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A large set of three-dimensional structures of 264 protein-protein complexes with known nonsynonymous single nucleotide polymorphisms (nsSNPs) at the interface was built using homology-based methods. The nsSNPs were mapped on the proteins' structures and their effect on the binding energy was investigated with CHARMM force field and continuum electrostatic calculations. Two sets of nsSNPs were studied: disease annotated Online Mendelian Inheritance in Man (OMIM) and nonannotated (non-OMIM). It was demonstrated that OMIM nsSNPs tend to destabilize the electrostatic component of the binding energy, in contrast with the effect of non-OMIM nsSNPs. In addition, it was shown that the change of the binding energy upon amino acid substitutions is not related to the conservation of the net charge, hydrophobicity, or hydrogen bond network at the interface. The results indicate that, generally, the effect of nsSNPs on protein-protein interactions cannot be predicted from amino acids' physico-chemical properties alone, since in many cases a substitution of a particular residue with another amino acid having completely different polarity or hydrophobicity had little effect on the binding energy. Analysis of sequence conservation showed that nsSNP at highly conserved positions resulted in a large variance of the binding energy changes. In contrast, amino acid substitutions corresponding to nsSNPs at nonconserved positions, on average, were not found to have a large effect on binding affinity. pKa calculations were performed and showed that amino acid substitutions could change the wild-type proton uptake/release and thus resulting in different pH-dependence of the binding energy.

Schizophrenia (SCZ) is a serious psychiatric disorder that affects 1% of general population and places a heavy burden worldwide. The underlying genetic mechanism of SCZ remains unknown, but studies indicate that the disease is associated with a global gene expression disturbance across many genes. Next-generation sequencing, particularly of RNA sequencing (RNA-Seq), provides a powerful genome-scale technology to investigate the pathological processes of SCZ. RNA-Seq has been used to analyze the gene expressions and identify the novel splice isoforms and rare transcripts associated with SCZ. This paper provides an overview on the genetics of SCZ, the advantages of RNA-Seq for transcriptome analysis, the accomplishments of RNA-Seq in SCZ cohorts, and the applications of induced pluripotent stem cells and RNA-Seq in SCZ research.

field with online, open access to highly relevant scholarly articles by leading international researchers. In a field where the literature is ever-expanding, researchers increasingly need access to up-to-date, high quality scholarly articles on areas of specific contemporary interest. This supplement aims to address this by presenting high-quality articles that allow readers to distinguish the signal from the noise. The editor in chief hopes that through this effort, practitioners and researchers will be aided in finding answers to some of the most complex and pressing issues of our time. At the discretion of the guest editors other articles on other relevant topics within the scope of the supplement may be included. This supplement is intended to focus on RNA sequence analysis. Topics of interest include, but are not limited to, the following: Data preprocessing and mappingHybrid Computing § § Gene expression § § Novel genes and transcripts discovery and De novo § § transcript assembly Alternative splicing § § RNA editing § § Single nucleotide variation discovery § § Fusion gene detection § § Coexpression networks § § Pathway and master regulator analysis § §

We report here the cloning and tissue distribution of the human B3GALT7 gene, a member of the β1,3-Glycosyltransferase family, structurally related to the β1,3-Galactosyltransferase family and β1,3-N-acetylglucosaminyltransferase family, isolated from a human lung cDNA library. B3GALT7 is mapped to chromosome 19q13.2 by browsing the UCSC genomic database. It contains an ORF with length of 1191bp, encoding a protein with a signal peptide sequence and galactosyl-T domain, and its molecular weight and isoelectric point is predicted to be 43.3 kDa and 8.67 respectively. The molecular weight of the protein when expressed in E. coli corresponded to that expected. Northern blotting showed that B3GALT7 was highly expressed in lung, throat and ileum, whereas the expression level was low in tongue, breast, uteri, testis. In addition, it was also demonstrated that B3GALT7 is differentially transcribed in human tumor cell lines. Published in 2003.

