PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.17.332197v1?rss=1

Authors: Sun, X., Ho, J., Gao, H., Evangelou, E., Yao, C., Huan, T., Hwang, S.-J., Courchesne, P., Larson, M., Levy, D., Ma, J., Liu, C.

Abstract:

The relationship between alcohol consumption, circulating proteins, and cardiovascular disease (CVD) risk has not been well studied. We performed association analyses of alcohol consumption with three CVD risk factors and 71 CVD-related circulating proteins measured in 6,745 Framingham Heart Study participants (mean age, 49 years; 53% women). We found that an increase in alcohol consumption was associated with a higher risk of incident hypertension (P=7.2E-3) but a lower risk of incident obesity (P=5.7E-4) and type 2 diabetes (P=1.4E-5) in a 14-year of follow-up. Using independent discovery (n=4,348) and validation (n=2,397) samples, we identified 20 alcohol-associated proteins (FDR<0.05 in discovery and P<0.05/n in validation), with majority (18 of 20 proteins) inversely associated with alcohol consumption. The alcohol-protein associations remained similar after removing heavy drinkers. Four proteins demonstrated consistent triangular relationships, as expected, with alcohol consumption and CVD risk factors. For example, a greater level of APOA1, which was associated with a higher alcohol consumption (P=1.2E-65), was associated with a lower risk of type 2 diabetes (P=3.1E-5). However, several others showed inconsistent triangular relationships, e.g., a greater level of GDF15, which was associated with a lower alcohol consumption (P=1.0E-13), was associated with an increased risk of hypertension (P=2.4E-4). In conclusion, we identified 20 alcohol-associated proteins and demonstrated complex relationships between alcohol consumption, circulating proteins and CVD risk factors. Future studies with integration of more proteomic markers and larger sample size are warranted to unravel the complex relationship between alcohol consumption and CVD risk.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.17.343962v1?rss=1

Authors: Gong, W., Guerler, A., Zhang, C., Warner, E., Li, C., Zhang, Y.

Abstract:

Genome-wide protein-protein interaction determination (or interactome) remains a significantly unsolved problem in structural biology. The difficulty is twofold since high-throughput experiments (THEs) have often high false-positive rate in assigning PPIs and PPI quaternary structure is much more difficult to solve than tertiary structure using traditional structural biology techniques. We proposed a uniform pipeline to address both difficulties which recognizes PPIs by combining multi-chain threading with high-throughput experimental (HTE) data through naive Bayesian classifiers, where quaternary structures are then constructed by mapping monomer models with the dimeric frameworks through interface-specific structural alignments. The pipeline was applied to the Escherichia coli genome and created 35,125 confident PPIs that is 4.5-fold higher than HTE alone. Graphic analyses of the PPI networks reveal a scale-free cluster size distribution, which was found critical to the robustness of genome evolution and the centrality of functionally important proteins that are essential to E. coli survival. Finally, high-confidence complex structure models were constructed for all predicted PPIs built on quaternary threading templates, which show a close consistency with the experimental structures solved after the modeling. These results demonstrate the significant usefulness of threading-based homologous modeling in both genome-wide PPI network detection and complex structural construction.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.17.343020v1?rss=1

Authors: Abante, J., Goutsias, J.

Abstract:

Motivation: Identifying regions of the genome that demonstrate significant differences in DNA methylation between groups of samples is an important problem in computational epigenetics. Available methods assume that methylation occurs in a statistically independent manner at individual cytosine-phosphate-guanine (CpG) sites or perform analysis using empirically estimated joint probability distributions of methylation patterns at no more than 4 contiguous CpG sites. These approaches can lead to poor detection performance and loss of reliability and reproducibility due to reduced specificity and sensitivity in the presence of insufficient data. Results: To accommodate data obtained with different bisulfite sequencing technologies, such as RRBS, ERRBS, and WGBS, and improve statistical power, we developed CpelTdm.jl, a Julia package for targeted differential analysis of DNA methylation stochasticity between groups of unmatched or matched samples. This package performs rigorous statistical analysis of methylation patterns within regions of the genome specified by the user that takes into account correlations in methylation and results in robust detection of genomic regions exhibiting statistically significant differences in methylation stochasticity. CpelTdm.jl does not only detect mean methylation differences, as it is commonly done by previous methods, but also differences in methylation entropy and, more generally, between probability distributions of methylation. Availability and Implementation: This Julia package is supported for Windows, macOS, and Linux, and can be freely downloaded from GitHub: https://github.com/jordiabante/CpelTdm.jl .

