Series of Three Lectures by
Eugene Shakhnovich,
Chemistry and Chemical Biology, Harvard University, 12 Oxford, Cambridge, MA
02138,
& invited professor at the Laboratory of
Theoretical Physics at ENS,
eugene@belok.harvard.edu
Climbing the scales ladder in Biology:
from protein physics to population genetics and back.
Biological
phenomena unfold in a broad range of scales ranging from molecules to cells to
populations and ecosystems. Mutations affect the molecular properties of
proteins and nucleic acids (abundance in the cytoplasm, thermodynamic
stability, activity, interaction with other macromolecules in cytoplasm).
Variations of molecular properties of biomolecules profoundly impact the ability
of cells to survive and propagate (fitness). Finally, the fate of a mutation is
decided by Darwinian selection on the level of the population, where three
outcomes are possible: fixation in the population, elimination by purifying
selection or separation in the population in a subdominant clone (polymorphism).
In this mini-series of lectures I will outline my labÕs and others efforts in
an emerging new field which merges molecular mechanism with evolution. The
advances in this new field became possible due to recent spectacular progress
in molecular biophysics, genomics, systems biology and population genetics. The
research along these lines has the potential to transform our understanding of
evolutionary dynamics from descriptive to quantitative and predictive with
potential biomedical applications that extend from proactive treatment of
infectious diseases to better modeling and treatment of cancer.
Lecture 1. Introduction to statistical
mechanics of protein folding.
I will
present the fundamental heteropolymer model of protein folding and its
statistical mechanical analysis, which uncovered the energy gap criterion - the
necessary and sufficient conditions for a heteropolymer sequence to encode a
foldable protein. The analogy and fundamental differences between heteropolymer
and spin glass models will be highlighted. I will also discuss how
understanding of basic principles of protein folding helps in our efforts to
design new proteins and decipher the ÔÕmessagesÕÕ hidden in multiple sequence
alignment. I will then discuss the analogy between sequence selection for
energy gaps and statistical mechanics of a class of generalized spin models.
The statistical mechanical view of sequence selection enjoyed renaissance with
the development of statistical methods to derive structural information about
proteins from the analysis of variation in multiple sequence alignment. Finally
I will discuss the relation between selection for foldable sequences and
thermodynamic and kinetic mechanisms of protein folding such as
first-order-like cooperativity.
Lecture 2. Multiscale biophysical models of
evolutionary dynamics.
In this lecture I will discuss recent efforts at modeling evolutionary
dynamics that merges molecular mechanisms with population genetics. Traditional
population genetics models are agnostic to the physical-chemical nature of
mutational effects. Rather they operate with an a priori assumed distributions
of fitness effects (DFE) of mutations from which evolutionary dynamics are
derived. Alternatively some population genetics models aim to derive DFE from
evolutionary observations. In departure with this tradition the novel
multiscale models integrate the molecular effects of mutations on physical
properties of proteins into physically intuitive yet detailed
genotype-phenotype relationship (GPR) assumptions. I will present a range of
models from simple analytical diffusion-based model on biophysical fitness
landscapes to more sophisticated computational models of populations of model
cells where genetic changes are mapped into molecular effects using biophysical
modeling of proteins and ensuing fitness changes determine the fate of
mutations in realistic population dynamics. Examples of insights derived from
biophysics-based multiscale models include the scale-free character of Protein
Universe, the fundamental limit of mutation rates in living organisms, physics
of thermal adaptation, co-evolution of protein interactions and abundances in
cytoplasm and related results, some of which I will present and discuss.
Lecture
3: Biophysical walks on fitness landscape: how a theorist became enchanted with
experiment
Multiscale biophysical modeling crucially relies on assumptions about
genotype-phenotype relationship (GPR). Fitness landscape (FL) is a common
metaphoric description of GPR. However its precise nature is not known: ÔÕAxesÓ
on the pictorial presentations of FL remain unlabeled. In this lecture I will
present experimental efforts, to outline FL in physical axes i.e. establish the
link between changes in molecular properties of proteins and fitness for model
living organisms. The effort encompasses multiple fields of experimental
biology: molecular biophysics, genetics, genomics, proteomics and cell biology.
The approach is bottom up and is based on our ability to edit genomes of
bacterial and eukaryotic cells. Rational genetic variation is introduced on the
chromosome of E. coli in the coding regions of essential genes dihydrofolate
reductase (DHFR) and adenylate kinase (AdK). The mutant proteins are purified
and their molecular properties are evaluated using the experimental tools of
molecular biophysics. The fitness effects of rationally introduced mutations
are determined using competition assays and are linked to known changes in molecular
properties of proteins. As a complementary approach we use inter-species
replacements of genes on E. coli chromosome, i.e. we replaced the gene encoding
DHFR with same genes from various other bacteria. This approach allows one to
explore a broader range of molecular variations inaccessible through point
mutations. We show how genetic variation introduces major fitness barriers that
can be overcome in evolutionary dynamics. Metabolomic, genomic and proteomic
comparative analyses of genomically edited and wild-type strains highlights major
elements in the GPR that encompasses molecular, system-level and organismal
traits providing a crucial feedback for computational modeling.
Dates
and place:
The
lectures will be given in room 236
(29 rue d'Ulm) from 9 to 11am, on May 13, 20 and 27, 2015.