BAPCP: A comprehensive and user-friendly web tool for identifying biomarkers from protein microarray technologies.

BACKGROUND AND OBJECTIVE: Proteome microarrays are one of the popular high-throughput screening methods for large-scale investigation of protein interactions in cells. These interactions can be measured on protein chips when coupled with fluorescence-labeled probes, helping indicate potential biomarkers or discover drugs. Several computational tools were developed to help analyze the protein chip results. However, existing tools fail to provide a user-friendly interface for biologists and present only one or two data analysis methods suitable for limited experimental designs, restricting the use cases. METHODS: In order to facilitate the biomarker examination using protein chips, we implemented a user-friendly and comprehensive web tool called BAPCP (Biomarker Analysis tool for Protein Chip Platforms) in this research to deal with diverse chip data distributions. RESULTS: BAPCP is well integrated with standard chip result files and includes 7 data normalization methods and 7 custom-designed quality control/differential analysis filters for biomarker extraction among experiment groups. Moreover, it can handle cost-efficient chip designs that repeat several blocks/samples within one single slide. Using experiments of the human coronavirus (HCoV) protein microarray and the E. coli proteome chip that helps study the immune response of Kawasaki disease as examples, we demonstrated that BAPCP can accelerate the time-consuming week-long manual biomarker identification process to merely 3 min. CONCLUSIONS: The developed BAPCP tool provides substantial analysis support for protein interaction studies and conforms to the necessity of expanding computer usage and exchanging information in bioscience and medicine. The web service of BAPCP is available at https://cosbi.ee.ncku.edu.tw/BAPCP/.

Identifying Human miRNA Target Sites via Learning the Interaction Patterns between miRNA and mRNA Segments.

miRNAs (microRNAs) target specific mRNA (messenger RNA) sites to regulate their translation expression. Although miRNA targeting can rely on seed region base pairing, animal miRNAs, including human miRNAs, typically cooperate with several cofactors, leading to various noncanonical pairing rules. Therefore, identifying the binding sites of animal miRNAs remains challenging. Because experiments for mapping miRNA targets are costly, computational methods are preferred for extracting potential miRNA-mRNA fragment binding pairs first. However, existing prediction tools can have significant false positives due to the prevalent noncanonical miRNA binding behaviors and the information-biased training negative sets that were used while constructing these tools. To overcome these obstacles, we first prepared an information-balanced miRNA binding pair ground-truth data set. A miRNA-mRNA interaction-aware model was then designed to help identify miRNA binding events. On the test set, our model (auROC = 94.4%) outperformed existing models by at least 2.8% in auROC. Furthermore, we showed that this model can suggest potential binding patterns for miRNA-mRNA sequence interacting pairs. Finally, we made the prepared data sets and the designed model available at http://cosbi2.ee.ncku.edu.tw/mirna_binding/download.

Structure-based pathogenicity relationship identifier for predicting effects of single missense variants and discovery of higher-order cancer susceptibility clusters of mutations.

We report the structure-based pathogenicity relationship identifier (SPRI), a novel computational tool for accurate evaluation of pathological effects of missense single mutations and prediction of higher-order spatially organized units of mutational clusters. SPRI can effectively extract properties determining pathogenicity encoded in protein structures, and can identify deleterious missense mutations of germ line origin associated with Mendelian diseases, as well as mutations of somatic origin associated with cancer drivers. It compares favorably to other methods in predicting deleterious mutations. Furthermore, SPRI can discover spatially organized pathogenic higher-order spatial clusters (patHOS) of deleterious mutations, including those of low recurrence, and can be used for discovery of candidate cancer driver genes and driver mutations. We further demonstrate that SPRI can take advantage of AlphaFold2 predicted structures and can be deployed for saturation mutation analysis of the whole human proteome.

YMLA: A comparative platform to carry out functional enrichment analysis for multiple gene lists in yeast.

