BASE: a web service for providing compound-protein binding affinity prediction datasets with reduced similarity bias.

BACKGROUND: Deep learning-based drug-target affinity (DTA) prediction methods have shown impressive performance, despite a high number of training parameters relative to the available data. Previous studies have highlighted the presence of dataset bias by suggesting that models trained solely on protein or ligand structures may perform similarly to those trained on complex structures. However, these studies did not propose solutions and focused solely on analyzing complex structure-based models. Even when ligands are excluded, protein-only models trained on complex structures still incorporate some ligand information at the binding sites. Therefore, it is unclear whether binding affinity can be accurately predicted using only compound or protein features due to potential dataset bias. In this study, we expanded our analysis to comprehensive databases and investigated dataset bias through compound and protein feature-based methods using multilayer perceptron models. We assessed the impact of this bias on current prediction models and proposed the binding affinity similarity explorer (BASE) web service, which provides bias-reduced datasets. RESULTS: By analyzing eight binding affinity databases using multilayer perceptron models, we confirmed a bias where the compound-protein binding affinity can be accurately predicted using compound features alone. This bias arises because most compounds show consistent binding affinities due to high sequence or functional similarity among their target proteins. Our Uniform Manifold Approximation and Projection analysis based on compound fingerprints further revealed that low and high variation compounds do not exhibit significant structural differences. This suggests that the primary factor driving the consistent binding affinities is protein similarity rather than compound structure. We addressed this bias by creating datasets with progressively reduced protein similarity between the training and test sets, observing significant changes in model performance. We developed the BASE web service to allow researchers to download and utilize these datasets. Feature importance analysis revealed that previous models heavily relied on protein features. However, using bias-reduced datasets increased the importance of compound and interaction features, enabling a more balanced extraction of key features. CONCLUSIONS: We propose the BASE web service, providing both the affinity prediction results of existing models and bias-reduced datasets. These resources contribute to the development of generalized and robust predictive models, enhancing the accuracy and reliability of DTA predictions in the drug discovery process. BASE is freely available online at https://synbi2024.kaist.ac.kr/base .

Artificial intelligence-based identification of octenidine as a Bcl-xL inhibitor.

Apoptosis plays an essential role in maintaining cellular homeostasis and preventing cancer progression. Bcl-xL, an anti-apoptotic protein, is an important modulator of the mitochondrial apoptosis pathway and is a promising target for anticancer therapy. In this study, we identified octenidine as a novel Bcl-xL inhibitor through structural feature-based deep learning and molecular docking from a library of approved drugs. The NMR experiments demonstrated that octenidine binds to the Bcl-2 homology 3 (BH3) domain-binding hydrophobic region that consists of the BH1, BH2, and BH3 domains in Bcl-xL. A structural model of the Bcl-xL/octenidine complex revealed that octenidine binds to Bcl-xL in a similar manner to that of the well-known Bcl-2 family protein antagonist ABT-737. Using the NanoBiT protein-protein interaction system, we confirmed that the interaction between Bcl-xL and Bak-BH3 domains within cells was inhibited by octenidine. Furthermore, octenidine inhibited the proliferation of MCF-7 breast and H1299 lung cancer cells by promoting apoptosis. Taken together, our results shed light on a novel mechanism in which octenidine directly targets anti-apoptotic Bcl-xL to trigger mitochondrial apoptosis in cancer cells.

Tertiary RNA Folding-Targeted Drug Screening Strategy Using a Protein Nanopore.

Bacterial riboswitch RNAs are attractive targets for novel antibiotics against antibiotic-resistant superbacteria. Their binding to cognate metabolites is essential for the regulation of bacterial gene expression. Despite the importance of RNAs as therapeutic targets, the development of RNA-targeted, small-molecule drugs is limited by current biophysical methods. Here, we monitored the specific interaction between the adenine-sensing riboswitch aptamer domain (ARS) and adenine at the single-molecule level using α-hemolysin (αHL) nanopores. During adenine-induced tertiary folding, adenine-bound ARS intermediates exhibited characteristic nanopore events, including a two-level ionic current blockade and a ∼ 5.6-fold longer dwell time than that of free RNA. In a proof-of-concept experiment, tertiary RNA folding-targeted drug screening was performed using a protein nanopore, which resulted in the discovery of three new ARS-targeting hit compounds from a natural compound library. Taken together, these results reveal that αHL nanopores are a valuable platform for ultrasensitive, label-free, and single-molecule-based drug screening against therapeutic RNA targets.

