scholarly journals CABS-dock standalone: a toolbox for flexible protein–peptide docking

2019 ◽  
Vol 35 (20) ◽  
pp. 4170-4172 ◽  
Author(s):  
Mateusz Kurcinski ◽  
Maciej Pawel Ciemny ◽  
Tymoteusz Oleniecki ◽  
Aleksander Kuriata ◽  
Aleksandra E Badaczewska-Dawid ◽  
...  
Keyword(s):  
Docking Simulation ◽  
Computational Time ◽  
Backbone Flexibility ◽  
Non Profit ◽  
Peptide Docking ◽  
Protein Receptor ◽  
Analysis Of Results ◽  
Peptide System ◽  
Flexible Protein ◽  
Python Package

AbstractSummaryCABS-dock standalone is a multiplatform Python package for protein–peptide docking with backbone flexibility. The main feature of the CABS-dock method is its ability to simulate significant backbone flexibility of the entire protein–peptide system in a reasonable computational time. In the default mode, the package runs a simulation of fully flexible peptide searching for a binding site on the surface of a flexible protein receptor. The flexibility level of the molecules may be defined by the user. Furthermore, the CABS-dock standalone application provides users with full control over the docking simulation from the initial setup to the analysis of results. The standalone version is an upgrade of the original web server implementation—it introduces a number of customizable options, provides support for large-sized systems and offers a framework for deeper analysis of docking results.Availability and implementationCABS-dock standalone is distributed under the MIT licence, which is free for academic and non-profit users. It is implemented in Python and Fortran. The CABS-dock standalone source code, wiki with documentation and examples of use and installation instructions for Linux, macOS and Windows are available in the CABS-dock standalone repository at https://bitbucket.org/lcbio/cabsdock.

scholarly journals Viola: a structural variant signature extractor with user-defined classifications

2021 ◽  
Author(s):  
Itsuki Sugita ◽  
Shohei Matsuyama ◽  
Hiroki Dobashi ◽  
Daisuke Komura ◽  
Shumpei Ishikawa
Keyword(s):  
Large Scale ◽  
Computational Time ◽  
Structural Variant ◽  
Analytical Functions ◽  
Dna Variations ◽  
Python Package

Here, we present Viola, a Python package that provides structural variant (SV; large scale genome DNA variations that can result in disease, e.g., cancer) signature analytical functions and utilities for custom SV classification, merging multi-SV-caller output files, and SV annotation. We demonstrate that Viola can extract biologically meaningful SV signatures from publicly available SV data for cancer and we evaluate the computational time necessary for annotation of the data.

scholarly journals Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249404
Author(s):  
Jeongtae Son ◽  
Dongsup Kim
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Drug Discovery ◽  
Ligand Binding ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Docking Simulation ◽  
Computational Time ◽  
Binding Affinities

Prediction of protein-ligand interactions is a critical step during the initial phase of drug discovery. We propose a novel deep-learning-based prediction model based on a graph convolutional neural network, named GraphBAR, for protein-ligand binding affinity. Graph convolutional neural networks reduce the computational time and resources that are normally required by the traditional convolutional neural network models. In this technique, the structure of a protein-ligand complex is represented as a graph of multiple adjacency matrices whose entries are affected by distances, and a feature matrix that describes the molecular properties of the atoms. We evaluated the predictive power of GraphBAR for protein-ligand binding affinities by using PDBbind datasets and proved the efficiency of the graph convolution. Given the computational efficiency of graph convolutional neural networks, we also performed data augmentation to improve the model performance. We found that data augmentation with docking simulation data could improve the prediction accuracy although the improvement seems not to be significant. The high prediction performance and speed of GraphBAR suggest that such networks can serve as valuable tools in drug discovery.

scholarly journals Insights Into Amentoflavone: A Natural Multifunctional Biflavonoid

2021 ◽  
Vol 12 ◽  
Author(s):  
Xifeng Xiong ◽  
Nan Tang ◽  
Xudong Lai ◽  
Jinli Zhang ◽  
Weilun Wen ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Biological Activities ◽  
Docking Simulation ◽  
Metabolism Regulation ◽  
Protein Receptor ◽  
Main Protease ◽  
Extracellular Signal ◽  
Anti Cancer ◽  
Selaginella Tamariscina ◽  
Phosphoinositide 3 Kinase

