scholarly journals ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides

Molecules ◽  
2019 ◽  
Vol 24 (10) ◽  
pp. 1973 ◽  
Author(s):  
Nalini Schaduangrat ◽  
Chanin Nantasenamat ◽  
Virapong Prachayasittikul ◽  
Watshara Shoombuatong
Keyword(s):  
Computational Models ◽  
Helical Structure ◽  
Support Vector ◽  
Disulfide Bridges ◽  
Cell Penetration ◽  
Anticancer Activities ◽  
New Class ◽  
Anticancer Peptides ◽  
Small Molecule Drugs

Anticancer peptides (ACPs) have emerged as a new class of therapeutic agent for cancer treatment due to their lower toxicity as well as greater efficacy, selectivity and specificity when compared to conventional small molecule drugs. However, the experimental identification of ACPs still remains a time-consuming and expensive endeavor. Therefore, it is desirable to develop and improve upon existing computational models for predicting and characterizing ACPs. In this study, we present a bioinformatics tool called the ACPred, which is an interpretable tool for the prediction and characterization of the anticancer activities of peptides. ACPred was developed by utilizing powerful machine learning models (support vector machine and random forest) and various classes of peptide features. It was observed by a jackknife cross-validation test that ACPred can achieve an overall accuracy of 95.61% in identifying ACPs. In addition, analysis revealed the following distinguishing characteristics that ACPs possess: (i) hydrophobic residue enhances the cationic properties of α-helical ACPs resulting in better cell penetration; (ii) the amphipathic nature of the α-helical structure plays a crucial role in its mechanism of cytotoxicity; and (iii) the formation of disulfide bridges on β-sheets is vital for structural maintenance which correlates with its ability to kill cancer cells. Finally, for the convenience of experimental scientists, the ACPred web server was established and made freely available online.

Characterization of 2 linear peptides without disulfide bridges from the venom of the spider Lycosa poonaensis (Lycosidae)

2021 ◽  
Author(s):  
Alhussin M A Megaly ◽  
Yusuke Yoshimoto ◽  
Yugo Tsunoda ◽  
Masahiro Miyashita ◽  
Mohammed Abdel-Wahab ◽  
...  
Keyword(s):  
Antibacterial Activity ◽  
Complex Mixture ◽  
Helical Structure ◽  
The Other ◽  
Disulfide Bridges ◽  
Bioactive Components ◽  
Activity Test ◽  
Weak Activity ◽  
Weak Antibacterial Activity

ABSTRACT Spider venom is a complex mixture of bioactive components, in which peptides play an important role by showing neurotoxicity or cytotoxicity. Disulfide-rich peptides are major components in the venom, but linear peptides without disulfide bridges are also present and often show antimicrobial activity. In this study, we analyzed the venom of the spider Lycosa poonaensis (Lycosidae) to find novel antimicrobial peptides using mass spectrometry. The result revealed that 120 out of 401 detected components were nondisulfide-bridged peptides. From them, the sequence of 2 peptides (lyp2370 and lyp1987) were determined by MS/MS analysis. The biological activity test revealed that lyp2370 has only weak antibacterial activity. On the other hand, lyp1987, which is identical to M-lycotoxin-Ls3b from the Lycosa singoriensi venom, showed significant antibacterial activity. The weak activity of lyp2370 was found to be due to the presence of a Glu residue on the hydrophilic face of its amphipathic α-helical structure.

Targeting IDH Mutations in AML: Wielding the Double-edged Sword of Differentiation

2020 ◽  
Vol 20 (7) ◽  
pp. 490-500 ◽  
Author(s):  
Justin S. Becker ◽  
Amir T. Fathi
Keyword(s):  
Genome Structure ◽  
Cellular Metabolism ◽  
Standard Of Care ◽  
Competitive Inhibitor ◽  
Driver Mutations ◽  
New Class ◽  
Regulatory Enzymes ◽  
Idh Mutations ◽  
Genomic Characterization

