Two-step devitrification of ultrastable glasses.

The discovery of ultrastable glasses raises novel challenges about glassy systems. Recent experiments studied the macroscopic devitrification of ultrastable glasses into liquids upon heating but lacked microscopic resolution. We use molecular dynamics simulations to analyze the kinetics of this transformation. In the most stable systems, devitrification occurs after a very large time, but the liquid emerges in two steps. At short times, we observe the rare nucleation and slow growth of isolated droplets containing a liquid maintained under pressure by the rigidity of the surrounding glass. At large times, pressure is released after the droplets coalesce into large domains, which accelerates devitrification. This two-step process produces pronounced deviations from the classical Avrami kinetics and explains the emergence of a giant lengthscale characterizing the devitrification of bulk ultrastable glasses. Our study elucidates the nonequilibrium kinetics of glasses following a large temperature jump, which differs from both equilibrium relaxation and aging dynamics, and will guide future experimental studies.

A review of methods for predicting DNA N6-methyladenine sites.

Deoxyribonucleic acid(DNA) N6-methyladenine plays a vital role in various biological processes, and the accurate identification of its site can provide a more comprehensive understanding of its biological effects. There are several methods for 6mA site prediction. With the continuous development of technology, traditional techniques with the high costs and low efficiencies are gradually being replaced by computer methods. Computer methods that are widely used can be divided into two categories: traditional machine learning and deep learning methods. We first list some existing experimental methods for predicting the 6mA site, then analyze the general process from sequence input to results in computer methods and review existing model architectures. Finally, the results were summarized and compared to facilitate subsequent researchers in choosing the most suitable method for their work.

Comment on 'rigorous benchmarking of T cell receptor repertoire profiling methods for cancer RNA sequencing'.

Transcriptome sequencing has become common in cancer research, resulting in the generation of a substantial volume of RNA sequencing (RNA-Seq) data. The ability to extract immune repertoires from these data is crucial for obtaining information on infiltrating T- and B-lymphocyte clones when dedicated amplicon T-cell/B-cell receptors sequencing (TCR-Seq/BCR-Seq) methods are unavailable. In response to this demand, several dedicated computational methods have been developed, including MiXCR, TRUST and ImRep. In the recent publication in Briefings in Bioinformatics, Peng et al. have conducted an intensive, systematic comparison of the three previously mentioned tools. Although their effort is commendable, we do have a few constructive critiques regarding technical elements of their analysis.

A comprehensive review and evaluation of graph neural networks for non-coding RNA and complex disease associations.

Non-coding RNAs (ncRNAs) play a critical role in the occurrence and development of numerous human diseases. Consequently, studying the associations between ncRNAs and diseases has garnered significant attention from researchers in recent years. Various computational methods have been proposed to explore ncRNA-disease relationships, with Graph Neural Network (GNN) emerging as a state-of-the-art approach for ncRNA-disease association prediction. In this survey, we present a comprehensive review of GNN-based models for ncRNA-disease associations. Firstly, we provide a detailed introduction to ncRNAs and GNNs. Next, we delve into the motivations behind adopting GNNs for predicting ncRNA-disease associations, focusing on data structure, high-order connectivity in graphs and sparse supervision signals. Subsequently, we analyze the challenges associated with using GNNs in predicting ncRNA-disease associations, covering graph construction, feature propagation and aggregation, and model optimization. We then present a detailed summary and performance evaluation of existing GNN-based models in the context of ncRNA-disease associations. Lastly, we explore potential future research directions in this rapidly evolving field. This survey serves as a valuable resource for researchers interested in leveraging GNNs to uncover the complex relationships between ncRNAs and diseases.

Integrating massive RNA-seq data to elucidate transcriptome dynamics in Drosophila melanogaster.

