Archive

homology modeling

Wanted to share this exciting news with you! Biophysical Journal has created a collection of papers that describe tools and software that can be routinely used in biological research. Editor Prof. Leslie Loew mentions that the full-text of articles in this collection will be freely available until February 25, 2016.

…a year agoBiophysical Journal called for papers in a new class of articles called Computational Tools (CTs). These papers are limited to five pages in length and describe software for analysis of experimental data, modeling and/or simulation, or database services. All are required to be freely accessible and open to the research community. In addition to following the usual review criteria of novelty and importance, reviewers of CTs are asked to test drive the software and judge its usability.

Among the thirteen, some of them are directly related to Structural Biology and Bioinformatics. So, here’s my “curated” list.
ResearchBlogging.org
Article Title
CHARMM-GUI HMMM Builder for Membrane Simulations with the Highly Mobile Membrane-Mimetic Model

Weblink: http://www.charmm-gui.org/input/hmmm

Abstract
Slow diffusion of the lipids in conventional all-atom simulations of membrane systems makes it difficult to sample large rearrangements of lipids and protein-lipid interactions. Recently, Tajkhorshid and co-workers developed the highly mobile membrane-mimetic (HMMM) model with accelerated lipid motion by replacing the lipid tails with small organic molecules. The HMMM model provides accelerated lipid diffusion by one to two orders of magnitude, and is particularly useful in studying membrane-protein associations. However, building an HMMM simulation system is not easy, as it requires sophisticated treatment of the lipid tails. In this study, we have developed CHARMM-GUI HMMM Builder (http://www.charmm-gui.org/input/hmmm) to provide users with ready-to-go input files for simulating HMMM membrane systems with/without proteins. Various lipid-only and protein-lipid systems are simulated to validate the qualities of the systems generated by HMMM Builder with focus on the basic properties and advantages of the HMMM model. HMMM Builder supports all lipid types available in CHARMM-GUI and also provides a module to convert back and forth between an HMMM membrane and a full-length membrane. We expect HMMM Builder to be a useful tool in studying membrane systems with enhanced lipid diffusion.

Article Title
MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories

Weblink: http://mdtraj.org/latest/

Abstract
As molecular dynamics (MD) simulations continue to evolve into powerful computational tools for studying complex biomolecular systems, the necessity of flexible and easy-to-use software tools for the analysis of these simulations is growing. We have developed MDTraj, a modern, lightweight, and fast software package for analyzing MD simulations. MDTraj reads and writes trajectory data in a wide variety of commonly used formats. It provides a large number of trajectory analysis capabilities including minimal root-mean-square-deviation calculations, secondary structure assignment, and the extraction of common order parameters. The package has a strong focus on interoperability with the wider scientific Python ecosystem, bridging the gap between MD data and the rapidly growing collection of industry-standard statistical analysis and visualization tools in Python. MDTraj is a powerful and user-friendly software package that simplifies the analysis of MD data and connects these datasets with the modern interactive data science software ecosystem in Python.

Article Title
MDN: A Web Portal for Network Analysis of Molecular Dynamics Simulations

Weblink: http://mdn.cheme.columbia.edu/

Abstract
We introduce a web portal that employs network theory for the analysis of trajectories from molecular dynamics simulations. Users can create protein energy networks following methodology previously introduced by our group, and can identify residues that are important for signal propagation, as well as measure the efficiency of signal propagation by calculating the network coupling. This tool, called MDN, was used to characterize signal propagation in Escherichia coli heat-shock protein 70-kDa. Two variants of this protein experimentally shown to be allosterically active exhibit higher network coupling relative to that of two inactive variants. In addition, calculations of partial coupling suggest that this quantity could be used as part of the criteria to determine pocket druggability in drug discovery studies.

