scholarly journals DDT - Drug Discovery Tool: a fast and intuitive graphics user interface for docking and molecular dynamics analysis

2019 ◽  
Author(s):  
Simone Aureli ◽  
Daniele Di Marino ◽  
Stefano Raniolo ◽  
Vittorio Limongelli
Keyword(s):  
Molecular Dynamics ◽  
User Interface ◽  
Drug Discovery ◽  
Protein Binding ◽  
Protein Complexes ◽  
Md Simulations ◽  
Supplementary Information ◽  
Chemical Information ◽  
Binding Interaction ◽  
Fast Analysis

Abstract Motivation The ligand/protein binding interaction is typically investigated by docking and molecular dynamics (MD) simulations. In particular, docking-based virtual screening (VS) is used to select the best ligands from database of thousands of compounds, while MD calculations assess the energy stability of the ligand/protein binding complexes. Considering the broad use of these techniques, it is of great demand to have one single software that allows a combined and fast analysis of VS and MD results. With this in mind, we have developed the Drug Discovery Tool (DDT) that is an intuitive graphics user interface able to provide structural data and physico-chemical information on the ligand/protein interaction. Results DDT is designed as a plugin for the Visual Molecular Dynamics (VMD) software and is able to manage a large number of ligand/protein complexes obtained from AutoDock4 (AD4) docking calculations and MD simulations. DDT delivers four main outcomes: i) ligands ranking based on an energy score; ii) ligand ranking based on a ligands’ conformation cluster analysis; iii) identification of the aminoacids forming the most occurrent interactions with the ligands; iv) plot of the ligands’ center-of-mass coordinates in the Cartesian space. The flexibility of the software allows saving the best ligand/protein complexes using a number of user-defined options. Availability and implementation DDT_site_1 (alternative DDT_site_2); the DDT tutorial movie is available here. Supplementary information Supplementary data are available at Bioinformatics online.

UNRES-Dock—protein–protein and peptide–protein docking by coarse-grained replica-exchange MD simulations

2020 ◽  
Author(s):  
Paweł Krupa ◽  
Agnieszka S Karczyńska ◽  
Magdalena A Mozolewska ◽  
Adam Liwo ◽  
Cezary Czaplewski
Keyword(s):  
Protein Complexes ◽  
Md Simulations ◽  
Protein Docking ◽  
Conformational Space ◽  
Coarse Grained ◽  
Supplementary Information ◽  
Replica Exchange ◽  
Variable Degree ◽  
Single Chain ◽  
Simulation Speed

Abstract Motivation The majority of the proteins in living organisms occur as homo- or hetero-multimeric structures. Although there are many tools to predict the structures of single-chain proteins or protein complexes with small ligands, peptide–protein and protein–protein docking is more challenging. In this work, we utilized multiplexed replica-exchange molecular dynamics (MREMD) simulations with the physics-based heavily coarse-grained UNRES model, which provides more than a 1000-fold simulation speed-up compared with all-atom approaches to predict structures of protein complexes. Results We present a new protein–protein and peptide–protein docking functionality of the UNRES package, which includes a variable degree of conformational flexibility. UNRES-Dock protocol was tested on a set of 55 complexes with size from 43 to 587 amino-acid residues, showing that structures of the complexes can be predicted with good quality, if the sampling of the conformational space is sufficient, especially for flexible peptide–protein systems. The developed automatized protocol has been implemented in the standalone UNRES package and in the UNRES server. Availability and implementation UNRES server: http://unres-server.chem.ug.edu.pl; UNRES package and data used in testing of UNRES-Dock: http://unres.pl. Supplementary information Supplementary data are available at Bioinformatics online.

A boosted unbiased molecular dynamics method for predicting ligands binding mechanisms: probing the binding pathway of dasatinib to Src-kinase

2020 ◽  
Vol 36 (18) ◽  
pp. 4714-4720
Author(s):  
Farzin Sohraby ◽  
Mostafa Javaheri Moghadam ◽  
Masoud Aliyar ◽  
Hassan Aryapour
Keyword(s):  
Molecular Dynamics ◽  
Ligand Binding ◽  
Md Simulation ◽  
Src Kinase ◽  
Deep Understanding ◽  
Md Simulations ◽  
Binding Mode ◽  
Supplementary Information ◽  
Computational Resources ◽  
Binding Mechanisms

