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Artikel ; Online: Protein Condensates and Protein Aggregates:

Venko, Katja / Žerovnik, Eva

Frontiers in bioscience (Landmark edition)

2023 Band 28, Heft 8, Seite(n) 183

Abstract: Similar to other polypeptides and electrolytes, proteins undergo phase transitions, obeying physicochemical laws. They can undergo liquid-to-gel and liquid-to-liquid phase transitions. Intrinsically disordered proteins are particularly susceptible to ... ...

Abstract	Similar to other polypeptides and electrolytes, proteins undergo phase transitions, obeying physicochemical laws. They can undergo liquid-to-gel and liquid-to-liquid phase transitions. Intrinsically disordered proteins are particularly susceptible to phase separation. After a general introduction, the principles of
Mesh-Begriff(e)	Protein Aggregates ; Computational Biology ; Phase Transition ; Protein Domains
Chemische Substanzen	Protein Aggregates
Sprache	Englisch
Erscheinungsdatum	2023-09-04
Erscheinungsland	Singapore
Dokumenttyp	Journal Article ; Review ; Research Support, Non-U.S. Gov't
ZDB-ID	2704569-9
ISSN	2768-6698 ; 2768-6698
ISSN (online)	2768-6698
ISSN	2768-6698
DOI	10.31083/j.fbl2808183
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: Merging Counter-Propagation and Back-Propagation Algorithms: Overcoming the Limitations of Counter-Propagation Neural Network Models.

Drgan, Viktor / Venko, Katja / Sluga, Janja / Novič, Marjana

International journal of molecular sciences

2024 Band 25, Heft 8

Abstract: Artificial neural networks (ANNs) are nowadays applied as the most efficient methods in the majority of machine learning approaches, including data-driven modeling for assessment of the toxicity of chemicals. We developed a combined neural network ... ...

Abstract	Artificial neural networks (ANNs) are nowadays applied as the most efficient methods in the majority of machine learning approaches, including data-driven modeling for assessment of the toxicity of chemicals. We developed a combined neural network methodology that can be used in the scope of new approach methodologies (NAMs) assessing chemical or drug toxicity. Here, we present QSAR models for predicting the physical and biochemical properties of molecules of three different datasets: aqueous solubility, acute fish toxicity toward fat head minnow, and bio-concentration factors. A novel neural network modeling method is developed by combining two neural network algorithms, namely, the counter-propagation modeling strategy (CP-ANN) with the back-propagation-of-errors algorithm (BPE-ANN). The advantage is a short training time, robustness, and good interpretability through the initial CP-ANN part, while the extension with BPE-ANN improves the precision of predictions in the range between minimal and maximal property values of the training data, regardless of the number of neurons in both neural networks, either CP-ANN or BPE-ANN.
Mesh-Begriff(e)	Neural Networks, Computer ; Algorithms ; Animals ; Quantitative Structure-Activity Relationship ; Machine Learning
Sprache	Englisch
Erscheinungsdatum	2024-04-09
Erscheinungsland	Switzerland
Dokumenttyp	Journal Article
ZDB-ID	2019364-6
ISSN	1422-0067 ; 1422-0067 ; 1661-6596
ISSN (online)	1422-0067
ISSN	1422-0067 ; 1661-6596
DOI	10.3390/ijms25084156
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel ; Online: An In Silico Approach for Assessment of the Membrane Transporter Activities of Phenols: A Case Study Based on Computational Models of Transport Activity for the Transporter Bilitranslocase.

Venko, Katja / Novič, Marjana

Molecules (Basel, Switzerland)

2019 Band 24, Heft 5

Abstract: Phenols are the most abundant naturally accessible antioxidants present in a human normal diet. Since numerous beneficial applications of phenols as preventive agents in various diseases were revealed, the evaluation of phenols bioavailability is of high ...

