scholarly journals Implications of AlphaFold2 for crystallographic phasing by molecular replacement

2022 ◽  
Vol 78 (1) ◽  
Author(s):  
Airlie J. McCoy ◽  
Massimo D. Sammito ◽  
Randy J. Read
Keyword(s):  
Structural Biology ◽  
In Silico ◽  
Structure Prediction ◽  
Sequence Similarity ◽  
Critical Assessment ◽  
Molecular Replacement ◽  
Mean Square ◽  
Mean Square Deviation ◽  
High Coverage ◽  
Correlated Mutations

The AlphaFold2 results in the 14th edition of Critical Assessment of Structure Prediction (CASP14) showed that accurate (low root-mean-square deviation) in silico models of protein structure domains are on the horizon, whether or not the protein is related to known structures through high-coverage sequence similarity. As highly accurate models become available, generated by harnessing the power of correlated mutations and deep learning, one of the aspects of structural biology to be impacted will be methods of phasing in crystallography. Here, the data from CASP14 are used to explore the prospects for changes in phasing methods, and in particular to explore the prospects for molecular-replacement phasing using in silico models.

scholarly journals Possible Implications of AlphaFold2 for Crystallographic Phasing by Molecular Replacement

2021 ◽  
Author(s):  
Airlie J McCoy ◽  
Massimo D Sammito ◽  
Randy J Read
Keyword(s):  
Structural Biology ◽  
In Silico ◽  
Structure Prediction ◽  
Sequence Similarity ◽  
Critical Assessment ◽  
Molecular Replacement ◽  
Mean Square ◽  
High Coverage ◽  
Correlated Mutations ◽  
In Silico Models

The AlphaFold2 results in the 14th edition of Critical Assessment of Structure Prediction (CASP14) showed that accurate (low root-mean-square deviation) in silico models of protein structure domains are on the horizon, whether or not the protein is related to known structures through high-coverage sequence similarity. As highly accurate models become available, generated by harnessing the power of correlated mutations and deep learning, one of the aspects of structural biology to be impacted will be methods of phasing in crystallography. We here use the data from CASP14 to explore the prospect for changes in phasing methods, and in particular to explore the prospects for molecular replacement phasing using in silico models.

POTENSI DAUN ASAM JAWA (Tamarindus indica L.) SEBAGAI ALTERNATIF ANTIINFLAMASI: STUDI IN SILICO

2019 ◽  
pp. 42-50
Author(s):  
Erma Yunita ◽  
Siti Fatimah ◽  
Deni Yulianto ◽  
Vedy Trikuncahyo ◽  
Zihan Khodijah
Keyword(s):  
Molecular Docking ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
In Silico ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Tamarindus Indica ◽  
Protein Target ◽  
Cox 2 ◽  
Cox 1

  Daun asam jawa (Tamarindus indica L.) merupakan tanaman yang memiliki banyak khasiat. Kandungan senyawa kimia yang terkandung salah satunya Kuersetin. Kuersetin merupakan senyawa flavonoid yang dapat digunakan sebagai anti inflamasi. Penelitian ini bertujuan untuk mengetahui potensi aktivitas Kuersetin dari daun asam jawa sebagai anti inflamasi terhadap protein COX-1 dan COX-2 secara in silico. Ekstrak daun asam jawa diperoleh dengan maserasi bertingkat menggunakan heksan dan etanol. Kadar Kuersetinnya dihitung secara spektrofotometri UVVis. Konfirmasi aktivitas antiinflamasi dilakukan secara in silico. Protein yang digunakan adalah 6COX, 3PGH, dan 1EQH. Kuersetin sebagai senyawa aktif sedangkan Aspirin digunakan sebagai zat pembanding. Preparasi ligan Kuersetin menggunakan MarvinSketch kemudian preparasi protein target 6COX, 1EQH, dan 3PGH menggunakan YASARA. Selanjutnya melakukan molecular docking menggunakan program PLANTS. Parameter evaluasi validasi dapat dilihat dari nilai Root Mean Square Deviation (RMSD), dimana nilai RMSD yang diterima adalah kurang dari 2Å. Kadar Kuersetin yang diperoleh dalam ekstrak dalam daun asam jawa sebesar 31,26 mg/g. Hasil docking menunjukkan bahwa Kuersetin mampu berinteraksi dengan 1EQH, 3PGH, dan 6COX dimana skor dockingnya masing-masing adalah -77,6195; -75,1344; dan -82,2454, sedangkan hasil docking Aspirin masing-masing adalah -69,8784; -75,2421; dan - 72,0884. Kuersetin memiliki potensi sebagai anti inflamasi yang lebih baik dibandingkan dengan Aspirin namun memiliki resiko lebih tinggi menyebabkan ulkus lambung dibanding Aspirin.