Comparative analysis among multiple gene lists on their functional features is now a routine task due to the advancement of high-throughput experiments. Several enrichment analysis tools were developed in the past. However, these tools mainly focus on one gene list and contain only gene ontology or interaction features. What makes it worse, comparative investigation and customized feature set reanalysis are still unavailable. Therefore, we constructed the YMLA (Yeast Multiple List Analyzer) platform in this research. YMLA includes 39 yeast features and facilitates comparative analysis among multiple gene lists via tabular views, heatmaps, and network plots. Moreover, the customized feature set reanalysis function was implemented in YMLA to help form mechanism hypotheses based on a selected enriched feature subset. We demonstrated the biological applicability of YMLA via example lists consisting of genes with top/bottom translation efficiency values. The analysis results provided by YMLA reveal novel facts consistent with previous experiments. YMLA is available at https://cosbi7.ee.ncku.edu.tw/YMLA/.

Structural and functional insights into ABHD5, a ligand-regulated lipase co-activator.

Alpha/beta hydrolase domain-containing protein 5 (ABHD5) is a highly conserved protein that regulates various lipid metabolic pathways via interactions with members of the perilipin (PLIN) and Patatin-like phospholipase domain-containing protein (PNPLA) protein families. Loss of function mutations in ABHD5 result in Chanarin-Dorfman Syndrome (CDS), characterized by ectopic lipid accumulation in numerous cell types and severe ichthyosis. Recent data demonstrates that ABHD5 is the target of synthetic and endogenous ligands that might be therapeutic beneficial for treating metabolic diseases and cancers. However, the structural basis of ABHD5 functional activities, such as protein-protein interactions and ligand binding is presently unknown. To address this gap, we constructed theoretical structural models of ABHD5 by comparative modeling and topological shape analysis to assess the spatial patterns of ABHD5 conformations computed in protein dynamics. We identified functionally important residues on ABHD5 surface for lipolysis activation by PNPLA2, lipid droplet targeting and PLIN-binding. We validated the computational model by examining the effects of mutating key residues in ABHD5 on an array of functional assays. Our integrated computational and experimental findings provide new insights into the structural basis of the diverse functions of ABHD5 as well as pathological mutations that result in CDS.

Identifying piRNA targets on mRNAs in C. elegans using a deep multi-head attention network.

BACKGROUND: Piwi-interacting RNAs (piRNAs) are the small non-coding RNAs (ncRNAs) that silence genomic transposable elements. And researchers found out that piRNA also regulates various endogenous transcripts. However, there is no systematic understanding of the piRNA binding patterns and how piRNA targets genes. While various prediction methods have been developed for other similar ncRNAs (e.g., miRNAs), piRNA holds distinctive characteristics and requires its own computational model for binding target prediction. RESULTS: Recently, transcriptome-wide piRNA binding events in C. elegans were probed by PRG-1 CLASH experiments. Based on the probed piRNA-messenger RNAs (mRNAs) binding pairs, in this research, we devised the first deep learning architecture based on multi-head attention to computationally identify piRNA targeting mRNA sites. In the devised deep network, the given piRNA and mRNA segment sequences are first one-hot encoded and undergo a combined operation of convolution and squeezing-extraction to unravel motif patterns. And we incorporate a novel multi-head attention sub-network to extract the hidden piRNA binding rules that can simulate the biological piRNA target recognition process. Finally, the true piRNA-mRNA binding pairs are identified by a deep fully connected sub-network. Our model obtains a supreme discriminatory power of AUC [Formula: see text] 93.3% on an independent test set and successfully extracts the verified binding pattern of a synthetic piRNA. These results demonstrated that the devised model achieves high prediction performance and suggests testable potential biological piRNA binding rules. CONCLUSIONS: In this research, we developed the first deep learning method to identify piRNA targeting sites on C. elegans mRNAs. And the developed deep learning method is demonstrated to be of high accuracy and can provide biological insights into piRNA-mRNA binding patterns. The piRNA binding target identification network can be downloaded from http://cosbi2.ee.ncku.edu.tw/data_download/piRNA_mRNA_binding .

