| 1. |
- Muvva, Charuvaka, et al.
(author)
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Assessment of Amyloid Forming Tendency of Peptide Sequences from Amyloid Beta and Tau Proteins Using Force-Field, Semi-Empirical, and Density Functional Theory Calculations
- 2021
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In: International Journal of Molecular Sciences. - : MDPI AG. - 1661-6596 .- 1422-0067. ; 22:6, s. 3244-
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Journal article (peer-reviewed)abstract
- 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-beta proteins, while the intracellular aggregates are formed from microtubule-binding tau proteins. The amyloid forming peptide sequences in the amyloid-beta 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 beta-sheet like secondary structures of sequences from amyloid-beta 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 alpha-helix like secondary structure. For the parallel and anti-parallel dimer of KLVFFA, double well potentials were obtained with the minima corresponding to alpha-helix and beta-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.
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| 2. |
- Muvva, Charuvaka, et al.
(author)
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Unraveling the Unbinding Pathways of Products Formed in Catalytic Reactions Involved in SIRT1-3 : A Random Acceleration Molecular Dynamics Simulation Study
- 2019
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In: Journal of Chemical Information and Modeling. - : AMER CHEMICAL SOC. - 1549-9596 .- 1549-960X. ; 59:10, s. 4100-4115
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Research review (peer-reviewed)abstract
- 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.
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| 3. |
- Natarajan Arul, Murugan, et al.
(author)
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Performance of Force-Field- and Machine Learning-Based Scoring Functions in Ranking MAO-B Protein-Inhibitor Complexes in Relevance to Developing Parkinson's Therapeutics
- 2020
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In: International Journal of Molecular Sciences. - : MDPI. - 1661-6596 .- 1422-0067. ; 21:20
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Journal article (peer-reviewed)abstract
- 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.
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