Modelling of the Citrus CCD4 Family Members: In Silico Analysis of Membrane Binding and Substrate Preference

Coloring is one of the most important characteristics in commercial flowers and fruits, generally due to the accumulation of carotenoid pigments. Enzymes of the CCD4 family in citrus intervene in the generation of β-citraurin, an apocarotenoid responsible for the reddish-orange color of mandarins. Citrus CCD4s enzymes could be capable of interacting with the thylakoid membrane inside chloroplasts. However, to date, this interaction has not been studied in detail. In this work, we present three new complete models of the CCD4 family members (CCD4a, CCD4b, and CCD4c), modeled with a lipid membrane. To identify the preference for substrates, typical carotenoids were inserted in the active site of the receptors and the protein–ligand interaction energy was evaluated. The results show a clear preference of CCD4s for xanthophylls over aliphatic carotenes. Our findings indicate the ability to penetrate the membrane and maintain a stable interaction through the N-terminal α-helical domain, spanning a contact surface of 2250 to 3250 Å2. The orientation and depth of penetration at the membrane surface suggest that CCD4s have the ability to extract carotenoids directly from the membrane through a tunnel consisting mainly of hydrophobic residues that extends up to the catalytic center of the enzyme.

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Identification of two novel COL10A1 heterozygous mutations in two Chinese pedigrees with Schmid-type metaphyseal chondrodysplasia

10.21203/rs.2.11650/v2 ◽

2019 ◽

Author(s):

Lingchi Kong ◽

Li Shi ◽

Wenbo Wang ◽

Rongtai Zuo ◽

Mengwei Wang ◽

...

Keyword(s):

Family Members ◽

In Silico Analysis ◽

Coxa Vara ◽

Missense Mutations ◽

Autosomal Dominant Disorder ◽

Metaphyseal Chondrodysplasia ◽

Whole Exome ◽

Col10a1 Gene ◽

Silico Analysis ◽

Nc1 Domain

Abstract Background: Schmid-type metaphyseal chondrodysplasia (MCDS) is an autosomal dominant disorder caused by COL10A1 mutations, which is characterized by short stature, waddling gait, coxa vara and bowing of the long bones. However, descriptions of the expressivity of MCDS are rare. Methods: Two probands and available family members affected with MCDS were subjected to clinical and radiological examination. Genomic DNA of all affected individuals was subjected to whole-exome sequencing, and candidate mutations were verified by Sanger sequencing in all available family members and in 250 normal control donors. A spatial model of the type X collagen (α1) C-terminal noncollagenous (NC1) domain was further constructed. Results: We found that the phenotype of affected family members exhibited irregular dominance. Mutation analysis indicated that there were two novel heterozygous missense mutations, [c.1765T>A (p.Phe589Ile)] and [c.1846A>G (p.Lys616Glu)] in the COL10A1 gene in family 1 and 2, respectively. The two novel substitution sites were highly conserved and the mutations were predicted to be deleterious by in silico analysis. Furthermore, protein modeling revealed that the two substitutions were located in the NC1 domain of collagen X (α1), which potentially impacted the trimerization of collagen X (α1) and combination with molecules in the pericellular matrix. Conclusion: Two novel mutations were identified in the present study, which will facilitate diagnosis of MCDS and further expand the spectrum of the COL10A1 mutations associated with MCDS patients. In addition, our research revealed the phenomenon of irregular dominance in MCDS.

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A Genotype-Phenotype Correlation of Haemophilia a in Victorian Patients with a Description of Novel Mutations

Blood ◽

10.1182/blood-2018-99-112721 ◽

2018 ◽

Vol 132 (Supplement 1) ◽

pp. 2498-2498

Author(s):

Shreerang Sirdesai ◽

Kerryn Weekes ◽

Asif Alam ◽

Huyen A Tran ◽

Christopher Barnes ◽

...