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.15.340455v1?rss=1

Authors: Zhou, X., Li, Y., Zhang, C., Zheng, W., Zhang, G., Zhang, Y.

Abstract:

Progress in cryo-electron microscopy (cryo-EM) has provided the potential for large-size protein structure determination. However, the solution rate for multi-domain proteins remains low due to the difficulty in modeling inter-domain orientations. We developed DEMO-EM, an automatic method to assemble multi-domain structures from cryo-EM maps through a progressive structural refinement procedure combining rigid-body domain fitting and flexible assembly simulations with deep neural network inter-domain distance profiles. The method was tested on a large-scale benchmark set of proteins containing up to twelve continuous and discontinuous domains with medium-to-low-resolution density maps, where DEMO-EM produced models with correct inter-domain orientations (TM-score >0.5) for 98% of cases and significantly outperformed the state-of-the-art methods. DEMO-EM was applied to SARS-CoV-2 coronavirus genome and generated models with average TM-score/RMSD of 0.97/1.4[A] to the deposited structures. These results demonstrated an efficient pipeline that enables automated and reliable large-scale multi-domain protein structure modeling with atomic-level accuracy from cryo-EM maps.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.15.339168v1?rss=1

Authors: Lu, S., Li, Y., Wang, F., Nan, X., Zhang, S.

Abstract:

Antibodies consisting of variable and constant regions, are a special type of proteins playing a vital role in immune system of the vertebrate. They have the remarkable ability to bind a large range of diverse antigens with extraordinary affinity and specificity. This malleability of binding makes antibodies an important class of biological drugs and biomarkers. In this article, we propose a method to identify which amino acid residues of an antibody directly interact with its associated antigen based on the features from sequence and structure. Our algorithm uses convolution neural networks (CNNs) linked with graph convolution networks (GCNs) to make use of information from both sequential and spatial neighbors to understand more about the local environment of the target amino acid residue. Furthermore, we process the antigen partner of an antibody by employing an attention layer. Our method improves on the state-of-the-art methodology.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.15.341149v1?rss=1

Authors: Yadahalli, S., Verma, C. S.

Abstract:

Targeting intracellular pathways with peptide drugs is becoming increasingly desirable but often limited in application due to their poor cell permeability. Understanding cellular permeability of peptides remains a major challenge with very little structure-activity relationship known. Fortunately, there exist a class of peptides called Cell-Penetrating Peptides (CPPs), which have the ability to cross cell membranes and are also capable of delivering biologically active cargo into cells. Discovering patterns that make peptides cell-permeable have a variety of applications in drug delivery. In the current study, we build prediction models for CPPs exploring features covering a range of properties based on amino acid sequences, using Random forest classifiers which are often more interpretable than other ensemble machine learning algorithms. While obtaining prediction accuracies of ~96%, we also interpret our prediction models using TreeInterpreter, LIME and SHAP to decipher the contributions of important features and optimal feature space for CPP class. We propose that our work might offer an intuitive guide for incorporating features that impart cell-penetrability into the design of novel CPPs.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.15.341941v1?rss=1

Authors: Gangwar, S. P., Bandyopadhyay, A., Saxena, A. K.

Abstract:

