Post-genomic Age Research Articles

ICDI-PseFpt: Identify the channel–drug interaction in cellular networking with PseAAC and molecular fingerprints

Many crucial functions in life, such as heartbeat, sensory transduction and central nervous system response, are controlled by cell signalings via various ion channels. Therefore, ion channels have become an excellent drug target, and study of ion channel–drug interaction networks is an important topic for drug development. However, it is both time-consuming and costly to determine whether a drug and a protein ion channel are interacting with each other in a cellular network by means of experimental techniques. Although some computational methods were developed in this regard based on the knowledge of the 3D (three-dimensional) structure of protein, unfortunately their usage is quite limited because the 3D structures for most protein ion channels are still unknown. With the avalanche of protein sequences generated in the post-genomic age, it is highly desirable to develop the sequence-based computational method to address this problem. To take up the challenge, we developed a new predictor called iCDI-PseFpt, in which the protein ion-channel sample is formulated by the PseAAC (pseudo amino acid composition) generated with the gray model theory, the drug compound by the 2D molecular fingerprint, and the operation engine is the fuzzy K-nearest neighbor algorithm. The overall success rate achieved by iCDI-PseFpt via the jackknife cross-validation was 87.27%, which is remarkably higher than that by any of the existing predictors in this area. As a user-friendly web-server, iCDI-PseFpt is freely accessible to the public at the website http://www.jci-bioinfo.cn/iCDI-PseFpt/. Furthermore, for the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated math equations presented in the paper just for its integrity. It has not escaped our notice that the current approach can also be used to study other drug–target interaction networks.

IHSP-PseRAAAC: Identifying the heat shock protein families using pseudo reduced amino acid alphabet composition

Heat shock proteins (HSPs) are a type of functionally related proteins present in all living organisms, both prokaryotes and eukaryotes. They play essential roles in protein–protein interactions such as folding and assisting in the establishment of proper protein conformation and prevention of unwanted protein aggregation. Their dysfunction may cause various life-threatening disorders, such as Parkinson’s, Alzheimer’s, and cardiovascular diseases. Based on their functions, HSPs are usually classified into six families: (i) HSP20 or sHSP, (ii) HSP40 or J-class proteins, (iii) HSP60 or GroEL/ES, (iv) HSP70, (v) HSP90, and (vi) HSP100. Although considerable progress has been achieved in discriminating HSPs from other proteins, it is still a big challenge to identify HSPs among their six different functional types according to their sequence information alone. With the avalanche of protein sequences generated in the post-genomic age, it is highly desirable to develop a high-throughput computational tool in this regard. To take up such a challenge, a predictor called iHSP-PseRAAAC has been developed by incorporating the reduced amino acid alphabet information into the general form of pseudo amino acid composition. One of the remarkable advantages of introducing the reduced amino acid alphabet is being able to avoid the notorious dimension disaster or overfitting problem in statistical prediction. It was observed that the overall success rate achieved by iHSP-PseRAAAC in identifying the functional types of HSPs among the aforementioned six types was more than 87%, which was derived by the jackknife test on a stringent benchmark dataset in which none of HSPs included has ⩾40% pairwise sequence identity to any other in the same subset. It has not escaped our notice that the reduced amino acid alphabet approach can also be used to investigate other protein classification problems. As a user-friendly web server, iHSP-PseRAAAC is accessible to the public at http://lin.uestc.edu.cn/server/iHSP-PseRAAAC.

Race in a bottle: the story of BiDil and racialized medicine in a post-genomic age

Race in a bottle: the story of BiDil and racialized medicine in a post-genomic age

Recent Progresses in Identifying Nuclear Receptors and Their Families

Nuclear receptors (NRs) are members of a large superfamily of evolutionarily related DNA-binding transcription factors. They regulate diverse functions, such as homeostasis, reproduction, development and metabolism. As nuclear receptors bind small molecules that can easily be modified by drug design, and control functions associated with major diseases (e.g. cancer, osteoporosis and diabetes), they are promising pharmacological targets. According to their different action mechanisms or functions, NR superfamily has been classified into seven families: NR1 (thyroid hormone like), NR2 (HNF4-like), NR3 (estrogen like), NR4 (nerve growth factor IB-like), NR5 (fushi tarazu-F1 like), NR6 (germ cell nuclear factor like), and NR0 (knirps or DAX like). With the avalanche of protein sequences generated in the postgenomic age, Scientists are facing the following challenging problems. Given an uncharacterized protein sequence, how can we identify whether it is a nuclear receptor? If it is, what family even subfamily it belongs to? To address these problems, many cheminformatics tools have been developed for nuclear receptor prediction. The current review is mainly focused on this field, including the functions, computational methods and limitations of these tools.