Applying in silico simulations and in vitro experiments, the amino acid proline was proved to have a profound influence on Streptomyces griseus trypsinogen, and the hydrogen bond between H 57 and D 102 was found to be crucial for trypsin activity. By introducing an artificial propeptide, IVEF, the titer of trypsin was increased 6.71-fold.

Many studies have shown that missense mutations might play an important role in carcinogenesis. However, the extent to which cancer mutations might affect biomolecular interactions remains unclear. Here, we map glioblastoma missense mutations on the human protein interactome, model the structures of affected protein complexes and decipher the effect of mutations on protein-protein, protein-nucleic acid and protein-ion binding interfaces. Although some missense mutations over-stabilize protein complexes, we found that the overall effect of mutations is destabilizing, mostly affecting the electrostatic component of binding energy. We also showed that mutations on interfaces resulted in more drastic changes of amino acid physico-chemical properties than mutations occurring outside the interfaces. Analysis of glioblastoma mutations on interfaces allowed us to stratify cancer-related interactions, identify potential driver genes, and propose two dozen additional cancer biomarkers, including those specific to functions of the nervous system. Such an analysis also offered insight into the molecular mechanism of the phenotypic outcomes of mutations, including effects on complex stability, activity, binding and turnover rate. As a result of mutated protein and gene network analysis, we observed that interactions of proteins with mutations mapped on interfaces had higher bottleneck properties compared to interactions with mutations elsewhere on the protein or unaffected interactions. Such observations suggest that genes with mutations directly affecting protein binding properties are preferably located in central network positions and may influence critical nodes and edges in signal transduction networks.

A structure-based approach is described for predicting the effects of amino acid substitutions on protein function. Structures were predicted using a homology modelling method. Folding and binding energy differences between wild-type and mutant structures were computed to quantitatively assess the effects of amino acid substitutions on protein stability and protein protein interaction, respectively. We demonstrated that pathogenic mutations at the interaction interface could affect binding energy and destabilise protein complex, whereas mutations at the non-interface might reduce folding energy and destabilise monomer structure. The results suggest that the structure-based analysis can provide useful information for understanding the molecular mechanisms of diseases.

Protein sumoylation play essential roles in the eukaryotic cell and any alterations in this process may cause various human diseases. This paper describes a new machine learning approach for the sumoylation site prediction from protein sequence information. Random Forests (RFs), which can handle a large number of input variables and avoid model overfitting, were trained with the data collected from literature. To construct accurate classifiers, forty sequence features were selected for input vector encoding. The results suggested that RF classifier performance was affected by the sequence context of sumoylation sites, and the use of eighteen residues with the core motif ψKXE in the middle gave the highest performance (ROC AUC = 0.9328). The RF classifiers were also found to outperform support vector machine (SVM) models on the same dataset. Thus, the RF algorithm appears to be the best choice for accurate prediction of protein sumoylation sites from sequence features.

The Snyder-Robinson syndrome is caused by missense mutations in the spermine sythase gene that encodes a protein (SMS) of 529 amino acids. Here we investigate, in silico, the molecular effect of three missense mutations, c.267G>A (p.G56S), c.496T>G (p.V132G), and c.550T>C (p.I150T) in SMS that were clinically identified to cause the disease. Single-point energy calculations, molecular dynamics simulations, and pKa calculations revealed the effects of these mutations on SMS's stability, flexibility, and interactions. It was predicted that the catalytic residue, Asp276, should be protonated prior binding the substrates. The pKa calculations indicated the p.I150T mutation causes pKa changes with respect to the wild-type SMS, which involve titratable residues interacting with the S-methyl-5'-thioadenosine (MTA) substrate. The p.I150T missense mutation was also found to decrease the stability of the C-terminal domain and to induce structural changes in the vicinity of the MTA binding site. The other two missense mutations, p.G56S and p.V132G, are away from active site and do not perturb its wild-type properties, but affect the stability of both the monomers and the dimer. Specifically, the p.G56S mutation is predicted to greatly reduce the affinity of monomers to form a dimer, and therefore should have a dramatic effect on SMS function because dimerization is essential for SMS activity.

Protein destabilization is a common mechanism by which amino acid substitutions cause human diseases. In this study, a new machine learning method has been developed for sequence-based prediction of protein stability changes upon single amino acid substitutions. Support vector machines were trained with data from experimental studies on the free energy change of protein stability upon mutations. To construct accurate classifiers, twenty biological features were examined for input vector encoding. It was shown that classifier performance varied significantly by the use of different features. The most accurate classifier was constructed using a combination of several biological features. This classifier achieved an overall accuracy of 82.24% with 75.24% sensitivity and 85.36% specificity. Predictive results at this level of accuracy may be used in human genetic studies to distinguish between deleterious and tolerant alterations in disease candidate genes.

Almost all (99.9%) nucleotide bases are exactly the same in all people, however, the remaining 0.1% account for about 1.4 million locations where single-base DNA differences/polymorphisms (SNPs) occur in humans. Some of these SNPs, called non-synonymous SNPs (nsSNPs), result in a change of the amino acid sequences of the corresponding proteins affecting protein functions and interactions. This review summarizes the plausible mechanisms that nsSNPs may affect the normal cellular function. It outlines the approaches that have been developed in the past to predict the effects caused by nsSNPs with special emphasis on the methods that use structural information. The review provides systematic information on the available resources for predicting the effects of nsSNPs and includes a comprehensive list of existing SNP databases and their features. While nsSNPs resulting in amino acid substitution in the core of a protein may affect protein stability irreversibly, the effect of an nsSNP resulting to a mutation at the surface of a protein or at the interface of protein-protein complexes, could, in principle be, subject of drug therapy. The importance of understanding the effects caused by nsSNP mutations at the protein-protein and protein-DNA interfaces is outlined.

Background Protein destabilization is a common mechanism by which amino acid substitutions cause human diseases. Although several machine learning methods have been reported for predicting protein stability changes upon amino acid substitutions, the previous studies did not utilize relevant sequence features representing biological knowledge for classifier construction. Results In this study, a new machine learning method has been developed for sequence feature-based prediction of protein stability changes upon amino acid substitutions. Support vector machines were trained with data from experimental studies on the free energy change of protein stability upon mutations. To construct accurate classifiers, twenty sequence features were examined for input vector encoding. It was shown that classifier performance varied significantly by using different sequence features. The most accurate classifier in this study was constructed using a combination of six sequence features. This classifier achieved an overall accuracy of 84.59% with 70.29% sensitivity and 90.98% specificity. Conclusions Relevant sequence features can be used to accurately predict protein stability changes upon amino acid substitutions. Predictive results at this level of accuracy may provide useful information to distinguish between deleterious and tolerant alterations in disease candidate genes. To make the classifier accessible to the genetics research community, we have developed a new web server, called MuStab (http://bioinfo.ggc.org/mustab/).

Background Understanding how genes are expressed specifically in particular tissues is a fundamental question in developmental biology. Many tissue-specific genes are involved in the pathogenesis of complex human diseases. However, experimental identification of tissue-specific genes is time consuming and difficult. The accurate predictions of tissue-specific gene targets could provide useful information for biomarker development and drug target identification. Results In this study, we have developed a machine learning approach for predicting the human tissue-specific genes using microarray expression data. The lists of known tissue-specific genes for different tissues were collected from UniProt database, and the expression data retrieved from the previously compiled dataset according to the lists were used for input vector encoding. Random Forests (RFs) and Support Vector Machines (SVMs) were used to construct accurate classifiers. The RF classifiers were found to outperform SVM models for tissue-specific gene prediction. The results suggest that the candidate genes for brain or liver specific expression can provide valuable information for further experimental studies. Our approach was also applied for identifying tissue-selective gene targets for different types of tissues. Conclusions A machine learning approach has been developed for accurately identifying the candidate genes for tissue specific/selective expression. The approach provides an efficient way to select some interesting genes for developing new biomedical markers and improve our knowledge of tissue-specific expression.