Predicting flavin and nicotinamide adenine dinucleotide-binding sites in proteins using the fragment transformation method

A novel fusion technology utilizing complex network and sequence information for FAD-binding site identification

Flavin adenine dinucleotide (FAD) binding sites play an increasingly important role as useful targets for inhibiting bacterial infections. To reveal protein topological structural information as a reasonable complement for the identification FAD-binding sites, we designed a novel fusion technology according to sequence and complex network. The specially designed feature vectors were combined and fed into CatBoost for model construction. Moreover, due to the minority class (positive samples) is more significant for biological researches, a random under-sampling technique was applied to solve the imbalance. Compared with the previous methods, our methods achieved the best results for two independent test datasets. Especially, the MCC obtained by FADsite and FADsite_seq were 14.37 %-53.37 % and 21.81 %-60.81 % higher than the results of existing methods on Test6; and they showed improvements ranging from 6.03 % to 21.96 % and 19.77 %-35.70 % on Test4. Meanwhile, statistical tests show that our methods significantly differ from the state-of-the-art methods and the cross-entropy loss shows that our methods have high certainty. The excellent results demonstrated the effectiveness of using sequence and complex network information in identifying FAD-binding sites. It may be complementary to other biological studies. The data and resource codes are available at https://github.com/Kangxiaoneuq/FADsite.

Identification of Iron-Sulfur (Fe-S) Cluster and Zinc (Zn) Binding Sites Within Proteomes Predicted by DeepMind's AlphaFold2 Program Dramatically Expands the Metalloproteome

DeepMind's AlphaFold2 software has ushered in a revolution in high quality, 3D protein structure prediction. In very recent work by the DeepMind team, structure predictions have been made for entire proteomes of twenty-one organisms, with >360,000 structures made available for download. Here we show that thousands of novel binding sites for iron-sulfur (Fe-S) clusters and zinc (Zn) ions can be identified within these predicted structures by exhaustive enumeration of all potential ligand-binding orientations. We demonstrate that AlphaFold2 routinely makes highly specific predictions of ligand binding sites: for example, binding sites that are comprised exclusively of four cysteine sidechains fall into three clusters, representing binding sites for 4Fe-4S clusters, 2Fe-2S clusters, or individual Zn ions. We show further: (a) that the majority of known Fe-S cluster and Zn binding sites documented in UniProt are recovered by the AlphaFold2 structures, (b) that there are occasional disputes between AlphaFold2 and UniProt with AlphaFold2 predicting highly plausible alternative binding sites, (c) that the Fe-S cluster binding sites that we identify in E. coli agree well with previous bioinformatics predictions, (d) that cysteines predicted here to be part of ligand binding sites show little overlap with those shown via chemoproteomics techniques to be highly reactive, and (e) that AlphaFold2 occasionally appears to build erroneous disulfide bonds between cysteines that should instead coordinate a ligand. These results suggest that AlphaFold2 could be an important tool for the functional annotation of proteomes, and the methodology presented here is likely to be useful for predicting other ligand-binding sites.

Identification of the key functional genes in salt-stress tolerance of Cyanobacterium Phormidium tenue using in silico analysis

The development of artificial biocrust using cyanobacterium Phormidium tenue has been suggested as an effective strategy to prevent soil degradation. Here, a combination of in silico approaches with growth rate, photosynthetic pigment, morphology, and transcript analysis was used to identify specific genes and their protein products in response to 500 mM NaCl in P. tenue. The results show that 500 mM NaCl induces the expression of genes encoding glycerol-3-phosphate dehydrogenase (glpD) as a Flavoprotein, ribosomal protein S12 methylthiotransferase (rimO), and a hypothetical protein (sll0939). The constructed co-expression network revealed a group of abiotic stress-responsive genes. Using the Basic Local Alignment Search Tool (BLAST), the homologous proteins of rimO, glpD, and sll0939 were identified in the P. tenue genome. Encoded proteins of glpD, rimO, and DUF1622 genes, respectively, contain (DAO and DAO C), (UPF0004, Radical SAM and TRAM 2), and (DUF1622) domains. The predicted ligand included 22B and MG for DUF1622, FS5 for rimO, and FAD for glpD protein. There was no direct disruption in ligand-binding sites of these proteins by Na+, Cl-, or NaCl. The growth rate, photosynthetic pigment, and morphology of P. tenue were investigated, and the result showed an acceptable tolerance rate of this microorganism under salt stress. The quantitative real-time polymerase chain reaction (qRT-PCR) results revealed the up-regulation of glpD, rimO, and DUF1622 genes under salt stress. This is the first report on computational and experimental analyses of the glpD, rimO, and DUF1622 genes in P. tenue under salt stress to the best of our knowledge.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-021-03050-w.

Predicting FAD Interacting Residues with Feature Selection and Comprehensive Sequence Descriptors

The function of a flavoprotein is determined to a great extent by the binding sites on its surface that interacts with flavin adenine dinucleotide (FAD). Malfunction or dysregulation of FAD binding leads to a series of diseases. Therefore, accurately identifying FAD interacting residues (FIRs) provides insights into the molecular mechanisms of flavoprotein-related biological processes and disease progression. In this paper, a new computational method is proposed for identifying FIRs from protein sequences. Various sequence-derived discriminative features are explored. We analyze the distinctions of these features between FIRs and non-FIRs. We also investigate the predictive capabilities of both individual features and combinations of features. A relief algorithm followed by incremental feature selection (relief-IFS) is then adopted to search the optimal features. Finally, a random forest (RF) module is used to predict FIRs based on the optimal features. Using a 5-fold cross-validation test, the proposed method performs well, with a sensitivity of 0.847, a specificity of 0.933, an accuracy of 0.890, and a Matthews correlation coefficient (MCC) of 0.782, thereby outperforming previous methods. These results indicate that our method is relatively successful at predicting FIRs.