The volume of ribonucleic acid (RNA)-seq data has increased exponentially, providing numerous new insights into various biological processes. However, due to significant practical challenges, such as data heterogeneity, it is still difficult to ensure the quality of these data when integrated. Although some quality control methods have been developed, sample consistency is rarely considered and these methods are susceptible to artificial factors. Here, we developed MassiveQC, an unsupervised machine learning-based approach, to automatically download and filter large-scale high-throughput data. In addition to the read quality used in other tools, MassiveQC also uses the alignment and expression quality as model features. Meanwhile, it is user-friendly since the cutoff is generated from self-reporting and is applicable to multimodal data. To explore its value, we applied MassiveQC to Drosophila RNA-seq data and generated a comprehensive transcriptome atlas across 28 tissues from embryogenesis to adulthood. We systematically characterized fly gene expression dynamics and found that genes with high expression dynamics were likely to be evolutionarily young and expressed at late developmental stages, exhibiting high nonsynonymous substitution rates and low phenotypic severity, and they were involved in simple regulatory programs. We also discovered that human and Drosophila had strong positive correlations in gene expression in orthologous organs, revealing the great potential of the Drosophila system for studying human development and disease.

Contiguously hydrophobic sequences are functionally significant throughout the human exome.

Hydrophobic interactions have long been established as essential for stabilizing struc-tured proteins as well as drivers of aggregation, but the impact of hydrophobicity on thefunctional significance of sequence variants has rarely been considered in a genome-wide context. Here we test the role of hydrophobicity on functional impact across70,000 disease- and non­disease-associated single-nucleotide polymorphisms (SNPs),using enrichment of disease association as an indicator of functionality. We find thatfunctional impact is uncorrelated with hydrophobicity of the SNP itself and only weaklycorrelated with the average local hydrophobicity, but is strongly correlated with boththe size and minimum hydrophobicity of the contiguously hydrophobic sequence (or"blob") that contains the SNP. Disease association is found to vary by more than sixfoldas a function of contiguous hydrophobicity parameters, suggesting utility as a prior foridentifying causal variation. We further find signatures of differential selective constrainton hydrophobic blobs and that SNPs splitting a long hydrophobic blob or joiningtwo short hydrophobic blobs are particularly likely to be disease associated. Trends arepreserved for both aggregating and nonaggregating proteins, indicating that the role ofcontiguous hydrophobicity extends well beyond aggregation risk.

Review of predicting protein stability changes upon variations.

Forecasting alterations in protein stability caused by variations holds immense importance. Improving the thermal stability of proteins is important for biomedical and industrial applications. This review discusses the latest methods for predicting the effects of mutations on protein stability, databases containing protein mutations and thermodynamic parameters, and experimental techniques for efficiently assessing protein stability in high-throughput settings. Various publicly available databases for protein stability prediction are introduced. Furthermore, state-of-the-art computational approaches for anticipating protein stability changes due to variants are reviewed. Each method's types of features, base algorithm, and prediction results are also detailed. Additionally, some experimental approaches for verifying the prediction results of computational methods are introduced. Finally, the review summarizes the progress and challenges of protein stability prediction and discusses potential models for future research directions.

Welch-weighted Egger regression reduces false positives due to correlated pleiotropy in Mendelian randomization.

Modern population-scale biobanks contain simultaneous measurements of many phenotypes, providing unprecedented opportunity to study the relationship between biomarkers and disease. However, inferring causal effects from observational data is notoriously challenging. Mendelian randomization (MR) has recently received increased attention as a class of methods for estimating causal effects using genetic associations. However, standard methods result in pervasive false positives when two traits share a heritable, unobserved common cause. This is the problem of correlated pleiotropy. Here, we introduce a flexible framework for simulating traits with a common genetic confounder that generalizes recently proposed models, as well as a simple approach we call Welch-weighted Egger regression (WWER) for estimating causal effects. We show in comprehensive simulations that our method substantially reduces false positives due to correlated pleiotropy while being fast enough to apply to hundreds of phenotypes. We apply our method first to a subset of the UK Biobank consisting of blood traits and inflammatory disease, and then to a broader set of 411 heritable phenotypes. We detect many effects with strong literature support, as well as numerous behavioral effects that appear to stem from physician advice given to people at high risk for disease. We conclude that WWER is a powerful tool for exploratory data analysis in ever-growing databases of genotypes and phenotypes.

Computational methods, databases and tools for synthetic lethality prediction.

Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of both genes results in cell death. SL-based therapy has become one of the most promising targeted cancer therapies in the last decade as PARP inhibitors achieve great success in the clinic. The key point to exploiting SL-based cancer therapy is the identification of robust SL pairs. Although many wet-lab-based methods have been developed to screen SL pairs, known SL pairs are less than 0.1% of all potential pairs due to large number of human gene combinations. Computational prediction methods complement wet-lab-based methods to effectively reduce the search space of SL pairs. In this paper, we review the recent applications of computational methods and commonly used databases for SL prediction. First, we introduce the concept of SL and its screening methods. Second, various SL-related data resources are summarized. Then, computational methods including statistical-based methods, network-based methods, classical machine learning methods and deep learning methods for SL prediction are summarized. In particular, we elaborate on the negative sampling methods applied in these models. Next, representative tools for SL prediction are introduced. Finally, the challenges and future work for SL prediction are discussed.

Biomedical data, computational methods and tools for evaluating disease-disease associations.

In recent decades, exploring potential relationships between diseases has been an active research field. With the rapid accumulation of disease-related biomedical data, a lot of computational methods and tools/platforms have been developed to reveal intrinsic relationship between diseases, which can provide useful insights to the study of complex diseases, e.g. understanding molecular mechanisms of diseases and discovering new treatment of diseases. Human complex diseases involve both external phenotypic abnormalities and complex internal molecular mechanisms in organisms. Computational methods with different types of biomedical data from phenotype to genotype can evaluate disease-disease associations at different levels, providing a comprehensive perspective for understanding diseases. In this review, available biomedical data and databases for evaluating disease-disease associations are first summarized. Then, existing computational methods for disease-disease associations are reviewed and classified into five groups in terms of the usages of biomedical data, including disease semantic-based, phenotype-based, function-based, representation learning-based and text mining-based methods. Further, we summarize software tools/platforms for computation and analysis of disease-disease associations. Finally, we give a discussion and summary on the research of disease-disease associations. This review provides a systematic overview for current disease association research, which could promote the development and applications of computational methods and tools/platforms for disease-disease associations.

An updated overview of experimental and computational approaches to identify non-canonical DNA/RNA structures with emphasis on G-quadruplexes and R-loops.

Multiple types of non-canonical nucleic acid structures play essential roles in DNA recombination and replication, transcription, and genomic instability and have been associated with several human diseases. Thus, an increasing number of experimental and bioinformatics methods have been developed to identify these structures. To date, most reviews have focused on the features of non-canonical DNA/RNA structure formation, experimental approaches to mapping these structures, and the association of these structures with diseases. In addition, two reviews of computational algorithms for the prediction of non-canonical nucleic acid structures have been published. One of these reviews focused only on computational approaches for G4 detection until 2020. The other mainly summarized the computational tools for predicting cruciform, H-DNA and Z-DNA, in which the algorithms discussed were published before 2012. Since then, several experimental and computational methods have been developed. However, a systematic review including the conformation, sequencing mapping methods and computational prediction strategies for these structures has not yet been published. The purpose of this review is to provide an updated overview of conformation, current sequencing technologies and computational identification methods for non-canonical nucleic acid structures, as well as their strengths and weaknesses. We expect that this review will aid in understanding how these structures are characterised and how they contribute to related biological processes and diseases.

Cell-cell communication inference and analysis in the tumour microenvironments from single-cell transcriptomics: data resources and computational strategies.

Carcinomas are complex ecosystems composed of cancer, stromal and immune cells. Communication between these cells and their microenvironments induces cancer progression and causes therapy resistance. In order to improve the treatment of cancers, it is essential to quantify crosstalk between and within various cell types in a tumour microenvironment. Focusing on the coordinated expression patterns of ligands and cognate receptors, cell-cell communication can be inferred through ligand-receptor interactions (LRIs). In this manuscript, we carry out the following work: (i) introduce pipeline for ligand-receptor-mediated intercellular communication estimation from single-cell transcriptomics and list a few available LRI-related databases and visualization tools; (ii) demonstrate seven classical intercellular communication scoring strategies, highlight four types of representative intercellular communication inference methods, including network-based approaches, machine learning-based approaches, spatial information-based approaches and other approaches; (iii) summarize the evaluation and validation avenues for intercellular communication inference and analyze the advantages and limitations for the above four types of cell-cell communication methods; (iv) comment several major challenges while provide further research directions for intercellular communication analysis in the tumour microenvironments. We anticipate that this work helps to better understand intercellular crosstalk and to further develop powerful cell-cell communication estimation tools for tumor-targeted therapy.

Quantitative evaluation of lesion response heterogeneity for superior prognostication of clinical outcome.

PURPOSE: Standardized reporting of treatment response in oncology patients has traditionally relied on methods like RECIST, PERCIST and Deauville score. These endpoints assess only a few lesions, potentially overlooking the response heterogeneity of all disease. This study hypothesizes that comprehensive spatial-temporal evaluation of all individual lesions is necessary for superior prognostication of clinical outcome. METHODS: [18F]FDG PET/CT scans from 241 patients (127 diffuse large B-cell lymphoma (DLBCL) and 114 non-small cell lung cancer (NSCLC)) were retrospectively obtained at baseline and either during chemotherapy or post-chemoradiotherapy. An automated TRAQinform IQ software (AIQ Solutions) analyzed the images, performing quantification of change in regions of interest suspicious of cancer (lesion-ROI). Multivariable Cox proportional hazards (CoxPH) models were trained to predict overall survival (OS) with varied sets of quantitative features and lesion-ROI, compared by bootstrapping with C-index and t-tests. The best-fit model was compared to automated versions of previously established methods like RECIST, PERCIST and Deauville score. RESULTS: Multivariable CoxPH models demonstrated superior prognostic power when trained with features quantifying response heterogeneity in all individual lesion-ROI in DLBCL (C-index = 0.84, p < 0.001) and NSCLC (C-index = 0.71, p < 0.001). Prognostic power significantly deteriorated (p < 0.001) when using subsets of lesion-ROI (C-index = 0.78 and 0.67 for DLBCL and NSCLC, respectively) or excluding response heterogeneity (C-index = 0.67 and 0.70). RECIST, PERCIST, and Deauville score could not significantly associate with OS (C-index < 0.65 and p > 0.1), performing significantly worse than the multivariable models (p < 0.001). CONCLUSIONS: Quantitative evaluation of response heterogeneity of all individual lesions is necessary for the superior prognostication of clinical outcome.

An evaluation of computational methods for aggregate data meta-analyses of diagnostic test accuracy studies.

BACKGROUND: A Generalized Linear Mixed Model (GLMM) is recommended to meta-analyze diagnostic test accuracy studies (DTAs) based on aggregate or individual participant data. Since a GLMM does not have a closed-form likelihood function or parameter solutions, computational methods are conventionally used to approximate the likelihoods and obtain parameter estimates. The most commonly used computational methods are the Iteratively Reweighted Least Squares (IRLS), the Laplace approximation (LA), and the Adaptive Gauss-Hermite quadrature (AGHQ). Despite being widely used, it has not been clear how these computational methods compare and perform in the context of an aggregate data meta-analysis (ADMA) of DTAs. METHODS: We compared and evaluated the performance of three commonly used computational methods for GLMM - the IRLS, the LA, and the AGHQ, via a comprehensive simulation study and real-life data examples, in the context of an ADMA of DTAs. By varying several parameters in our simulations, we assessed the performance of the three methods in terms of bias, root mean squared error, confidence interval (CI) width, coverage of the 95% CI, convergence rate, and computational speed. RESULTS: For most of the scenarios, especially when the meta-analytic data were not sparse (i.e., there were no or negligible studies with perfect diagnosis), the three computational methods were comparable for the estimation of sensitivity and specificity. However, the LA had the largest bias and root mean squared error for pooled sensitivity and specificity when the meta-analytic data were sparse. Moreover, the AGHQ took a longer computational time to converge relative to the other two methods, although it had the best convergence rate. CONCLUSIONS: We recommend practitioners and researchers carefully choose an appropriate computational algorithm when fitting a GLMM to an ADMA of DTAs. We do not recommend the LA for sparse meta-analytic data sets. However, either the AGHQ or the IRLS can be used regardless of the characteristics of the meta-analytic data.

A comprehensive performance evaluation, comparison, and integration of computational methods for detecting and estimating cross-contamination of human samples in cancer next-generation sequencing analysis.

