Protein Bioinformatics Infrastructure for the Integration and Analysis of Multiple High-Throughput "omics" Data. Adv Bioinformatics. 2010;423589. [PMID: 20369061 PMCID: PMC2847380 DOI: 10.1155/2010/423589]

MLapRVFL: Protein sequence prediction based on Multi-Laplacian Regularized Random Vector Functional Link

Protein sequence classification is a crucial research field in bioinformatics, playing a vital role in facilitating functional annotation, structure prediction, and gaining a deeper understanding of protein function and interactions. With the rapid development of high-throughput sequencing technologies, a vast amount of unknown protein sequence data is being generated and accumulated, leading to an increasing demand for protein classification and annotation. Existing machine learning methods still have limitations in protein sequence classification, such as low accuracy and precision of classification models, rendering them less valuable in practical applications. Additionally, these models often lack strong generalization capabilities and cannot be widely applied to various types of proteins. Therefore, accurately classifying and predicting proteins remains a challenging task. In this study, we propose a protein sequence classifier called Multi-Laplacian Regularized Random Vector Functional Link (MLapRVFL). By incorporating Multi-Laplacian and L2,1-norm regularization terms into the basic Random Vector Functional Link (RVFL) method, we effectively improve the model's generalization performance, enhance the robustness and accuracy of the classification model. The experimental results on two commonly used datasets demonstrate that MLapRVFL outperforms popular machine learning methods and achieves superior predictive performance compared to previous studies. In conclusion, the proposed MLapRVFL method makes significant contributions to protein sequence prediction.

Current progress in metabolomics of gestational diabetes mellitus

Gestational diabetes mellitus (GDM) is one of the most common metabolic disorders of pregnancy and can cause short- and long-term adverse effects in both pregnant women and their offspring. However, the etiology and pathogenesis of GDM are still unclear. As a metabolic disease, GDM is well suited to metabolomics study, which can monitor the changes in small molecular metabolites induced by maternal stimuli or perturbations in real time. The application of metabolomics in GDM can be used to discover diagnostic biomarkers, evaluate the prognosis of the disease, guide the application of diet or drugs, evaluate the curative effect, and explore the mechanism. This review provides comprehensive documentation of metabolomics research methods and techniques as well as the current progress in GDM research. We anticipate that the review will contribute to identifying gaps in the current knowledge or metabolomics technology, provide evidence-based information, and inform future research directions in GDM.

New Technologies for Monitoring Marine Mammal Health

Omics technologies have been developed in recent decades and applied to different subjects, although the greatest advancements have been achieved in human biology and disease. Genome sequencing and the exploration of its coding and noncoding regions are rapidly yielding meaningful answers to diverse questions, relating genome information to protein activity to environmental changes. In the past, marine mammal genetic and transcriptional studies have been restricted due to the lack of reference genomes. But the advance of high-throughput sequencing is revolutionizing the life sciences technologies. As long-lived organisms, at the top of the food chain, marine mammals play an important role in marine ecosystems and while their protected status is in favor of conservation of the species, it also complicates the researcher's approach to traditional measurements of health. Omics data generated by high-throughput technologies will represent an important key for improving the scientific basis for understanding both marine mammal and environment health.

Minimum redundancy maximum relevance feature selection approach for temporal gene expression data

BACKGROUND

Feature selection, aiming to identify a subset of features among a possibly large set of features that are relevant for predicting a response, is an important preprocessing step in machine learning. In gene expression studies this is not a trivial task for several reasons, including potential temporal character of data. However, most feature selection approaches developed for microarray data cannot handle multivariate temporal data without previous data flattening, which results in loss of temporal information. We propose a temporal minimum redundancy - maximum relevance (TMRMR) feature selection approach, which is able to handle multivariate temporal data without previous data flattening. In the proposed approach we compute relevance of a gene by averaging F-statistic values calculated across individual time steps, and we compute redundancy between genes by using a dynamical time warping approach.

RESULTS

The proposed method is evaluated on three temporal gene expression datasets from human viral challenge studies. Obtained results show that the proposed method outperforms alternatives widely used in gene expression studies. In particular, the proposed method achieved improvement in accuracy in 34 out of 54 experiments, while the other methods outperformed it in no more than 4 experiments.

CONCLUSION

We developed a filter-based feature selection method for temporal gene expression data based on maximum relevance and minimum redundancy criteria. The proposed method incorporates temporal information by combining relevance, which is calculated as an average F-statistic value across different time steps, with redundancy, which is calculated by employing dynamical time warping approach. As evident in our experiments, incorporating the temporal information into the feature selection process leads to selection of more discriminative features.

