Statistical Geometry and Topology of Real and Model Protein Structures

2006 ◽  
Author(s):  
Todd J. Taylor
Keyword(s):  
Protein Structures ◽  
Statistical Geometry ◽  
And Topology ◽  
Model Protein

scholarly journals Protein Structure Idealization: How accurately is it possible to model protein structures with dihedral angles?

2013 ◽  
Vol 8 (1) ◽  
pp. 5 ◽  
Author(s):  
Xuefeng Cui ◽  
Shuai Cheng Li ◽  
Dongbo Bu ◽  
Babak Alipanahi ◽  
Ming Li
Keyword(s):  
Protein Structure ◽  
Protein Structures ◽  
Dihedral Angles ◽  
Model Protein

scholarly journals Exploring Protein Fold Space

Biomolecules ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 193 ◽  
Author(s):  
William R. Taylor
Keyword(s):  
Protein Structures ◽  
Pairwise Comparison ◽  
Secondary Structures ◽  
Beta Sheets ◽  
Protein Fold ◽  
Helical Packing ◽  
Model Protein ◽  
Protein Fold Space ◽  
Open Question ◽  
Generate Model

The model of protein folding proposed by Ptitsyn and colleagues involves the accretion of secondary structures around a nucleus. As developed by Efimov, this model also provides a useful way to view the relationships among structures. Although somewhat eclipsed by later databases based on the pairwise comparison of structures, Efimov’s approach provides a guide for the more automatic comparison of proteins based on an encoding of their topology as a string. Being restricted to layers of secondary structures based on beta sheets, this too has limitations which are partly overcome by moving to a more generalised secondary structure lattice that can encompass both open and closed (barrel) sheets as well as helical packing of the type encoded by Murzin and Finkelstein on small polyhedra. Regular (crystalline) lattices, such as close-packed hexagonals, were found to be too limited so pseudo-latticses were investigated including those found in quasicrystals and the Bernal tetrahedron-based lattice that he used to represent liquid water. The Bernal lattice was considered best and used to generate model protein structures. These were much more numerous than those seen in Nature, posing the open question of why this might be.

scholarly journals From systems to structure — using genetic data to model protein structures

2022 ◽  
Author(s):  
Hannes Braberg ◽  
Ignacia Echeverria ◽  
Robyn M. Kaake ◽  
Andrej Sali ◽  
Nevan J. Krogan
Keyword(s):  
Protein Structures ◽  
Genetic Data ◽  
Model Protein

scholarly journals Structure and Topology Prediction of Phage Adhesion Devices Using AlphaFold2: The Case of Two Oenococcus oeni Phages

2021 ◽  
Vol 9 (10) ◽  
pp. 2151
Author(s):  
Adeline Goulet ◽  
Christian Cambillau
Keyword(s):  
Protein Structures ◽  
Oenococcus Oeni ◽  
Economic Losses ◽  
Carbohydrate Binding ◽  
And Topology ◽  
Carbohydrate Binding Modules ◽  
Predict Protein Structure ◽  
Infection Mechanisms ◽  
Dependent Processes ◽  
Binding Mechanisms

Lactic acid bacteria (LAB) are important microorganisms in food fermentation. In the food industry, bacteriophages (phages or bacterial viruses) may cause the disruption of LAB-dependent processes with product inconsistencies and economic losses. LAB phages use diverse adhesion devices to infect their host, yet the overall picture of host-binding mechanisms remains incomplete. Here, we aimed to determine the structure and topology of the adhesion devices of two lytic siphophages, OE33PA and Vinitor162, infecting the wine bacteria Oenococcus oeni. These phages possess adhesion devices with a distinct composition and morphology and likely use different infection mechanisms. We primarily used AlphaFold2, an algorithm that can predict protein structure with unprecedented accuracy, to obtain a 3D model of the adhesion devices’ components. Using our prior knowledge of the architecture of the LAB phage host-binding machineries, we also reconstituted the topology of OE33PA and Vinitor162 adhesion devices. While OE33PA exhibits original structures in the assembly of its bulky adhesion device, Vinitor162 harbors several carbohydrate-binding modules throughout its long and extended adhesion device. Overall, these results highlight the ability of AlphaFold2 to predict protein structures and illustrate its great potential in the study of phage structures and host-binding mechanisms.

How Accurately Can We Model Protein Structures with Dihedral Angles?

2012 ◽  
pp. 274-287
Author(s):  
Xuefeng Cui ◽  
Shuai Cheng Li ◽  
Dongbo Bu ◽  
Babak Alipanahi Ramandi ◽  
Ming Li
Keyword(s):  
Protein Structures ◽  
Dihedral Angles ◽  
Model Protein

Refined template selection and combination algorithm significantly improves template-based modeling accuracy

2019 ◽  
Vol 17 (02) ◽  
pp. 1950006 ◽  
Author(s):  
Ashish Runthala ◽  
Shibasish Chowdhury
Keyword(s):  
Structural Information ◽  
Protein Structures ◽  
Comparative Modeling ◽  
Target Sequence ◽  
Sequence Alignments ◽  
Multiple Sequence ◽  
Multiple Sequence Alignments ◽  
Modeling Accuracy ◽  
Model Protein ◽  
Rank And Select

In contrast to ab-initio protein modeling methodologies, comparative modeling is considered as the most popular and reliable algorithm to model protein structure. However, the selection of the best set of templates is still a major challenge. An effective template-ranking algorithm is developed to efficiently select only the reliable hits for predicting the protein structures. The algorithm employs the pairwise as well as multiple sequence alignments of template hits to rank and select the best possible set of templates. It captures several key sequences and structural information of template hits and converts into scores to effectively rank them. This selected set of templates is used to model a target. Modeling accuracy of the algorithm is tested and evaluated on TBM-HA domain containing CASP8, CASP9 and CASP10 targets. On an average, this template ranking and selection algorithm improves GDT-TS, GDT-HA and TM_Score by 3.531, 4.814 and 0.022, respectively. Further, it has been shown that the inclusion of structurally similar templates with ample conformational diversity is crucial for the modeling algorithm to maximally as well as reliably span the target sequence and construct its near-native model. The optimal model sampling also holds the key to predict the best possible target structure.

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