No subject

viewed as sets of interacting molecules. To unravel their inner workings an=
d
to modulate their activities, a fundamental problem in computational biolog=
y
is probing mechanism and specific design of molecular interactions. In this
talk, I will address both aspects as optimization problems and explore new
insights into formulating and solving the problem.

The first part of the talk examines a molecule=92s ability to change its
conformation when binding to other molecules. This phenomenon, known as
conformational flexibility or molecular plasticity, has been hypothesized a=
s
a mechanism to facilitate tight and specific interactions. To test this
hypothesis and to incorporate it into molecular design, we extended charge
optimization theory to treat flexible molecules. Specifically, we framed an=
d
solved a single-objective optimization problem for binding affinity and a
corresponding multi-objective optimization problem for binding affinity and
specificity by adjusting the charge distribution of one binding partner to
be complementary for the other while permitting varying levels of
conformational flexibility. Application to a ligand that was developed to
inhibit HIV-1 protease identified ways to improve its binding affinity, and
indicated that a more favorable Pareto frontier of the binding
affinity=96specificity trade-off can be achieved with a more flexible ligan=
d.

The second part aims to understand molecular mechanisms by which small
molecules can exhibit binding promiscuity and to develop design strategies
to implement such promiscuity. We chose to study the inhibition of HIV-1
protease, which remains a tremendous challenge in the face of an evolving
viral population. Using computational design we constructed small-molecule
inhibitors targeting a set of wild-type and drug-resistant mutant HIV-1
proteases. Each design was solved as a combinatorial optimization problem i=
n
a discrete chemical and conformational space. Subsequent statistical
analysis revealed significant trends for promiscuous inhibitors: they tende=
d
to be smaller, more flexible, and more hydrophobic compared to highly
selective ones. Furthermore, structural analysis indicated that flexible
inhibitors often achieved promiscuity by conformational adaptations to
mutations in proteases. Our flexibility measure also showed its potential a=
s
a design criterion for promiscuous inhibitors because inhibitors with highe=
r
flexibility measures were more likely to be promiscuous. Finally, as no
inhibitor covered all variants, we designed small cocktails of inhibitors t=
o
do so through solution of a set cover problem.

This talk examines two perspectives on molecular complementarity through
analysis and design in an optimization framework.

--=20
Liv Leader
Faculty Services

Toyota Technological Institute
6045 S Kenwood Ave, #504
Chicago, IL 60637
Phone- (773) 834-2567
Fax-     (773) 834-9881
Email-  lleader at ttic.edu <jam at ttic.edu>
Web-   www.ttic.edu

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Content-Type: text/html; charset=windows-1252
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<u>REMINDER:</u><br><br>What:=A0=A0=A0=A0 <b>Tuesday, March 8 @ 11</b><br><=
br>Where:=A0=A0 <b>TTIC Conference Room #526</b>, 6045 S. Kenwood Ave, 5th =
Floor<br><br>Who:=A0=A0=A0=A0 <b>Yang Shen</b>, MIT<br><br>Title:=A0=A0=A0=
=A0=A0 <b>Biomolecular Analysis, Design, and Engineering Through Optimizati=
on</b><br>



<br>From the perspective of networks, biological systems such as cells=20
can be viewed as sets of interacting molecules. To unravel their inner=20
workings and to modulate their activities, a fundamental problem in=20
computational biology is probing mechanism and specific design of=20
molecular interactions. In this talk, I will address both aspects as=20
optimization problems and explore new insights into formulating and=20
solving the problem.<br>

<br>The first part of the talk examines a molecule=92s ability to change=20
its conformation when binding to other molecules. This phenomenon, known
 as conformational flexibility or molecular plasticity, has been=20
hypothesized as a mechanism to facilitate tight and specific=20
interactions. To test this hypothesis and to incorporate it into=20
molecular design, we extended charge optimization theory to treat=20
flexible molecules. Specifically, we framed and solved a=20
single-objective optimization problem for binding affinity and a=20
corresponding multi-objective optimization problem for binding affinity=20
and specificity by adjusting the charge distribution of one binding=20
partner to be complementary for the other while permitting varying=20
levels of conformational flexibility. Application to a ligand that was=20
developed to inhibit HIV-1 protease identified ways to improve its=20
binding affinity, and indicated that a more favorable Pareto frontier of
 the binding affinity=96specificity trade-off can be achieved with a more=
=20
flexible ligand.<br>

<br>The second part aims to understand molecular mechanisms by which=20
small molecules can exhibit binding promiscuity and to develop design=20
strategies to implement such promiscuity. We chose to study the=20
inhibition of HIV-1 protease, which remains a tremendous challenge in=20
the face of an evolving viral population. Using computational design we=20
constructed small-molecule inhibitors targeting a set of wild-type and=20
drug-resistant mutant HIV-1 proteases. Each design was solved as a=20
combinatorial optimization problem in a discrete chemical and=20
conformational space. Subsequent statistical analysis revealed=20
significant trends for promiscuous inhibitors: they tended to be=20
smaller, more flexible, and more hydrophobic compared to highly=20
selective ones. Furthermore, structural analysis indicated that flexible
 inhibitors often achieved promiscuity by conformational adaptations to=20
mutations in proteases. Our flexibility measure also showed its=20
potential as a design criterion for promiscuous inhibitors because=20
inhibitors with higher flexibility measures were more likely to be=20
promiscuous. Finally, as no inhibitor covered all variants, we designed=20
small cocktails of inhibitors to do so through solution of a set cover=20
problem.<br>

<br>This talk examines two perspectives on molecular complementarity throug=
h analysis and design in an optimization framework.<br clear=3D"all"><br>--=
 <br>Liv Leader<br>Faculty Services<br><br>Toyota Technological Institute <=
br>

6045 S Kenwood Ave, #504<br>

Chicago, IL 60637<br>Phone- (773) 834-2567<br>Fax-=A0 =A0=A0 (773) 834-9881=
<br>Email-=A0 <a href=3D"mailto:jam at ttic.edu" target=3D"_blank">lleader at tti=
c.edu</a><br>Web-=A0=A0 <a href=3D"http://www.ttic.edu/" target=3D"_blank">=
www.ttic.edu</a><br>



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