scholarly journals Structural and Functional Analysis of Proteins Using Rigidity Theory

2021 ◽  
pp. 337-367
Author(s):  
Adnan Sljoka
Keyword(s):  
Big Data ◽  
Intrinsically Disordered Proteins ◽  
Protein Structures ◽  
Computational Modelling ◽  
Biological Data ◽  
Rigidity Theory ◽  
Cellular Functions ◽  
Mechanical Heterogeneity ◽  
Large Scale Analysis ◽  
Living Organisms

AbstractOver the past two decades, we have witnessed an unprecedented explosion in available biological data. In the age of big data, large biological datasets have created an urgent need for the development of bioinformatics methods and innovative fast algorithms. Bioinformatics tools can enable data-driven hypothesis and interpretation of complex biological data that can advance biological and medicinal knowledge discovery. Advances in structural biology and computational modelling have led to the characterization of atomistic structures of many biomolecular components of cells. Proteins in particular are the most fundamental biomolecules and the key constituent elements of all living organisms, as they are necessary for cellular functions. Proteins play crucial roles in immunity, catalysis, metabolism and the majority of biological processes, and hence there is significant interest to understand how these macromolecules carry out their complex functions. The mechanical heterogeneity of protein structures and a delicate mix of rigidity and flexibility, which dictates their dynamic nature, is linked to their highly diverse biological functions. Mathematical rigidity theory and related algorithms have opened up many exciting opportunities to accurately analyse protein dynamics and probe various biological enigmas at a molecular level. Importantly, rigidity theoretical algorithms and methods run in almost linear time complexity, which makes it suitable for high-throughput and big-data style analysis. In this chapter, we discuss the importance of protein flexibility and dynamics and review concepts in mathematical rigidity theory for analysing stability and the dynamics of protein structures. We then review some recent breakthrough studies, where we designed rigidity theory methods to understand complex biological events, such as allosteric communication, large-scale analysis of immune system antibody proteins, the highly complex dynamics of intrinsically disordered proteins and the validation of Nuclear Magnetic Resonance (NMR) solved protein structures.

scholarly journals Co-Evolution of Intrinsically Disordered Proteins with Folded Partners Witnessed by Evolutionary Couplings

2018 ◽  
Vol 19 (11) ◽  
pp. 3315 ◽  
Author(s):  
Rita Pancsa ◽  
Fruzsina Zsolyomi ◽  
Peter Tompa
Keyword(s):  
Large Scale ◽  
Intrinsically Disordered Proteins ◽  
Protein Structures ◽  
Disordered Proteins ◽  
Cellular Interaction ◽  
Structural Constraints ◽  
Protein Residues ◽  
Intrinsically Disordered ◽  
Evolutionary Changes ◽  
Folded Proteins

Although improved strategies for the detection and analysis of evolutionary couplings (ECs) between protein residues already enable the prediction of protein structures and interactions, they are mostly restricted to conserved and well-folded proteins. Whereas intrinsically disordered proteins (IDPs) are central to cellular interaction networks, due to the lack of strict structural constraints, they undergo faster evolutionary changes than folded domains. This makes the reliable identification and alignment of IDP homologs difficult, which led to IDPs being omitted in most large-scale residue co-variation analyses. By preforming a dedicated analysis of phylogenetically widespread bacterial IDP–partner interactions, here we demonstrate that partner binding imposes constraints on IDP sequences that manifest in detectable interprotein ECs. These ECs were not detected for interactions mediated by short motifs, rather for those with larger IDP–partner interfaces. Most identified coupled residue pairs reside close (<10 Å) to each other on the interface, with a third of them forming multiple direct atomic contacts. EC-carrying interfaces of IDPs are enriched in negatively charged residues, and the EC residues of both IDPs and partners preferentially reside in helices. Our analysis brings hope that IDP–partner interactions difficult to study could soon be successfully dissected through residue co-variation analysis.

scholarly journals FrustratometeR: an R-package to compute Local frustration in protein structures, point mutants and MD simulations

