scholarly journals Challenges and Limitations in the Studies of Glycoproteins: A Computational Chemist’s Perspective

2021 ◽  
Author(s):  
Oyku Balli ◽  
Vladimir Uversky ◽  
Serdar Durdagi ◽  
Orkid Coskuner-Weber
Keyword(s):  
Computational Biology ◽  
Structural Properties ◽  
Structural Property ◽  
Computational Studies ◽  
Computational Tools ◽  
Future Developments ◽  
High Flexibility

Experimenters face challenges and limitations while analyzing glycoproteins due to their high flexibility, stereochemistry, anisotropic effects, and hydration phenomena. Computational studies complement experiments and have been used in characterization of the structural properties of glycoproteins. However, recent investigations revealed that computational studies face significant challenges as well. Here, we introduce and discuss some of these challenges and weaknesses in the investigations of glycoproteins. We also present requirements of future developments in computational biochemistry and computational biology areas that could be necessary for providing more accurate structural property analyses of glycopro-teins using computational tools. Further theoretical strategies that need to be and can be developed are discussed herein.

scholarly journals Computational biology for ageing

2011 ◽  
Vol 366 (1561) ◽  
pp. 51-63 ◽  
Author(s):  
Daniela Wieser ◽  
Irene Papatheodorou ◽  
Matthias Ziehm ◽  
Janet M. Thornton
Keyword(s):  
Gene Expression ◽  
Computational Biology ◽  
High Throughput ◽  
Computational Analysis ◽  
Data Interpretation ◽  
Easy Access ◽  
Computational Tools ◽  
Computational Approaches ◽  
Future Developments ◽  
Pathway Analyses

High-throughput genomic and proteomic technologies have generated a wealth of publicly available data on ageing. Easy access to these data, and their computational analysis, is of great importance in order to pinpoint the causes and effects of ageing. Here, we provide a description of the existing databases and computational tools on ageing that are available for researchers. We also describe the computational approaches to data interpretation in the field of ageing including gene expression, comparative and pathway analyses, and highlight the challenges for future developments. We review recent biological insights gained from applying bioinformatics methods to analyse and interpret ageing data in different organisms, tissues and conditions.

Current Challenges and Limitations in the Studies of Intrinsically Disordered Proteins in Neurodegenerative Diseases by Computer Simulations

2020 ◽  
Vol 17 ◽  
Author(s):  
Ibrahim Yagiz Akbayrak ◽  
Sule Irem Caglayan ◽  
Zilan Ozcan ◽  
Vladimir N. Uversky ◽  
Orkid Coskuner-Weber
Keyword(s):  
Neurodegenerative Diseases ◽  
Computer Simulations ◽  
Conformational Changes ◽  
Intrinsically Disordered Proteins ◽  
Disordered Proteins ◽  
Computational Studies ◽  
Accurate Data ◽  
Aggregation Propensity ◽  
Intrinsically Disordered ◽  
Future Developments

: Experiments face challenges in the analysis of intrinsically disordered proteins in solution due to fast conformational changes and enhanced aggregation propensity. Computational studies complement experiments, being widely used in the analyses of intrinsically disordered proteins, especially those positioned at the centers of neurodegenerative diseases. However, recent investigations – including our own – revealed that computer simulations face significant challenges and limitations themselves. In this review, we introduced and discussed some of the scientific challenges and limitations of computational studies conducted on intrinsically disordered proteins. We also outlined the importance of future developments in the areas of computational chemistry and computational physics that would be needed for generating more accurate data for intrinsically disordered proteins from computer simulations. Additional theoretical strategies that can be developed are discussed herein.

Characterization of Structural Properties in High Reynolds Hydraulic Jump Based on CFD and Physical Modeling Approaches

2020 ◽  
Vol 146 (12) ◽  
pp. 04020079 ◽  
Author(s):  
Juan Francisco Macián-Pérez ◽  
Arnau Bayón ◽  
Rafael García-Bartual ◽  
P. Amparo López-Jiménez ◽  
Francisco José Vallés-Morán
Keyword(s):  
Structural Properties ◽  
Physical Modeling ◽  
Hydraulic Jump ◽  
Modeling Approaches ◽  
High Reynolds

Structural Property Analysis of a Kind of Petri Net Synthesis

2011 ◽  
Vol 255-260 ◽  
pp. 1989-1993
Author(s):  
Chuan Liang Xia ◽  
Zhen Dong Liu ◽  
Peng Sun
Keyword(s):  
Structural Properties ◽  
Structural Property ◽  
Petri Net ◽  
The State ◽  
Property Analysis ◽  
Petri Net Synthesis

Petri net synthesis can avoid the state exploration problem by guaranteeing the correctness in the Petri net while incrementally expanding the net. This paper proposes the conditions imposed on a synthesis shared a kind of subnet under which the following structural properties will be preserved: repetitiveness, consistency, structural boundedness, conservativeness, structural liveness, P-invariant and T-invariant.

Structure Based Grading of Kinematic Chains

2014 ◽  
Vol 575 ◽  
pp. 501-506 ◽  
Author(s):  
Shubhashis Sanyal ◽  
G.S. Bedi
Keyword(s):  
Structural Properties ◽  
Structural Property ◽  
Kinematic Chain ◽  
Degree Of Freedom ◽  
Kinematic Chains ◽  
Structural Differences ◽  
The Individual ◽  
Independent Loops

Kinematic chains differ due to the structural differences between them. The location of links, joints and loops differ in each kinematic chain to make it unique. Two similar kinematic chains will produce similar motion properties and hence are avoided. The performance of these kinematic chains also depends on the individual topology, i.e. the placement of its entities. In the present work an attempt has been made to compare a family of kinematic chains based on its structural properties. The method is based on identifying the chains structural property by using its JOINT LOOP connectivity table. Nomenclature J - Number of joints, F - Degree of freedom of the chain, N - Number of links, L - Number of basic loops (independent loops plus one peripheral loop).

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