Constraint Logic Programming approach to protein structure prediction

Palù, Alessandro Dal; Dovier, Agostino; Fogolari, Federico

doi:10.1186/1471-2105-5-186

Cited by 54 publications

(8 citation statements)

References 35 publications

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“…Since we completely omit the secondary structure information of the protein when solving the problem, these results are not comparable to those using secondary structure information (eg. [ 16 - 18 ]). All of the experiments have been performed on an Intel(R) Xeon(R) CPU E5440 at 2.83 GHz with 32 GB memory.…”

Section: Resultsmentioning

confidence: 99%

“…Authors in [16,18] added some extra constraints to disallow angles of 60° and 180° for three consecutive amino acids. Also their definition of contact distance is somewhat different from us.…”

Section: Methodsmentioning

confidence: 99%

“…A better application of CP technique to protein folding is to encode the problem directly under constraint programming model and to search for the minimum energy structure. Palu et al [ 16 ] showed heuristic approaches to protein folding problem using Constraint Logic Programming over finite domains (CLP(FD)). Despite satisfactory performances on small/medium sized instances, it was proved ineffective in scaling to larger instances of the problem [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

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A hybrid approach to protein folding problem integrating constraint programming with local search

Ullah

Steinhöfel

2010

BMC Bioinformatics

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BackgroundThe protein folding problem remains one of the most challenging open problems in computational biology. Simplified models in terms of lattice structure and energy function have been proposed to ease the computational hardness of this optimization problem. Heuristic search algorithms and constraint programming are two common techniques to approach this problem. The present study introduces a novel hybrid approach to simulate the protein folding problem using constraint programming technique integrated within local search.ResultsUsing the face-centered-cubic lattice model and 20 amino acid pairwise interactions energy function for the protein folding problem, a constraint programming technique has been applied to generate the neighbourhood conformations that are to be used in generic local search procedure. Experiments have been conducted for a few small and medium sized proteins. Results have been compared with both pure constraint programming approach and local search using well-established local move set. Substantial improvements have been observed in terms of final energy values within acceptable runtime using the hybrid approach.ConclusionConstraint programming approaches usually provide optimal results but become slow as the problem size grows. Local search approaches are usually faster but do not guarantee optimal solutions and tend to stuck in local minima. The encouraging results obtained on the small proteins show that these two approaches can be combined efficiently to obtain better quality solutions within acceptable time. It also encourages future researchers on adopting hybrid techniques to solve other hard optimization problems.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A hybrid approach to protein folding problem integrating constraint programming with local search

Ullah

Steinhöfel

2010

BMC Bioinformatics

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“…Jafari et al [16] demonstrated that logic-based methods are useful for improving PLOS ONE static conceptual models in molecular biology. Palù et al [17] demonstrated that logic-based models can be used effectively for predicting protein structures and functions.…”

Section: Description Of Current Approachesmentioning

confidence: 99%

Personizing the prediction of future susceptibility to a specific disease

et al. 2021

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A traceable biomarker is a member of a disease’s molecular pathway. A disease may be associated with several molecular pathways. Each different combination of these molecular pathways, to which detected traceable biomarkers belong, may serve as an indicative of the elicitation of the disease at a different time frame in the future. Based on this notion, we introduce a novel methodology for personalizing an individual’s degree of future susceptibility to a specific disease. We implemented the methodology in a working system called Susceptibility Degree to a Disease Predictor (SDDP). For a specific disease d, let S be the set of molecular pathways, to which traceable biomarkers detected from most patients of d belong. For the same disease d, let S′ be the set of molecular pathways, to which traceable biomarkers detected from a certain individual belong. SDDP is able to infer the subset S′′ ⊆{S-S′} of undetected molecular pathways for the individual. Thus, SDDP can infer undetected molecular pathways of a disease for an individual based on few molecular pathways detected from the individual. SDDP can also help in inferring the combination of molecular pathways in the set {S′+S′′}, whose traceable biomarkers collectively is an indicative of the disease. SDDP is composed of the following four components: information extractor, interrelationship between molecular pathways modeler, logic inferencer, and risk indicator. The information extractor takes advantage of the exponential increase of biomedical literature to automatically extract the common traceable biomarkers for a specific disease. The interrelationship between molecular pathways modeler models the hierarchical interrelationships between the molecular pathways of the traceable biomarkers. The logic inferencer transforms the hierarchical interrelationships between the molecular pathways into rule-based specifications. It employs the specification rules and the inference rules for predicate logic to infer as many as possible undetected molecular pathways of a disease for an individual. The risk indicator outputs a risk indicator value that reflects the individual’s degree of future susceptibility to the disease. We evaluated SDDP by comparing it experimentally with other methods. Results revealed marked improvement.

