Dataset Information

Identification, purification and characterization of a novel human blood protein with binding affinity for prostate secretory protein of 94 amino acids.

ABSTRACT: PSP94 (prostate secretory protein of 94 amino acids), an abundant protein within semen, has reported local functions within the reproductive tract and reported systemic functions. Mechanisms of action remain poorly understood, but binding to undefined molecules within the prostate, pituitary, testis and blood may initiate some of these actions. PSP94 serum measurements, especially of bound and free forms, have potential clinical utility in prostate cancer management. Identification of the binding molecules will help in the understanding of PSP94's action, and enable further development of PSP94 serum assays. PSPBP (PSP94-binding protein) was purified from human serum by ammonium sulphate fractionation, ion-exchange and affinity chromatography. The glycosylated protein ran as two bands on SDS/PAGE (70 and 95 kDa). N-terminal sequencing yielded a 30-amino-acid sequence, identical with the translated N-terminal region of a previously published cDNA (GenBank accession number AX136261). Reverse transcriptase PCR and plaque hybridization demonstrated PSPBP mRNA in peripheral blood leucocytes and in a prostate cDNA library. Northern blotting showed 2 kb mRNA species in prostate, testis, ovary and intestine. Immunohistochemistry demonstrated PSPBP in tissues, including pituitary and Leydig cells, supporting a role for PSP94 in hormonal control at the pituitary gonadal axis. ELISA demonstrated that PSPBP levels were significantly lower (P=0.0014) in the serum of a prostate cancer population (n=65) compared with a control population (n=70). PSPBP identification will help the understanding of PSP94's functions and facilitate ELISA development to address the clinical value of PSP94 serum assays.

SUBMITTER: Reeves JR

PROVIDER: S-EPMC1134678 | biostudies-literature | 2005 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification, purification and characterization of a novel human blood protein with binding affinity for prostate secretory protein of 94 amino acids.

Reeves Jonathan R JR Xuan Jim W JW Arfanis Katerina K Morin Catherine C Garde Seema V SV Ruiz Marcia T MT Wisniewski Jan J Panchal Chandra C Tanner Jerome E JE

The Biochemical journal 20050101 Pt 1

PSP94 (prostate secretory protein of 94 amino acids), an abundant protein within semen, has reported local functions within the reproductive tract and reported systemic functions. Mechanisms of action remain poorly understood, but binding to undefined molecules within the prostate, pituitary, testis and blood may initiate some of these actions. PSP94 serum measurements, especially of bound and free forms, have potential clinical utility in prostate cancer management. Identification of the bindin ...[more]

PMID: 15344909

Similar Datasets

Project description:Protein-protein interactions (PPIs) play a central role in nearly all cellular processes. The strength of the binding in a PPI is characterized by the binding affinity (BA) and is a key factor in controlling protein-protein complex formation and defining the structure-function relationship. Despite advancements in understanding protein-protein binding, much remains unknown about the interfacial region and its association with BA. New models are needed to predict BA with improved accuracy for therapeutic design. Here, we use machine learning approaches to examine how well different types of interfacial contacts can be used to predict experimentally determined BA and to reveal the impact of the specific amino acids at the binding interface on BA. We create a series of multivariate linear regression models incorporating different contact features at both residue and atomic levels and examine how different methods of identifying and characterizing these properties impact the performance of these models. Particularly, we introduce a new and simple approach to predict BA based on the quantities of specific amino acids at the protein-protein interface. We found that the numbers of specific amino acids at the protein-protein interface were correlated with BA. We show that the interfacial numbers of amino acids can be used to produce models with consistently good performance across different data sets, indicating the importance of the identities of interfacial amino acids in underlying BA. When trained on a diverse set of complexes from two benchmark data sets, the best performing BA model was generated with an explicit linear equation involving six amino acids. Tyrosine, in particular, was identified as the key amino acid in controlling BA, as it had the strongest correlation with BA and was consistently identified as the most important amino acid in feature importance studies. Glycine and serine were identified as the next two most important amino acids in predicting BA. The results from this study further our understanding of PPIs and can be used to make improved predictions of BA, giving them implications for drug design and screening in the pharmaceutical industry.

Project description:BACKGROUND: Many learning approaches to predicting RNA-binding residues in a protein sequence construct a non-redundant training dataset based on the sequence similarity. The sequence similarity-based method either takes a whole sequence or discards it for a training dataset. However, similar sequences or even identical sequences can have different interaction sites depending on their interaction partners, and this information is lost when the sequences are removed. Furthermore, a training dataset constructed by the sequence similarity-based method may contain redundant data when the remaining sequence contains similar subsequences within the sequence. In addition to the problem with the training dataset, most approaches do not consider the interacting partner (i.e., RNA) of a protein when they predict RNA-binding amino acids. Thus, they always predict the same RNA-binding sites for a given protein sequence even if the protein binds to different RNA molecules. RESULTS: We developed a feature vector-based method that removes data redundancy for a non-redundant training dataset. The feature vector-based method constructed a larger training dataset than the standard sequence similarity-based method, yet the dataset contained no redundant data. We identified effective features of protein and RNA (the interaction propensity of amino acid triplets, global features of the protein sequence, and RNA feature) for predicting RNA-binding residues. Using the method and features, we built a support vector machine (SVM) model that predicted RNA-binding residues in a protein sequence. Our SVM model showed an accuracy of 84.2%, an F-measure of 76.1%, and a correlation coefficient of 0.41 with 5-fold cross validation on a non-redundant dataset from 3,149 protein-RNA interacting pairs. In an independent test dataset that does not include the 3,149 pairs and were not used in training the SVM model, it achieved an accuracy of 90.3%, an F-measure of 72.8%, and a correlation coefficient of 0.24. Comparison with other methods on the same datasets demonstrated that our model was better than the others. CONCLUSIONS: The feature vector-based redundancy reduction method is powerful for constructing a non-redundant training dataset for a learning model since it generates a larger dataset with non-redundant data than the standard sequence similarity-based method. Including the features of both RNA and protein sequences in a feature vector results in better performance than using the protein features only when predicting the RNA-binding residues in a protein sequence.

Dataset Information

Identification, purification and characterization of a novel human blood protein with binding affinity for prostate secretory protein of 94 amino acids.

Publications

Identification, purification and characterization of a novel human blood protein with binding affinity for prostate secretory protein of 94 amino acids.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets