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Homology modeling of 3d structure of human chitinase-like protein CHI3L2

CENTRAL EUROPEAN JOURNAL OF BIOLOGY 2010年第4期5卷 407-420页

作者： Iershov, Anton Odynets, Konstantin Kornelyuk, Alexander Kavsan, Vadim Natl Acad Sci Ukraine Dept Biosynth Nucle Acids Inst Mol Biol & Genet UA-03680 Kiev Ukraine Natl Acad Sci Ukraine Dept Prot Engn & Bioinformat Inst Mol Biol & Genet UA-03680 Kiev Ukraine

The human genome encodes six proteins of family 18 glycosyl hydrolases, two active chitinases and four chitinase-like lectins (chi-lectins) lacking catalytic activity. The present article is dedicated to homology modeling of 3d structure of human chitinase 3-like 2 protein (CHI3L2), which is overexpressed in glial brain tumors, and its structural comparison with homologous chi-lectin CHI3L1. Two crystal structures of CHI3L1 in free state (protein data Bank codes 1HJX and 1NWR) were used as structural templates for the homology modeling by Modeller 9.7 program, and the best quality model structure was selected from the obtained model ensemble. Analysis of potential oligosaccharide-binding groove structures of CHI3L1 and CHI3L2 revealed significant differences between these two homologous proteins. 8 of 19 amino acid residues important for ligand binding are substituted in CHI3L2: Tyr(34)/Asp(39), Trp(69)/Lys(74), Trp(71)/Lys(76), Trp(99)/Tyr(104), Asn(100)/Leu(105), Met(204)/Leu(210), Tyr(206)/Phe(212) and Arg(263)/His(271). The differences between these residues could influence the structure of the ligand-binding groove and substantially change the ability of CHI3L2 to bind oligosaccharide ligands.

关键词： Chitinase 3-like 2 protein (CHI3L2, YKL-39) Chitinase 3-like 1 protein (CHI3L1, YKL-40) Chi-lectins Homology modeling protein 3d structure Oligosaccharide binding

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Probing protein 3d structures and Conformational Changes Using Electrochemistry-Assisted Isotope Labeling Cross-Linking Mass Spectrometry

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JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2016年第5期27卷 864-875页

作者： Zheng, Qiuling Zhang, Hao Wu, Shiyong Chen, Hao Ohio Univ Dept Chem & Biochem Edison Biotechnol Inst Ctr Intelligent Chem Instrumentat Athens OH 45701 USA Washington Univ Dept Chem St Louis MO 63130 USA

This study presents a new chemical cross-linking mass spectrometry (MS) method in combination with electrochemistry and isotope labeling strategy for probing both protein three-dimensional (3d) structures and conformational changes. For the former purpose, the target protein/protein complex is cross-linked with equal mole of premixed light and heavy isotope labeled cross-linkers carrying electrochemically reducible disulfide bonds (i.e., dSP-d(0) and dSP-d(8) in this study, dSP = dithiobis[succinimidyl propionate]), digested and then electrochemically reduced followed with online MS analysis. Cross-links can be quickly identified because of their reduced intensities upon electrolysis and the presence of doublet isotopic peak characteristics. In addition, electroreduction converts cross-links into linear peptides, facilitating MS/MS analysis to gain increased information about their sequences and modification sites. For the latter purpose of probing protein conformational changes, an altered procedure is adopted, in which the protein in two different conformations is cross-linked using dSP-d(0) and dSP-d(8) separately, and then the two protein samples are mixed in 1:1 molar ratio. The merged sample is subjected to digestion and electrochemical mass spectrometric analysis. In such a comparative cross-linking experiment, cross-links could still be rapidly recognized based on their responses to electrolysis. More importantly, the ion intensity ratios of light and heavy isotope labeled cross-links reveal the conformational changes of the protein, as exemplified by examining the effect of Ca2+ on calmodulin conformation alternation. This new cross-linking MS method is fast and would have high value in structural biology.

