Evolutionary model of protein secondary structure capable of revealing new biological relationships.

Ancestral sequence reconstruction has had recent success in decoding the origins and the determinants of complex protein functions. However, phylogenetic analyses of remote homologues must handle extreme amino acid sequence diversity resulting from extended periods of evolutionary change. We exploited the wealth of protein structures to develop an evolutionary model based on protein secondary structure. The approach follows the differences between discrete secondary structure states observed in modern proteins and those hypothesized in their immediate ancestors. We implemented maximum likelihood-based phylogenetic inference to reconstruct ancestral secondary structure. The predictive accuracy from the use of the evolutionary model surpasses that of comparative modeling and sequence-based prediction; the reconstruction extracts information not available from modern structures or the ancestral sequences alone. Based on a phylogenetic analysis of a sequence-diverse protein family, we showed that the model can highlight relationships that are evolutionarily rooted in structure and not evident in amino acid-based analysis.

NAD+ cleavage activity by animal and plant TIR domains in cell death pathways.

SARM1 (sterile alpha and TIR motif containing 1) is responsible for depletion of nicotinamide adenine dinucleotide in its oxidized form (NAD+) during Wallerian degeneration associated with neuropathies. Plant nucleotide-binding leucine-rich repeat (NLR) immune receptors recognize pathogen effector proteins and trigger localized cell death to restrict pathogen infection. Both processes depend on closely related Toll/interleukin-1 receptor (TIR) domains in these proteins, which, as we show, feature self-association-dependent NAD+ cleavage activity associated with cell death signaling. We further show that SARM1 SAM (sterile alpha motif) domains form an octamer essential for axon degeneration that contributes to TIR domain enzymatic activity. The crystal structures of ribose and NADP+ (the oxidized form of nicotinamide adenine dinucleotide phosphate) complexes of SARM1 and plant NLR RUN1 TIR domains, respectively, reveal a conserved substrate binding site. NAD+ cleavage by TIR domains is therefore a conserved feature of animal and plant cell death signaling pathways.

Lipid exposure prediction enhances the inference of rotational angles of transmembrane helices.

BACKGROUND: Since membrane protein structures are challenging to crystallize, computational approaches are essential for elucidating the sequence-to-structure relationships. Structural modeling of membrane proteins requires a multidimensional approach, and one critical geometric parameter is the rotational angle of transmembrane helices. Rotational angles of transmembrane helices are characterized by their folded structures and could be inferred by the hydrophobic moment; however, the folding mechanism of membrane proteins is not yet fully understood. The rotational angle of a transmembrane helix is related to the exposed surface of a transmembrane helix, since lipid exposure gives the degree of accessibility of each residue in lipid environment. To the best of our knowledge, there have been few advances in investigating whether an environment descriptor of lipid exposure could infer a geometric parameter of rotational angle. RESULTS: Here, we present an analysis of the relationship between rotational angles and lipid exposure and a support-vector-machine method, called TMexpo, for predicting both structural features from sequences. First, we observed from the development set of 89 protein chains that the lipid exposure, i.e., the relative accessible surface area (rASA) of residues in the lipid environment, generated from high-resolution protein structures could infer the rotational angles with a mean absolute angular error (MAAE) of 46.32˚. More importantly, the predicted rASA from TMexpo achieved an MAAE of 51.05˚, which is better than 71.47˚ obtained by the best of the compared hydrophobicity scales. Lastly, TMexpo outperformed the compared methods in rASA prediction on the independent test set of 21 protein chains and achieved an overall Matthew's correlation coefficient, accuracy, sensitivity, specificity, and precision of 0.51, 75.26%, 81.30%, 69.15%, and 72.73%, respectively. TMexpo is publicly available at http://bio-cluster.iis.sinica.edu.tw/TMexpo. CONCLUSIONS: TMexpo can better predict rASA and rotational angles than the compared methods. When rotational angles can be accurately predicted, free modeling of transmembrane protein structures in turn may benefit from a reduced complexity in ensembles with a significantly less number of packing arrangements. Furthermore, sequence-based prediction of both rotational angle and lipid exposure can provide essential information when high-resolution structures are unavailable and contribute to experimental design to elucidate transmembrane protein functions.

Computational comparative study of tuberculosis proteomes using a model learned from signal peptide structures.

Secretome analysis is important in pathogen studies. A fundamental and convenient way to identify secreted proteins is to first predict signal peptides, which are essential for protein secretion. However, signal peptides are highly complex functional sequences that are easily confused with transmembrane domains. Such confusion would obviously affect the discovery of secreted proteins. Transmembrane proteins are important drug targets, but very few transmembrane protein structures have been determined experimentally; hence, prediction of the structures is essential. In the field of structure prediction, researchers do not make assumptions about organisms, so there is a need for a general signal peptide predictor.To improve signal peptide prediction without prior knowledge of the associated organisms, we present a machine-learning method, called SVMSignal, which uses biochemical properties as features, as well as features acquired from a novel encoding, to capture biochemical profile patterns for learning the structures of signal peptides directly.We tested SVMSignal and five popular methods on two benchmark datasets from the SPdb and UniProt/Swiss-Prot databases, respectively. Although SVMSignal was trained on an old dataset, it performed well, and the results demonstrate that learning the structures of signal peptides directly is a promising approach. We also utilized SVMSignal to analyze proteomes in the entire HAMAP microbial database. Finally, we conducted a comparative study of secretome analysis on seven tuberculosis-related strains selected from the HAMAP database. We identified ten potential secreted proteins, two of which are drug resistant and four are potential transmembrane proteins.SVMSignal is publicly available at http://bio-cluster.iis.sinica.edu.tw/SVMSignal. It provides user-friendly interfaces and visualizations, and the prediction results are available for download.

Genomic splice site prediction algorithm based on nucleotide sequence pattern for RNA viruses.

Splice site prediction on an RNA virus has two potential difficulties seriously degrading the performance of most conventional splice site predictors. One is a limited number of strains available for a virus species and the other is the diversified sequence patterns around the splice sites caused by the high mutation frequency. To overcome these two difficulties, a new algorithm called Genomic Splice Site Prediction (GSSP) algorithm, was proposed for splice site prediction of RNA viruses. The key idea of the GSSP algorithm was to characterize the interdependency among the nucleotides and base positions based on the eigen-patterns. Identified by a sequence pattern mining technique, each eigen-pattern specified a unique composition of the base positions and the nucleotides occurring at the positions. To remedy the problem of insufficient training data due to the limited number of strains for an RNA virus, a cross-species strategy was employed in this study. The GSSP algorithm was shown to be effective and superior to two conventional methods in predicting the splice sites of five RNA species in the Orthomyxoviruses family. The sensitivity and specificity achieved by the GSSP algorithm was higher than 99 and 94%, respectively, for the donor sites, and was higher than 96 and 92%, respectively, for the acceptor sites. Supplementary data associated with this work are freely available for academic use at http://homepage.ntu.edu.tw/ approximately d91548013/.