Cross-sample contamination is one of the major issues in next-generation sequencing (NGS)-based molecular assays. This type of contamination, even at very low levels, can significantly impact the results of an analysis, especially in the detection of somatic alterations in tumor samples. Several contamination identification tools have been developed and implemented as a crucial quality-control step in the routine NGS bioinformatic pipeline. However, no study has been published to comprehensively and systematically investigate, evaluate, and compare these computational methods in the cancer NGS analysis. In this study, we comprehensively investigated nine state-of-the-art computational methods for detecting cross-sample contamination. To explore their application in cancer NGS analysis, we further compared the performance of five representative tools by qualitative and quantitative analyses using in silico and simulated experimental NGS data. The results showed that Conpair achieved the best performance for identifying contamination and predicting the level of contamination in solid tumors NGS analysis. Moreover, based on Conpair, we developed a Python script, Contamination Source Predictor (ConSPr), to identify the source of contamination. We anticipate that this comprehensive survey and the proposed tool for predicting the source of contamination will assist researchers in selecting appropriate cross-contamination detection tools in cancer NGS analysis and inspire the development of computational methods for detecting sample cross-contamination and identifying its source in the future.

Identification of Phytochemicals with Inhibitory Potential Against Beta-lactamase Enzymes via Computer-aided Approach.

INTRODUCTION: Antibacterial drugs have been widely used for the past century to treat diseases, but their efficacy has been limited by multi-resistant pathogens, particularly those that utilize beta-lactamase enzymes. The inhibition of beta-lactamase enzymes holds great promise for reducing the influence of such pathogens. OBJECTIVE: This study aims to evaluate the mechanism of inhibition of phytochemicals with antibacterial activity against two classes of beta-lactamases using computational methods. METHODS: To achieve this objective, a total of thirty phytochemicals were docked against SHV-1 beta-lactamase and AmpC beta-lactamase after procurement from Protein Data Bank. The pharmacokinetics (ADMET) and density functional theory (DFT) analysis study were also conducted to unravel the nature of the top six most promising compounds on each protein. RESULTS: The results showed that a significant percentage of the compounds had binding affinities greater than that of avibactam, the positive control. Quercetin-3-O-rutinoside showed the most promising results against SHV-1 beta-lactamase with an affinity of -9.4 kcal/mol, while luteolin was found to be the most promising candidate against AmpC beta-lactamase with an affinity of -8.5 kcal/mol. DFT analysis demonstrated the reactivity of these compounds, and the ADMET study indicated that they were relatively safe. CONCLUSION: In conclusion, the study's findings suggest that the selected compounds have significant potential to inhibit beta-lactamase and may be used in combination with antibiotics against organisms that produce beta-lactamase. This study provides a basis for further research in a wet-lab setting to validate the results.

Interrogating the microenvironmental landscape of tumors with computational image analysis approaches.

The tumor microenvironment is an interacting heterogeneous collection of cancer cells, resident as well as infiltrating host cells, secreted factors, and extracellular matrix proteins. With the growing importance of immunotherapies, it has become crucial to be able to characterize the composition and the functional orientation of the microenvironment. The development of novel computational image analysis methodologies may enable the robust quantification and localization of immune and related biomarker-expressing cells within the microenvironment. The aim of the review is to concisely highlight a selection of current and significant contributions pertinent to methodological advances coupled with biomedical or translational applications. A further aim is to concisely present computational advances that, to our knowledge, have currently very limited use for the assessment of the microenvironment but have the potential to enhance image analysis pipelines; on this basis, an example is shown for the detection and segmentation of cells of the microenvironment using a published pipeline and a public dataset. Finally, a general proposal is presented on the conceptual design of automation-optimized computational image analysis workflows in the biomedical and clinical domain.

Trypanosoma cruzi killing and immune response boosting by novel phenoxyhydrazine-thiazole against Chagas disease.

