Single-particle tracking (SPT) grants unprecedented insight into cellular
function at the molecular scale [1]. Throughout the cell, the movement of
single-molecules is generally heterogeneous and complex. Hence, there is an
imperative to understand the multi-scale nature of single-molecule dynamics in
biological systems. We have previously shown that with high-density SPT,
spatial maps of the parameters that dictate molecule motion can be generated to
intricately describe cellular environments [2,3,4]. To date, however, there
exist no publically available tools that reconcile trajectory data to generate
the aforementioned maps. We address this void in the SPT community with
InferenceMAP: an interactive software package that uses a powerful Bayesian
method to map the dynamic cellular space experienced by individual
biomolecules.Comment: 56 page

Beheiry, Mohamed El

Dahan, Maxime

Masson, Jean-Baptiste

English

arXiv

El Beheiry, Mohamed

Elsevier - Publisher Connector 

472a Tuesday, February 10, 2015process, but our theory is based on stochastic energetics, which is thermody-
namics in fluctuating small world.
Here, I will show energetics of single active diffusion trajectories, where the
instantaneous diffusion coefficient (IDC) as a new diffusion analysis quantity
is derived. Considering decomposition of energy dissipation in a non-
equilibrium steady state, we can make implication of the IDC clear. An advan-
tage of our theory is to be able to discuss the meso scale energetics from only
single-particle tracking data without measuring response.
2379-Pos Board B516
Analysis of Amino Acid Properties in Interaction Surfaces of Decoys
Generated by Re-Docking Scheme
Nobuyuki Uchikoga1, Yuri Matsuzaki2, Masahito Ohue3,4,
Takatsugu Hirokawa5, Yutaka Akiyama2,4.
1Department of Physics, Chuo University, Tokyo, Japan, 2ACLS, Tokyo Inst.
Tech., Tokyo, Japan, 3JSPS Research Fellow, Tokyo, Japan, 4Grad. Sch.
Inform. Sci. and Eng., Tokyo Inst. Tech., Tokyo, Japan, 5MolProf, AIST,
Tokyo, Japan.
Rigid-body docking processes generate many protein complexes (decoys) for
searching near-native decoys (NN-decoys) in post-docking processes with
analyzing various pairs of surfaces on two input proteins, using properties of
electrostatics and desolvations between two molecules or proteins. Many
powerful docking computer softwares were developed and were used for
resolving various categories of problems, for example, analysis of protein inter-
action networks or drug design. Rigid-body docking process is popular and
useful in such works. However, there are some unfortunate cases, which
could not obtain NN-decoys. Then, we developed Re-docking scheme using
interaction fingerprints (IFPs). Re-docking scheme is the process of iterating
rigid-body docking for generating more NN-decoys. After initial-docking, we
classify decoys into several interaction surfaces. Thereafter, other docking
processes are performed with more fine searching limited in every interaction
surfaces classified. We could obtain NN-decoys even if no NN-decoys in
initial-docking process [Uchikoga et. al. (2013) PLOS ONE 8:e69365].
Then, we approach a problem of prediction of protein-protein interactions
by using IFPs, which gives us properties of physico-chemical interactions
because IFPs are composed of interacting amino acid pairs [Uchikoga & Hir-
okawa (2010) BMC Bioinform. 11:236]. By using IFPs, we can obtain these
properties easily and trace interaction surfaces in docking processes. In this
work, docking search spaces become to be seen by using amino acid properties
involved in molecular surfaces of many decoys, generating by Re-docking
scheme. Then, we would like to discuss about understanding interaction
mechanisms.
2380-Pos Board B517
Inferencemap: Mapping of Single-Molecule Dynamics using Bayesian
Inference
Mohamed El Beheiry1, Maxime Dahan1, Jean-Baptiste Masson2,3.
1Physical Chemistry, Institut Curie, Paris, France, 2Physics of Biological
Systems, Institut Pasteur, Paris, France, 3Janelia Farm Research Campus,
Ashburn, VA, USA.
Single-molecule imaging has become ubiquitous in biophysics, biology,
biochemistry and biotechnology, covering a large range of in vitro and in
vivo applications. This ever-growing field now requires new and reliable statis-
tical tools for data analysis. This is especially true for high-density single-mole-
cule tracking methods that yield massive amounts of data and invite the use of
statistics-based methods for analysis. Of particular importance is the extraction
of dynamic properties (such as diffusion and transport parameters) and the abil-
ity to map these properties at different spatial scales (up to the full extent of the
cell).
Bayesian analysis is a powerful method that has recently garnered interest in
the treatment of single-molecule trajectories. Previously, we have shown that
it provides an efficient means for estimating the relevant physical parameters
that characterize the motion of individual molecules. Of particular importance,
we have shown that interaction fields (which are systematically neglected in
most approaches) play a paramount role in the long-term dynamics of
biomolecules.
With this motivation, we present InferenceMAP, an interactive software tool
that uses a powerful Bayesian technique to extract the parameters that describe