Amentoflavone is an active phenolic compound isolated from Selaginella tamariscina over 40 years. Amentoflavone has been extensively recorded as a molecule which displays multifunctional biological activities. Especially, amentoflavone involves in anti-cancer activity by mediating various signaling pathways such as extracellular signal-regulated kinase (ERK), nuclear factor kappa-B (NF-κB) and phosphoinositide 3-kinase/protein kinase B (PI3K/Akt), and emerges anti-SARS-CoV-2 effect via binding towards the main protease (Mpro/3CLpro), spike protein receptor binding domain (RBD) and RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2. Therefore, amentoflavone is considered to be a promising therapeutic agent for clinical research. Considering the multifunction of amentoflavone, the current review comprehensively discuss the chemistry, the progress in its diverse biological activities, including anti-inflammatory, anti-oxidation, anti-microorganism, metabolism regulation, neuroprotection, radioprotection, musculoskeletal protection and antidepressant, specially the fascinating role against various types of cancers. In addition, the bioavailability and drug delivery of amentoflavone, the molecular mechanisms underlying the activities of amentoflavone, the molecular docking simulation of amentoflavone through in silico approach and anti-SARS-CoV-2 effect of amentoflavone are discussed.

scholarly journals Highly Flexible Protein-Peptide Docking Using CABS-Dock

2017 ◽  
pp. 69-94 ◽  
Author(s):  
Maciej Paweł Ciemny ◽  
Mateusz Kurcinski ◽  
Konrad Jakub Kozak ◽  
Andrzej Kolinski ◽  
Sebastian Kmiecik
Keyword(s):  
Peptide Docking ◽  
Flexible Protein

Aggrescan3D standalone package for structure-based prediction of protein aggregation properties

2019 ◽  
Vol 35 (19) ◽  
pp. 3834-3835 ◽  
Author(s):  
Aleksander Kuriata ◽  
Valentin Iglesias ◽  
Mateusz Kurcinski ◽  
Salvador Ventura ◽  
Sebastian Kmiecik
Keyword(s):  
Protein Aggregation ◽  
Rational Design ◽  
Protein Solubility ◽  
Automated Analysis ◽  
Computational Framework ◽  
Computational Tools ◽  
Non Profit ◽  
Recent Experimental Study ◽  
The Impact ◽  
Python Package

Abstract Summary Aggrescan3D (A3D) standalone is a multiplatform Python package for structure-based prediction of protein aggregation properties and rational design of protein solubility. A3D allows the re-design of protein solubility by combining structural aggregation propensity and stability predictions, as demonstrated by a recent experimental study. It also enables predicting the impact of protein conformational fluctuations on the aggregation properties. The standalone A3D version is an upgrade of the original web server implementation—it introduces a number of customizable options, automated analysis of multiple mutations and offers a flexible computational framework for merging it with other computational tools. Availability and implementation A3D standalone is distributed under the MIT license, which is free for academic and non-profit users. It is implemented in Python. The A3D standalone source code, wiki with documentation and examples of use, and installation instructions for Linux, macOS and Windows are available in the A3D standalone repository at https://bitbucket.org/lcbio/aggrescan3d.

scholarly journals Copper-Catalyzed Synthesis, Bio-Evaluation, and in Silico Studies of 2-Aryl-N-alkylbenzimidazoles as Neuroprotective Agents

Catalysts ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. 433 ◽  
Author(s):  
Yun-Xin Yao ◽  
Nan-Nan Jia ◽  
Ya-Nan Cao ◽  
Xing-Xiu Chen ◽  
Feng Gao ◽  
...  
Keyword(s):  
In Silico ◽  
Topoisomerase Ii ◽  
Metabotropic Glutamate Receptor ◽  
Docking Simulation ◽  
Histone Deacetylase 6 ◽  
Metabotropic Glutamate ◽  
Aryl Bromides ◽  
Protein Receptor ◽  
In Silico Studies

2-aryl-N-alkylbenzimidazole derivatives synthesized by CuI/PPh3 promoted direct coupling of N-alkylbenzimidazoles with aryl bromides. In vitro neurotoxicities of 20 compounds were evaluated, and the neuroprotective abilities of low-neurotoxic compounds (3b, 3g, 3h, 3i, 3j, 3k, 3o, 3q, 3s and 3t) were investigated against toxicity induced by 1-methyl-4-phenylpyridinium ion (MPP+) in SH-SY5Y neuronal cells. In silico studies revealed that compound 3g could have molecule docking with the following proteins: the bone morphogenetic protein receptor type 1B (BMPR1B), human cytochrome P450 1B1(CYP1B1), Metabotropic glutamate receptor 7 (GRM7), histone deacetylase 6 (HDAC6), 5-hydroxytryptamine receptor 5A (HTR5A), human topoisomerase II beta (TOP2B). A molecular docking simulation of model compound 3g and model protein CYP1B1 has been shown.