Development and Validation of Tumor Immunogenicity Based Gene Signature for Skin Cancer Risk Stratification.

Melanoma is one of the most aggressive types of skin cancer, with significant heterogeneity in overall survival. Currently, tumor-node-metastasis (TNM) staging is insufficient to provide accurate survival prediction and appropriate treatment decision making for several types of tumors, such as those in melanoma patients. Therefore, the identification of more reliable prognosis biomarkers is urgently essential. Recent studies have shown that low immune cells infiltration is significantly associated with unfavorable clinical outcome in melanoma patients. Here we constructed a prognostic-related gene signature for melanoma risk stratification by quantifying the levels of several cancer hallmarks and identify the Wnt/ß-catenin activation pathway as a primary risk factor for low tumor immunity. A series of bioinformatics and statistical methods were combined and applied to construct a Wnt-immune-related prognosis gene signature. With this gene signature, we computed risk scores for individual patients that can predict overall survival. To evaluate the robustness of the result, we validated the signature in multiple independent GEO datasets. Finally, an overall survival-related nomogram was established based on the gene signature and clinicopathological features. The Wnt-immune-related prognostic risk score could better predict overall survival compared with standard clinicopathological features. Our results provide a comprehensive map of the oncogene-immune-related gene signature that can serve as valuable biomarkers for better clinical decision making.

Solution structure of MUL1-RING domain and its interaction with p53 transactivation domain.

Mitochondrial E3 ubiquitin ligase 1 (MUL1) is a multifunctional mitochondrial protein involved in various biological processes such as mitochondrial dynamics, cell growth, apoptosis, and mitophagy. MUL1 mediates the ubiquitylation of mitochondrial p53 for proteasomal degradation. Although the interaction of MUL1-RING domain with its substrate, p53, is a unique mechanism in RING-mediated ubiquitylation, the molecular basis of this process remains unknown. In this study, we determined the solution structure of the MUL1-RING domain and characterized its interaction with the p53 transactivation domain (p53-TAD) by nuclear magnetic resonance (NMR) spectroscopy. The overall structure of the MUL1-RING domain is similar to those of RING domains of other E3 ubiquitinases. The MUL1-RING domain adopts a ßßαß fold with three anti-parallel ß-strands and one α-helix, containing a canonical cross-brace motif for the ligation of two zinc ions. Through NMR chemical shift perturbation experiments, we determined the p53-TAD-binding site in the MUL1-RING domain and showed that the MUL1-RING domain interacts mainly with the p53-TAD2 subdomain composed of residues 39-57. Taken together, our results provide a molecular basis for the novel recognition mechanism of the p53-TAD substrate by the MUL1-RING domain.

A novel mechanism of irinotecan targeting MDM2 and Bcl-xL.

Irinotecan is a strong anticancer drug whose mechanism of action has been reported only for the inhibition of DNA topoisomerase I (Topo I) through its active metabolite SN-38. In this study, we present a new mechanism of Irinotecan which inhibits the activities of MDM2, an E3 ligase of tumour suppressor p53, and Bcl-xL, an anti-apoptotic protein, through direct binding. In our structure modelling study, Irinotecan could fit to the binding sites of MDM2 and Bcl-xL for their known drugs, Nutlin-3 and ABT-737, with a better binding affinity than to Topo I. The direct binding of Irinotecan to both proteins was confirmed through a NMR study. We further showed that Irinotecan increased the amount of p53 only in the presence of MDM2 and inhibited the physical interaction of Bcl-xL with Bim, a core pro-apoptotic protein. In addition, we demonstrated that Irinotecan induced the down regulation of proliferation and strong G2/M arrest in HCT116 colon cancer cells shortly after treatment. Collectively, we suggest a new mechanism of action for Irinotecan as a dual target inhibitor of MDM2 and Bcl-xL facilitating the anticancer activities mediated by p53 and Bcl-xL interaction partners.

JAB1 regulates CPNE1-related differentiation via direct binding to CPNE1 in HiB5 hippocampal progenitor cells.

