DeepPhoPred: Accurate Deep Learning Model to Predict Microbial Phosphorylation.

Phosphorylation is a substantial posttranslational modification of proteins that refers to adding a phosphate group to the amino acid side chain after translation process in the ribosome. It is vital to coordinate cellular functions, such as regulating metabolism, proliferation, apoptosis, subcellular trafficking, and other crucial physiological processes. Phosphorylation prediction in a microbial organism can assist in understanding pathogenesis and host-pathogen interaction, drug and antibody design, and antimicrobial agent development. Experimental methods for predicting phosphorylation sites are costly, slow, and tedious. Hence low-cost and high-speed computational approaches are highly desirable. This paper presents a new deep learning tool called DeepPhoPred for predicting microbial phospho-serine (pS), phospho-threonine (pT), and phospho-tyrosine (pY) sites. DeepPhoPred incorporates a two-headed convolutional neural network architecture with the squeeze and excitation blocks followed by fully connected layers that jointly learn significant features from the peptide's structural and evolutionary information to predict phosphorylation sites. Our empirical results demonstrate that DeepPhoPred significantly outperforms the existing microbial phosphorylation site predictors with its highly efficient deep-learning architecture. DeepPhoPred as a standalone predictor, all its source codes, and our employed datasets are publicly available at https://github.com/faisalahm3d/DeepPhoPred.

Resolving single-cell copy number profiling for large datasets.

The advances of single-cell DNA sequencing (scDNA-seq) enable us to characterize the genetic heterogeneity of cancer cells. However, the high noise and low coverage of scDNA-seq impede the estimation of copy number variations (CNVs). In addition, existing tools suffer from intensive execution time and often fail on large datasets. Here, we propose SeCNV, an efficient method that leverages structural entropy, to profile the copy numbers. SeCNV adopts a local Gaussian kernel to construct a matrix, depth congruent map (DCM), capturing the similarities between any two bins along the genome. Then, SeCNV partitions the genome into segments by minimizing the structural entropy from the DCM. With the partition, SeCNV estimates the copy numbers within each segment for cells. We simulate nine datasets with various breakpoint distributions and amplitudes of noise to benchmark SeCNV. SeCNV achieves a robust performance, i.e. the F1-scores are higher than 0.95 for breakpoint detections, significantly outperforming state-of-the-art methods. SeCNV successfully processes large datasets (>50 000 cells) within 4 min, while other tools fail to finish within the time limit, i.e. 120 h. We apply SeCNV to single-nucleus sequencing datasets from two breast cancer patients and acoustic cell tagmentation sequencing datasets from eight breast cancer patients. SeCNV successfully reproduces the distinct subclones and infers tumor heterogeneity. SeCNV is available at https://github.com/deepomicslab/SeCNV.

MMDB: Multimodal dual-branch model for multi-functional bioactive peptide prediction.

Bioactive peptides can hinder oxidative processes and microbial spoilage in foodstuffs and play important roles in treating diverse diseases and disorders. While most of the methods focus on single-functional bioactive peptides and have obtained promising prediction performance, it is still a significant challenge to accurately detect complex and diverse functions simultaneously with the quick increase of multi-functional bioactive peptides. In contrast to previous research on multi-functional bioactive peptide prediction based solely on sequence, we propose a novel multimodal dual-branch (MMDB) lightweight deep learning model that designs two different branches to effectively capture the complementary information of peptide sequence and structural properties. Specifically, a multi-scale dilated convolution with Bi-LSTM branch is presented to effectively model the different scales sequence properties of peptides while a multi-layer convolution branch is proposed to capture structural information. To the best of our knowledge, this is the first effective extraction of peptide sequence features using multi-scale dilated convolution without parameter increase. Multimodal features from both branches are integrated via a fully connected layer for multi-label classification. Compared to state-of-the-art methods, our MMDB model exhibits competitive results across metrics, with a 9.1% Coverage increase and 5.3% and 3.5% improvements in Precision and Accuracy, respectively.

Structure-based rational design of hydroxysteroid dehydrogenases for improving and diversifying steroid synthesis.

