Targeting Tmprss2, S-Protein:Ace2, and 3Clpro for Synergetic Inhibitory Engagement Mathew Coban1, Juliet Morrison Phd2, William
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Open Babel Documentation Release 2.3.1
Open Babel Documentation Release 2.3.1 Geoffrey R Hutchison Chris Morley Craig James Chris Swain Hans De Winter Tim Vandermeersch Noel M O’Boyle (Ed.) December 05, 2011 Contents 1 Introduction 3 1.1 Goals of the Open Babel project ..................................... 3 1.2 Frequently Asked Questions ....................................... 4 1.3 Thanks .................................................. 7 2 Install Open Babel 9 2.1 Install a binary package ......................................... 9 2.2 Compiling Open Babel .......................................... 9 3 obabel and babel - Convert, Filter and Manipulate Chemical Data 17 3.1 Synopsis ................................................. 17 3.2 Options .................................................. 17 3.3 Examples ................................................. 19 3.4 Differences between babel and obabel .................................. 21 3.5 Format Options .............................................. 22 3.6 Append property values to the title .................................... 22 3.7 Filtering molecules from a multimolecule file .............................. 22 3.8 Substructure and similarity searching .................................. 25 3.9 Sorting molecules ............................................ 25 3.10 Remove duplicate molecules ....................................... 25 3.11 Aliases for chemical groups ....................................... 26 4 The Open Babel GUI 29 4.1 Basic operation .............................................. 29 4.2 Options ................................................. -
Foldx As Protein Engineering Tool: Better Than Random Based Approaches?
Computational and Structural Biotechnology Journal 16 (2018) 25–33 Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/csbj FoldX as Protein Engineering Tool: Better Than Random Based Approaches? Oliver Buß ⁎,JensRudat,KatrinOchsenreither Institute of Process Engineering in Life Sciences, Section II: Technical Biology, Karlsruhe Institute of Technology, Karlsruhe, Germany article info abstract Article history: Improving protein stability is an important goal for basic research as well as for clinical and industrial applications Received 31 October 2017 but no commonly accepted and widely used strategy for efficient engineering is known. Beside random ap- Received in revised form 21 December 2017 proaches like error prone PCR or physical techniques to stabilize proteins, e.g. by immobilization, in silico ap- Accepted 20 January 2018 proaches are gaining more attention to apply target-oriented mutagenesis. In this review different algorithms Available online 03 February 2018 for the prediction of beneficial mutation sites to enhance protein stability are summarized and the advantages Keywords: and disadvantages of FoldX are highlighted. The question whether the prediction of mutation sites by the algo- FoldX rithm FoldX is more accurate than random based approaches is addressed. Fold-X © 2018 Buß et al. Published by Elsevier B.V. on behalf of the Research Network of Computational and Structural Thermostability Biotechnology. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Protein stabilization Protein engineering Enzyme engineering 1. Introduction per unit of product [10–12][13]. For industrial enzymes improved sta- bility against heat, solvents and other relevant process parameters, Increasing protein stability is a desirable goal for different life science e.g. -
Foldx Accurate Structural Protein–DNA Binding
3852–3863 Nucleic Acids Research, 2018, Vol. 46, No. 8 Published online 28 March 2018 doi: 10.1093/nar/gky228 FoldX accurate structural protein–DNA binding prediction using PADA1 (Protein Assisted DNA Assembly 1) Downloaded from https://academic.oup.com/nar/article-abstract/46/8/3852/4955761 by Biblioteca de la Universitat Pompeu Fabra user on 25 November 2019 Javier Delgado Blanco1,†, Leandro Radusky1,†,Hector´ Climente-Gonzalez´ 1 and Luis Serrano1,2,3,* 1Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Dr. Aiguader 88, 08003 Barcelona, Spain, 2Universitat Pompeu Fabra (UPF), Barcelona, Spain and 3Institucio´ Catalana de Recerca i Estudis Avanc¸ats (ICREA), Pg. Lluis Companys 23, 08010 Barcelona, Spain Received January 18, 2018; Revised March 12, 2018; Editorial Decision March 13, 2018; Accepted March 20, 2018 