scholarly journals Prediction of polyproline II secondary structure propensity in proteins

2020 ◽  
Vol 7 (1) ◽  
pp. 191239 ◽  
Author(s):  
Kevin T. O’Brien ◽  
Catherine Mooney ◽  
Cyril Lopez ◽  
Gianluca Pollastri ◽  
Denis C. Shields
Keyword(s):  
Secondary Structure ◽  
Large Scale ◽  
External Validation ◽  
Three Dimensional ◽  
Binding Domains ◽  
Left Handed ◽  
Polyproline Ii ◽  
Polyproline Ii Helix ◽  
Binding Peptides ◽  
Validation Set

Background: The polyproline II helix (PPIIH) is an extended protein left-handed secondary structure that usually but not necessarily involves prolines. Short PPIIHs are frequently, but not exclusively, found in disordered protein regions, where they may interact with peptide-binding domains. However, no readily usable software is available to predict this state. Results: We developed PPIIPRED to predict polyproline II helix secondary structure from protein sequences, using bidirectional recurrent neural networks trained on known three-dimensional structures with dihedral angle filtering. The performance of the method was evaluated in an external validation set. In addition to proline, PPIIPRED favours amino acids whose side chains extend from the backbone (Leu, Met, Lys, Arg, Glu, Gln), as well as Ala and Val. Utility for individual residue predictions is restricted by the rarity of the PPIIH feature compared to structurally common features. Conclusion: The software, available at http://bioware.ucd.ie/PPIIPRED , is useful in large-scale studies, such as evolutionary analyses of PPIIH, or computationally reducing large datasets of candidate binding peptides for further experimental validation.

scholarly journals Left-handed polyproline-II helix revisited: proteins causing proteopathies

2016 ◽  
Vol 35 (12) ◽  
pp. 2701-2713 ◽  
Author(s):  
Alexei A. Adzhubei ◽  
Anastasia A. Anashkina ◽  
Alexander A. Makarov
Keyword(s):  
Left Handed ◽  
Polyproline Ii ◽  
Polyproline Ii Helix

scholarly journals Super Secondary Structure Consisting of a Polyproline II Helix and a β-Turn in Leucine Rich Repeats in Bacterial Type III Secretion System Effectors

2018 ◽  
Vol 37 (3) ◽  
pp. 223-236 ◽  
Author(s):  
Dashdavaa Batkhishig ◽  
Khurelbaatar Bilguun ◽  
Purevjav Enkhbayar ◽  
Hiroki Miyashita ◽  
Robert H. Kretsinger ◽  
...  
Keyword(s):  
Secondary Structure ◽  
Type Iii Secretion ◽  
Type Iii Secretion System ◽  
Secretion System ◽  
Type Iii ◽  
Polyproline Ii ◽  
Polyproline Ii Helix ◽  
Leucine Rich Repeats ◽  
Bacterial Type

Polyproline II Helix as a Recognition Motif of Plant Peptide Hormones and Flagellin Peptide flg22

2019 ◽  
Vol 26 (9) ◽  
pp. 684-690
Author(s):  
Norio Matsushima ◽  
Hiroki Miyashita ◽  
Shinsuke Tamaki ◽  
Robert H. Kretsinger
Keyword(s):  
Bound States ◽  
Peptide Hormones ◽  
Structural Features ◽  
Small Peptides ◽  
Left Handed ◽  
Polyproline Ii ◽  
Polyproline Ii Helix ◽  
Signaling Peptides ◽  
Leucine Rich Repeats ◽  
Atomic Coordinates

