Biomedical subjects

Zhengdeng Lei

Publications and source records attributed to Zhengdeng Lei.

4 recordsLinked to original sources

PubMed2006

Assessing protein similarity with Gene Ontology and its use in subnuclear localization prediction.

BACKGROUND: The accomplishment of the various genome sequencing projects resulted in accumulation of massive amount of gene sequence information. This calls for a large-scale computational method for predicting protein localization from sequence. The protein localization can provide valuable information about its molecular function, as well as the biological pathway in which it participates. The prediction of localization of a protein at subnuclear level is a challenging task. In our previous work we proposed an SVM-based system using protein sequence information for this prediction task. In this work, we assess protein similarity with Gene Ontology (GO) and then improve the performance of the system by adding a module of nearest neighbor classifier using a similarity measure derived from the GO annotation terms for protein sequences. RESULTS: The performance of the new system proposed here was compared with our previous system using a set of proteins resided within 6 localizations collected from the Nuclear Protein Database (NPD). The overall MCC (accuracy) is elevated from 0.284 (50.0%) to 0.519 (66.5%) for single-localization proteins in leave-one-out cross-validation; and from 0.420 (65.2%) to 0.541 (65.2%) for an independent set of multi-localization proteins. The new system is available at http://array.bioengr.uic.edu/subnuclear.htm. CONCLUSION: The prediction of protein subnuclear localizations can be largely influenced by various definitions of similarity for a pair of proteins based on different similarity measures of GO terms. Using the sum of similarity scores over the matched GO term pairs for two proteins as the similarity definition produced the best predictive outcome. Substantial improvement in predicting protein subnuclear localizations has been achieved by combining Gene Ontology with sequence information.

Algorithms↗

PubMed2005

An SVM-based system for predicting protein subnuclear localizations.

BACKGROUND: The large gap between the number of protein sequences in databases and the number of functionally characterized proteins calls for the development of a fast computational tool for the prediction of subnuclear and subcellular localizations generally applicable to protein sequences. The information on localization may reveal the molecular function of novel proteins, in addition to providing insight on the biological pathways in which they function. The bulk of past work has been focused on protein subcellular localizations. Furthermore, no specific tool has been dedicated to prediction at the subnuclear level, despite its high importance. In order to design a suitable predictive system, the extraction of subtle sequence signals that can discriminate among proteins with different subnuclear localizations is the key. RESULTS: New kernel functions used in a support vector machine (SVM) learning model are introduced for the measurement of sequence similarity. The k-peptide vectors are first mapped by a matrix of high-scored pairs of k-peptides which are measured by BLOSUM62 scores. The kernels, measuring the similarity for sequences, are then defined on the mapped vectors. By combining these new encoding methods, a multi-class classification system for the prediction of protein subnuclear localizations is established for the first time. The performance of the system is evaluated with a set of proteins collected in the Nuclear Protein Database (NPD). The overall accuracy of prediction for 6 localizations is about 50% (vs. random prediction 16.7%) for single localization proteins in the leave-one-out cross-validation; and 65% for an independent set of multi-localization proteins. This integrated system can be accessed at http://array.bioengr.uic.edu/subnuclear.htm. CONCLUSION: The integrated system benefits from the combination of predictions from several SVMs based on selected encoding methods. Finally, the predictive power of the system is expected to improve as more proteins with known subnuclear localizations become available.

Algorithms↗

PubMed2003

Immobilized metal-ion chelating capillary microreactor for peptide mapping analysis of proteins by matrix assisted laser desorption/ ionization-time of flight-mass spectrometry.

Peptide mass mapping analysis, utilizing a regenerable enzyme microreactor with metal-ion chelated adsorption of enzyme, combined with matrix assisted laser desorption/ionization-time of flight-mass spectrometry (MALDI-TOF-MS) was developed. Different procedures from the conventional approaches were adopted to immobilize the chelator onto the silica supports, that is, the metal chelating agent of iminodiacetic acid (IDA) was reacted with glycidoxypropyltrimethoxysilane (GLYMO) before its immobilization onto the inner wall of the fused-silica capillary pretreated with NH(4)HF(2). The metal ion of copper and subsequently enzyme was specifically adsorbed onto the surface to form the immobilized enzyme capillary microreactor, which was combined with MALDI-TOF-MS to apply for the mass mapping analysis of nL amounts of protein samples. The results revealed that the peptide mapping could routinely be generated from 0.5 pmol protein sample in 15 min at 50 degrees C, even 20 fmol cytochrome c could be well digested and detected.

Adsorption↗

PubMed2002

Study of competitive binding of enantiomers to protein by affinity capillary electrochromatography.

Affinity capillary electrochromatography (CEC) with zonal elution method was used to probe the competitive interactions of enantiomers with protein. In this approach, a known concentration of a competing agent is continuously applied to a CEC column with bovine serum albumin (BSA) physically adsorbed on SAX packing while injections of a small amount of analyte are made. The binding sites of solutes on the BSA molecule were determined by the changes in the retention factors of the solutes resulted from the addition of competitive agent. By using D- or L-tryptophan as competitive agents and D-, L-tryptophan and benzoin enantiomers as injected analytes showed that BSA molecule has a primary site to strongly bind L-tryptophan, but D-tryptophan dose not bind at this site; D- and L-tryptophan share a weak binding site on the BSA molecule. Benzoin enantiomers do not share any binding sites with either D- or L-tryptophan. Non-chiral compounds of trichloroacetic acid and n-hexanoic acid were applied as the competitive agents to study the binding of warfarin enantiomers to BSA, it was observed that trichloroacetic acid and n-hexanoic acid had a same binding site for warfarin enantiomers binding to BSA molecule.

Binding, Competitive↗