Generic Considerations in Ishiguro's Never Let Me Go

Never Let Me Go , Kazuo Ishiguro's novel about human clones raised for organs, is most often critiqued as science fiction or dystopian literature by the scholarly community. Yet focusing on the institutional implementation of cloning obscures a more critically fertile theme: sentiment. As demonstrated in this article, the novel has deeper affinities with sentimental and abolitionist literature of the eighteenth and nineteenth centuries than with speculative fiction. This generic reevaluation makes way for a broader critical approach to the novel's notion of humanness in the post-genome age.

ISNO-PseAAC: predict cysteine S-nitrosylation sites in proteins by incorporating position specific amino acid propensity into pseudo amino acid composition.

Posttranslational modifications (PTMs) of proteins are responsible for sensing and transducing signals to regulate various cellular functions and signaling events. S-nitrosylation (SNO) is one of the most important and universal PTMs. With the avalanche of protein sequences generated in the post-genomic age, it is highly desired to develop computational methods for timely identifying the exact SNO sites in proteins because this kind of information is very useful for both basic research and drug development. Here, a new predictor, called iSNO-PseAAC, was developed for identifying the SNO sites in proteins by incorporating the position-specific amino acid propensity (PSAAP) into the general form of pseudo amino acid composition (PseAAC). The predictor was implemented using the conditional random field (CRF) algorithm. As a demonstration, a benchmark dataset was constructed that contains 731 SNO sites and 810 non-SNO sites. To reduce the homology bias, none of these sites were derived from the proteins that had pairwise sequence identity to any other. It was observed that the overall cross-validation success rate achieved by iSNO-PseAAC in identifying nitrosylated proteins on an independent dataset was over 90%, indicating that the new predictor is quite promising. Furthermore, a user-friendly web-server for iSNO-PseAAC was established at http://app.aporc.org/iSNO-PseAAC/, by which users can easily obtain the desired results without the need to follow the mathematical equations involved during the process of developing the prediction method. It is anticipated that iSNO-PseAAC may become a useful high throughput tool for identifying the SNO sites, or at the very least play a complementary role to the existing methods in this area.

IRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition

Meiotic recombination is an important biological process. As a main driving force of evolution, recombination provides natural new combinations of genetic variations. Rather than randomly occurring across a genome, meiotic recombination takes place in some genomic regions (the so-called ‘hotspots’) with higher frequencies, and in the other regions (the so-called ‘coldspots’) with lower frequencies. Therefore, the information of the hotspots and coldspots would provide useful insights for in-depth studying of the mechanism of recombination and the genome evolution process as well. So far, the recombination regions have been mainly determined by experiments, which are both expensive and time-consuming. With the avalanche of genome sequences generated in the postgenomic age, it is highly desired to develop automated methods for rapidly and effectively identifying the recombination regions. In this study, a predictor, called ‘iRSpot-PseDNC’, was developed for identifying the recombination hotspots and coldspots. In the new predictor, the samples of DNA sequences are formulated by a novel feature vector, the so-called ‘pseudo dinucleotide composition’ (PseDNC), into which six local DNA structural properties, i.e. three angular parameters (twist, tilt and roll) and three translational parameters (shift, slide and rise), are incorporated. It was observed by the rigorous jackknife test that the overall success rate achieved by iRSpot-PseDNC was >82% in identifying recombination spots in Saccharomyces cerevisiae, indicating the new predictor is promising or at least may become a complementary tool to the existing methods in this area. Although the benchmark data set used to train and test the current method was from S. cerevisiae, the basic approaches can also be extended to deal with all the other genomes. Particularly, it has not escaped our notice that the PseDNC approach can be also used to study many other DNA-related problems. As a user-friendly web-server, iRSpot-PseDNC is freely accessible at http://lin.uestc.edu.cn/server/iRSpot-PseDNC.

