scholarly journals Computational Screening for the Anticancer Potential of Seed-Derived Antioxidant Peptides: A Cheminformatic Approach

Molecules ◽  
2021 ◽  
Vol 26 (23) ◽  
pp. 7396
Author(s):  
Tsun-Thai Chai ◽  
Jiun-An Koh ◽  
Clara Chia-Ci Wong ◽  
Mohamad Zulkeflee Sabri ◽  
Fai-Chu Wong
Keyword(s):  
Xanthine Oxidase ◽  
Dynamics Simulation ◽  
Virtual Library ◽  
Antioxidant Peptides ◽  
Tyrosine Residues ◽  
Anticancer Peptides ◽  
Computational Screening ◽  
Computational Alanine Scanning ◽  
Wet Lab ◽  
Key Residues

Some seed-derived antioxidant peptides are known to regulate cellular modulators of ROS production, including those proposed to be promising targets of anticancer therapy. Nevertheless, research in this direction is relatively slow owing to the inevitable time-consuming nature of wet-lab experimentations. To help expedite such explorations, we performed structure-based virtual screening on seed-derived antioxidant peptides in the literature for anticancer potential. The ability of the peptides to interact with myeloperoxidase, xanthine oxidase, Keap1, and p47phox was examined. We generated a virtual library of 677 peptides based on a database and literature search. Screening for anticancer potential, non-toxicity, non-allergenicity, non-hemolyticity narrowed down the collection to five candidates. Molecular docking found LYSPH as the most promising in targeting myeloperoxidase, xanthine oxidase, and Keap1, whereas PSYLNTPLL was the best candidate to bind stably to key residues in p47phox. Stability of the four peptide-target complexes was supported by molecular dynamics simulation. LYSPH and PSYLNTPLL were predicted to have cell- and blood-brain barrier penetrating potential, although intolerant to gastrointestinal digestion. Computational alanine scanning found tyrosine residues in both peptides as crucial to stable binding to the targets. Overall, LYSPH and PSYLNTPLL are two potential anticancer peptides that deserve deeper exploration in future.

Xanthine oxidase inhibitory activity of Euphorbia peplus L. phenolics

2021 ◽  
Vol 24 ◽  
Author(s):  
Emadeldin M. Kamel ◽  
Noha A. Ahmed ◽  
Ashraf A. El-Bassuony ◽  
Omnia E. Hussein ◽  
Barakat Alrashdi ◽  
...  
Keyword(s):  
Xanthine Oxidase ◽  
Active Site ◽  
Inhibitory Activity ◽  
Drug Binding ◽  
Dynamics Simulation ◽  
Solvent Accessible Surface Area ◽  
Radius Of Gyration ◽  
Euphorbia Peplus

Background: Various phenolics show inhibitory activity towards xanthine oxidase (XO), an enzyme that generates reactive oxygen species which cause oxidative damage. Objective: This study investigated the XO inhibitory activity of Euphorbia peplus phenolics. Methods: The dried powdered aerial parts of E. peplus were extracted, fractioned and phenolics were isolated and identified. The XO inhibitory activity of E. peplus extract (EPE) and the isolated phenolics was investigated in vitro and in vivo. Results: Three phenolics were isolated from the ethyl acetate fraction of E. peplus. All isolated compounds and the EPE showed inhibitory activity towards XO in vitro. In hyperuricemic rats, EPE and the isolated phenolics decreased uric acid and XO activity. Molecular docking showed the binding modes of isolated phenolics with XO, depicting significant interactions with the active site amino acid residues. Molecular dynamics simulation trajectories confirmed the interaction of isolated phenolics with XO by forming hydrogen bonds with the active site residues. Also, the root mean square (RMS) deviations of XO and phenolics-XO complexes achieved equilibrium and fluctuated during the 10 ns MD simulations. The radius of gyration and solvent accessible surface area investigations showed that different systems were stabilized at ≈ 2500 ps. The RMS fluctuations profile depicted that the drug binding site exhibited a rigidity behavior during the simulation. Conclusion: In vitro, in vivo and computational investigations showed the XO inhibitory activity of E. peplus phenolics. These phenolics might represent promising candidates for the development of XO inhibitors.

scholarly journals Impact of key residues within chloroplast thioredoxin-f on recognition for reduction and oxidation of target proteins

