Current and emerging tools of computational biology to improve the detoxification of mycotoxins

2021 ◽  
Author(s):  
Natalie Sandlin ◽  
Darius Russell Kish ◽  
John Kim ◽  
Marco Zaccaria ◽  
Babak Momeni
Keyword(s):  
Computational Biology ◽  
Empirical Data ◽  
Detoxifying Enzymes ◽  
Computational Tools ◽  
Cellular Processes ◽  
Food And Feed ◽  
Biological Organisms

Biological organisms carry a rich potential for removing toxins from our environment, but identifying suitable candidates and improving them remain challenging. We explore the use of computational tools to discover strains and enzymes that detoxify harmful compounds. In particular, we will focus on mycotoxins—fungi-produced toxins that contaminate food and feed—and biological enzymes that are capable of rendering them less harmful. We discuss the use of established and novel computational tools to complement existing empirical data in three directions: discovering the prospect of detoxification among underexplored organisms, finding important cellular processes that contribute to detoxification, and improving the performance of detoxifying enzymes. We hope to create a synergistic conversation between researchers in computational biology and those in the bioremediation field. We showcase open bioremediation questions where computational researchers can contribute and highlight relevant existing and emerging computational tools that could benefit bioremediation researchers.

scholarly journals Computational biology for enzymatic removal of mycotoxins

2021 ◽  
Author(s):  
Natalie Sandlin ◽  
Darius Russell Kish ◽  
John Kim ◽  
Marco Zaccaria ◽  
Babak Momeni
Keyword(s):  
Computational Biology ◽  
Empirical Data ◽  
Computational Tools ◽  
Cellular Processes ◽  
Food And Feed ◽  
Biological Organisms

Biological organisms carry a rich potential for removing toxins from our environment, but the search to identify suitable candidates remains challenging. We survey and explore the use of computational tools to discover and optimize the detoxification of harmful compounds. In particular, we will focus on mycotoxins—fungi-produced toxins that contaminate food and feed—and biological enzymes that are capable of rendering them less harmful. We discuss the use of computational tools to complement existing empirical data in three main directions: discovering the prospect of detoxification among underexplored organisms, finding important cellular processes that contribute to detoxification, and optimizing the performance of enzymes with detoxification capability.

Carbonyl Cytotoxicity Affects Plant Cellular Processes and Detoxifying Enzymes Scavenge These Compounds to Improve Stress Tolerance

2020 ◽  
Vol 68 (23) ◽  
pp. 6237-6247
Author(s):  
Vemanna S. Ramu ◽  
V. Preethi ◽  
K. N. Nisarga ◽  
Kinshuk R. Srivastava ◽  
M. S. Sheshshayee ◽  
...  
Keyword(s):  
Stress Tolerance ◽  
Detoxifying Enzymes ◽  
Cellular Processes

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 Practical Guidance in Genome-Wide RNA:DNA Triple Helix Prediction

2020 ◽  
Vol 21 (3) ◽  
pp. 830 ◽  
Author(s):  
Elena Matveishina ◽  
Ivan Antonov ◽  
Yulia A. Medvedeva
Keyword(s):  
Secondary Structure ◽  
Prediction Accuracy ◽  
Triple Helix ◽  
Noncoding Rnas ◽  
Computational Tools ◽  
Cellular Processes ◽  
Genome Wide ◽  
Specific Manner ◽  
Triple Helices ◽  
Practical Guidance

Long noncoding RNAs (lncRNAs) play a key role in many cellular processes including chromatin regulation. To modify chromatin, lncRNAs often interact with DNA in a sequence-specific manner forming RNA:DNA triple helices. Computational tools for triple helix search do not always provide genome-wide predictions of sufficient quality. Here, we used four human lncRNAs (MEG3, DACOR1, TERC and HOTAIR) and their experimentally determined binding regions for evaluating triplex parameters that provide the highest prediction accuracy. Additionally, we combined triplex prediction with the lncRNA secondary structure and demonstrated that considering only single-stranded fragments of lncRNA can further improve DNA-RNA triplexes prediction.

scholarly journals Tools and strategies for physiological genomics: the Rat Genome Database

2005 ◽  
Vol 23 (2) ◽  
pp. 246-256 ◽  
Author(s):  
Simon N. Twigger ◽  
Dean Pasko ◽  
Jeff Nie ◽  
Mary Shimoyama ◽  
Susan Bromberg ◽  
...  
Keyword(s):  
Comparative Genomics ◽  
Computational Biology ◽  
Computational Techniques ◽  
Genome Database ◽  
Computational Tools ◽  
Functional Roles ◽  
Genomics Research ◽  
Human And Mouse ◽  
Rat Genome

The broad goal of physiological genomics research is to link genes to their functions using appropriate experimental and computational techniques. Modern genomics experiments enable the generation of vast quantities of data, and interpretation of this data requires the integration of information derived from many diverse sources. Computational biology and bioinformatics offer the ability to manage and channel this information torrent. The Rat Genome Database (RGD; http://rgd.mcw.edu ) has developed computational tools and strategies specifically supporting the goal of linking genes to their functional roles in rat and, using comparative genomics, to human and mouse. We present an overview of the database with a focus on these unique computational tools and describe strategies for the use of these resources in the area of physiological genomics.

scholarly journals Functions and Mechanisms of Lysine Glutarylation in Eukaryotes

2021 ◽  
Vol 9 ◽  
Author(s):  
Longxiang Xie ◽  
Yafei Xiao ◽  
Fucheng Meng ◽  
Yongqiang Li ◽  
Zhenyu Shi ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Post Translational Modification ◽  
Computational Tools ◽  
Chromatin Dynamics ◽  
Biochemical Processes ◽  
Development History ◽  
Cellular Processes ◽  
Regulation Of Metabolism ◽  
Recent Developments ◽  
History Of

