Identification of structurally conserved residues of proteins in absence of structural homologs using neural network ensemble

Deciphering the associations between gene expression and copy number alteration using a sparse double Laplacian shrinkage approach

MOTIVATION

Both gene expression levels (GEs) and copy number alterations (CNAs) have important biological implications. GEs are partly regulated by CNAs, and much effort has been devoted to understanding their relations. The regulation analysis is challenging with one gene expression possibly regulated by multiple CNAs and one CNA potentially regulating the expressions of multiple genes. The correlations among GEs and among CNAs make the analysis even more complicated. The existing methods have limitations and cannot comprehensively describe the regulation.

RESULTS

A sparse double Laplacian shrinkage method is developed. It jointly models the effects of multiple CNAs on multiple GEs. Penalization is adopted to achieve sparsity and identify the regulation relationships. Network adjacency is computed to describe the interconnections among GEs and among CNAs. Two Laplacian shrinkage penalties are imposed to accommodate the network adjacency measures. Simulation shows that the proposed method outperforms the competing alternatives with more accurate marker identification. The Cancer Genome Atlas data are analysed to further demonstrate advantages of the proposed method.

AVAILABILITY AND IMPLEMENTATION

R code is available at http://works.bepress.com/shuangge/49/.

Defining and predicting structurally conserved regions in protein superfamilies

MOTIVATION

The structures of homologous proteins are generally better conserved than their sequences. This phenomenon is demonstrated by the prevalence of structurally conserved regions (SCRs) even in highly divergent protein families. Defining SCRs requires the comparison of two or more homologous structures and is affected by their availability and divergence, and our ability to deduce structurally equivalent positions among them. In the absence of multiple homologous structures, it is necessary to predict SCRs of a protein using information from only a set of homologous sequences and (if available) a single structure. Accurate SCR predictions can benefit homology modelling and sequence alignment.

RESULTS

Using pairwise DaliLite alignments among a set of homologous structures, we devised a simple measure of structural conservation, termed structural conservation index (SCI). SCI was used to distinguish SCRs from non-SCRs. A database of SCRs was compiled from 386 SCOP superfamilies containing 6489 protein domains. Artificial neural networks were then trained to predict SCRs with various features deduced from a single structure and homologous sequences. Assessment of the predictions via a 5-fold cross-validation method revealed that predictions based on features derived from a single structure perform similarly to ones based on homologous sequences, while combining sequence and structural features was optimal in terms of accuracy (0.755) and Matthews correlation coefficient (0.476). These results suggest that even without information from multiple structures, it is still possible to effectively predict SCRs for a protein. Finally, inspection of the structures with the worst predictions pinpoints difficulties in SCR definitions.

AVAILABILITY

The SCR database and the prediction server can be found at http://prodata.swmed.edu/SCR.

CONTACT

91huangi@gmail.com or grishin@chop.swmed.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics Online.

Identification in silico and experimental validation of novel phosphodiesterase 7 inhibitors with efficacy in experimental autoimmune encephalomyelitis mice

A neural network model has been developed to predict the inhibitory capacity of any chemical structure to be a phosphodiesterase 7 (PDE7) inhibitor, a new promising kind of drugs for the treatment of neurological disorders. The numerical definition of the structures was achieved using CODES program. Through the validation of this neural network model, a novel family of 5-imino-1,2,4-thiadiazoles (ITDZs) has been identified as inhibitors of PDE7. Experimental extensive biological studies have demonstrated the ability of ITDZs to inhibit PDE7 and to increase intracellular levels of cAMP. Among them, the derivative 15 showed a high in vitro potency with desirable pharmacokinetic profile (safe genotoxicity and blood brain barrier penetration). Administration of ITDZ 15 in an experimental autoimmune encephalomyelitis (EAE) mouse model results in a significant attenuation of clinical symptoms, showing the potential of ITDZs, especially compound 15, for the effective treatment of multiple sclerosis.

SMpred: a support vector machine approach to identify structural motifs in protein structure without using evolutionary information

Knowledge of three dimensional structure is essential to understand the function of a protein. Although the overall fold is made from the whole details of its sequence, a small group of residues, often called as structural motifs, play a crucial role in determining the protein fold and its stability. Identification of such structural motifs requires sufficient number of sequence and structural homologs to define conservation and evolutionary information. Unfortunately, there are many structures in the protein structure databases have no homologous structures or sequences. In this work, we report an SVM method, SMpred, to identify structural motifs from single protein structure without using sequence and structural homologs. SMpred method was trained and tested using 132 proteins domains containing 581 motifs. SMpred method achieved 78.79% accuracy with 79.06% sensitivity and 78.53% specificity. The performance of SMpred was evaluated with MegaMotifBase using 188 proteins containing 1161 motifs. Out of 1161 motifs, SMpred correctly identified 1503 structural motifs reported in MegaMotifBase. Further, we showed that SMpred is useful approach for the length deviant superfamilies and single member superfamilies. This result suggests the usefulness of our approach for facilitating the identification of structural motifs in protein structure in the absence of sequence and structural homologs. The dataset and executable for the SMpred algorithm is available at http://www3.ntu.edu.sg/home/EPNSugan/index_files/SMpred.htm.