Copine1 (CPNE1), has tandem C2 domains and an A domain. We previously demonstrated that CPNE1 directly induces neuronal differentiation via Protein kinase B (AKT) phosphorylation in the hippocampal progenitor cell line, HiB5. To better understand its cellular function, we carried out a yeast two-hybrid screening to find CPNE1 binding partners. Among the identified proteins, Jun activation domain-binding protein 1 (JAB1) appears to directly interact with CPNE1. Between CPNE1 and JAB1, the physical interaction was confirmed in vitro and in vivo. In addition the specific binding regions of CPNE1 and JAB1 was confirmed with truncated mutant assay. Furthermore, our results also demonstrate that AKT phosphorylation and expression of the neuronal marker protein are increased when JAB1 is overexpressed in CPNE1 high expressed HiB5 cells. Moreover, overexpression of both CPNE1 and JAB1 in HiB5 cells effectively increased neurite outgrowth. Collectively, our findings suggest that JAB1 activates the neuronal differentiation ability of CPNE1 through the binding of C2A domain in CPNE1 with MPN domain in JAB1.

Copine1 regulates neural stem cell functions during brain development.

Copine 1 (CPNE1) is a well-known phospholipid binding protein in plasma membrane of various cell types. In brain cells, CPNE1 is closely associated with AKT signaling pathway, which is important for neural stem cell (NSC) functions during brain development. Here, we investigated the role of CPNE1 in the regulation of brain NSC functions during brain development and determined its underlying mechanism. In this study, abundant expression of CPNE1 was observed in neural lineage cells including NSCs and immature neurons in human. With mouse brain tissues in various developmental stages, we found that CPNE1 expression was higher at early embryonic stages compared to postnatal and adult stages. To model developing brain in vitro, we used primary NSCs derived from mouse embryonic hippocampus. Our in vitro study shows decreased proliferation and multi-lineage differentiation potential in CPNE1 deficient NSCs. Finally, we found that the deficiency of CPNE1 downregulated mTOR signaling in embryonic NSCs. These data demonstrate that CPNE1 plays a key role in the regulation of NSC functions through the activation of AKT-mTOR signaling pathway during brain development.

Inhibition of euchromatin histone-lysine N-methyltransferase 2 sensitizes breast cancer cells to tumor necrosis factor-related apoptosis-inducing ligand through reactive oxygen species-mediated activating transcription factor 4-C/EBP homologous protein-death receptor 5 pathway activation.

Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) has been characterized as an anti-cancer therapeutic agent with prominent cancer cell selectivity over normal cells. However, breast cancer cells are generally resistant to TRAIL, thus limiting its therapeutic potential. In this study, we found that BIX-01294, a selective inhibitor of euchromatin histone methyltransferase 2/G9a, is a strong TRAIL sensitizer in breast cancer cells. The combination of BIX-01294 and TRAIL decreased cell viability and led to an increase in the annexin V/propidium iodide-positive cell population, DNA fragmentation, and caspase activation. BIX-01294 markedly increased death receptor 5 (DR5) expression, while silencing of DR5 using small interfering RNAs abolished the TRAIL-sensitizing effect of BIX-01294. Specifically, BIX-01294 induced C/EBP homologous protein (CHOP)-mediated DR5 gene transcriptional activation and DR5 promoter activation was induced by upregulation of the protein kinase R-like endoplasmic reticulum kinase-mediated activating transcription factor 4 (ATF4). Moreover, inhibition of reactive oxygen species by N-acetyl-L-cysteine efficiently blocked BIX-01294-induced DR5 upregulation by inhibiting ATF4/CHOP expression, leading to diminished sensitization to TRAIL. These findings suggest that BIX-01294 sensitizes breast cancer cells to TRAIL by upregulating ATF4/CHOP-dependent DR5 expression with a reactive oxygen species-dependent manner. Furthermore, combination treatment with BIX-01294 and TRAIL suppressed tumor growth and induced apoptosis in vivo. In conclusion, we found that epigenetic regulation can contribute to the development of resistance to cancer therapeutics such as TRAIL, and further studies of unfolded protein responses and the associated epigenetic regulatory mechanisms may lead to the discovery of new molecular targets for effective cancer therapy.

14-3-3γ regulates Copine1-mediated neuronal differentiation in HiB5 hippocampal progenitor cells.