Lysine glutarylation (Kglu) is a newly discovered post-translational modification (PTM), which is considered to be reversible, dynamic, and conserved in prokaryotes and eukaryotes. Recent developments in the identification of Kglu by mass spectrometry have shown that Kglu is mainly involved in the regulation of metabolism, oxidative damage, chromatin dynamics and is associated with various diseases. In this review, we firstly summarize the development history of glutarylation, the biochemical processes of glutarylation and deglutarylation. Then we focus on the pathophysiological functions such as glutaric acidemia 1, asthenospermia, etc. Finally, the current computational tools for predicting glutarylation sites are discussed. These emerging findings point to new functions for lysine glutarylation and related enzymes, and also highlight the mechanisms by which glutarylation regulates diverse cellular processes.

scholarly journals Noncoding RNAs in Cardiovascular Disease: Current Knowledge, Tools and Technologies for Investigation, and Future Directions: A Scientific Statement From the American Heart Association

2020 ◽  
Vol 13 (4) ◽  
Author(s):  
Saumya Das ◽  
Ravi Shah ◽  
Stefanie Dimmeler ◽  
Jane E. Freedman ◽  
Christopher Holley ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Noncoding Rna ◽  
Functional Role ◽  
Unmet Need ◽  
Noncoding Rnas ◽  
Rapid Expansion ◽  
Cardiovascular Development ◽  
Computational Tools ◽  
Cellular Processes

Background: The discovery that much of the non–protein-coding genome is transcribed and plays a diverse functional role in fundamental cellular processes has led to an explosion in the development of tools and technologies to investigate the role of these noncoding RNAs in cardiovascular health. Furthermore, identifying noncoding RNAs for targeted therapeutics to treat cardiovascular disease is an emerging area of research. The purpose of this statement is to review existing literature, offer guidance on tools and technologies currently available to study noncoding RNAs, and identify areas of unmet need. Methods: The writing group used systematic literature reviews (including MEDLINE, Web of Science through 2018), expert opinion/statements, analyses of databases and computational tools/algorithms, and review of current clinical trials to provide a broad consensus on the current state of the art in noncoding RNA in cardiovascular disease. Results: Significant progress has been made since the initial studies focusing on the role of miRNAs (microRNAs) in cardiovascular development and disease. Notably, recent progress on understanding the role of novel types of noncoding small RNAs such as snoRNAs (small nucleolar RNAs), tRNA (transfer RNA) fragments, and Y-RNAs in cellular processes has revealed a noncanonical function for many of these molecules. Similarly, the identification of long noncoding RNAs that appear to play an important role in cardiovascular disease processes, coupled with the development of tools to characterize their interacting partners, has led to significant mechanistic insight. Finally, recent work has characterized the unique role of extracellular RNAs in mediating intercellular communication and their potential role as biomarkers. Conclusions: The rapid expansion of tools and pipelines for isolating, measuring, and annotating these entities suggests that caution in interpreting results is warranted until these methodologies are rigorously validated. Most investigators have focused on investigating the functional role of single RNA entities, but studies suggest complex interaction between different RNA molecules. The use of network approaches and advanced computational tools to understand the interaction of different noncoding RNA species to mediate a particular phenotype may be required to fully comprehend the function of noncoding RNAs in mediating disease phenotypes.

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.

COMPUTATIONAL BIOLOGY

2018 ◽  
Vol 8 (6) ◽  
pp. 66
Author(s):  
Matthew N. O. Sadiku ◽  
Yonghui Wang ◽  
Suxia Cui ◽  
Sarhan M. Musa
Keyword(s):  
Molecular Biology ◽  
Computational Biology ◽  
Computational Tools

Computation is an integral part of a larger revolution that will affect how science is conducted.  Computational biology is an important emerging field of biology which is uniquely enabled by computation. It involves using computers to model biological problems and interpret data, especially problems in evolutionary and molecular biology. The application of computational tools to all areas of biology is producing excitements and insights into biological problems too complex for conventional approaches. This paper provides a brief introduction on computational biology.

scholarly journals Computational Structural Biology: Successes, Future Directions, and Challenges

Molecules ◽  
2019 ◽  
Vol 24 (3) ◽  
pp. 637 ◽  
Author(s):  
Ruth Nussinov ◽  
Chung-Jung Tsai ◽  
Amarda Shehu ◽  
Hyunbum Jang
Keyword(s):  
Computational Biology ◽  
Structural Biology ◽  
Special Focus ◽  
Conformational Sampling ◽  
Future Directions ◽  
Cellular Processes ◽  
Disease Associated Genes ◽  
The Brain ◽  
Insight Into ◽  
Computational Structural Biology

Computational biology has made powerful advances. Among these, trends in human health have been uncovered through heterogeneous ‘big data’ integration, and disease-associated genes were identified and classified. Along a different front, the dynamic organization of chromatin is being elucidated to gain insight into the fundamental question of genome regulation. Powerful conformational sampling methods have also been developed to yield a detailed molecular view of cellular processes. when combining these methods with the advancements in the modeling of supramolecular assemblies, including those at the membrane, we are finally able to get a glimpse into how cells’ actions are regulated. Perhaps most intriguingly, a major thrust is on to decipher the mystery of how the brain is coded. Here, we aim to provide a broad, yet concise, sketch of modern aspects of computational biology, with a special focus on computational structural biology. We attempt to forecast the areas that computational structural biology will embrace in the future and the challenges that it may face. We skirt details, highlight successes, note failures, and map directions.

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