scholarly journals Bayesian Fitting of Multi-Gaussian Expansion Models to Galaxy Images

2021 ◽  
Vol 923 (1) ◽  
pp. 124
Author(s):  
Tim B. Miller ◽  
Pieter van Dokkum
Keyword(s):  
Surface Brightness ◽  
Hubble Space Telescope ◽  
Computation Time ◽  
Large Samples ◽  
Point Spread ◽  
Spread Function ◽  
Analysis Of Results ◽  
Validation Tests ◽  
Degraded Images ◽  
Python Package

Abstract Fitting parameterized models to images of galaxies has become the standard for measuring galaxy morphology. This forward-modeling technique allows one to account for the point-spread function to effectively study semi-resolved galaxies. However, using a specific parameterization for a galaxy’s surface brightness profile can bias measurements if it is not an accurate representation. Furthermore, it can be difficult to assess systematic errors in parameterized profiles. To overcome these issues we employ the Multi-Gaussian expansion (MGE) method of representing a galaxy’s profile together with a Bayesian framework for fitting images. MGE flexibly represents a galaxy’s profile using a series of Gaussians. We introduce a novel Bayesian inference approach that uses pre-rendered Gaussian components, which greatly speeds up computation time and makes it feasible to run the fitting code on large samples of galaxies. We demonstrate our method with a series of validation tests. By injecting galaxies, with properties similar to those observed at z ∼ 1.5, into deep Hubble Space Telescope observations we show that it can accurately recover total fluxes and effective radii of realistic galaxies. Additionally we use degraded images of local galaxies to show that our method can recover realistic galaxy surface brightness and color profiles. Our implementation is available in an open source python package imcascade, which contains all methods needed for the preparation of images, fitting, and analysis of results.

scholarly journals wFReDoW: A Cloud-Based Web Environment to Handle Molecular Docking Simulations of a Fully Flexible Receptor Model

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Renata De Paris ◽  
Fábio A. Frantz ◽  
Osmar Norberto de Souza ◽  
Duncan D. A. Ruiz
Keyword(s):  
Molecular Docking ◽  
High Performance ◽  
Coming Of Age ◽  
Docking Simulation ◽  
Docking Simulations ◽  
Web Environment ◽  
Protein Receptor ◽  
Important Challenge ◽  
Performance Computing

Molecular docking simulations of fully flexible protein receptor (FFR) models are coming of age. In our studies, an FFR model is represented by a series of different conformations derived from a molecular dynamic simulation trajectory of the receptor. For each conformation in the FFR model, a docking simulation is executed and analyzed. An important challenge is to perform virtual screening of millions of ligands using an FFR model in a sequential mode since it can become computationally very demanding. In this paper, we propose a cloud-based web environment, called web Flexible Receptor Docking Workflow (wFReDoW), which reduces the CPU time in the molecular docking simulations of FFR models to small molecules. It is based on the new workflow data pattern called self-adaptive multiple instances (P-SaMIs) and on a middleware built on Amazon EC2 instances. P-SaMI reduces the number of molecular docking simulations while the middleware speeds up the docking experiments using a High Performance Computing (HPC) environment on the cloud. The experimental results show a reduction in the total elapsed time of docking experiments and the quality of the new reduced receptor models produced by discarding the nonpromising conformations from an FFR model ruled by the P-SaMI data pattern.

Accurate Prediction of Complex Structure and Affinity for a Flexible Protein Receptor and Its Inhibitor

2017 ◽  
Vol 13 (6) ◽  
pp. 2389-2399 ◽  
Author(s):  
Gert-Jan Bekker ◽  
Narutoshi Kamiya ◽  
Mitsugu Araki ◽  
Ikuo Fukuda ◽  
Yasushi Okuno ◽  
...  
Keyword(s):  
Complex Structure ◽  
Accurate Prediction ◽  
Protein Receptor ◽  
Flexible Protein
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