Sequence Similarity Approaches Research Articles

Ab Initio Modelling of the Structure of ToxA-like and MAX Fungal Effector Proteins

Pathogenic fungal diseases in crops are mediated by the release of effector proteins that facilitate infection. Characterising the structure of these fungal effectors is vital to understanding their virulence mechanisms and interactions with their hosts, which is crucial in the breeding of plant cultivars for disease resistance. Several effectors have been identified and validated experimentally; however, their lack of sequence conservation often impedes the identification and prediction of their structure using sequence similarity approaches. Structural similarity has, nonetheless, been observed within fungal effector protein families, creating interest in validating the use of computational methods to predict their tertiary structure from their sequence. We used Rosetta ab initio modelling to predict the structures of members of the ToxA-like and MAX effector families for which experimental structures are known to validate this method. An optimised approach was then used to predict the structures of phenotypically validated effectors lacking known structures. Rosetta was found to successfully predict the structure of fungal effectors in the ToxA-like and MAX families, as well as phenotypically validated but structurally unconfirmed effector sequences. Interestingly, potential new effector structural families were identified on the basis of comparisons with structural homologues and the identification of associated protein domains.

ProAll-D: protein allergen detection using long short term memory - a deep learning approach.

BackgroundAn allergic reaction is the immune system's overreacting to a previously encountered, typically benign molecule, frequently a protein. Allergy reactions can result in rashes, itching, mucous membrane swelling, asthma, coughing, and other bizarre symptoms. To anticipate allergies, a wide range of principles and methods have been applied in bioinformatics. The sequence similarity approach's positive predictive value is very low and ineffective for methods based on FAO/WHO criteria, making it difficult to predict possible allergens.MethodThis work advocated the use of a deep learning model LSTM (Long Short-Term Memory) to overcome the limitations of traditional approaches and machine learning lower performance models in predicting the allergenicity of dietary proteins. A total of 2,427 allergens and 2,427 non-allergens, from a variety of sources, including the Central Science Laboratory and the NCBI are used. The data was divided 80:20 for training and testing purposes. These techniques have all been implemented in Python. To describe the protein sequences of allergens and non-allergens, five E-descriptors were used. E1 (hydrophilic character of peptides), E2 (length), E3(propensity to form helices), E4(abundance and dispersion), and E5 (propensity of beta strands) are used to make the variable-length protein sequence to uniform length using ACC transformation. A total of eight machine learning techniques have been taken into consideration.ResultsThe Gaussian Naive Bayes as accuracy of 64.14 %, Radius Neighbour's Classifier with 49.2 %, Bagging Classifier was 85.8 %, ADA Boost was 76.9 %, Linear Discriminant Analysis has 76.13 %, Quadratic Discriminant Analysis was 84.2 %, Extra Tree Classifier was 90%, and LSTM is 91.5 %.ConclusionAs the LSTM, has an AUC value of 91.5 % is regarded best in predicting allergens. A web server called ProAll-D has been created that successfully identifies novel allergens using the LSTM approach. Users can use the link https://doi.org/10.17632/tjmt97xpjf.1 to access the ProAll-D server and data.

Evolutionary Adaptation of the Thyroid Hormone Signaling Toolkit in Chordates.

The specification of the endostyle in non-vertebrate chordates and of the thyroid gland in vertebrates are fundamental steps in the evolution of the thyroid hormone (TH) signaling to coordinate development and body physiology in response to a range of environmental signals. The physiology and biology of TH signaling in vertebrates have been studied in the past, but a complete understanding of such a complex system is still lacking. Non-model species from non-vertebrate chordates may greatly improve our understanding of the evolution of this complex endocrine pathway. Adaptation of already existing proteins in order to perform new roles is a common feature observed during the course of evolution. Through sequence similarity approaches, we investigated the presence of bona fide thyroid peroxidase (TPO), iodothyronine deiodinase (DIO), and thyroid hormone receptors (THRs) in non-vertebrate and vertebrate chordates. Additionally, we determined both the conservation and divergence degrees of functional domains at the protein level. This study supports the hypothesis that non-vertebrate chordates have a functional thyroid hormone signaling system and provides additional information about its possible evolutionary adaptation.

Identification of Allelic Variation in Drought Responsive Dehydrin Gene Based on Sequence Similarity in Chickpea (Cicer arietinum L.).