Copine1 (CPNE1), known as a calcium-dependent membrane-binding protein, has tandem C2 domains and an A domain. We previously demonstrated that CPNE1 directly induces neuronal differentiation via Protein kinase B (AKT) phosphorylation in the hippocampal progenitor cell line, HiB5. To better understand its cellular function, we carried out a yeast two-hybrid screening to find CPNE1 binding partners. Among the identified proteins, 14-3-3γ appears to directly interact with CPNE1. Between CPNE1 and 14-3-3γ, the physical interaction as well as the specific binding regions of CPNE1 was confirmed in vitro and in vivo. Furthermore, among the seven 14-3-3 isotypes, only 14-3-3γ directly interacts with CPNE1. Our results also demonstrate that AKT phosphorylation, neurite outgrowth and expression of the neuronal marker protein are increased when 14-3-3γ is overexpressed in CPNE1 high expressed HiB5 cells. Furthermore, the neighboring Ser54 amino acids residue of C2A domain in CPNE1 has an important role in binding with 14-3-3γ, and in differentiation-related function of CPNE1. Moreover, mutation of Ser54 amino acids residue in CPNE1 effectively decreased association with 14-3-3γ and neuronal differentiation of HiB5 cells. Collectively, our findings indicate that 14-3-3γ regulates the differentiation ability of CPNE1 through the binding with C2A domain of CPNE1 in HiB5 cells.

Prediction of Novel Anoctamin1 (ANO1) Inhibitors Using 3D-QSAR Pharmacophore Modeling and Molecular Docking.

Recently, anoctamin1 (ANO1), a calcium-activated chloride channel, has been considered an important drug target, due to its involvement in various physiological functions, as well as its possibility for treatment of cancer, pain, diarrhea, hypertension, and asthma. Although several ANO1 inhibitors have been discovered by high-throughput screening, a discovery of new ANO1 inhibitors is still in the early phase, in terms of their potency and specificity. Moreover, there is no computational model to be able to identify a novel lead candidate of ANO1 inhibitor. Therefore, three-dimensional quantitative structure-activity relationship (3D-QSAR) pharmacophore modeling approach was employed for identifying the essential chemical features to be required in the inhibition of ANO1. The pharmacophore hypothesis 2 (Hypo2) was selected as the best model based on the highest correlation coefficient of prediction on the test set (0.909). Hypo2 comprised a hydrogen bond acceptor, a hydrogen bond donor, a hydrophobic, and a ring aromatic feature with good statistics of the total cost (73.604), the correlation coefficient of the training set (0.969), and the root-mean-square deviation (RMSD) value (0.946). Hypo2 was well assessed by the test set, Fischer randomization, and leave-one-out methods. Virtual screening of the ZINC database with Hypo2 retrieved the 580 drug-like candidates with good potency and ADMET properties. Finally, two compounds were selected as novel lead candidates of ANO1 inhibitor, based on the molecular docking score and the interaction analysis. In this study, the best pharmacophore model, Hypo2, with notable predictive ability was successfully generated, and two potential leads of ANO1 inhibitors were identified. We believe that these compounds and the 3D-QSAR pharmacophore model could contribute to discovering novel and potent ANO1 inhibitors in the future.

Drug repositioning for enzyme modulator based on human metabolite-likeness.

BACKGROUND: Recently, the metabolite-likeness of the drug space has emerged and has opened a new possibility for exploring human metabolite-like candidates in drug discovery. However, the applicability of metabolite-likeness in drug discovery has been largely unexplored. Moreover, there are no reports on its applications for the repositioning of drugs to possible enzyme modulators, although enzyme-drug relations could be directly inferred from the similarity relationships between enzyme's metabolites and drugs. METHODS: We constructed a drug-metabolite structural similarity matrix, which contains 1,861 FDA-approved drugs and 1,110 human intermediary metabolites scored with the Tanimoto similarity. To verify the metabolite-likeness measure for drug repositioning, we analyzed 17 known antimetabolite drugs that resemble the innate metabolites of their eleven target enzymes as the gold standard positives. Highly scored drugs were selected as possible modulators of enzymes for their corresponding metabolites. Then, we assessed the performance of metabolite-likeness with a receiver operating characteristic analysis and compared it with other drug-target prediction methods. We set the similarity threshold for drug repositioning candidates of new enzyme modulators based on maximization of the Youden's index. We also carried out literature surveys for supporting the drug repositioning results based on the metabolite-likeness. RESULTS: In this paper, we applied metabolite-likeness to repurpose FDA-approved drugs to disease-associated enzyme modulators that resemble human innate metabolites. All antimetabolite drugs were mapped with their known 11 target enzymes with statistically significant similarity values to the corresponding metabolites. The comparison with other drug-target prediction methods showed the higher performance of metabolite-likeness for predicting enzyme modulators. After that, the drugs scored higher than similarity score of 0.654 were selected as possible modulators of enzymes for their corresponding metabolites. In addition, we showed that drug repositioning results of 10 enzymes were concordant with the literature evidence. CONCLUSIONS: This study introduced a method to predict the repositioning of known drugs to possible modulators of disease associated enzymes using human metabolite-likeness. We demonstrated that this approach works correctly with known antimetabolite drugs and showed that the proposed method has better performance compared to other drug target prediction methods in terms of enzyme modulators prediction. This study as a proof-of-concept showed how to apply metabolite-likeness to drug repositioning as well as potential in further expansion as we acquire more disease associated metabolite-target protein relations.

