14,851 research outputs found
Predicting protein function by machine learning on amino acid sequences – a critical evaluation
Copyright @ 2007 Al-Shahib et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background: Predicting the function of newly discovered proteins by simply inspecting their amino acid sequence is one of the major challenges of post-genomic computational biology, especially when done without recourse to experimentation or homology information. Machine learning classifiers are able to discriminate between proteins belonging to different functional classes. Until now, however, it has been unclear if this ability would be transferable to proteins of unknown function, which may show distinct biases compared to experimentally more tractable proteins. Results: Here we show that proteins with known and unknown function do indeed differ significantly. We then show that proteins from different bacterial species also differ to an even larger and very surprising extent, but that functional classifiers nonetheless generalize successfully across species boundaries. We also show that in the case of highly specialized proteomes classifiers from a different, but more conventional, species may in fact outperform the endogenous species-specific classifier. Conclusion: We conclude that there is very good prospect of successfully predicting the function of yet uncharacterized proteins using machine learning classifiers trained on proteins of known function
PRED-CLASS: cascading neural networks for generalized protein classification and genome-wide applications
A cascading system of hierarchical, artificial neural networks (named
PRED-CLASS) is presented for the generalized classification of proteins into
four distinct classes-transmembrane, fibrous, globular, and mixed-from
information solely encoded in their amino acid sequences. The architecture of
the individual component networks is kept very simple, reducing the number of
free parameters (network synaptic weights) for faster training, improved
generalization, and the avoidance of data overfitting. Capturing information
from as few as 50 protein sequences spread among the four target classes (6
transmembrane, 10 fibrous, 13 globular, and 17 mixed), PRED-CLASS was able to
obtain 371 correct predictions out of a set of 387 proteins (success rate
approximately 96%) unambiguously assigned into one of the target classes. The
application of PRED-CLASS to several test sets and complete proteomes of
several organisms demonstrates that such a method could serve as a valuable
tool in the annotation of genomic open reading frames with no functional
assignment or as a preliminary step in fold recognition and ab initio structure
prediction methods. Detailed results obtained for various data sets and
completed genomes, along with a web sever running the PRED-CLASS algorithm, can
be accessed over the World Wide Web at http://o2.biol.uoa.gr/PRED-CLAS
Toward a multilevel representation of protein molecules: comparative approaches to the aggregation/folding propensity problem
This paper builds upon the fundamental work of Niwa et al. [34], which
provides the unique possibility to analyze the relative aggregation/folding
propensity of the elements of the entire Escherichia coli (E. coli) proteome in
a cell-free standardized microenvironment. The hardness of the problem comes
from the superposition between the driving forces of intra- and inter-molecule
interactions and it is mirrored by the evidences of shift from folding to
aggregation phenotypes by single-point mutations [10]. Here we apply several
state-of-the-art classification methods coming from the field of structural
pattern recognition, with the aim to compare different representations of the
same proteins gathered from the Niwa et al. data base; such representations
include sequences and labeled (contact) graphs enriched with chemico-physical
attributes. By this comparison, we are able to identify also some interesting
general properties of proteins. Notably, (i) we suggest a threshold around 250
residues discriminating "easily foldable" from "hardly foldable" molecules
consistent with other independent experiments, and (ii) we highlight the
relevance of contact graph spectra for folding behavior discrimination and
characterization of the E. coli solubility data. The soundness of the
experimental results presented in this paper is proved by the statistically
relevant relationships discovered among the chemico-physical description of
proteins and the developed cost matrix of substitution used in the various
discrimination systems.Comment: 17 pages, 3 figures, 46 reference
Identification of functionally related enzymes by learning-to-rank methods
Enzyme sequences and structures are routinely used in the biological sciences
as queries to search for functionally related enzymes in online databases. To
this end, one usually departs from some notion of similarity, comparing two
enzymes by looking for correspondences in their sequences, structures or
surfaces. For a given query, the search operation results in a ranking of the
enzymes in the database, from very similar to dissimilar enzymes, while
information about the biological function of annotated database enzymes is
ignored.
In this work we show that rankings of that kind can be substantially improved
by applying kernel-based learning algorithms. This approach enables the
detection of statistical dependencies between similarities of the active cleft
and the biological function of annotated enzymes. This is in contrast to
search-based approaches, which do not take annotated training data into
account. Similarity measures based on the active cleft are known to outperform
sequence-based or structure-based measures under certain conditions. We
consider the Enzyme Commission (EC) classification hierarchy for obtaining
annotated enzymes during the training phase. The results of a set of sizeable
experiments indicate a consistent and significant improvement for a set of
similarity measures that exploit information about small cavities in the
surface of enzymes
Introduction to Protein Structure Prediction
This chapter gives a graceful introduction to problem of protein three-
dimensional structure prediction, and focuses on how to make structural sense
out of a single input sequence with unknown structure, the 'query' or 'target'
sequence. We give an overview of the different classes of modelling techniques,
notably template-based and template free. We also discuss the way in which
structural predictions are validated within the global com- munity, and
elaborate on the extent to which predicted structures may be trusted and used
in practice. Finally we discuss whether the concept of a sin- gle fold
pertaining to a protein structure is sustainable given recent insights. In
short, we conclude that the general protein three-dimensional structure
prediction problem remains unsolved, especially if we desire quantitative
predictions. However, if a homologous structural template is available in the
PDB model or reasonable to high accuracy may be generated
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