Cancer DEIso: An integrative analysis platform for investigating differentially expressed gene-level and isoform-level human cancer markers.

Transcript isoforms regulated by alternative splicing can substantially impact carcinogenesis, leading to a need to obtain clues for both gene differential expression and malfunctions of isoform distributions in cancer studies. The Cancer Genome Atlas (TCGA) project was launched in 2008 to collect cancer-related genome mutation raw data from the population. While many repositories tried to add insights into the raw data in TCGA, no existing database provides both comprehensive gene-level and isoform-level cancer stage marker investigation and survival analysis. We constructed Cancer DEIso to facilitate in-depth analyses for both gene-level and isoform-level human cancer studies. Patient RNA-seq data, sample sheets, patient clinical data, and human genome datasets were collected and processed in Cancer DEIso. And four functions to search differentially expressed genes/isoforms between cancer stages were implemented: (i) Search potential gene/isoform markers for a specified cancer type and its two stages; (ii) Search potentially induced cancer types and stages for a gene/isoform; (iii) Expression survival analysis on a given gene/isoform for some cancer; (iv) Gene/isoform stage expression comparison visualization. As an example, we demonstrate that Cancer DEIso can indicate potential colorectal cancer isoform diagnostic markers that are not easily detected when only gene-level expressions are considered. Cancer DEIso is available at http://cosbi4.ee.ncku.edu.tw/DEIso/.

YPIBP: A repository for phosphoinositide-binding proteins in yeast.

Phosphoinositides (PIs) are a family of eight lipids consisting of phosphatidylinositol (PtdIns) and its seven phosphorylated forms. PIs have important regulatory functions in the cell including lipid signaling, protein transport, and membrane trafficking. Yeast has been recognized as a eukaryotic model system to study lipid-protein interactions. Hundreds of yeast PI-binding proteins have been identified, but this research knowledge remains scattered. Besides, the complete PI-binding spectrum and potential PI-binding domains have not been interlinked. No comprehensive databases are available to support the lipid-protein interaction research on phosphoinositides. Here we constructed the first knowledgebase of Yeast Phosphoinositide-Binding Proteins (YPIBP), a repository consisting of 679 PI-binding proteins collected from high-throughput proteome-array and lipid-array studies, QuickGO, and a rigorous literature mining. The YPIBP also contains protein domain information in categories of lipid-binding domains, lipid-related domains and other domains. The YPIBP provides search and browse modes along with two enrichment analyses (PI-binding enrichment analysis and domain enrichment analysis). An interactive visualization is given to summarize the PI-domain-protein interactome. Finally, three case studies were given to demonstrate the utility of YPIBP. The YPIBP knowledgebase consolidates the present knowledge and provides new insights of the PI-binding proteins by bringing comprehensive and in-depth interaction network of the PI-binding proteins. YPIBP is available at http://cosbi7.ee.ncku.edu.tw/YPIBP/.

A tool for analyzing and visualizing ribo-seq data at the isoform level.

BACKGROUND: Translational regulation is one important aspect of gene expression regulation. Dysregulation of translation results in abnormal cell physiology and leads to diseases. Ribosome profiling (RP), also called ribo-seq, is a powerful experimental technique to study translational regulation. It can capture a snapshot of translation by deep sequencing of ribosome-protected mRNA fragments. Many ribosome profiling data processing tools have been developed. However, almost all tools analyze ribosome profiling data at the gene level. Since different isoforms of a gene may produce different proteins with distinct biological functions, it is advantageous to analyze ribosome profiling data at the isoform level. To meet this need, previously we developed a pipeline to analyze 610 public human ribosome profiling data at the isoform level and constructed HRPDviewer database. RESULTS: To allow other researchers to use our pipeline as well, here we implement our pipeline as an easy-to-use software tool called RPiso. Compared to Ribomap (a widely used tool which provides isoform-level ribosome profiling analyses), our RPiso (1) estimates isoform abundance more accurately, (2) supports analyses on more species, and (3) provides a web-based viewer for interactively visualizing ribosome profiling data on the selected mRNA isoforms. CONCLUSIONS: In this study, we developed RPiso software tool ( http://cosbi7.ee.ncku.edu.tw/RPiso/ ) to provide isoform-level ribosome profiling analyses. RPiso is very easy to install and execute. RPiso also provides a web-based viewer for interactively visualizing ribosome profiling data on the selected mRNA isoforms. We believe that RPiso is a useful tool for researchers to analyze and visualize their own ribosome profiling data at the isoform level.

