Assessment of Amyloid Forming Tendency of Peptide Sequences from Amyloid Beta and Tau Proteins Using Force-Field, Semi-Empirical, and Density Functional Theory Calculations.

A wide variety of neurodegenerative diseases are characterized by the accumulation of protein aggregates in intraneuronal or extraneuronal brain regions. In Alzheimer's disease (AD), the extracellular aggregates originate from amyloid-ß proteins, while the intracellular aggregates are formed from microtubule-binding tau proteins. The amyloid forming peptide sequences in the amyloid-ß peptides and tau proteins are responsible for aggregate formation. Experimental studies have until the date reported many of such amyloid forming peptide sequences in different proteins, however, there is still limited molecular level understanding about their tendency to form aggregates. In this study, we employed umbrella sampling simulations and subsequent electronic structure theory calculations in order to estimate the energy profiles for interconversion of the helix to ß-sheet like secondary structures of sequences from amyloid-ß protein (KLVFFA) and tau protein (QVEVKSEKLD and VQIVYKPVD). The study also included a poly-alanine sequence as a reference system. The calculated force-field based free energy profiles predicted a flat minimum for monomers of sequences from amyloid and tau proteins corresponding to an α-helix like secondary structure. For the parallel and anti-parallel dimer of KLVFFA, double well potentials were obtained with the minima corresponding to α-helix and ß-sheet like secondary structures. A similar double well-like potential has been found for dimeric forms for the sequences from tau fibril. Complementary semi-empirical and density functional theory calculations displayed similar trends, validating the force-field based free energy profiles obtained for these systems.

Performance of Force-Field- and Machine Learning-Based Scoring Functions in Ranking MAO-B Protein-Inhibitor Complexes in Relevance to Developing Parkinson's Therapeutics.

Monoamine oxidase B (MAOB) is expressed in the mitochondrial membrane and has a key role in degrading various neurologically active amines such as benzylamine, phenethylamine and dopamine with the help of Flavin adenine dinucleotide (FAD) cofactor. The Parkinson's disease associated symptoms can be treated using inhibitors of MAO-B as the dopamine degradation can be reduced. Currently, many inhibitors are available having micromolar to nanomolar binding affinities. However, still there is demand for compounds with superior binding affinity and binding specificity with favorable pharmacokinetic properties for treating Parkinson's disease and computational screening methods can be majorly recruited for this. However, the accuracy of currently available force-field methods for ranking the inhibitors or lead drug-like compounds should be improved and novel methods for screening compounds need to be developed. We studied the performance of various force-field-based methods and data driven approaches in ranking about 3753 compounds having activity against the MAO-B target. The binding affinities computed using autodock and autodock-vina are shown to be non-reliable. The force-field-based MM-GBSA also under-performs. However, certain machine learning approaches, in particular KNN, are found to be superior, and we propose KNN as the most reliable approach for ranking the complexes to reasonable accuracy. Furthermore, all the employed machine learning approaches are also computationally less demanding.

Governing the Inhibition of Reconstituted Collagen Type I Assemblies Mediated Through Noncovalent Forces of (±)-α Lipoic Acid.

Type I collagen is a fibrous protein, which is highly biocompatible and biodegradable and exhibits low immunogenicity with its unique feature of undergoing a spontaneous self-assembly process. However, the excessive accumulation of collagen may lead to a condition known as fibrosis in vertebrates. Recently, saturated fatty acids have gained much attention as biomedical and therapeutic agents. Therefore, drawing inspiration from the biological and structural tunability of these fatty acids, this work aims to inhibit the self-assembly of type I collagen using (±)-α-lipoic acid (ALA). Reconstituted collagen and its blends with (±)-ALA under physiological conditions were subjected to fibril growth kinetics measurements, which exhibited the decrease in the rate of fibrillogenesis ( t1/2) with an increase in the concentration of ALA. Variations in the viscoelasticity of collagen and ALA blend with respect to rate and frequency showed significant changes. Further, the frequency shifts of different functional groups via FT-IR (ATR) and the morphological changes associated with fibril inhibition were visualized using a cryoscanning electron microscope. Molecular dynamics simulation of the collagen-like peptide with the (±)-ALA molecule at different molar ratios proved that (±)-ALA had a strong potential to bind at various sites of collagen mediated by conventional secondary or noncovalent forces. Thus, the protein-small molecule interaction dominates the forces prevailing between protein-protein binding, leading to the inhibition of the self-assembly process. Such inhibitory effects by a fatty acid may unfold newer avenues for development of targeted and sustainable drug delivery systems for fibrotic diseases.

Unraveling the Unbinding Pathways of Products Formed in Catalytic Reactions Involved in SIRT1-3: A Random Acceleration Molecular Dynamics Simulation Study.