A group of steroidogenic enzymes, hydroxysteroid dehydrogenases are involved in steroid metabolism which is very important in the cell: signaling, growth, reproduction, and energy homeostasis. The enzymes show an inherent function in the interconversion of ketosteroids and hydroxysteroids in a position- and stereospecific manner on the steroid nucleus and side-chains. However, the biocatalysis of steroids reaction is a vital and demanding, yet challenging, task to produce the desired enantiopure products with non-natural substrates or non-natural cofactors, and/or in non-physiological conditions. This has driven the use of protein design strategies to improve their inherent biosynthetic efficiency or activate their silent catalytic ability. In this review, the innate features and catalytic characteristics of enzymes based on sequence-structure-function relationships of steroidogenic enzymes are reviewed. Combining structure information and catalytic mechanisms, progress in protein redesign to stimulate potential function, for example, substrate specificity, cofactor dependence, and catalytic stability are discussed.

Kernel Estimation Using Total Variation Guided GAN for Image Super-Resolution.

Various super-resolution (SR) kernels in the degradation model deteriorate the performance of the SR algorithms, showing unpleasant artifacts in the output images. Hence, SR kernel estimation has been studied to improve the SR performance in several ways for more than a decade. In particular, a conventional research named KernelGAN has recently been proposed. To estimate the SR kernel from a single image, KernelGAN introduces generative adversarial networks(GANs) that utilize the recurrence of similar structures across scales. Subsequently, an enhanced version of KernelGAN, named E-KernelGAN, was proposed to consider image sharpness and edge thickness. Although it is stable compared to the earlier method, it still encounters challenges in estimating sizable and anisotropic kernels because the structural information of an input image is not sufficiently considered. In this paper, we propose a kernel estimation algorithm called Total Variation Guided KernelGAN (TVG-KernelGAN), which efficiently enables networks to focus on the structural information of an input image. The experimental results show that the proposed algorithm accurately and stably estimates kernels, particularly sizable and anisotropic kernels, both qualitatively and quantitatively. In addition, we compared the results of the non-blind SR methods, using SR kernel estimation techniques. The results indicate that the performance of the SR algorithms was improved using our proposed method.

1DE-MS Profiling for Proteoform-Correlated Proteomic Analysis, by Combining SDS-PAGE, Whole-Gel Slicing, Quantitative LC-MS/MS, and Reconstruction of Gel Distributions of Several Thousands of Proteins.

SDS-PAGE has often been used in proteomic analysis, but generally for sample prefractionation although the technique separates proteins by molecular masses (Mws) and the information would contribute to proteoform-level analysis. Here, we report a method that combines SDS-PAGE, whole-gel slicing, and quantitative LC-MS/MS for establishing gel distributions of several thousand proteins in a proteome. A previously obtained data set on rat cerebral cortex with cerebral ischemia-reperfusion injury1 was analyzed, and the gel distributions of 5906 proteins were reconstructed. These distributions, referred to as 1DE-MS profiles, revealed that about 30% of the proteins had more than one proteoform detected in the gels. The profiles were categorized into six types by distribution (narrow, dispersed, or broad) and relative deviations between the abundance-peak apparent Mws and calculated Mws. Only 56% of the proteins showed narrow distributions and matched Mws, while the others had rather complex profiles. Bioinformatic analysis on example profiles showed the resolved proteoforms involved alternative splicing, proteolytic processing, glycosylation and ubiquitination, fragmentation, and probably transmembrane structures. Profile-based differential analysis revealed that many of the disease-caused changes were proteoform dependent. This work provided a proteome-scale view of protein distributions in SDS-PAGE gels, and the method would be useful to obtain proteoform-correlated information for in-depth proteomics.

Complementing sequence-derived features with structural information extracted from fragment libraries for protein structure prediction.

BACKGROUND: Fragment libraries play a key role in fragment-assembly based protein structure prediction, where protein fragments are assembled to form a complete three-dimensional structure. Rich and accurate structural information embedded in fragment libraries has not been systematically extracted and used beyond fragment assembly. METHODS: To better leverage the valuable structural information for protein structure prediction, we extracted seven types of structural information from fragment libraries. We broadened the usage of such structural information by transforming fragment libraries into protein-specific potentials for gradient-descent based protein folding and encoding fragment libraries as structural features for protein property prediction. RESULTS: Fragment libraires improved the accuracy of protein folding and outperformed state-of-the-art algorithms with respect to predicted properties, such as torsion angles and inter-residue distances. CONCLUSION: Our work implies that the rich structural information extracted from fragment libraries can complement sequence-derived features to help protein structure prediction.

Characterization of Lignin Compounds at the Molecular Level: Mass Spectrometry Analysis and Raw Data Processing.