Keyword(s):

In Silico ◽

Family Members ◽

In Silico Analysis ◽

Clinical Severity ◽

Missense Mutations ◽

Phenotype Correlation ◽

Novel Mutations ◽

Mild Phenotype ◽

Genotype Phenotype Correlation ◽

Silico Analysis

Abstract Aim: Hemophilia A (HA) is caused by abnormalities in the Factor VIII gene. Certain abnormalities correlate with disease severity. Here, we report the genotype-phenotype correlation for all Victorian HA patients. Methods: Using the Australian Bleeding Disorders Registry, Victorian HA patients were identified. All genetic testing was conducted at Southern Health. The testing algorithm is summarized in Figure 1. Mutations were compared with the list of known Factor 8 mutations on the Champ and EAHAD F8 Variant Databases. A PubMed search was undertaken for any mutations not on either database. If this too was unrevealing, the mutation was designated novel. In-silico analysis was conducted on all novel mutations using three open-access, online prediction tools: a) Mutation Taster; b) Poly-Phen 2; c) Human Splice Site Predictor. Results: 318 patients with matched clinical and genetic records were identified. 275 had known FVIII mutations and 36 novel FVIII mutations were discovered. Eight patients (3%) had no mutations identified. (Table 1) In severe HA the intron-22 inversion was the most common mutation (47/122, 38%). Missense mutations predominated in mild and moderate HA. Inhibitors were present in 44/318 patients, the majority of whom had 26/44 (59%) severe HA. 20/36 novel mutations (55%) were associated with severe HA, 12/36 (33%) with mild HA and 4/36 (11%) with a moderate HA. Novel mutations associated with non-severe phenotypes were mostly missense mutations (15/16); More diversity was seen in the novel mutations causing a severe HA with a fairly even distribution of mutations: missense (7/20), nonsense (4/20) and small deletions and insertions (8/20). One large deletion involving a 6.5kb region of exon 26, as well as one duplication of exons 7 to 9 - was seen in the severe group. In-silico analysis predicted that all novel severe HA mutations were likely to be pathogenic.Inhibitors were seen in 7 patients with novel mutations. Of the 36 novel mutations we described, 9/36 (25%) were seen in other family members - often female carriers. All 9 mutations caused a severe phenotype which is not unexpected given that the screening and testing of family members would be unlikely to take place in patients who have a mild phenotype and rarely require supportive medical care Conclusion: This study adds 36 novel mutations to the currently known FVIII haemophilic mutations. It also confirms that the frequency and correlative clinical severity of known genetic mutations in the Victorian HA cohort is similar to that described internationally. Disclosures No relevant conflicts of interest to declare.

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Interaction of Spike protein and lipid membrane of SARS-CoV-2 with Ursodeoxycholic acid, an in-silico analysis

Scientific Reports ◽

10.1038/s41598-021-01705-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Francisco Javier Rodal Canales ◽

Laura Pérez-Campos Mayoral ◽

María Teresa Hernández-Huerta ◽

Luis Manuel Sánchez Navarro ◽

Carlos Alberto Matias-Cervantes ◽

...

Keyword(s):