2019 ◽  
Vol 294 (46) ◽  
pp. 17437-17450 ◽  
Author(s):  
Yuichi Yokochi ◽  
Kazunori Sugiura ◽  
Kazuhiro Takemura ◽  
Keisuke Yoshida ◽  
Satoshi Hara ◽  
...  
Keyword(s):  
Disulfide Bonds ◽  
Redox Regulation ◽  
Target Recognition ◽  
Dynamics Simulation ◽  
Target Proteins ◽  
Specific Target ◽  
Redox Responsive ◽  
Key Residues ◽  
Protein Reduction ◽  
The Impact

Thioredoxin (Trx) is a redox-responsive protein that modulates the activities of its target proteins mostly by reducing their disulfide bonds. In chloroplasts, five Trx isoforms (Trx-f, Trx-m, Trx-x, Trx-y, and Trx-z) regulate various photosynthesis-related enzymes with distinct target selectivity. To elucidate the determinants of the target selectivity of each Trx isoform, here we investigated the residues responsible for target recognition by Trx-f, the most well-studied chloroplast-resident Trx. As reported previously, we found that positively-charged residues on the Trx-f surface are involved in the interactions with its targets. Moreover, several residues that are specifically conserved in Trx-f (e.g. Cys-126 and Thr-158) were also involved in interactions with target proteins. The validity of these residues was examined by the molecular dynamics simulation. In addition, we validated the impact of these key residues on target protein reduction by studying (i) Trx-m variants into which we introduced the key residues for Trx-f and (ii) Trx-like proteins, named atypical Cys His-rich Trx 1 (ACHT1) and ACHT2a, that also contain these key residues. These artificial or natural protein variants could reduce Trx-f–specific targets, indicating that the key residues for Trx-f are critical for Trx-f–specific target recognition. Furthermore, we demonstrate that ACHT1 and ACHT2a efficiently oxidize some Trx-f–specific targets, suggesting that its target selectivity also contributes to the oxidative regulation process. Our results reveal the key residues for Trx-f–specific target recognition and uncover ACHT1 and ACHT2a as oxidation factors of their target proteins, providing critical insight into redox regulation of photosynthesis.

scholarly journals Residues R1075, D1090, R1095, and C1130 Are Critical in ADAMTS13 TSP8-Spacer Interaction Predicted by Molecular Dynamics Simulation

Molecules ◽  
2021 ◽  
Vol 26 (24) ◽  
pp. 7525
Author(s):  
Zhiwei Wu ◽  
Junxian Yang ◽  
Xubin Xie ◽  
Guangjian Liu ◽  
Ying Fang ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Homology Modelling ◽  
Microvascular Disease ◽  
Dynamics Simulation ◽  
Salt Bridges ◽  
Key Residues ◽  
Von Willebrand ◽  
Willebrand Factor

ADAMTS13 (A Disintegrin and Metalloprotease with Thrombospondin type 1 repeats, member 13) cleaves von Willebrand Factor (VWF) multimers to limit the prothrombotic function of VWF. The deficiency of ADAMTS13 causes a lethal thrombotic microvascular disease, thrombotic thrombocytopenic purpura (TTP). ADAMTS13 circulates in a “closed” conformation with the distal domain associating the Spacer domain to avoid off-target proteolysis or recognition by auto-antibodies. However, the interactions of the distal TSP8 domain and the Spacer domain remain elusive. Here, we constructed the TSP8-Spacer complex by a combination of homology modelling and flexible docking. Molecular dynamics simulation was applied to map the binding sites on the TSP8 or Spacer domain. The results predicted that R1075, D1090, R1095, and C1130 on the TSP8 domain were key residues that interacted with the Spacer domain. R1075 and R1095 bound exosite-4 tightly, D1090 formed multiple hydrogen bonds and salt bridges with exosite-3, and C1130 interacted with both exosite-3 and exosite-4. Specific mutations of exosite-3 (R568K/F592Y/R660K/Y661F/Y665F) or the four key residues (R1075A/D1090A/R1095A/C1130A) impaired the binding of the TSP8 domain to the Spacer domain. These results shed new light on the understanding of the auto-inhibition of ADAMTS13.

scholarly journals Computational Investigation of Structural Dynamics of SARS-CoV-2 Methyltransferase-Stimulatory Factor Heterodimer Nsp16/nsp10 Bound to the Cofactor SAM

2020 ◽  
Author(s):  
Md Fulbabu Sk ◽  
Nisha Amarnath Jonniya ◽  
Rajarshi Roy ◽  
Sayan Poddar ◽  
Parimal Kar
Keyword(s):  
Protein Interactions ◽  
Electrostatic Interactions ◽  
Immune Escape ◽  
Hydrophobic Interactions ◽  
Conformational Dynamics ◽  
Rna Stability ◽  
Protein Translation ◽  
Dynamics Simulation ◽  
Mtase Activity ◽  
Computational Alanine Scanning

