Function prediction of hypothetical proteins without sequence similarity to proteins of known function

Analysis of spatial and temporal protein expression in the cerebral cortex after ischemia-reperfusion injury

Background and Purpose

Hypoxia, or ischemia, is a common cause of neurological deficits in the elderly. This study elucidated the mechanisms underlying ischemia-induced brain injury that results in neurological sequelae.

Methods

Cerebral ischemia was induced in male Sprague-Dawley rats by transient ligation of the left carotid artery followed by 60 min of hypoxia. A two-dimensional differential proteome analysis was performed using matrix-assisted laser desorption ionization-time-of-flight mass spectrometry to compare changes in protein expression on the lesioned side of the cortex relative to that on the contralateral side at 0, 6, and 24 h after ischemia.

Results

The expressions of the following five proteins were up-regulated in the ipsilateral cortex at 24 h after ischemia-reperfusion injury compared to the contralateral (i.e., control) side: aconitase 2, neurotensin-related peptide, hypothetical protein XP-212759, 60-kDa heat-shock protein, and aldolase A. The expression of one protein, dynamin-1, was up-regulated only at the 6-h time point. The level of 78-kDa glucose-regulated protein precursor on the lesioned side of the cerebral cortex was found to be high initially, but then down-regulated by 24 h after the induction of ischemia-reperfusion injury. The expressions of several metabolic enzymes and translational factors were also perturbed soon after brain ischemia.

Conclusions

These findings provide insights into the mechanisms underlying the neurodegenerative events that occur following cerebral ischemia.

Structural analysis of hypothetical proteins from Helicobacter pylori: an approach to estimate functions of unknown or hypothetical proteins

Helicobacter pylori (H. pylori) have a unique ability to survive in extreme acidic environments and to colonize the gastric mucosa. It can cause diverse gastric diseases such as peptic ulcers, chronic gastritis, mucosa-associated lymphoid tissue (MALT) lymphoma, gastric cancer, etc. Based on genomic research of H. pylori, over 1600 genes have been functionally identified so far. However, H. pylori possess some genes that are uncharacterized since: (i) the gene sequences are quite new; (ii) the function of genes have not been characterized in any other bacterial systems; and (iii) sometimes, the protein that is classified into a known protein based on the sequence homology shows some functional ambiguity, which raises questions about the function of the protein produced in H. pylori. Thus, there are still a lot of genes to be biologically or biochemically characterized to understand the whole picture of gene functions in the bacteria. In this regard, knowledge on the 3D structure of a protein, especially unknown or hypothetical protein, is frequently useful to elucidate the structure-function relationship of the uncharacterized gene product. That is, a structural comparison with known proteins provides valuable information to help predict the cellular functions of hypothetical proteins. Here, we show the 3D structures of some hypothetical proteins determined by NMR spectroscopy and X-ray crystallography as a part of the structural genomics of H. pylori. In addition, we show some successful approaches of elucidating the function of unknown proteins based on their structural information.

Unscrambling genetic information at the RNA level

Genomics aims at unraveling the blueprint of life; however, DNA sequence alone does not always reveal the proteins and structural RNAs encoded by the genome. The reason is that genetic information is often encrypted. Recognizing the logic of encryption, and understanding how living cells decode hidden information--at the level of DNA, RNA or protein--is challenging. RNA-level decryption includes topical RNA editing and more 'macroscopic' transcript rearrangements. The latter events involve the four types of introns recognized to date, notably spliceosomal, group I, group II, and archaeal/tRNA splicing. Intricate variants, such as alternative splicing and trans-splicing, have been reported for each intron type, but the biological significance has not always been confirmed. Novel RNA-level unscrambling processes were recently discovered in mitochondria of dinoflagellates and diplonemids, and potentially euglenids. These processes seem not to rely on known introns, and the corresponding molecular mechanisms remain to be elucidated.

A multi-label classifier for predicting the subcellular localization of gram-negative bacterial proteins with both single and multiple sites

Prediction of protein subcellular localization is a challenging problem, particularly when the system concerned contains both singleplex and multiplex proteins. In this paper, by introducing the “multi-label scale” and hybridizing the information of gene ontology with the sequential evolution information, a novel predictor called iLoc-Gneg is developed for predicting the subcellular localization of Gram-positive bacterial proteins with both single-location and multiple-location sites. For facilitating comparison, the same stringent benchmark dataset used to estimate the accuracy of Gneg-mPLoc was adopted to demonstrate the power of iLoc-Gneg. The dataset contains 1,392 Gram-negative bacterial proteins classified into the following eight locations: (1) cytoplasm, (2) extracellular, (3) fimbrium, (4) flagellum, (5) inner membrane, (6) nucleoid, (7) outer membrane, and (8) periplasm. Of the 1,392 proteins, 1,328 are each with only one subcellular location and the other 64 are each with two subcellular locations, but none of the proteins included has pairwise sequence identity to any other in a same subset (subcellular location). It was observed that the overall success rate by jackknife test on such a stringent benchmark dataset by iLoc-Gneg was over 91%, which is about 6% higher than that by Gneg-mPLoc. As a user-friendly web-server, iLoc-Gneg is freely accessible to the public at http://icpr.jci.edu.cn/bioinfo/iLoc-Gneg. Meanwhile, a step-by-step guide is provided on how to use the web-server to get the desired results. Furthermore, for the user's convenience, the iLoc-Gneg web-server also has the function to accept the batch job submission, which is not available in the existing version of Gneg-mPLoc web-server. It is anticipated that iLoc-Gneg may become a useful high throughput tool for Molecular Cell Biology, Proteomics, System Biology, and Drug Development.

