scholarly journals Dynamical important residue network (DIRN): network inference via conformational change

2019 ◽  
Vol 35 (22) ◽  
pp. 4664-4670 ◽  
Author(s):  
Quan Li ◽  
Ray Luo ◽  
Hai-Feng Chen
Keyword(s):  
Network Inference ◽  
Protein Structures ◽  
Interaction Network ◽  
Structural Data ◽  
Supplementary Information ◽  
Residue Interaction ◽  
Protein Functions ◽  
Important Residue ◽  
Dynamics Simulations ◽  
Dynamical Information

Abstract Motivation Protein residue interaction network has emerged as a useful strategy to understand the complex relationship between protein structures and functions and how functions are regulated. In a residue interaction network, every residue is used to define a network node, adding noises in network post-analysis and increasing computational burden. In addition, dynamical information is often necessary in deciphering biological functions. Results We developed a robust and efficient protein residue interaction network method, termed dynamical important residue network, by combining both structural and dynamical information. A major departure from previous approaches is our attempt to identify important residues most important for functional regulation before a network is constructed, leading to a much simpler network with the important residues as its nodes. The important residues are identified by monitoring structural data from ensemble molecular dynamics simulations of proteins in different functional states. Our tests show that the new method performs well with overall higher sensitivity than existing approaches in identifying important residues and interactions in tested proteins, so it can be used in studies of protein functions to provide useful hypotheses in identifying key residues and interactions. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Enlighten2: molecular dynamics simulations of protein–ligand systems made accessible

2020 ◽  
Vol 36 (20) ◽  
pp. 5104-5106
Author(s):  
Kirill Zinovjev ◽  
Marc W van der Kamp
Keyword(s):  
Molecular Dynamics ◽  
Expert Knowledge ◽  
Structural Data ◽  
Md Simulations ◽  
Enzyme Engineering ◽  
Protein Ligand Interactions ◽  
Ligand Interactions ◽  
Dynamics Simulations ◽  
User Friendly ◽  
Dynamical Information

Abstract Motivation Experimental structural data can allow detailed insight into protein structure and protein–ligand interactions, which is crucial for many areas of bioscience, including drug design and enzyme engineering. Typically, however, little more than a static picture of protein–ligand interactions is obtained, whereas dynamical information is often required for deeper understanding and to assess the effect of mutations. Molecular dynamics (MD) simulations can provide such information, but setting up and running these simulations is not straightforward and requires expert knowledge. There is thus a need for a tool that makes protein–ligand simulation easily accessible to non-expert users. Results We present Enlighten2: efficient simulation protocols for protein–ligand systems alongside a user-friendly plugin to the popular visualization program PyMOL. With Enlighten2, non-expert users can straightforwardly run and visualize MD simulations on protein–ligand models of interest. There is no need to learn new programs and all underlying tools are free and open source. Availability and implementation The Enlighten2 Python package and PyMOL plugin are free to use under the GPL3.0 licence and can be found at https://enlighten2.github.io. We also provide a lightweight Docker image via DockerHub that includes Enlighten2 with all the required utilities.

scholarly journals Fold and flexibility: what can proteins' mechanical properties tell us about their folding nucleus?

2015 ◽  
Vol 12 (112) ◽  
pp. 20150876 ◽  
Author(s):  
Sophie Sacquin-Mora
Keyword(s):  
Mechanical Properties ◽  
Brownian Dynamics ◽  
Protein Structures ◽  
Interaction Network ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Folding Nucleus ◽  
Dynamics Simulations ◽  
Brownian Dynamics Simulations

The determination of a protein's folding nucleus, i.e. a set of native contacts playing an important role during its folding process, remains an elusive yet essential problem in biochemistry. In this work, we investigate the mechanical properties of 70 protein structures belonging to 14 protein families presenting various folds using coarse-grain Brownian dynamics simulations. The resulting rigidity profiles combined with multiple sequence alignments show that a limited set of rigid residues, which we call the consensus nucleus, occupy conserved positions along the protein sequence. These residues' side chains form a tight interaction network within the protein's core, thus making our consensus nuclei potential folding nuclei. A review of experimental and theoretical literature shows that most (above 80%) of these residues were indeed identified as folding nucleus member in earlier studies.

scholarly journals Streamlined use of protein structures in variant analysis

2021 ◽  
Author(s):  
Sandeep Kaur ◽  
Neblina Sikta ◽  
Andrea Schafferhans ◽  
Nicola Bordin ◽  
Mark J. Cowley ◽  
...  
Keyword(s):  
Protein Function ◽  
Molecular Mechanisms ◽  
Structural Information ◽  
Protein Structures ◽  
Structural Data ◽  
Supplementary Information ◽  
3D Structures ◽  
Link Type ◽  
Variant Analysis ◽  
Many Sources

AbstractMotivationVariant analysis is a core task in bioinformatics that requires integrating data from many sources. This process can be helped by using 3D structures of proteins, which can provide a spatial context that can provide insight into how variants affect function. Many available tools can help with mapping variants onto structures; but each has specific restrictions, with the result that many researchers fail to benefit from valuable insights that could be gained from structural data.ResultsTo address this, we have created a streamlined system for incorporating 3D structures into variant analysis. Variants can be easily specified via URLs that are easily readable and writable, and use the notation recommended by the Human Genome Variation Society (HGVS). For example, ‘https://aquaria.app/SARS-CoV-2/S/?N501Y’ specifies the N501Y variant of SARS-CoV-2 S protein. In addition to mapping variants onto structures, our system provides summary information from multiple external resources, including COSMIC, CATH-FunVar, and PredictProtein. Furthermore, our system identifies and summarizes structures containing the variant, as well as the variant-position. Our system supports essentially any mutation for any well-studied protein, and uses all available structural data — including models inferred via very remote homology — integrated into a system that is fast and simple to use. By giving researchers easy, streamlined access to a wealth of structural information during variant analysis, our system will help in revealing novel insights into the molecular mechanisms underlying protein function in health and disease.AvailabilityOur resource is freely available at the project home page (https://aquaria.app). After peer review, the code will be openly available via a GPL version 2 license at https://github.com/ODonoghueLab/Aquaria. PSSH2, the database of sequence-to-structure alignments, is also freely available for download at https://zenodo.org/record/[email protected] informationNone.

ClusFCM: AN ALGORITHM FOR PREDICTING PROTEIN FUNCTIONS USING HOMOLOGIES AND PROTEIN INTERACTIONS

2008 ◽  
Vol 06 (01) ◽  
pp. 203-222 ◽  
Author(s):  
CAO NGUYEN ◽  
MICHAEL MANNINO ◽  
KATHELEEN GARDINER ◽  
KRZYSZTOF J. CIOS
Keyword(s):  
Protein Interaction ◽  
Protein Interactions ◽  
Direct Interaction ◽  
Interaction Network ◽  
Roc Curves ◽  
Fuzzy Cognitive Maps ◽  
Supplementary Information ◽  
Protein Interaction Data ◽  
Protein Functions ◽  
Grid Database

We introduce a new algorithm, called ClusFCM, which combines techniques of clustering and fuzzy cognitive maps (FCM) for prediction of protein functions. ClusFCM takes advantage of protein homologies and protein interaction network topology to improve low recall predictions associated with existing prediction methods. ClusFCM exploits the fact that proteins of known function tend to cluster together and deduce functions not only through their direct interaction with other proteins, but also from other proteins in the network. We use ClusFCM to annotate protein functions for Saccharomyces cerevisiae (yeast), Caenorhabditis elegans (worm), and Drosophila melanogaster (fly) using protein–protein interaction data from the General Repository for Interaction Datasets (GRID) database and functional labels from Gene Ontology (GO) terms. The algorithm's performance is compared with four state-of-the-art methods for function prediction — Majority, χ2 statistics, Markov random field (MRF), and FunctionalFlow — using measures of Matthews correlation coefficient, harmonic mean, and area under the receiver operating characteristic (ROC) curves. The results indicate that ClusFCM predicts protein functions with high recall while not lowering precision. Supplementary information is available at .

scholarly journals Free Energy Landscapes and Residue Network Analysis for Six SARS-CoV-2 Targets in Complex with Plausible Phytochemical Inhibitors from Withania Somnifera: 1 µS Molecular Dynamics Simulations