Lignin is the second most abundant natural biopolymer, which is a potential alternative to conventional fossil fuels. It is also a promising material for the recovery of valuable chemicals such as aromatic compounds as well as an important biomarker for terrestrial organic matter. Lignin is currently produced in large quantities as a by-product of chemical pulping and cellulosic ethanol processes. Consequently, analytical methods are required to assess the content of valuable chemicals contained in these complex lignin wastes. This review is devoted to the application of mass spectrometry, including data analysis strategies, for the elemental and structural elucidation of lignin products. We describe and critically evaluate how these methods have contributed to progress and trends in the utilization of lignin in chemical synthesis, materials, energy, and geochemistry.

Sparse multiple co-Inertia analysis with application to integrative analysis of multi -Omics data.

BACKGROUND: Multiple co-inertia analysis (mCIA) is a multivariate analysis method that can assess relationships and trends in multiple datasets. Recently it has been used for integrative analysis of multiple high-dimensional -omics datasets. However, its estimated loading vectors are non-sparse, which presents challenges for identifying important features and interpreting analysis results. We propose two new mCIA methods: 1) a sparse mCIA method that produces sparse loading estimates and 2) a structured sparse mCIA method that further enables incorporation of structural information among variables such as those from functional genomics. RESULTS: Our extensive simulation studies demonstrate the superior performance of the sparse mCIA and structured sparse mCIA methods compared to the existing mCIA in terms of feature selection and estimation accuracy. Application to the integrative analysis of transcriptomics data and proteomics data from a cancer study identified biomarkers that are suggested in the literature related with cancer disease. CONCLUSION: Proposed sparse mCIA achieves simultaneous model estimation and feature selection and yields analysis results that are more interpretable than the existing mCIA. Furthermore, proposed structured sparse mCIA can effectively incorporate prior network information among genes, resulting in improved feature selection and enhanced interpretability.

Hollow Multi-Shelled Structure with Metal-Organic-Framework-Derived Coatings for Enhanced Lithium Storage.

Herein, we present heterogeneous hollow multi-shelled structures (HoMSs) prepared by exploiting the properties of the metal-organic framework (MOFs) casing. Through accurately controlling the transformation of MOF layer into different heterogeneous casings, we can precisely design HoMSs of SnO2 @Fe2 O3 (MOF) and SnO2 @FeOx -C(MOF), which not only retain properties of the original SnO2 -HoMSs, but also structural information from the MOFs. Tested as anode materials in LIBs, SnO2 @Fe2 O3 (MOF)-HoMSs demonstrate superior lithium-storage capacity and cycling stability to the original SnO2 -HoMSs, which can be attributed to the topological features from the MOF casing. Making a sharp contrast to the electrodes of SnO2 @Fe2 O3 (particle)-HoMSs fabricated by hydrothermal method, the capacity retention after 100 cycles for the SnO2 @Fe2 O3 (MOF)-HoMSs is about eight times higher than that of the SnO2 @Fe2 O3 (particle)-HoMS.

Integrative analysis of transcriptomic and metabolomic data via sparse canonical correlation analysis with incorporation of biological information.

Integrative analysis of high dimensional omics data is becoming increasingly popular. At the same time, incorporating known functional relationships among variables in analysis of omics data has been shown to help elucidate underlying mechanisms for complex diseases. In this article, our goal is to assess association between transcriptomic and metabolomic data from a Predictive Health Institute (PHI) study that includes healthy adults at a high risk of developing cardiovascular diseases. Adopting a strategy that is both data-driven and knowledge-based, we develop statistical methods for sparse canonical correlation analysis (CCA) with incorporation of known biological information. Our proposed methods use prior network structural information among genes and among metabolites to guide selection of relevant genes and metabolites in sparse CCA, providing insight on the molecular underpinning of cardiovascular disease. Our simulations demonstrate that the structured sparse CCA methods outperform several existing sparse CCA methods in selecting relevant genes and metabolites when structural information is informative and are robust to mis-specified structural information. Our analysis of the PHI study reveals that a number of gene and metabolic pathways including some known to be associated with cardiovascular diseases are enriched in the set of genes and metabolites selected by our proposed approach.

Incorporating biological information in sparse principal component analysis with application to genomic data.

