Design of peptidomimetics for inhibition of HER2 receptor dimerization by a combination of virtual screening, MD simulations, and QSAR in silico methods

Unraveling the Role of Hydrogen Bonds in Thrombin via Two Machine Learning Methods

Hydrogen bonds play a critical role in the folding and stability of proteins, such as proteins and nucleic acids, by providing strong and directional interactions. They help to maintain the secondary and 3D structure of proteins, and structural changes in these molecules often result from the formation or breaking of hydrogen bonds. To gain insights into these hydrogen bonding networks, we applied two machine learning models - a logistic regression model and a decision tree model - to study four variants of thrombin: wild-type, ΔK9, E8K, and R4A. Our results showed that both models have their unique advantages. The logistic regression model highlighted potential key residues (GLU295) in thrombin's allosteric pathways, while the decision tree model identified important hydrogen bonding motifs. This information can aid in understanding the mechanisms of folding in proteins and has potential applications in drug design and other therapies. The use of these two models highlights their usefulness in studying hydrogen bonding networks in proteins.

In silico identification and design of potent peptide inhibitors against PDZ-3 domain of Postsynaptic Density Protein (PSD-95)

Unique intrinsic properties of peptides like low toxicity, high biological activity, and specificity make them attractive therapeutic agents. PDZ-binding peptide inhibitors have been demonstrated for curing of Alzheimer, Parkinson, Dementia, and other central nervous system ailments. In this article, we report the successful use of an integrated computational protocol to analyze the structural basis of how peptides bind to the shallow groove of the third PDZ domain (PDZ-3) from the postsynaptic density (PSD-95) protein. This protocol employs careful and precise computational techniques for design of new strategy for predicting novel and potent peptides against PDZ protein. We attempted to generate a pharmacophore model using crystal structure of peptide inhibitor bound to the PDZ-3. A highly specific and sensitive generated pharmacophore model was used for screening virtual database generated using different combination of amino acid substitutions as well as decoy peptide database for its sensitivity and specificity. Identified hit peptides were further analyzed by docking studies, and their stability analyzed using solvated molecular dynamics. Quantum Mechanics/Molecular Mechanics (QM/MM) interaction energy and GMX-PBSA scoring schemes were used for ranking of stable peptides. Computational approach applied here generated encouraging results for identifying peptides against PDZ interaction model. The workflow can be further exercised as a virtual screening technique for reducing the search space for candidate target peptides against PDZ domains.

Construction of Epidermal Growth Factor Receptor Peptide Magnetic Nanovesicles with Lipid Bilayers for Enhanced Capture of Liver Cancer Circulating Tumor Cells

Highly effective targeted tumor recognition via vectors is crucial for cancer detection. In contrast to antibodies and proteins, peptides are direct targeting ligands with a low molecular weight. In the present study, a peptide magnetic nanovector platform containing a lipid bilayer was designed using a peptide amphiphile (PA) as a skeleton material in a controlled manner without surface modification. Fluorescein isothiocyanate-labeled epidermal growth factor receptor (EGFR) peptide nanoparticles (NPs) could specifically bind to EGFR-positive liver tumor cells. EGFR peptide magnetic vesicles (EPMVs) could efficiently recognize and separate hepatoma carcinoma cells from cell solutions and treated blood samples (ratio of magnetic EPMVs versus anti-EpCAM NPs: 3.5 ± 0.29). Analysis of the circulating tumor cell (CTC) count in blood samples from 32 patients with liver cancer showed that EPMVs could be effectively applied for CTC capture. Thus, this nanoscale, targeted cargo-packaging technology may be useful for designing cancer diagnostic systems.

Design and Synthesis of Near-Infrared Peptide for in Vivo Molecular Imaging of HER2

We report the development, characterization, and validation of a peptide specific for the extracellular domain of HER2. This probe chemistry was developed for molecular imaging by using a structural model to select an optimal combination of amino acids that maximize the likelihood for unique hydrophobic and hydrophilic interactions with HER2 domain 3. The sequence KSPNPRF was identified and conjugated with either FITC or Cy5.5 via a GGGSK linker using Fmoc-mediated solid-phase synthesis to demonstrate flexibility for this chemical structure to be labeled with different fluorophores. A scrambled sequence was developed for control by altering the conformationally rigid spacer and moving both hydrophobic and hydrophilic amino acids on the C-terminus. We validated peptide specificity for HER2 in knockdown and competition experiments using human colorectal cancer cells in vitro, and measured a binding affinity of kd = 21 nM and time constant of k = 0.14 min(-1) (7.14 min). We used this peptide with either topical or intravenous administration in a preclinical model of colorectal cancer to demonstrate specific uptake in spontaneous adenomas and to show feasibility for real time in vivo imaging with near-infrared fluorescence. We used this peptide in immunofluorescence studies of human proximal colon specimens to evaluate specificity for sessile serrated and sporadic adenomas. Improved visualization can be used endoscopically to guide tissue biopsy and detect premalignant lesions that would otherwise be missed. Our peptide design for specificity to HER2 is promising for clinical translation in molecular imaging methods for early cancer detection.

Sequential application of ligand and structure based modeling approaches to index chemicals for their hH4R antagonism

The human histamine H4 receptor (hH4R), a member of the G-protein coupled receptors (GPCR) family, is an increasingly attractive drug target. It plays a key role in many cell pathways and many hH4R ligands are studied for the treatment of several inflammatory, allergic and autoimmune disorders, as well as for analgesic activity. Due to the challenging difficulties in the experimental elucidation of hH4R structure, virtual screening campaigns are normally run on homology based models. However, a wealth of information about the chemical properties of GPCR ligands has also accumulated over the last few years and an appropriate combination of these ligand-based knowledge with structure-based molecular modeling studies emerges as a promising strategy for computer-assisted drug design. Here, two chemoinformatics techniques, the Intelligent Learning Engine (ILE) and Iterative Stochastic Elimination (ISE) approach, were used to index chemicals for their hH4R bioactivity. An application of the prediction model on external test set composed of more than 160 hH4R antagonists picked from the chEMBL database gave enrichment factor of 16.4. A virtual high throughput screening on ZINC database was carried out, picking ∼ 4000 chemicals highly indexed as H4R antagonists' candidates. Next, a series of 3D models of hH4R were generated by molecular modeling and molecular dynamics simulations performed in fully atomistic lipid membranes. The efficacy of the hH4R 3D models in discrimination between actives and non-actives were checked and the 3D model with the best performance was chosen for further docking studies performed on the focused library. The output of these docking studies was a consensus library of 11 highly active scored drug candidates. Our findings suggest that a sequential combination of ligand-based chemoinformatics approaches with structure-based ones has the potential to improve the success rate in discovering new biologically active GPCR drugs and increase the enrichment factors in a synergistic manner.