Prediction of FAD binding sites in electron transport proteins according to efficient radial basis function networks and significant amino acid pairs

BACKGROUND

Cellular respiration is a catabolic pathway for producing adenosine triphosphate (ATP) and is the most efficient process through which cells harvest energy from consumed food. When cells undergo cellular respiration, they require a pathway to keep and transfer electrons (i.e., the electron transport chain). Due to oxidation-reduction reactions, the electron transport chain produces a transmembrane proton electrochemical gradient. In case protons flow back through this membrane, this mechanical energy is converted into chemical energy by ATP synthase. The convert process is involved in producing ATP which provides energy in a lot of cellular processes. In the electron transport chain process, flavin adenine dinucleotide (FAD) is one of the most vital molecules for carrying and transferring electrons. Therefore, predicting FAD binding sites in the electron transport chain is vital for helping biologists understand the electron transport chain process and energy production in cells.

RESULTS

We used an independent data set to evaluate the performance of the proposed method, which had an accuracy of 69.84 %. We compared the performance of the proposed method in analyzing two newly discovered electron transport protein sequences with that of the general FAD binding predictor presented by Mishra and Raghava and determined that the accuracy of the proposed method improved by 9-45 % and its Matthew's correlation coefficient was 0.14-0.5. Furthermore, the proposed method enabled reducing the number of false positives significantly and can provide useful information for biologists.

CONCLUSIONS

We developed a method that is based on PSSM profiles and SAAPs for identifying FAD binding sites in newly discovered electron transport protein sequences. This approach achieved a significant improvement after we added SAAPs to PSSM features to analyze FAD binding proteins in the electron transport chain. The proposed method can serve as an effective tool for predicting FAD binding sites in electron transport proteins and can help biologists understand the functions of the electron transport chain, particularly those of FAD binding sites. We also developed a web server which identifies FAD binding sites in electron transporters available for academics.

NIMS: a database on nucleobase compounds and their interactions in macromolecular structures

The intense exploration of nucleotide-binding protein structures has created a whirlwind in the field of structural biology and bioinformatics. This has led to the conception and birth of NIMS. This database is a collection of detailed data on the nucleobases, nucleosides and nucleotides, along with their analogues as well as the protein structures to which they bind. Interaction details such as the interacting residues and all associated values have been made available. As a pioneering step, the diffraction precision index for protein structures, the atomic uncertainty for each atom, and the computed errors on the interatomic distances and angles are available in the database. Apart from the above, provision has been made to visualize the three-dimensional structures of both ligands and protein–ligand structures and their interactions inJmolas well asJSmol. One of the salient features of NIMS is that it has been interfaced with a user-friendly and query-based efficient search engine. It was conceived and developed with the aim of serving a significant section of researchers working in the area of protein and nucleobase complexes. NIMS is freely available online at http://iris.physics.iisc.ernet.in/nims and it is hoped that it will prove to be an invaluable asset.

Construction of dynamic probabilistic protein interaction networks for protein complex identification

Background

Recently, high-throughput experimental techniques have generated a large amount of protein-protein interaction (PPI) data which can construct large complex PPI networks for numerous organisms. System biology attempts to understand cellular organization and function by analyzing these PPI networks. However, most studies still focus on static PPI networks which neglect the dynamic information of PPI.

Results

The gene expression data under different time points and conditions can reveal the dynamic information of proteins. In this study, we used an active probability-based method to distinguish the active level of proteins at different active time points. We constructed dynamic probabilistic protein networks (DPPN) to integrate dynamic information of protein into static PPI networks. Based on DPPN, we subsequently proposed a novel method to identify protein complexes, which could effectively exploit topological structure as well as dynamic information of DPPN. We used three different yeast PPI datasets and gene expression data to construct three DPPNs. When applied to three DPPNs, many well-characterized protein complexes were accurately identified by this method.

Conclusion

The shift from static PPI networks to dynamic PPI networks is essential to accurately identify protein complex. This method not only can be applied to identify protein complex, but also establish a framework to integrate dynamic information into static networks for other applications, such as pathway analysis.

In silico verification and parallel reaction monitoring prevalidation of potential prostate cancer biomarkers

PURPOSE

Targeted proteomics of potential biomarkers is often challenging. Hence, we developed an intermediate workflow to streamline potential urinary biomarkers of prostate cancer (PCa).

MATERIALS & METHODS

Using previously discovered potential PCa biomarkers; we selected proteotypic peptides for targeted validation. Preliminary in silico immunohistochemical and single reaction monitoring (SRM) verification was performed. Successful PTPs were then prevalidated using parallel reaction monitoring (PRM) and reconfirmed in 15 publicly available databases.

RESULTS

Stringency-based targetable potential biomarkers were shortlisted following in silico screening. PRM reveals top 12 potential biomarkers including the top ranking seven in silico verification-based biomarkers. Database reconfirmation showed differential expression between PCa and benign/normal prostatic urine samples.

CONCLUSION

The pragmatic penultimate screening step, described herein, would immensely improve targeted proteomics validation of potential disease biomarkers.

