Computational and Structural Biotechnology Journal

Journal: Computational and Structural Biotechnology Journal

Publisher

Elsevier

ISSN

2001-0370

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Publications 1 - 10 of 10

G–PLIP: Knowledge graph neural network for structure-free protein–ligand bioactivity prediction
Crouzet, Simon J.; Lieberherr, Anja Maria; Atz, Kenneth; et al. (2024)
Computational and Structural Biotechnology Journal
Protein–ligand interactions (PLIs) determine the efficacy and safety profiles of small molecule drugs. Existing methods rely on either structural information or resource-intensive computations to predict PLI, casting doubt on whether it is possible to perform structure-free PLI predictions at low computational cost. Here we show that a light-weight graph neural network (GNN), trained with quantitative PLIs of a small number of proteins and ligands, is able to predict the strength of unseen PLIs. The model has no direct access to structural information about the protein–ligand complexes. Instead, the predictive power is provided by encoding the entire chemical and proteomic space in a single heterogeneous graph, encapsulating primary protein sequence, gene expression, the protein–protein interaction network, and structural similarities between ligands. This novel approach performs competitively with, or better than, structure-aware models. Our results suggest that existing PLI prediction methods may be improved by incorporating representation learning techniques that embed biological and chemical knowledge.
Dynamics in Transcriptomics: Advancements in RNA-seq Time Course and Downstream Analysis
Spies, Daniel; Ciaudo, Constance (2015)
Computational and Structural Biotechnology Journal
Analysis of gene expression has contributed to a plethora of biological and medical research studies. Microarrays have been intensively used for the profiling of gene expression during diverse developmental processes, treatments and diseases. New massively parallel sequencing methods, often named as RNA-sequencing (RNA-seq) are extensively improving our understanding of gene regulation and signaling networks. Computational methods developed originally for microarrays analysis can now be optimized and applied to genome-wide studies in order to have access to a better comprehension of the whole transcriptome. This review addresses current challenges on RNA-seq analysis and specifically focuses on new bioinformatics tools developed for time series experiments. Furthermore, possible improvements in analysis, data integration as well as future applications of differential expression analysis are discussed.
Introducing the CSP Analyzer: A novel Machine Learning-based application for automated analysis of two-dimensional NMR spectra in NMR fragment-based screening
Fino, Roberto; Byrne, Ryan; Softley, Charlotte A.; et al. (2020)
Computational and Structural Biotechnology Journal
NMR-based screening, especially fragment-based drug discovery is a valuable approach in early-stage drug discovery. Monitoring fragment-binding in protein-detected 2D NMR experiments requires analysis of hundreds of spectra to detect chemical shift perturbations (CSPs) in the presence of ligands screened. Computational tools are available that simplify the tracking of CSPs in 2D NMR spectra. However, to the best of our knowledge, an efficient automated tool for the assessment and binning of multiple spectra for ligand binding has not yet been described. We present a novel and fast approach for analysis of multiple 2D HSQC spectra based on machine-learning-driven statistical discrimination. The CSP Analyzer features a C# frontend interfaced to a Python ML classifier. The software allows rapid evaluation of 2D screening data from large number of spectra, reducing user-introduced bias in the evaluation. The CSP Analyzer software package is available on GitHub https://github.com/rubbs14/CSP-Analyzer/releases/tag/v1.0 under the GPL license 3.0 and is free to use for academic and commercial uses.
Iron deficiency triggered transcriptome changes in bread wheat
Wang, Meng; Gong, Jiazhen; Bhullar, Navreet Kaur (2020)
Computational and Structural Biotechnology Journal