Article Title
Multidomain Assembler (MDA) Generates Models of Large Multidomain Proteins

Weblink: http://www.rbvi.ucsf.edu/chimera/docs/UsersGuide/midas/mda.html AND http://www.cell.com/biophysj/biophysj/supplemental/S0006-3495(15)00339-2

Abstract
Homology modeling predicts protein structures using known structures of related proteins as templates. We developed MULTIDOMAIN ASSEMBLER (MDA) to address the special problems that arise when modeling proteins with large numbers of domains, such as fibronectin with 30 domains, as well as cases with hundreds of templates. These problems include how to spatially arrange nonoverlapping template structures, and how to get the best template coverage when some sequence regions have hundreds of available structures while other regions have a few distant homologs. MDA automates the tasks of template searching, visualization, and selection followed by multidomain model generation, and is part of the widely used molecular graphics package UCSF CHIMERA (University of California, San Francisco). We demonstrate applications and discuss MDA’s benefits and limitations.

Article Title
RedMDStream: Parameterization and Simulation Toolbox for Coarse-Grained Molecular Dynamics Models

Weblink: https://bionano.cent.uw.edu.pl/Software/RedMD

Abstract
Coarse-grained (CG) models in molecular dynamics (MD) are powerful tools to simulate the dynamics of large biomolecular systems on micro- to millisecond timescales. However, the CG model, potential energy terms, and parameters are typically not transferable between different molecules and problems. So parameterizing CG force fields, which is both tedious and time-consuming, is often necessary. We present RedMDStream, a software for developing, testing, and simulating biomolecules with CG MD models. Development includes an automatic procedure for the optimization of potential energy parameters based on metaheuristic methods. As an example we describe the parameterization of a simple CG MD model of an RNA hairpin.

Article Title
A Web Interface for Easy Flexible Protein-Protein Docking with ATTRACT

Weblink: http://www.attract.ph.tum.de/services/ATTRACT/attract.html

Abstract
Protein-protein docking programs can give valuable insights into the structure of protein complexes in the absence of an experimental complex structure. Web interfaces can facilitate the use of docking programs by structural biologists. Here, we present an easy web interface for protein-protein docking with the ATTRACT program. While aimed at nonexpert users, the web interface still covers a considerable range of docking applications. The web interface supports systematic rigid-body protein docking with the ATTRACT coarse-grained force field, as well as various kinds of protein flexibility. The execution of a docking protocol takes up to a few hours on a standard desktop computer.

Article Title
ReaDDyMM: Fast Interacting Particle Reaction-Diffusion Simulations Using Graphical Processing Units

Weblink: https://github.com/readdy

Abstract
ReaDDy is a modular particle simulation package combining off-lattice reaction kinetics with arbitrary particle interaction forces. Here we present a graphical processing unit implementation of ReaDDy that employs the fast multiplatform molecular dynamics package OpenMM. A speedup of up to two orders of magnitude is demonstrated, giving us access to timescales of multiple seconds on single graphical processing units. This opens up the possibility of simulating cellular signal transduction events while resolving all protein copies.

Article Title
Local Perturbation Analysis: A Computational Tool for Biophysical Reaction-Diffusion Models

Weblink: http://www.cell.com/biophysj/biophysj/supplemental/S0006-3495(14)04670-0

Abstract
Diffusion and interaction of molecular regulators in cells is often modeled using reaction-diffusion partial differential equations. Analysis of such models and exploration of their parameter space is challenging, particularly for systems of high dimensionality. Here, we present a relatively simple and straightforward analysis, the local perturbation analysis, that reveals how parameter variations affect model behavior. This computational tool, which greatly aids exploration of the behavior of a model, exploits a structural feature common to many cellular regulatory systems: regulators are typically either bound to a membrane or freely diffusing in the interior of the cell. Using well-documented, readily available bifurcation software, the local perturbation analysis tracks the approximate early evolution of an arbitrarily large perturbation of a homogeneous steady state. In doing so, it provides a bifurcation diagram that concisely describes various regimes of the model’s behavior, reducing the need for exhaustive simulations to explore parameter space. We explain the method and provide detailed step-by-step guides to its use and application.