Prediction of scaffold proteins based on protein interaction and domain architectures.

BACKGROUND: Scaffold proteins are known for being crucial regulators of various cellular functions by assembling multiple proteins involved in signaling and metabolic pathways. Identification of scaffold proteins and the study of their molecular mechanisms can open a new aspect of cellular systemic regulation and the results can be applied in the field of medicine and engineering. Despite being highlighted as the regulatory roles of dozens of scaffold proteins, there was only one known computational approach carried out so far to find scaffold proteins from interactomes. However, there were limitations in finding diverse types of scaffold proteins because their criteria were restricted to the classical scaffold proteins. In this paper, we will suggest a systematic approach to predict massive scaffold proteins from interactomes and to characterize the roles of scaffold proteins comprehensively. RESULTS: From a total of 10,419 basic scaffold protein candidates in protein interactomes, we classified them into three classes according to the structural evidences for scaffolding, such as domain architectures, domain interactions and protein complexes. Finally, we could define 2716 highly reliable scaffold protein candidates and their characterized functional features. To assess the accuracy of our prediction, the gold standard positive and negative data sets were constructed. We prepared 158 gold standard positive data and 844 gold standard negative data based on the functional information from Gene Ontology consortium. The precision, sensitivity and specificity of our testing was 80.3, 51.0, and 98.5 % respectively. Through the function enrichment analysis of highly reliable scaffold proteins, we could confirm the significantly enriched functions that are related to scaffold protein binding. We also identified functional association between scaffold proteins and their recruited proteins. Furthermore, we checked that the disease association of scaffold proteins is higher than kinases. CONCLUSIONS: In conclusion, we could predict larger volume of scaffold proteins and analyzed their functional characteristics. Deeper understandings about the roles of scaffold proteins from this study will provide a higher opportunity to find therapeutic or engineering applications of scaffold proteins using their functional characteristics.

Ubiquitination-dependent degradation of p73 by the mitochondrial E3 ubiquitin ligase Hades.

p73 is a member of the p53 family of transcription factors which plays an essential role in tumor suppression. p73 is associated with the sensitivity of cancer cells to chemotherapy and the prognosis of many cancers. In this study, we showed the ubiquitination-dependent degradation of p73 by the mitochondrial E3 ubiquitin ligase Hades. First, the binding between p73 and Hades was identified by co-immunoprecipitation experiments, and it was found that the Hades RING-finger domain mediates the interaction with p73. Immunofluorescence analysis showed that p73 moves to the mitochondria and colocalizes with Hades during etoposide-induced apoptosis. By performing in vivo and in vitro ubiquitination assays, we observed that the Hades RING-finger domain promotes ubiquitination of p73. Finally, it was shown that SiRNA-mediated depletion of Hades stabilizes p73. Taken together, our results showed that Hades mediates the ubiquitination-dependent degradation of mitochondrial p73 under apoptotic conditions. These findings suggest that Hades-mediated p73 ubiquitination is a novel regulatory mechanism for the exonuclear function of p73.

Disease association and inter-connectivity analysis of human brain specific co-expressed functional modules.