Chickpea (Cicer arietinum L.) is an economically important food legume grown in arid and semi-arid regions of the world. Chickpea is cultivated mainly in the rainfed, residual moisture, and restricted irrigation condition. The crop is always prone to drought stress which is resulting in flower drop, unfilled pods, and is a major yield reducer in many parts of the world. The present study elucidates the association between candidate gene and morpho-physiological traits for the screening of drought tolerance in chickpea. Abiotic stress-responsive gene Dehydrin (DHN) was identified in some of the chickpea genotypes based on the sequence similarity approach to play a major role in drought tolerance. Analysis of variance revealed a significant effect of drought on relative water content, membrane stability index, plant height, and yield traits. The genotypes Pusa1103, Pusa362, and ICC4958 were found most promising genotypes for drought tolerance as they maintained the higher value of osmotic regulations and yield characters. The results were further supported by a sequence similarity approach for the dehydrin gene when analyzed for the presence of single nucleotide polymorphisms (SNPs) and indels. Homozygous indels and single nucleotide polymorphisms were found after the sequencing in some of the selected genotypes.

A multiclass SVM classifier with teaching learning based feature subset selection for enzyme subclass classification

The functional characterization of proteins aims to understand life at macroscopic and microscopic levels, thus having wide and extensive applications in biological and pharmaceutical research. The classical methods used for the functional characterization of proteins rely on the sequence similarity approach i.e. identifying a similar protein whose functions are known. However, the performance of these methods reduces in the event of the sequences exhibiting less similarity. Hence, to tide over this impediment, many computational function prediction methods that do not use the sequence similarity approach were developed. This paper focuses on a Support Vector Machine (SVM) based method which classifies a protein at three levels. At the first level, a protein is identified and earmarked as an enzyme or non-enzyme. In the case of it being an enzyme, its functional class, and subclass are predicted in one step by taking 7 EC classes with 63 of their subclasses. Each protein is initially represented by its 32 physicochemical properties. Then a multiclass SVM (MSVM) classifier, that solves an n class classification problem with log2n binary classifiers, along with a Modified Teaching Learning Based Optimization method (MTLBO) to identify the significant features for classification, is designed to predict the functional class, and subclass of an enzyme in one step. The recall of the proposed MSVM-MTLBO using only 25 features ranges from 92.97% to 98.14% for class label prediction and 60% to 98.25% for subclass label prediction, which is significantly better than the existing methods for class and subclass classification. Moreover, the proposed method predicts the subclass labels with less number of binary classifiers than a standard multiclass SVM.

Physio-morphological and molecular analysis for salt tolerance in chickpea (Cicer arietinum)

After drought salinity is the major abiotic stress that severely affects agricultural productivity globally. Chickpea (Cicer arietinum L.) is the important grain legume which suffers approximately 8-10% of total global yield loss due to salinity. Screening for salt stress is difficult and traits that correlate salinity tolerance are least understood. The present study was carried out at ICAR-IARI, New Delhi 2017-18, deals with the important morphological and physiological traits like RWC (Relative water content), EL (Electrolyte Leakage), Na/K (sodium and potassium ratio) to characterize the salt tolerant genotypes under hydroponic condition which is a quick and easy method to screen large number of chickpea genotypes at initial stage under salt stress condition. Genotypes showing high RWC, low EL and Na/K ratio were tolerant like ICCV 10, JG 11, JG 62 and CSG-8962 whereas genotypes like ICC4958 and Pusa362 fall under moderately tolerant genotypes and DCP 93-3, Pusa 256, Phule G5 and SBD 377 were classified as susceptible genotypes. This study also attempts to understand the candidate genes responsible for salt-stress related pathways in chickpea genotypes based on sequence similarity approach exploiting known salt-stress responsive genes from model crops or other crop species.

PaPrBaG: A machine learning approach for the detection of novel pathogens from NGS data

The reliable detection of novel bacterial pathogens from next-generation sequencing data is a key challenge for microbial diagnostics. Current computational tools usually rely on sequence similarity and often fail to detect novel species when closely related genomes are unavailable or missing from the reference database. Here we present the machine learning based approach PaPrBaG (Pathogenicity Prediction for Bacterial Genomes). PaPrBaG overcomes genetic divergence by training on a wide range of species with known pathogenicity phenotype. To that end we compiled a comprehensive list of pathogenic and non-pathogenic bacteria with human host, using various genome metadata in conjunction with a rule-based protocol. A detailed comparative study reveals that PaPrBaG has several advantages over sequence similarity approaches. Most importantly, it always provides a prediction whereas other approaches discard a large number of sequencing reads with low similarity to currently known reference genomes. Furthermore, PaPrBaG remains reliable even at very low genomic coverages. CombiningPaPrBaG with existing approaches further improves prediction results.

