Your browser doesn't support javascript.
loading
Show: 20 | 50 | 100
Results 1 - 3 de 3
Filter
Add more filters










Database
Language
Publication year range
1.
Inorg Chem ; 61(30): 11519-11523, 2022 Aug 01.
Article in English | MEDLINE | ID: mdl-35849848

ABSTRACT

Fe-modified Ru nanosheets are achieved via preintercalated Al species serving as the self-sacrificial template. Benefiting from the amphoteric feature of Al and strong corrosion of Fe3+ ions, Fe is effectively incorporated into pristine Ru nanosheets. Correspondingly, the surface oxophilicity is improved, promoting the Volmer step. The charge density redistribution weakens hydrogen combination on Ru and thus accelerates the desorption kinetics (Heyrovsky step). Meanwhile, more defective sites are exposed, leading to an enhanced hydrogen production in pH-universal electrolytes.

2.
Bioinformatics ; 37(21): 3752-3759, 2021 11 05.
Article in English | MEDLINE | ID: mdl-34473228

ABSTRACT

MOTIVATION: Protein model quality assessment (QA) is an essential component in protein structure prediction, which aims to estimate the quality of a structure model and/or select the most accurate model out from a pool of structure models, without knowing the native structure. QA remains a challenging task in protein structure prediction. RESULTS: Based on the inter-residue distance predicted by the recent deep learning-based structure prediction algorithm trRosetta, we developed QDistance, a new approach to the estimation of both global and local qualities. QDistance works for both single- and multi-models inputs. We designed several distance-based features to assess the agreement between the predicted and model-derived inter-residue distances. Together with a few widely used features, they are fed into a simple yet powerful linear regression model to infer the global QA scores. The local QA scores for each structure model are predicted based on a comparative analysis with a set of selected reference models. For multi-models input, the reference models are selected from the input based on the predicted global QA scores. For single-model input, the reference models are predicted by trRosetta. With the informative distance-based features, QDistance can predict the global quality with satisfactory accuracy. Benchmark tests on the CASP13 and the CAMEO structure models suggested that QDistance was competitive with other methods. Blind tests in the CASP14 experiments showed that QDistance was robust and ranked among the top predictors. Especially, QDistance was the top 3 local QA method and made the most accurate local QA prediction for unreliable local region. Analysis showed that this superior performance can be attributed to the inclusion of the predicted inter-residue distance. AVAILABILITY AND IMPLEMENTATION: http://yanglab.nankai.edu.cn/QDistance. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.


Subject(s)
Computational Biology , Proteins , Computational Biology/methods , Proteins/chemistry , Algorithms
3.
Bioinformatics ; 38(1): 94-98, 2021 12 22.
Article in English | MEDLINE | ID: mdl-34450651

ABSTRACT

MOTIVATION: The solvent accessible surface is an essential structural property measure related to the protein structure and protein function. Relative solvent accessible area (RSA) is a standard measure to describe the degree of residue exposure in the protein surface or inside of protein. However, this computation will fail when the residues information is missing. RESULTS: In this article, we proposed a novel method for estimation RSA using the Cα atom distance matrix with the deep learning method (EAGERER). The new method, EAGERER, achieves Pearson correlation coefficients of 0.921-0.928 on two independent test datasets. We empirically demonstrate that EAGERER can yield better Pearson correlation coefficients than existing RSA estimators, such as coordination number, half sphere exposure and SphereCon. To the best of our knowledge, EAGERER represents the first method to estimate the solvent accessible area using limited information with a deep learning model. It could be useful to the protein structure and protein function prediction. AVAILABILITYAND IMPLEMENTATION: The method is free available at https://github.com/cliffgao/EAGERER. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.


Subject(s)
Deep Learning , Membrane Proteins , Solvents/chemistry
SELECTION OF CITATIONS
SEARCH DETAIL
...