2021 ◽  
Author(s):  
Pratap Kumar Parida ◽  
Dipak Paul ◽  
Debamitra Chakravorty
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Molecular Dynamics Simulations ◽  
Withania Somnifera ◽  
Minimum Energy ◽  
Interaction Network ◽  
Energy Landscapes ◽  
Residue Interaction ◽  
Free Energy Landscapes ◽  
Dynamics Simulations

The pandemic is here to stay- evident from the second wave that is severely affecting global population. Though vaccination is now available, the population size restricts its efficacy, especially in the third world countries. Therefore, to avoid a third wave, natural preventive therapeutics are the need of the hour. In this work the efficiency of phytochemicals from <i>Withania somnifera</i> to bind to a total of six SARS-CoV-2 targets have been shown.1 µs molecular dynamics simulations and essential dynamic analyses shed light on the changes induced by the phytochemicals and highlights their multipotent capabilities- 27-Hydroxywithanolide B was able to bind to three targets. Relative free energy of binding for all the phytochemicals were calculated by MM/PBSA. Minimum energy structures were extracted from their free energy landscapes and were subjected to PSN-ENM-NMA and network centrality analysis. Results showed that the phytochemical binding changes the residue-residue interaction network. Network communities increase while hubs and links decrease. Metapath rewiring occurs through residues Phe456 in spike protein, Thr26 and Tyr118 in main protease, Val49 and Phe156 in NSP3, Leu98 in NSP9, Leu4345 in NSP10, Phe440 and Phe843 in NSP12. This work tries to understand the mechanism of possible inhibition by the phytochemicals to combat SARS-CoV-2 with their capability of targeting multiple proteins. The insight from this study can be of great relevance to explore the changes in network properties induced by reported potential inhibitors against SARS-CoV-2 targets.

scholarly journals Free Energy Landscapes and Residue Network Analysis for Six SARS-CoV-2 Targets in Complex with Plausible Phytochemical Inhibitors from Withania Somnifera: 1 µS Molecular Dynamics Simulations

2021 ◽  
Author(s):  
Pratap Kumar Parida ◽  
Dipak Paul ◽  
Debamitra Chakravorty
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Molecular Dynamics Simulations ◽  
Withania Somnifera ◽  
Minimum Energy ◽  
Interaction Network ◽  
Energy Landscapes ◽  
Residue Interaction ◽  
Free Energy Landscapes ◽  
Dynamics Simulations

The pandemic is here to stay- evident from the second wave that is severely affecting global population. Though vaccination is now available, the population size restricts its efficacy, especially in the third world countries. Therefore, to avoid a third wave, natural preventive therapeutics are the need of the hour. In this work the efficiency of phytochemicals from <i>Withania somnifera</i> to bind to a total of six SARS-CoV-2 targets have been shown.1 µs molecular dynamics simulations and essential dynamic analyses shed light on the changes induced by the phytochemicals and highlights their multipotent capabilities- 27-Hydroxywithanolide B was able to bind to three targets. Relative free energy of binding for all the phytochemicals were calculated by MM/PBSA. Minimum energy structures were extracted from their free energy landscapes and were subjected to PSN-ENM-NMA and network centrality analysis. Results showed that the phytochemical binding changes the residue-residue interaction network. Network communities increase while hubs and links decrease. Metapath rewiring occurs through residues Phe456 in spike protein, Thr26 and Tyr118 in main protease, Val49 and Phe156 in NSP3, Leu98 in NSP9, Leu4345 in NSP10, Phe440 and Phe843 in NSP12. This work tries to understand the mechanism of possible inhibition by the phytochemicals to combat SARS-CoV-2 with their capability of targeting multiple proteins. The insight from this study can be of great relevance to explore the changes in network properties induced by reported potential inhibitors against SARS-CoV-2 targets.