Systematic characterization of hypothetical proteins in Synechocystis sp. PCC 6803 reveals proteins functionally relevant to stress responses

We described here a global detection and functional inference of hypothetical proteins involved in stress response in Synechocystis sp. PCC 6803. In the study, we first applied an iTRAQ-LC–MS/MS based quantitative proteomics to the Synechocystis cells grown under five stress conditions. The analysis detected a total of 807 hypothetical proteins with high confidence. Among them, 480 were differentially regulated. We then applied a Weighted Gene Co-expression Network Analysis approach to construct transcriptional networks for Synechocystis under nutrient limitation and osmotic stress conditions using transcriptome datasets. The analysis showed that 305 and 467 coding genes of hypothetical proteins were functionally relevant to nutrient limitation and osmotic stress, respectively. A comparison of responsive hypothetical proteins to all stress conditions allowed identification of 22 hypothetical proteins commonly responsive to all stresses, suggesting they may be part of the core stress responses in Synechocystis. Finally, functional inference of these core stress responsive proteins using both sequence similarity and non-similarity approaches was conducted. The study provided new insights into the stress response networks in Synechocystis, and also demonstrated that a combination of experimental “OMICS” and bioinformatics methodologies could improve functional annotation for hypothetical proteins.

Sequence similarity based identification of abiotic stress responsive genes in chickpea.

Chickpea (Cicer arietinum L.) is an important food legume crop, particularly for the arid regions including Indian subcontinent. Considering the detrimental effect of drought, temperature and salt stress on crop yield, efforts have been initiated in the direction of developing improved varieties and designing alternate strategies to sustain chickpea production in adverse environmental conditions. Identification of genes that confer abiotic stress tolerance in plants remains a challenge in contemporary plant breeding. The present study focused on the identification of abiotic stress responsive genes in chickpea based on sequence similarity approach exploiting known abiotic stress responsive genes from model crops or other plant species. Ten abiotic stress responsive genes identified in other plants were partially amplified from eight chickpea genotypes and their presence in chickpea was confirmed after sequencing the PCR products. These genes have been functionally validated and reported to play significant role in stress response in model plants like Arabidopsis, rice and other legume crops. Chickpea EST sequences available at NCBI EST database were used for the identification of abiotic stress responsive genes. A total of 8,536 unique coding long sequences were used for identification of chickpea homologues of these abiotic stress responsive genes by sequence similarity search (BLASTN and BLASTX). These genes can be further explored towards achieving the goal of developing superior chickpea varieties providing improved yields under stress conditions using modern molecular breeding approaches.

Barcoding the major Mediterranean leguminous crops by combining universal chloroplast and nuclear DNA sequence targets

The ability to discriminate all species is the ultimate target in barcoding. The Mediterranean basin is a center of origin for legumes and thus they have played a key role in feeding the Mediterranean population. It is also a region with important protected designation of origin and protected geographical indication legumes that provide income in rural areas. We evaluated the use of two chloroplast regions, trnL and rpoC1, and a nuclear internal transcriber region, ITS2, for their efficiency to barcode the main Mediterranean leguminous crops. Twenty-five legume species were studied. Plant material of pasture and legumes was obtained from the Greek GenBank and the Fodder Crops and Pastures Institute (National Agricultural Research Foundation). DNA was extracted with the Qiagen DNeasy plant mini-kit and PCR amplification was performed using the Kapa Taq DNA polymerase using primers amplifying the chloroplast trnL and rpoC1 regions or the nuclear region ITS2. PCR products were sequenced and the sequences were aligned using CLUSTAL W. Species identification based on the sequence similarity approach was performed using the GenBank database. In order to evaluate intraspecific and interspecific divergence in legumes we used Molecular Evolutionary Genetics Analysis 5 and for pairwise Kimura 2-parameter distance calculations for all 3 DNA regions (2 chloroplast regions, trnL and rpoC1, and the nuclear region ITS2). Four tree-based methods (neighbor joining and maximum parsimony, maximum likelihood, and Bayesian inference analyses) were used to exhibit the molecular identification results to represent differences as an uprooted dendrogram. Additionally, the sequence character-based method was used with DnaSP and the information from each site was treated as a character to distinguish the species from one another. The DNA regions trnL and ITS2 successfully (100%) discriminated the Mediterranean crop legume species used, while rpoC1 identified only 72% of them. Furthermore, the use of the trnL region enabled the discrimination of even very closely related species, like Phaseolus lunatus and P. coccineus or Vicia faba subsp major with V. faba subsp minor, which are so closely related that even in NCBI they were both referred as Phaseolus vulgaris and V. faba, respectively. We conclude that trnL and ITS2 are efficient DNA barcoding target regions in order to discriminate Mediterranean leguminous crops and provide a reliable and efficient tool for the scientific, agricultural and industrial community.

