scholarly journals Structure Identification and Quality Assessment of Laccase (Lac InaCC) from Neurospora crassa by Using a Structure Prediction

2021 ◽  
Vol 28 (1) ◽  
pp. 1
Author(s):  
Rini Kurniasih ◽  
Laksmi Ambarsari ◽  
Setyanto Tri Wahyudi
Keyword(s):  
Neurospora Crassa ◽  
Structure Prediction ◽  
Md Simulation ◽  
Structure Identification ◽  
Copper Binding ◽  
Sequence Motifs ◽  
Typical Structure ◽  
Stable Conformation ◽  
Structure Quality ◽  
Typical Sequence

Laccases are multi-copper oxidase enzyme, developed for being applied widely. The laccase gene in this study was isolated from local isolates of Neurospora crassa (LAC inaCC). The structure of this enzyme has not been known and there is no laccase structure of Neurospora crassa based on protein structure development in database. Here, we aimed to analyze the characteristics of the sequence and prediction structure, the structure quality after refinement through the molecular dynamics (MD) simulation method. LAC inaCC has been identified with typical sequence motifs (HWH, HSH, HXXH) which played role in copper-binding on 274(HWH)G-DG-T-CP on CBL-1, 314GT-WY(HSH)FS-QYG-G on CBL-2, and 607HPIHL on CBL-3. The four copper atoms have an important role in the catalytic activity. LAC inaCC is a multi-subunit enzyme consisted of three functional domains with structural motifs of Greek-key β barrel which is typical structure motif. Refinement in the prediction structure through the MD simulation showed that this method was proven to be able to improve the structure quality. The increase on the most favoured area on Ramachandran plot, clashcore percentile score, and molprobity score showed that the laccase structure headed to conformation change, to be more stable conformation with better resolution compared to earlier prediction structure.

Computational Evidences of Phytochemical Mediated Disruption of PLpro Driven Replication of SARS-CoV-2: A Therapeutic Approach Against COVID-19

2020 ◽  
Vol 21 ◽  
Author(s):  
Acharya Balkrishna ◽  
Rashmi Mittal ◽  
Vedpriya Arya
Keyword(s):  
Molecular Docking ◽  
Md Simulation ◽  
Bond Distance ◽  
Immunological Response ◽  
Docking Study ◽  
Receptor Interaction ◽  
Molecular Docking Study ◽  
Replication Process ◽  
Stable Conformation ◽  
Distance Analysis

Background:: COVID-19 caused by SARS-CoV-2 has been declared as global pandemic by WHO. Comprehensive analysis of this unprecedented outbreak may help to fight against the disease and may play a pivotal role in decreasing the mortality rate linked with it. Papain like protease (PLpro), a multifunctional polyprotein facilitates the replication of SARS-CoV-2 and evades it from the host immunological response by antagonizing cytokines, interferons and may be considered as potential drug target to combat the current pandemic. Methods:: Natural moieties obtained from medicinal plants were analysed for their potency to target PLpro of SARS-CoV-2 by molecular docking study and were compared with synthetic analogs named as remdesivir, chloroquine and favipiravir. The stability of complexes of top hits was analysed by MD Simulation and interaction energy was calculated. Furthermore, average RMSD values were computed and deepsite ligand binding pockets were predicted using Play Molecule. Drug like abilities of these moieties were determined using ADMET and bond distance between the ligand and active site was assessed to predict the strength of interaction. Results:: Nimbocinol (-7.6 Kcal/mol) and sage (-7.3 Kcal/mol) exhibited maximum BA against PLpro SARS-CoV-2 as evident from molecular docking study which was found to be even better than remdesivir (-6.1 Kcal/mol), chloroquine (-5.3 Kcal/mol) and favipiravir (-5.7 Kcal/mol). Both nimbocinol-PLpro and sage-PLpro SARS-CoV-2 complex exhibited stable conformation during MD Simulation of 101ns at 310 K and potential, kinetic and electrostatic interaction energies were computed which was observed to be concordant with results of molecular docking study. RMSD average values were found to be 0.496 ± 0.015 Å and 0.598 ± 0.023 Å for nimbocinol and sage respectively thus revealing that both the deviation and fluctuations during MD Simulation were observed to be least. Deepsite prediction disclosed that both compounds occupied cryptic pockets in receptor and non-bond distance analysis revealed the formation of hydrogen bonds during ligand-receptor interaction. ADMET exploration further validated the drug like properties of these compounds. Conclusion:: Present study revealed that active constituents of Azadirachta indica and Salvia officinalis can be potentially used to target SARS-CoV-2 by hindering its replication process.

scholarly journals Polypeptide p41 of a Norwalk-Like Virus Is a Nucleic Acid-Independent Nucleoside Triphosphatase

