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Performance of Forc...
Performance of Force-Field- and Machine Learning-Based Scoring Functions in Ranking MAO-B Protein-Inhibitor Complexes in Relevance to Developing Parkinson's Therapeutics
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- Natarajan Arul, Murugan (författare)
- KTH,Teoretisk kemi och biologi
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- Muvva, Charuvaka (författare)
- BiomAILS India Pvt Ltd, Hyderabad 500090, India.
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- Jeyarajpandian, Chitra (författare)
- Dr Umayal Ramanathan Coll Women, Dept Biotechnol, Karaikkudi 630004, Tamil Nadu, India.
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- Jeyakanthan, Jeyaraman (författare)
- Alagappa Univ, Dept Bioinformat, Karaikkudi 630004, Tamil Nadu, India.
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- Subramanian, Venkatesan (författare)
- Cent Leather Res Inst, CSIR, Ctr High Comp, Chennai 600020, Tamil Nadu, India.
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(creator_code:org_t)
- 2020-10-16
- 2020
- Engelska.
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Ingår i: International Journal of Molecular Sciences. - : MDPI. - 1661-6596 .- 1422-0067. ; 21:20
- Relaterad länk:
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https://doi.org/10.3...
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https://doi.org/10.3...
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https://urn.kb.se/re...
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https://doi.org/10.3...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Psykiatri (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Psychiatry (hsv//eng)
Nyckelord
- binding free energy calculations
- molecular docking
- monoamine oxidase B
- Parkinson’
- s disease
- machine learning approach
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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