Background: Plant peptide hormones play a crucial role in plant growth and development. A group of these peptide hormones are signaling peptides with 5 - 23 amino acids. Flagellin peptide (flg22) also elicits an immune response in plants. The functions are expressed through recognition of the peptide hormones and flg22. This recognition relies on membrane localized receptor kinases with extracellular leucine rich repeats (LRR-RKs). The structures of plant peptide hormones - AtPep1, IDA, IDL1, RGFs 1- 3, TDIF/CLE41 - and of flg22 complexed with LRR domains of corresponding LRRRKs and co-receptors SERKs have been determined. However, their structures are well not analyzed and characterized in detail. The structures of PIP, CEP, CIF, and HypSys are still unknown. Objective: Our motivation is to clarify structural features of these plant, small peptides and Flg22 in their bound states. Methods: In this article, we performed secondary structure assignments and HELFIT analyses (calculating helix axis, pitch, radius, residues per turn, and handedness) based on the atomic coordinates from the crystal structures of AtPep1, IDA, IDL1, RGFs 1- 3, TDIF/CLE41 - and of flg22. We also performed sequence analysis of the families of PIP, CEP, CIF, and HypSys in order to predict their secondary structures. Results: Following AtPep1 with 23 residues adopts two left handed polyproline helices (PPIIs) with six and four residues. IDA, IDL1, RGFs 1 - 2, and TDIF/CLE41 with 12 or 13 residues adopt a four residue PPII; RGF3 adopts two PPIIs with four residues. Flg22 with 22 residues also adopts a six residue PPII. The other peptide hormones – PIP, CEP, CIF, and HypSys – that are rich in proline or hydroxyproline presumably prefer PPII. Conclusion: The present analysis indicates that PPII helix in the plant small peptide hormones and in flg22 is crucial for recognition of the LRR domains in receptors.

Host−Guest Study of Left-Handed Polyproline II Helix Formation†

Biochemistry ◽  
2001 ◽  
Vol 40 (48) ◽  
pp. 14376-14383 ◽  
Author(s):  
Melissa A. Kelly ◽  
Brian W. Chellgren ◽  
Adam L. Rucker ◽  
Jerry M. Troutman ◽  
Michael G. Fried ◽  
...  
Keyword(s):  
Helix Formation ◽  
Left Handed ◽  
Polyproline Ii ◽  
Polyproline Ii Helix

scholarly journals Structural and Functional Analyses of PolyProline-II helices in Globular Proteins

2016 ◽  
Author(s):  
Prasun Kumar ◽  
Manju Bansal
Keyword(s):  
Protein Structures ◽  
Three Dimensional ◽  
Aromatic Amino Acids ◽  
Dimensional Structure ◽  
Protein Chain ◽  
Left Handed ◽  
Polyproline Ii ◽  
Backbone Torsion Angle ◽  
Functional Analyses ◽  
Flanking Regions

PolyProline-II (PPII) helices are defined as a continuous stretch of a protein chain in which the constituent residues have the backbone torsion angle (φ,ψ) values of (-75°, 145°) and take up extended left handed conformation, lacking any intra-helical hydrogen bonds. They are found to occur very frequently in protein structures with their number exceeding that of π-helices, though it is considerably less than that of α-helices and β-strands. A relatively new procedure, ASSP, for the identification of regular secondary structures using Cα trace identifies 3597 PPII helices in 3582 protein chains, solved at resolution ≤ 2.5Å. Taking advantage of this significantly expanded database of PPII-helices, we have analyzed the functional and structural roles of PPII helices as well as determined the amino acid propensity within and around them. Though Pro residues are highly preferred, it is not a mandatory condition for the formation of PPII-helices, since ~40% PPII-helices were found to contain no Proline residues. Aromatic amino acids are avoided within this helix, while Gly, Asn and Asp residues are preferred in the proximal flanking regions. These helices range from 3 to 13 residues in length with the average twist and rise being -121.2°±9.2° and 3.0ű0.1Å respectively. A majority (~72%) of PPII-helices were found to occur in conjunction with α-helices and β-strands, and serve as linkers as well. The analysis of various intra-helical non-bonded interactions revealed frequent presence of C-H...O H-bonds. PPII-helices participate in maintaining the three-dimensional structure of proteins and are important constituents of binding motifs involved in various biological functions.

scholarly journals Density of Deep Eutectic Solvents: The Path Forward Cheminformatics-Driven Reliable Predictions for Mixtures