References:

  1. http://www.cell.com/biophysj/collections/computational-tools
  2. Qi Y, Cheng X, Lee J, Vermaas JV, Pogorelov TV, Tajkhorshid E, Park S, Klauda JB, & Im W (2015). CHARMM-GUI HMMM Builder for Membrane Simulations with the Highly Mobile Membrane-Mimetic Model. Biophysical journal, 109 (10), 2012-22 PMID: 26588561
  3. McGibbon RT, Beauchamp KA, Harrigan MP, Klein C, Swails JM, Hernández CX, Schwantes CR, Wang LP, Lane TJ, & Pande VS (2015). MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories. Biophysical journal, 109 (8), 1528-32 PMID: 26488642
  4. Ribeiro AA, & Ortiz V (2015). MDN: A Web Portal for Network Analysis of Molecular Dynamics Simulations. Biophysical journal, 109 (6), 1110-6 PMID: 26143656
  5. Hertig S, Goddard TD, Johnson GT, & Ferrin TE (2015). Multidomain Assembler (MDA) Generates Models of Large Multidomain Proteins. Biophysical journal, 108 (9), 2097-102 PMID: 25954868
  6. Leonarski F, & Trylska J (2015). RedMDStream: Parameterization and Simulation Toolbox for Coarse-Grained Molecular Dynamics Models. Biophysical journal, 108 (8), 1843-7 PMID: 25902423
  7. de Vries SJ, Schindler CE, Chauvot de Beauchêne I, & Zacharias M (2015). A web interface for easy flexible protein-protein docking with ATTRACT. Biophysical journal, 108 (3), 462-5 PMID: 25650913
  8. Biedermann J, Ullrich A, Schöneberg J, & Noé F (2015). ReaDDyMM: Fast interacting particle reaction-diffusion simulations using graphical processing units. Biophysical journal, 108 (3), 457-61 PMID: 25650912
  9. Holmes WR, Mata MA, & Edelstein-Keshet L (2015). Local perturbation analysis: a computational tool for biophysical reaction-diffusion models. Biophysical journal, 108 (2), 230-6 PMID: 25606671

 

The Mosquito Net (1912) by John Singer Sargent. Licensed under Public Domain via Wikimedia Commons

The Mosquito Net (1912) by John Singer Sargent. Licensed under Public Domain via Wikimedia Commons

 

We all know how pesky mosquitoes can be. Did you know that the ability of a mosquito to find a suitable host to feed is due to thermotaxis? This behavior, being attracted/repelled due to high/low temperature, is seen in other organisms as well such as Drosophila melanogaster and Caenorhabditis elegans. 

However, the behaviour is more pronounced among blood-feeding pests (kissing bugs, bedbugs, Ticks, and mosquitoes including Aedes aegypti). Aedes aegypti is a vector for many flaviviral diseases (Dengue fever, Yellow fever, etc.) Until now, it was well established that thermotaxis requires specific thermosensors that activate the sensory signals for a subsequent flight response in a mosquito. However, how exactly they function was not resolved.

ResearchBlogging.orgIn a recent paper by Corfas and Vosshall [1] describe the use of zinc-finger nuclease-mediated genome editing method to identify the role of two receptors TRPA1 and GR19 in Aedes aegypti‘s attraction to heat. It was found that these receptors help the mosquito to identify the host for feeding (in the temperature range of 43-50 deg Celcius), however they avoid surfaces that exhibit above 50 deg Celcius. [Read the recent editorial on genome editing in Genome Biology]

The sequence (923 residues long) of this receptor (Uniprot id: Q0IFQ4) has at least five transmembrane regions that are approximately 20-25 residues long. A cursory glance at homologous sequences shows that it shares 37% sequence identity with the a de novo designed protein (PDB id:2xeh).