BACKGROUND: Sparse principal component analysis (PCA) is a popular tool for dimensionality reduction, pattern recognition, and visualization of high dimensional data. It has been recognized that complex biological mechanisms occur through concerted relationships of multiple genes working in networks that are often represented by graphs. Recent work has shown that incorporating such biological information improves feature selection and prediction performance in regression analysis, but there has been limited work on extending this approach to PCA. In this article, we propose two new sparse PCA methods called Fused and Grouped sparse PCA that enable incorporation of prior biological information in variable selection. RESULTS: Our simulation studies suggest that, compared to existing sparse PCA methods, the proposed methods achieve higher sensitivity and specificity when the graph structure is correctly specified, and are fairly robust to misspecified graph structures. Application to a glioblastoma gene expression dataset identified pathways that are suggested in the literature to be related with glioblastoma. CONCLUSIONS: The proposed sparse PCA methods Fused and Grouped sparse PCA can effectively incorporate prior biological information in variable selection, leading to improved feature selection and more interpretable principal component loadings and potentially providing insights on molecular underpinnings of complex diseases.

Efficient dynamic programming algorithm with prior knowledge for protein ß-strand alignment.

One of the main tasks towards the prediction of protein ß-sheet structure is to predict the native alignment of ß-strands. The alignment of two ß-strands defines similar regions that may reflect functional, structural, or evolutionary relationships between them. Therefore, any improvement in ß-strands alignment not only reduces the computational search space but also improves ß-sheet structure prediction accuracy. To define the alignment scores, previous studies utilized predicted residue-residue contacts (contact maps). However, there are two serious problems using them. First, the precision of contact map prediction techniques, especially for long-range contacts (i.e., ß-residues), is still not satisfactory. Second, the residue-residue contact predictors usually utilize general properties of amino acids and disregard the structural features of ß-residues. In this paper, we consider ß-structure information, which is estimated from protein ß-sheet data sets, as alignment scores. However, the predicted contact maps are used as a prior knowledge about residues. They are used for strengthening or weakening the alignment scores in our algorithm. Thus, we can utilize both ß-residues and ß-structure information in alignment of ß-strands. The structure of dynamic programming of the alignment algorithm is changed in order to work with our prior knowledge. Moreover, the Four Russians method is applied to the proposed alignment algorithm in order to reduce the time complexity of the problem. For evaluating the proposed method, we applied it to the state-of-the-art ß-sheet structure prediction methods. The experimental results on the BetaSheet916 data set showed significant improvements in the execution time, the accuracy of ß-strands' alignment and consequently ß-sheet structure prediction accuracy. The results are available at http://conceptsgate.com/BetaSheet.

Conformation Generation: The State of the Art.

The generation of conformations for small molecules is a problem of continuing interest in cheminformatics and computational drug discovery. This review will present an overview of methods used to sample conformational space, focusing on those methods designed for organic molecules commonly of interest in drug discovery. Different approaches to both the sampling of conformational space and the scoring of conformational stability will be compared and contrasted, with an emphasis on those methods suitable for conformer sampling of large numbers of drug-like molecules. Particular attention will be devoted to the appropriate utilization of information from experimental solid-state structures in validating and evaluating the performance of these tools. The review will conclude with some areas worthy of further investigation.

Structural aspects of metal-organic framework-based energy materials research at Diamond.

Large-scale central facilities such as Diamond Light Source fulfil an increasingly pivotal role in many large-scale scientific research programmes. We illustrate these developments by reference to energy-centred projects at the University of Nottingham, the progress of which depends crucially on access to these facilities. Continuing access to beamtime has now become a major priority for those who direct such programmes.

GNN-based structural information to improve DNN-based basal ganglia segmentation in children following early brain lesion.

Analyzing the basal ganglia following an early brain lesion is crucial due to their noteworthy role in sensory-motor functions. However, the segmentation of these subcortical structures on MRI is challenging in children and is further complicated by the presence of a lesion. Although current deep neural networks (DNN) perform well in segmenting subcortical brain structures in healthy brains, they lack robustness when faced with lesion variability, leading to structural inconsistencies. Given the established spatial organization of the basal ganglia, we propose enhancing the DNN-based segmentation through post-processing with a graph neural network (GNN). The GNN conducts node classification on graphs encoding both class probabilities and spatial information regarding the regions segmented by the DNN. In this study, we focus on neonatal arterial ischemic stroke (NAIS) in children. The approach is evaluated on both healthy children and children after NAIS using three DNN backbones: U-Net, UNETr, and MSGSE-Net. The results show an improvement in segmentation performance, with an increase in the median Dice score by up to 4% and a reduction in the median Hausdorff distance (HD) by up to 93% for healthy children (from 36.45 to 2.57) and up to 91% for children suffering from NAIS (from 40.64 to 3.50). The performance of the method is compared with atlas-based methods. Severe cases of neonatal stroke result in a decline in performance in the injured hemisphere, without negatively affecting the segmentation of the contra-injured hemisphere. Furthermore, the approach demonstrates resilience to small training datasets, a widespread challenge in the medical field, particularly in pediatrics and for rare pathologies.

