AlloSigMA: allosteric signaling and mutation analysis server
Bioinformatics, 33(24), 2017, 3996–3998
doi: 10.1093/bioinformatics/btx430
Advance Access Publication Date: 6 July 2017
Applications Note
Structural bioinformatics
AlloSigMA: allosteric signaling and mutation
analysis server
1
Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore 138671,
Singapore and 2Department of Biological Sciences (DBS), National University of Singapore (NUS), Singapore
117597, Singapore
*To whom correspondence should be addressed.
†
The authors wish it to be known that these authors contributed equally to the work.
Associate Editor: Alfonso Valencia
Received on March 1, 2017; revised on June 5, 2017; editorial decision on June 29, 2017; accepted on July 3, 2017
Abstract
Motivation: Allostery is an omnipresent mechanism of the function modulation in proteins via either effector binding or mutations in the exosites. Despite the growing number of online servers
and databases devoted to prediction/classification of allosteric sites and their characteristics, there
is a lack of resources for an efficient and quick estimation of the causality and energetics of allosteric communication.
Results: The AlloSigMA server implements a unique approach on the basis of the recently
introduced structure-based statistical mechanical models of allosteric signaling. It provides an
interactive framework for estimating the allosteric free energy as a result of the ligand(s) binding,
mutation(s) and their combinations. Latent regulatory exosites and allosteric effect of mutations
can be detected and explored, facilitating the research efforts in protein engineering and allosteric
drug design.
Availability and implementation: The AlloSigMA server is freely available at
http://allosigma.bii.a-star.edu.sg/home/.
Contact:
1 Introduction
One of the consequences of the pervasive presence of the allosteric
signaling phenomena in the wide spectrum protein of types
(Berezovsky et al., 2017; Guarnera and Berezovsky, 2016;
Gunasekaran et al., 2004) and molecular machines (Cui and
Karplus, 2008; Guarnera and Berezovsky, 2016; Mitternacht and
Berezovsky, 2011) is the development of many web-based resources
dedicated to the detection/listing of allosteric sites (Goncearenco
et al., 2013; Guarnera and Berezovsky, 2016; Shen et al., 2016).
However, efficient online applications for the physics-based
(Guarnera and Berezovsky, 2016; Rodgers et al., 2013) analysis of
allosteric signaling, which would allow one to quickly estimate the
causality and energetics of the process are still lacking. Additionally,
recently reported enrichment of allosteric sites with deleterious mutations (Shen et al., 2017) shows that the analysis of allosteric effects
of mutations is an important component in the understanding of the
mechanisms of cancerogenesis, calling for the development of relevant computational approaches and their web implementations.
AlloSigMA server is aimed at providing a quantitative tool for the
analysis of the energetics of allosteric communication, allowing
users to quickly estimate in energy terms the allosteric effects of ligand binding, mutations, and their combinations. The quantification
of allosteric effects offers a rational guide to the experimental researcher in the selection of allosterically relevant binding sites and/
or mutations, which can facilitate the design of experimental efforts
towards modulation of the protein activity.
C The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail:
V
3996
Enrico Guarnera1,†, Zhen Wah Tan1,†, Zejun Zheng1,† and
Igor N. Berezovsky1,2,*
Allosteric signaling and mutation analysis server
3997
2 Methods
2.2 Input, preprocessing and processing
2.1 Theoretical background
We use here the structure-based statistical mechanical model of
allostery (SBSMMA), which allows one to explore the causality and
energetics of allosteric signaling in the general case of a protein perturbed by the allosteric ligand(s) (Guarnera and Berezovsky, 2016)
and mutation(s) (Kurochkin et al., 2017). The resulting per-residue
allosteric free energy is obtained by solving the statistical mechanical
problem for the ensemble of all possible protein local configurations
in the unbound/wild-type (0), bound (B), mutated (M) and bound/
mutated (BM) states, respectively, leading to the relations
ðBÞ
ðMÞ
l;i
l;i
X el;i
X el;i
1
1
ðMÞ
¼ kB T
ln ð0Þ ; Dgi ¼ kB T
ln ð0Þ ;
2
2
e
e
l
l
ðBMÞ
Dgi
¼
ðBÞ
Dgi
þ
(1)
ðMÞ
Dgi
ðPÞ
where i is the residue index. The el; i are parameters associated to
the normal modes elðPÞ of the protein in a state (P), and they are components of the allosteric potential:
ðPÞ
Ui ðrÞ ¼
1X
2
ðPÞ
el; i r2l ;
(2)
l
where r ¼ ðr1 ; . . . ; rl ; . . .Þ is a vector of Gaussian variables with
ðPÞ
variance 1=el; i , each of which is associated with a corresponding
normal mode. The allosteric free energies are thus obtained by integrating over all the Ca residue displacements
2identified by the vector r.
P ðPÞ
ðPÞ
ðPÞ
The parameters el; i ¼ j el; i el; j are calculated from the
modes eðPÞ
l that characterize the dynamics of a protein in either one
of considered states: unbound/wild-type (0), bound (B) or mutated
(M). They are obtained as the orthonormal modes of the Hessian
matrices KðPÞ ¼ @ 2 EðPÞ =@r i @r j , with EðPÞ ðr Þ the harmonic energies
associated with the corresponding protein state (P).
The energy function associated with Ca harmonic model of the
protein in the unbound/wild-type (0) is
2
X
E r r0 ¼
k
di; j di;0 j ;
hi; ji i; j
ð0Þ
(3)
where di; j and di;0 j are the interatomic distances between Ca atoms
in the generic and reference structures, respectively, and ki; j is a
distance-dependent force constant. The energy function of the protein bound state (B) for a particular site S is
2
X
EðBÞ r r 0 ; S ¼
k
di; j di;0 j
hi; ji62S i; j
2
X
þa
k
di; j di;0 j
hi; ji i; j
(4)
where the second term defines binding as a harmonic restraint with a
being the corresponding stiffening parameter (a ¼ 100, see Guarnera
and Berezovsky, 2016). The energy function associated with a
mutated protein state (M), with point mutation on residue m is
2
X
EðMÞ r r 0 ; m ¼
k
di; j di;0 j
hi; ji:i62m i; j
2
X
þ h hm; ji ki; j di; j di;0 j
(5)
where h determines the type of mutation. Two types of mutations
are defined: UP-mutation (" M; h ¼ 100), which models the situation of an actual mutation to a bulky residue with over-stabilizing
effects on the local contact network; conversely, DOWN-mutation
(# M; h ¼ 102 ) models the destabilization of residue’s contact network similarly to Ala/Gly-like mutations.
2.3 Implementation
AlloSigMA server is written in Python using the Flask framework
(http://flask.pocoo.org/). The calculation of the allosteric free energy
is implemented in Python. The interactive web interface is powered
by the JavaScript libraries jQuery (http://www.jquery.com/) and D3.j (...truncated)