Identifying Primate ACE2 Variants That Confer Resistance to SARS-CoV-2.

SARS-CoV-2 infects humans through the binding of viral S-protein (spike protein) to human angiotensin I converting enzyme 2 (ACE2). The structure of the ACE2-S-protein complex has been deciphered and we focused on the 27 ACE2 residues that bind to S-protein. From human sequence databases, we identified nine ACE2 variants at ACE2-S-protein binding sites. We used both experimental assays and protein structure analysis to evaluate the effect of each variant on the binding affinity of ACE2 to S-protein. We found one variant causing complete binding disruption, two and three variants, respectively, strongly and mildly reducing the binding affinity, and two variants strongly enhancing the binding affinity. We then collected the ACE2 gene sequences from 57 nonhuman primates. Among the 6 apes and 20 Old World monkeys (OWMs) studied, we found no new variants. In contrast, all 11 New World monkeys (NWMs) studied share four variants each causing a strong reduction in binding affinity, the Philippine tarsier also possesses three such variants, and 18 of the 19 prosimian species studied share one variant causing a strong reduction in binding affinity. Moreover, one OWM and three prosimian variants increased binding affinity by >50%. Based on these findings, we proposed that the common ancestor of primates was strongly resistant to and that of NWMs was completely resistant to SARS-CoV-2 and so is the Philippine tarsier, whereas apes and OWMs, like most humans, are susceptible. This study increases our understanding of the differences in susceptibility to SARS-CoV-2 infection among primates.

YQFC: a web tool to compare quantitative biological features between two yeast gene lists.

Nowadays high-throughput omics technologies are routinely used in biological research. From the omics data, researchers can easily get two gene lists (e.g. stress-induced genes vs. stress-repressed genes) related to their biological question. The next step would be to apply enrichment analysis tools to identify distinct functional/regulatory features between these two gene lists for further investigation. Although various enrichment analysis tools are already available, two challenges remain to be addressed. First, most existing tools are designed to analyze only one gene list, so they cannot directly compare two gene lists. Second, almost all existing tools focus on identifying the enriched qualitative features (e.g. gene ontology [GO] terms, pathways, domains, etc.). Many quantitative features (e.g. number of mRNA isoforms of a gene, mRNA half-life, protein half-life, transcriptional plasticity, translational efficiency, etc.) are available in the yeast, but no existing tools provide analyses on these quantitative features. To address these two challenges, here we present Yeast Quantitative Features Comparator (YQFC) that can directly compare various quantitative features between two yeast gene lists. In YQFC, we comprehensively collected and processed 85 quantitative features from the yeast literature and yeast databases. For each quantitative feature, YQFC provides three statistical tests (t-test, U test and KS test) to test whether this quantitative feature is statistically different between the two input yeast gene lists. The distinct quantitative features identified by YQFC may help researchers to study the underlying molecular mechanisms that differentiate the two input yeast gene lists. We believe that YQFC is a useful tool to expedite the biological research that uses high-throughput omics technologies. DATABASE URL: http://cosbi2.ee.ncku.edu.tw/YQFC/.

Many human RNA viruses show extraordinarily stringent selective constraints on protein evolution.