2001 ◽  
Vol 75 (4) ◽  
pp. 1611-1619 ◽  
Author(s):  
Thomas Pfister ◽  
Eckard Wimmer
Keyword(s):  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Open Reading Frames ◽  
Nucleoside Triphosphate ◽  
Sequence Information ◽  
Sequence Motifs ◽  
Alpha Helices ◽  
Consensus Sequences ◽  
First Time

ABSTRACT Southampton virus (SHV) is a member of the Norwalk-like viruses (NLVs), one of four genera of the family Caliciviridae. The genome of SHV contains three open reading frames (ORFs). ORF 1 encodes a polyprotein that is autocatalytically processed into six proteins, one of which is p41. p41 shares sequence motifs with protein 2C of picornaviruses and superfamily 3 helicases. We have expressed p41 of SHV in bacteria. Purified p41 exhibited nucleoside triphosphate (NTP)-binding and NTP hydrolysis activities. The NTPase activity was not stimulated by single-stranded nucleic acids. SHV p41 had no detectable helicase activity. Protein sequence comparison between the consensus sequences of NLV p41 and enterovirus protein 2C revealed regions of high similarity. According to secondary structure prediction, the conserved regions were located within a putative central domain of alpha helices and beta strands. This study reveals for the first time an NTPase activity associated with a calicivirus-encoded protein. Based on enzymatic properties and sequence information, a functional relationship between NLV p41 and enterovirus 2C is discussed in regard to the role of 2C-like proteins in virus replication.

scholarly journals Motif Discovery in Protein Structure Databases

1999 ◽  
Author(s):  
Janice Glasgow ◽  
Evan Steeg
Keyword(s):  
Protein Structure ◽  
Protein Sequence ◽  
Structure Prediction ◽  
Sequence Motifs ◽  
Computational Molecular Biology ◽  
X Ray ◽  
X Ray Crystallography ◽  
Efficiency And Effectiveness ◽  
Structure Prediction Program ◽  
Automated Discovery

The field of knowledge discovery is concerned with the theory and processes involved in the representation and extraction of patterns or motifs from large databases. Discovered patterns can be used to group data into meaningful classes, to summarize data, or to reveal deviant entries. Motifs stored in a database can be brought to bear on difficult instances of structure prediction or determination from X-ray crystallography or nuclear magnetic resonance (NMR) experiments. Automated discovery techniques are central to understanding and analyzing the rapidly expanding repositories of protein sequence and structure data. This chapter deals with the discovery of protein structure motifs. A motif is an abstraction over a set of recurring patterns observed in a dataset; it captures the essential features shared by a set of similar or related objects. In many domains, such as computer vision and speech recognition, there exist special regularities that permit such motif abstraction. In the protein science domain, the regularities derive from evolutionary and biophysical constraints on amino acid sequences and structures. The identification of a known pattern in a new protein sequence or structure permits the immediate retrieval and application of knowledge obtained from the analysis of other proteins. The discovery and manipulation of motifs—in DNA, RNA, and protein sequences and structures—is thus an important component of computational molecular biology and genome informatics. In particular, identifying protein structure classifications at varying levels of abstraction allows us to organize and increase our understanding of the rapidly growing protein structure datasets. Discovered motifs are also useful for improving the efficiency and effectiveness of X-ray crystallographic studies of proteins, for drug design, for understanding protein evolution, and ultimately for predicting the structure of proteins from sequence data. Motifs may be designed by hand, based on expert knowledge. For example, the Chou-Fasman protein secondary structure prediction program (Chou and Fasman, 1978), which dominated the field for many years, depended on the recognition of predefined, user-encoded sequence motifs for α-helices and β-sheets. Several hundred sequence motifs have been cataloged in PROSITE (Bairoch, 1992); the identification of one of these motifs in a novel protein often allows for immediate function interpretation.

scholarly journals Identify High-Quality Protein Structural Models by EnhancedK-Means

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Hongjie Wu ◽  
Haiou Li ◽  
Min Jiang ◽  
Cheng Chen ◽  
Qiang Lv ◽  
...  
Keyword(s):  
Structure Prediction ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Structural Models ◽  
Comparative Modeling ◽  
Critical Issue ◽  
Dimensional Structure ◽  
Structure Identification ◽  
High Quality ◽  
High Quality Protein

Background.One critical issue in protein three-dimensional structure prediction using either ab initio or comparative modeling involves identification of high-quality protein structural models from generated decoys. Currently, clustering algorithms are widely used to identify near-native models; however, their performance is dependent upon different conformational decoys, and, for some algorithms, the accuracy declines when the decoy population increases.Results.Here, we proposed two enhancedK-means clustering algorithms capable of robustly identifying high-quality protein structural models. The first one employs the clustering algorithm SPICKER to determine the initial centroids for basicK-means clustering (SK-means), whereas the other employs squared distance to optimize the initial centroids (K-means++). Our results showed thatSK-means andK-means++ were more robust as compared with SPICKER alone, detecting 33 (59%) and 42 (75%) of 56 targets, respectively, with template modeling scores better than or equal to those of SPICKER.Conclusions.We observed that the classicK-means algorithm showed a similar performance to that of SPICKER, which is a widely used algorithm for protein-structure identification. BothSK-means andK-means++ demonstrated substantial improvements relative to results from SPICKER and classicalK-means.

