Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction

AbstractA plethora of biological functions are performed through various types of protein-peptide binding. Prime examples include the protein kinase phosphorylation on peptide substrates and the binding of major histocompatibility complex to neoantigens in the immune system. Understanding the specificity of protein-peptide interactions is critical for unraveling the architectures of functional pathways and the mechanisms of cellular processes in human cells. Despite mass-spectrometric techniques were developed for the identification of protein-peptide interactions, our understanding of the preferences of proteins on their binding peptides is still rudimentary. As a complementary direction, a line of computational prediction methods has been recently proposed to predict protein-peptide bindings which efficiently provide rich functional annotations on a large scale. To achieve a high prediction accuracy, these computational methods require a sufficient amount of data to build the prediction model. However, the number of experimentally verified protein-peptide bindings is often limited in real cases. For example, a majority of protein kinases have very few experimentally verified phosphorylation sites (e.g., less than 30 sites) in existing databases. These methods are thus limited to building accurate prediction models for only well-characterized proteins with a large volume of known binding peptides and cannot be extended to predict new binding peptides for less-studied proteins. In this paper, we introduce a generic framework to address this issue of data scarcity in protein binding prediction. We demonstrate the applicability of our framework in predicting kinase-specific phosphorylation sites. Our method uses an effective training strategy to build a prediction model with robust transferability. The model is able to predict the phosphorylation sites of a less-studied kinase, even if there is only a small number of phosphorylation sites known for this kinase. To achieve this, we train the model via a meta-learning phase followed by a few-shot learning phase. We demonstrate our framework has better transferability than state-of-the-art methods and is effective in utilizing limited data to accurately predict phosphorylation sites for less-characterized kinases. The implementation of our framework is available at https://github.com/luoyunan/MetaKinase.

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Protein binding prediction using non-metric multidimensional scaling method

2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) ◽

10.1109/bibmw.2011.6112516 ◽

2011 ◽

Cited By ~ 1

Author(s):

Hiromu Mogi ◽

Y.-h. Taguchi

Keyword(s):

Multidimensional Scaling ◽

Protein Binding ◽

Binding Prediction ◽

Scaling Method ◽

Metric Multidimensional Scaling

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Predicting Residues Involved in Anti-DNA Autoantibodies with Limited Neural Networks

10.1101/2020.08.06.240101 ◽

2020 ◽

Author(s):

Rachel St.Clair ◽

Michael Teti ◽

Mirjana Pavlovic ◽

William Hahn ◽

Elan Barenholtz

Keyword(s):

Protein Binding ◽

Domain Knowledge ◽

Short Term Memory ◽

Network Models ◽

Strongly Correlated ◽

Neural Network Models ◽

Binding Prediction ◽

Autoimmune Antibodies ◽

Primary Sequence Analysis ◽

Computational Resources

AbstractComputer-aided rational vaccine design (RVD) and synthetic pharmacology are rapidly developing fields that leverage existing datasets for developing compounds of interest. Computational proteomics utilizes algorithms and models to probe proteins for functional prediction. A potentially strong target for such a computational approach is autoimmune antibodies which are the result of broken tolerance in the immune system where it cannot distinguish “self” from “non-self” resulting in attack of its own structures (proteins and DNA, mainly). The information on structure, function and pathogenicity of autoantibodies may assist in engineering RVD against autoimmune diseases. Current computational approaches exploit large datasets curated with extensive domain knowledge, most of which include the need for many computational resources and have been applied indirectly to problems of interest for DNA, RNA, and monomer protein binding. Here, we present a novel method for discovering potential binding sites. We employed long short-term memory (LSTM) models trained on FASTA primary sequences directly to predict protein binding in DNA-binding hydrolytic antibodies (abzymes). We also employed CNN models applied to the same dataset. While the CNN model outperformed the LSTM on the primary task of binding prediction, analysis of internal model representations of both models showed that the LSTM models highlighted sub-sequences that were more strongly correlated with sites known to be involved in binding. These results demonstrate that analysis of internal processes of recurrent neural network models may serve as a powerful tool for primary sequence analysis.

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MechRNA: prediction of lncRNA mechanisms from RNA-RNA and RNA-protein interactions

10.1101/285965 ◽

2018 ◽

Author(s):

Alexander R. Gawronski ◽

Michael Uhl ◽

Yajia Zhang ◽

Yen-Yi Lin ◽

Yashar S. Niknafs ◽

...

