Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Taiwan
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    MIB2 attempts to overcome the limitation of structure-based prediction approaches, with many proteins lacking a solved structure. MIB2 also offers more accurate prediction performance and more metal ion types. MIB2 utilizes both the (PS)2 method and the AlphaFold Protein Structure Database to acquire predicted structures to perform metal ion docking and predict binding residues. MIB2 offers marked improvements over MIB by collecting more metal ion-binding residue templates and using the metal ion type-specific scoring function. It offers a total of 18 types of metal ions for binding site predictions.





    CanSavPre is a structure-based cancer-related single amino acid variation prediction system. This system predicted the cancer-related SAVs and provided the critical features used to estimate the relationship between their properties and cancer caused by SAVs. Moreover, CanSavPre developed by the machine learning methods and its five-fold cross-validation performance is reached 89.73% for accuracy, 0.74 for the Matthews correlation coefficient, and 0.81 for the F1 score.





    MIB is a binding sites prediction server for metal ions, and this server provides an accurate, integrated method to search the residues in metal ion-binding sites by using the fragment transformation method. Eleven kinds of metal ions (Ca2+, Cu2+, Fe3+, Mg2+, Mn2+, Zn2+, Cd2+, Fe2+ ,Ni2+, Hg2+ and Co2+) binding residues prediction are supported. MIB also provides the metal ions modeling after prediction. Ultimately, for an FPR threshold of 5% our method achieved an overall 94.6% accuracy with a TPR of 60.5%, which is a substantial improvement over other prediction methods currently available. Therefore, our method may find use as a predictor of putative metal ion binding proteins and their binding.





    CELLO2GO is a web-based system for screening various properties of a targeted protein and its subcellular localization. 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. 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.





    CELLO is a subCELlular LOcalization predictive system. It is developed by using support vector machine (SVM) and trained by multiple feature vectors based on n-peptide compositions. We compare our approach with a global sequence alignment approach and other existing approaches for two benchmark data sets: one comprising prokaryotic sequences and the other eukaryotic sequences. Its performance is comparable to the homology search method in the high homology regions and better than the homology search method in the low homology regions. Because the function of protein is usually related to its subcellular localization, the ability to predict subcellular localization directly from protein sequences will be useful for inferring protein functions. This system may be applied to a wide range of sequence identity and thus provide a practical tool for biologists.