Abstract	Phenols are the most abundant naturally accessible antioxidants present in a human normal diet. Since numerous beneficial applications of phenols as preventive agents in various diseases were revealed, the evaluation of phenols bioavailability is of high interest of researchers, consumers and drug manufacturers. The hydrophilic nature of phenols makes a cell membrane penetration difficult, which imply an alternative way of uptake via membrane transporters. However, the structural and functional data of membrane transporters are limited, thus the in silico modelling is really challenging and urgent tool in elucidation of transporter ligands. Focus of this research was a particular transporter bilitranslocase (BTL). BTL has a broad tissue expression (vascular endothelium, absorptive and excretory epithelia) and can transport wide variety of poly-aromatic compounds. With available BTL data (pKi [mmol/L] for 120 organic compounds) a robust and reliable QSAR models for BTL transport activity were developed and extrapolated on 300 phenolic compounds. For all compounds the transporter profiles were assessed and results show that dietary phenols and some drug candidates are likely to interact with BTL. Moreover, synopsis of predictions from BTL models and hits/predictions of 20 transporters from Metrabase and Chembench platforms were revealed. With such joint transporter analyses a new insights for elucidation of BTL functional role were acquired. Regarding limitation of models for virtual profiling of transporter interactions the computational approach reported in this study could be applied for further development of reliable in silico models for any transporter, if in vitro experimental data are available.
Mesh-Begriff(e)	Biological Transport ; Biological Transport, Active ; Cell Membrane/enzymology ; Ceruloplasmin/metabolism ; Computer Simulation ; Databases, Pharmaceutical ; Humans ; Phenols/metabolism
Chemische Substanzen	Phenols ; Ceruloplasmin (EC 1.16.3.1) ; bilitranslocase (EC 1.16.3.1)
Sprache	Englisch
Erscheinungsdatum	2019-02-27
Erscheinungsland	Switzerland
Dokumenttyp	Journal Article
ZDB-ID	1413402-0
ISSN	1420-3049 ; 1431-5165 ; 1420-3049
ISSN (online)	1420-3049
ISSN	1431-5165 ; 1420-3049
DOI	10.3390/molecules24050837
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel: Prediction of Transmembrane Regions, Cholesterol, and Ganglioside Binding Sites in Amyloid-Forming Proteins Indicate Potential for Amyloid Pore Formation.

Venko, Katja / Novič, Marjana / Stoka, Veronika / Žerovnik, Eva

Frontiers in molecular neuroscience

2021 Band 14, Seite(n) 619496

Abstract: Besides amyloid fibrils, amyloid pores (APs) represent another mechanism of amyloid induced toxicity. Since hypothesis put forward by Arispe and collegues in 1993 that amyloid-beta makes ion-conducting channels and that Alzheimer's disease may be due to ... ...

Abstract	Besides amyloid fibrils, amyloid pores (APs) represent another mechanism of amyloid induced toxicity. Since hypothesis put forward by Arispe and collegues in 1993 that amyloid-beta makes ion-conducting channels and that Alzheimer's disease may be due to the toxic effect of these channels, many studies have confirmed that APs are formed by prefibrillar oligomers of amyloidogenic proteins and are a common source of cytotoxicity. The mechanism of pore formation is still not well-understood and the structure and imaging of APs in living cells remains an open issue. To get closer to understand AP formation we used predictive methods to assess the propensity of a set of 30 amyloid-forming proteins (AFPs) to form transmembrane channels. A range of amino-acid sequence tools were applied to predict AP domains of AFPs, and provided context on future experiments that are needed in order to contribute toward a deeper understanding of amyloid toxicity. In a set of 30 AFPs we predicted their amyloidogenic propensity, presence of transmembrane (TM) regions, and cholesterol (CBM) and ganglioside binding motifs (GBM), to which the oligomers likely bind. Noteworthy, all pathological AFPs share the presence of TM, CBM, and GBM regions, whereas the functional amyloids seem to show just one of these regions. For comparative purposes, we also analyzed a few examples of amyloid proteins that behave as biologically non-relevant AFPs. Based on the known experimental data on the β-amyloid and α-synuclein pore formation, we suggest that many AFPs have the potential for pore formation. Oligomerization and α-TM helix to β-TM strands transition on lipid rafts seem to be the common key events.
Sprache	Englisch
Erscheinungsdatum	2021-02-10
Erscheinungsland	Switzerland
Dokumenttyp	Journal Article
ZDB-ID	2452967-9
ISSN	1662-5099
ISSN	1662-5099
DOI	10.3389/fnmol.2021.619496
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel: A Comprehensive Cheminformatics Analysis of Structural Features Affecting the Binding Activity of Fullerene Derivatives.