Trypanosoma cruzi (T. cruzi) causes Chagas, which is a neglected tropical disease (NTD). WHO estimates that 6 to 7 million people are infected worldwide. Current treatment is done with benznidazole (BZN), which is very toxic and effective only in the acute phase of the disease. In this work, we designed, synthesized, and characterized thirteen new phenoxyhydrazine-thiazole compounds and applied molecular docking and in vitro methods to investigate cell cytotoxicity, trypanocide activity, nitric oxide (NO) production, cell death, and immunomodulation. We observed a higher predicted affinity of the compounds for the squalene synthase and 14-alpha demethylase enzymes of T. cruzi. Moreover, the compounds displayed a higher predicted affinity for human TLR2 and TLR4, were mildly toxic in vitro for most mammalian cell types tested, and LIZ531 (IC50 2.8 µM) was highly toxic for epimastigotes, LIZ311 (IC50 8.6 µM) for trypomastigotes, and LIZ331 (IC50 1.9 µM) for amastigotes. We observed that LIZ311 (IC50 2.5 µM), LIZ431 (IC50 4.1 µM) and LIZ531 (IC50 5 µM) induced 200 µg/mL of NO and JM14 induced NO production in three different concentrations tested. The compound LIZ331 induced the production of TNF and IL-6. LIZ311 induced the secretion of TNF, IFNγ, IL-2, IL-4, IL-10, and IL-17, cell death by apoptosis, decreased acidic compartment formation, and induced changes in the mitochondrial membrane potential. Taken together, LIZ311 is a promising anti-T. cruzi compound is not toxic to mammalian cells and has increased antiparasitic activity and immunomodulatory properties.

AI-Driven Deep Learning Techniques in Protein Structure Prediction.

Protein structure prediction is important for understanding their function and behavior. This review study presents a comprehensive review of the computational models used in predicting protein structure. It covers the progression from established protein modeling to state-of-the-art artificial intelligence (AI) frameworks. The paper will start with a brief introduction to protein structures, protein modeling, and AI. The section on established protein modeling will discuss homology modeling, ab initio modeling, and threading. The next section is deep learning-based models. It introduces some state-of-the-art AI models, such as AlphaFold (AlphaFold, AlphaFold2, AlphaFold3), RoseTTAFold, ProteinBERT, etc. This section also discusses how AI techniques have been integrated into established frameworks like Swiss-Model, Rosetta, and I-TASSER. The model performance is compared using the rankings of CASP14 (Critical Assessment of Structure Prediction) and CASP15. CASP16 is ongoing, and its results are not included in this review. Continuous Automated Model EvaluatiOn (CAMEO) complements the biennial CASP experiment. Template modeling score (TM-score), global distance test total score (GDT_TS), and Local Distance Difference Test (lDDT) score are discussed too. This paper then acknowledges the ongoing difficulties in predicting protein structure and emphasizes the necessity of additional searches like dynamic protein behavior, conformational changes, and protein-protein interactions. In the application section, this paper introduces some applications in various fields like drug design, industry, education, and novel protein development. In summary, this paper provides a comprehensive overview of the latest advancements in established protein modeling and deep learning-based models for protein structure predictions. It emphasizes the significant advancements achieved by AI and identifies potential areas for further investigation.

Computational Methods Reveal a Series of Cyclic and Linear Lichenysins and Surfactins from the Vietnamese Marine Sediment-Derived Streptomyces Strain G222.

The Streptomyces strain G222, isolated from a Vietnamese marine sediment, was confidently identified by 16S rRNA gene sequencing. Its AcOEt crude extract was successfully analyzed using non-targeted LC-MS/MS analysis, and molecular networking, leading to a putative annotation of its chemical diversity thanks to spectral libraries from GNPS and in silico metabolite structure prediction obtained from SIRIUS combined with the bioinformatics tool conCISE (Consensus Annotation Propagation of in silico Elucidations). This dereplication strategy allowed the identification of an interesting cluster of a series of putative cyclic and linear lipopeptides of the lichenysin and surfactin families. Lichenysins (3-7) were isolated from the sub-fraction, which showed significant anti-biofilm activity against Pseudomonas aeruginosa MUC-N1. Their structures were confirmed by detailed 1D and 2D NMR spectroscopy (COSY, HSQC, HMBC, TOCSY, ROESY) recorded in CD3OH, and their absolute configurations were determined using the modified Marfey's method. The isolated lichenysins showed anti-biofilm activity at a minimum concentration of 100 µM. When evaluated for antibacterial activity against a panel of Gram-positive and Gram-negative strains, two isolated lichenysins exhibited selective activity against the MRSA strain without affecting its growth curve and without membranotropic activity. This study highlights the power of the MS/MS spectral similarity strategy using computational methods to obtain a cross-validation of the annotated molecules from the complex metabolic profile of a marine sediment-derived Streptomyces extract. This work provides the first report from a Streptomyces strain of combined cyclic and linear lichenysins and surfactins, known to be characteristic compounds of the genus Bacillus.