ABSTRACT are involved in numerous processes including DNA repli- cation, DNA repair, gene regulation, recombination, DNA The speed at which new genomes are being se- packing, etc. Currently, although there are >120 000 struc- quenced highlights the need for genome-wide meth- tures deposited in the PDB (3), only ∼5000 of them in- ods capable of predicting protein–DNA interactions. volve DNA–protein complexes. When considering the rate Here, we present PADA1, a generic algorithm that ac- at which new genomes are being sequenced, the resolu- curately models structural complexes and predicts tion of novel DNA–protein structures is relatively scarce. the DNA-binding regions of resolved protein struc- As such, we need to develop methods not only capable of tures. PADA1 relies on a library of protein and double- predicting whether a protein can interact with DNA, but stranded DNA fragment pairs obtained from a train- also capable of determining the protein’s 3D binding re- ing set of 2103 DNA–protein complexes. -
Expanding Molecular Modeling and Design Tools to Nonnatural Sidechains
WWW.C-CHEM.ORG FULL PAPER Expanding Molecular Modeling and Design Tools to Non-Natural Sidechains David Gfeller,[a] Olivier Michielin,*[a,b,c] and Vincent Zoete*[a] Protein–protein interactions encode the wiring diagram of increase peptide ligand binding affinity. Our results obtained on cellular signaling pathways and their deregulations underlie a non-natural mutants of a BCL9 peptide targeting beta-catenin variety of diseases, such as cancer. Inhibiting protein–protein show very good correlation between predicted and interactions with peptide derivatives is a promising way to experimental binding free-energies, indicating that such develop new biological and therapeutic tools. Here, we develop predictions can be used to design new inhibitors. Data a general framework to computationally handle hundreds of generated in this work, as well as PyMOL and UCSF Chimera non-natural amino acid sidechains and predict the effect of plug-ins for user-friendly visualization of non-natural sidechains, inserting them into peptides or proteins. We first generate all are all available at http://www.swisssidechain.ch. Our results structural files (pdb and mol2), as well as parameters and enable researchers to rapidly and efficiently work with hundreds topologies for standard molecular mechanics software of non-natural sidechains. VC 2012 Wiley Periodicals, Inc. (CHARMM and Gromacs). Accurate predictions of rotamer probabilities are provided using a novel combined knowledge and physics based strategy. Non-natural sidechains are useful to DOI: 10.1002/jcc.22982 Introduction are currently expanding peptide screening technologies to include non-natural sidechains. Correct interpretation of these Protein–protein interactions are fundamental to most biologi- results at the atomic level requires structural and biochemical cal and biochemical processes. -
BIOINFORMATICS APPLICATIONS NOTE Doi:10.1093/Bioinformatics/Btu426
Vol. 30 no. 20 2014, pages 2981–2982 BIOINFORMATICS APPLICATIONS NOTE doi:10.1093/bioinformatics/btu426 Structural bioinformatics Advance Access publication July 4, 2014 YASARA View—molecular graphics for all devices—from smartphones to workstations Elmar Krieger* and Gert Vriend Centre for Molecular and Biomolecular Informatics, NCMLS, Radboud University Nijmegen Medical Centre, 6500 HB Nijmegen, the Netherlands Downloaded from https://academic.oup.com/bioinformatics/article-abstract/30/20/2981/2422272 by guest on 17 March 2019 Associate Editor: Janet Kelso ABSTRACT 2 METHODS Summary: Today’s graphics processing units (GPUs) compose the The general idea is very simple and has been used ever since texture scene from individual triangles. As about 320 triangles are needed to mapping became part of 3D graphics: if an object is too complex (like approximate a single sphere—an atom—in a convincing way, visualiz- the 960 triangles required to draw a single water molecule in Fig. 1A) it is ing larger proteins with atomic details requires tens of millions of tri- replaced with ‘impostors’, i.e. fewer triangles that have precalculated tex- angles, far too many for smooth interactive frame rates. We describe a tures attached, which make them look like the original object. Texture mapping means that a triangle is not rendered with a single color, but an new approach to solve this ‘molecular graphics problem’, which image (the texture) is attached to it instead. For each of the three triangle shares the work between GPU and multiple CPU cores, generates vertices, the programmer can specify the corresponding 2D coordinates in high-quality results with perfectly round spheres, shadows and ambi- the texture, and the hardware interpolates in between. -