2020 ◽  
Author(s):  
Atilio O. Rausch ◽  
Maria I. Freiberger ◽  
Cesar O. Leonetti ◽  
Diego M. Luna ◽  
Leandro G. Radusky ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Protein Structures ◽  
Md Simulations ◽  
R Package ◽  
Protein Protein Interactions ◽  
Large Scale Analysis ◽  
Functional Aspects ◽  
Catalytic Sites ◽  
Polypeptide Chains

Once folded natural protein molecules have few energetic conflicts within their polypeptide chains. Many protein structures do however contain regions where energetic conflicts remain after folding, i.e. they have highly frustrated regions. These regions, kept in place over evolutionary and physiological timescales, are related to several functional aspects of natural proteins such as protein-protein interactions, small ligand recognition, catalytic sites and allostery. Here we present FrustratometeR, an R package that easily computes local energetic frustration on a personal computer or a cluster. This package facilitates large scale analysis of local frustration, point mutants and MD trajectories, allowing straightforward integration of local frustration analysis in to pipelines for protein structural analysis.Availability and implementation: https://github.com/proteinphysiologylab/frustratometeR

Semiotic Content of Visuals and Communication

2014 ◽  
pp. 174-192
Keyword(s):  
Data Visualization ◽  
Verbal Communication ◽  
Biological Data ◽  
Scientific Study ◽  
Visual Design ◽  
Basic Information ◽  
Natural Processes ◽  
Sign Systems ◽  
Visualization Tools ◽  
Living Organisms

The semiotic content of visual design makes a foundation for non-verbal communication applied to practice, especially for visualizing knowledge. The ways signs convey meaning define the notion of semiotics. After inspection of the notions of sign systems, codes, icons, and symbols further text examines how to tie a sign or symbol to that for which it stands, combine images, and think figuratively or metaphorically. Further text introduces basic information about communication through metaphors, analogies, and about the scientific study of biosemiotics, which examines communication in living organisms aimed at conveying meaning, communicating knowledge about natural processes, and designing the biological data visualization tools.

The Role of Big Data in Radiation Oncology

Big Data ◽  
2016 ◽  
pp. 1519-1542
Author(s):  
Issam El Naqa
Keyword(s):  
Big Data ◽  
Cancer Genetics ◽  
Clinical Decision ◽  
Biological Data ◽  
Imaging Features ◽  
Treatment Regimens ◽  
Patient Demographics ◽  
Complex Interactions ◽  
Advanced Stages ◽  
And Biological Markers

More than half of cancer patients receive ionizing radiation as part of their treatment and it is the main modality at advanced stages of disease. Treatment outcomes in radiotherapy are determined by complex interactions between cancer genetics, treatment regimens, and patient-related variables. A typical radiotherapy treatment scenario can generate a large pool of data, “Big data,” that is comprised of patient demographics, dosimetry, imaging features, and biological markers. Radiotherapy data constitutes a unique interface between physical and biological data interactions. In this chapter, the authors review recent advances and discuss current challenges to interrogate big data in radiotherapy using top-bottom and bottom-top approaches. They describe the specific nature of big data in radiotherapy and discuss issues related to bioinformatics tools for data aggregation, sharing, and confidentiality. The authors also highlight the potential opportunities in this field for big data research from bioinformaticians as well as clinical decision-makers' perspectives.

Influence of Genomic and Other Biological Data Sets in the Understanding of Protein Structures, Functions and Interactions

2011 ◽  
Vol 2 (1) ◽  
pp. 24-44
Author(s):  
N. Srinivasan ◽  
G. Agarwal ◽  
R. M. Bhaskara ◽  
R. Gadkari ◽  
O. Krishnadev ◽  
...  
Keyword(s):  
Protein Structures ◽  
Biological Properties ◽  
Biological Data ◽  
Biological Information ◽  
Biological Databases ◽  
Homology Detection ◽  
Putative Gene ◽  
Remote Homology ◽  
Strategic Integration ◽  
Remote Homology Detection

