We have constructed a map of the ''protein structure space'' by using the pairwise structural similarity scores calculated for all nonredundant protein structures determined experimentally. As expected, proteins with similar structures clustered together in the map and the overall distribution of structural classes of this map followed closely that of the map of the ''protein fold space'' we have reported previously. Consequently, proteins sharing similar molecular functions also were found to colocalize in the protein structure space map, pointing toward a previously undescribed scheme for structure-based functional inference for remote homologues based on the proximity in the map of the protein structure space. We found that this scheme consistently outperformed other predictions made by using either the raw scores or normalized Z-scores of pairwise DALI structure alignment. global map of protein universe ͉ multivariate analysis ͉ protein function prediction ͉ protein structure universe T he molecular functions of a protein can be inferred from either its sequence or structure information. Sequence-based function inference methods annotate molecular function of a protein from its sequence homologues. Most genome-wide functional annotations are carried out with this scheme, by using sequence alignment tools such as BLAST (1), or motif͞profile-based search tools such as PROSITE (2, 3) and PFAM (4, 5). However, when two functionally similar proteins do not share detectable sequence homology, molecular function cannot be inferred based solely on sequence information. Low sequence homology results either from an early branching point at the protein evolution (also known as remote homologues) or a convergent evolution. Many studies were focused on the detection of remote homologues (6-8). In general, methods using statistical models extracted from multiply aligned sequences perform better than pairwise sequence comparison methods (9). However, even these improved methods fail to recognize remote homologues with sequence identity Ͻ25-30%, which is estimated to be Ͼ25% of all sequenced proteins.Structure-based function inference, however, depends less on sequence information. During protein evolution, homology on sequence level is far less preserved compared with homology on structure level. Because proteins fold into specific structures to perform their molecular functions, structure-based functional inference is able to characterize remote homologous relationships of proteins that are impossible to detect by using sequences. By using different random sampling methods and similarity measuring functions, a large number of structural alignment algorithms have been developed to measure similarity of a pair of protein structures. Among these algorithms, DALI (10), SSAP (11), CE (12), and VAST (13) have been widely used, and their performances have been assessed [see Koehl (14) for a review].The issue of predicting the function of remote homologues has become more prominent recently: the Structural Genomics initiative (15...

The effect of algae growth on aerobic granulation and nutrients removal was studied in two identical sequencing batch reactors (SBRs). Sunlight exposure promoted the growth of algae in the SBR (Rs), forming an algal-bacterial symbiosis in aerobic granules. Compared to the control SBR (Rc), Rs had a slower granulation process with granules of loose structure and smaller particle size. Moreover, the specific oxygen uptake rate was significantly decreased for the granules from Rs with secretion of 25.7% and 22.5% less proteins and polysaccharides respectively in the extracellular polymeric substances. Although little impact was observed on chemical oxygen demand (COD) removal, algal-bacterial symbiosis deteriorated N and P removals, about 40.7-45.4% of total N and 44% of total P in Rs in contrast to 52.9-58.3% of TN and 90% of TP in Rc, respectively. In addition, the growth of algae altered the microbial community in Rs, especially unfavorable for Nitrospiraceae and Nitrosomonadaceae.

Objective: To automatically detect focal cortical dysplasia (FCD) lesion by combining quantitative multimodal surface-based features with machine learning and to assess its clinical value.Methods: Neuroimaging data and clinical information for 74 participants (40 with histologically proven FCD type II) was retrospectively included. The morphology, intensity and function-based features characterizing FCD lesions were calculated vertex-wise on each cortical surface and fed to an artificial neural network. The classifier performance was quantitatively and qualitatively assessed by performing statistical analysis and conventional visual analysis.Results: The accuracy, sensitivity, specificity of the neural network classifier based on multimodal surface-based features were 70.5%, 70.0%, and 69.9%, respectively, which outperformed the unimodal classifier. There was no significant difference in the detection rate of FCD subtypes (Pearson’s Chi-Square = 0.001, p = 0.970). Cohen’s kappa score between automated detection outcomes and post-surgical resection region was 0.385 (considered as fair).Conclusion: Automated machine learning with multimodal surface features can provide objective and intelligent detection of FCD lesion in pre-surgical evaluation and can assist the surgical strategy. Furthermore, the optimal parameters, appropriate surface features and efficient algorithm are worth exploring.