Sirtuins are a family of nicotinamide adenine dinucleotide (NAD+)-dependent enzymes, which undergo robust deacetylase activity, resulting in the production of nicotinamide. It is well known that nicotinamide, which is one of the products, can also act as an inhibitor for further deacetylation process by forming NAD+ again. Hence, the removal of nicotinamide from sirtuins is a demanding process, and the mechanistic understanding of the process remains elusive. In this investigation, we have made an attempt to unravel the unbinding pathways of nicotinamide from SIRT1, SIRT2, and SIRT3 (SIRT1-3) using Random Acceleration Molecular Dynamics (RAMD) Simulations, and we have successfully identified various unbinding channels. The selectivity of the egression channel is determined by using a thorough analysis of the frequency of egression trajectories. Similarly, various inhibitors have been docked with the active sites of SIRT1-3, and their egression pathways have been investigated to understand whether they follow the same egression pathway as that of nicotinamide. The residues that are responsible for the unbinding pathways have been determined from the analysis of RAMD trajectories. From these results, it is clear that phenylalanine and histidine residues play major roles in the egression of inhibitors. Additionally, the key residues Leu, Pro, Met, Phe, Tyr, and Ile are found to control the release by acting as gateway residues. The role of these residues from different egression channels has been studied by carrying out mutations with alanine residue. This is the first report on sirtuins, which demonstrates the novel unbinding pathways for nicotinamide/inhibitors. This work provides new insights for developing more promising SIRT1-3 inhibitors.

Glycolipids Derived from the Korean Endemic Plant Aruncus aethusifolius Inducing Glucose Uptake in Mouse Skeletal Muscle C2C12 Cells.

Aruncus spp. has been used as a traditional folk medicine worldwide for its anti-inflammatory, hemostatic, and detoxifying properties. The well-known species A. dioicus var. kamtschaticus has long been used for multifunctional purposes in Eastern Asia. Recently, it was reported that its extract has antioxidant and anti-diabetic effects. In this respect, it is likely that other Aruncus spp. possess various biological activities; however, little research has been conducted thus far. The present study aims to biologically identify active compounds against diabetes in the Korean endemic plant A. aethusifolius and evaluate the underlying mechanisms. A. aethusifolius extract enhanced glucose uptake without toxicity to C2C12 cells. A bioassay-guided isolation of A. aethusifolius yielded two pure compounds, and their structures were characterized as glycolipid derivatives, gingerglycolipid A, and (2S)-3-linolenoylglycerol-O-ß-d-galactopyranoside by an interpretation of nuclear magnetic resonance and high-resolution mass spectrometric data. Both compounds showed glucose uptake activity, and both compounds increased the phosphorylation levels of insulin receptor substrate 1 (IRS-1) and 5'-AMP-activated protein kinase (AMPK) and protein expression of peroxisome proliferator-activated receptor γ (PPARγ). Gingerglycolipid A docked computationally into the active site of IRS-1, AMPK1, AMPK2, and PPARγ (-5.8, -6.9, -6.8, and -6.8 kcal/mol).

PLAS-5k: Dataset of Protein-Ligand Affinities from Molecular Dynamics for Machine Learning Applications.

Computational methods and recently modern machine learning methods have played a key role in structure-based drug design. Though several benchmarking datasets are available for machine learning applications in virtual screening, accurate prediction of binding affinity for a protein-ligand complex remains a major challenge. New datasets that allow for the development of models for predicting binding affinities better than the state-of-the-art scoring functions are important. For the first time, we have developed a dataset, PLAS-5k comprised of 5000 protein-ligand complexes chosen from PDB database. The dataset consists of binding affinities along with energy components like electrostatic, van der Waals, polar and non-polar solvation energy calculated from molecular dynamics simulations using MMPBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) method. The calculated binding affinities outperformed docking scores and showed a good correlation with the available experimental values. The availability of energy components may enable optimization of desired components during machine learning-based drug design. Further, OnionNet model has been retrained on PLAS-5k dataset and is provided as a baseline for the prediction of binding affinities.

MMpI: A WideRange of Available Compounds of Matrix Metalloproteinase Inhibitors.

Matrix metalloproteinases (MMPs) are a family of zinc-dependent proteinases involved in the regulation of the extracellular signaling and structural matrix environment of cells and tissues. MMPs are considered as promising targets for the treatment of many diseases. Therefore, creation of database on the inhibitors of MMP would definitely accelerate the research activities in this area due to its implication in above-mentioned diseases and associated limitations in the first and second generation inhibitors. In this communication, we report the development of a new MMpI database which provides resourceful information for all researchers working in this field. It is a web-accessible, unique resource that contains detailed information on the inhibitors of MMP including small molecules, peptides and MMP Drug Leads. The database contains entries of ~3000 inhibitors including ~72 MMP Drug Leads and ~73 peptide based inhibitors. This database provides the detailed molecular and structural details which are necessary for the drug discovery and development. The MMpI database contains physical properties, 2D and 3D structures (mol2 and pdb format files) of inhibitors of MMP. Other data fields are hyperlinked to PubChem, ChEMBL, BindingDB, DrugBank, PDB, MEROPS and PubMed. The database has extensive searching facility with MMpI ID, IUPAC name, chemical structure and with the title of research article. The MMP inhibitors provided in MMpI database are optimized using Python-based Hierarchical Environment for Integrated Xtallography (Phenix) software. MMpI Database is unique and it is the only public database that contains and provides the complete information on the inhibitors of MMP. Database URL: http://clri.res.in/subramanian/databases/mmpi/index.php.