关键词： protein 3d structure protein conformational change Isotope-labeling Chemical cross-linking Mass spectrometry Electrochemistry

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Persistent homology reveals strong phylogenetic signal in 3d protein structures

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PNAS NEXUS 2024年第4期3卷 pgae158页

作者： Bou dagher, Lea Madern, dominique Malbos, Philippe Brochier-Armanet, Celine Univ Claude Bernard Lyon 1 CNRS VetAgro Sup Lab Biometrie & Biol Evolut F-69622 Villeurbanne France Univ Claude Bernard Lyon 1 CNRS UMR5208 Inst Camille Jordan F-69622 Villeurbanne France Univ Libanaise Lab Math Ecole Doctorale Sci & Technol POB 5 Hadath Lebanon Univ Grenoble Alpes CEA CNRS IBS F-38000 Grenoble France

Changes that occur in proteins over time provide a phylogenetic signal that can be used to decipher their evolutionary history and the relationships between organisms. Sequence comparison is the most common way to access this phylogenetic signal, while those based on 3d structure comparisons are still in their infancy. In this study, we propose an effective approach based on Persistent Homology Theory (PH) to extract the phylogenetic information contained in protein structures. PH provides efficient and robust algorithms for extracting and comparing geometric features from noisy datasets at different spatial resolutions. PH has a growing number of applications in the life sciences, including the study of proteins (e.g. classification, folding). However, it has never been used to study the phylogenetic signal they may contain. Here, using 518 protein families, representing 22,940 protein sequences and structures, from 10 major taxonomic groups, we show that distances calculated with PH from protein structures correlate strongly with phylogenetic distances calculated from protein sequences, at both small and large evolutionary scales. We test several methods for calculating PH distances and propose some refinements to improve their relevance for addressing evolutionary questions. This work opens up new perspectives in evolutionary biology by proposing an efficient way to access the phylogenetic signal contained in protein structures, as well as future developments of topological analysis in the life sciences.

关键词： topological data analysis phylogenetics persistent homology protein 3d structure

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divide and conquer: cleavable cross-linkers to study protein conformation and protein-protein interactions

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ANALYTICAL ANd BIOANALYTICAL CHEMISTRY 2017年第1期409卷 33-44页

作者： Sinz, Andrea Martin Luther Univ Halle Wittenberg Inst Pharm Dept Pharmaceut Chem & Bioanalyt Wolfgang Langenbeck Str 4 D-06120 Halle Saale Germany

Chemical cross-linking combined with mass spectrometry (MS) and computational modeling has evolved as an alternative method to address fundamental questions in structural biology. The constraints revealed by the cross-links yield valuable distance information and allow one to deduce three-dimensional structural information on very large and transient protein complexes. during the past few years, technical advances in the cross-linking/MS approach have been enormous, mainly owing to the fantastic advances in MS technology, and it is easily overlooked that significant progress has been made in the design of novel cross-linking reagents. In this review, the advent of cleavable cross-linking reagents will be highlighted. In particular, gas-phase (MS-) cleavable cross-linkers offer unique properties for an automated, data-dependent assignment of cross-linked products based on the generation of characteristic fragment ion signatures in MS/MS and MS3 spectra. Therefore, MS-cleavable cross-linkers are envisioned to hold the key for proteome-wide applications of the chemical cross-linking/MS approach, not only to delineate the conformation of single proteins but also to decipher protein interaction networks.

关键词： Chemical cross-linking Cleavable cross-linkers Mass spectrometry protein 3d structure protein interaction networks

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deePROG: deep Attention-Based Model for diseased Gene Prognosis by Fusing Multi-Omics data

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY ANd BIOINFORMATICS 2022年第5期19卷 2770-2781页

作者： dutta, Pratik Patra, Aditya Prakash Saha, Sriparna Indian Inst Technol Dept Comp Sci & Engn Patna 801103 Bihar India

An in-depth exploration of gene prognosis using different methodologies aids in understanding various biological regulations of genes in disease pathobiology and molecular functions. Interpreting gene functions at biological and molecular levels remains a daunting yet crucial task in domains such as drug design, personalized medicine, and next-generation diagnostics. Recent advancements in omics technologies have produced diverse heterogeneous genomic datasets like micro-array gene expression, miRNA expression, dNA sequence, 3d structures, which are significant resources for understanding the gene functions. In this paper, we propose a novel self-attention based deep multi-modal model, named deePROG, for the prognosis of disease affected genes based on heterogeneous omics data. We use three NCBI datasets covering three modalities, namely gene expression profile, the underlying dNA sequence, and the 3d protein structures. To extract useful features from each modality, we develop several context-specific deep learning models. Besides, we develop three attention-based deep bi-modal architectures along with deePROG to leverage the prognosis of the underlying biomedical data. We assess the performance of the models' in terms of computational assessment of function annotation (CAFA2) metrics. Moreover, we analyze the results in terms of receiver operating characteristics (ROC) curve in high-class imbalance data setting and perform statistical significance tests in terms of Welch's t-test. Experiment results show that deePROG significantly outperforms baseline models across in terms of performance metrics. The source code and all preprocessed datasets used in this study are available at https://***/duttaprat/deePROG.