scholarly journals Identification of Terpenoids From Abrus precatorius Against Parkinson’s Disease Proteins Using In Silico Approach

2021 ◽  
Vol 15 ◽  
pp. 117793222110507
Author(s):  
Damilola Alex Omoboyowa ◽  
Toheeb Adewale Balogun ◽  
Oluwaseun Motunrayo Omomule ◽  
Oluwatosin A Saibu
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
In Silico ◽  
High Performance ◽  
Md Simulation ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Abrus Precatorius

Parkinson’s disease (PD) is the second major neuro-degenrative disorder that causes morbidity and mortality among older populations. Terpenoids were reported as potential neuro-protective agents. Therefore, this study seeks to unlock the inhibitory potential of terpenoids from Abrus precatorius seeds against proteins involve in PD pathogenesis. In this study, in silico molecular docking of 5 terpenoids derived from high-performance liquid chromatography (HPLC) analysis of A. precatorius seeds against α-synuclein, catechol-o-methyltransferase, and monoamine oxidase B which are markers of PD was performed using Autodock vina. The absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) of the hits were done using Swiss ADME predictor and molecular dynamic (MD) simulation of the hit-protein complex was performed using Desmond Schrodinger software. Five out of 6 compounds satisfied the ADME/Tox parameters and showed varying degrees of binding affinities with selected proteins. Drimenin-α-synuclein complex showed the lowest binding energy of −9.1 kcal/mol followed by interaction with key amino acid residues necessary for α-synuclein inhibition. The selection of this complex was justified by its stability in MD simulation conducted for 10 ns and exhibited stable interaction in terms of root mean square deviation (RMSD) and root mean square deviation error fluctuation (RMSF) values.

scholarly journals The Treatment of Alzheimer’s Disease through Molecular Docking Studies using Phyltetralin against Kallikrein_6: A Bioinformatic Approach

2021 ◽  
Vol 9 (VI) ◽  
pp. 3992-3999
Author(s):  
Sarita Negi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Molecular Docking ◽  
Binding Energy ◽  
In Silico ◽  
Receptor Protein ◽  
Mean Square ◽  
Mean Square Deviation ◽  
In Silico Study ◽  
Kallikrein 6

Alzheimer's disease (AD) is a neurodegenerative disease that generally begins leisurely and gets worse with time. Alzheimer’s disease (AD) dementia is the specific beginning of age-related declination of cognitive abilities and function, which eventually leads to death. Alzheimer’s disease (AD) is one of the neurodeteriorating disorders which is one of the mostcritical complications that our current health care system faces. The phenomenon of molecular docking has progressively become a strong tool in the field of pharmaceutical research including drug discovery. The aim of the presentin silico study was to inhibit the expression of KLK-6 (kallikrein-6) which is a target or receptor protein by its interaction with three distinct secondary metabolites for treating Alzheimer's disease (AD) through molecular docking. Methods: The in-silico study was based on molecular docking. Docking was executed amidst ligands- Quercetin (CID: 5280343), Ricinoleic Acid (CID: 643684), Phyltetralin (CID: 11223782), and the target or receptor protein Kallikrein-6 (PDB ID: 1LO6). The protein and the ligands were downloaded in the required format. Through PyRx, the ligands were virtually screened after importing them in the PyRx window. The results of PyRx and SwissADME were analyzed and the best ligand was finalized. Among the three, Phyltetralin was the best ligand contrary to KLK-6 having minimum binding energy and it was following Lipinski’s five rules along with 0 violations. Results: The final docking was carried out between Phyltetralin and KLK-6 through AutoDock Vina. The outcome showed 9 poses with distinct binding energy, RSMD LB (root mean square deviation lower bound) and RSMD UB (root mean square deviation upper bound). With the help of PyMOL which is an open-access tool for molecular visualization, the interaction amidst Phyltetralin and KLK-6 can be visualized. Conclusion: Based on this in silico study it can be concluded that KLK-6 (kallikrein-6) which is responsible for causing AD can be inhibited by ligand Phyltetralin and for the treatment of AD, phyltetralin might act as a potential drug. Thus, in future studies, Phyltetralin from natural sources can prevent Alzheimer's disease and can be proved as a promising and efficient drug for treating Alzheimer's disease.

scholarly journals A C++ library for protein sub-structure search

2020 ◽  
Author(s):  
Jianfu Zhou ◽  
Gevorg Grigoryan
Keyword(s):  
Data Mining ◽  
Structural Biology ◽  
Protein Structures ◽  
Structural Motif ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Structure Search ◽  
Program Interface ◽  
Programmatic Access