the motion of individual molecules from single-molecule trajectories. The main
features of our tool include:
,A versatile calculation platform for estimating dynamic parameters, including
the ability to specify relevant prior probabilities.
,Adaptive meshing methods to conform to different temporal and spatial scales
,The ability to generate vast three-dimensional landscapes of single-molecule
dynamicsWe present relevant applications inside lipid rafts, glycine receptors, and HIV
assembly platforms.
2381-Pos Board B518
Neuroimage: A Novel Highly Efficient Tool for Image Processing of in vivo
Neural Networks
Christian Heusinger, Andre´ Drews, Aune Koitma¨e, Robert Blick.
Physics, University of Hamburg, Hamburg, Germany.
Simulation of biological neural networks has been of great interest in the last
years with well-established simulation tools like NEURON being constantly
developed on, while tools for image processing of experimental neuron data
are still lacking.
We present an image processing tool that takes confocal fluorescence micro-
scopic 2D data of a neural network and converts it into a NeuroML file contain-
ing the network morphology. Thereby the network data is ready to be imported
into neuroConstruct or any NeuroML compliant tool, where the network then
can be visualised, manipulated and simulated using a broad range of already
available simulators and cell models.
We investigate a neural network of in-vivo neuron cells extracted from mouse
brain tissue and grown on a semiconductor substrate. Besides obtaining the po-
sitions of somae and axons together with their network topology we identify so-
called micro tubes which are built for future experiments seeking to measure
the action potentials of axons going through them. Our image processing is
used here to analyse the preparation process, in particular the positioning of
cells on the substrate and success rate of axons growing through micro tubes.
Furthermore the tool offers the interface for subsequent simulation in neuro-
Construct and NEURON.
Simulation of a simple electrophysiological input and output will be presented
and will allow comparison with future experimental data.
2382-Pos Board B519
Integrative Modeling Approaches to Interpret High-Resolution cryo-EM
Reconstructions
Alejandro Jesu´s Canosa-Valls, Erney Ramı´rez-Aportela, Pablo Chaco´n,
Jose´ R. Lo´pez-Blanco.
Biological Physical Chemistry, Institute of Physical Chemistry, Madrid,
Spain.
Modeling 3D-EM reconstructions with computational tools currently enables
the interpretation at near-atomic resolution of different functional states of
macromolecules, thereby deciphering the functional mechanism of biologically
relevant complexes. Recent advances in cryo-EM, such as direct electron detec-
tors, specimen preparation, image processing, and data automation, are
increasing the number of determined structures, particularly at high (<5 A˚) res-
olutions. Here, several new integrative approaches are presented to retrieve
structural information from these accurate reconstructions by incorporating
modeling constraints from complementary biophysical techniques (crystallog-
raphy, SAXS, FRET, etc.) or any other source of structural information (cross-
linking, mutagenesis, prediction data, etc.). First, a two-step integrative
approach was developed to unravel the topology of helical bundles using
cryo-EM maps, distance restraints, and secondary structure predictions. This
method unambiguously localized all helices of a key unassigned proteasome
helical bundle and provided a topologically correct model that was later
confirmed by crystallography. Second, our normal mode based flexible fitting
algorithm, iMODFIT, was accelerated and adapted to deal with high resolution
cryo-EM maps and other experimental constraints. Third, a fast loop-closure
algorithm (RCD) was combined with integrative fitting strategies for modeling
loops into unfilled densities. We strongly believe that these tools will facilitate
the interpretation of the incoming high-resolution maps.
2383-Pos Board B520
Energy Tabulation Strategies for Accelerated Monte Carlo Simulation at
Multiple Length Scales
Justin M. Spiriti, Daniel M. Zuckerman.
Computational and Systems Biology, University of Pittsburgh, Pittsburgh,
PA, USA.
We have constructed novel coarse-grained models based on dividing biomole-
cular systems into rigid fragments and constructing six-dimensional tables of
the interaction energy between them as a function of their relative displacement
and orientation. The approach can be used for simulations on two length scales:
conformational sampling within a macromolecule (e.g., protein) and interac-
tions between elements of a multi-molecular complex (e.g., viral capsid). For
conformational sampling of proteins, we tabulate interaction and solvation en-
ergies for small rigid fragments, obtained from an underlying atomistic force
field. By applying potential energy smoothing techniques to these tables, we
are able to improve sampling of these proteins, while maintaining secondary
structure without added restraints. We use a similar tabulation strategy, based


Inferencemap: Mapping of Single-Molecule Dynamics using Bayesian Inference 

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InferenceMAP: Mapping of Single-Molecule Dynamics with Bayesian Inference

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