CELLO2GO (http://cello.life.nctu.edu.tw/cello2go/) is a publicly available, web-based system for screening various properties of a targeted protein and its subcellular localization. Herein, we describe how this platform is used to obtain a brief or detailed gene ontology (GO)-type categories, including subcellular localization(s), for the queried proteins by combining the CELLO localization-predicting and BLAST homology-searching approaches. Given a query protein sequence, CELLO2GO uses BLAST to search for homologous sequences that are GO annotated in an in-house database derived from the UniProt KnowledgeBase database. At the same time, CELLO attempts predict at least one subcellular localization on the basis of the species in which the protein is found. When homologs for the query sequence have been identified, the number of terms found for each of their GO categories, i.e., cellular compartment, molecular function, and biological process, are summed and presented as pie charts representing possible functional annotations for the queried protein. Although the experimental subcellular localization of a protein may not be known, and thus not annotated, CELLO can confidentially suggest a subcellular localization. CELLO2GO should be a useful tool for research involving complex subcellular systems because it combines CELLO and BLAST into one platform and its output is easily manipulated such that the user-specific questions may be readily addressed.

The structure of a protein determines its biological function(s) and its interactions with other factors; the binding regions tend to be conserved in sequence and structure, and the interacting residues involved are usually in close 3D space. The Protein Data Bank currently contains more than 110 000 protein structures, approximately one-third of which contain metal ions. Identifying and characterizing metal ion-binding sites is thus essential for investigating a protein’s function(s) and interactions. However, experimental approaches are time-consuming and costly. The web server reported here was built to predict metal ion-binding residues and to generate the predicted metal ion-bound 3D structure. Binding templates have been constructed for regions that bind 12 types of metal ion-binding residues have been used to construct binding templates. The templates include residues within 3.5 Å of the metal ion, and the fragment transformation method was used for structural comparison between query proteins and templates without any data training. Through the adjustment of scoring functions, which are based on the similarity of structure and binding residues. Twelve kinds of metal ions (Ca²⁺, Cu²⁺, Fe³⁺, Mg²⁺, Mn²⁺, Zn²⁺, Cd²⁺, Fe²⁺, Ni²⁺, Hg²⁺, Co²⁺, and Cu⁺) binding residues prediction are supported. MIB also provides the metal ions docking after prediction. The MIB server is available at http://bioinfo.cmu.edu.tw/MIB/.

The structure of a protein determines its function and its interactions with other factors. Regions of proteins that interact with ligands, substrates, and/or other proteins, tend to be conserved both in sequence and structure, and the residues involved are usually in close spatial proximity. More than 70,000 protein structures are currently found in the Protein Data Bank, and approximately one-third contain metal ions essential for function. Identifying and characterizing metal ion–binding sites experimentally is time-consuming and costly. Many computational methods have been developed to identify metal ion–binding sites, and most use only sequence information. For the work reported herein, we developed a method that uses sequence and structural information to predict the residues in metal ion–binding sites. Six types of metal ion–binding templates– those involving Ca²⁺, Cu²⁺, Fe³⁺, Mg²⁺, Mn²⁺, and Zn²⁺–were constructed using the residues within 3.5 Å of the center of the metal ion. Using the fragment transformation method, we then compared known metal ion–binding sites with the templates to assess the accuracy of our method. Our method achieved an overall 94.6 % accuracy with a true positive rate of 60.5 % at a 5 % false positive rate and therefore constitutes a significant improvement in metal-binding site prediction.