Ursodeoxycholic Acid ◽

Lipid Membrane ◽

In Silico Analysis ◽

Spike Protein ◽

Heptad Repeat ◽

Alpha Helices ◽

Cholesterol Gallstones ◽

A Value ◽

Cholesterol Solubilization ◽

Silico Analysis

AbstractNumerous repositioned drugs have been sought to decrease the severity of SARS-CoV-2 infection. It is known that among its physicochemical properties, Ursodeoxycholic Acid (UDCA) has a reduction in surface tension and cholesterol solubilization, it has also been used to treat cholesterol gallstones and viral hepatitis. In this study, molecular docking was performed with the SARS-CoV-2 Spike protein and UDCA. In order to confirm this interaction, we used Molecular Dynamics (MD) in “SARS-CoV-2 Spike protein-UDCA”. Using another system, we also simulated MD with six UDCA residues around the Spike protein at random, naming this “SARS-CoV-2 Spike protein-6UDCA”. Finally, we evaluated the possible interaction between UDCA and different types of membranes, considering the possible membrane conformation of SARS-CoV-2, this was named “SARS-CoV-2 membrane-UDCA”. In the “SARS-CoV-2 Spike protein-UDCA”, we found that UDCA exhibits affinity towards the central region of the Spike protein structure of − 386.35 kcal/mol, in a region with 3 alpha helices, which comprises residues from K986 to C1032 of each monomer. MD confirmed that UDCA remains attached and occasionally forms hydrogen bonds with residues R995 and T998. In the presence of UDCA, we observed that the distances between residues atoms OG1 and CG2 of T998 in the monomers A, B, and C in the prefusion state do not change and remain at 5.93 ± 0.62 and 7.78 ± 0.51 Å, respectively, compared to the post-fusion state. Next, in “SARS-CoV-2 Spike protein-6UDCA”, the three UDCA showed affinity towards different regions of the Spike protein, but only one of them remained bound to the region between the region's heptad repeat 1 and heptad repeat 2 (HR1 and HR2) for 375 ps of the trajectory. The RMSD of monomer C was the smallest of the three monomers with a value of 2.89 ± 0.32, likewise, the smallest RMSF was also of the monomer C (2.25 ± 056). In addition, in the simulation of “SARS-CoV-2 membrane-UDCA”, UDCA had a higher affinity toward the virion-like membrane; where three of the four residues remained attached once they were close (5 Å, to the centre of mass) to the membrane by 30 ns. However, only one of them remained attached to the plasma-like membrane and this was in a cluster of cholesterol molecules. We have shown that UDCA interacts in two distinct regions of Spike protein sequences. In addition, UDCA tends to stay bound to the membrane, which could potentially reduce the internalization of SARS-CoV-2 in the host cell.

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Lifetime of actin-dependent protein nanoclusters

10.1101/2022.01.06.475299 ◽

2022 ◽

Author(s):

Sumantra Sarkar ◽

Debanjan Goswami

Keyword(s):

Spatial Organization ◽

Lipid Membrane ◽

Simple Extension ◽

Ligand Interaction ◽

Cellular Processes ◽

Wide Range ◽

Dependent Protein ◽

Protein Ligand Interaction ◽

Kinetics Of

Protein nanoclusters (PNCs) are dynamic collections of a few proteins that spatially organize in nanometer length clusters. PNCs are one of the principal forms of spatial organization of membrane proteins and they have been shown or hypothesized to be important in various cellular processes, including cell signaling. PNCs show remarkable diversity in size, shape, and lifetime. In particular, the lifetime of PNCs can vary over a wide range of timescales. The diversity in size and shape can be explained by the interaction of the clustering proteins with the actin cytoskeleton or the lipid membrane, but very little is known about the processes that determine the lifetime of the nanoclusters. In this paper, using mathematical modelling of the cluster dynamics, we model the biophysical processes that determine the lifetime of actin-dependent PNCs. In particular, we investigated the role of actin aster fragmentation, which had been suggested to be a key determinant of the PNC lifetime, and found that it is important only for a small class of PNCs. A simple extension of our model allowed us to investigate the kinetics of protein-ligand interaction near PNCs. We found an anomalous increase in the lifetime of ligands near PNCs, which agrees remarkably well with experimental data on RAS-RAF kinetics. In particular, analysis of the RAS-RAF data through our model provides falsifiable predictions and novel hypotheses that will not only shed light on the role of RAS-RAF kinetics in various cancers, but also will be useful in studying membrane protein clustering in general.

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In-silico analysis of angiotensin converting enzyme 2 (ACE2) of livestock, pet and poultry animals to determine its susceptibility to SARS–CoV- 2 infection

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207323666201110144542 ◽

2020 ◽

Vol 23 ◽

Author(s):

Aman Kumar ◽

Anil Panwar ◽

Kanisht Batra ◽

Sachinandan Dey ◽

Sushila Maan

Keyword(s):