The genomic characterization of acute myeloid leukemia (AML) by DNA sequencing has illuminated subclasses of the disease, with distinct driver mutations, that might be responsive to targeted therapies. Approximately 15-23% of AML genomes harbor mutations in one of two isoforms of isocitrate dehydrogenase (IDH1 or IDH2). These enzymes are constitutive mediators of basic cellular metabolism, but their mutated forms in cancer synthesize an abnormal metabolite, 2- hydroxyglutarate, that in turn acts as a competitive inhibitor of multiple gene regulatory enzymes. As a result, leukemic IDH mutations cause changes in genome structure and gene activity, culminating in an arrest of normal myeloid differentiation. These discoveries have motivated the development of a new class of selective small molecules with the ability to inhibit the mutant IDH enzymes while sparing normal cellular metabolism. These agents have shown promising anti-leukemic activity in animal models and early clinical trials, and are now entering Phase 3 study. This review will focus on the growing preclinical and clinical data evaluating IDH inhibitors for the treatment of IDH-mutated AML. These data suggest that inducing cellular differentiation is central to the mechanism of clinical efficacy for IDH inhibitors, while also mediating toxicity for patients who experience IDH Differentiation Syndrome. Ongoing trials are studying the efficacy of IDH inhibitors in combination with other AML therapies, both to evaluate potential synergistic combinations as well as to identify the appropriate place for IDH inhibitors within existing standard-of-care regimens.

A Novel Amino Acid Sequence-based Computational Approach to Predicting Cell-penetrating Peptides

2019 ◽  
Vol 15 (3) ◽  
pp. 206-211 ◽  
Author(s):  
Jihui Tang ◽  
Jie Ning ◽  
Xiaoyan Liu ◽  
Baoming Wu ◽  
Rongfeng Hu
Keyword(s):  
Machine Learning ◽  
Amino Acid ◽  
Amino Acid Position ◽  
Cell Penetrating Peptides ◽  
Support Vector ◽  
Cell Penetration ◽  
Drug Candidates ◽  
Machine Learning Model ◽  
Cell Penetrating ◽  
Novel Method

<P>Introduction: Machine Learning is a useful tool for the prediction of cell-penetration compounds as drug candidates. </P><P> Materials and Methods: In this study, we developed a novel method for predicting Cell-Penetrating Peptides (CPPs) membrane penetrating capability. For this, we used orthogonal encoding to encode amino acid and each amino acid position as one variable. Then a software of IBM spss modeler and a dataset including 533 CPPs, were used for model screening. </P><P> Results: The results indicated that the machine learning model of Support Vector Machine (SVM) was suitable for predicting membrane penetrating capability. For improvement, the three CPPs with the most longer lengths were used to predict CPPs. The penetration capability can be predicted with an accuracy of close to 95%. </P><P> Conclusion: All the results indicated that by using amino acid position as a variable can be a perspective method for predicting CPPs membrane penetrating capability.</P>

scholarly journals Synthesis and Characterization of a Fullerenol Derivative for Potential Biological Applications

2020 ◽  
Vol 4 (1) ◽  
pp. 15
Author(s):  
Eduardo Ravelo-Nieto ◽  
Alvaro Duarte-Ruiz ◽  
Luis H. Reyes ◽  
Juan C. Cruz
Keyword(s):  
Phase Transfer ◽  
Protein A ◽  
Structural Features ◽  
Cellular Level ◽  
Cellulose Membrane ◽  
Cell Penetration ◽  
Covalent Functionalization ◽  
Dialysis Tubing ◽  
Surface Functionality

Several biological barriers are generally responsible for the limited delivery of cargoes at the cellular level. Fullerenols have unique structural features and possess suitable properties for interaction with the cells. This study aimed to synthesize and characterize a fullerenol derivative with desirable characteristics (size, charge, functionality) to develop cell penetration vehicles. Fullerenol was synthesized from fullerene (C60) solubilized in toluene, followed by hydroxylation with hydrogen peroxide and tetra-n-butylammonium hydroxide (TBAH) as a phase transfer catalyst. The obtained product was purified by a Florisil chromatography column (water as the eluent), followed by dialysis (cellulose membrane dialysis tubing) and freeze-drying (yield 66%). Subsequently, a silane coupling agent was conjugated on the fullerenol surface to render free amine functional groups for further covalent functionalization with other molecules. Characterization via UV–VIS, FTIR-ATR, Raman, DLS, and SEM techniques was conducted to evaluate the composition, size, morphology, surface functionality, and structural properties. We are currently working on the conjugation of the potent cell-penetrating agents Buforin II (BUFII) and the Outer Membrane Protein A (OmpA) on the surface of the fullerenol to estimate whether cell penetration and endosome escape are improved concerning conventional polymeric vehicles and our previous developments with iron oxide nanoparticles.

