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TMVisDB: Annotation and 3D-visualization of Transmembrane Proteins

Journal of Molecular biology 2025年 168997页

作者： Olenyi, Tobias Marquet, Céline Grekova, Anastasia Houri, Leen Heinzinger, Michael Dallago, Christian Rost, Burkhard School of Computation Information and Technology (CIT) Faculty of Informatics Chair of Bioinformatics & Computational Biology TUM (Technical University of Munich) Garching/Munich 85748 Germany Structural and Computational Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg 69117 Germany TUM School of Life Sciences Weihenstephan (WZW) Alte Akademie 8 Freising Germany NVIDIA DE GmbH Einsteinstraße 172 Munich 81677 Germany

Since the rise of cellular life, transmembrane proteins (TMPs) have been crucial to various cellular processes through their central role as gates and gatekeepers. Despite their importance, experimental high-resolution structures for TMPs remain underrepresented due to experimental challenges. Given its performance leap, structure predictions have begun to close the gap. However, identifying the membrane regions and topology in three-dimensional structure files on a large scale still requires additional in silico predictions. Here, we introduce TMVisDB to sieve through millions of predicted structures for TMPs. This resource enables both browsing through 46 million predicted TMPs and visualizing them along with their topological annotations without having to tap into costly predictions of the AlphaFold3-style. TMVisDB joins AlphaFoldDB structure predictions and transmembrane topology predictions from the protein language model (pLM) based method TMbed. We showcase the utility of TMVisDB for the analysis of proteins through two use cases, namely the B-lymphocyte antigen CD20 (Homo sapiens) and the cellulose synthase (Novosphingobium sp. P6W). We demonstrate the value of TMVisDB for large-scale analyses through findings pertaining to all TMPs predicted for the human proteome. TMVisDB is freely available at https://***. © 2025 The Author(s)

关键词： AlphaFold protein language models transmembrane protein prediction transmembrane topology

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Comparative study of the gut microbiomes between Western and Indigenous cultures – Implications for health and disease

Microbe (Netherlands)

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Microbe (Netherlands) 2025年 7卷

作者： Sreenikitha K Das, Santanu Andrighetti, Tahila Sudhakar, Padhmanand Molecular Biology and Microbial Biotechnology Laboratory Division of Life Sciences Institute of Advanced Study in Science and Technology (IASST) Guwahati India Department of Molecular Biology and Biotechnology Cotton University Guwahati India Laboratory of Structural Bioinformatics and Computational Biology (SBCB) Institute of Informatics Federal University of Rio Grande do Sul (UFRGS) Av. Bento Gonçalves 9500 RS Porto Alegre 91501-970 Brazil Department of Biotechnology Kumaraguru College of Technology Tamil Nadu Coimbatore India

The mammalian gut microbiome plays a critical role in multiple functions related to health and a large number of metabolic and chronic diseases/disorders. Recent studies have profiled the influence of lifestyles and dietary behavior by comparing the gut microbiome of populations with different cultural underpinnings. We aim to provide an overview of the studies that report the influence on the gut microbial composition of dietary and lifestyle patterns in different contexts such as Western industrialized countries and Indigenous cultures, and how this association may influence health and disease. An electronic search was performed on PubMed using defined search terms focused on the gut microbiome, diets, lifestyles, traditional/Indigenous populations, western/industrialized populations, human nutrition, disease, and health. Based on research articles profiling the gut microbiomes of healthy subjects from twelve human populations with different cultural backgrounds, dietary patterns, and lifestyles, we identified differentially enriched bacterial taxa. None of the taxa were found to be common to all the populations although taxa such as Prevotella was abundant in eight of the twelve populations. We also found additional independent evidence linking altered bacterial taxa to human health and disease outcomes, including ailments of the gastrointestinal tract such as Crohn's disease and Ulcerative colitis. © 2025 The Authors

关键词： Diet Gastrointestinal diseases Gut health Gut microbiome Industrialized/non-industrialized/traditional populations