Annotating Genes of Known and Unknown Function by Large-Scale Coexpression Analysis

About 40% of the proteins encoded in eukaryotic genomes are proteins of unknown function (PUFs). Their functional characterization remains one of the main challenges in modern biology. In this study we identified the PUF encoding genes from Arabidopsis (Arabidopsis thaliana) using a combination of sequence similarity, domain-based, and empirical approaches. Large-scale gene expression analyses of 1,310 publicly available Affymetrix chips were performed to associate the identified PUF genes with regulatory networks and biological processes of known function. To generate quality results, the study was restricted to expression sets with replicated samples. First, genome-wide clustering and gene function enrichment analysis of clusters allowed us to associate 1,541 PUF genes with tightly coexpressed genes for proteins of known function (PKFs). Over 70% of them could be assigned to more specific biological process annotations than the ones available in the current Gene Ontology release. The most highly overrepresented functional categories in the obtained clusters were ribosome assembly, photosynthesis, and cell wall pathways. Interestingly, the majority of the PUF genes appeared to be controlled by the same regulatory networks as most PKF genes, because clusters enriched in PUF genes were extremely rare. Second, large-scale analysis of differentially expressed genes was applied to identify a comprehensive set of abiotic stress-response genes. This analysis resulted in the identification of 269 PKF and 104 PUF genes that responded to a wide variety of abiotic stresses, whereas 608 PKF and 206 PUF genes responded predominantly to specific stress treatments. The provided coexpression and differentially expressed gene data represent an important resource for guiding future functional characterization experiments of PUF and PKF genes. Finally, the public Plant Gene Expression Database (http://bioweb.ucr.edu/PED) was developed as part of this project to provide efficient access and mining tools for the vast gene expression data of this study.

Gene analogue finder: a GRID solution for finding functionally analogous gene products.

BackgroundTo date more than 2,1 million gene products from more than 100000 different species have been described specifying their function, the processes they are involved in and their cellular localization using a very well defined and structured vocabulary, the gene ontology (GO). Such vast, well defined knowledge opens the possibility of compare gene products at the level of functionality, finding gene products which have a similar function or are involved in similar biological processes without relying on the conventional sequence similarity approach. Comparisons within such a large space of knowledge are highly data and computing intensive. For this reason this project was based upon the use of the computational GRID, a technology offering large computing and storage resources.ResultsWe have developed a tool, GENe AnaloGue FINdEr (ENGINE) that parallelizes the search process and distributes the calculation and data over the computational GRID, splitting the process into many sub-processes and joining the calculation and the data on the same machine and therefore completing the whole search in about 3 days instead of occupying one single machine for more than 5 CPU years. The results of the functional comparison contain potential functional analogues for more than 79000 gene products from the most important species. 46% of the analyzed gene products are well enough described for such an analysis to individuate functional analogues, such as well-known members of the same gene family, or gene products with similar functions which would never have been associated by standard methods.ConclusionENGINE has produced a list of potential functionally analogous relations between gene products within and between species using, in place of the sequence, the gene description of the GO, thus demonstrating the potential of the GO. However, the current limiting factor is the quality of the associations of many gene products from non-model organisms that often have electronic associations, since experimental information is missing. With future improvements of the GO, this limit will be reduced. ENGINE will manifest its power when it is applied to the whole GODB of more than 2,1 million gene products from more than 100000 organisms. The data produced by this search is planed to be available as a supplement to the GO database as soon as we are able to provide regular updates.