关键词： Gene expression diseases Three-dimensional displays Prognostics and health management Computer architecture proteins Feature extraction Multi-modal architecture gene prognosis gene expression profile protein 3d structure nucleotide sequence attention mechanism deep learning

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An ice nucleation protein from Fusarium acuminatum: cloning, expression, biochemical characterization and computational modeling

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BIOTECHNOLOGY LETTERS 2014年第10期36卷 2043-2051页

作者： Lagzian, Milad Latifi, Ali Mohammad Bassami, Mohammad Reza Mirzaei, Morteza Ferdowsi Univ Mashhad Fac Vet Med Div Biotechnol Mashhad Iran Ferdowsi Univ Mashhad Inst Biotechnol Mashhad Iran Baqiyatallah Univ Med Sci Appl Biotechnol Res Ctr Tehran Iran

Ice nucleation proteins (INP) are a major cause of frost damage in plants and crops. Here, an INP gene from Fusarium acuminatum was optimized, synthesized, expressed in *** and subsequently purified and characterized. The protein belongs to the second class of ice nucleation proteins with an optimum pH 5.5, relative activity and stability between pH 5 and 9.5 and up to 45 A degrees C. The protein was fully active and stable in the presence of dimethyl sulfoxide (dMSO), dioxane, acetone and ethyl acetate. Moreover, it retained over 50 % of its original activity in the presence of polyvinyl alcohol. The 3d structure model of the INP-F indicated the protein had three distinct domains as exist in other ice nucleation proteins with some variations. Considering these promising results, INP-F could be a novel candidate for industrial applications.

关键词： Cloning and expression Fusarium acuminatum Ice nucleation protein protein 3d structure

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Rinmaker: a fast, versatile and reliable tool to determine residue interaction networks in proteins

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BMC BIOINFORMATICS 2023年第1期24卷 1-14页

作者： Spano, Alvise Fanton, Lorenzo Pizzolato, davide Moi, Jacopo Vinci, Francesco Pesce, Alberto Foumthuim, Cedrix J. dongmo Giacometti, Achille Simeoni, Marta Univ Ca Foscari Venice Dept Environm Sci Comp Sci & Stat Via Torino 155 I-30172 Venice Italy Univ Ca Foscari Venice Dept Mol Sci & Nanosyst Via Torino 155 I-30172 Venice Italy European Ctr Living Technol ECLT Dorsoduro 3911 I-30123 Venice Italy

Background: Residue Interaction Networks (RINs) map the crystallographic description of a protein into a graph, where amino acids are represented as nodes and noncovalent bonds as edges. determination and visualization of a protein as a RIN provides insights on the topological properties (and hence their related biological functions) of large proteins without dealing with the full complexity of the three-dimensional description, and hence it represents an invaluable tool of modern bioinformatics. Results: We present RINmaker, a fast, flexible, and powerful tool for determining and visualizing RINs that include all standard non-covalent interactions. RINmaker is offered as a cross-platform and open source software that can be used either as a command-line tool or through a web application or a web API service. We benchmark its efficiency against the main alternatives and provide explicit tests to show its performance and its correctness. Conclusions: RINmaker is designed to be fully customizable, from a simple and handy support for experimental research to a sophisticated computational tool that can be embedded into a large computational pipeline. Hence, it paves the way to bridge the gap between data-driven/machine learning approaches and numerical simulations of simple, physically motivated, models.