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ConsensuSV-ONT – A modern method for accurate structural variant calling

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Scientific Reports 2025年第1期15卷 1-9页

作者： Pietryga, Antoni Chiliński, Mateusz Gadakh, Sachin Plewczynski, Dariusz Laboratory of Bioinformatics and Computational Genomics Faculty of Mathematics and Information Science Warsaw University of Technology Warsaw Poland Faculty of Electrical Engineering Warsaw University of Technology Warsaw Poland Laboratory of Functional and Structural Genomics Centre of New Technologies University of Warsaw Warsaw Poland Section for Computational and RNA Biology Department of Biology University of Copenhagen Copenhagen Denmark

Improvements in sequencing technology make the development of new tools for detection of structural variance more and more common. However, since the tools available for the long-read Oxford Nanopore sequencing are limited, and the selection of the optimal tool presents a challenge, there is a need to create a tool based on Consensus that combines existing work to identify a set of high-quality, reliable structural variants that can be used for further downstream analysis. The field has also been subject to revolution in machine learning techniques, especially deep learning. To address the aforementioned need and developments, we propose a novel, fully automated ConsensuSV-ONT algorithm. The method uses six independent, state-of-the-art structural variant callers for long-read sequencing along with a convolutional neural network for filtering high-quality variants. We provide a runtime environment in the form of a docker image, wrapping a nextflow pipeline for efficient processing using parallel computing. The solution is complete in its form and is ready to use not only by computer scientists but accessible and easy to use for everyone working with Oxford Nanopore long-read sequencing data. © The Author(s) 2025.

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Patulin interference with ATP binding cassette transferring auto inducer −2 in Salmonella typhi and biofilm inhibition via quorum sensing

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Informatics in Medicine Unlocked 2018年 11卷 9-14页

作者： Vijayababu, Princy Samykannu, Gopinath Antonyraj, Christian Bharathi Thomas, Jebastin Narayanan, SundaraBaalaji Basheer Ahamed, Syed Ibrahim Piramanayagam, Shanmughavel Structural Biology Laboratory Department of Bioinformatics Bharathiar University Coimbatore Tamil Nadu India Centre for Bioinformatics Pondicherry University Pondicherry India Computational Biology Laboratory Department of Bioinformatics Bharathiar University Coimbatore Tamil Nadu India

Salmonella typhi are Gram-negative pathogens that infect many hosts including humans and animals and cause diseases ranging from gastroenteritis and diarrhea to life-threatening systemic syndrome. Owing to the metabolic versatility, they will colonize as multi cellular aggregates on various surfaces to enhance the virulence by forming a biofilm in which bacterial cells are more resistant to antibiotics than planktonic cells. Quorum Sensing (QS) is a cell-to-cell communication mechanism in the bacterial system to coordinate group behaviors such as biofilms formation and virulence factors production. In QS system ATP Binding Cassette (ABC) transporter component, LsrA, plays a key role to transport autoinducer-2 (AI-2) to increase the cell density. In order to reduce biofilm formation, patulin was selected as a natural QS inhibitor and its function was studied by biofilm inhibitory assays. Significant differences in the spectroscopic values were obtained between antibiotic resistance of kanamycin (30 μg/ml) and patulin (30 μg/ml). Furthermore, to distinguish the molecular level interaction of LsrA, patulin and AI-2 were docked. Both the compounds were interact in the same pose with Glide score of −4.237 kcal/mol and −7.126 kcal/mol respectively. These results will suggest that patulin is the efficient Quorum Sensing Inhibitor to control biofilm formation in ***. © 2018

关键词： AI-2 LsrA Patulin Quorum sensing Salmonella typhi

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PDB-IHM: A System for Deposition, Curation, Validation, and Dissemination of Integrative Structures

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Journal of Molecular biology 2025年 168963页