How negative selection, positive selection, and population size contribute to the large variation in nucleotide substitution rates among RNA viruses remains unclear. Here, we studied the ratios of nonsynonymous-to-synonymous substitution rates (dN/dS) in protein-coding genes of human RNA and DNA viruses and mammals. Among the 21 RNA viruses studied, 18 showed a genome-average dN/dS from 0.01 to 0.10, indicating that over 90% of nonsynonymous mutations are eliminated by negative selection. Only HIV-1 showed a dN/dS (0.31) higher than that (0.22) in mammalian genes. By comparing the dN/dS values among genes in the same genome and among species or strains, we found that both positive selection and population size play significant roles in the dN/dS variation among genes and species. Indeed, even in flaviviruses and picornaviruses, which showed the lowest ratios among the 21 species studied, positive selection appears to have contributed significantly to dN/dS We found the view that positive selection occurs much more frequently in influenza A subtype H3N2 than subtype H1N1 holds only for the hemagglutinin and neuraminidase genes, but not for other genes. Moreover, we found no support for the view that vector-borne RNA viruses have lower dN/dS ratios than non-vector-borne viruses. In addition, we found a correlation between dN and dS, implying a correlation between dN and the mutation rate. Interestingly, only 2 of the 8 DNA viruses studied showed a dN/dS < 0.10, while 4 showed a dN/dS > 0.22. These observations increase our understanding of the mechanisms of RNA virus evolution.

Predicting Oncogenic Missense Mutations.

With the rapid progress of cancer genome studies, many missense mutations in populations of somatic cells of different cancer types and at different stages have been identified. However, it is challenging to understand the implications of these cancer-related variants. We have developed a computational method that integrates structural, topographical, and evolutionary information for assessments of biochemical effects and the extent of deleteriousness of the cancer-related variants. We have mapped somatic missense mutations from the Catalogue of Somatic Mutations In Cancer (COSMIC) to 3D structures in the Protein Data Bank (PDB). Our results show that a large portion of these missense mutations is located on protein surface pockets, which often serve as a structural and functional unit of cancer variants. We provide detailed analysis of several examples and assessment on the importance of these variants, including prediction of previously unreported cancer-variants, along with independent evidence from the literature. Furthermore, we show our predictions can inform on the functional roles and the mechanism of predicted cancer variants.

YHMI: a web tool to identify histone modifications and histone/chromatin regulators from a gene list in yeast.

Post-translational modifications of histones (e.g. acetylation, methylation, phosphorylation and ubiquitination) play crucial roles in regulating gene expression by altering chromatin structures and creating docking sites for histone/chromatin regulators. However, the combination patterns of histone modifications, regulatory proteins and their corresponding target genes remain incompletely understood. Therefore, it is advantageous to have a tool for the enrichment/depletion analysis of histone modifications and histone/chromatin regulators from a gene list. Many ChIP-chip/ChIP-seq datasets of histone modifications and histone/chromatin regulators in yeast can be found in the literature. Knowing the needs and having the data motivate us to develop a web tool, called Yeast Histone Modifications Identifier (YHMI), which can identify the enriched/depleted histone modifications and the enriched histone/chromatin regulators from a list of yeast genes. Both tables and figures are provided to visualize the identification results. Finally, the high-quality and biological insight of the identification results are demonstrated by two case studies. We believe that YHMI is a valuable tool for yeast biologists to do epigenetics research.

Automatic extraction of protein-protein interactions using grammatical relationship graph.