In the post-genomic era, biological databases are growing at a tremendous rate. Despite rapid accumulation of biological information, functions and other biological properties of many putative gene products of various organisms remain either unknown or obscure. This paper examines how strategic integration of large biological databases and combinations of various biological information helps address some of the fundamental questions on protein structure, function and interactions. New developments in function recognition by remote homology detection and strategic use of sequence databases aid recognition of functions of newly discovered proteins. Knowledge of 3-D structures and combined use of sequences and 3-D structures of homologous protein domains expands the ability of remote homology detection enormously. The authors also demonstrate how combined consideration of functions of individual domains of multi-domain proteins helps in recognizing gross biological attributes. This paper also discusses a few cases of combining disparate biological datasets or combination of disparate biological information in obtaining new insights about protein-protein interactions across a host and a pathogen. Finally, the authors discuss how combinations of low resolution structural data, obtained using cryoEM studies, of gigantic multi-component assemblies, and atomic level 3-D structures of the components is effective in inferring finer features in the assembly.

scholarly journals Armc8 is an evolutionarily conserved armadillo protein involved in cell–cell adhesion complexes through multiple molecular interactions

2019 ◽  
Vol 39 (8) ◽  
Author(s):  
Ismail Sahin Gul ◽  
Paco Hulpiau ◽  
Ellen Sanders ◽  
Frans van Roy ◽  
Jolanda van Hengel
Keyword(s):  
Intracellular Signaling ◽  
Protein Structures ◽  
Hybrid Approach ◽  
Evolutionary Relationship ◽  
Similar Process ◽  
Cellular Functions ◽  
Human Ortholog ◽  
Armadillo Repeat ◽  
Relationship Of ◽  
Cell Cell

Abstract Armadillo-repeat-containing protein 8 (Armc8) belongs to the family of armadillo-repeat containing proteins, which have been found to be involved in diverse cellular functions including cell–cell contacts and intracellular signaling. By comparative analyses of armadillo repeat protein structures and genomes from various premetazoan and metazoan species, we identified orthologs of human Armc8 and analyzed in detail the evolutionary relationship of Armc8 genes and their encoded proteins. Armc8 is a highly ancestral armadillo protein although not present in yeast. Consequently, Armc8 is not the human ortholog of yeast Gid5/Vid28. Further, we performed a candidate approach to characterize new protein interactors of Armc8. Interactions between Armc8 and specific δ-catenins (plakophilins-1, -2, -3 and p0071) were observed by the yeast two-hybrid approach and confirmed by co-immunoprecipitation and co-localization. We also showed that Armc8 interacts specifically with αE-catenin but neither with αN-catenin nor with αT-catenin. Degradation of αE-catenin has been reported to be important in cancer and to be regulated by Armc8. A similar process may occur with respect to plakophilins in desmosomes. Deregulation of desmosomal proteins has been considered to contribute to tumorigenesis.

scholarly journals State of the Art of Fuzzy Methods for Gene Regulatory Networks Inference

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Tuqyah Abdullah Al Qazlan ◽  
Aboubekeur Hamdi-Cherif ◽  
Chafia Kara-Mohamed
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Ad Hoc ◽  
Cellular Level ◽  
Biological Information ◽  
Cellular Functions ◽  
Fuzzy Methods ◽  
Gene Regulatory ◽  
Living Organisms ◽  
Different Levels

To address one of the most challenging issues at the cellular level, this paper surveys the fuzzy methods used in gene regulatory networks (GRNs) inference. GRNs represent causal relationships between genes that have a direct influence, trough protein production, on the life and the development of living organisms and provide a useful contribution to the understanding of the cellular functions as well as the mechanisms of diseases. Fuzzy systems are based on handling imprecise knowledge, such as biological information. They provide viable computational tools for inferring GRNs from gene expression data, thus contributing to the discovery of gene interactions responsible for specific diseases and/orad hoccorrecting therapies. Increasing computational power and high throughput technologies have provided powerful means to manage these challenging digital ecosystems at different levels from cell to society globally. The main aim of this paper is to report, present, and discuss the main contributions of this multidisciplinary field in a coherent and structured framework.

