With increasing computational power (aka GPU) that can be accessed these days, it is no wonder that performing all-atom molecular dynamics simulation for a longer time, with duplicates and/or triplicates, has become easier.
Two publications report all-atom MD data that have significant implication in two diverse areas. The first one is the popular CRISPR-Cas9 system and the second one is Dengue virus.

With these data it should pave way for more insights.

CRISPR-Cas9 all atom simulation (total of 400-600ns data)
Zuo Z, & Liu J (2016). Cas9-catalyzed DNA Cleavage Generates Staggered Ends: Evidence from Molecular Dynamics Simulations. Scientific reports, 5 PMID: 27874072

Entire Dengue viral envelope complex simluation (1 microsecond data)
Marzinek JK, Holdbrook DA, Huber RG, Verma C, & Bond PJ (2016). Pushing the Envelope: Dengue Viral Membrane Coaxed into Shape by Molecular Simulations. Structure (London, England : 1993), 24 (8), 1410-20 PMID: 27396828



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.
Article Title
CHARMM-GUI HMMM Builder for Membrane Simulations with the Highly Mobile Membrane-Mimetic Model


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 ( 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


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


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: AND

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


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


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


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


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.


  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

I am sharing this guest post of mine that was published in Cell’s Crosstalk: Biology in 3D Blog. Yes, the journal Cell!

Here is the link:


Why Do I Blog about Structural Bioinformatics?:Biology in 3D

When someone says that they have a blog, the stereotypical response would be, “About your travels?” or “Hmmm … Recipes … Must be a delicious blog!” And when one confesses that said blog is about scientific research, the jaw drops. I presume it has to do with the notion that blogging science is not that much fun!

Two things inspired me to become a blogger: (1) an amazing community of scientific bloggers at Research Blogging, who inspired me with their wonderful posts; and (2) my view that structural biology and structural bioinformatics are not getting the exposure they deserve. Thus inspired and motivated, I begun blogging about four years ago, and was able to channel some of my thoughts and energy into my blog, called Getting to Know Structural Bioinformatics.

Guest author and blogger
Raghu Yennamalli

Why do I blog? Blogging is fun! For me, blogging is about sharing with the world recent research and tidbits on structural biology and bioinformatics. Most importantly, it is about sharing the excitement that I feel after reading a paper. In some sense, blogging about research is similar to a journal club, where I am able to share the latest research with my peers. However, unlike a journal club, the audience for my blog is the entire world.

Blogging is also dynamic and interactive, because it allows me to engage in conversation with others (specifically students) when they weigh in with their comments. Below I highlight some of the best practices that I’ve developed over the years that help me with balancing my research, teaching, and personal responsibilities with my blogging.

Selecting the paper

The main way I find articles that I want to blog about is by scouring through the table of contents of the journals I am interested in. Sometimes I also hear about exciting protein structures via friends and other blogs that I follow. I try to have a balanced approach and highlight structural work on systems that are “hot topics” as well as papers that just captured my interest and fancy.

In the early days of my blogging, I was trying to collate and compile tools and techniques that would come in handy for students working with protein structures. I wanted my blog to be a handy place for myself and others to find tips and tricks. Over time, the range of topics and papers I cover has broadened, and although I still cover a lot of method development work, I cover other topics as well. In general, once I make up my mind about the paper I want to blog about, I start reading it, give myself some time to soak in the method and outcome of the paper, and try to think critically as to what possible gaps or methods that the authors could have done to make the paper better. Alternatively, I also analyze the paper’s novelty with respect to structural bioinformatics.

Composing the blog post

I should confess that the monthly posts in Protein Spotlight by Vivienne Baillie Gerritsen are my inspiration while composing posts. I love her writing style and also the manner in which artwork is included in every post, to make it fun to read. Like Protein Spotlight, blogs have the advantage of including other multimedia items, for example using animated gifs and YouTube videos that make the post much easier for the reader. So, I start finding an appropriate image from an art database that best fits the topic (of course, giving credit where it is due). When it is about a tool/software, I figure the best approach is to use said tool/software and include a “first-hand” experience of how I perceived it. Also, I try to include an additional tidbit or information that the authors mention in passing.