A series of complex transport, storage and regulation mechanisms control iron metabolism and thereby maintain iron homeostasis in plants. Despite several studies on iron deficiency responses in different plant species, these mechanisms remain unclear in the allohexaploid wheat, which is the most widely cultivated commercial crop. We used RNA sequencing to reveal transcriptomic changes in the wheat flag leaves and roots, when subjected to iron limited conditions. We identified 5969 and 2591 differentially expressed genes (DEGs) in the flag leaves and roots, respectively. Genes involved in the synthesis of iron ligands i.e., nicotianamine (NA) and deoxymugineic acid (DMA) were significantly up-regulated during iron deficiency. In total, 337 and 635 genes encoding transporters exhibited altered expression in roots and flag leaves, respectively. Several genes related to MAJOR FACILITATOR SUPERFAMILY (MFS), ATP-BINDING CASSETTE (ABC) transporter superfamily, NATURAL RESISTANCE ASSOCIATED MACROPHAGE PROTEIN (NRAMP) family and OLIGOPEPTIDE TRANSPORTER (OPT) family were regulated, indicating their important roles in combating iron deficiency stress. Among the regulatory factors, the genes encoding for transcription factors of BASIC HELIX-LOOP-HELIX (bHLH) family were highly up-regulated in both roots and the flag leaves. The jasmonate biosynthesis pathway was significantly altered but with notable expression differences between roots and flag leaves. Homoeologs expression and induction bias analysis revealed subgenome specific differential expression. Our findings provide an integrated overview on regulated molecular processes in response to iron deficiency stress in wheat. This information could potentially serve as a guideline for breeding iron deficiency stress tolerant crops as well as for designing appropriate wheat iron biofortification strategies.
Leri: A web-server for identifying protein functional networks from evolutionary couplings
Cheung, Ngaam J.; John Peter, Arun Thomas; Kornmann, Benoit (2021)
Computational and Structural Biotechnology Journal
Information on the co-evolution of amino acid pairs in a protein can be used for endeavors such as protein engineering, mutation design, and structure prediction. Here we report a method that captures significant determinants of proteins using estimated co-evolution information to identify networks of residues, termed ”residue communities”, relevant to protein function. On the benchmark dataset (67 proteins with both catalytic and allosteric residues), the Pearson’s correlation between the identified residues in the communities at functional sites is 0.53, and it is higher than 0.8 by taking account of conserved residues derived from the method. On the endoplasmic reticulum-mitochondria encounter structure complex, the results indicate three distinguishable residue communities that are relevant to functional roles in the protein family, suggesting that the residue communities could be general evolutionary signatures in proteins. Based on the method, we provide a webserver for the scientific community to explore the signatures in protein families, which establishes a powerful tool to analyze residue-level profiling for the discovery of functional sites and biological pathway identification. This web-server is freely available for non-commercial users at https://kornmann.bioch.ox.ac.uk/leri/services/ecs.html, neither login nor e-mail required.
Structures to complement the archaeo-eukaryotic primases catalytic cycle description: What's next?
Boudet, Julien; Devillier, Jean-Christophe; Allain, Frédéric H.-T.; et al. (2015)
Computational and Structural Biotechnology Journal
DNA replication is a crucial stage in the transfer of genetic information from parent to daughter cells. This mechanism involves multiple proteins with one key player being the primase. Primases are single-stranded DNA dependent RNA polymerases. On the leading strand, they synthesize the primer once allowing DNA elongation while on the lagging strand primers are generated repeatedly (Okazaki fragments). Primases have the unique ability to create the first phosphodiester bond yielding a dinucleotide which is initially elongated by primases and then by DNA polymerases. Primase activity has been studied in the last decades but the detailed molecular steps explaining some unique features remain unclear. High-resolution structures of free and bound primases domains have brought significant insights in the understanding of the primase reaction cycle. Here, we give a short review of the structural work conducted in the field of archaeo-eukaryotic primases and we underline the missing “pictures” of the active forms of the enzyme which are of major interest. We organized our analysis with respect to the progression through the catalytic pathway.
Combining data integration and molecular dynamics for target identification in alpha-Synuclein-aggregating neurodegenerative diseases: Structural insights on Synaptojanin-1 (Synj1)
Jenkins, Kirsten; Mateeva, Teodora; Szabo, Istvan; et al. (2020)
Computational and Structural Biotechnology Journal