Understanding Protein-RNA Interactions for Alternative Splicing
Understanding Protein-RNA Interactions for Alternative Splicing During Gene Expression Using Molecular Dynamic Simulations Joseph Daou Department of Chemistry & Chemical Engineering/B.S. Chemistry Dequan Xiao Ph.D. Abstract The alternative splicing during gene expression is regulated by a type of protein called splicing factors. Recently, a splicing factor, SRSF2 protein was formed to recognize guanines and cytosines at the binding site of the RNA segment. A series of mutations were performed in experiments on SRSF2 to understand the binding interactions of the protein and RNA. However, the changes of binding interactions at the atomistic level due to the mutations have not been revealed. Here, we used molecular dynamics simulations to investigate the conformational changes and protein-RNA binding free energy changes due to a series of mutations. Through the extrapolation of intermediate states of molecular interactions during a fixed length of time throughout point mutations, we used free energy perturbation methods to compute the binding affinity between the mutated proteins and RNA segments. In addition, we also computed the binding free energy by comparing the free energy difference between the folded and unfolded states of the proteins. Our computed free energy changes due to mutations showed consistency with experimental findings. In addition, by molecular dynamic simulations for the solvated protein-RNA complexes, we revealed here the detailed interactions between the protein residues and the RNA for different mutants. Our results provide new insights on understanding the interaction between the splicing factor proteins and the RNA, which can be linked to understanding the pathological mechanisms of Leukemia and cancers. Introduction the option to solvate the RRM system for an in-vitro model Biophysical researchers have shown, comparable the environment of the protein-RNA complex. -
“One Ring to Rule Them All”
OpenMM library “One ring to rule them all” https://simtk.org/home/openmm CHARMM * Abalone Presto * ACEMD NAMD * ADUN * Ascalaph Gromacs AMBER * COSMOS ??? HOOMD * Desmond * Culgi * ESPResSo Tinker * GROMOS * GULP * Hippo LAMMPS * Kalypso MD * LPMD * MacroModel * MDynaMix * MOLDY * Materials Studio * MOSCITO * ProtoMol The OpenM(olecular)M(echanics) API * RedMD * YASARA * ORAC https://simtk.org/home/openmm * XMD Goals of OpenMM l Complete library l provide what is most needed l easy to learn and use l Fast, general, extensible l optimize for speed l hide hardware specifics l support new hardware l add new force fields, integration methods etc. l Available APIs in C++, Fortran95, Python l Advantages l Object oriented l Modular l Extensible l Disadvantages l Programs written in other languages need to invoke it through a layer of C++ code Features • Electrostatics: cut-off, Ewald, PME • Implicit solvent • Integrators: Verlet, Langevin, Brownian, custom • Thermostat: Andersen • Barostat: MonteCarlo • Others: energy minimization, virtual sites 5 The OpenMM Architecture Public Interface OpenMM Public API Platform Independent Code OpenMM Implementation Layer Platform Abstraction Layer OpenMM Low Level API Computational Kernels CUDA/OpenCL/Brook/MPI etc. Public API Classes (1) l System l A collection of interaction particles l Defines the mass of each particle (needed for integration) l Specifies distance restraints l Contains a list of Force objects that define the interactions l OpenMMContext l Contains all state information for a particular simulation − Positions, velocities, other parmeters Public API Classes (2) l Force l Anything that affects the system's behavior: forces, barostats, thermostats, etc. l A Force may: − Apply forces to particles − Contribute to the potential energy − Define adjustable parameters − Modify positions, velocities, and parameters at the start of each time step l Existing Force subclasses − HarmonicBond, HarmonicAngle, PeriodicTorsion, RBTorsion, Nonbonded, GBSAOBC, AndersenThermostat, CMMotionRemover.. -
Foldx 5.0: Working with RNA, Small Molecules and a New Graphical Interface Javier Delgado1,†, Leandro G