Protein sequence classification with improved extreme learning machine algorithms

Precisely classifying a protein sequence from a large biological protein sequences database plays an important role for developing competitive pharmacological products. Comparing the unseen sequence with all the identified protein sequences and returning the category index with the highest similarity scored protein, conventional methods are usually time-consuming. Therefore, it is urgent and necessary to build an efficient protein sequence classification system. In this paper, we study the performance of protein sequence classification using SLFNs. The recent efficient extreme learning machine (ELM) and its invariants are utilized as the training algorithms. The optimal pruned ELM is first employed for protein sequence classification in this paper. To further enhance the performance, the ensemble based SLFNs structure is constructed where multiple SLFNs with the same number of hidden nodes and the same activation function are used as ensembles. For each ensemble, the same training algorithm is adopted. The final category index is derived using the majority voting method. Two approaches, namely, the basic ELM and the OP-ELM, are adopted for the ensemble based SLFNs. The performance is analyzed and compared with several existing methods using datasets obtained from the Protein Information Resource center. The experimental results show the priority of the proposed algorithms.

Transcriptional landscapes of emerging autoimmunity: transient aberrations in the targeted tissue's extracellular milieu precede immune responses in Sjögren's syndrome

Introduction

Our understanding of autoimmunity is skewed considerably towards the late stages of overt disease and chronic inflammation. Defining the targeted organ’s role during emergence of autoimmune diseases is, however, critical in order to define their etiology, early and covert disease phases and delineate their molecular basis.

Methods

Using Sjögren’s syndrome (SS) as an exemplary rheumatic autoimmune disease and temporal global gene-expression profiling, we systematically mapped the transcriptional landscapes and chronological interrelationships between biological themes involving the salivary glands’ extracellular milieu. The time period studied spans from pre- to subclinical and ultimately to onset of overt disease in a well-defined model of spontaneous SS, the C57BL/6.NOD-Aec1Aec2 strain. In order to answer this aim of great generality, we developed a novel bioinformatics-based approach, which integrates comprehensive data analysis and visualization within interactive networks. The latter are computed by projecting the datasets as a whole on a priori-defined consensus-based knowledge.

Results

Applying these methodologies revealed extensive susceptibility loci-dependent aberrations in salivary gland homeostasis and integrity preceding onset of overt disease by a considerable amount of time. These alterations coincided with innate immune responses depending predominantly on genes located outside of the SS-predisposing loci Aec1 and Aec2. Following a period of transcriptional stability, networks mapping the onset of overt SS displayed, in addition to natural killer, T- and B-cell-specific gene patterns, significant reversals of focal adhesion, cell-cell junctions and neurotransmitter receptor-associated alterations that had prior characterized progression from pre- to subclinical disease.

Conclusions

This data-driven methodology advances unbiased assessment of global datasets an allowed comprehensive interpretation of complex alterations in biological states. Its application delineated a major involvement of the targeted organ during the emergence of experimental SS.

Integration of cardiac proteome biology and medicine by a specialized knowledgebase

RATIONALE

Omics sciences enable a systems-level perspective in characterizing cardiovascular biology. Integration of diverse proteomics data via a computational strategy will catalyze the assembly of contextualized knowledge, foster discoveries through multidisciplinary investigations, and minimize unnecessary redundancy in research efforts.

OBJECTIVE

The goal of this project is to develop a consolidated cardiac proteome knowledgebase with novel bioinformatics pipeline and Web portals, thereby serving as a new resource to advance cardiovascular biology and medicine.

METHODS AND RESULTS

We created Cardiac Organellar Protein Atlas Knowledgebase (COPaKB; www.HeartProteome.org), a centralized platform of high-quality cardiac proteomic data, bioinformatics tools, and relevant cardiovascular phenotypes. Currently, COPaKB features 8 organellar modules, comprising 4203 LC-MS/MS experiments from human, mouse, drosophila, and Caenorhabditis elegans, as well as expression images of 10,924 proteins in human myocardium. In addition, the Java-coded bioinformatics tools provided by COPaKB enable cardiovascular investigators in all disciplines to retrieve and analyze pertinent organellar protein properties of interest.

CONCLUSIONS

COPaKB provides an innovative and interactive resource that connects research interests with the new biological discoveries in protein sciences. With an array of intuitive tools in this unified Web server, nonproteomics investigators can conveniently collaborate with proteomics specialists to dissect the molecular signatures of cardiovascular phenotypes.

When one and one gives more than two: challenges and opportunities of integrative omics

Since the dawn of the post-genomic era a myriad of novel high-throughput technologies have been developed that are capable of measuring thousands of biological molecules at once, giving rise to various “omics” platforms. These advances offer the unique opportunity to study how individual parts of a biological system work together to produce emerging phenotypes. Today, many research laboratories are moving toward applying multiple omics platforms to analyze the same biological samples. In addition, network information of interacting molecules is being incorporated more and more into the analysis and interpretation of these multiple omics datasets, which provides novel ways to integrate multiple layers of heterogeneous biological information into a single coherent picture. Here, we provide a perspective on how such recent “integrative omics” efforts are likely going to shift biological paradigms once again, and what challenges lie ahead.