SuperTAD-Fast: Accelerating Topologically Associating Domains Detection Through Discretization.

High-throughput chromosome conformation capture (Hi-C) technology captures spatial interactions of DNA sequences into matrices, and software tools are developed to identify topologically associating domains (TADs) from the Hi-C matrices. With structural information theory, SuperTAD adopted a dynamic programming approach to find the TAD hierarchy with minimal structural entropy. However, the algorithm suffers from high time complexity. To accelerate this algorithm, we design and implement an approximation algorithm with a theoretical performance guarantee. We implemented a package, SuperTAD-Fast. Using Hi-C matrices and simulated data, we demonstrated that SuperTAD-Fast achieved great runtime improvement compared with SuperTAD. SuperTAD-Fast shows high consistency and significant enrichment of structural proteins from Hi-C data of human cell lines in comparison with the existing six hierarchical TADs detecting methods.

Discovery of Virus-Host interactions using bioinformatic tools.

Viruses are a diverse biological group capable of infecting several hosts such as bacteria, plants, and animals, including humans. Viral infections constitute a threat to the human population as they may cause high mortality rates, decrease food production, and generate large economical losses. Viruses co-evolve with their hosts and this constant evolution must be clarified to better predict possible viral outbreaks, and to develop improved diagnostic methods and therapeutical approaches. In this review, we summarize several viral databases that store key information retrieved from a variety of omics approaches. Furthermore, we explore the use of such databases to predict Virus-Host interactions through artificial intelligence algorithms, focusing on the latest methodologies to characterize biological networks.

Disulfide Bond Engineering for Enhancing the Thermostability of the Maltotetraose-Forming Amylase from Pseudomonas saccharophila STB07.

Maltooligosaccharides are a novel type of functional oligosaccharides with potential applications in food processing and can be produced by glycosyl hydrolases hydrolyzing starch. However, the main obstacle in industrial applications is the balance between the high temperature of the process and the stability of enzymes. In this study, based on the structural information and in silico tools (DSDBASE-MODIP, Disulfide by Design2 and FoldX), two disulfide bond mutants (A211C-S214C and S409C-Q412C) of maltotetraose-forming amylase from Pseudomonas saccharophila STB07 (MFAps) were generated to improve its thermostability. The mutation A211C-S214C was closer to the catalytic center and showed significantly improved thermostability with a 2.6-fold improved half-life at 60 °C and the thermal transition mid-point increased by 1.6 °C, compared to the wild-type. However, the thermostability of mutant S409C-Q412C, whose mutation sites are closely to CBM20, did not change observably. Molecular dynamics simulations revealed that both disulfide bonds A211C-S214C and S409C-Q412C rigidified the overall structure of MFAps, however, the impact on thermostability depends on the position and distance from the catalytic center.

Nuclear Norm Regularized Deep Neural Network for EEG-Based Emotion Recognition.

Electroencephalography (EEG) based emotion recognition enables machines to perceive users' affective states, which has attracted increasing attention. However, most of the current emotion recognition methods neglect the structural information among different brain regions, which can lead to the incorrect learning of high-level EEG feature representation. To mitigate possible performance degradation, we propose a novel nuclear norm regularized deep neural network framework (NRDNN) that can capture the structural information among different brain regions in EEG decoding. The proposed NRDNN first utilizes deep neural networks to learn high-level feature representations of multiple brain regions, respectively. Then, a set of weights indicating the contributions of each brain region can be automatically learned using a region-attention layer. Subsequently, the weighted feature representations of multiple brain regions are stacked into a feature matrix, and the nuclear norm regularization is adopted to learn the structural information within the feature matrix. The proposed NRDNN method can learn the high-level representations of EEG signals within multiple brain regions, and the contributions of them can be automatically adjusted by assigning a set of weights. Besides, the structural information among multiple brain regions can be captured in the learning procedure. Finally, the proposed NRDNN can perform in an efficient end-to-end manner. We conducted extensive experiments on publicly available emotion EEG dataset to evaluate the effectiveness of the proposed NRDNN. Experimental results demonstrated that the proposed NRDNN can achieve state-of-the-art performance by leveraging the structural information.