AbstractSummaryMASTER is a previously published algorithm for protein sub-structure search. Given a database of protein structures and a query structural motif, composed of multiple disjoint segments, it finds all sub-structures from the database that align onto the query to within a pre-specified backbone root-mean-square deviation. Here, we present an improved version of the algorithm, MASTER v.2, in the form of an open-source C++ Application Program Interface library, thereby providing programmatic access to structure search functionality. An entirely reorganized approach to database representation now enables large structural databases to be stored in memory, further simplifying development of automated search-based methods. Given the increasingly important role of structure-based data mining, our improved implementation should find ample uses in structural biology applications.AvailabilityMASTER is available at https://grigoryanlab.org/master/[email protected]

In Silico Study of Potential Cross-Kingdom Plant MicroRNA Based Regulation in Chronic Myeloid Leukemia

2020 ◽  
Vol 17 (2) ◽  
pp. 125-132
Author(s):  
Marjanu Hikmah Elias ◽  
Noraziah Nordin ◽  
Nazefah Abdul Hamid
Keyword(s):  
Targeted Therapy ◽  
In Silico ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Target Prediction ◽  
In Silico Analysis ◽  
Microrna Target ◽  
Good Target

Background: Chronic Myeloid Leukaemia (CML) is associated with the BCRABL1 gene, which plays a central role in the pathogenesis of CML. Thus, it is crucial to suppress the expression of BCR-ABL1 in the treatment of CML. MicroRNA is known to be a gene expression regulator and is thus a good candidate for molecularly targeted therapy for CML. Objective: This study aims to identify the microRNAs from edible plants targeting the 3’ Untranslated Region (3’UTR) of BCR-ABL1. Methods: In this in silico analysis, the sequence of 3’UTR of BCR-ABL1 was obtained from Ensembl Genome Browser. PsRNATarget Analysis Server and MicroRNA Target Prediction (miRTar) Server were used to identify miRNAs that have binding conformity with 3’UTR of BCR-ABL1. The MiRBase database was used to validate the species of plants expressing the miRNAs. The RNAfold web server and RNA COMPOSER were used for secondary and tertiary structure prediction, respectively. Results: In silico analyses revealed that cpa-miR8154, csi-miR3952, gma-miR4414-5p, mdm-miR482c, osa-miR1858a and osa-miR1858b show binding conformity with strong molecular interaction towards 3’UTR region of BCR-ABL1. However, only cpa-miR- 8154, osa-miR-1858a and osa-miR-1858b showed good target site accessibility. Conclusion: It is predicted that these microRNAs post-transcriptionally inhibit the BCRABL1 gene and thus could be a potential molecular targeted therapy for CML. However, further studies involving in vitro, in vivo and functional analyses need to be carried out to determine the ability of these miRNAs to form the basis for targeted therapy for CML.

scholarly journals GNINA 1.0: molecular docking with deep learning

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Andrew T. McNutt ◽  
Paul Francoeur ◽  
Rishal Aggarwal ◽  
Tomohide Masuda ◽  
Rocco Meli ◽  
...  
Keyword(s):  
Molecular Docking ◽  
Root Mean Square ◽  
Root Mean Square Deviation ◽  
Computational Cost ◽  
Scoring Function ◽  
Binding Pocket ◽  
Protein Docking ◽  
Mean Square ◽  
Mean Square Deviation ◽  
Autodock Vina

AbstractMolecular docking computationally predicts the conformation of a small molecule when binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline as they determine the fitness of sampled poses. Here we describe and evaluate the 1.0 release of the Gnina docking software, which utilizes an ensemble of convolutional neural networks (CNNs) as a scoring function. We also explore an array of parameter values for Gnina 1.0 to optimize docking performance and computational cost. Docking performance, as evaluated by the percentage of targets where the top pose is better than 2Å root mean square deviation (Top1), is compared to AutoDock Vina scoring when utilizing explicitly defined binding pockets or whole protein docking. Gnina, utilizing a CNN scoring function to rescore the output poses, outperforms AutoDock Vina scoring on redocking and cross-docking tasks when the binding pocket is defined (Top1 increases from 58% to 73% and from 27% to 37%, respectively) and when the whole protein defines the binding pocket (Top1 increases from 31% to 38% and from 12% to 16%, respectively). The derived ensemble of CNNs generalizes to unseen proteins and ligands and produces scores that correlate well with the root mean square deviation to the known binding pose. We provide the 1.0 version of Gnina under an open source license for use as a molecular docking tool at https://github.com/gnina/gnina.

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