scholarly journals CheSPI: Chemical shift Secondary structure Population Inference

2021 ◽  
Author(s):  
Jakob Toudahl Nielsen ◽  
Frans A.A. Mulder
Keyword(s):  
Intrinsically Disordered Proteins ◽  
Chemical Shifts ◽  
Protein Structures ◽  
Random Coil ◽  
Disordered Proteins ◽  
Residual Structure ◽  
Intrinsically Disordered ◽  
Population Inference ◽  
Local Protein Structure ◽  
Structural Classes

AbstractNMR chemical shifts (CSs) are delicate reporters of local protein structure, and recent advances in random coil CS (RCCS) prediction and interpretation now offer the compelling prospect of inferring small populations of structure from small deviations from RCCSs. Here, we present CheSPI, a simple and efficient method that provides unbiased and sensitive aggregate measures of local structure and disorder. It is demonstrated that CheSPI can predict even very small amounts of residual structure and robustly delineate subtle differences into four structural classes for intrinsically disordered proteins. For structured regions and proteins, CheSPI can assign up to eight structural classes, which coincide with the well-known DSSP classification. The program is freely available, and can either be invoked from URL www.protein-nmr.org as a web implementation, or run locally from command line as a python program. CheSPI generates comprehensive numeric and graphical output for intuitive annotation and visualization of protein structures. A number of examples are provided.

scholarly journals Living organisms and sedimentary remains from high mountain lakes in the Alps

2021 ◽  
Author(s):  
Aldo Marchetto ◽  
Angela Boggero ◽  
Diego Fontaneto ◽  
Andrea Lami ◽  
André F. Lotter ◽  
...  
Keyword(s):  
Aquatic Organisms ◽  
Mountain Lakes ◽  
Biological Data ◽  
Environmental Data ◽  
High Mountain ◽  
High Mountain Lakes ◽  
Data Set ◽  
Lake Bottom ◽  
The Alps ◽  
Living Organisms

We publish a data set of environmental and biological data collected in 2000 during the ice-free period in high mountain lakes located above the local timberline in the Alps, in Italy, Switzerland and Austria. Environmental data include coordinates, geographical attributes and detailed information on vegetation, bedrock and land use in lake catchments. Chemical analyses of a sample for each lake collected at the lake surface in Summer 2000 are also reported. Biological data include phytoplankton (floating algae and cyanobacteria), zooplankton (floating animals), macroinvertebrates (aquatic organisms visible to the naked eye living in contact with sediments on lake bottom), benthic diatoms. Diatoms, cladocera and chironomids remains and algal and bacterial pigments were also analysed in lake sediments.

scholarly journals Multiscale dynamics of COVID-19 and model-based recommendations for 105 countries

2020 ◽  
Author(s):  
Jithender J. Timothy ◽  
Vijaya Holla ◽  
Günther Meschke
Keyword(s):  
Large Scale ◽  
Multiple Scales ◽  
Computational Modelling ◽  
Real Data ◽  
Mitigation Measures ◽  
Agent Based ◽  
Multiscale Dynamics ◽  
Large Scale Analysis ◽  
Disease Spreading ◽  
Country Specific

We analyse the dynamics of COVID-19 using computational modelling at multiple scales. For large scale analysis, we propose a 2-scale lattice extension of the classical SIR-type compartmental model with spatial interactions called the Lattice-SIRQL model. Computational simulations show that global quantifiers are not completely representative of the actual dynamics of the disease especially when mitigation measures such as quarantine and lockdown are applied. Furthermore, using real data of confirmed COVID-19 cases, we calibrate the Lattice-SIRQL model for 105 countries. The calibrated model is used to make country specific recommendations for lockdown relaxation and lockdown continuation. Finally, using an agent-based model we analyse the influence of cluster level relaxation rate and lockdown duration on disease spreading.

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