Because the protein's function is usually related to its subcellular localization, the ability to predict subcellular localization directly from protein sequences will be useful for inferring protein functions. Recent years have seen a surging interest in the development of novel computational tools to predict subcellular localization. At present, these approaches, based on a wide range of algorithms, have achieved varying degrees of success for specific organisms and for certain localization categories. A number of authors have noticed that sequence similarity is useful in predicting subcellular localization. For example, Nair and Rost (Protein Sci 2002;11:2836-2847) have carried out extensive analysis of the relation between sequence similarity and identity in subcellular localization, and have found a close relationship between them above a certain similarity threshold. However, many existing benchmark data sets used for the prediction accuracy assessment contain highly homologous sequences-some data sets comprising sequences up to 80-90% sequence identity. Using these benchmark test data will surely lead to overestimation of the performance of the methods considered. Here, we develop an approach based on a two-level support vector machine (SVM) system: the first level comprises a number of SVM classifiers, each based on a specific type of feature vectors derived from sequences; the second level SVM classifier functions as the jury machine to generate the probability distribution of decisions for possible localizations. 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. Furthermore, we carried out all-against-all sequence alignment for several data sets to investigate the relationship between sequence homology and subcellular localization. Our results, which are consistent with previous studies, indicate that the homology search approach performs well down to 30% sequence identity, although its performance deteriorates considerably for sequences sharing lower sequence identity. A data set of high homology levels will undoubtedly lead to biased assessment of the performances of the predictive approaches-especially those relying on homology search or sequence annotations. Our two-level classification system based on SVM does not rely on homology search; therefore, its performance remains relatively unaffected by sequence homology. When compared with other approaches, our approach performed significantly better. Furthermore, we also develop a practical hybrid method, which combines the two-level SVM classifier and the homology search method, as a general tool for the sequence annotation of subcellular localization.
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 (Ca, Cu, Fe, Mg, Mn, Zn, Cd, Fe, Ni, Hg, Co, 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/ .
It has recently been shown that in proteins the atomic mean-square displacement (or B-factor) can be related to the number of the neighboring atoms (or protein contact number), and that this relationship allows one to compute the B-factor profiles directly from protein contact number. This method, referred to as the protein contact model, is appealing, since it requires neither trajectory integration nor matrix diagonalization. As a result, the protein contact model can be applied to very large proteins and can be implemented as a high-throughput computational tool to compute atomic fluctuations in proteins. Here, we show that this relationship can be further refined to that between the atomic mean-square displacement and the weighted protein contact-number, the weight being the square of the reciprocal distance between the contacting pair. In addition, we show that this relationship can be utilized to compute the cross-correlation of atomic motion (the B-factor is essentially the auto-correlation of atomic motion). For a nonhomologous dataset comprising 972 high-resolution X-ray protein structures (resolution <2.0 A and sequence identity <25%), the mean correlation coefficient between the X-ray and computed B-factors based on the weighted protein contact-number model is 0.61, which is better than those of the original contact-number model (0.51) and other methods. We also show that the computed correlation maps based on the weighted contact-number model are globally similar to those computed through normal model analysis for some selected cases. Our results underscore the relationship between protein dynamics and protein packing. We believe that our method will be useful in the study of the protein structure-dynamics relationship.
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.
Psoriasis is a chronic inflammatory skin disorder that affects ~2%–3% of the worldwide population. Inappropriate and excessive activation of endosomal Toll-like receptors 7, 8, and 9 (TLRs 7–9) at the psoriatic site has been shown to play a pathogenic role in the onset of psoriasis. Macrophage is a major inflammatory cell type that can be differentiated into phenotypes M1 and M2. M1 macrophages produce proinflammatory cytokines, and M2 macrophages produce anti-inflammatory cytokines. The balance between these two types of macrophages determines the progression of various inflammatory diseases; however, whether macrophage polarization plays a role in psoriatic inflammation activated by endosomal TLRs has not been investigated. In this study, we investigated the function and mechanism of macrophages related to the pathogenic role of TLRs 7–9 in the progression of psoriasis. Analysis of clinical data in database revealed significantly increased expression of macrophage markers and inflammatory cytokines in psoriatic tissues over those in normal tissues. In animal studies, depletion of macrophages in mice ameliorated imiquimod, a TLR 7 agonist-induced psoriatic response. Imiquimod induced expression of genes and cytokines that are signature of M1 macrophage in the psoriatic lesions. In addition, treatment with this TLR 7 agonist shifted macrophages in the psoriatic lesions to a higher M1/M2 ratio. Both of the exogenous and endogenous TLR 7–9 ligands activated M1 macrophage polarization. M1 macrophages expressed higher levels of proinflammatory cytokines and TLRs 7–9 than M2 macrophages. These results suggest that by rendering macrophages into a more inflammatory status and capable of response to their ligands in the psoriatic sites, TLR 7–9 activation drives them to participate in endosomal TLR-activated psoriatic inflammation, resulting in an amplified inflammatory response. Our results also suggest that blocking M1 macrophage polarization could be a strategy which enables inhibition of psoriatic inflammation activated by these TLRs.
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