3,741 research outputs found
Cooperative "folding transition" in the sequence space facilitates function-driven evolution of protein families
In the protein sequence space, natural proteins form clusters of families
which are characterized by their unique native folds whereas the great majority
of random polypeptides are neither clustered nor foldable to unique structures.
Since a given polypeptide can be either foldable or unfoldable, a kind of
"folding transition" is expected at the boundary of a protein family in the
sequence space. By Monte Carlo simulations of a statistical mechanical model of
protein sequence alignment that coherently incorporates both short-range and
long-range interactions as well as variable-length insertions to reproduce the
statistics of the multiple sequence alignment of a given protein family, we
demonstrate the existence of such transition between natural-like sequences and
random sequences in the sequence subspaces for 15 domain families of various
folds. The transition was found to be highly cooperative and two-state-like.
Furthermore, enforcing or suppressing consensus residues on a few of the
well-conserved sites enhanced or diminished, respectively, the natural-like
pattern formation over the entire sequence. In most families, the key sites
included ligand binding sites. These results suggest some selective pressure on
the key residues, such as ligand binding activity, may cooperatively facilitate
the emergence of a protein family during evolution. From a more practical
aspect, the present results highlight an essential role of long-range effects
in precisely defining protein families, which are absent in conventional
sequence models.Comment: 13 pages, 7 figures, 2 tables (a new subsection added
Probabilistic methods in the analysis of protein interaction networks
Imperial Users onl
Pair HMM based gap statistics for re-evaluation of indels in alignments with affine gap penalties: Extended Version
Although computationally aligning sequence is a crucial step in the vast
majority of comparative genomics studies our understanding of alignment biases
still needs to be improved. To infer true structural or homologous regions
computational alignments need further evaluation. It has been shown that the
accuracy of aligned positions can drop substantially in particular around gaps.
Here we focus on re-evaluation of score-based alignments with affine gap
penalty costs. We exploit their relationships with pair hidden Markov models
and develop efficient algorithms by which to identify gaps which are
significant in terms of length and multiplicity. We evaluate our statistics
with respect to the well-established structural alignments from SABmark and
find that indel reliability substantially increases with their significance in
particular in worst-case twilight zone alignments. This points out that our
statistics can reliably complement other methods which mostly focus on the
reliability of match positions.Comment: 17 pages, 7 figure
The posterior-Viterbi: a new decoding algorithm for hidden Markov models
Background: Hidden Markov models (HMM) are powerful machine learning tools
successfully applied to problems of computational Molecular Biology. In a
predictive task, the HMM is endowed with a decoding algorithm in order to
assign the most probable state path, and in turn the class labeling, to an
unknown sequence. The Viterbi and the posterior decoding algorithms are the
most common. The former is very efficient when one path dominates, while the
latter, even though does not guarantee to preserve the automaton grammar, is
more effective when several concurring paths have similar probabilities. A
third good alternative is 1-best, which was shown to perform equal or better
than Viterbi. Results: In this paper we introduce the posterior-Viterbi (PV) a
new decoding which combines the posterior and Viterbi algorithms. PV is a two
step process: first the posterior probability of each state is computed and
then the best posterior allowed path through the model is evaluated by a
Viterbi algorithm.
Conclusions: We show that PV decoding performs better than other algorithms
first on toy models and then on the computational biological problem of the
prediction of the topology of beta-barrel membrane proteins.Comment: 23 pages, 3 figure
Accurate reconstruction of insertion-deletion histories by statistical phylogenetics
The Multiple Sequence Alignment (MSA) is a computational abstraction that
represents a partial summary either of indel history, or of structural
similarity. Taking the former view (indel history), it is possible to use
formal automata theory to generalize the phylogenetic likelihood framework for
finite substitution models (Dayhoff's probability matrices and Felsenstein's
pruning algorithm) to arbitrary-length sequences. In this paper, we report
results of a simulation-based benchmark of several methods for reconstruction
of indel history. The methods tested include a relatively new algorithm for
statistical marginalization of MSAs that sums over a stochastically-sampled
ensemble of the most probable evolutionary histories. For mammalian
evolutionary parameters on several different trees, the single most likely
history sampled by our algorithm appears less biased than histories
reconstructed by other MSA methods. The algorithm can also be used for
alignment-free inference, where the MSA is explicitly summed out of the
analysis. As an illustration of our method, we discuss reconstruction of the
evolutionary histories of human protein-coding genes.Comment: 28 pages, 15 figures. arXiv admin note: text overlap with
arXiv:1103.434
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
Automated Protein Structure Classification: A Survey
Classification of proteins based on their structure provides a valuable
resource for studying protein structure, function and evolutionary
relationships. With the rapidly increasing number of known protein structures,
manual and semi-automatic classification is becoming ever more difficult and
prohibitively slow. Therefore, there is a growing need for automated, accurate
and efficient classification methods to generate classification databases or
increase the speed and accuracy of semi-automatic techniques. Recognizing this
need, several automated classification methods have been developed. In this
survey, we overview recent developments in this area. We classify different
methods based on their characteristics and compare their methodology, accuracy
and efficiency. We then present a few open problems and explain future
directions.Comment: 14 pages, Technical Report CSRG-589, University of Toront
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