Blinov, Michael L.

Loew, Leslie M.

Moraru, Ion I.

Schaff, James C.

Vasilescu, Dan

Elsevier - Publisher Connector 

Using Rules & Pathway Databases to Create Quantitative Mechanistic Models in Virtual Cell 

Tuesday, February 10, 2015 473aon an underlying coarse-grained model, to study interactions among hepatitis B
capsid subunit dimers, treating each entire dimer as a rigid fragment. The tables
reduce the computation time of the capsid simulations by 2-3 orders of magni-
tude, potentially enabling the study pathways for the assembly of capsids using
more realistic models than previously possible.
2384-Pos Board B521
G-Losa: An Efficient Computational Tool for Local Structure-Centric
Biological Studies
Hui Sun Lee, Wonpil Im.
The University of Kansas, Lawrence, KS, USA.
Molecular recognition such as ligand binding and protein-protein interaction
(PPI) is a fundamental way for biological molecules to play their functional
roles. These specific interactions are involved in the local regions of the mol-
ecules, rather than global structures. Therefore, accurate characterization of
local structures in protein is needed to better understand biological mechanisms
and to rationally design effective drugs. G-LoSA, a recently developed local
structure alignment tool, has the advantages of not only predicting the ligand
binding sites with high accuracy, but also identifying a single template ligand
that is highly similar to the target ligand. Here, we present an improved version
of G-LoSA aiming at extending its applicability to broad local structure-centric
biological studies. The method generates all possible alignments between two
local structures by iterative maximum clique search and fragment superposition
and then determines the final optimal alignment by a G-LoSA alignment
scoring function, GA-score. GA-score is a length-independent and physico-
chemical property-based scoring function to measure structural similarity
between two local structures. G-LoSA outperforms its previous version in
identifying ligand templates and also shows robust performance in detecting
similar ligand binding pockets and PPI interfaces from the benchmark sets.
Finally, we introduce its application to in silico fragment-based drug design.
As demonstrated by this work, G-LoSA is a promising computational tool
that can be universally applied to diverse local structure-centric biological
studies.
2385-Pos Board B522
Multi-Scale Deep Neural Network Microscopic Image Segmentation
Xundong Wu1, Yong Wu1, Enrico Stefani2.
1UCLA, Los angeles, CA, USA, 2UCLA, los angeles, CA, USA.
With digital technology and fast imaging speed, huge amount of microscopic
images are produced in biomedical research every day. A key procedure in pro-
cessing and analyzing the images involves recognition and segmentation of fea-
tures, patterns, or regions that are of interest to the researchers. Such work is
often manually done by humans, which is very time-consuming and thus
impractical when image data are massive. Traditional image segmentation al-
gorithms have difficulty in identifying high-level features (e.g. a particular
morphology of a cell type or an organelle). Plus, many microscopic images,
especially super-resolution STED images, are highly noisy. The analysis of
noisy images poses an even higher challenge to traditional algorithms.
Compared with computer algorithms, humans are incredibly good at identi-
fying high-level features. Machine learning, which let computer learn human
behaviors, is thus a suitable solution. Deep learning is a set of emerging ma-
chine learning methods that is being successfully applied in many applications,
such as face recognition and speech recognition. It has also been used to pro-
cess specific types of microscopic images.
We have designed a multi-scale convolution neural network for segmentizing
noisy microscopic images. Images at original resolution and down-sampled im-
ages at multiple scales are feed to the network for information extraction. The
purpose of adopting this multi-scale architecture is to obtain local fine resolu-
tion while maintain larger field of views, which gives network low classifica-
tion error rate while maintain reasonable processing speed. The network has
been tested on segmentation of mitochondria labeled with mitotracker.
Network separate image pixels into two categories: ‘‘background’’ and ‘‘mito-
chondria’’. Training and testing of the network is done with standard back-
propagation algorithm, with human labeled segmentation data. Preliminary
result shows the trained network well outperformed multiple traditional image
segmentation methods.
2386-Pos Board B523
Using Rules & Pathway Databases to Create Quantitative Mechanistic
Models in Virtual Cell
Michael L. Blinov, Dan Vasilescu, James C. Schaff, Ion I. Moraru,
Leslie M. Loew.
Center for Cell Analysis and Modeling, University of Connecticut Health
Center, Farmington, CT, USA.Recent technological advances have resulted in an accumulation of experi-