Abstract Summary Small molecules such as metabolites and drugs play essential roles in biological processes and pharmaceutical industry. Knowing their interactions with biomacromolecular targets demands a deep understanding of binding mechanisms. Dozens of papers have suggested that discovering of the binding event by means of conventional unbiased molecular dynamics (MD) simulation urges considerable amount of computational resources, therefore, only one who holds a cluster or a supercomputer can afford such extensive simulations. Thus, many researchers who do not own such resources are reluctant to take the benefits of running unbiased MD simulation, in full atomistic details, when studying a ligand binding pathway. Many researchers are impelled to be content with biased MD simulations which seek its validation due to its intrinsic preconceived framework. In this work, we have presented a workable stratagem to encourage everyone to perform unbiased (unguided) MD simulations, in this case a protein–ligand binding process, by typical desktop computers and so achieve valuable results in nanosecond time scale. Here, we have described a dynamical binding’s process of an anticancer drug, the dasatinib, to the c-Src kinase in full atomistic details for the first time, without applying any biasing force or potential which may lead the drug to artificial interactions with the protein. We have attained multiple independent binding events which occurred in the nanosecond time scales, surprisingly as little as ∼30 ns. Both the protonated and deprotonated forms of the dasatinib reached the crystallographic binding mode without having any major intermediate state during induction. Availability and implementation The links of the tutorial and technical documents are accessible in the article. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals In SilicoIdentification of Potent PPAR-γAgonists from Traditional Chinese Medicine: A Bioactivity Prediction, Virtual Screening, and Molecular Dynamics Study

2014 ◽  
Vol 2014 ◽  
pp. 1-19 ◽  
Author(s):  
Kuan-Chung Chen ◽  
Calvin Yu-Chian Chen
Keyword(s):  
Molecular Dynamics ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Virtual Screening ◽  
Prediction Models ◽  
Protein Complexes ◽  
Md Simulations ◽  
Peroxisome Proliferator ◽  
Peroxisome Proliferator Activated Receptors ◽  
Key Residues

The peroxisome proliferator-activated receptors (PPARs) related to regulation of lipid metabolism, inflammation, cell proliferation, differentiation, and glucose homeostasis by controlling the related ligand-dependent transcription of networks of genes. They are used to be served as therapeutic targets against metabolic disorder, such as obesity, dyslipidemia, and diabetes; especially, PPAR-γis the most extensively investigated isoform for the treatment of dyslipidemic type 2 diabetes. In this study, we filter compounds of traditional Chinese medicine (TCM) using bioactivities predicted by three distinct prediction models before the virtual screening. For the top candidates, the molecular dynamics (MD) simulations were also utilized to investigate the stability of interactions between ligand and PPAR-γprotein. The top two TCM candidates, 5-hydroxy-L-tryptophan and abrine, have an indole ring and carboxyl group to form the H-bonds with the key residues of PPAR-γprotein, such as residues Ser289 and Lys367. The secondary amine group of abrine also stabilized an H-bond with residue Ser289. From the figures of root mean square fluctuations (RMSFs), the key residues were stabilized in protein complexes with 5-Hydroxy-L-tryptophan and abrine as control. Hence, we propose 5-hydroxy-L-tryptophan and abrine as potential lead compounds for further study in drug development process with the PPAR-γprotein.

scholarly journals MODE-TASK: Large-scale protein motion tools

2017 ◽  
Author(s):  
Caroline Ross ◽  
Bilal Nizami ◽  
Michael Glenister ◽  
Olivier Sheik Amamuddy ◽  
Ali Rana Atilgan ◽  
...  
Keyword(s):  
Large Scale ◽  
Protein Complexes ◽  
Normal Mode Analysis ◽  
Md Simulations ◽  
Supplementary Information ◽  
Mode Analysis ◽  
Analysis Tool ◽  
Link Type ◽  
Supplementary Material ◽  
Anisotropic Network

AbstractSummaryMODE-TASK, a novel software suite, comprises Principle Component Analysis, Multidimensional Scaling, and t-Distributed Stochastic Neighbor Embedding techniques using molecular dynamics trajectories. MODE-TASK also includes a Normal Mode Analysis tool based on Anisotropic Network Model so as to provide a variety of ways to analyse and compare large-scale motions of protein complexes for which long MD simulations are prohibitive.Availability and ImplementationMODE-TASK has been open-sourced, and is available for download from https://github.com/RUBi-ZA/MODE-TASK, implemented in Python and C++.Supplementary informationDocumentation available at http://mode-task.readthedocs.io.