Analysis of protein sequence and interaction data for candidate disease gene prediction

Linkage analysis is a successful procedure to associate diseases with specific genomic regions. These regions are often large, containing hundreds of genes, which make experimental methods employed to identify the disease gene arduous and expensive. We present two methods to prioritize candidates for further experimental study: Common Pathway Scanning (CPS) and Common Module Profiling (CMP). CPS is based on the assumption that common phenotypes are associated with dysfunction in proteins that participate in the same complex or pathway. CPS applies network data derived from protein–protein interaction (PPI) and pathway databases to identify relationships between genes. CMP identifies likely candidates using a domain-dependent sequence similarity approach, based on the hypothesis that disruption of genes of similar function will lead to the same phenotype. Both algorithms use two forms of input data: known disease genes or multiple disease loci. When using known disease genes as input, our combined methods have a sensitivity of 0.52 and a specificity of 0.97 and reduce the candidate list by 13-fold. Using multiple loci, our methods successfully identify disease genes for all benchmark diseases with a sensitivity of 0.84 and a specificity of 0.63. Our combined approach prioritizes good candidates and will accelerate the disease gene discovery process.

A new measure for functional similarity of gene products based on Gene Ontology.

BackgroundGene Ontology (GO) is a standard vocabulary of functional terms and allows for coherent annotation of gene products. These annotations provide a basis for new methods that compare gene products regarding their molecular function and biological role.ResultsWe present a new method for comparing sets of GO terms and for assessing the functional similarity of gene products. The method relies on two semantic similarity measures; simRel and funSim. One measure (simRel) is applied in the comparison of the biological processes found in different groups of organisms. The other measure (funSim) is used to find functionally related gene products within the same or between different genomes. Results indicate that the method, in addition to being in good agreement with established sequence similarity approaches, also provides a means for the identification of functionally related proteins independent of evolutionary relationships. The method is also applied to estimating functional similarity between all proteins in Saccharomyces cerevisiae and to visualizing the molecular function space of yeast in a map of the functional space. A similar approach is used to visualize the functional relationships between protein families.ConclusionThe approach enables the comparison of the underlying molecular biology of different taxonomic groups and provides a new comparative genomics tool identifying functionally related gene products independent of homology. The proposed map of the functional space provides a new global view on the functional relationships between gene products or protein families.

Conservation of RET Regulatory Function from Human to Zebrafish Without Sequence Similarity

Evolutionary sequence conservation is an accepted criterion to identify noncoding regulatory sequences. We have used a transposon-based transgenic assay in zebrafish to evaluate noncoding sequences at the zebrafish ret locus, conserved among teleosts, and at the human RET locus, conserved among mammals. Most teleost sequences directed ret-specific reporter gene expression, with many displaying overlapping regulatory control. The majority of human RET noncoding sequences also directed ret-specific expression in zebrafish. Thus, vast amounts of functional sequence information may exist that would not be detected by sequence similarity approaches.

Recognition and Classification of Histones Using Support Vector Machine

Histones are DNA-binding proteins found in the chromatin of all eukaryotic cells. They are highly conserved and can be grouped into five major classes: H1/H5, H2A, H2B, H3, and H4. Two copies of H2A, H2B, H3, and H4 bind to about 160 base pairs of DNA forming the core of the nucleosome (the repeating structure of chromatin) and H1/H5 bind to its DNA linker sequence. Overall, histones have a high arginine/lysine content that is optimal for interaction with DNA. This sequence bias can make the classification of histones difficult using standard sequence similarity approaches. Therefore, in this paper, we applied support vector machine (SVM) to recognize and classify histones on the basis of their amino acid and dipeptide composition. On evaluation through a five-fold cross-validation, the SVM-based method was able to distinguish histones from nonhistones (nuclear proteins) with an accuracy around 98%. Similarly, we obtained an overall >95% accuracy in discriminating the five classes of histones through the application of 1-versus-rest (1-v-r) SVM. Finally, we have applied this SVM-based method to the detection of histones from whole proteomes and found a comparable sensitivity to that accomplished by hidden Markov motifs (HMM) profiles.

Using GO-PseAA predictor to predict enzyme sub-class

Enzyme function is much less conserved than anticipated, i.e., the requirement for sequence similarity that implies similarity in enzymatic function is much higher than the requirement that implies similarity in protein structure. This is because the function of an enzyme is an extremely complicated problem that may involve very subtle structural details as well as many other physical chemistry factors. Accordingly, if simply based on the sequence similarity approach, it would hardly get a decent success rate in predicting enzyme sub-class even for a dataset consisting of samples with ⩾50% sequence identity. To cope with such a situation, the GO-PseAA predictor was adopted to identify the sub-class for each of the six main enzyme families. It has been observed that, even for the much more stringent datasets in which none of the enzymes has ⩾25% sequence identity to any others, the overall success rates are 73–95%, suggesting that the GO-PseAA predictor can catch the core features of the statistical samples concerned and may become a useful high throughput tool in proteomics and bioinformatics.

