scholarly journals Inference of Protein Function from Protein Structure

Structure ◽  
2005 ◽  
Vol 13 (1) ◽  
pp. 121-130 ◽  
Author(s):  
Debnath Pal ◽  
David Eisenberg
Keyword(s):  
Protein Structure ◽  
Protein Function

scholarly journals The Impact of Experimental, Protein Structure on our Ability to Model Protein Function

2016 ◽  
Vol 110 (3) ◽  
pp. 544a
Author(s):  
Oleg Y. Borbulevych ◽  
Lance M. Westerhoff
Keyword(s):  
Protein Structure ◽  
Protein Function ◽  
Model Protein ◽  
The Impact

scholarly journals PointSite: a point cloud segmentation tool for identification of protein ligand binding atoms

2019 ◽  
Author(s):  
Zhen Li ◽  
Xu Yan ◽  
Qing Wei ◽  
Xin Gao ◽  
Sheng Wang ◽  
...  
Keyword(s):  
Protein Structure ◽  
Ligand Binding ◽  
Protein Function ◽  
Feature Learning ◽  
Point Clouds ◽  
Accurate Identification ◽  
3D Protein Structure ◽  
Fine Grained ◽  
Atom Level ◽  
Cascaded Filter

AbstractAccurate identifications of ligand binding sites (LBS) on protein structure is critical for understanding protein function and designing structure-based drug. As the previous pocket-centric methods are usually based on the investigation of pseudo surface points (PSPs) outside the protein structure, thus inherently cannot incorporate the local connectivity and global 3D geometrical information of the protein structure. In this paper, we propose a novel point clouds segmentation method, PointSite, for accurate identification of protein ligand binding atoms, which performs protein LBS identification at the atom-level in a protein-centric manner. Specifically, we first transfer the original 3D protein structure to point clouds and then conduct segmentation through Submanifold Sparse Convolution (SSC) based U-Net. With the fine-grained atom-level binding atoms representation and enhanced feature learning, PointSite can outperform previous methods in atom-IoU by a large margin. Furthermore, our segmented binding atoms can work as a filter on predictions achieved by previous pocket-centric approaches, which significantly decreases the false-positive of LBS candidates. Through cascaded filter and re-ranking aided by the segmented atoms, state-of-the-art performance can be achieved over various canonical benchmarks and CAMEO hard targets in terms of the commonly used DCA criteria. Our code is publicly available through https://github.com/PointSite.

scholarly journals Bent into shape: Folded peptides to mimic protein structure and modulate protein function

2020 ◽  
Vol 112 (1) ◽  
Author(s):  
Haley I. Merritt ◽  
Nicholas Sawyer ◽  
Paramjit S. Arora
Keyword(s):  
Protein Structure ◽  
Protein Function

Precise estimation of residue relative solvent accessible area from Cα atom distance matrix using a deep learning method

2021 ◽  
Author(s):  
Jianzhao Gao ◽  
Shuangjia Zheng ◽  
Mengting Yao ◽  
Peikun Wu
Keyword(s):  
Deep Learning ◽  
Protein Structure ◽  
Protein Function ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Distance Matrix ◽  
Supplementary Information ◽  
Learning Method ◽  
Solvent Accessible Area ◽  
Accessible Area

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.

scholarly journals Counteraction of urea destabilization of protein structure by methylamine osmoregulatory compounds of elasmobranch fishes

1979 ◽  
Vol 183 (2) ◽  
pp. 317-323 ◽  
Author(s):  
Paul H. Yancey ◽  
George N. Somero
Keyword(s):  
Amino Acids ◽  
Protein Structure ◽  
Protein Function ◽  
Tertiary Structure ◽  
Total Concentration ◽  
Amino Acid Sequences ◽  
Protein Amino Acid ◽  
Evolutionary Changes ◽  
Structural And Functional Properties ◽  
Elasmobranch Fishes

Intracellular fluids of marine elasmobranchs (sharks, skates and rays), holocephalans and the coelacanth contain urea at concentrations averaging 0.4m, high enough to significantly affect the structural and functional properties of many proteins. Also present in the cells of these fishes are a family of methylamine compounds, largely trimethylamine N-oxide with some betaine and sarcosine, and certain free amino acids, mainly β-alanine and taurine, whose total concentration is approx. 0.2m. These methylamine compounds and amino acids have been found to be effective stabilizers of protein structure, and, at a 1:2 molar concentration ratio of these compounds to urea, perturbations of protein structure by urea are largely or fully offset. These counteracting effects of solutes on proteins are seen for: (1) thermal stability of protein secondary and tertiary structure (bovine ribonuclease); (2) the rate and extent of enzyme renaturation after acid denaturation (rabbit and shark lactate dehydrogenases); and (3) the reactivity of thiol groups of an enzyme (bovine glutamate dehydrogenase). Attaining osmotic equilibrium with seawater by these fishes has thus involved the selective accumulation of certain nitrogenous metabolites that individually have significant effects on protein structure, but that have virtually no net effects on proteins when these solutes are present at elasmobranch physiological concentrations. These experiments indicate that evolutionary changes in intracellular solute compositions as well as in protein amino acid sequences can have important roles in intracellular protein function.

scholarly journals Understanding the Role of Protein Glycation in the Amyloid Aggregation Process

2021 ◽  
Vol 22 (12) ◽  
pp. 6609
Author(s):  
Ivana Sirangelo ◽  
Clara Iannuzzi
Keyword(s):  
Protein Structure ◽  
Protein Function ◽  
The Body ◽  
Protein Glycation ◽  
Amyloid Formation ◽  
Aggregation Process ◽  
Amyloid Aggregation ◽  
Post Translational Modification

Protein function and flexibility is directly related to the native distribution of its structural elements and any alteration in protein architecture leads to several abnormalities and accumulation of misfolded proteins. This phenomenon is associated with a range of increasingly common human disorders, including Alzheimer and Parkinson diseases, type II diabetes, and a number of systemic amyloidosis characterized by the accumulation of amyloid aggregates both in the extracellular space of tissues and as intracellular deposits. Post-translational modifications are known to have an active role in the in vivo amyloid aggregation as able to affect protein structure and dynamics. Among them, a key role seems to be played by non-enzymatic glycation, the most unwanted irreversible modification of the protein structure, which strongly affects long-living proteins throughout the body. This study provided an overview of the molecular effects induced by glycation on the amyloid aggregation process of several protein models associated with misfolding diseases. In particular, we analyzed the role of glycation on protein folding, kinetics of amyloid formation, and amyloid cytotoxicity in order to shed light on the role of this post-translational modification in the in vivo amyloid aggregation process.

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