Keyword(s):

Protein Binding ◽

Protein Interactions ◽

Large Scale ◽

Mechanistic Explanation ◽

Supplementary Information ◽

Target Pair ◽

P Value ◽

Binding Prediction ◽

Large Scale Analysis ◽

Rna Interaction

AbstractMotivationLong non-coding RNAs (lncRNAs) are defined as transcripts longer than 200 nucleotides that do not get translated into proteins. Often these transcripts are processed (spliced, capped, polyadenylated) and some are known to have important biological functions. However, most lncRNAs have unknown or poorly understood functions. Nevertheless, because of their potential role in cancer, lncRNAs are receiving a lot of attention, and the need for computational tools to predict their possible mechanisms of action is more than ever. Fundamentally, most of the known lncRNA mechanisms involve RNA-RNA and/or RNA-protein interactions. Through accurate predictions of each kind of interaction and integration of these predictions, it is possible to elucidate potential mechanisms for a given lncRNA.ApproachHere we introduce MechRNA, a pipeline for corroborating RNA-RNA interaction prediction and protein binding prediction for identifying possible lncRNA mechanisms involving specific targets or on a transcriptome-wide scale. The first stage uses a version of IntaRNA2 with added functionality for efficient prediction of RNA-RNA interactions with very long input sequences, allowing for large-scale analysis of lncRNA interactions with little or no loss of optimality. The second stage integrates protein binding information pre-computed by GraphProt, for both the lncRNA and the target. The final stage involves inferring the most likely mechanism for each lncRNA/target pair. This is achieved by generating candidate mechanisms from the predicted interactions, the relative locations of these interactions and correlation data, followed by selection of the most likely mechanistic explanation using a combined p-value.ResultsWe applied MechRNA on a number of recently identified cancer-related lncRNAs (PCAT1, PCAT29, ARLnc1) and also on two well-studied lncRNAs (PCA3 and 7SL). This led to the identification of hundreds of high confidence potential targets for each lncRNA and corresponding mechanisms. These predictions include the known competitive mechanism of 7SL with HuR for binding on the tumor suppressor TP53, as well as mechanisms expanding what is known about PCAT1 and ARLn1 and their targets BRCA2 and AR, respectively. For PCAT1-BRCA2, the mechanism involves competitive binding with HuR, which we confirmed using HuR immunoprecipitation assays.AvailabilityMechRNA is available for download athttps://bitbucket.org/compbio/[email protected],[email protected] informationSupplementary data are available atBioinformaticsonline.

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Transient Prealbumin- Associated Hyperthyroxinemia in TSH-Producing Pituitary Adenoma

Nuklearmedizin ◽

10.1055/s-0038-1629511 ◽

1990 ◽

Vol 29 (01) ◽

pp. 40-43 ◽

Cited By ~ 3

Author(s):

W. Langsteger ◽

P. Költringer ◽

P. Wakonig ◽

B. Eber ◽

M. Mokry ◽

...

Keyword(s):

Computed Tomography ◽

Case Report ◽

Pituitary Adenoma ◽

Thyroid Hormones ◽

Protein Binding ◽

Alpha Subunit ◽

Normal Range ◽

Thyroxine Binding Globulin ◽

Free Thyroid Hormones ◽

Transmission Computed Tomography

This case report describes a 38-year-old male who was hospitalized for further clarification of clinically mild hyperthyroidism. His increased total hormone levels, the elevated free thyroid hormones and the elevated basal TSH with blunted response to TRH strongly suggested a pituitary adenoma with inappropriate TSH incretion. Transmission computed tomography showed an intrasellar expansion, 16 mm in diameter. The neoplastic TSH production was confirmed by an elevated alpha-subunit and a raised molar alpha-sub/ATSH ratio. However, T4 distribution on prealbumin (PA, TTR), albumin (A) and thyroxine binding globulin (TBG) showed a clearly increased binding to PA (39%), indicating additional prealbumin-associated hyperthyroxinemia. The absolute values of PA, A and TBG were within the normal range. After removal of the TSH-producing adenoma, basal TSH, the free thyroid hormones and T4 binding to prealbumin returned to normal. Therefore, the prealbumin-associated hyperthyroxinemia had to be interpreted as a transitory phenomenon related to secondary hyperthyroidism (T4 shift from thyroxine binding globulin to prealbumin) rather than a genetically conditioned anomaly of protein binding.

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Absorption, Metabolic Stability and Protein Binding Properties of Sutherlandioside B from Sutherlandia frutescens

Planta Medica ◽

10.1055/s-2008-1075256 ◽

2008 ◽

Vol 74 (03) ◽

Author(s):

VLM Madgula ◽

B Avula ◽

X Fu ◽

XC Li ◽

TJ Smillie ◽

...