The biomineralisation of metal phosphates is a promising approach to develop more efficient nanobiocatalysts; however, the interactions between the protein and the inorganic mineral are poorly understood. Elucidating which protein regions most likely participate in the mineral formation will guide the fabrication of more efficient biocatalysts based on metal-phosphate nanoflowers. We have biomineralised the lipase from Thermomyces lanuginosus using three calcium, zinc and copper phosphates to fabricate different types of bio-inorganic nanoflowers. To better understand how the biomineralisation process affects the enzyme properties, we have computationally predicted the protein regions with a higher propensity for binding Ca²⁺, Cu²⁺ and Zn²⁺. These binding sites can be considered as presumable nucleation points where the biomineralisation process starts and explain why different metals can form bio-inorganic nanoflowers of the same enzyme with different functional properties. The formation of calcium, copper and zinc phosphates in the presence of this lipase gives rise to nanoflowers with different morphologies and different enzymatic properties such as activity, stability, hyperactivation and activity–pH profile; these functional differences are supported by structural studies based on fluorescence spectroscopy and can be explained by the different locations of the predicted nucleation sites for the different metals. Among the three metals used herein, the mineralisation of this lipase with zinc-phosphate enables the fabrication of bio-inorganic nanoflowers 34 times more stable than the soluble enzyme. These bio-inorganic nanoflowers were reused for 8 reaction cycles achieving 100% yield in the hydrolysis of p-nitrophenol butyrate but losing more than 50% of their initial activity after 6 operational cycles. Finally, this heterogeneous biocatalyst was more active and enantioselective than the soluble enzyme (ee = 79%(R)) towards the kinetic resolution of rac-1-phenylethyl acetate yielding the R enantiomer with ee = 84%.

Glioblastoma (GBM) is one of the most common malignant and incurable brain tumors. The identification of a gene signature for GBM may be helpful for its diagnosis, treatment, prediction of prognosis and even the development of treatments. In this study, we used the GSE108474 database to perform GSEA and machine learning analysis, and identified a 33-gene signature of GBM by examining astrocytoma or non-GBM glioma differential gene expression. The 33 identified signature genes included the overexpressed genes COL6A2, ABCC3, COL8A1, FAM20A, ADM, CTHRC1, PDPN, IBSP, MIR210HG, GPX8, MYL9 and PDLIM4, as well as the underexpressed genes CHST9, CSDC2, ENHO, FERMT1, IGFN1, LINC00836, MGAT4C, SHANK2 and VIPR2. Protein functional analysis by CELLO2GO implied that these signature genes might be involved in regulating various aspects of biological function, including anatomical structure development, cell proliferation and adhesion, signaling transduction and many of the genes were annotated in response to stress. Of these 33 signature genes, 23 have previously been reported to be functionally correlated with GBM; the roles of the remaining 10 genes in glioma development remain unknown. Our results were the first to reveal that GBM exhibited the overexpressed GPX8 gene and underexpressed signature genes including CHST9, CSDC2, ENHO, FERMT1, IGFN1, LINC00836, MGAT4C and SHANK2, which might play crucial roles in the tumorigenesis of different gliomas.

Eradicating influenza A virus (IAV) is difficult, due to its genetic drift and reassortment ability. As the infectious cycle is initiated by the influenza glycoprotein, hemagglutinin (HA), which mediates the binding of virions to terminal sialic acids moieties, HA is a tempting target of anti-influenza inhibitors. However, the complexity of the HA structure has prevented delineation of the structural characterization of the HA protein–ligand complex. Our computational strategy efficiently analyzed > 200,000 records of compounds held in the United States National Cancer Institute (NCI) database and identified potential HA inhibitors, by modeling the sialic acid (SA) receptor binding site (RBS) for the HA structure. Our modeling revealed that compound NSC85561 showed significant antiviral activity against the IAV H1N1 strain with EC₅₀ values ranging from 2.31 to 2.53 µM and negligible cytotoxicity (CC₅₀ > 700 µM). Using the NSC85561 compound as the template to generate 12 derivatives, robust bioassay results revealed the strongest antiviral efficacies with NSC47715 and NSC7223. Virtual screening clearly identified three SA receptor binding site inhibitors that were successfully validated in experimental data. Thus, our computational strategy has identified SA receptor binding site inhibitors against HA that show IAV-associated antiviral activity.