Fjodorova, Natalja / Novič, Marjana / Venko, Katja / Rasulev, Bakhtiyor

Nanomaterials (Basel, Switzerland)

2020 Band 10, Heft 1

Abstract: Nanostructures like fullerene derivatives (FDs) belong to a new family of nano-sized organic compounds. Fullerenes have found a widespread application in material science, pharmaceutical, biomedical, and medical fields. This fact caused the importance of ...

Abstract	Nanostructures like fullerene derivatives (FDs) belong to a new family of nano-sized organic compounds. Fullerenes have found a widespread application in material science, pharmaceutical, biomedical, and medical fields. This fact caused the importance of the study of pharmacological as well as toxicological properties of this relatively new family of chemicals. In this work, a large set of 169 FDs and their binding activity to 1117 proteins was investigated. The structure-based descriptors widely used in drug design (so-called drug-like descriptors) were applied to understand cheminformatics characteristics related to the binding activity of fullerene nanostructures. Investigation of applied descriptors demonstrated that polarizability, topological diameter, and rotatable bonds play the most significant role in the binding activity of FDs. Various cheminformatics methods, including the counter propagation artificial neural network (CPANN) and Kohonen network as visualization tool, were applied. The results of this study can be applied to compose the priority list for testing in risk assessment related to the toxicological properties of FDs. The pharmacologist can filter the data from the heat map to view all possible side effects for selected FDs.
Sprache	Englisch
Erscheinungsdatum	2020-01-02
Erscheinungsland	Switzerland
Dokumenttyp	Journal Article
ZDB-ID	2662255-5
ISSN	2079-4991
ISSN	2079-4991
DOI	10.3390/nano10010090
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel: How fullerene derivatives (FDs) act on therapeutically important targets associated with diabetic diseases

Fjodorova, Natalja / Novič, Marjana / Venko, Katja / Drgan, Viktor / Rasulev, Bakhtiyor / Türker Saçan, Melek / Sağ Erdem, Safiye / Tugcu, Gulcin / Toropova, Alla P. / Toropov, Andrey A.

Computational and Structural Biotechnology Journal. 2022, v. 20

2022

Abstract: Fullerene derivatives (FDs) belong to a relatively new family of nano-sized organic compounds. They are widely applied in materials science, pharmaceutical industry, and (bio) medicine. This research focused on the study of FDs in terms of their ... ...

Abstract	Fullerene derivatives (FDs) belong to a relatively new family of nano-sized organic compounds. They are widely applied in materials science, pharmaceutical industry, and (bio) medicine. This research focused on the study of FDs in terms of their potential inhibitory effect on therapeutic targets associated with diabetic disease, as well as analysis of protein–ligand binding in order to identify the key binding characteristics of FDs. Therapeutic drug compounds when entering the biological system usually inevitably encounter and interact with a vast variety of biomolecules that are responsible for many different functions in organisms. Protein biomolecules are the most important functional components and used in this study as target structures. The structures of proteins [(PDB ID: 1BMQ, 1FM6, 1GPB, 1H5U, 1US0)] belonging to the class of anti-diabetes targets were obtained from the Protein Data Bank (PDB). Protein binding activity data (binding scores) were calculated for the dataset of 169 FDs related to these five proteins. Subsequently, the resulting data were analyzed using various machine learning and cheminformatics methods, including artificial neural network algorithms for variable selection and property prediction. The Quantitative Structure-Activity Relationship (QSAR) models for prediction of binding scores activity were built up according to five Organization for Economic Co-operation and Development (OECD) principles. All the data obtained can provide important information for further potential use of FDs with different functional groups as promising medical antidiabetic agents. Binding scores activity can be used for ranking of FDs in terms of their inhibitory activity (pharmacological properties) and potential toxicity.
Schlagwörter	biotechnology ; chemoinformatics ; data collection ; databases ; fullerene ; medicine ; neural networks ; new family ; pharmaceutical industry ; prediction ; quantitative structure-activity relationships ; therapeutics ; toxicity
Sprache	Englisch
Umfang	p. 913-924.
Erscheinungsort	Elsevier B.V.
Dokumenttyp	Artikel
ZDB-ID	2694435-2
ISSN	2001-0370
ISSN	2001-0370
DOI	10.1016/j.csbj.2022.02.006
Datenquelle	NAL Katalog (AGRICOLA)