The homology modeled structure showing coiled coil region (residues 189-338). Although, the eLife paper does not talk about structure, I felt that this paper deserves a mention here. The reason is the structural biology/bioinformatics possibilities with this novel target. It is a suitable target for designing inhibitors that would potentially act as mosquito repellents.

Also, combined with the method described in my previous post on mutating transmembrane proteins as a method of making them crystallize, I guess the 3D structure of this important protein will come to light sooner!

Homology modeled region of TRPA1, from ModBase

Homology modeled region of TRPA1 (189-338), from ModBase

 

References:

  1. Corfas RA, & Vosshall LB (2015). The cation channel TRPA1 tunes mosquito thermotaxis to host temperatures. eLife, 4 PMID: 26670734
  2. Greppi, Chloe and Budelli, Gonzalo and Garrity, Paul A (2015). Some like it hot, but not too hot. eLife, 4

In the 90s morphing of two unrelated images was popular and mostly it was used for entertainment purposes. For example: the famous video of Michael Jackson’s pop hit “Black or White”.

Courtesy: Google

Courtesy: Google

This morphing method was also used to analyze changes in protein motions, like in domain rearrangement. A popular webserver, where you can get an animated gif of your protein’s motion (assuming you have two distinct conformations), is the Morph server (http://www2.molmovdb.org/) from Gerstein’s Lab. In many cases this gave us insight of how the protein could dynamically change from one form to another.

ResearchBlogging.orgThe change in structural forms of a protein is not a trivial problem. We would need to generate ensembles of protein structures for many purposes. 1) Understand conformational transition paths, 2) Generating more realistic receptors for docking 3) in turn understand the flexible and rigid parts of the protein, and few other applications.

Till now, one could use Normal mode analysis and Molecular Dynamics methods to generate ensemble. It is here that ConTemplate tries to bring in fresh perspective to generate an ensemble of structures.

ConTemplate mines the PDB for existing structures and gives the user a set of possible conformations. The main presumptions are that for any given PDB structure, it has more than one available structure, and there are additional conformations available for proteins that undergo major conformational changes.

For the dataset created for ConTemplate the maximum RMSD between two structures of the same protein is 5 Angstroms. 69.2% of the proteins have less than 1 Angstroms RMSD. Thus, the method uses an interesting three-step process:

  1. using the query it searches for structural equivalents using GESAMT aligner. Here using the structural alignment sequence alignments are generated.
  2. it runs BLAST to identify additional conformations for all structural equivalents obtained in step 1. A representative template is identified
  3. Finally, Modeller is used to build model structures using this template in various conformations.

The advantage of ConTemplate is that it yields a more relevant set of conformations for the query protein. I tried running a query to the server and I would say that I got some interesting results. Screenshot below:

contemplate

Superposition of models created in ConTemplate for PDB id; 1ECE

Superposition of models created in ConTemplate for PDB id; 1ECE

References:
Narunsky A, Nepomnyachiy S, Ashkenazy H, Kolodny R, & Ben-Tal N (2015). ConTemplate Suggests Possible Alternative Conformations for a Query Protein of Known Structure. Structure (London, England : 1993), 23 (11), 2162-70 PMID: 26455800

I was reading a review that came in Structure’s recent issue. The article is titled “Protein Modeling: What Happened to the ‘Protein Structure Gap’?” [1] It was very interesting to read, especially the section called “Know your limits“.

Wouldn’t it be a great idea to put all these homology modeled structures that were published (of course, in a peer-reviewed journal) in one place? For some researchers, homology models are usually considered with a pinch (sorry a bucket!) of salt. Still, why should I spend time on modeling the protein, if a model exists already?