SCOT: Rethinking the classification of secondary structure elements

2019 ◽  
Vol 36 (8) ◽  
pp. 2417-2428
Author(s):  
Tobias Brinkjost ◽  
Christiane Ehrt ◽  
Oliver Koch ◽  
Petra Mutzel
Keyword(s):  
Secondary Structure ◽  
Structure Prediction ◽  
Secondary Structure Prediction ◽  
Structural Alignment ◽  
Secondary Structure Element ◽  
Structure Alignment ◽  
Supplementary Information ◽  
Structure Quality ◽  
Alignment Algorithms ◽  
Geometric Consistency

Abstract Motivation Secondary structure classification is one of the most important issues in structure-based analyses due to its impact on secondary structure prediction, structural alignment and protein visualization. There are still open challenges concerning helix and sheet assignments which are currently not addressed by a single multi-purpose software. Results We introduce SCOT (Secondary structure Classification On Turns) as a novel secondary structure element assignment software which supports the assignment of turns, right-handed α-, 310- and π-helices, left-handed α- and 310-helices, 2.27- and polyproline II helices, β-sheets and kinks. We demonstrate that the introduction of helix Purity values enables a clear differentiation between helix classes. SCOT’s unique strengths are highlighted by comparing it to six state-of-the-art methods (DSSP, STRIDE, ASSP, SEGNO, DISICL and SHAFT). The assignment approaches were compared concerning geometric consistency, protein structure quality and flexibility dependency and their impact on secondary structure element-based structural alignments. We show that only SCOT’s combination of hydrogen bonds, geometric criteria and dihedral angles enables robust assignments independent of the structure quality and flexibility. We demonstrate that this combination and the elaborate kink detection lead to SCOT’s clear superiority for protein alignments. As the resulting helices and strands are provided in a PDB conform output format, they can immediately be used for structure alignment algorithms. Taken together, the application of our new method and the straight-forward visualization using the accompanying PyMOL scripts enable the comprehensive analysis of regular backbone geometries in proteins. Availability and implementation https://this-group.rocks Supplementary information Supplementary data are available at Bioinformatics online.

The structural repertoire of Fusarium oxysporum f. sp. lycopersici effectors revealed by experimental and computational studies

2021 ◽  
Author(s):  
Daniel S. Yu ◽  
Megan A Outram ◽  
Ashley Smith ◽  
Carl L McCombe ◽  
Pravin B Khambalkar ◽  
...  
Keyword(s):  
Fusarium Oxysporum ◽  
Structure Prediction ◽  
Plant Pathogens ◽  
Plant Immunity ◽  
Sequence Motifs ◽  
Structural Class ◽  
X Ray Crystallography ◽  
Pathogen Virulence ◽  
Plant Infection ◽  
Dual Domain

Plant pathogens secrete proteins, known as effectors, that function in the apoplast and inside plant cells to promote virulence. Effectors can also be detected by cell-surface and cytosolic receptors, resulting in the activation of defence pathways and plant immunity. Our understanding of fungal effector function and detection by immunity receptors is limited largely due to high sequence diversity and lack of identifiable sequence motifs precluding prediction of structure or function. Recent studies have demonstrated that fungal effectors can be grouped into structural classes despite significant sequence variation. Using protein x-ray crystallography, we identify a new structural class of effectors hidden within the secreted in xylem (SIX) effectors from Fusarium oxysporum f. sp. lycopersici (Fol). The recognised effectors Avr1 (SIX4) and Avr3 (SIX1) represent the founding members of the Fol dual-domain (FOLD) effector class. Using AlphaFold ab initio protein structure prediction, benchmarked against the experimentally determined structures, we demonstrate SIX6 and SIX13 are FOLD effectors. We show that the conserved N-domain of Avr1 and Avr3 is sufficient for recognition by their corresponding, but structurally-distinct, immunity receptors. Additional structural prediction and comparison indicate that 11 of the 14 SIX effectors group into four structural families. This revealed that genetically linked effectors are related structurally, and we provide direct evidence for a physical association between one divergently-transcribed effector pair. Collectively, these data indicate that Fol secretes groups of structurally-related molecules during plant infection, an observation that has broad implications for our understanding of pathogen virulence and the engineering of plant immunity receptors.

Sign in / Sign up

Export Citation Format

Share Document