Parkinson's disease (PD), Alzheimer's disease (AD) and Amyotrophic lateral sclerosis (ALS) are neurodegenerative diseases hallmarked by the formation of toxic protein aggregates. However, targeting these aggregates therapeutically have thus far shown no success. The treatment of AD has remained particularly problematic since no new drugs have been approved in the last 15 years. Therefore, novel therapeutic targets need to be identified and explored. Here, through the integration of genomic and proteomic data, a set of proteins with strong links to alpha-synuclein-aggregating neurodegenerative diseases was identified. We propose 17 protein targets that are likely implicated in neurodegeneration and could serve as potential targets. The human phosphatidylinositol 5-phosphatase synaptojanin-1, which has already been independently confirmed to be implicated in Parkinson's and Alzheimer's disease, was among those identified. Despite its involvement in PD and AD, structural aspects are currently missing at the molecular level. We present the first atomistic model of the 5-phosphatase domain of synaptojanin-1 and its binding to its substrate phosphatidylinositol 4,5-bisphosphate (PIP2). We determine structural information on the active site including membrane-embedded molecular dynamics simulations. Deficiency of charge within the active site of the protein is observed, which suggests that a second divalent cation is required to complete dephosphorylation of the substrate. The findings in this work shed light on the protein's binding to phosphatidylinositol 4,5-bisphosphate (PIP2) and give additional insight for future targeting of the protein active site, which might be of interest in neurodegenerative diseases where synaptojanin-1 is overexpressed. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.
Measuring the microbiome: Best practices for developing and benchmarking microbiomics methods
Bokulich, Nicholas; Ziemski, Michal; Robeson II, Michael S.; et al. (2020)
Computational and Structural Biotechnology Journal
Microbiomes are integral components of diverse ecosystems, and increasingly recognized for their roles in the health of humans, animals, plants, and other hosts. Given their complexity (both in composition and function), the effective study of microbiomes (microbiomics) relies on the development, optimization, and validation of computational methods for analyzing microbial datasets, such as from marker-gene (e.g., 16S rRNA gene) and metagenome data. This review describes best practices for benchmarking and implementing computational methods (and software) for studying microbiomes, with particular focus on unique characteristics of microbiomes and microbiomics data that should be taken into account when designing and testing microbiomics methods.
Structural and functional characterization of the receptor binding proteins of Escherichia coli O157 phages EP75 and EP335
Witte, Sander; Zinsli, Léa V.; Gonzalez-Serrano, Rafael; et al. (2021)
Computational and Structural Biotechnology Journal
Tackling microbial threats in agriculture with integrative imaging and computational approaches
Singh, Nikhil K.; Dutta, Anik; Puccetti, Guido; et al. (2021)
Computational and Structural Biotechnology Journal
Pathogens and pests are one of the major threats to agricultural productivity worldwide. For decades, targeted resistance breeding was used to create crop cultivars that resist pathogens and environmental stress while retaining yields. The often decade-long process of crossing, selection, and field trials to create a new cultivar is challenged by the rapid rise of pathogens overcoming resistance. Similarly, antimicrobial compounds can rapidly lose efficacy due to resistance evolution. Here, we review three major areas where computational, imaging and experimental approaches are revolutionizing the management of pathogen damage on crops. Recognizing and scoring plant diseases have dramatically improved through high-throughput imaging techniques applicable both under well-controlled greenhouse conditions and directly in the field. However, computer vision of complex disease phenotypes will require significant improvements. In parallel, experimental setups similar to high-throughput drug discovery screens make it possible to screen thousands of pathogen strains for variation in resistance and other relevant phenotypic traits. Confocal microscopy and fluorescence can capture rich phenotypic information across pathogen genotypes. Through genome-wide association mapping approaches, phenotypic data helps to unravel the genetic architecture of stress- and virulence-related traits accelerating resistance breeding. Finally, joint, large-scale screenings of trait variation in crops and pathogens can yield fundamental insights into how pathogens face trade-offs in the adaptation to resistant crop varieties. We discuss how future implementations of such innovative approaches in breeding and pathogen screening can lead to more durable disease control.

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