scholarly journals Maximizing resource usage in multifold molecular dynamics with rCUDA

2019 ◽  
Vol 34 (1) ◽  
pp. 5-19
Author(s):  
Javier Prades ◽  
Baldomero Imbernón ◽  
Carlos Reaño ◽  
Jorge Peña-García ◽  
Jose Pedro Cerón-Carrasco ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Drug Discovery ◽  
Graphics Processing Units ◽  
Materials Science ◽  
Dynamical Behavior ◽  
Md Simulations ◽  
Atomic Scale ◽  
Power Budget ◽  
Single Node ◽  
Time Required

The full-understanding of the dynamics of molecular systems at the atomic scale is of great relevance in the fields of chemistry, physics, materials science, and drug discovery just to name a few. Molecular dynamics (MD) is a widely used computer tool for simulating the dynamical behavior of molecules. However, the computational horsepower required by MD simulations is too high to obtain conclusive results in real-world scenarios. This is mainly motivated by two factors: (1) the long execution time required by each MD simulation (usually in the nanoseconds and microseconds scale, and beyond) and (2) the large number of simulations required in drug discovery to study the interactions between a large library of compounds and a given protein target. To deal with the former, graphics processing units (GPUs) have come up into the scene. The latter has been traditionally approached by launching large amounts of simulations in computing clusters that may contain several GPUs on each node. However, GPUs are targeted as a single node that only runs one MD instance at a time, which translates into low GPU occupancy ratios and therefore low throughput. In this work, we propose a strategy to increase the overall throughput of MD simulations by increasing the GPU occupancy through virtualized GPUs. We use the remote CUDA (rCUDA) middleware as a tool to decouple GPUs from CPUs, and thus enabling multi-tenancy of the virtual GPUs. As a working test in the drug discovery field, we studied the binding process of a novel flavonol to DNA with the GROningen MAchine for Chemical Simulations (GROMACS) MD package. Our results show that the use of rCUDA provides with a 1.21× speed-up factor compared to the CUDA counterpart version while requiring a similar power budget.

scholarly journals Data-Driven Molecular Dynamics: A Multifaceted Challenge

2020 ◽  
Vol 13 (9) ◽  
pp. 253
Author(s):  
Mattia Bernetti ◽  
Martina Bertazzo ◽  
Matteo Masetti
Keyword(s):  
Molecular Dynamics ◽  
Big Data ◽  
Drug Discovery ◽  
Drug Design ◽  
Md Simulations ◽  
Biomolecular Systems ◽  
Biomolecular Simulations ◽  
The Impact ◽  
Opening Up ◽  
Computational Drug Discovery

The big data concept is currently revolutionizing several fields of science including drug discovery and development. While opening up new perspectives for better drug design and related strategies, big data analysis strongly challenges our current ability to manage and exploit an extraordinarily large and possibly diverse amount of information. The recent renewal of machine learning (ML)-based algorithms is key in providing the proper framework for addressing this issue. In this respect, the impact on the exploitation of molecular dynamics (MD) simulations, which have recently reached mainstream status in computational drug discovery, can be remarkable. Here, we review the recent progress in the use of ML methods coupled to biomolecular simulations with potentially relevant implications for drug design. Specifically, we show how different ML-based strategies can be applied to the outcome of MD simulations for gaining knowledge and enhancing sampling. Finally, we discuss how intrinsic limitations of MD in accurately modeling biomolecular systems can be alleviated by including information coming from experimental data.

scholarly journals NAPS update: network analysis of molecular dynamics data and protein–nucleic acid complexes

2019 ◽  
Vol 47 (W1) ◽  
pp. W462-W470 ◽  
Author(s):  
Broto Chakrabarty ◽  
Varun Naganathan ◽  
Kanak Garg ◽  
Yash Agarwal ◽  
Nita Parekh
Keyword(s):  
Molecular Dynamics ◽  
Nucleic Acid ◽  
Network Analysis ◽  
Conformational Changes ◽  
Protein Complexes ◽  
Md Simulations ◽  
Network Construction ◽  
Dynamic Correlation ◽  
Residue Interaction ◽  
Protein Nucleic Acid