Role of Preferential Ions of Ammonium Ionic Liquid in Destabilization of Collagen.

Ions play a key role in the destabilization of collagen. This study explores the effect of diethyl methyl ammonium methane sulfonate (AMS), an ionic liquid (IL), on different hierarchical orderings of collagen, namely, at the molecular and fibrillar levels. The rheological behavior and secondary structural changes reveal changes in the hydrogen-bonding environment of collagen, leading to alterations in the triple helical structure of collagen. An increase in the concentration of AMS resulted in swelling of rat-tail tendon fibers, and also, decreased thermal stability signifies that ions are obliged to destabilize collagen at the fibrillar level. Molecular modeling studies confirm that anions are judiciously held responsible for structural deformities in collagen, whereas cations have a tenuous effect. Thus, the preferential role of ions present in an ammonium IL has been elucidated in this study.

Structure-based virtual screening of novel, high-affinity BRD4 inhibitors.

Bromodomains (BRDs) are a diverse family of evolutionarily conserved protein-interaction modules. Among various members of the bromodomain and extra terminal domain family, BRD4 is found to be an important target for many diseases such as cancer, acute myeloid leukemia, multiple myeloma, Burkitt's lymphoma, etc. Therefore, in this study an attempt has been made to screen compounds from NCI Diversity, Drug Bank and Toslab Databases targeting the Kac binding site of BRD4 using molecular docking, molecular dynamics simulations, MM-PB/GBSA binding free energy calculations and steered molecular dynamics simulations. Using virtual screening and docking, we have identified 11 inhibitors. These new inhibitors exhibit binding energy values higher than that of the (+)JQ1 inhibitor which is effective against BRD4. However, due to the toxicity of (+)JQ1, the designing of new inhibitors becomes significantly important. Thus, these new 11 ligands were systematically analyzed using other computational investigations. Results reveal that the compounds ZINC01411240, ZINC19632618 and ZINC04818522 could be potential drug candidates for targeting BRD4. It can also be seen from the results that there is a linear relationship between the results obtained from the SMD simulation and free energy obtained from the MM-PBSA/GBSA approach. This study clearly illustrates that the steered molecular dynamics can be effectively used for the design of new inhibitors.

Prediction of the three-dimensional structure of aflatoxin of Aspergillus flavus by homology modelling.

Aflatoxins are polyketide-derived secondary metabolites produced by Aspergillus spp. The toxic effects of aflatoxins have adverse consequences for human health and agricultural economics. The aflR gene, a regulatory gene for aflatoxin biosynthesis, encodes a protein containing a zinc-finger DNA-binding motif. AFLR-Protein three-dimensional model was generated using Robetta server. The modeled AFLR-Protein was further optimization and validation using Rampage. In the simulations, we monitored the backbone atoms and the C-α-helix of the modeled protein. The low RMSD and the simulation time indicate that, as expected, the 3D structural model of AFLR-protein represents a stable folding conformation. This study paves the way for generating computer molecular models for proteins whose crystal structures are not available and which would aid in detailed molecular mechanism of inhibition of aflatoxin.

Molecular docking and virtual screening for novel protein tyrosine phosphatase 1B (PTP1B) inhibitors.

Protein tyrosine phosphatase 1B (PTP1B) functions as major negative regulator of insulin and leptin signaling pathways. In view of this, PTP1B is an significant target for drug development against cancer, diabetes and obesity. The aim of the current study is to identify PTP1B inhibitors by means of virtual screening with docking. 523,366 molecules from ZINC database have been screened and based on DOCK grid scores and hydrogen bonding interactions five new potential inhibitors were identified. ZINC12502589, ZINC13213457, ZINC25721858, ZINC31392733 and ZINC04096400 were identified as potential lead molecules for inhibition of PTP1B. The identified molecules were subjected to Lipinski's rule of five parameters and found that they did not violate any rule. More specific analysis of pharmacological parameters may be scrutinized through a complete ADME/Tox evaluation. Pharma algorithm was used to Calculate ADME-Tox profiles for such molecules. In general, all the molecules presented advantages and as well as disadvantages when compared to each other. No marked difference in health effects and toxicity profiles were observed among these molecules.

In silico identification of miRNAs and their targets from the expressed sequence tags of Raphanus sativus.

MicroRNAs (miRNAs) are a novel growing family of endogenous, small, non- coding, single-stranded RNA molecules directly involved in regulating gene expression at the posttranscriptional level. High conservation of miRNAs in plant provides the foundation for identification of new miRNAs in other plant species through homology alignment. Here, previous known plant miRNAs were BLASTed against the Expressed Sequence Tag (EST) database of Raphanus sativus, and according to a series of filtering criteria, a total of 48 miRNAs belonging to 9 miRNA families were identified, and 16 potential target genes of them were subsequently predicted, most of which seemed to encode transcription factors or enzymes participating in regulation of development, growth and other physiological processes. Overall, our findings lay the foundation for further researches of miRNAs function in R.sativus.