The Pros and Cons of Predicting Protein Contact Maps

Bartoli, Lisa; Capriotti, Emidio; Fariselli, Piero; Martelli, Pier Luigi; Casadio, Rita

doi:10.1007/978-1-59745-574-9_8

Cited by 28 publications

(33 citation statements)

References 27 publications

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“…Moreover, the graph theory and the extensive works on interaction networks provide a broad class of tools to study the network-based approaches. Thus, the algorithms proposed to reconstruct the tertiary structures are insensitive to random noise and are also robust so that it is not necessary to correctly predict all contacts (Bartoli et al, 2008). Second, a model is a means to study a phenomenon that possesses itself some new features.…”

Section: Discussionmentioning

confidence: 99%

“…This specific representation conveys strong information about the protein 3D structure (Bartoli et al, 2008). Moreover, the graph theory and the extensive works on interaction networks provide a broad class of tools to study the network-based approaches.…”

Section: Discussionmentioning

confidence: 99%

“…Thus the studies are not always done with the same hypothesis and the results are then not universal. In addition, the prediction of amino acid interaction networks is known as a non-local problem: the prediction of contacts between two residues poses constraints on the feasibilities of all other contacts (Bartoli et al, 2008).…”

Section: Discussionmentioning

confidence: 99%

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Community structure description in amino acid interaction networks

Gaci

2011

Interdiscip Sci Comput Life Sci

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In this paper, we represent proteins by amino acid interaction networks. This is a graph whose vertices are the protein's amino acids and whose edges are the interactions between them. We begin by identifying the main topological properties of these interaction networks using graph theory measures. We observe that the amino acids interact specifically, according to their structural role, and depending on whether they participate or not in the secondary structure. Thus, certain amino acids tend to group together to form local clouds. Then, we study the formation of node aggregations through community structure detections. We observe that the composition of organizations confirms a specific aggregation between loops around a core composed of secondary.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Community structure description in amino acid interaction networks

Gaci

2011

Interdiscip Sci Comput Life Sci

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“…Contact maps (CMs) provide a simple but powerful means for protein structure comparison and alignment [18,19], prediction [20,21] and visualization of protein structural features [22]. Here we show how CMs can be used to identify solenoid proteins and to calculate lengths of individual units, even for very divergent repeats.…”

Section: Methodsmentioning

confidence: 99%

ConSole: using modularity of Contact maps to locate Solenoid domains in protein structures

Hrabe

Godzik

2014

BMC Bioinformatics

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BackgroundPeriodic proteins, characterized by the presence of multiple repeats of short motifs, form an interesting and seldom-studied group. Due to often extreme divergence in sequence, detection and analysis of such motifs is performed more reliably on the structural level. Yet, few algorithms have been developed for the detection and analysis of structures of periodic proteins.ResultsConSole recognizes modularity in protein contact maps, allowing for precise identification of repeats in solenoid protein structures, an important subgroup of periodic proteins. Tests on benchmarks show that ConSole has higher recognition accuracy as compared to Raphael, the only other publicly available solenoid structure detection tool. As a next step of ConSole analysis, we show how detection of solenoid repeats in structures can be used to improve sequence recognition of these motifs and to detect subtle irregularities of repeat lengths in three solenoid protein families.ConclusionsThe ConSole algorithm provides a fast and accurate tool to recognize solenoid protein structures as a whole and to identify individual solenoid repeat units from a structure. ConSole is available as a web-based, interactive server and is available for download at http://console.sanfordburnham.org.

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“…We introduce three contact-based descriptors including contact number, absolute contact number, and residue-wise contact order. These 1D descriptors are related to the 2D contact map descriptor [37], which is out of scope of this review. 4.…”

Section: Categorization Of Descriptorsmentioning

confidence: 99%

Structural Protein Descriptors in 1-Dimension and their Sequence-Based Predictions

Kurgan¹,

Disfani

2011

CPPS

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The last few decades observed an increasing interest in development and application of 1-dimensional (1D) descriptors of protein structure. These descriptors project 3D structural features onto 1D strings of residue-wise structural assignments. They cover a wide-range of structural aspects including conformation of the backbone, burying depth/solvent exposure and flexibility of residues, and inter-chain residue-residue contacts. We perform first-of-its-kind comprehensive comparative review of the existing 1D structural descriptors. We define, review and categorize ten structural descriptors and we also describe, summarize and contrast over eighty computational models that are used to predict these descriptors from the protein sequences. We show that the majority of the recent sequence-based predictors utilize machine learning models, with the most popular being neural networks, support vector machines, hidden Markov models, and support vector and linear regressions. These methods provide high-throughput predictions and most of them are accessible to a non-expert user via web servers and/or stand-alone software packages. We empirically evaluate several recent sequence-based predictors of secondary structure, disorder, and solvent accessibility descriptors using a benchmark set based on CASP8 targets. Our analysis shows that the secondary structure can be predicted with over 80% accuracy and segment overlap (SOV), disorder with over 0.9 AUC, 0.6 Matthews Correlation Coefficient (MCC), and 75% SOV, and relative solvent accessibility with PCC of 0.7 and MCC of 0.6 (0.86 when homology is used). We demonstrate that the secondary structure predicted from sequence without the use of homology modeling is as good as the structure extracted from the 3D folds predicted by top-performing template-based methods.

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The Pros and Cons of Predicting Protein Contact Maps

Cited by 28 publications

References 27 publications

Community structure description in amino acid interaction networks

Community structure description in amino acid interaction networks

ConSole: using modularity of Contact maps to locate Solenoid domains in protein structures

Structural Protein Descriptors in 1-Dimension and their Sequence-Based Predictions

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