated using a probabilistic graphical model, the conditional random field. iGFP showed robust accuracy even when substantial amount of GO annotations were missing. The perspective of 'group' function annotation opens up novel approaches for understanding functional nature of proteins in biological systems.Availability and implementation: http://kiharalab.org/iGFP/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: It is important to characterize individual relatedness in terms of familial relationships and underlying population structure in genome-wide association studies for correct downstream analysis. The characterization of individual relatedness becomes vital if the cohort is to be used as reference panel in other studies for association tests and for identifying ethnic diversities. In this paper, we propose a kinship visualization tool to detect cryptic relatedness between subjects. We utilize multi-dimensional scaling, bar charts, heat maps and node-link visualizations to enable analysis of relatedness information. AVAILABILITY AND IMPLEMENTATION: Available online as well as can be downloaded at http://shiny-vis.qcri.org/public/kinvis/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: Protein solubility plays a vital role in pharmaceutical research and production yield. For a given protein, the extent of its solubility can represent the quality of its function, and is ultimately defined by its sequence. Thus, it is imperative to develop novel, highly accurate in silico sequence-based protein solubility predictors. In this work we propose, DeepSol, a novel Deep Learning-based protein solubility predictor. The backbone of our framework is a convolutional neural network that exploits k-mer structure and additional sequence and structural features extracted from the protein sequence. Results: DeepSol outperformed all known sequence-based state-of-the-art solubility prediction methods and attained an accuracy of 0.77 and Matthew's correlation coefficient of 0.55. The superior prediction accuracy of DeepSol allows to screen for sequences with enhanced production capacity and can more reliably predict solubility of novel proteins. Availability and implementation: DeepSol's best performing models and results are publicly deposited at https://doi.org/10.5281/zenodo.1162886 (Khurana and Mall, 2018). Supplementary information: Supplementary data are available at Bioinformatics online.

Motivation: Protein solubility can be a decisive factor in both research and production efficiency, and in silico sequence-based predictors that can accurately estimate solubility outcomes are highly sought. Results: In this study, we present a novel approach termed PRotein SolubIlity Predictor (PaRSnIP), which uses a gradient boosting machine algorithm as well as an approximation of sequence and structural features of the protein of interest. Based on an independent test set, PaRSnIP outperformed other state-of-the-art sequence-based methods by more than 9% in accuracy and 0.17 in Matthew's correlation coefficient, with an overall accuracy of 74% and Matthew's correlation coefficient of 0.48. Additionally, PaRSnIP provides importance scores for all features used in training. We observed higher fractions of exposed residues to associate positively with protein solubility and tripeptide stretches with multiple histidines to associate negatively with solubility. The improved prediction accuracy of PaRSnIP should enable it to predict protein solubility with greater reliability and to screen for sequence variants with enhanced manufacturability. Availability and implementation: PaRSnIP software is available for download under GitHub (https://github.com/RedaRawi/PaRSnIP). Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Anti-cancer therapies have different responses to different patients. There is a need to identify biomarkers for effectiveness of drugs beside the biomarkers for the diseases like cancer to advance the field personalized medicine. We have used a panel of cancer cell lines from Genomics of Drug Sensitivity in Cancer (GDSC) to capture the sensitivity of drugs. By combining the genetic information such as mutations, copy number variations and gene expression levels from Catalogue of Somatic Mutations in Cancer (COSMIC) and The Cancer Genome ATLAS (TCGA) for the afore mentioned cell lines, we are able to identify genomic features associated with the sensitivity of the drugs.

Genome Wide Association Studies (GWAS) examine genetic variants in different individuals to detect variants associated to specific diseases. The 1000 Genomes project is such a collaborative research effort to sequence the genomes of at least 1000 participants of 26 different ethnicities, to establish a detailed summary of human genetic variation. The kinship information is a measure of individuals ancestor relationships within the considered populations. We study the design of kinship data visualizations allowing the experts to discover anomalies in GWAS data. The visual analysis of the 1000 Genomes Project kinship data reveals inconsistencies which call for a deeper analysis of the data quality within this project.

BACKGROUND: The post-genomic era with its wealth of sequences gave rise to a broad range of protein residue-residue contact detecting methods. Although various coevolution methods such as PSICOV, DCA and plmDCA provide correct contact predictions, they do not completely overlap. Hence, new approaches and improvements of existing methods are needed to motivate further development and progress in the field. We present a new contact detecting method, COUSCOus, by combining the best shrinkage approach, the empirical Bayes covariance estimator and GLasso. RESULTS: Using the original PSICOV benchmark dataset, COUSCOus achieves mean accuracies of 0.74, 0.62 and 0.55 for the top L/10 predicted long, medium and short range contacts, respectively. In addition, COUSCOus attains mean areas under the precision-recall curves of 0.25, 0.29 and 0.30 for long, medium and short contacts and outperforms PSICOV. We also observed that COUSCOus outperforms PSICOV w.r.t. Matthew's correlation coefficient criterion on full list of residue contacts. Furthermore, COUSCOus achieves on average 10% more gain in prediction accuracy compared to PSICOV on an independent test set composed of CASP11 protein targets. Finally, we showed that when using a simple random forest meta-classifier, by combining contact detecting techniques and sequence derived features, PSICOV predictions should be replaced by the more accurate COUSCOus predictions. CONCLUSION: We conclude that the consideration of superior covariance shrinkage approaches will boost several research fields that apply the GLasso procedure, amongst the presented one of residue-residue contact prediction as well as fields such as gene network reconstruction.

HIV-1 Env glycoprotein complex is the key protein that mediates binding and entry of HIV-1 into human host cells. The complex entry process involves three main steps. First, the attachment, the interaction of gp120 and CD4. Second, the coreceptor binding, where gp120 binds the chemokine receptor CCR5 or CXCR4, and finally, the fusion of viral and host cell membranes. Despite the fact that several coordinate structures of HIV-1 Env in unliganded state exist (as well as in complex with CD4, CD4 mimics, or various antibodies, and of gp41 in intermediate and post-fusion state), a comprehensive understanding of structural arrangements and communication within gp120 and gp41 domains during the entry is far from complete. In this study, we applied a direct amino acid interaction detecting method to analyse the function and structure of HIV-1 Env protein sequences representing all group M subtypes. We identified more than 400 coevolving residue pairs within Env, of which the majority are real contacts and proximal in the available coordinate structures, or have functional implications such as receptor binding, variable loop, gp120-gp41, and interdomain interactions. This work provides a new dimension of information in HIV research, important in assisting protein coordinate structure prediction and in designing new and effective entry inhibitors.

SUMMARY: CHOYCE is a web server for homology modelling of protein components and the fitting of those components into cryo electron microscopy (cryoEM) maps of their assemblies. It provides an interactive approach to improving the selection of models based on the quality of their fit into the EM map. AVAILABILITY: http://choyce.ismb.lon.ac.uk/ CONTACT: [email protected]; [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.