Bioinformatics, 35(20), 2019, 4168–4169 doi: 10.1093/bioinformatics/btz184 Advance Access Publication Date: 15 March 2019 Applications Note Structural bioinformatics FoldX 5.0: working with RNA, small molecules and a new graphical interface Javier Delgado1,†, Leandro G. Radusky1,†, Damiano Cianferoni1 and Luis Serrano1,2,3,* 1Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, 08003 Barcelona, Spain, 2Universitat Pompeu Fabra (UPF), Barcelona, Spain and 3Institucio´ Catalana de Recerca i Estudis Avanc¸ats (ICREA), 08010 Barcelona, Spain *To whom correspondence should be addressed. †The authors wish it to be known that, in their opinion, the first two authors should be regarded as Joint First Authors. Associate Editor: Alfonso Valencia Received on November 20, 2018; revised on January 29, 2019; editorial decision on March 9, 2019; accepted on March 14, 2019 Abstract Summary: A new version of FoldX, whose main new features allows running classic FoldX com- mands on structures containing RNA molecules and includes a module that allows parametrization of ligands or small molecules (ParamX) that were not previously recognized in old versions, has been released. An extended FoldX graphical user interface has also being developed (available as a python plugin for the YASARA molecular viewer) allowing user-friendly parametrization of new custom user molecules encoded using JSON format. Availability and implementation: http://foldxsuite.crg.eu/ Contact: [email protected] 1 Introduction 2 Implementation and requirements The FoldX toolsuite (Guerois et al., 2002; Schymkowitz et al., FoldX 5.0 was written in Cþþ, and has been compiled as a univer- 2005) was developed for the rapid evaluation of the effect of muta- sal and portable binary file for each of the three main platforms tions on the stability, folding and dynamics of proteins. -
ACPYPE - Antechamber Python Parser Interface Alan W Sousa Da Silva1,2,3* and Wim F Vranken2,4
da Silva and Vranken BMC Research Notes 2012, 5:367 http://www.biomedcentral.com/1756-0500/5/367 TECHNICAL NOTE Open Access ACPYPE - AnteChamber PYthon Parser interfacE Alan W Sousa da Silva1,2,3* and Wim F Vranken2,4 Abstract Background: ACPYPE (or AnteChamber PYthon Parser interfacE) is a wrapper script around the ANTECHAMBER software that simplifies the generation of small molecule topologies and parameters for a variety of molecular dynamics programmes like GROMACS, CHARMM and CNS. It is written in the Python programming language and was developed as a tool for interfacing with other Python based applications such as the CCPN software suite (for NMR data analysis) and ARIA (for structure calculations from NMR data). ACPYPE is open source code, under GNU GPL v3, and is available as a stand-alone application at http://www.ccpn.ac.uk/acpype and as a web portal application at http://webapps.ccpn.ac.uk/acpype. Findings: We verified the topologies generated by ACPYPE in three ways: by comparing with default AMBER topologies for standard amino acids; by generating and verifying topologies for a large set of ligands from the PDB; and by recalculating the structures for 5 protein–ligand complexes from the PDB. Conclusions: ACPYPE is a tool that simplifies the automatic generation of topology and parameters in different formats for different molecular mechanics programmes, including calculation of partial charges, while being object oriented for integration with other applications. Keywords: MD, GROMACS, AMBER, CNS, ANTECHAMBER, NMR, Ligand, Topology Findings Background Here we introduce ACPYPE, a tool based on Molecular Mechanics (MM) has evolved substantially ANTECHAMBER [1] for generating automatic topolo- over the last decades, not only because of major advances gies and parameters in different formats for different in computational power, but also due to more accurate molecular mechanics programmes, including calcula- and diverse force field descriptions. -
Ballview a Molecular Viewer and Modeling Tool
BALLView A molecular viewer and modeling tool Dissertation zur Erlangung des Grades des Doktors der Ingenieurwissenschaften der Naturwissenschaftlich– Technischen Fakultäten der Universität des Saarlandes vorgelegt von Diplom-Biologe Andreas Moll Saarbrücken im Mai 2007 Tag des Kolloquiums: 18. Juli 2007 Dekan: Prof. Dr. Thorsten Herfet Mitglieder des Prüfungsausschusses: Prof. Dr. Philipp Slusallek Prof. Dr. Hans-Peter Lenhof Prof. Dr. Oliver Kohlbacher Dr. Dirk Neumann Acknowledgments The work on this thesis was carried out during the years 2002-2007 at the Center for Bioinformatics in the group of Prof. Dr. Hans-Peter Lenhof who also was the supervisor of the thesis. With his deeply interesting lecture on bioinformatics, Prof. Dr. Hans-Peter Lenhof