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“…Logic-based approaches have been successfully applied to solve complex problems in bioinformatics by viewing these problems as binary classification tasks. For example, [3] achieved acceptable results for predicting protein structures using constraint logic programming techniques. [4] presented a methodology that successfully predicted the tertiary structure of a protein using constraint logic programming.…”

Section: Introductionmentioning

confidence: 99%

Predicting protein functions by applying predicate logic to biomedical literature

Taha¹,

Iraqi²,

Aamri³

2019

BMC Bioinformatics

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BackgroundA large number of computational methods have been proposed for predicting protein functions. The underlying techniques adopted by most of these methods revolve around predicting the functions of an unannotated protein p from already annotated proteins that have similar characteristics as p. Recent Information Extraction methods take advantage of the huge growth of biomedical literature to predict protein functions. They extract biological molecule terms that directly describe protein functions from biomedical texts. However, they consider only explicitly mentioned terms that co-occur with proteins in texts. We observe that some important biological molecule terms pertaining functional categories may implicitly co-occur with proteins in texts. Therefore, the methods that rely solely on explicitly mentioned terms in texts may miss vital functional information implicitly mentioned in the texts.ResultsTo overcome the limitations of methods that rely solely on explicitly mentioned terms in texts to predict protein functions, we propose in this paper an Information Extraction system called PL-PPF. The proposed system employs techniques for predicting the functions of proteins based on their co-occurrences with explicitly and implicitly mentioned biological molecule terms that pertain functional categories in biomedical literature. That is, PL-PPF employs a combination of statistical-based explicit term extraction techniques and logic-based implicit term extraction techniques. The statistical component of PL-PPF predicts some of the functions of a protein by extracting the explicitly mentioned functional terms that directly describe the functions of the protein from the biomedical texts associated with the protein. The logic-based component of PL-PPF predicts additional functions of the protein by inferring the functional terms that co-occur implicitly with the protein in the biomedical texts associated with it. First, the system employs its statistical-based component to extract the explicitly mentioned functional terms. Then, it employs its logic-based component to infer additional functions of the protein. Our hypothesis is that important biological molecule terms pertaining functional categories of proteins are likely to co-occur implicitly with the proteins in biomedical texts. We evaluated PL-PPF experimentally and compared it with five systems. Results revealed better prediction performance.ConclusionsThe experimental results showed that PL-PPF outperformed the other five systems. This is an indication of the effectiveness and practical viability of PL-PPF’s combination of explicit and implicit techniques. We also evaluated two versions of PL-PPF: one adopting the complete techniques (i.e., adopting both the implicit and explicit techniques) and the other adopting only the explicit terms co-occurrence extraction techniques (i.e., without the inference rules for predicate logic). The experimental results showed that the complete version outperformed significantly the other version. This is attributed to t...

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Constraint Logic Programming approach to protein structure prediction

Cited by 54 publications

References 35 publications

A hybrid approach to protein folding problem integrating constraint programming with local search

A hybrid approach to protein folding problem integrating constraint programming with local search

Personizing the prediction of future susceptibility to a specific disease

Predicting protein functions by applying predicate logic to biomedical literature

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