BACKGROUND: Relationships between bio-entities (genes, proteins, diseases, etc.) constitute a significant part of our knowledge. Most of this information is documented as unstructured text in different forms, such as books, articles and on-line pages. Automatic extraction of such information and storing it in structured form could help researchers more easily access such information and also make it possible to incorporate it in advanced integrative analysis. In this study, we developed a novel approach to extract bio-entity relationships information using Nature Language Processing (NLP) and a graph-theoretic algorithm. METHODS: Our method, called GRGT (Grammatical Relationship Graph for Triplets), not only extracts the pairs of terms that have certain relationships, but also extracts the type of relationship (the word describing the relationships). In addition, the directionality of the relationship can also be extracted. Our method is based on the assumption that a triplet exists for a pair of interactions. A triplet is defined as two terms (entities) and an interaction word describing the relationship of the two terms in a sentence. We first use a sentence parsing tool to obtain the sentence structure represented as a dependency graph where words are nodes and edges are typed dependencies. The shortest paths among the pairs of words in the triplet are then extracted, which form the basis for our information extraction method. Flexible pattern matching scheme was then used to match a triplet graph with unknown relationship to those triplet graphs with labels (True or False) in the database. RESULTS: We applied the method on three benchmark datasets to extract the protein-protein-interactions (PPIs), and obtained better precision than the top performing methods in literature. CONCLUSIONS: We have developed a method to extract the protein-protein interactions from biomedical literature. PPIs extracted by our method have higher precision among other methods, suggesting that our method can be used to effectively extract PPIs and deposit them into databases. Beyond extracting PPIs, our method could be easily extended to extracting relationship information between other bio-entities.

HRPDviewer: human ribosome profiling data viewer.

Translational regulation plays an important role in protein synthesis. Dysregulation of translation causes abnormal cell physiology and leads to diseases such as inflammatory disorders and cancers. An emerging technique, called ribosome profiling (ribo-seq), was developed to capture a snapshot of translation. It is based on deep sequencing of ribosome-protected mRNA fragments. A lot of ribo-seq data have been generated in various studies, so databases are needed for depositing and visualizing the published ribo-seq data. Nowadays, GWIPS-viz, RPFdb and TranslatomeDB are the three largest databases developed for this purpose. However, two challenges remain to be addressed. First, GWIPS-viz and RPFdb databases align the published ribo-seq data to the genome. Since ribo-seq data aim to reveal the actively translated mRNA transcripts, there are advantages of aligning ribo-req data to the transcriptome over the genome. Second, TranslatomeDB does not provide any visualization and the other two databases only provide visualization of the ribo-seq data around a specific genomic location, while simultaneous visualization of the ribo-seq data on multiple mRNA transcripts produced from the same gene or different genes is desired. To address these two challenges, we developed the Human Ribosome Profiling Data viewer (HRPDviewer). HRPDviewer (i) contains 610 published human ribo-seq datasets from Gene Expression Omnibus, (ii) aligns the ribo-seq data to the transcriptome and (iii) provides visualization of the ribo-seq data on the selected mRNA transcripts. Using HRPDviewer, researchers can compare the ribosome binding patterns of multiple mRNA transcripts from the same gene or different genes to gain an accurate understanding of protein synthesis in human cells. We believe that HRPDviewer is a useful resource for researchers to study translational regulation in human.Database URL: http://cosbi4.ee.ncku.edu.tw/HRPDviewer/ or http://cosbi5.ee.ncku.edu.tw/HRPDviewer/.

YARG: A repository for arsenic-related genes in yeast.

Arsenic is a toxic metalloid. Moderate levels of arsenic exposure from drinking water can cause various human health problems such as skin lesions, circulatory disorders and cancers. Thus, arsenic toxicity is a key focus area for environmental and toxicological investigations. Many arsenic-related genes in yeast have been identified by experimental strategies such as phenotypic screening and transcriptional profiling. These identified arsenic-related genes are valuable information for studying arsenic toxicity. However, the literature about these identified arsenic-related genes is widely dispersed and cannot be easily acquired by researchers. This prompts us to develop YARG (Yeast Arsenic-Related Genes) database, which comprehensively collects 3396 arsenic-related genes in the literature. For each arsenic-related gene, the number and types of experimental evidence (phenotypic screening and/or transcriptional profiling) are provided. Users can use both search and browse modes to query arsenic-related genes in YARG. We used two case studies to show that YARG can return biologically meaningful arsenic-related information for the query gene(s). We believe that YARG is a useful resource for arsenic toxicity research. YARG is available at http://cosbi4.ee.ncku.edu.tw/YARG/.