Keyword(s):

Protein Binding ◽

Metabolic Stability ◽

Binding Properties ◽

Sutherlandia Frutescens

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INCORPORATION OF IODIDE INTO ORGANIC COMPOUNDS IN THE GUINEA PIG THYROID

Acta Endocrinologica ◽

10.1530/acta.0.0430110 ◽

1963 ◽

Vol 43 (1) ◽

pp. 110-118 ◽

Cited By ~ 2

Author(s):

R. Ekholm ◽

T. Zelander ◽

P.-S. Agrell

Keyword(s):

Guinea Pig ◽

Protein Binding ◽

Organic Compounds ◽

Guinea Pigs ◽

Microsomal Fraction ◽

Thyroid Tissue ◽

Particulate Fraction ◽

Supernatant Fraction ◽

Hydrolysis Of

ABSTRACT Guinea pigs, kept on a iodine-sufficient diet, were injected with Na131I and the thyroids excised from 45 seconds to 5 days later. The thyroid tissue was homogenized and separated into a combined nuclear-mitochondrial-microsomal fraction and a supernatant fraction by centrifugation at 140 000 g for one hour. Protein bound 131iodine (PB131I) and free 131iodide were determined in the fractions and the PB131I was analysed for monoiodotyrosine (MIT), diiodotyrosine (DIT) and thyroxine after hydrolysis of PB131I. As early as only 20 minutes after the Na131I-injection almost 100% of the particulate fraction 131I was protein bound. In the supernatant fraction the protein binding was somewhat less rapid and PB131I values above 90% of total supernatant 131I were not found until 3 hours after the injection. In all experiments the total amount of PB131I was higher in the supernatant than in the corresponding particulate fraction. The ratio between supernatant PB131I and pellet PB131I was lower in experiments up to 3 minutes and from 2 to 5 days than in experiments of 6 minutes to 20 hours. Hydrolysis of PB131I yielded, even in the shortest experiments, both MIT and DIT. The DIT/MIT ratio was lower in the experiments up to 2 hours than in those of 3 hours and over.

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EVALUATION OF TISSUE STEROID BINDING IN VITRO

Acta Endocrinologica ◽

10.1530/acta.0.068s223 ◽

1971 ◽

Vol 68 (1_Suppl) ◽

pp. S223-S246 ◽

Cited By ~ 2

Author(s):

C. R. Wira ◽

H. Rochefort ◽

E. E. Baulieu

Keyword(s):

Protein Binding ◽

Binding Sites ◽

Experimental System ◽

Specific Protein ◽

Coupling Mechanism ◽

Target Tissue ◽

Protein Binding Sites ◽

Definition Of ◽

Hormone Specificity

ABSTRACT The definition of a RECEPTOR* in terms of a receptive site, an executive site and a coupling mechanism, is followed by a general consideration of four binding criteria, which include hormone specificity, tissue specificity, high affinity and saturation, essential for distinguishing between specific and nonspecific binding. Experimental approaches are proposed for choosing an experimental system (either organized or soluble) and detecting the presence of protein binding sites. Techniques are then presented for evaluating the specific protein binding sites (receptors) in terms of the four criteria. This is followed by a brief consideration of how receptors may be located in cells and characterized when extracted. Finally various examples of oestrogen, androgen, progestagen, glucocorticoid and mineralocorticoid binding to their respective target tissues are presented, to illustrate how researchers have identified specific corticoid and mineralocorticoid binding in their respective target tissue receptors.

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EVALUATION OF A SIMPLIFIED METHOD FOR DETERMINING SERUM THYROXINE BY COMPETITIVE PROTEIN BINDING ANALYSIS

Acta Endocrinologica ◽

10.1530/acta.0.0640630 ◽

1970 ◽

Vol 64 (4) ◽

pp. 630-636 ◽

Cited By ~ 8

Author(s):

Stephen C. Thorson ◽

Ronald Tsujikawa ◽

James L. Brown ◽

Robert T. Morrison ◽

Hamish W. McIntosh

Keyword(s):

Protein Binding ◽

Clinical Application ◽

Thyroid Status ◽

Serum Thyroxine ◽

Binding Analysis ◽

Simplified Method ◽

Competitive Protein Binding ◽

Hypothyroid Patients

ABSTRACT Serum thyroxine concentrations were determined in 66 euthyroid, 30 hyperthyroid and 13 hypothyroid patients using both the established Murphy method and a simplified method of competitive protein binding analysis. A diagnosis compatibility of 96% was found with both methods indicating that the simplified method has comparable clinical application as an initial screen of thyroid status.

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