Abstract Network theory is now a method of choice to gain insights in understanding protein structure, folding and function. In combination with molecular dynamics (MD) simulations, it is an invaluable tool with widespread applications such as analyzing subtle conformational changes and flexibility regions in proteins, dynamic correlation analysis across distant regions for allosteric communications, in drug design to reveal alternative binding pockets for drugs, etc. Updated version of NAPS now facilitates network analysis of the complete repertoire of these biomolecules, i.e., proteins, protein–protein/nucleic acid complexes, MD trajectories, and RNA. Various options provided for analysis of MD trajectories include individual network construction and analysis of intermediate time-steps, comparative analysis of these networks, construction and analysis of average network of the ensemble of trajectories and dynamic cross-correlations. For protein–nucleic acid complexes, networks of the whole complex as well as that of the interface can be constructed and analyzed. For analysis of proteins, protein–protein complexes and MD trajectories, network construction based on inter-residue interaction energies with realistic edge-weights obtained from standard force fields is provided to capture the atomistic details. Updated version of NAPS also provides improved visualization features, interactive plots and bulk execution. URL: http://bioinf.iiit.ac.in/NAPS/

scholarly journals Protein-Protein Interactions: Insight from Molecular Dynamics Simulations and Nanoparticle Tracking Analysis

Molecules ◽  
2021 ◽  
Vol 26 (18) ◽  
pp. 5696
Author(s):  
Wei Lim Chong ◽  
Koollawat Chupradit ◽  
Sek Peng Chin ◽  
Mai Mai Khoo ◽  
Sook Mei Khor ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Free Energy ◽  
Protein Binding ◽  
Protein Interactions ◽  
Binding Free Energy ◽  
Single Point ◽  
Md Simulations ◽  
Nanoparticle Tracking Analysis ◽  
Buffer Solution ◽  
Dynamics Simulations

Protein-protein interaction plays an essential role in almost all cellular processes and biological functions. Coupling molecular dynamics (MD) simulations and nanoparticle tracking analysis (NTA) assay offered a simple, rapid, and direct approach in monitoring the protein-protein binding process and predicting the binding affinity. Our case study of designed ankyrin repeats proteins (DARPins)—AnkGAG1D4 and the single point mutated AnkGAG1D4-Y56A for HIV-1 capsid protein (CA) were investigated. As reported, AnkGAG1D4 bound with CA for inhibitory activity; however, it lost its inhibitory strength when tyrosine at residue 56 AnkGAG1D4, the most key residue was replaced by alanine (AnkGAG1D4-Y56A). Through NTA, the binding of DARPins and CA was measured by monitoring the increment of the hydrodynamic radius of the AnkGAG1D4-gold conjugated nanoparticles (AnkGAG1D4-GNP) and AnkGAG1D4-Y56A-GNP upon interaction with CA in buffer solution. The size of the AnkGAG1D4-GNP increased when it interacted with CA but not AnkGAG1D4-Y56A-GNP. In addition, a much higher binding free energy (∆GB) of AnkGAG1D4-Y56A (−31 kcal/mol) obtained from MD further suggested affinity for CA completely reduced compared to AnkGAG1D4 (−60 kcal/mol). The possible mechanism of the protein-protein binding was explored in detail by decomposing the binding free energy for crucial residues identification and hydrogen bond analysis.

scholarly journals Implementation of Geometry Dependent Charge Flux into Polarizable AMOEBA+ Potential

2019 ◽  
Author(s):  
Chengwen Liu ◽  
Jean-Philip Piquemal ◽  
Pengyu Ren
Keyword(s):  
Molecular Dynamics ◽  
Atomic Charge ◽  
Md Simulations ◽  
Water Model ◽  
Local Geometry ◽  
Important Ingredient ◽  
Binding Interaction ◽  
Molecular Systems ◽  
Water Models ◽  
Charge Flux

Molecular dynamics (MD) simulations employing classical force fields (FFs) have been widely used to model molecular systems. The important ingredient of the current FFs, atomic charge, remains fixed during MD simulations despite the atomic environment or local geometry changes. This approximation hinders the transferability of the potential being used in multiple phases. Here we implement a geometry dependent charge flux (GDCF) model into the multipole-based AMOEBA+ polarizable potential. The CF in the current work explicitly depends on the local geometry (<i>bond and angle</i>) of the molecule. To our knowledge, this is the first study that derives energy and force expressions due to GDCF in a multipole-based polarizable FF framework. Due to the inclusion of GDCF, the AMOEBA+ water model is noticeably improved in terms of describing the monomer properties, cluster binding/interaction energy and a variety of liquid properties, including the infrared spectra that previous flexible water models were not able to capture.

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