EGPred: prediction of eukaryotic genes using ab initio methods after combining with sequence similarity approaches.

EGPred is a Web-based server that combines ab initio methods and similarity searches to predict genes, particularly exon regions, with high accuracy. The EGPred program proceeds in the following steps: (1) an initial BLASTX search of genomic sequence against the RefSeq database is used to identify protein hits with an E-value <1; (2) a second BLASTX search of genomic sequence against the hits from the previous run with relaxed parameters (E-values <10) helps to retrieve all probable coding exon regions; (3) a BLASTN search of genomic sequence against the intron database is then used to detect probable intron regions; (4) the probable intron and exon regions are compared to filter/remove wrong exons; (5) the NNSPLICE program is then used to reassign splicing signal site positions in the remaining probable coding exons; and (6) finally ab initio predictions are combined with exons derived from the fifth step based on the relative strength of start/stop and splice signal sites as obtained from ab initio and similarity search. The combination method increases the exon level performance of five different ab initio programs by 4%-10% when evaluated on the HMR195 data set. Similar improvement is observed when ab initio programs are evaluated on the Burset/Guigo data set. Finally, EGPred is demonstrated on an approximately 95-Mbp fragment of human chromosome 13. The list of predicted genes from this analysis are available in the supplementary material. The EGPred program is computationally intensive due to multiple BLAST runs during each analysis. The EGPred server is available at http://www.imtech.res.in/raghava/egpred/.

A search method for homologs of small proteins. Ubiquitin-like proteins in prokaryotic cells?

The question of protein homology versus analogy arises when proteins share a common function or a common structural fold without any statistically significant amino acid sequence similarity. Even though two or more proteins do not have similar sequences but share a common fold and the same or closely related function, they are assumed to be homologs, descendant from a common ancestor. The problem of homolog identification is compounded in the case of proteins of 100 or less amino acids. This is due to a limited number of basic single domain folds and to a likelihood of identifying by chance sequence similarity. The latter arises from two conditions: first, any search of the currently very large protein database is likely to identify short regions of chance match; secondly, a direct sequence comparison among a small set of short proteins sharing a similar fold can detect many similar patterns of hydrophobicity even if proteins do not descend from a common ancestor. In an effort to identify distant homologs of the many ubiquitin proteins, we have developed a combined structure and sequence similarity approach that attempts to overcome the above limitations of homolog identification. This approach results in the identification of 90 probable ubiquitin-related proteins, including examples from the two prokaryotic domains of life, Archaea and Bacteria.

Partial Characterization of a Cerebral Thyroid Hormone-Responsive Protein

The thyroid hormone-responsive protein (THRP) is expressed in rat cerebral tissue and has 83% overall sequence homology with c-Abl interactor protein, Abi-2, which is a substrate for the tyrosine kinase activity of c-Abl. Within the core region of the two proteins, the sequence similarity approaches 99%. To determine whether THRP is a rat homologue of Abi-2 or is a distinct protein with unique properties, the tissue distribution of THRP and Abi-2 mRNA's was examined using a sensitive ribonuclease protection assay and probes specific for THRP and Abi-2, respectively. The THRP mRNA content of cerebral tissue (1340.0 ± 126.5 arbitrary units) was 2.3-fold higher than Abi-2 mRNA (581.3 ± 73.7), while the ratio of hepatic content of THRP mRNA (209.0 ± 49.1) to hepatic Abi-2 mRNA (2923.0 ± 378.7) was only 0.07 (P < 0.004). Very low levels of Abi-2 mRNA, but not THRP mRNA, were also found in the heart and small intestine. Experiments with PC12 cells transfected with the full-length THRP cDNA and grown in the presence or absence of a tyrosine kinase inhibitor, along with experiments where PC12 cells were cotransfected with the THRP cDNA with or without the wild-type or mutant (tyrosine kinase deficient) c-Abl cDNA, showed that THRP is tyrosine phosphorylated; however, it is not a substrate for c-Abl. These studies demonstrate that THRP and Abi-2 have distinct tissue distribution and distinct biological properties.