Balancing things

With an active teaching and research schedule, finding time to blog does become a challenge. I try to make it a fun process, so that it does not feel cumbersome. If one looks at the frequency of my posts, I try to maintain at least one post per month. Looking at others’ blogs at Research Blogging, I realize that one post a month is a low turnout, and I try to post as frequently as possible. Sometimes, the problem is sheer lack of time or not finding exciting enough material to blog about. However, this does not mean that exciting research is not out there. The key is to find a balance between blogging and other duties. I have had discussions with other bloggers who blog on other nonscience topics, and we observed that the main turnoff in blogging is when one delves deeper and over time a particular post becomes “work.” Maneuvering that roadblock is key to maintaining a successful blog.

In the end, as at the beginning, it all comes down to having fun and sharing with the world my excitement about the type of scientific research I enjoy. I think this is probably the feeling others who blog share as well, and I can see it in some of the blogs I follow, such as the following:

Raghu Yennamalli completed his PhD in Computational Biology and Bioinformatics in 2008 from Jawaharlal Nehru University. He conducted postdoctoral research at Iowa State University, University of Wisconsin-Madison, and Rice University. Currently, he is an Assistant Professor at Jaypee University of Information Technology. He can be contacted at ragothaman AT gmail DOT com.

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 ( 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:


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

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

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

ResearchBlogging.orgReblogging this blog post

Professor Meiering and her colleagues were able to incorporate both structure and function into the design process by using bioinformatics to leverage information from nature. They then analyzed what they made and measured how long it took for the folded, functional protein to unfold and breakdown.

Using a combination of biophysical and computational analyses, the team discovered this kinetic stability can be successfully modeled based on the extent to which the protein chain loops back on itself in the folded structure. Because their approach to stability is also quantitative, the protein’s stability can be adjusted to naturally break down when it is no longer needed.


Broom A, Ma SM, Xia K, Rafalia H, Trainor K, Colón W, Gosavi S, & Meiering EM (2015). Designed protein reveals structural determinants of extreme kinetic stability. Proceedings of the National Academy of Sciences of the United States of America, 112 (47), 14605-10 PMID: 26554002


Fire, by Giuseppe Arcimboldo. 1566 Oil on wood, 67 x 51 cm Kunsthistorisches Museum, Vienna

Fire, by Giuseppe Arcimboldo.
Oil on wood, 67 x 51 cm
Kunsthistorisches Museum, Vienna
The allegory of Fire combines objects that are more or less directly related to fire in a bizarre profile head. The cheek is formed by a large firestone, the neck and chin are formed by a burning candle and an oil lamp, the nose and ear are contoured by firesteels; a blond moustache is formed by a crossed bundle of wood shavings for kindling, the eye is an extinguished candle stub, the forehead area is a wound-up fuse, the hair of the head forms a crown of blazing logs. The breast is composed of fire weapons: mortar and canon barrels together with the respective gunpowder shovel and a pistol barrel.

In protein engineering studies, mutating a residue to increase thermostability without affecting the activity of the protein/enzyme is a major consideration for researchers. The laborious method is list the number of possible mutations and then finding out the stability and activity for each mutant, one after another.

This method becomes more time consuming if the protein is a membrane proteins and especially determining their 3D structure. I like to call membrane proteins as “diva” proteins. The reason being that they are high maintenance and tend to be picky about what conditions require for them to crystallize. It has been reported that when thermostability is introduced in membrane proteins, their solubility increases, thus increasing the chances of getting a good crystal for diffraction. [1]

Not everyone could avail high-throughput mutation experiments to screen for thermostable membrane proteins. Here is where Bioinformatics based analysis comes to help in faster screening and selecting a few mutants among the hundreds that can be tested experimentally. In the recent issue of Biophysical Journal, Sauer et al have come up with two methods to identify potential “thermoadaptive” sequences. [2]

The first method or global method, involves generating a heatmap of amino acid frequency differences between the thermophilic and mesophilic sequences. So, residues that are either most represented or less represented are identified.