The Mycobacterium DprE2 is a NADH-dependent enzyme and converts the decaprenylphosphoryl-beta-D-ribose (DPX) to decaprenylphosphoryl-{beta}-D-arabinofuranose (DPA). The FAD-containing oxidoreductase MtbDprE1 and NADH-dependent reductase MtbDprE2 enzymes catalyses together the epimerization reaction, which coverts DPR to DPA. Here, MtbDprE2 enzyme was purified and structurally characterized using circular dichroism, molecular modelling and dynamics simulation techniques. The MtbDprE2 was purified, which eluted as oligomer from size exclusion column. The circular dichroism analysis yielded ~ 47.6% alpha-helix, ~ 19.8% beta-sheet and ~ 32.6% random coil structures in MtbDprE2 enzyme and showed highly thermostability. The molecular modelling of MtbDprE2 and its complex with NADH showed that it contains two domains (i) the large domain consists of central twisted seven beta-sheets decorated by eight alpha-helices and (ii) a small domain contains two short alpha-helices connect by loop. Overall, the MtbDprE2 adopts a typical short-chain dehydrogenase rossmann fold and NADH binds to Asp69, Ser147, Tyr160, Lys164 of catalytic triad and Gly16, Ser19, Glu20, Ile21 of Gly-rich motif of MtbDprE2. 1 ns dynamics simulation was performed on apo and NADH bound MtbDprE2, which indicated the small conformational change in ligand binding site, which resulted more closed pocket than open pocket observed in apo enzyme. Small conformational changes were observed in active site residues and orientation between large and small domains of MtbDprE2 upon NADH binding. Current knowledge of MtbDprE2 structure and its NADH binding mechanism will contribute significantly in development of specific inhibitors against M. tuberculosis.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.15.340737v1?rss=1

Authors: Kalinka, A. T.

Abstract:

High-throughput studies, in which thousands of hypothesis tests are conducted simultaneously, can be under-powered when effect sizes are small and there are few replicates. Here, I describe an approach to estimate the FDR for a given experiment such that the ground truth is known. A decision boundary between true and false positive calls can then be learned from the data itself along the axes of fold change and expression level. By excluding hits that fall into the false positive space, the FDR of any given method can be controlled providing a means to employ less conservative methods for detecting differential expression without incurring the usual loss of precision. I show that coupling this approach with a feature-selection method - an elastic-net logistic regression - can increase sensitivity 10-fold above what is achievable with the prevailing methods of the day. An R package implementing these methods is available at https://github.com/alextkalinka/delboy.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.15.340802v1?rss=1

Authors: Pantazis, Y., Tselas, C., Lakiotaki, K., Lagani, V., Tsamardinos, I.

Abstract:

High-throughput technologies such as microarrays and RNA-sequencing (RNA-seq) allow to precisely quantify transcriptomic profiles, generating datasets that are inevitably high-dimensional. In this work, we investigate whether the whole human transcriptome can be represented in a compressed, low dimensional latent space without loosing relevant information. We thus constructed low-dimensional latent feature spaces of the human genome, by utilizing three dimensionality reduction approaches and a diverse set of curated datasets. We applied standard Principal Component Analysis (PCA), kernel PCA and Autoencoder Neural Networks on 1360 datasets from four different measurement technologies. The latent feature spaces are tested for their ability to (a) reconstruct the original data and (b) improve predictive performance on validation datasets not used during the creation of the feature space. While linear techniques show better reconstruction performance, nonlinear approaches, particularly, neural-based models seem to be able to capture non-additive interaction effects, and thus enjoy stronger predictive capabilities. Our results show that low dimensional representations of the human transcriptome can be achieved by integrating hundreds of datasets, despite the limited sample size of each dataset and the biological / technological heterogeneity across studies. The created space is two to three orders of magnitude smaller compared to the raw data, offering the ability of capturing a large portion of the original data variability and eventually reducing computational time for downstream analyses.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.15.340810v1?rss=1

Authors: Barry, D. J., Gerri, C., Bell, D. M., D'Antuono, R., Niakan, K. K.

Abstract:

The study of cellular and developmental processes in physiologically relevant three-dimensional (3D) systems facilitates an understanding of mechanisms underlying cell fate, disease and injury. While cutting-edge microscopy technologies permit the routine acquisition of 3D datasets, there is currently a lack of user-friendly, open-source software to analyse such images. Here we describe GIANI (djpbarry.github.io/Giani), new software for the analysis of 3D images, implemented as a plugin for the popular FIJI platform. The design primarily facilitates segmentation of nuclei and cells, followed by quantification of morphology and protein expression. GIANI enables routine and reproducible batch-processing of large numbers of images and also comes with scripting and command line tools, such that users can incorporate its functionality into their own scripts and easily run GIANI on a high-performance computing cluster. We demonstrate the utility of GIANI by quantifying cell morphology and protein expression in mouse early embryos. More generally, we anticipate that GIANI will be a useful tool for researchers in a variety of biomedical fields.

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