Forecasting residue–residue contact prediction accuracy

Wozniak, Pawel P.; Konopka, Bogumił M.; Xu, Jinbo; Vriend, Gert; Kotulska, Małgorzata

doi:10.1093/bioinformatics/btx416

Cited by 5 publications

(4 citation statements)

References 66 publications

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“…Since even those native contacts can hinder reconstruction (as indicated by negative SR scores), it becomes evident that the correct ranking of contacts 21,36 has a serious influence on reconstruction quality when subsets of contacts are considered. This knowledge also has implications for the design and training of contact prediction techniques.…”

Section: Resultsmentioning

confidence: 99%

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StructureDistiller: Structural relevance scoring identifies the most informative entries of a contact map

Bittrich

Schroeder

Labudde

2019

Sci Rep

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Protein folding and structure prediction are two sides of the same coin. Contact maps and the related techniques of constraint-based structure reconstruction can be considered as unifying aspects of both processes. We present the Structural Relevance (SR) score which quantifies the information content of individual contacts and residues in the context of the whole native structure. The physical process of protein folding is commonly characterized with spatial and temporal resolution: some residues are Early Folding while others are Highly Stable with respect to unfolding events. We employ the proposed SR score to demonstrate that folding initiation and structure stabilization are subprocesses realized by distinct sets of residues. The example of cytochrome c is used to demonstrate how StructureDistiller identifies the most important contacts needed for correct protein folding. This shows that entries of a contact map are not equally relevant for structural integrity. The proposed StructureDistiller algorithm identifies contacts with the highest information content; these entries convey unique constraints not captured by other contacts. Identification of the most informative contacts effectively doubles resilience toward contacts which are not observed in the native contact map. Furthermore, this knowledge increases reconstruction fidelity on sparse contact maps significantly by 0.4 Å.

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Section: Resultsmentioning

confidence: 99%

“…Peculiarly, such false positive predictions are difficult to spot 17 and in reconstruction they impair the feasibility of all other contacts 35 . Thus, dedicated methods were designed to validate contact maps 34,36 . Other studies 23 tried to elucidate the optimal contact definition by assessing its influence on reconstruction performance.…”

Section: Introductionmentioning

confidence: 99%

StructureDistiller: Structural relevance scoring identifies the most informative entries of a contact map

Bittrich

Schroeder

Labudde

2019

Sci Rep

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“…The search for methods to choose K given an MSA of given size and statistical characteristics was only initiated recently [43,44] and is yet to be developed fully. Let us note that a commonly used rule of thumb has been to retain about as many predictions as the data dimension L; from a statistical point of view this is inappropriate unless the MSA is square or a thin matrix, i.e.…”

Section: Introductionmentioning

confidence: 99%

Correlation-compressed direct-coupling analysis

2018

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Learning Ising or Potts models from data has become an important topic in statistical physics and computational biology, with applications to predictions of structural contacts in proteins and other areas of biological data analysis. The corresponding inference problems are challenging since the normalization constant (partition function) of the Ising/Potts distributions cannot be computed efficiently on large instances. Different ways to address this issue have hence given size to a substantial methodological literature. In this paper we investigate how these methods could be used on much larger datasets than studied previously. We focus on a central aspect, that in practice these inference problems are almost always severely under-sampled, and the operational result is almost always a small set of leading (largest) predictions. We therefore explore an approach where the data is pre-filtered based on empirical correlations, which can be computed directly even for very large problems. Inference is only used on the much smaller instance in a subsequent step of the analysis. We show that in several relevant model classes such a combined approach gives results of almost the same quality as the computationally much more demanding inference on the whole dataset. We also show that results on whole-genome epistatic couplings that were obtained in a recent computation-intensive study can be retrieved by the new approach. The method of this paper hence opens up the possibility to learn parameters describing pair-wise dependencies in whole genomes in a computationally feasible and expedient manner.ily related to spatial contacts [13,18,33].The actual use of DCA is comprised of two parts: (1) to run the inference procedure of choice on an MSA which consists of N samples, each of dimension L; and (2) to keep only a subset of largest predictions for further assessment and use. We will refer to the columns of the MSA as loci, the variables in each column as alleles, and the rows as samples. The MSA hence consists of N samples with each sample being a list of L alleles. Alternatively we will refer to L as the data dimension and N as the sample size. The total number of parameters in the inferred Ising or Potts models is proportional to L 2 and will here be denoted P. The number of retained predictions will be denoted K.Exact frequentist or Bayesian-point estimate methods, i.e. maximum likelihood (ML) or maximum a posteriori (MAP), are not computationally feasible for the data dimensions of current practical interest, and many approximate inference methods have therefore been developed [26]. Additionally statistical identifiability demands that K cannot be larger than N ; one cannot learn more features from the data than there are examples. This has indeed mostly been the case in the examples above. However, in the intermediate step P parameters are inferred, and in many (if not all) cases of interest P ∼ L 2 has been much larger than N . On top of inference being approximate it must therefore also be regularized.The setting whe...

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“…Contact maps do not provide all the necessary information though, allowing for models of mirror chirality difficult to filter out. The knowledge of even incomplete maps allows selecting misfolded from properly folded proteins [2]. We designed a regression model that forecasts the accuracy of contact prediction for individual proteins with an average error of 7 percentage points [3].…”

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confidence: 99%

Residue-residue contacts in modeling protein structure

2018

11-Ая Международная Конференция По Биоинформатике Регуляции И Структуры Геномов И Системной Биологии

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Forecasting residue–residue contact prediction accuracy

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 5 publications

References 66 publications

StructureDistiller: Structural relevance scoring identifies the most informative entries of a contact map

StructureDistiller: Structural relevance scoring identifies the most informative entries of a contact map

Correlation-compressed direct-coupling analysis

Residue-residue contacts in modeling protein structure

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