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Artikel: Computational Approaches for Revealing the Structure of Membrane Transporters: Case Study on Bilitranslocase

Venko, Katja / A. Roy Choudhury / Marjana Novič

Computational and Structural Biotechnology Journal. 2017, v. 15

2017

Abstract: The structural and functional details of transmembrane proteins are vastly underexplored, mostly due to experimental difficulties regarding their solubility and stability. Currently, the majority of transmembrane protein structures are still unknown and ... ...

Abstract	The structural and functional details of transmembrane proteins are vastly underexplored, mostly due to experimental difficulties regarding their solubility and stability. Currently, the majority of transmembrane protein structures are still unknown and this present a huge experimental and computational challenge. Nowadays, thanks to X-ray crystallography or NMR spectroscopy over 3000 structures of membrane proteins have been solved, among them only a few hundred unique ones. Due to the vast biological and pharmaceutical interest in the elucidation of the structure and the functional mechanisms of transmembrane proteins, several computational methods have been developed to overcome the experimental gap. If combined with experimental data the computational information enables rapid, low cost and successful predictions of the molecular structure of unsolved proteins. The reliability of the predictions depends on the availability and accuracy of experimental data associated with structural information. In this review, the following methods are proposed for in silico structure elucidation: sequence-dependent predictions of transmembrane regions, predictions of transmembrane helix–helix interactions, helix arrangements in membrane models, and testing their stability with molecular dynamics simulations. We also demonstrate the usage of the computational methods listed above by proposing a model for the molecular structure of the transmembrane protein bilitranslocase. Bilitranslocase is bilirubin membrane transporter, which shares similar tissue distribution and functional properties with some of the members of the Organic Anion Transporter family and is the only member classified in the Bilirubin Transporter Family. Regarding its unique properties, bilitranslocase is a potentially interesting drug target.
Schlagwörter	bilirubin ; biotechnology ; case studies ; chemical structure ; drugs ; functional properties ; models ; molecular dynamics ; nuclear magnetic resonance spectroscopy ; prediction ; solubility ; tissue distribution ; transmembrane proteins ; transporters ; X-ray diffraction
Sprache	Englisch
Umfang	p. 232-242.
Erscheinungsort	Elsevier B.V.
Dokumenttyp	Artikel
ISSN	2001-0370
DOI	10.1016/j.csbj.2017.01.008
Datenquelle	NAL Katalog (AGRICOLA)

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Artikel ; Online: Cheminformatics and Machine Learning Approaches to Assess Aquatic Toxicity Profiles of Fullerene Derivatives.

Fjodorova, Natalja / Novič, Marjana / Venko, Katja / Rasulev, Bakhtiyor / Türker Saçan, Melek / Tugcu, Gulcin / Sağ Erdem, Safiye / Toropova, Alla P / Toropov, Andrey A

International journal of molecular sciences

2023 Band 24, Heft 18

Abstract: Fullerene derivatives (FDs) are widely used in nanomaterials production, the pharmaceutical industry and biomedicine. In the present study, we focused on the potential toxic effects of FDs on the aquatic environment. First, we analyzed the binding ... ...