SM22α suppresses cytokine-induced inflammation and the transcription of NF-κB inducing kinase (Nik) by modulating SRF transcriptional activity in vascular smooth muscle cells.

Vascular smooth muscle cell (VSMC) phenotypic modulation is characterized by the downregulation of SMC actin cytoskeleton proteins. Our published study shows that depletion of SM22α (aka SM22, Transgelin, an actin cytoskeleton binding protein) promotes inflammation in SMCs by activating NF-κB signal pathways both in cultured VSMCs and in response to vascular injury. The goal of this study is to investigate the underlying molecular mechanisms whereby SM22 suppresses NF-κB signaling pathways under inflammatory condition. NF-κB inducing kinase (Nik, aka MAP3K14, activated by the LTßR) is a key upstream regulator of NF-κB signal pathways. Here, we show that SM22 overexpression suppresses the expression of NIK and its downstream NF-κB canonical and noncanonical signal pathways in a VSMC line treated with a LTßR agonist. SM22 regulates NIK expression at both transcriptional and the proteasome-mediated post-translational levels in VSMCs depending on the culture condition. By qPCR, chromatin immunoprecipitation and luciferase assays, we found that Nik is a transcription target of serum response factor (SRF). Although SM22 is known to be expressed in the cytoplasm, we found that SM22 is also expressed in the nucleus where SM22 interacts with SRF to inhibit the transcription of Nik and prototypical SRF regulated genes including c-fos and Egr3. Moreover, carotid injury increases NIK expression in Sm22-/- mice, which is partially relieved by adenovirally transduced SM22. These findings reveal for the first time that SM22 is expressed in the nucleus in addition to the cytoplasm of VSMCs to regulate the transcription of Nik and its downstream proinflammatory NF-kB signal pathways as a modulator of SRF during vascular inflammation.

YGMD: a repository for yeast cooperative transcription factor sets and their target gene modules.

Database URL: http://cosbi4.ee.ncku.edu.tw/YGMD/ , http://cosbi5.ee.ncku.edu.tw/YGMD/ or http://cosbi.ee.ncku.edu.tw/YGMD/.

Molecular Basis of ABHD5 Lipolysis Activation.

Alpha-beta hydrolase domain-containing 5 (ABHD5), the defective gene in human Chanarin-Dorfman syndrome, is a highly conserved regulator of adipose triglyceride lipase (ATGL)-mediated lipolysis that plays important roles in metabolism, tumor progression, viral replication, and skin barrier formation. The structural determinants of ABHD5 lipolysis activation, however, are unknown. We performed comparative evolutionary analysis and structural modeling of ABHD5 and ABHD4, a functionally distinct paralog that diverged from ABHD5 ~500 million years ago, to identify determinants of ABHD5 lipolysis activation. Two highly conserved ABHD5 amino acids (R299 and G328) enabled ABHD4 (ABHD4 N303R/S332G) to activate ATGL in Cos7 cells, brown adipocytes, and artificial lipid droplets. The corresponding ABHD5 mutations (ABHD5 R299N and ABHD5 G328S) selectively disrupted lipolysis without affecting ATGL lipid droplet translocation or ABHD5 interactions with perilipin proteins and ABHD5 ligands, demonstrating that ABHD5 lipase activation could be dissociated from its other functions. Structural modeling placed ABHD5 R299/G328 and R303/G332 from gain-of-function ABHD4 in close proximity on the ABHD protein surface, indicating they form part of a novel functional surface required for lipase activation. These data demonstrate distinct ABHD5 functional properties and provide new insights into the functional evolution of ABHD family members and the structural basis of lipase regulation.