This research aims to identify protein-protein interactions in Anopheles gambiae that could be targeted by ligands to block transmission of malaria. Computational methods were used to predict nearly 10,000 putative interactions and 100 interaction modules. The interactions were identified using various data sources and techniques including gene expression, regulatory motifs, orthology, and literature mining. Validation with additional methods like structural analysis could help confirm some of the predicted interactions. The goal is to develop novel drugs, insecticides or repellents by disrupting interactions essential for malaria transmission.

1. RESEARCH POSTER PRESENTATION DESIGN © 2015
www.PosterPresentations.com
• Use computational methods to predict a set
of candidate protein-protein interactions in
Anopheles gambiae.
• Filter candidates keeping the interactions
that are most likely, through molecular
dynamics or protein docking tools.
• Protein interactions of medical significance
are considered for screening against ligands
to block the interaction.
Using computational methods, we obtained nearly
10,000 putative protein-protein interactions, and
around 100 protein-protein interaction modules, in
the primary malarial vector, Anopheles gambiae.
REFERENCES
[1] WHO-Global-Malaria-Programme, “World Malaria Report,” Geneva, 2013.
[2] Daniel E. Neafsey,*† Robert M. Waterhouse, “Highly evolvable malaria
vectors: The genomes of 16 Anopheles mosquitoes,” Science (80-. )., vol. 347,
no. 6217, p. 43, 2014.
[3] M. Pellegrini, E. M. Marcotte, M. J. Thompson, D. Eisenberg, and T. O.
Yeates, “Assigning protein functions by comparative genome analysis: protein
phylogenetic profiles,” Proc. Natl. Acad. Sci. U. S. A., vol. 96, no. 8, pp. 4285–
4288, 1999.
[5] R. M. Maccallum, S. N. Redmond, and G. K. Christophides, “An expression
map for Anopheles gambiae.,” BMC Genomics, vol. 12, no. 1, p. 620, Jan.
2011.
[6] E. Sprinzak and H. Margalit, “Correlated sequence-signatures as markers of
protein-protein interaction.,” J. Mol. Biol., vol. 311, no. 4, pp. 681–92, Aug.
2001.
[7] A. Franceschini, D. Szklarczyk, S. Frankild, M. Kuhn, M. Simonovic, A.
Roth, J. Lin, P. Minguez, P. Bork, C. von Mering, and L. J. Jensen, “STRING
v9.1: protein-protein interaction networks, with increased coverage and
integration.,” Nucleic Acids Res., vol. 41, no. Database issue, pp. D808–15, Jan.
2013.
HPC-LEAP
Andrew Brockman, Giulia Rossetti, Robert MacCallum, Paolo Carloni, George
Christophides
Protein-protein interactions in Anopheles gambiae
Malaria causes an enormous burden to
global public health, with an estimated six
hundred thousand deaths per year [1]. The
majority of these deaths occur in sub-
Saharan Africa and are attributable to
Plasmodium falciparum, one of the malarial
parasite species transmitted via Anopheline
mosquitoes, most notably Anopheles
gambiae.
Here, we plan to identify ligand blocking
protein-protein interactions of medical
significance in Anopheles gambiae and
Plasmodium falciparum, leading to the
development of novel drugs, insecticides
and repellants. And with 16 newly
sequenced genomes of malarial
mosquitoes published in 2014 [2], we have
a wealth of new data to make
computational predictions feasible.
Protein Interactomics is a recent field in
bioinformatics that is concerned with
detecting and characterizing protein-protein
interactions in species such as Anopheles
gambiae. Proteins often interact to fulfill
biological roles, such interactions can for
example allow proteins to form larger
complexes to carry out DNA replication.
Genome Proteome Interactome
Proteins: perform
biological work
Interactions:
achieve biological
functions
Genes: basic
units of hereditary
Phylogenetic profiling
The joint presence of two traits across large number of species is
used to infer a biological interaction [3], since it suggests the
proteins highly depend on one another’s evolutionary changes.
Orthology
Proteins with similar species trees, may have been co-evolving due
to theirs being involved in a tight interaction. Softwares include
MirrorTree and ContextTree [4], however, we used SOM clustering,
a type of neural network machine learning, to predict modules of
interacting proteins.
Gene expression
Co-expressed genes are likely to be co-regulated and may interact.
We have used VectorBase’s Expression Map [5], which uses SOM
clustering to predict modules of interacting proteins.
Regulatory motifs
Proteins composed of similar regulatory sequence motifs are likely
to be co-regulated and may interact [6]. We developed a HPC
pipeline that discovered 350 motifs in A. gambiae.
Literature mining
Text-mining based methodologies, aim to extract information for
proteins and their interactions from literature. Interaction data of this
kind were scraped from the STRING database [7].
Machine learning
Classification methods use data to train a classifier to distinguish
positive examples of interacting protein pairs with negative
examples, but could not be used due to lack of a sufficient training
set (6 verified interactions in BioGrid).
Method Data Result
Phylo-profiling 16 x mosquito genomes 0
Regulatory Motifs 350 x sequence motifs 0
Gene expression
>10k x genes, 93 x
conditions
101 PPI
modules
Structure-based
X-ray crystallography,
etc.
14 PPIs
(9 p.
falciparum)
Orthology-based Gene trees (most genes)
12 PPI
modules
Machine learning 2-6 x PPIs (training set) 0
Combined methods Various of the above
9364 PPIs
Literature mining Proteomics literature
OBP17-with-OBP7GSTE2-with-GSTD1TEP1-with-TEP1
METHODSINTRODUCTION RESULTS
OBJECTIVES
For a closer glance into our predicted protein-protein
interactions (PPIs), we focus on 14 PPIs predicted by
my own structure-based method, SplinterBotPPI
(github.com/a1ultima/SplinterBotPPI)
CONTACT
andrew.i.brockman@gmail.com