作者： Vallat, Brinda Webb, Benjamin M. Zalevsky, Arthur Tangmunarunkit, Hongsuda Sekharan, Monica R. Voinea, Serban Shafaeibejestan, Aref Sagendorf, Jared Hoch, Jeffrey C. Kurisu, Genji Morris, Kyle L. Velankar, Sameer Kesselman, Carl Burley, Stephen K. Berman, Helen M. Sali, Andrej Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine Rutgers The State University of New Jersey Piscataway 08854 NJ United States Rutgers Cancer Institute Rutgers The State University of New Jersey New Brunswick 08901 NJ United States Research Collaboratory for Structural Bioinformatics Protein Data Bank Department of Bioengineering and Therapeutic Sciences Quantitative Biosciences Institute (QBI) Department of Pharmaceutical Chemistry University of California San Francisco. San Francisco 94157 CA United States Information Sciences Institute Viterbi School of Engineering University of Southern California Los Angeles CA United States Biological Magnetic Resonance Data Bank Department of Molecular Biology and Biophysics University of Connecticut Farmington 06030-3305 CT United States Protein Data Bank Japan Institute for Protein Research Osaka University Osaka Suita 565-0871 Japan Electron Microscopy Data Bank European Molecular Biology Laboratory European Bioinformatics Institute Cambridge Hinxton CB10 1SD United Kingdom Protein Data Bank in Europe European Molecular Biology Laboratory European Bioinformatics Institute Cambridge Hinxton CB10 1SD United Kingdom Research Collaboratory for Structural Bioinformatics Protein Data Bank San Diego Supercomputer Center University of California La Jolla 92093 CA United States Department of Chemistry and Chemical Biology Rutgers The State University of New Jersey Piscataway 08854 NJ United States Rutgers Artificial Intelligence and Data Science (RAD) Collaboratory Rutgers The State University of New Jersey Piscataway 08854 NJ United States Department of Quantitative and Computational Biology University of Southern California Los Angeles 90089 CA United States

Structures of many large biomolecular assemblies are now being determined using integrative approaches. In these approaches, information derived from multiple experimental and computational methods is combined to compute three-dimensional structures of multi-protein complexes and other macromolecular machines. A standalone prototype data resource for integrative structures called PDB-Dev was built, based on recommendations of the Integrative and Hybrid Methods (IHM) Task Force of the Worldwide Protein Data Bank (wwPDB). This effort included developing data standards and software tools for collecting, curating, validating, visualizing, archiving, and disseminating integrative structures that span diverse spatiotemporal scales and conformational states. Mechanisms have been created to validate integrative structures based on the experimental data underpinning them. Building upon this foundational framework, PDB-Dev has been further expanded to handle large dynamic macromolecular systems and integrative structures that combine, for example, experimental restraints with atomic coordinates computed by machine learning algorithms. Data standards and supporting tools have also been extended to capture information about biomolecular dynamics, such as conformational transitions and related kinetic data derived from biophysical methods. Recently, PDB-Dev was unified with the PDB archive and rebranded as PDB-IHM (***), further promoting FAIR (Findable, Accessible, Interoperable, and Reusable) principles of data stewardship for integrative structural biology. © 2025 The Author(s)

关键词： IHMCIF integrative modeling PDB PDBx/mmCIF structure validation

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GFF-Ex: A genome feature extraction package

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BMC Research Notes 2014年第1期7卷 1-3页

作者： Rastogi, Achal Gupta, Dinesh Bioinformatics Laboratory Structural and Computational Biology Group International Center for Genetic Engineering and Biotechnology New Delhi 110067 Aruna Asaf Ali Marg India

