Computational prediction of side-chain conformation is an important component of protein structure prediction. Accurate side-chain prediction is crucial for practical applications of protein structure models that need atomic detailed resolution such as protein and ligand design. We evaluated the accuracy of eight side-chain prediction methods in reproducing the side-chain conformations of experimentally solved structures deposited to the Protein Data Bank. Prediction accuracy was evaluated for a total of four different structural environments (buried, surface, interface, and membrane-spanning) in three different protein types (monomeric, multimeric, and membrane). Overall, the highest accuracy was observed for buried residues in monomeric and multimeric proteins. Notably, side-chains at protein interfaces and membrane-spanning regions were better predicted than surface residues even though the methods did not all use multimeric and membrane proteins for training. Thus, we conclude that the current methods are as practically useful for modeling protein docking interfaces and membrane-spanning regions as for modeling monomers.
Protein tertiary structure prediction methods have matured in recent years. However, some proteins defy accurate prediction due to factors such as inadequate template structures. While existing model quality assessment methods predict global model quality relatively well, there is substantial room for improvement in local quality assessment, i.e. assessment of the error at each residue position in a model. Local quality is a very important information for practical applications of structure models such as interpreting/designing site-directed mutagenesis of proteins. We have developed a novel local quality assessment method for protein tertiary structure models. The method, named Graph-based Model Quality assessment method (GMQ), explicitly considers the predicted quality of spatially neighboring residues using a graph representation of a query protein structure model. GMQ uses conditional random field as its core of the algorithm, and performs a binary prediction of the quality of each residue in a model, indicating if a residue position is likely to be within an error cutoff or not. The accuracy of GMQ was improved by considering larger graphs to include quality information of more surrounding residues. Moreover, we found that using different edge weights in graphs reflecting different secondary structures further improves the accuracy. GMQ showed competitive performance on a benchmark for quality assessment of structure models from the Critical Assessment of Techniques for Protein Structure Prediction (CASP).
The larval stage of the tapeworm Echinococcus granulosus sensu lato (E. granulosus s.l.) caused a chronic infection, known as cystic echinococcosis (CE), which is a worldwide public health problem. The human secondary CE is caused by the dissemination of protoscoleces (PSCs) when fertile cysts are accidentally ruptured, followed by development of PSCs into new metacestodes. The local immune mechanisms responsible for the establishment and established phases after infection with E. granulosus s.l. are not clear. Here, we showed that T cells were involved in the formation of the immune environment in the liver in CE patients and Echinococcus granulosus sensu strict (E. granulosus s.s.)-infected mice, with CD4+ T cells being the dominant immune cells; this process was closely associated with cyst viability and establishment. Local T2-type responses in the liver were permissive for early infection establishment by E. granulosus s.s. between 4 and 6 weeks in the experimental model. CD4+ T-cell deficiency promoted PSC development into cysts in the liver in E. granulosus s.s.-infected mice. In addition, CD4+ T-cell-mediated cellular immune responses and IL-10-producing CD8+ T cells play a critical role in the establishment phase of secondary E. granulosus s.s. PSC infection. These data contribute to the understanding of local immune responses to CE and the design of new therapies by restoring effective immune responses and blocking evasion mechanisms during the establishment phase of infection.
Splice sites detection is helpful to the analysis of gene structure and contributes to the prediction of gene products, so it is one of the most important topics in bioinformatics. With the rapid increase of biological data, faster splice sites detecting methods are more appreciated. Markov model and hidden Markov model not only are low-time-cost, but also easy to understand. Besides the detecting result, we can often obtain some biological features from the models, so they are widely used in DNA sites detection. However, the detecting accuracy of the conventional Markov and hidden Markov model is not satisfactory, so in order to improve their detecting accuracy, we propose a dinucleotide-based hidden Markov model, and then combine the Markov model with the dinucleotide-based hidden Markov model to construct an ensemble model. The experiment results show that our models can further improve the detecting accuracy than those of the conventional models and some present models. Keywords-markov model; hidden markov model; support vector machine; splice sites detectionI.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.