Recently, a highly contagious novel coronavirus (COVID-19 or SARS-CoV-2) has emerged as a global threat in people's health and global economies. Identification of the potential targets and development of a vaccine or antiviral drugs is an urgent demand. The 5’-capping mechanism of eukaryotic mRNA and some viruses such as coronaviruses (CoVs) are essential for maintaining the RNA stability, protein translation, and for viral immune escape. SARSCoV encodes S-adenosyl-L-methionine dependent (SAM) methyltransferase (MTase) enzyme characterized by nsp16 (2’-O-MTase) for generating the capped structure. The present article highlights the binding mechanisms of nsp16 and nsp10 to identify the role of nsp10 in MTase activity. Furthermore, the conformational dynamics and energetic behind the SAM binding to nsp16 in its monomer and dimer form was analyzed by using an extensive molecular dynamics simulation along with the Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA). Our results show that the presence of nsp10 increases the favorable van der Waal and electrostatic interactions between the SAM and nsp16, thus nsp10 acts as a stimulator for its strong binding. The interaction profile suggests that hydrophobic interactions were predominately identified for protein-protein interactions. Also, the stable hydrogen bond between Ala83 (nsp16) and Tyr96 (nsp10), and between Gln87 (nsp16) and Leu45 (nsp10) plays a vital role in the nsp16-nsp10 interface. Further, Computational Alanine Scanning (CAS) mutagenesis was performed, which revealed hotspot mutants, namely I40A, V104A, and R86A for the dimer association. Therefore, the dimer interface of nsp16/nsp10 could also be a potential target to suppress the 2’-O-MTase activity of SARS-CoV-2. Overall, our study provides a comprehensive understanding of the dynamic and thermodynamic process of binding of nsp16 and nsp10 that will contribute to the novel design of peptide inhibitors based on nsp16.

scholarly journals Incorporating Support Vector Machine With Sequential Minimal Optimization to Identify Anticancer Peptides

2020 ◽  
Author(s):  
Yu Wan ◽  
Zhuo Wang ◽  
Tzong-Yi Lee
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Sequential Minimal Optimization ◽  
Anticancer Peptides ◽  
Performance Accuracy ◽  
Wet Lab ◽  
Optimized Model ◽  
Scoring Matrix ◽  
New Feature ◽  
Research Show

Abstract BackgroundCancer is a major cause of death worldwide. To treat cancer, the use of anticancer peptides (ACPs) has received increasing attention in recent years. ACPs are a unique group of small molecules that can target and kill cancer cells fast and directly. However, identifying ACPs by wet-lab experiments is time-consuming and labor-intensive. Therefore, it is significant to develop computational tools for ACPs prediction.ResultsThis study chose amino acid composition (AAC), N5C5, k-space, position-specific scoring matrix (PSSM) as features, and analyzed them by machine learning methods, including support vector machine (SVM) and sequential minimal optimization (SMO) to build a model (model 2) distinguishing ACPs from non-ACPs. Since a growing number of studies have shown that some antimicrobial peptides (AMPs) exhibit anticancer function, a model (model 1) to distinguish ACPs from AMPs is also been developed. Comparing to previous models, models developed in this research show better performance (accuracy: 82.5% for model 1 and 93.5% for model 2).ConclusionsThis work utilizes a new feature, PSSM, which contributes to better performance than other features. In addition to SVM, SMO is used in this research for optimizing SVM and the SMO-models show better performance than unoptimized models. Last but not least, this work provides two different functions, including distinguishing ACPs from AMPs and distinguishing ACPs from all peptides. The second SMO-optimized model, which utilizes PSSM as feature, performs better than all other existing tools.

Discovery and identification of Cdc37-derived peptides targeting the Hsp90–Cdc37 protein–protein interaction

RSC Advances ◽  
2015 ◽  
Vol 5 (116) ◽  
pp. 96138-96145 ◽  
Author(s):  
Lei Wang ◽  
Qi-Chao Bao ◽  
Xiao-Li Xu ◽  
Fen Jiang ◽  
Kai Gu ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Molecular Dynamics Simulation ◽  
Protein Interaction ◽  
Peptide Inhibitors ◽  
Dynamics Simulation ◽  
Protein Protein Interaction ◽  
Binding Interface ◽  
Combined Strategy ◽  
Key Residues

In order to explore the key residues of the Hsp90–Cdc37 binding interface for further design of peptide inhibitors, a combined strategy of molecular dynamics simulation and MM-PBSA analysis was performed.

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