In Silico ◽

Binding Capacity ◽

In Silico Analysis ◽

Ligand Interaction ◽

Angiotensin Converting Enzyme 2 ◽

Animal Management ◽

In Silico Study ◽

Protein Ligand Interaction ◽

Novel Coronavirus ◽

Almost All

Background: Novel coronavirus SARS-CoV-2 is responsible of COVID-19 pandemic. It was first reported in Wuhan, China in December, 2019 and despite the tremendous efforts to control the disease, it has now spread almost all over the world.The interaction of SARS-CoV-2spike protein and its acceptor protein ACE2 is an important issue in determining viralhost range and cross-species infection, while the binding capacity of spike protein toACE2 of different species is unknown. Objective: The present study has been conducted to determine the susceptibility of livestock, poultry and pets to SARSCoV-2. Methods: We evaluated the receptor-utilizing capability of ACE2sfrom various species by sequence alignment,phylogenetic clustering and protein-ligand interaction studies with the currently knownACE2s utilized by SARS-CoV-2. Result: In-silico study predicted that SARS-CoV-2 tends to utilize ACE2s ofvarious animal species with varied possible interactions and theprobability ofthe receptor utilization will be greater in horse and poor in chicken followed by ruminants. Conclusion: Present studypredicted that SARS-CoV-2 tends to utilize ACE2s ofvarious livestock and poultry species with greater probability in equine and poor in chicken. Study may provide important insights into the animal models for SARSCoV-2 and animal management for COVID-19 control.

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A systematic review with in silico analysis on transcriptomic profile of gallbladder carcinoma

Seminars in Oncology ◽

10.1053/j.seminoncol.2020.02.012 ◽

2020 ◽

Vol 47 (6) ◽

pp. 398-408

Author(s):

Sonam Tulsyan ◽

Showket Hussain ◽

Balraj Mittal ◽

Sundeep Singh Saluja ◽

Pranay Tanwar ◽

...

Keyword(s):

Systematic Review ◽

Gallbladder Carcinoma ◽

In Silico ◽

In Silico Analysis ◽

Transcriptomic Profile ◽

Silico Analysis

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In Silico Analysis of Single Nucleotide Polymorphism in Human Prothrombin Gene

10.1055/s-0039-1680245 ◽

2019 ◽

Author(s):

I. Farah ◽

A. El-Mubark ◽

M. Osman ◽

A. Soliman ◽

F. Ali ◽

...

Keyword(s):

Single Nucleotide Polymorphism ◽

In Silico ◽

In Silico Analysis ◽

Nucleotide Polymorphism ◽

Single Nucleotide ◽

Prothrombin Gene ◽

Silico Analysis

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Enalos Suite of Tools: Enhancing Cheminformatics and Nanoinfor - matics through KNIME

Current Medicinal Chemistry ◽

10.2174/0929867327666200727114410 ◽

2020 ◽

Vol 27 (38) ◽

pp. 6523-6535 ◽

Cited By ~ 3

Author(s):

Antreas Afantitis ◽

Andreas Tsoumanis ◽

Georgia Melagraki

Keyword(s):

Data Analysis ◽

Virtual Screening ◽

In Silico ◽

Model Development ◽

In Silico Analysis ◽

Material Design ◽

Efficient Solutions ◽

Biological Data ◽

Cost Efficient ◽

Silico Analysis

Drug discovery as well as (nano)material design projects demand the in silico analysis of large datasets of compounds with their corresponding properties/activities, as well as the retrieval and virtual screening of more structures in an effort to identify new potent hits. This is a demanding procedure for which various tools must be combined with different input and output formats. To automate the data analysis required we have developed the necessary tools to facilitate a variety of important tasks to construct workflows that will simplify the handling, processing and modeling of cheminformatics data and will provide time and cost efficient solutions, reproducible and easier to maintain. We therefore develop and present a toolbox of >25 processing modules, Enalos+ nodes, that provide very useful operations within KNIME platform for users interested in the nanoinformatics and cheminformatics analysis of chemical and biological data. With a user-friendly interface, Enalos+ Nodes provide a broad range of important functionalities including data mining and retrieval from large available databases and tools for robust and predictive model development and validation. Enalos+ Nodes are available through KNIME as add-ins and offer valuable tools for extracting useful information and analyzing experimental and virtual screening results in a chem- or nano- informatics framework. On top of that, in an effort to: (i) allow big data analysis through Enalos+ KNIME nodes, (ii) accelerate time demanding computations performed within Enalos+ KNIME nodes and (iii) propose new time and cost efficient nodes integrated within Enalos+ toolbox we have investigated and verified the advantage of GPU calculations within the Enalos+ nodes. Demonstration data sets, tutorial and educational videos allow the user to easily apprehend the functions of the nodes that can be applied for in silico analysis of data.

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