mental data and a growing interest in using this data to build quantitative
models of biological processes. The traditional approach to build such models
involves manual formulation of model hypothesis and data search. This is
becoming inadequate due to the degree of complexity in the models and the
great variety of data available. In particular, for understanding and modeling
the dynamics of protein-protein interactions, the mechanisms of interactions
have to be described at the level of protein sites, the parts of proteins that are
responsible for protein-protein interactions, such as protein phosphorylation
sites and interaction domains. The rule-based approach provides modelers
with an opportunity to efficiently use such information. However, despite the
high relevance of the site-specific details of protein-protein interactions for
understanding system behavior, rule-based models incorporating these details
are not very common, because of difficulties in mining and using this
information for modeling. To address this need, we developed 2 new capabil-
ities within the Virtual Cell modeling and simulation framework. The first
one is BioNetGen@VCell, which enables the user to create rule-based
models and combine rules and reactions in a single interface. The other,
PathwayCommons@VCell, enables the user to easily extract information
from external pathway databases and create computational models of path-
ways. We describe the technology underlying these tools and present an
example model that makes use of them.
2387-Pos Board B524
A Statistical Overview of Experimentally Defined Anisotropic Displace-
ment Parameters (ADP) Currently Deposited in the RCSB PDB
Christopher B. Waite, Lei Zhou.
Physiology and Biophysics, Virginia Commonwealth University, Richmond,
VA, USA.
The potential of X-ray crystallography to provide information for protein
dynamics has not yet been extensively investigated. In addition to atom
XYZ coordinates, typical PDB files provide the temperature factor, also
referred to as isotropic B-factor, which describes the magnitude of atomic
fluctuations. Currently, approximately 5% (~5500 unique pdb files) of the
structures deposited in the RCSB PDB are high resolution (< 1.4 A˚). These
high resolution structures generally provide an additional line for each
atom entry, with the second containing a symmetric tensor with six integers
that describes an ellipsoidal, instead of spherical, distribution of electron
densities. Importantly, this asymmetric distribution of electron density reflects
the anisotropic movement of the atomic nuclei. A diagonalization of this
3x3 tensor yields three eigenvalues (W1, W2, and W3), which define the
distribution of electron density along those three prominent axes. The degree
of anisotropy is defined as the ratio of W1 to W3 and varies between
0 (extremely anisotropic, like a rod or a pancake) and 1 (a sphere). Our pre-
liminary analysis over the ADPs derived from ~1500 high resolution struc-
tures revealed that interestingly, the backbone carbon atoms are more
anisotropic than the C-a and C-b atoms. The C-a atoms show the lowest
degree of anisotropy, and proceeding out along the side chain from C-a to
C-ε, the degree of anisotropy gradually increases. We also analyzed the trend
of isotropic B-factors. Indeed, the value of isotropic B-factors increases out-
ward from the backbone C to the C-ε atom. In summary, we performed a sta-
tistical analysis over the ADPs of structures in the RCSB PDB, with the aim
to obtain useful information in order to gain a better understanding of the
physical basis of ADP.
2388-Pos Board B525
Prediction of Functionally Linked Interface (FLIP) Regions in Residue
Interaction Network (RIN) Models of Protein Structures
Isha D. Mehta, Brian W. Beck.
Biology, Texas Womans University, Denton, TX, USA.
Various cellular processes involve participation of proteins as monomers or
oligomers. The function of the oligomers is often related to their structural
stability and interaction capabilities. Here, we continue our work on the
characterization of the physico-chemical patterns of amino acid residues
involved in quaternary interactions that form Functionally Linked Interfaces
of Proteins (FLIPs). Proteins are represented as a network with residues as
nodes and proximity between residues (both bonded and non-bonded interac-
tions) being the edges (a residue interaction network or RIN). Our previous
studies of RINs have shown that FLIPS can be distinguished from
Functionally uncorrelated Contacts (FunCs) with ~74% accuracy, indicating
residues show organizational differences in the interfaces of FLIPS and
FunCS. In the current work, we identify threshold values of network centrality
features that predictively distinguish FLIP, FunC, and non-interface regions
of RINs.


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Using Rules & Pathway Databases to Create Quantitative Mechanistic Models in Virtual Cell

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