scholarly journals ssbio: a Python framework for structural systems biology

2018 ◽  
Vol 34 (12) ◽  
pp. 2155-2157 ◽  
Author(s):  
Nathan Mih ◽  
Elizabeth Brunk ◽  
Ke Chen ◽  
Edward Catoiu ◽  
Anand Sastry ◽  
...  
Keyword(s):  
Structural Information ◽  
Protein Structures ◽  
Structural Data ◽  
Third Party ◽  
Supplementary Information ◽  
Scale Models ◽  
Protein Properties ◽  
Scale Network ◽  
Structural Systems Biology ◽  
Genome Scale

Abstract Summary Working with protein structures at the genome-scale has been challenging in a variety of ways. Here, we present ssbio, a Python package that provides a framework to easily work with structural information in the context of genome-scale network reconstructions, which can contain thousands of individual proteins. The ssbio package provides an automated pipeline to construct high quality genome-scale models with protein structures (GEM-PROs), wrappers to popular third-party programs to compute associated protein properties, and methods to visualize and annotate structures directly in Jupyter notebooks, thus lowering the barrier of linking 3D structural data with established systems workflows. Availability and implementation ssbio is implemented in Python and available to download under the MIT license at http://github.com/SBRG/ssbio. Documentation and Jupyter notebook tutorials are available at http://ssbio.readthedocs.io/en/latest/. Interactive notebooks can be launched using Binder at https://mybinder.org/v2/gh/SBRG/ssbio/master?filepath=Binder.ipynb. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals COSIFER: a Python package for the consensus inference of molecular interaction networks

2020 ◽  
Author(s):  
Matteo Manica ◽  
Charlotte Bunne ◽  
Roland Mathis ◽  
Joris Cadow ◽  
Mehmet Eren Ahsen ◽  
...  
Keyword(s):  
High Throughput ◽  
Network Inference ◽  
State Of The Art ◽  
Interaction Network ◽  
Molecular Networks ◽  
Supplementary Information ◽  
Web Based ◽  
Molecular Interaction Networks ◽  
Inference Methods ◽  
User Friendly

Abstract Summary The advent of high-throughput technologies has provided researchers with measurements of thousands of molecular entities and enable the investigation of the internal regulatory apparatus of the cell. However, network inference from high-throughput data is far from being a solved problem. While a plethora of different inference methods have been proposed, they often lead to non-overlapping predictions, and many of them lack user-friendly implementations to enable their broad utilization. Here, we present Consensus Interaction Network Inference Service (COSIFER), a package and a companion web-based platform to infer molecular networks from expression data using state-of-the-art consensus approaches. COSIFER includes a selection of state-of-the-art methodologies for network inference and different consensus strategies to integrate the predictions of individual methods and generate robust networks. Availability and implementation COSIFER Python source code is available at https://github.com/PhosphorylatedRabbits/cosifer. The web service is accessible at https://ibm.biz/cosifer-aas. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals ssbio: A Python Framework for Structural Systems Biology

2017 ◽  
Author(s):  
Nathan Mih ◽  
Elizabeth Brunk ◽  
Ke Chen ◽  
Edward Catoiu ◽  
Anand Sastry ◽  
...  
Keyword(s):  
Structural Information ◽  
Protein Structures ◽  
Structural Data ◽  
Third Party ◽  
Supplementary Information ◽  
Link Type ◽  
Protein Properties ◽  
Scale Network ◽  
Structural Systems Biology ◽  
Genome Scale

AbstractSummaryWorking with protein structures at the genome-scale has been challenging in a variety of ways. Here, we present ssbio, a Python package that provides a framework to easily work with structural information in the context of genome-scale network reconstructions, which can contain thousands of individual proteins. The ssbio package provides an automated pipeline to construct high quality genome-scale models with protein structures (GEM-PROs), wrappers to popular third-party programs to compute associated protein properties, and methods to visualize and annotate structures directly in Jupyter notebooks, thus lowering the barrier of linking 3D structural data with established systems workflows.Availability and Implementationssbio is implemented in Python and available to download under the MIT license at http://github.com/SBRG/ssbio. Documentation and Jupyter notebook tutorials are available at http://ssbio.readthedocs.io/en/latest/. Interactive notebooks can be launched using Binder at https://mybinder.org/v2/gh/SBRG/ssbio/[email protected] InformationSupplementary data are available at Bioinformatics online.

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