397 research outputs found
On the entropy of protein families
Proteins are essential components of living systems, capable of performing a
huge variety of tasks at the molecular level, such as recognition, signalling,
copy, transport, ... The protein sequences realizing a given function may
largely vary across organisms, giving rise to a protein family. Here, we
estimate the entropy of those families based on different approaches, including
Hidden Markov Models used for protein databases and inferred statistical models
reproducing the low-order (1-and 2-point) statistics of multi-sequence
alignments. We also compute the entropic cost, that is, the loss in entropy
resulting from a constraint acting on the protein, such as the fixation of one
particular amino-acid on a specific site, and relate this notion to the escape
probability of the HIV virus. The case of lattice proteins, for which the
entropy can be computed exactly, allows us to provide another illustration of
the concept of cost, due to the competition of different folds. The relevance
of the entropy in relation to directed evolution experiments is stressed.Comment: to appear in Journal of Statistical Physic
Identification of drug resistance mutations in HIV from constraints on natural evolution
Human immunodeficiency virus (HIV) evolves with extraordinary rapidity.
However, its evolution is constrained by interactions between mutations in its
fitness landscape. Here we show that an Ising model describing these
interactions, inferred from sequence data obtained prior to the use of
antiretroviral drugs, can be used to identify clinically significant sites of
resistance mutations. Successful predictions of the resistance sites indicate
progress in the development of successful models of real viral evolution at the
single residue level, and suggest that our approach may be applied to help
design new therapies that are less prone to failure even where resistance data
is not yet available.Comment: 5 pages, 3 figure
For T Cell Receptors, Some Breakups Might Not Last Forever
Does the affinity or half-life of peptide-MHC-T cell receptor (TCR) interactions determine T cell activation? In this issue of Immunity, Aleksic et al. (2010) propose a role for the on rate through multiple rebindings to the same TCR
From HIV protein sequences to viral fitness landscapes: a new paradigm for in silico vaccine design
Background: An inexpensive prophylactic vaccine offers the best hope to curb the HIV/AIDS epidemic gripping sub-Saharan Africa. Systematic means to guide the design of an effective immunogen for this, and other, infectious diseases are not available. What is required is a method to chart the peaks and valleys of viral fitness as a function of amino acid sequence. An efficacious vaccine would eject the virus from the high fitness peaks, and drive it into the valleys where its compromised fitness impairs its ability to replicate and inflict damage to the host.
Methods: Appealing to spin glass models in statistical physics, we present a novel approach to translate viral sequence databases into landscapes of viral fitness. These inferred models furnish a quantitative description of viral replicative capacity as a function of amino acid sequence. We illustrate this approach in the development of landscapes for the proteins of HIV-1 clade B Gag.
Results: In comparisons to experimental and clinical data, our inferred landscapes demonstrate excellent agreement with: 1) in vitro replicative fitness measurements, 2) clinically observed high-fitness circulating viral strains, 3) documented HLA associated CTL escape mutations, and 4) intra-host temporal adaptation pathways revealed by deep sequencing. These favorable comparisons support our landscapes as reflections of intrinsic viral fitness. We illustrate the value of such descriptions in the computational design of a CTL Gag immunogen.
Conclusion: We present a novel methodology to translate viral sequence data into quantitative landscapes of viral fitness. In an application to HIV-1 Gag, we illustrate excellent agreement of our model predictions with experimental and clinical data, and demonstrate a powerful new approach for HIV immunogen design. We anticipate that this approach may represent a heretofore unprecedented means to synthesize fitness landscapes for diverse pathogens, and may provide the basis for the design of improved prophylactic and therapeutic strategies
Deconvolving mutational patterns of poliovirus outbreaks reveals its intrinsic fitness landscape.
Vaccination has essentially eradicated poliovirus. Yet, its mutation rate is higher than that of viruses like HIV, for which no effective vaccine exists. To investigate this, we infer a fitness model for the poliovirus viral protein 1 (vp1), which successfully predicts in vitro fitness measurements. This is achieved by first developing a probabilistic model for the prevalence of vp1 sequences that enables us to isolate and remove data that are subject to strong vaccine-derived biases. The intrinsic fitness constraints derived for vp1, a capsid protein subject to antibody responses, are compared with those of analogous HIV proteins. We find that vp1 evolution is subject to tighter constraints, limiting its ability to evade vaccine-induced immune responses. Our analysis also indicates that circulating poliovirus strains in unimmunized populations serve as a reservoir that can seed outbreaks in spatio-temporally localized sub-optimally immunized populations
Student perception on the implementation of an end-of-the-day quiz and its impact on student learning and class attendance in a three-year accelerated doctorate program of pharmacy.
Positive Feedback Regulation Results in Spatial Clustering and Fast Spreading of Active Signaling Molecules on a Cell Membrane
Positive feedback regulation is ubiquitous in cell signaling networks, often
leading to binary outcomes in response to graded stimuli. However, the role of
such feedbacks in clustering, and in spatial spreading of activated molecules,
has come to be appreciated only recently. We focus on the latter, using a
simple model developed in the context of Ras activation with competing negative
and positive feedback mechanisms. We find that positive feedback, in the
presence of slow diffusion, results in clustering of activated molecules on the
plasma membrane, and rapid spatial spreading as the front of the cluster
propagates with a constant velocity (dependent on the feedback strength). The
advancing fronts of the clusters of the activated species are rough, with
scaling consistent with the Kardar-Parisi-Zhang (KPZ) equation in one
dimension. Our minimal model is general enough to describe signal transduction
in a wide variety of biological networks where activity in the
membrane-proximal region is subject to feedback regulation.Comment: 37 pages, 8 figures. Journal of Chemical Physics (in press
How nonuniform contact profiles of T cell receptors modulate thymic selection outcomes
T cell receptors (TCRs) bind foreign or self-peptides attached to major
histocompatibility complex (MHC) molecules, and the strength of this
interaction determines T cell activation. Optimizing the ability of T cells to
recognize a diversity of foreign peptides yet be tolerant of self-peptides is
crucial for the adaptive immune system to properly function. This is achieved
by selection of T cells in the thymus, where immature T cells expressing
unique, stochastically generated TCRs interact with a large number of
self-peptide-MHC; if a TCR does not bind strongly enough to any
self-peptide-MHC, or too strongly with at least one self-peptide-MHC, the T
cell dies. Past theoretical work cast thymic selection as an extreme value
problem, and characterized the statistical enrichment or depletion of amino
acids in the post-selection TCR repertoire, showing how T cells are selected to
be able to specifically recognize peptides derived from diverse pathogens, yet
have limited self-reactivity. Here, we investigate how the degree of enrichment
is modified by nonuniform contacts that a TCR makes with peptide-MHC.
Specifically, we were motivated by recent experiments showing that amino acids
at certain positions of a TCR sequence have large effects on thymic selection
outcomes, and crystal structure data that reveal a nonuniform contact profile
between a TCR and its peptide-MHC ligand. Using a representative TCR contact
profile as an illustration, we show via simulations that the degree of
enrichment now varies by position according to the contact profile, and,
importantly, it depends on the implementation of nonuniform contacts during
thymic selection. We explain these nontrivial results analytically. Our study
has implications for understanding the selection forces that shape the
functionality of the post-selection TCR repertoire.Comment: 10 pages, 4 figures, submitted to Phys. Rev.
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