Some remarks on protein attribute prediction and pseudo amino acid composition

With the accomplishment of human genome sequencing, the number of sequence-known proteins has increased explosively. In contrast, the pace is much slower in determining their biological attributes. As a consequence, the gap between sequence-known proteins and attribute-known proteins has become increasingly large. The unbalanced situation, which has critically limited our ability to timely utilize the newly discovered proteins for basic research and drug development, has called for developing computational methods or high-throughput automated tools for fast and reliably identifying various attributes of uncharacterized proteins based on their sequence information alone. Actually, during the last two decades or so, many methods in this regard have been established in hope to bridge such a gap. In the course of developing these methods, the following things were often needed to consider: (1) benchmark dataset construction, (2) protein sample formulation, (3) operating algorithm (or engine), (4) anticipated accuracy, and (5) web-server establishment. In this review, we are to discuss each of the five procedures, with a special focus on the introduction of pseudo amino acid composition (PseAAC), its different modes and applications as well as its recent development, particularly in how to use the general formulation of PseAAC to reflect the core and essential features that are deeply hidden in complicated protein sequences.

Comparing performances of logistic regression and neural networks for predicting melatonin excretion patterns in the rat exposed to ELF magnetic fields

Various studies have been reported on the bioeffects of magnetic field exposure; however, no consensus or guideline is available for experimental designs relating to exposure conditions as yet. In this study, logistic regression (LR) and artificial neural networks (ANNs) were used in order to analyze and predict the melatonin excretion patterns in the rat exposed to extremely low frequency magnetic fields (ELF-MF). Subsequently, on a database containing 33 experiments, performances of LR and ANNs were compared through resubstitution and jackknife tests. Predictor variables were more effective parameters and included frequency, polarization, exposure duration, and strength of magnetic fields. Also, five performance measures including accuracy, sensitivity, specificity, Matthew's Correlation Coefficient (MCC) and normalized percentage, better than random (S) were used to evaluate the performance of models. The LR as a conventional model obtained poor prediction performance. Nonetheless, LR distinguished the duration of magnetic fields as a statistically significant parameter. Also, horizontal polarization of magnetic fields with the highest logit coefficient (or parameter estimate) with negative sign was found to be the strongest indicator for experimental designs relating to exposure conditions. This means that each experiment with horizontal polarization of magnetic fields has a higher probability to result in "not changed melatonin level" pattern. On the other hand, ANNs, a more powerful model which has not been introduced in predicting melatonin excretion patterns in the rat exposed to ELF-MF, showed high performance measure values and higher reliability, especially obtaining 0.55 value of MCC through jackknife tests. Obtained results showed that such predictor models are promising and may play a useful role in defining guidelines for experimental designs relating to exposure conditions. In conclusion, analysis of the bioelectromagnetic data could result in finding a relationship between electromagnetic fields and different biological processes.

Identification of proteases and their types

Called by many as biology's version of Swiss army knives, proteases cut long sequences of amino acids into fragments and regulate most physiological processes. They are vitally important in the life cycle. Different types of proteases have different action mechanisms and biological processes. With the avalanche of protein sequences generated during the postgenomic age, it is highly desirable for both basic research and drug design to develop a fast and reliable method for identifying the types of proteases according to their sequences or even just for whether they are proteases or not. In this article, three recently developed identification methods in this regard are discussed: (i) FunD-PseAAC, (ii) GO-PseAAC, and (iii) FunD-PsePSSM. The first two were established by hybridizing the FunD (functional domain) approach and the GO (gene ontology) approach, respectively, with the PseAAC (pseudo amino acid composition) approach. The third method was established by fusing the FunD approach with the PsePSSM (pseudo position-specific scoring matrix) approach. Of these three methods, only FunD-PsePSSM has provided a server called ProtIdent (protease identifier), which is freely accessible to the public via the website at http://www.csbio.sjtu.edu.cn/bioinf/Protease. For the convenience of users, a step-by-step guide on how to use ProtIdent is illustrated. Meanwhile, the caveat in using ProtIdent and how to understand the success expectancy rate of a statistical predictor are discussed. Finally, the essence of why ProtIdent can yield a high success rate in identifying proteases and their types is elucidated.

Unassigned MURF1 of kinetoplastids codes for NADH dehydrogenase subunit 2

Background

In a previous study, we conducted a large-scale similarity-free function prediction of mitochondrion-encoded hypothetical proteins, by which the hypothetical gene murf1 (maxicircle unidentified reading frame 1) was assigned as nad2, encoding subunit 2 of NADH dehydrogenase (Complex I of the respiratory chain). This hypothetical gene occurs in the mitochondrial genome of kinetoplastids, a group of unicellular eukaryotes including the causative agents of African sleeping sickness and leishmaniasis. In the present study, we test this assignment by using bioinformatics methods that are highly sensitive in identifying remote homologs and confront the prediction with available biological knowledge.

Results

Comparison of MURF1 profile Hidden Markov Model (HMM) against function-known profile HMMs in Pfam, Panther and TIGR shows that MURF1 is a Complex I protein, but without specifying the exact subunit. Therefore, we constructed profile HMMs for each individual subunit, using all available sequences clustered at various identity thresholds. HMM-HMM comparison of these individual NADH subunits against MURF1 clearly identifies this hypothetical protein as NAD2. Further, we collected the relevant experimental information about kinetoplastids, which provides additional evidence in support of this prediction.

Conclusion

Our in silico analyses provide convincing evidence for MURF1 being a highly divergent member of NAD2.