In silico design of potential Mcl-1 peptide-based inhibitors.

CONTEXT: BIM (Bcl-2 interacting mediator of apoptosis)-derived peptides that specifically target over-expressed Mcl-1 (myeloid cell leukemia-1) protein and induce apoptosis are potentially anti-cancer agents. Since the helicity of BIM-derived peptides has a crucial role in their functionality, a range of strategies have been used to increase the helicity including the introduction of unnatural residues and stapling methods that have some drawbacks such as the accumulation in the liver. To avoid these drawbacks, this study aimed to design a more helical peptide by utilizing bioinformatics algorithms and molecular dynamics simulations without exploiting unnatural residues and stapling methods. MM-PBSA results showed that the mutations of A4fE and A2eE in analogue 5 demonstrate a preference towards binding with Mcl-1. As evidenced by Circular dichroism results, the helicity increases from 18 to 34%, these findings could enhance the potential of analogue 5 as an anti-cancer agent targeting Mcl-1. The applied strategies in this research could shed light on the in silico peptide design. Moreover, analogue 5 as a drug candidate can be evaluated in vitro and in vivo studies. METHODS: The sequence of the lead peptide was determined using the ApInAPDB database and PRALINE program. Contact finder and PDBsum web server softwares were used to determine the contact involved amino acids in complex with Mcl-1. All identified salt bridge contributing residues were unaltered to preserve the binding affinity. After proposing novel analogues, their secondary structures were predicted by Cham finder web server software and GOR, Neural Network, and Chou-Fasman algorithms. Finally, molecular dynamics simulations run for 100 ns were done using the GROMACS, version 5.0.7, with the CHARMM36 force field. MM-PBSA was used to assess binding affinity specificity in targeting Mcl-1 and Bcl-xL (B-cell lymphoma extra-large).

ApInAPDB: a database of apoptosis-inducing anticancer peptides.

ApInAPDB (Apoptosis-Inducing Anticancer Peptides Database) consists of 818 apoptosis-inducing anticancer peptides which are manually collected from research articles. The database provides scholars with peptide related information such as function, binding target and affinity, IC50 and etc. In addition, GRAVY (grand average of hydropathy), net charge at pH 7, hydrophobicity and other physicochemical properties are calculated and presented. Another category of information are structural information includes 3D modeling, secondary structure prediction and descriptors for QSAR (quantitative structure-activity relationship) modeling. In order to facilitate the browsing process, three types of user-friendly searching tools are provided: top categories browser, simple search and advanced search. Overall ApInAPDB as the first database presenting apoptosis-inducing anticancer peptides can be useful in the field of peptide design and especially cancer therapy. Researchers can freely access the database at http://bioinf.modares.ac.ir/software/ApInAPDB/ .

Sensitive and convenient detection of miRNA-145 using a gold nanoparticle-HCR coupled system: computational and in vitro validations.

Multiple sclerosis (MS) remains a challenging disease that requires timely diagnosis. Therefore, an ultrasensitive optical biosensor based on hybridization chain reaction (HCR) was developed to detect microRNA-145 (miRNA-145) as an MS biomarker. To construct such a sensor, HCR occurred between specific hairpin probes, as MB1 contains a poly-cytosine nucleotide loop and MB2 has a poly-guanine nucleotide sticky end. By introducing miR-145 as a target sequence, long-range dsDNA polymers are formed. Then, positively charged gold nanoparticles (AuNPs) were incubated with the HCR product, which adsorbed onto the dsDNA polymers due to electrostatic adsorption. This resulted in the precipitation of the AuNPs. By incubating different concentrations of miR-145 with AuNPs, the changes in the UV-vis spectrum of the supernatant were analyzed. The proposed biosensor showed a great ability to detect miR-145 in a wide linear range from 1 pM-1 nM with an excellent detection limit (LOD) of 0.519 nM. Furthermore, the developed biosensor indicated considerable selectivity in discriminating between miR-145 and mismatched sequences. It shows high selectivity in differentiating targets. Interestingly, the proposed method was also able to detect miRNA-145 in the diluted serum samples. In conclusion, this sensing platform exhibits high selectivity and specificity for the detection of circulating microRNAs, which holds great promise for translation to routine clinical applications.

Quantitative structure-activity relationship for the oxidation of organic contaminants by peracetic acid using GA-MLR method.

Peracetic acid (PAA) is considered as an effective and powerful oxidant for eliminating organic contaminants in wastewater treatment. The second-order rate constant (kapp) for the reaction of PAA with organic contaminants is practically important for evaluating their removal efficiency in wastewater treatment, but only limited numbers of kapp values are available. In this study, 70 organic compounds with various structures were selected, and the kapp of PAA with each organic compound was used to develop two quantitative structure-activity relationship (QSAR) models based on three kinds of descriptors including constitutional, quantum chemical, and the PaDEL descriptors. The genetic algorithm (GA) was applied to select the molecular descriptors, then the models developed by multiple linear regression (MLR). The most important descriptors that explain the reactivity of organic compounds with PAA are the EHOMO for the model with the constitutional and quantum chemical descriptors. The maxHdsCH and minHdCH2 are two most important descriptors for the model with only PaDEL descriptors. The developed models can be used to predict kapp for a wide range of organic contaminants. The accuracy of the developed models was proved by the internal, external validation and the Y-scrambling technique. The developed QSAR models using the GA-MLR method can be used as a screening tool for predicting the elimination of organic contaminants by PAA and increasing the understanding of chemical pollutant fate.