7,596 research outputs found
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
Towards Structural Classification of Proteins based on Contact Map Overlap
A multitude of measures have been proposed to quantify the similarity between
protein 3-D structure. Among these measures, contact map overlap (CMO)
maximization deserved sustained attention during past decade because it offers
a fine estimation of the natural homology relation between proteins. Despite
this large involvement of the bioinformatics and computer science community,
the performance of known algorithms remains modest. Due to the complexity of
the problem, they got stuck on relatively small instances and are not
applicable for large scale comparison. This paper offers a clear improvement
over past methods in this respect. We present a new integer programming model
for CMO and propose an exact B &B algorithm with bounds computed by solving
Lagrangian relaxation. The efficiency of the approach is demonstrated on a
popular small benchmark (Skolnick set, 40 domains). On this set our algorithm
significantly outperforms the best existing exact algorithms, and yet provides
lower and upper bounds of better quality. Some hard CMO instances have been
solved for the first time and within reasonable time limits. From the values of
the running time and the relative gap (relative difference between upper and
lower bounds), we obtained the right classification for this test. These
encouraging result led us to design a harder benchmark to better assess the
classification capability of our approach. We constructed a large scale set of
300 protein domains (a subset of ASTRAL database) that we have called Proteus
300. Using the relative gap of any of the 44850 couples as a similarity
measure, we obtained a classification in very good agreement with SCOP. Our
algorithm provides thus a powerful classification tool for large structure
databases
Simultaneous Coherent Structure Coloring facilitates interpretable clustering of scientific data by amplifying dissimilarity
The clustering of data into physically meaningful subsets often requires
assumptions regarding the number, size, or shape of the subgroups. Here, we
present a new method, simultaneous coherent structure coloring (sCSC), which
accomplishes the task of unsupervised clustering without a priori guidance
regarding the underlying structure of the data. sCSC performs a sequence of
binary splittings on the dataset such that the most dissimilar data points are
required to be in separate clusters. To achieve this, we obtain a set of
orthogonal coordinates along which dissimilarity in the dataset is maximized
from a generalized eigenvalue problem based on the pairwise dissimilarity
between the data points to be clustered. This sequence of bifurcations produces
a binary tree representation of the system, from which the number of clusters
in the data and their interrelationships naturally emerge. To illustrate the
effectiveness of the method in the absence of a priori assumptions, we apply it
to three exemplary problems in fluid dynamics. Then, we illustrate its capacity
for interpretability using a high-dimensional protein folding simulation
dataset. While we restrict our examples to dynamical physical systems in this
work, we anticipate straightforward translation to other fields where existing
analysis tools require ad hoc assumptions on the data structure, lack the
interpretability of the present method, or in which the underlying processes
are less accessible, such as genomics and neuroscience
A Structural Model of the Cytochrome c Reductase/Oxidase Supercomplex from Yeast Mitochondria
Mitochondrial respiratory chain complexes are arranged in supercomplexes within the inner membrane. Interaction of cytochrome c reductase (complex III) and cytochrome c oxidase (complex IV) was investigated in Saccharomyces cerevisiae. Projection maps at 15 Ă… resolution of supercomplexes III2 + IV1 and III2 + IV2 were obtained by electron microscopy. Based on a comparison of our maps with atomic x-ray structures for complexes III and IV we present a pseudo-atomic model of their precise interaction. Two complex IV monomers are specifically attached to dimeric complex III with their convex sides. The opposite sides, which represent the complex IV dimer interface in the x-ray structure, are open for complex IV-complex IV interactions. This could lead to oligomerization of III2 + IV2 supercomplexes, but this was not detected. Instead, binding of cytochrome c to the supercomplexes was revealed. It was calculated that cytochrome c has to move less than 40 Ă… at the surface of the supercomplex for electron transport between complex III2 and complex IV. Hence, the prime function of the supercomplex III2 + IV2 is proposed to be a scaffold for effective electron transport between complexes III and IV.
Protein contact map prediction using multi-stage hybrid intelligence inference systems
AbstractProteins are one of the most important molecules in organisms. Protein function can be inferred from its 3D structure. The gap between the number of discovered protein sequences and the number of structures determined by the experimental methods is increasing. Accurate prediction of protein contact map is an important step toward the reconstruction of the protein’s 3D structure. In spite of continuous progress in developing contact map predictors, highly accurate prediction is still unresolved problem. In this paper, we introduce a new predictor, JUSTcon, which consists of multiple parallel stages that are based on adaptive neuro-fuzzy inference System (ANFIS) and K nearest neighbors (KNNs) classifier. A smart filtering operation is performed on the final outputs to ensure normal connectivity behaviors of amino acids pairs. The window size of the filter is selected by a simple expert system. The dataset was divided into testing dataset of 50 proteins and training dataset of 450 proteins. The system produced an average accuracy of 45.2% for the sequence separation of six amino acids. In addition, JUSTcon outperformed SVMcon and PROFcon predictors in the cases of large separation distances. JUSTcon produced an average accuracy of 15% for the sequence separation of 24 amino acids after applying it on CASP9 targets
Allo-network drugs: Extension of the allosteric drug concept to protein-protein interaction and signaling networks
Allosteric drugs are usually more specific and have fewer side effects than orthosteric drugs targeting the same
protein. Here, we overview the current knowledge on allosteric signal transmission from the network point of view, and show that most intra-protein conformational changes may be dynamically transmitted across protein-protein interaction and signaling networks of the cell. Allo-network drugs influence the pharmacological target protein indirectly using specific inter-protein network pathways. We show that allo-network drugs may have a higher efficiency to change the networks of human cells than those of other organisms, and can be designed to have specific effects on cells in a diseased state. Finally, we summarize possible methods to identify allo-network drug targets and sites, which may develop to a promising new area of systems-based drug design
Exploring the potential of 3D Zernike descriptors and SVM for protein\u2013protein interface prediction
Abstract Background The correct determination of protein–protein interaction interfaces is important for understanding disease mechanisms and for rational drug design. To date, several computational methods for the prediction of protein interfaces have been developed, but the interface prediction problem is still not fully understood. Experimental evidence suggests that the location of binding sites is imprinted in the protein structure, but there are major differences among the interfaces of the various protein types: the characterising properties can vary a lot depending on the interaction type and function. The selection of an optimal set of features characterising the protein interface and the development of an effective method to represent and capture the complex protein recognition patterns are of paramount importance for this task. Results In this work we investigate the potential of a novel local surface descriptor based on 3D Zernike moments for the interface prediction task. Descriptors invariant to roto-translations are extracted from circular patches of the protein surface enriched with physico-chemical properties from the HQI8 amino acid index set, and are used as samples for a binary classification problem. Support Vector Machines are used as a classifier to distinguish interface local surface patches from non-interface ones. The proposed method was validated on 16 classes of proteins extracted from the Protein–Protein Docking Benchmark 5.0 and compared to other state-of-the-art protein interface predictors (SPPIDER, PrISE and NPS-HomPPI). Conclusions The 3D Zernike descriptors are able to capture the similarity among patterns of physico-chemical and biochemical properties mapped on the protein surface arising from the various spatial arrangements of the underlying residues, and their usage can be easily extended to other sets of amino acid properties. The results suggest that the choice of a proper set of features characterising the protein interface is crucial for the interface prediction task, and that optimality strongly depends on the class of proteins whose interface we want to characterise. We postulate that different protein classes should be treated separately and that it is necessary to identify an optimal set of features for each protein class
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