Background: Genomic features of whole genome sequences emerging from various sequencing and annotation projects are represented and stored in several formats. Amongst these formats, the GFF (Generic/General Feature Format) has emerged as a widely accepted, portable and successfully used flat file format for genome annotation storage. With an increasing interest in genome annotation projects and secondary and meta-analysis, there is a need for efficient tools to extract sequences of interests from GFF files. Findings. We have developed GFF-Ex to automate feature-based extraction of sequences from a GFF file. In addition to automated sequence extraction of the features described within a feature file, GFF-Ex also assigns boundaries for the features (introns, intergenic, regions upstream to genes), which are not explicitly specified in the GFF format, and exports the corresponding primary sequence information into predefined feature specific output files. GFF-Ex package consists of several UNIX Shell and PERL scripts. Conclusions: Compared to other available GFF parsers, GFF-Ex is a simpler tool, which permits sequence retrieval based on additional inferred features. GFF-Ex can also be integrated with any genome annotation or analysis pipeline. GFF-Ex is freely available at. © 2014 Rastogi and Gupta;licensee BioMed Central Ltd.

关键词： Annotation Genomics GFF Sequence parser

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Dormancy heterogeneity among Arabidopsis thaliana seeds is linked to individual seed size

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Plant Communications 2024年第2期5卷 215-227页

作者： Michal Krzyszton Sebastian P.Sacharowski Veena Halale Manjunath Katarzyna Muter Grzegorz Bokota Ce Wang Dariusz Plewczynski Tereza Dobisova Szymon Swiezewski Laboratory of Seeds Molecular Biology Institute of Biochemistry and BiophysicsPAS02-106 WarsawPoland Laboratory of Functional and Structural Genomics Centre of New TechnologiesUniversity of WarsawWarsawPoland Laboratory of Bioinformatics and Computational Genomics Faculty of Mathematics and Information ScienceWarsaw University of TechnologyWarsawPoland Labdeers Ltd. 680-01 BoskoviceCzech Republic

Production of morphologically and physiologically variable seeds is an important strategy that helps plants to survive in unpredictable natural ***,the model plant Arabidopsis thaliana and most agronomically essential crops produce visually homogenous *** automated phenotype analysis,we observed that small seeds in Arabidopsis tend to have higher primary and secondary dormancy levels than large *** analysis revealed distinct gene expression profiles between large and small *** seeds have higher expression of translation-related genes implicated in germination *** contrast,small seeds have elevated expression of many positive regulators of dormancy,including a key regulator of this process,the DOG1 *** in DOG1 expression are associated with differential production of its alternative cleavage and polyadenylation isoforms;in small seeds,the proximal poly(A)site is selected,resulting in a short mRNA ***,single-seed RNA sequencing analysis demonstrated that large seeds resemble DOG1 knockout mutant ***,on the single-seed level,expression of genes affected by seed size is correlated with expression of genes that position seeds on the path toward *** results demonstrate an unexpected link between seed size and dormancy phenotypes in a species that produces highly homogenous seed pools,suggesting that the correlation between seed morphology and physiology is more widespread than initially assumed.

关键词： seed size dormancy DOG1 Arabidopsis

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Predicting dynamic cellular protein-RNA interactions by deep learning using in vivo RNA structures

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细胞研究（英文版） 2021年第5期31卷 495-516页

作者： Lei Sun Kui Xu Wenze Huang Yucheng T.Yang Pan Li Lei Tang Tuanlin Xiong Qiangfeng Cliff Zhang MOE Key Laboratory of Bioinformatics Beijing Advanced Innovation Center for Structural Biology and Frontier Research Center for Biological StructureCenter for Synthetic and Systems BiologySchool of Life SciencesTsinghua UniversityBeijing 100084China Tsinghua-Peking Center for Life Sciences Beijing 100084China Program in Computational Biology and Bioinformatics Yale UniversityNew HavenCT 06520USA Department of Molecular Biophysics and Biochemistry Yale UniversityNew HavenCT 06520USA