BACKGROUND: In the recent studies, it is suggested that the analysis of transcriptomic change of functional modules instead of individual genes would be more effective for system-wide identification of cellular functions. This could also provide a new possibility for the better understanding of difference between human and chimpanzee. RESULTS: In this study, we analyzed to find molecular characteristics of human brain functions from the difference of transcriptome between human and chimpanzee's brain using the functional module-centric co-expression analysis. We performed analysis of brain disease association and systems-level connectivity of species-specific co-expressed functional modules. CONCLUSIONS: Throughout the analyses, we found human-specific functional modules and significant overlap between their genes in known brain disease genes, suggesting that human brain disorder could be mediated by the perturbation of modular activities emerged in human brain specialization. In addition, the human-specific modules having neurobiological functions exhibited higher networking than other functional modules. This finding suggests that the expression of neural functions are more connected than other functions, and the resulting high-order brain functions could be identified as a result of consolidated inter-modular gene activities. Our result also showed that the functional module based transcriptome analysis has a potential to expand molecular understanding of high-order complex functions like cognitive abilities and brain disorders.

Discovering transnosological molecular basis of human brain diseases using biclustering analysis of integrated gene expression data.

BACKGROUND: It has been reported that several brain diseases can be treated as transnosological manner implicating possible common molecular basis under those diseases. However, molecular level commonality among those brain diseases has been largely unexplored. Gene expression analyses of human brain have been used to find genes associated with brain diseases but most of those studies were restricted either to an individual disease or to a couple of diseases. In addition, identifying significant genes in such brain diseases mostly failed when it used typical methods depending on differentially expressed genes. RESULTS: In this study, we used a correlation-based biclustering approach to find coexpressed gene sets in five neurodegenerative diseases and three psychiatric disorders. By using biclustering analysis, we could efficiently and fairly identified various gene sets expressed specifically in both single and multiple brain diseases. We could find 4,307 gene sets correlatively expressed in multiple brain diseases and 3,409 gene sets exclusively specified in individual brain diseases. The function enrichment analysis of those gene sets showed many new possible functional bases as well as neurological processes that are common or specific for those eight diseases. CONCLUSIONS: This study introduces possible common molecular bases for several brain diseases, which open the opportunity to clarify the transnosological perspective assumed in brain diseases. It also showed the advantages of correlation-based biclustering analysis and accompanying function enrichment analysis for gene expression data in this type of investigation.

E3Net: a system for exploring E3-mediated regulatory networks of cellular functions.

Ubiquitin-protein ligase (E3) is a key enzyme targeting specific substrates in diverse cellular processes for ubiquitination and degradation. The existing findings of substrate specificity of E3 are, however, scattered over a number of resources, making it difficult to study them together with an integrative view. Here we present E3Net, a web-based system that provides a comprehensive collection of available E3-substrate specificities and a systematic framework for the analysis of E3-mediated regulatory networks of diverse cellular functions. Currently, E3Net contains 2201 E3s and 4896 substrates in 427 organisms and 1671 E3-substrate specific relations between 493 E3s and 1277 substrates in 42 organisms, extracted mainly from MEDLINE abstracts and UniProt comments with an automatic text mining method and additional manual inspection and partly from high throughput experiment data and public ubiquitination databases. The significant functions and pathways of the extracted E3-specific substrate groups were identified from a functional enrichment analysis with 12 functional category resources for molecular functions, protein families, protein complexes, pathways, cellular processes, cellular localization, and diseases. E3Net includes interactive analysis and navigation tools that make it possible to build an integrative view of E3-substrate networks and their correlated functions with graphical illustrations and summarized descriptions. As a result, E3Net provides a comprehensive resource of E3s, substrates, and their functional implications summarized from the regulatory network structures of E3-specific substrate groups and their correlated functions. This resource will facilitate further in-depth investigation of ubiquitination-dependent regulatory mechanisms. E3Net is freely available online at http://pnet.kaist.ac.kr/e3net.

Biclustering for the comprehensive search of correlated gene expression patterns using clustered seed expansion.

BACKGROUND: In a functional analysis of gene expression data, biclustering method can give crucial information by showing correlated gene expression patterns under a subset of conditions. However, conventional biclustering algorithms still have some limitations to show comprehensive and stable outputs. RESULTS: We propose a novel biclustering approach called "BIclustering by Correlated and Large number of Individual Clustered seeds (BICLIC)" to find comprehensive sets of correlated expression patterns in biclusters using clustered seeds and their expansion with correlation of gene expression. BICLIC outperformed competing biclustering algorithms by completely recovering implanted biclusters in simulated datasets with various types of correlated patterns: shifting, scaling, and shifting-scaling. Furthermore, in a real yeast microarray dataset and a lung cancer microarray dataset, BICLIC found more comprehensive sets of biclusters that are significantly enriched to more diverse sets of biological terms than those of other competing biclustering algorithms. CONCLUSIONS: BICLIC provides significant benefits in finding comprehensive sets of correlated patterns and their functional implications from a gene expression dataset.