Molecules ◽  
2021 ◽  
Vol 26 (19) ◽  
pp. 5779
Author(s):  
Amit Kumar Halder ◽  
Reza Haghbakhsh ◽  
Iuliia V. Voroshylova ◽  
Ana Rita C. Duarte ◽  
M. Natalia D. S. Cordeiro
Keyword(s):  
Large Scale ◽  
Predictive Accuracy ◽  
External Validation ◽  
Deep Eutectic Solvents ◽  
Structural Features ◽  
Industrial Applications ◽  
Structure Property ◽  
Consensus Prediction ◽  
Validation Set ◽  
Set Up

Deep eutectic solvents (DES) are often regarded as greener sustainable alternative solvents and are currently employed in many industrial applications on a large scale. Bearing in mind the industrial importance of DES—and because the vast majority of DES has yet to be synthesized—the development of cheminformatic models and tools efficiently profiling their density becomes essential. In this work, after rigorous validation, quantitative structure-property relationship (QSPR) models were proposed for use in estimating the density of a wide variety of DES. These models were based on a modelling dataset previously employed for constructing thermodynamic models for the same endpoint. The best QSPR models were robust and sound, performing well on an external validation set (set up with recently reported experimental density data of DES). Furthermore, the results revealed structural features that could play crucial roles in ruling DES density. Then, intelligent consensus prediction was employed to develop a consensus model with improved predictive accuracy. All models were derived using publicly available tools to facilitate easy reproducibility of the proposed methodology. Future work may involve setting up reliable, interpretable cheminformatic models for other thermodynamic properties of DES and guiding the design of these solvents for applications.

Host-guest scale of left-handed polyproline II helix formation

2003 ◽  
Vol 53 (1) ◽  
pp. 68-75 ◽  
Author(s):  
Adam L. Rucker ◽  
Cara T. Pager ◽  
Margaret N. Campbell ◽  
Joseph E. Qualls ◽  
Trevor P. Creamer
Keyword(s):  
Helix Formation ◽  
Left Handed ◽  
Polyproline Ii ◽  
Polyproline Ii Helix

Identification of local variations within secondary structures of proteins

2015 ◽  
Vol 71 (5) ◽  
pp. 1077-1086 ◽  
Author(s):  
Prasun Kumar ◽  
Manju Bansal
Keyword(s):  
Protein Structure ◽  
Secondary Structure ◽  
Structural Biology ◽  
Operating Systems ◽  
Protein Secondary Structure ◽  
Secondary Structures ◽  
Data Set ◽  
Left Handed ◽  
Polyproline Ii ◽  
Helical Parameters

Secondary-structure elements (SSEs) play an important role in the folding of proteins. Identification of SSEs in proteins is a common problem in structural biology. A new method,ASSP(Assignment ofSecondaryStructure inProteins), using only the path traversed by the Cαatoms has been developed. The algorithm is based on the premise that the protein structure can be divided into continuous or uniform stretches, which can be defined in terms of helical parameters, and depending on their values the stretches can be classified into different SSEs, namely α-helices, 310-helices, π-helices, extended β-strands and polyproline II (PPII) and other left-handed helices. The methodology was validated using an unbiased clustering of these parameters for a protein data set consisting of 1008 protein chains, which suggested that there are seven well defined clusters associated with different SSEs. Apart from α-helices and extended β-strands, 310-helices and π-helices were also found to occur in substantial numbers.ASSPwas able to discriminate non-α-helical segments from flanking α-helices, which were often identified as part of α-helices by other algorithms.ASSPcan also lead to the identification of novel SSEs. It is believed thatASSPcould provide a better understanding of the finer nuances of protein secondary structure and could make an important contribution to the better understanding of comparatively less frequently occurring structural motifs. At the same time, it can contribute to the identification of novel SSEs. A standalone version of the program for the Linux as well as the Windows operating systems is freely downloadable and a web-server version is also available at http://nucleix.mbu.iisc.ernet.in/assp/index.php.

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