关键词： protein 3d structure Residue interaction network (RIN) Non-covalent bonds

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Characterizing Secretion System Effector proteins With structure-Aware Graph Neural Networks and Pre-Trained Language Models

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IEEE JOURNAL OF BIOMEdICAL ANd HEALTH INFORMATICS 2024年第9期28卷 5649-5657页

作者： Ran, Zixu Wang, Cong Sun, Heyun Pan, Shirui Li, Fuyi Northwest A&F Univ Coll Informat Engn Xianyang 712100 Peoples R China Univ Adelaide South Australian immunoGEN Canc Inst SAiGENCI Adelaide SA 5000 Australia Griffith Univ Sch Informat & Commun Technol Brisbane Qld 4222 Australia

The Type III Secretion Systems (T3SSs) play a pivotal role in host-pathogen interactions by mediating the secretion of type III secretion system effectors (T3SEs) into host cells. These T3SEs mimic host cell protein functions, influencing interactions between Gram-negative bacterial pathogens and their hosts. Identifying T3SEs is essential in biomedical research for comprehending bacterial pathogenesis and its implications on human cells. This study presents EdIFIER, a novel multi-channel model designed for accurate T3SE prediction. It incorporates a graph structural channel, utilizing graph convolutional networks (GCN) to capture protein 3d structural features and a sequence channel based on the proteinBERT pre-trained model to extract the sequence context features of T3SEs. Rigorous benchmarking tests, including ablation studies and comparative analysis, validate that EdIFIER outperforms current state-of-the-art tools in T3SE prediction. To enhance EdIFIER's accessibility to the broader scientific community, we developed a webserver that is publicly accessible at http://***/. We anticipate EdIFIER will contribute to the field by providing reliable T3SE predictions, thereby advancing our understanding of host-pathogen dynamics.

关键词： proteins Feature extraction Three-dimensional displays Amino acids Solvents Bioinformatics Benchmark testing deep learning host-pathogen interaction protein 3d structure secreted protein

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Homology modeling in the time of collective and artificial intelligence

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COMPUTATIONAL ANd STRUCTURAL BIOTECHNOLOGY JOURNAL 2020年 18卷 3494-3506页

作者： Hameduh, Tareq Haddad, Yazan Adam, Vojtech Heger, Zbynek Mendel Univ Brno Dept Chem & Biochem Zemedelska 1 CZ-61300 Brno Czech Republic Brno Univ Technol Cent European Inst Technol Purkynova 656-123 Brno 61200 Czech Republic

Homology modeling is a method for building protein 3d structures using protein primary sequence and utilizing prior knowledge gained from structural similarities with other proteins. The homology modeling process is done in sequential steps where sequence/structure alignment is optimized, then a backbone is built and later, side-chains are added. Once the low-homology loops are modeled, the whole 3d structure is optimized and validated. In the past three decades, a few collective and collaborative initiatives allowed for continuous progress in both homology and ab initio modeling. Critical Assessment of protein structure Prediction (CASP) is a worldwide community experiment that has historically recorded the progress in this field. Folding@Home and Rosetta@Home are examples of crowd-sourcing initiatives where the community is sharing computational resources, whereas RosettaCommons is an example of an initiative where a community is sharing a codebase for the development of computational algorithms. Foldit is another initiative where participants compete with each other in a protein folding video game to predict 3d structure. In the past few years, contact maps deep machine learning was introduced to the 3d structure prediction process, adding more information and increasing the accuracy of models significantly. In this review, we will take the reader in a journey of exploration from the beginnings to the most recent turnabouts, which have revolutionized the field of homology modeling. Moreover, we discuss the new trends emerging in this rapidly growing field. (C) 2020 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.

关键词： Homology modeling Machine learning protein 3d structure Structural bioinformatics Collective intelligence Artificial intelligence

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On the functional and structural characterization of hubs in protein-protein interaction networks

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BIOTECHNOLOGY AdVANCES 2013年第2期31卷 274-286页

作者： Bertolazzi, Paola Bock, Mary Ellen Guerra, Concettina IASI CNR Rome Italy Purdue Univ Dept Stat W Lafayette IN 47907 USA Georgia Inst Technol Coll Comp Atlanta GA 30332 USA

A number of interesting issues have been addressed on biological networks about their global and local properties. The connection between the topological properties of proteins in protein-protein Interaction (PPI) networks and their biological relevance has been investigated focusing on hubs, i.e. proteins with a large number of interacting partners. We will survey the literature trying to answer the following questions: do hub proteins have special biological properties? do they tend to be more essential than non-hub proteins? Are they more evolutionarily conserved? do they play a central role in modular organization of the protein interaction network? Are there structural properties that characterize hub proteins? (C) 2012 Elsevier Inc. All rights reserved.

关键词： protein-protein interaction networks Network topology Hubs protein 3d structure Evolution Lethality Structural disorder

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