kindled my interest in this field and gave me the freedom to do research in those areas that fascinated me most. The implementation of BALLView would have been unthinkable without the help of all the people who contributed code and ideas. In particular, I want to thank Prof. Dr. Oliver Kohlbacher for his splendid work on the BALL library, on which this thesis is based on. Furthermore, Prof. Kohlbacher had at any time an open ear for my questions. Next I want to thank Dr. Andreas Hildebrandt, who had good advices for the majority of problems that I was confronted with. In addition he contributed code for database access, field line calculations, spline points calculations, 2D depiction of molecules, and for the docking in- terface. Heiko Klein wrote the precursor of the VIEW library. Anne Dehof implemented the peptide builder, the secondary structure assignment, and a first version of the editing mo- de. -
Refinement of the Docking Component of Virtual Screening for Pparγ Therapeutics Using Pharmacophore Analysis and Molecular Dynamics
Refinement of the Docking Component of Virtual Screening for PPARγ Therapeutics Using Pharmacophore Analysis and Molecular Dynamics Stephanie Nicole Lewis Dissertation submitted to the faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of Doctor of Philosophy In Genetics, Bioinformatics, and Computational Biology David R. Bevan, Chair Josep Bassaganya-Riera Jill Sible Liqing Zhang June 11, 2013 Blacksburg, VA Keywords: Virtual screening, PPARγ, Drug discovery, Molecular docking, Pharmacophore screening, Molecular dynamics Copyright 2013, Stephanie Nicole Lewis Refinement of the Docking Component of Virtual Screening for PPARγ Therapeutics Using Pharmacophore Analysis and Molecular Dynamics Stephanie Nicole Lewis ABSTRACT Exploration of peroxisome proliferator-activated receptor-gamma (PPARγ) as a drug target holds applications for treating a wide variety of chronic inflammation-related diseases. Type 2 diabetes (T2D), which is a metabolic disease influenced by chronic inflammation, is quickly reaching epidemic proportions. Although some treatments are available to control T2D, more efficacious compounds with fewer side effects are in great demand. Drugs targeting PPARγ typically are compounds that function as agonists toward this receptor, which means they bind to and activate the protein. Identifying compounds that bind to PPARγ (i.e. binders) using computational docking methods has proven difficult given the large binding cavity of the protein, which yields a large target area and variations in ligand positions within the binding site. We applied a combined computational and experimental concept for characterizing PPARγ and identifying binders. The goal was to establish a time- and cost-effective way to screen a large, diverse compound database potentially containing natural and synthetic compounds for PPARγ agonists that are more efficacious and safer than currently available T2D treatments. -
Enabling Biomolecular Analysis and Engineering Along Structural Ensembles
bioRxiv preprint doi: https://doi.org/10.1101/2021.08.16.456210; this version posted August 17, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. pyFoldX: enabling biomolecular analysis and engineering along structural ensembles Leandro G. Radusky1 & Luis Serrano1,2,3 1 Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona 08003, Spain 2 Universitat Pompeu Fabra (UPF), Barcelona, Spain 3 ICREA, Pg. Lluis Companys 23, Barcelona 08010, Spain Abstract Recent years have seen an increase in the number of structures available, not only for new proteins but also for the same protein crystallized with different molecules and proteins. While protein design software have proven to be successful in designing and modifying proteins, they can also be overly sensitive to small conformational differences between structures of the same protein. To cope with this, we introduce here pyFoldX, a python library that allows the integrative analysis of structures of the same protein using FoldX, an established forcefield and modeling software. The library offers new functionalities for handling different structures of the same protein, an improved molecular parametrization module, and an easy integration with the data analysis ecosystem of the python programming language. Availability and implementation pyFoldX is an open-source library that uses the FoldX software for energy calculations and modelling. The latter can be downloaded upon registration in http://foldxsuite.crg.eu/ and is free of charge for academics.