scholarly journals A Methodology Based on FT-IR Data Combined with Random Forest Model to Generate Spectralprints for the Characterization of High-Quality Vinegars

Foods ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 1411
Author(s):  
José Luis P. Calle ◽  
Marta Ferreiro-González ◽  
Ana Ruiz-Rodríguez ◽  
Gerardo F. Barbero ◽  
José Á. Álvarez ◽  
...  
Keyword(s):  
Random Forest ◽  
Raw Materials ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Raw Material ◽  
Support Vector ◽  
Protected Designation Of Origin ◽  
Ft Ir

Sherry wine vinegar is a Spanish gourmet product under Protected Designation of Origin (PDO). Before a vinegar can be labeled as Sherry vinegar, the product must meet certain requirements as established by its PDO, which, in this case, means that it has been produced following the traditional solera and criadera ageing system. The quality of the vinegar is determined by many factors such as the raw material, the acetification process or the aging system. For this reason, mainly producers, but also consumers, would benefit from the employment of effective analytical tools that allow precisely determining the origin and quality of vinegar. In the present study, a total of 48 Sherry vinegar samples manufactured from three different starting wines (Palomino Fino, Moscatel, and Pedro Ximénez wine) were analyzed by Fourier-transform infrared (FT-IR) spectroscopy. The spectroscopic data were combined with unsupervised exploratory techniques such as hierarchical cluster analysis (HCA) and principal component analysis (PCA), as well as other nonparametric supervised techniques, namely, support vector machine (SVM) and random forest (RF), for the characterization of the samples. The HCA and PCA results present a clear grouping trend of the vinegar samples according to their raw materials. SVM in combination with leave-one-out cross-validation (LOOCV) successfully classified 100% of the samples, according to the type of wine used for their production. The RF method allowed selecting the most important variables to develop the characteristic fingerprint (“spectralprint”) of the vinegar samples according to their starting wine. Furthermore, the RF model reached 100% accuracy for both LOOCV and out-of-bag (OOB) sets.

scholarly journals Characterization of the Antibiotic Compound No. 70 Produced byStreptomycessp. IMV-70

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Lyudmila P. Trenozhnikova ◽  
Almagul K. Khasenova ◽  
Assya S. Balgimbaeva ◽  
Galina B. Fedorova ◽  
Genrikh S. Katrukha ◽  
...  
Keyword(s):  
Optimal Growth ◽  
Growth Conditions ◽  
Culture Broth ◽  
Producer Strain ◽  
New Class ◽  
New Antibiotics ◽  
Actinomycete Strain ◽  
Main Component

We describe the actinomycete strain IMV-70 isolated from the soils of Kazakhstan, which produces potent antibiotics with high levels of antibacterial activity. After the research of its morphological, chemotaxonomic, and cultural characteristics, the strain with potential to be developed further as a novel class of antibiotics with chemotherapeutics potential was identified asStreptomycessp. IMV-70. In the process of fermentation, the strainStreptomycesspp. IMV-70 produces the antibiotic no. 70, which was isolated from the culture broth by extraction with organic solvents. Antibiotic compound no. 70 was purified and separated into individual components by HPLC, TLC, and column chromatography methods. The main component of the compound is the antibiotic 70-A, which was found to be identical to the peptolide etamycin A. Two other antibiotics 70-B and 70-C have never been described and therefore are new antibiotics. The physical-chemical and biological characteristics of these preparations were described and further researched. Determination of the optimal growth conditions to cultivate actinomycete-producer strain IMV-70 and development of methods to isolate, purify, and accumulate preparations of the new antibiotic no. 70 enable us to research further the potential of this new class of antibiotics.

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