Interactions with RNA-binding proteins (RBPs) are integral to RNA function and cellular regulation,and dynamically reflect specific cellular ***,presently available tools for predicting RBP-RNA interactions employ RNA sequence and/or predicted RNA structures,and therefore do not capture their condition-dependent ***,after profiling transcriptome-wide in vivo RNA secondary structures in seven cell types,we developed PrismNet,a deep learning tool that integrates experimental in vivo RNA structure data and RBP binding data for matched cells to accurately predict dynamic RBP binding in various cellular *** results for 168 RBPs support its utility for both understanding CUP-seq results and largely extending such interaction data to accurately analyze additional cell ***,PrismNet employs an 'attention' strategy to computationally identify exact RBP-binding nucleotides,and we discovered enrichment among dynamic RBP-binding sites for structure-changing variants(riboSNitches),which can link genetic diseases with dysregulated RBP *** rich profiling data and deep learning-based prediction tool provide access to a previously inaccessible layer of cell-type-specific RBP-RNA interactions,with clear utility for understanding and treating human diseases.

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Accurate Prediction of Stability Changes in Bacteriophage T4 Lysozyme upon Single Amino Acid Replacements

Accurate Prediction of Stability Changes in Bacteriophage T4...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Majid Masso Tariq Alsheddi Iosif I. Vaisman Laboratory for Structural Bioinformatics Department of Bioinformatics and Computational Biology George Mason University Manassas VA USA

A computational mutagenesis methodology utilizing a four-body, knowledge-based, statistical contact potential is applied toward quantifying sequence-structure compatibility changes in bacteriophage T4 lysozyme upon single amino acid replacements. We show that these scalar scores correlate with experimentally measured stability changes to the protein due to the mutations. For each mutant, the approach also generates a vector of environmental perturbations occurring at every position in the protein. Implementation of the random forest algorithm, utilizing 521 experimental T4 lysozyme mutants each represented by its respective perturbation vector, correctly classifies mutants based on the direction of stability change with 88% cross-validation accuracy and 0.70 Matthew's correlation coefficient while achieving 0.91 area under the receiver operating characteristic curve. Learning curves are presented and reveal the dependence of training set size on model performance. The trained random forest model is used to infer stability changes for all remaining unexplored T4 lysozyme mutants.

关键词： Amino acids Protein engineering Thermal stability bioinformatics Network address translation Genetic mutations Crystallography Distributed computing Laboratories computational biology

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An evolutionary multi-agent algorithm to explore the high degree of selectivity in three-dimensional protein structures

An evolutionary multi-agent algorithm to explore the high de...

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2017 IEEE Congress on Evolutionary Computation, CEC 2017

作者： De Lima Corrêa, Leonardo Inostroza-Ponta, Mario Dorn, Márcio Institute of Informatics Laboratory of Structural Bioinformatics and Computational Biology Federal University of Rio Grande Do sul Porto Alegre- RS Brazil Departamento de Ingenieria Informatica Center for Biotechnology and Bioengineering Universidad de Santiago de Chile Santiago Chile

ISBN: (纸本)9781509046010

Tertiary protein structure prediction in silico is one of the most challenging problems in structural bioinformatics. The challenge arises due to the combinatorial explosion of plausible shapes, where a long amino acid chain ends up in one out of a vast number of three-dimensional conformations. The rules that govern the biological process are partially known, which difficult the development of robust prediction methods. Many computational methods and strategies were proposed over the last decades. Nevertheless, the problem remains open. The agent-based paradigm has been shown a useful technique for the applications that have repetitive and time-consuming activities, knowledge share and management, such as the integration of different knowledge sources and modeling of complex biological systems. In this paper, we propose a first principle method with database information for the 3-D protein structure prediction problem. We do so by designing a multi-agent approach that uses concepts of evolutionary algorithms to speed up the search phase by improving local candidate solutions from the protein conformational space. To validate our method, we tested our computational strategy on a test bed of eight protein sequences. Predicted structures were analyzed regarding root-mean-square deviation, global distance total score test and secondary structure arrangement. The obtained results were topologically compatible with their correspondent experimental structures, thus corroborating the effectiveness of our proposed method. As observed, the evolutionary multi-agent approach achieved good results in terms of the evaluted measures and was able to efficiently search the roughness of protein energy landscape. © 2017 IEEE.

关键词： Evolutionary algorithms

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