HMPAS: Human Membrane Protein Analysis System.

BACKGROUND: Membrane proteins perform essential roles in diverse cellular functions and are regarded as major pharmaceutical targets. The significance of membrane proteins has led to the developing dozens of resources related with membrane proteins. However, most of these resources are built for specific well-known membrane protein groups, making it difficult to find common and specific features of various membrane protein groups. METHODS: We collected human membrane proteins from the dispersed resources and predicted novel membrane protein candidates by using ortholog information and our membrane protein classifiers. The membrane proteins were classified according to the type of interaction with the membrane, subcellular localization, and molecular function. We also made new feature dataset to characterize the membrane proteins in various aspects including membrane protein topology, domain, biological process, disease, and drug. Moreover, protein structure and ICD-10-CM based integrated disease and drug information was newly included. To analyze the comprehensive information of membrane proteins, we implemented analysis tools to identify novel sequence and functional features of the classified membrane protein groups and to extract features from protein sequences. RESULTS: We constructed HMPAS with 28,509 collected known membrane proteins and 8,076 newly predicted candidates. This system provides integrated information of human membrane proteins individually and in groups organized by 45 subcellular locations and 1,401 molecular functions. As a case study, we identified associations between the membrane proteins and diseases and present that membrane proteins are promising targets for diseases related with nervous system and circulatory system. A web-based interface of this system was constructed to facilitate researchers not only to retrieve organized information of individual proteins but also to use the tools to analyze the membrane proteins. CONCLUSIONS: HMPAS provides comprehensive information about human membrane proteins including specific features of certain membrane protein groups. In this system, user can acquire the information of individual proteins and specified groups focused on their conserved sequence features, involved cellular processes, and diseases. HMPAS may contribute as a valuable resource for the inference of novel cellular mechanisms and pharmaceutical targets associated with the human membrane proteins. HMPAS is freely available at http://fcode.kaist.ac.kr/hmpas.

Finding type 2 diabetes causal single nucleotide polymorphism combinations and functional modules from genome-wide association data.

BACKGROUND: Due to the low statistical power of individual markers from a genome-wide association study (GWAS), detecting causal single nucleotide polymorphisms (SNPs) for complex diseases is a challenge. SNP combinations are suggested to compensate for the low statistical power of individual markers, but SNP combinations from GWAS generate high computational complexity. METHODS: We aim to detect type 2 diabetes (T2D) causal SNP combinations from a GWAS dataset with optimal filtration and to discover the biological meaning of the detected SNP combinations. Optimal filtration can enhance the statistical power of SNP combinations by comparing the error rates of SNP combinations from various Bonferroni thresholds and p-value range-based thresholds combined with linkage disequilibrium (LD) pruning. T2D causal SNP combinations are selected using random forests with variable selection from an optimal SNP dataset. T2D causal SNP combinations and genome-wide SNPs are mapped into functional modules using expanded gene set enrichment analysis (GSEA) considering pathway, transcription factor (TF)-target, miRNA-target, gene ontology, and protein complex functional modules. The prediction error rates are measured for SNP sets from functional module-based filtration that selects SNPs within functional modules from genome-wide SNPs based expanded GSEA. RESULTS: A T2D causal SNP combination containing 101 SNPs from the Wellcome Trust Case Control Consortium (WTCCC) GWAS dataset are selected using optimal filtration criteria, with an error rate of 10.25%. Matching 101 SNPs with known T2D genes and functional modules reveals the relationships between T2D and SNP combinations. The prediction error rates of SNP sets from functional module-based filtration record no significance compared to the prediction error rates of randomly selected SNP sets and T2D causal SNP combinations from optimal filtration. CONCLUSIONS: We propose a detection method for complex disease causal SNP combinations from an optimal SNP dataset by using random forests with variable selection. Mapping the biological meanings of detected SNP combinations can help uncover complex disease mechanisms.