Some remarks on predicting multi-label attributes in molecular biosystems

Many molecular biosystems and biomedical systems belong to the multi-label systems in which each of their constituent molecules possesses one or more than one function or feature, and hence needs one or more than one label to indicate its attribute(s). With the avalanche of biological sequences generated in the post genomic age, it is highly desirable to develop computational methods to timely and reliably identify their various kinds of attributes. Compared with the single-label systems, the multi-label systems are much more complicated and difficult to deal with. The current mini review focuses on the recent progresses in this area from both conceptual aspects and detailed mathematical formulations.

Computational Systems Biology

Computational Systems Biology

Editors’ Letter

Editors’ Letter

Opportunities for protein interaction network‐guided cellular engineering

As we move further into the postgenomics age where the mountain of systems biology-generated data keeps growing, as does the number of genomes that have been sequenced, we have the exciting opportunity to understand more deeply the biology of important systems, those that are amenable to genetic manipulation and metabolic engineering. This is, of course, if we can make 'head or tail' of what we have measured and use this for robust predictions. The use of modern mass spectrometry tools has greatly facilitated our understanding of which proteins are present in a particular phenotype, their relative and absolute abundances and their state of modifications. Coupled with modern bioinformatics and systems biology modelling tools, this has the opportunity of not just providing information and understanding but also to provide targets for engineering and suggest new genetic/metabolic designs. Cellular engineering, whether it be via metabolic engineering, synthetic biology or a combination of both approaches, offers exciting potential for biotechnological exploitation in fields as diverse as medicine and energy as well as fine and bulk chemicals production. At the heart of such effective designs, proteins' interactions with other proteins or with DNA will become increasingly important. In this work, we examine the work done until now in protein-protein interactions and how this network knowledge can be used to inform ambitious cellular engineering strategies. Some examples demonstrating small molecules/biofuels and biopharmaceuticals applications are presented.

A Comparison of Computational Methods for Identifying Virulence Factors

Bacterial pathogens continue to threaten public health worldwide today. Identification of bacterial virulence factors can help to find novel drug/vaccine targets against pathogenicity. It can also help to reveal the mechanisms of the related diseases at the molecular level. With the explosive growth in protein sequences generated in the postgenomic age, it is highly desired to develop computational methods for rapidly and effectively identifying virulence factors according to their sequence information alone. In this study, based on the protein-protein interaction networks from the STRING database, a novel network-based method was proposed for identifying the virulence factors in the proteomes of UPEC 536, UPEC CFT073, P. aeruginosa PAO1, L. pneumophila Philadelphia 1, C. jejuni NCTC 11168 and M. tuberculosis H37Rv. Evaluated on the same benchmark datasets derived from the aforementioned species, the identification accuracies achieved by the network-based method were around 0.9, significantly higher than those by the sequence-based methods such as BLAST, feature selection and VirulentPred. Further analysis showed that the functional associations such as the gene neighborhood and co-occurrence were the primary associations between these virulence factors in the STRING database. The high success rates indicate that the network-based method is quite promising. The novel approach holds high potential for identifying virulence factors in many other various organisms as well because it can be easily extended to identify the virulence factors in many other bacterial species, as long as the relevant significant statistical data are available for them.

Prediction of Protein Domain with mRMR Feature Selection and Analysis

The domains are the structural and functional units of proteins. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to develop effective methods for predicting the protein domains according to the sequences information alone, so as to facilitate the structure prediction of proteins and speed up their functional annotation. However, although many efforts have been made in this regard, prediction of protein domains from the sequence information still remains a challenging and elusive problem. Here, a new method was developed by combing the techniques of RF (random forest), mRMR (maximum relevance minimum redundancy), and IFS (incremental feature selection), as well as by incorporating the features of physicochemical and biochemical properties, sequence conservation, residual disorder, secondary structure, and solvent accessibility. The overall success rate achieved by the new method on an independent dataset was around 73%, which was about 28–40% higher than those by the existing method on the same benchmark dataset. Furthermore, it was revealed by an in-depth analysis that the features of evolution, codon diversity, electrostatic charge, and disorder played more important roles than the others in predicting protein domains, quite consistent with experimental observations. It is anticipated that the new method may become a high-throughput tool in annotating protein domains, or may, at the very least, play a complementary role to the existing domain prediction methods, and that the findings about the key features with high impacts to the domain prediction might provide useful insights or clues for further experimental investigations in this area. Finally, it has not escaped our notice that the current approach can also be utilized to study protein signal peptides, B-cell epitopes, HIV protease cleavage sites, among many other important topics in protein science and biomedicine.

Using Protein-protein Interaction Network Information to Predict the Subcellular Locations of Proteins in Budding Yeast

The information of protein subcellular localization is vitally important for in-depth understanding the intricate pathways that regulate biological processes at the cellular level. With the rapidly increasing number of newly found protein sequence in the Post-Genomic Age, many automated methods have been developed attempting to help annotate their subcellular locations in a timely manner. However, very few of them were developed using the protein-protein interaction (PPI) network information. In this paper, we have introduced a new concept called "tethering potential" by which the PPI information can be effectively fused into the formulation for protein samples. Based on such a network frame, a new predictor called Yeast-PLoc has been developed for identifying budding yeast proteins among their 19 subcellular location sites. Meanwhile, a purely sequence-based approach, called the "hybrid-property" method, is integrated into Yeast-PLoc as a fall-back to deal with those proteins without sufficient PPI information. The overall success rate by the jackknife test on the 4,683 yeast proteins in the training dataset was 70.25%. Furthermore, it was shown that the success rate by Yeast- PLoc on an independent dataset was remarkably higher than those by some other existing predictors, indicating that the current approach by incorporating the PPI information is quite promising. As a user-friendly web-server, Yeast-PLoc is freely accessible at http://yeastloc.biosino.org/.

INR-PhysChem: A Sequence-Based Predictor for Identifying Nuclear Receptors and Their Subfamilies via Physical-Chemical Property Matrix

Nuclear receptors (NRs) form a family of ligand-activated transcription factors that regulate a wide variety of biological processes, such as homeostasis, reproduction, development, and metabolism. Human genome contains 48 genes encoding NRs. These receptors have become one of the most important targets for therapeutic drug development. According to their different action mechanisms or functions, NRs have been classified into seven subfamilies. With the avalanche of protein sequences generated in the postgenomic age, we are facing the following challenging problems. Given an uncharacterized protein sequence, how can we identify whether it is a nuclear receptor? If it is, what subfamily it belongs to? To address these problems, we developed a predictor called iNR-PhysChem in which the protein samples were expressed by a novel mode of pseudo amino acid composition (PseAAC) whose components were derived from a physical-chemical matrix via a series of auto-covariance and cross-covariance transformations. It was observed that the overall success rate achieved by iNR-PhysChem was over 98% in identifying NRs or non-NRs, and over 92% in identifying NRs among the following seven subfamilies: NR1thyroid hormone like, NR2HNF4-like, NR3estrogen like, NR4nerve growth factor IB-like, NR5fushi tarazu-F1 like, NR6germ cell nuclear factor like, and NR0knirps like. These rates were derived by the jackknife tests on a stringent benchmark dataset in which none of protein sequences included has pairwise sequence identity to any other in a same subset. As a user-friendly web-server, iNR-PhysChem is freely accessible to the public at either http://www.jci-bioinfo.cn/iNR-PhysChem or http://icpr.jci.edu.cn/bioinfo/iNR-PhysChem. Also a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated mathematics involved in developing the predictor. It is anticipated that iNR-PhysChem may become a useful high throughput tool for both basic research and drug design.

An Ensemble Classifier for Eukaryotic Protein Subcellular Location Prediction Using Gene Ontology Categories and Amino Acid Hydrophobicity

With the rapid increase of protein sequences in the post-genomic age, it is challenging to develop accurate and automated methods for reliably and quickly predicting their subcellular localizations. Till now, many efforts have been tried, but most of which used only a single algorithm. In this paper, we proposed an ensemble classifier of KNN (k-nearest neighbor) and SVM (support vector machine) algorithms to predict the subcellular localization of eukaryotic proteins based on a voting system. The overall prediction accuracies by the one-versus-one strategy are 78.17%, 89.94% and 75.55% for three benchmark datasets of eukaryotic proteins. The improved prediction accuracies reveal that GO annotations and hydrophobicity of amino acids help to predict subcellular locations of eukaryotic proteins.