ResearchBlogging.orgI have been in situations, where I would come across a paper that describes a structure that was homology modeled and made conclusions from that. Usually, my first stop is at ModBase. Would the structure in ModBase is exactly as the model discussed? So I would scurry to find the coordinates of it in the supplementary materials or in the respective lab website. And, I would not always be lucky. That would start an awkward journey of contacting the authors and explain my ulterior motive of looking at the structure. (Sometimes, I would think the authors must be misunderstanding my motive as to find faults. No! I am not that bad! 🙂 )

Enter Protein Model Portal (PMP) [2] in the picture and the Model archive (currently in beta version). In PMP, one can give a query and get a list of structures (experimentally determined and modeled) to view. In case of only modeled structures, the structures from ModBase, Swissmodel are also listed. The next step is where it gets interesting. After selecting the radio buttons against each structure, one can compare the structural variability between the structures.There are other features in the website that tells the reliability, parameters and constraints of the model.

Below is a screenshot of a query protein of my current research in PMP (http://www.proteinmodelportal.org/)

Screen Shot 2013-09-04 at 5.14.38 PMModel archive, on the other hand, provides a DOI for each model deposited, exactly as in PDB. This is important since, if a peer-reviewed publication had a homology modeled structure, it needs to be made available to the public and other scientific colleagues to look at it. The argument is the same that was put across journals when PDB was founded. If you are publishing a paper with a homology modeled structure, deposit in Model archive and mention it in the manuscript. That the structures will be available to start working form and add more parameters and constraints is great!

The archive is still being established and I think it will take some time before this also becomes a crucial step in publishing articles on homology modeled structures. Thanks to Prof. Torsten Schwede, here is a screenshot of the two homology models of DapE [4] that were deposited in Model archive and the paper describing the protein was published in Metallomics [5] Click on this link to access the models.

Screenshot of DapE homology models deposited in Model archive

Screenshot of DapE homology models deposited in Model archiveDo spread the word about Model archive.

Friends, I don’t want to sound patronizing, but next time you model a structure, you realize that the homology models had some contribution in the conclusion, do think of submitting the structures to Model archive. Plus, mention a sentence as to where the models have been uploaded to. 🙂

Since, this is a community based effort and if you have any suggestions/comments do send them to Prof. Torsten Schwede (http://www.biozentrum.unibas.ch/research/groups-platforms/overview/unit/schwede/)

I will touch more on homology modeled structures on the next post. Adios amigos!

References:

  1. Torsten Schwede (2013). Protein Modeling: What Happened to the “Protein Structure Gap”? Structure, 21 (9), 1531-1540 DOI: 10.1016/j.str.2013.08.007
  2. Haas J, Roth S, Arnold K, Kiefer F, Schmidt T, Bordoli L, & Schwede T (2013). The Protein Model Portal–a comprehensive resource for protein structure and model information. Database : the journal of biological databases and curation, 2013 PMID: 23624946
  3. Schwede T, Sali A, Honig B, Levitt M, Berman HM, Jones D, Brenner SE, Burley SK, Das R, Dokholyan NV, Dunbrack RL Jr, Fidelis K, Fiser A, Godzik A, Huang YJ, Humblet C, Jacobson MP, Joachimiak A, Krystek SR Jr, Kortemme T, Kryshtafovych A, Montelione GT, Moult J, Murray D, Sanchez R, Sosnick TR, Standley DM, Stouch T, Vajda S, Vasquez M, Westbrook JD, & Wilson IA (2009). Outcome of a workshop on applications of protein models in biomedical research. Structure (London, England : 1993), 17 (2), 151-9 PMID: 19217386
  4. http://www.modelarchive.org/doi/10.5452/ma-a1nb6
  5. Narasimha Rao Uda, Grégory Upert, Gaetano Angelici, Stefan Nicolet, Tobias Schmidt, Torsten Schwede, & Marc Creus (2013). Zinc-selective inhibition of the promiscuous bacterial amide-hydrolase DapE: implications of metal heterogeneity for evolution and antibiotic drug design
    Metallomics DOI: 10.1039/c3mt00125c