The second method or pairwise method, involves pairwise comparison of thermophilic and mesophilic sequences and identify the differences.

A unique observation was that the the selected list of amino acids did not overlap from either of the methods and the global method identified potential mutants in the N-terminal domain of the test case and the pairwise method identified the potential C-terminal mutants only. This could be a case of thermostabilization for the protein tested, i.e., BsTetL – Tetracycline transporter from Bacillus subtilis.

The caveat is that there should be enough available sequences for identification of potential mutants, in any protein family. This does not, on the outset, seem like a limitation. Since, we have abundant protein sequences available and steadily increasing.

The main selling point is the speed of identifying the mutations given a particular target sequence, and its applicability in membrane protein crystallization. However, their success rate was 26-30%. Here, success indicates both thermostable mutant and maintaining the tetracycline resistance activity.


  1. Mancusso R, Karpowich NK, Czyzewski BK, & Wang DN (2011). Simple screening method for improving membrane protein thermostability. Methods (San Diego, Calif.), 55 (4), 324-9 PMID: 21840396
  2. Sauer DB, Karpowich NK, Song JM, & Wang DN (2015). Rapid Bioinformatic Identification of Thermostabilizing Mutations. Biophysical journal, 109 (7), 1420-8 PMID: 26445442

Image reproduced under Creative Commons licence. Source: Wikimedia commons

The Cellular Prion Protein (PrPc) like Dr. Jekyll converts into PrPSc , a fatal conformational form, like Mr. Hyde, and is responsible for a variety of neurodegenrative disorders. Unlike the use of a potion, this molecular Jekyll and Hyde undergoes conformational change in low pH environment, such as in endosomes. While, there has been many studies done in the past of how this conformational change happens,  a recent paper has tried to list the structural and dynamic properties using Molecular Dynamics.

ResearchBlogging.orgTo list these properties,three structures were taken into consideration; one NMR structure (PDB id: 1QLX) and two X-ray structures (PDB id: 2W9E and 3HAK). Interestingly the 3HAK structure is from a SNP variant of human PrPc, where the Met129 is replaced by Val129. Furthermore, those who genetically have this variant are less susceptible to Prion diseases!

Structural alignment of 1QLX (blue), 2W9E (red), and 3HAK (orange) with Met129/Val129 shown as sticks.

Structural alignment of 1QLX (blue), 2W9E (red), and 3HAK (orange) with Met129/Val129 shown as sticks. Image made using PyMOL

Using an in-house MD package called in lucem molecular mechanicsilmm for short, Chen et al simulated the three structures under two different pH conditions (pH 5 and pH 7) and under two different temperatures (298K/25C and 310K/37C), totaling for about 3.6 microseconds of simulation. (For each structure under each condition the MD simulation was performed in triplicates.)

Analyzing the MD results they found that at 37C and low pH the C-terminal globular domain had significant destabilization effects.

  • The helix HA and its neighboring loop S1-HA for the SNP variant was higher compared to other two structures at 37C and low pH. It is interesting to note that the S1-HA loop becomes a strand during the prion’s conversion.
  • At low pH, another helix HB destabilizes, where the His187 becomes solvent exposed, leading to partial unfolding of the C-terminus.
  • Two residues, Phe198 and Met134, converting from being part of the hydrophobic core to being exposed to the solvent may be involved in partial unfolding and might possibly provide aggregation sites.
  • The X-loop in the Val129 SNP variant’s structure took a different conformation that was not populated by the other two structures.
  • Formation of new secondary structures of the N-terminus region to either alpha and beta strands is spontaneous. While, in all two structures both alpha and beta strands formation was seen, in the SNP variant alpha strands were rarely formed. (This N-terminus region is missing from the solved structures and hence was modeled and in each starting structure this region was unstructured.)

These results give more insights into the conversion of the benign form of human Prion to the infectious form.


  1. Chen, W., van der Kamp, M., & Daggett, V. (2014). Structural and Dynamic Properties of the Human Prion Protein Biophysical Journal, 106 (5), 1152-1163 DOI: 10.1016/j.bpj.2013.12.053