Functional classification of secreted proteins by position specific scoring matrix and auto covariance

Secreted proteins play pivotal biological regulatory roles in eukaryotic cells with different functions, and have the potential for disease biomarkers and protein therapeutics. Furthermore, the comprehension of their functions is not only indispensable for helping genome annotation, but also a supplement to the existing methods. As the rapid increase of protein sequences generated in the post-genomic age, it is urgent to develop a computational method to effectively annotate the function types of numerous newly discovered secreted proteins. In view of this, a support vector machine (SVM)-based predictor is proposed to classify secreted proteins with different functions in this paper, including cytokine, hormone, immune system protein, protease, and protease inhibitor. Here, proteins are represented by position specific scoring matrix (PSSM) and auto covariance (AC), which incorporates the evolution and the sequence-order information of proteins. When distinguishing the five types of secreted proteins, an accuracy of 83.2%, 88.9%, 86.1%, 90.9%, and 90.6% is achieved for cytokine, hormone, immune system protein, protease and protease inhibitor, respectively. Particularly, when performed on an independent test set of 325 proteins, the method also yielded a satisfactory accuracy of 91.2%. It shows that this method can be a complementary tool for identifying different functions of secreted proteins. The code and all datasets used in this article are freely available at http://cic.scu.edu.cn/bioinformatics/fcSecretP.zip.

Race and IQ in the postgenomic age: The microcephaly case

A convergence of contextual factors, technological platforms and research frameworks in the genomics of the human brain and cognition has generated a new postgenomic model for the study of race and IQ. Centered on the case study of Bruce T. Lahn's 2005 claims about the genomic basis of racial differences in brain size and IQ, this article maps the disciplinary terrain of this research, analyzes its central claims and examines the rigor of critical debate within the genomics community about new race and IQ research. New postgenomic race and IQ research, while displaying some continuities with previous eras of racial science, also differs in important ways, both contextual and conceptual. In particular, this new research draws on methods and hypotheses that are widely accepted across many fields of the contemporary molecular genetic sciences. This has implications for the forms of critical engagement that science studies scholars might pursue.

Using Pseudo Amino Acid Composition to Predict Protein Attributes Via Cellular Automata and Other Approaches

With the avalanche of protein sequences generated in the post-genomic age, many typical topics in bioinformatics, proteomics and system biology are relevant to identification of various attributes of uncharacterized proteins or need this kind of knowledge. Unfortunately, it is both time-consuming and costly to acquire the desired information by purely conducting biochemical experiments. Therefore, it is highly desirable to develop automated methods for fast and accurately identifying various attributes of proteins based on their sequences information alone. This is the convergence between bioinformatics and artificial intelligence techniques (AI). To establish powerful computational methods in this regard, one of the key procedures is to find an effective mathematical expression for the protein samples that can truly reflect their intrinsic correlation with the target to be predicted. To realize this, the pseudo amino acid (PseAA) composition or PseAAC was proposed. Stimulated by the concept of PseAAC, a series of different modes of PseAAC were developed to deal with proteins or proteins-related systems. The current review is mainly focused on those PseAAC modes that were formulated via cellular automata. By using some optimal space-time evolvement rules of cellular automata, a protein sequence can be represented by a unique image, the so-called cellular automata (CA) image or CAI. Many important features, which are deeply hidden in piles of long and complicated amino acid sequences, can be clearly revealed through their CAIs. It is anticipated that, owing to its impressive power, intuitiveness and relative simplicity, the CAI approach holds a great potential in bioinformatics and other related areas. Keywords: Pseudo amino acid composition, cellular automata, sequence-derived features, protein sequence image expression, protein attributes, Fast Fourer transform, G-protein coupled receptors, Complexity Factor, Amino Acids

Commentary: Reading Plomin and Daniels in the post-genomic age

Commentary: Reading Plomin and Daniels in the post-genomic age