Predicting interresidue contacts using templates and pathways

Shao, Yu; Bystroff, Christopher

doi:10.1002/prot.10539

Cited by 68 publications

(42 citation statements)

References 29 publications

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“…It has successfully been used to predict protein three-dimensional local structures, secondary structures, to identify protein-coding ORFs, or to design a sequence to fit a structure. Recently, a two-dimensional approach has been developed with HMMSTR-CM [384]. The latter predicts the likelihood of pairwise inter-residue contacts.…”

Section: D-blastmentioning

confidence: 99%

Local Protein Structures

Offmann¹,

Tyagi²,

Brevern

2007

CBIO

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Protein structures are classically described as composed of two regular states, the α-helices and the β-strands and one non-regular and variable state, the coil. Nonetheless, this simple definition of secondary structures hides numerous limitations. In fact, the rules for secondary structure assignment are complex. Thus, numerous assignment methods based on different criteria have emerged leading to heterogeneous and diverging results. In the same way, 3 states may over-simplify the description of protein structure; 50% of all residues, i.e., the coil, are not genuinely described even when it encompass precise local protein structures.Description of local protein structures have hence focused on the elaboration of complete sets of small prototypes or "structural alphabets", able to analyze local protein structures and to approximate every part of the protein backbone. They have also been used to predict the protein backbone conformation and in ab initio / de novo methods. In this paper, we review different approaches towards the description of local structures, mainly through their description in terms of secondary structures and in terms of structural alphabets. We provide some insights into their potential applications.

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Section: D-blastmentioning

confidence: 99%

Local Protein Structures

Offmann¹,

Tyagi²,

Brevern

2007

CBIO

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“…Numerous sequence-based methods have been developed using machine learning algorithms such as artificial neural networks (ANNs) (Fariselli and Casadio, 1999;Fariselli et al, 2001;Pollastri et al, 2001;Punta and Rost, 2005;Tegge et al, 2009;Vullo et al, 2006;Xue et al, 2009;Zhang and Huang, 2004), support vector machines (SVMs) (Cheng and Baldi, 2007;Wu and Zhang, 2008;Zhao and Karypis, 2005), Hidden Markov Models (HMM) (Bjorkholm et al, 2009;Shao and Bystroff, 2003), Genetic Algorithm (GA) (Chen and Li, 2010;MacCallum, 2004), etc. The mean accuracy achieved by state-of-the-art RR predictors is often in the range of 20-30%, suggesting that it remains in need of improvement.…”

Section: Introductionmentioning

confidence: 99%

Predicting residue–residue contacts using random forest models

Fang

2011

Bioinformatics

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Motivation: Protein residue-residue contact prediction can be useful in predicting protein 3D structures. Current algorithms for such a purpose leave room for improvement. Results: We develop ProC_S3, a set of Random Forest algorithmbased models, for predicting residue-residue contact maps. The models are constructed based on a collection of 1490 nonredundant, high-resolution protein structures using >1280 sequencebased features. A new amino acid residue contact propensity matrix and a new set of seven amino acid groups based on contact preference are developed and used in ProC_S3. ProC_S3 delivers a 3-fold cross-validated accuracy of 26.9% with coverage of 4.7% for top L/5 predictions (L is the number of residues in a protein) of long-range contacts (sequence separation ≥ 24). Further benchmark tests deliver an accuracy of 29.7% and coverage of 5.6% for an independent set of 329 proteins. In the recently completed Ninth Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP9), ProC_S3 is ranked as No. 1, No. 3, and No. 2 accuracies in the top L/5, L/10 and best 5 predictions of long-range contacts, respectively, among 18 automatic prediction servers.

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“…Construction of residue contact maps for protein structures and statistical evaluation of residue colocations is a frequently used method for visualization and analyses of spatial connections between amino acids [27][28][29]. The amino acid co-locations in real protein structures is clearly not random [30,31] and therefore residue co-location matrices are often used to assist in the prediction of novel protein structures [32,33]. We have carefully examined the physicochemical properties of specifically interacting amino acids in and between protein structures, and we concluded that these interactions follows the well known physico-chemical rules of size, charge and hydrophobe compatibility (unpublished data) well in line with Anfinsen's prediction.…”

Section: Resultsmentioning

confidence: 99%

The Meaning of a Redundant Codon: There is Protein Folding Information in Nucleic Acids in Addition to the Genetic Code

Biro¹,

Biro²

2006

American J. of Biochemistry and Biotechnology

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All the information necessary for protein folding is supposed to be present in the amino acid sequence. It is still not possible to provide specific ab initio structure predictions by bioinformatical methods. It is suspected that additional folding information is present in protein coding nucleic acid sequences, which is not represented by the known genetic code. Nucleic acid subsequences comprising the 1st and/or 3rd codon residues in mRNAs express significantly higher free folding energy (FFE) than the subsequence containing only the 2nd residues (p<0.0001, n=81). This periodic FFE difference is not present in introns and therefore it is a specific physico-chemical characteristic of coding sequences and it might contribute to unambiguous definition of codon boundaries during translation. The FFE in the 1st and 3rd residues is additive, which suggests that these residues contain a significant number of complementary bases and contribute to selection for local RNA secondary structures in coding regions. This periodic, codon-related structure-forming of mRNAs indicates a connection between the structure of exons and the corresponding (translated) proteins. The folding energy dot plots of RNAs and the residue contact maps of the coded proteins are indeed similar. Residue contact statistics using 81 different protein structures confirmed that amino acids that are coded by partially reverse and complementary codons (Watson-Crick (WC) base pairs at the 1st and 3rd codon positions and translated in reverse orientation) are preferentially co-located in protein structures. Exons are distinguished from introns and codon boundaries are physico-chemically defined by periodically distributed FFE differences between codon positions. There is a selection for local RNA secondary structures in coding regions and this nucleic acid structure resembles the folding profiles of the coded proteins. The preferentially (specifically) interacting amino acids are coded by partially complementary codons, which strongly supports the connection between mRNA and the corresponding protein structures and indicates that there is protein folding information in nucleic acids that is not present in the genetic code. This might give some additional explanation of codon redundancy.

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Predicting interresidue contacts using templates and pathways

Cited by 68 publications

References 29 publications

Local Protein Structures

Local Protein Structures

Predicting residue–residue contacts using random forest models

The Meaning of a Redundant Codon: There is Protein Folding Information in Nucleic Acids in Addition to the Genetic Code

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