Abstract	Fullerene derivatives (FDs) are widely used in nanomaterials production, the pharmaceutical industry and biomedicine. In the present study, we focused on the potential toxic effects of FDs on the aquatic environment. First, we analyzed the binding affinity of 169 FDs to 10 human proteins (1D6U, 1E3K, 1GOS, 1GS4, 1H82, 1OG5, 1UOM, 2F9Q, 2J0D, 3ERT) obtained from the Protein Data Bank (PDB) and showing high similarity to proteins from aquatic species. Then, the binding activity of 169 FDs to the enzyme acetylcholinesterase (AChE)-as a known target of toxins in fathead minnows and
Sprache	Englisch
Erscheinungsdatum	2023-09-15
Erscheinungsland	Switzerland
Dokumenttyp	Journal Article
ZDB-ID	2019364-6
ISSN	1422-0067 ; 1422-0067 ; 1661-6596
ISSN (online)	1422-0067
ISSN	1422-0067 ; 1661-6596
DOI	10.3390/ijms241814160
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel: Computational Approaches for Revealing the Structure of Membrane Transporters: Case Study on Bilitranslocase.

Venko, Katja / Roy Choudhury, A / Novič, Marjana

Computational and structural biotechnology journal

2017 Band 15, Seite(n) 232–242

Abstract	The structural and functional details of transmembrane proteins are vastly underexplored, mostly due to experimental difficulties regarding their solubility and stability. Currently, the majority of transmembrane protein structures are still unknown and this present a huge experimental and computational challenge. Nowadays, thanks to X-ray crystallography or NMR spectroscopy over 3000 structures of membrane proteins have been solved, among them only a few hundred unique ones. Due to the vast biological and pharmaceutical interest in the elucidation of the structure and the functional mechanisms of transmembrane proteins, several computational methods have been developed to overcome the experimental gap. If combined with experimental data the computational information enables rapid, low cost and successful predictions of the molecular structure of unsolved proteins. The reliability of the predictions depends on the availability and accuracy of experimental data associated with structural information. In this review, the following methods are proposed for
Sprache	Englisch
Erscheinungsdatum	2017-01-31
Erscheinungsland	Netherlands
Dokumenttyp	Journal Article ; Review
ISSN	2001-0370
ISSN	2001-0370
DOI	10.1016/j.csbj.2017.01.008
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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Artikel: How fullerene derivatives (FDs) act on therapeutically important targets associated with diabetic diseases.

Fjodorova, Natalja / Novič, Marjana / Venko, Katja / Drgan, Viktor / Rasulev, Bakhtiyor / Türker Saçan, Melek / Sağ Erdem, Safiye / Tugcu, Gulcin / Toropova, Alla P / Toropov, Andrey A

Computational and structural biotechnology journal

2022 Band 20, Seite(n) 913–924

Abstract	Fullerene derivatives (FDs) belong to a relatively new family of nano-sized organic compounds. They are widely applied in materials science, pharmaceutical industry, and (bio) medicine. This research focused on the study of FDs in terms of their potential inhibitory effect on therapeutic targets associated with diabetic disease, as well as analysis of protein-ligand binding in order to identify the key binding characteristics of FDs. Therapeutic drug compounds when entering the biological system usually inevitably encounter and interact with a vast variety of biomolecules that are responsible for many different functions in organisms. Protein biomolecules are the most important functional components and used in this study as target structures. The structures of proteins [(PDB ID: 1BMQ, 1FM6, 1GPB, 1H5U, 1US0)] belonging to the class of anti-diabetes targets were obtained from the Protein Data Bank (PDB). Protein binding activity data (binding scores) were calculated for the dataset of 169 FDs related to these five proteins. Subsequently, the resulting data were analyzed using various machine learning and cheminformatics methods, including artificial neural network algorithms for variable selection and property prediction. The Quantitative Structure-Activity Relationship (QSAR) models for prediction of binding scores activity were built up according to five Organization for Economic Co-operation and Development (OECD) principles. All the data obtained can provide important information for further potential use of FDs with different functional groups as promising medical antidiabetic agents. Binding scores activity can be used for ranking of FDs in terms of their inhibitory activity (pharmacological properties) and potential toxicity.
Sprache	Englisch
Erscheinungsdatum	2022-02-12
Erscheinungsland	Netherlands
Dokumenttyp	Journal Article
ZDB-ID	2694435-2
ISSN	2001-0370
ISSN	2001-0370
DOI	10.1016/j.csbj.2022.02.006
Datenquelle	MEDical Literature Analysis and Retrieval System OnLINE

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