Improvement of a new rotation function for molecular replacement by designing new scoring functions and dynamic correlation coefficient

2010 ◽  
Vol 19 (10) ◽  
pp. 106101 ◽  
Author(s):  
Jiang Fan ◽  
Ding Wei
Keyword(s):  
Correlation Coefficient ◽  
Molecular Replacement ◽  
Scoring Functions ◽  
Dynamic Correlation ◽  
Rotation Function ◽  
New Scoring

scholarly journals Suitable Docking Protocol for the Design of Novel Coumarin Derivatives with Selective MAO-B Effects

2021 ◽  
Vol 1 (1) ◽  
pp. 40-47
Author(s):  
Emilio Viktorov Mateev ◽  
Iva Valkova ◽  
Maya Georgieva ◽  
Alexander Zlatkov
Keyword(s):  
Correlation Coefficient ◽  
Scoring Functions ◽  
Pearson’S Correlation ◽  
Chain Flexibility ◽  
Pearson’S Correlation Coefficient ◽  
Pairwise Correlation ◽  
Mao B ◽  
Docking Protocol ◽  
Pearson's Correlation ◽  
Pearson's Correlation Coefficient

Recently, the application of molecular docking is drastically increasing due to the rapid growth of resolved crystallographic receptors with co-crystallized ligands. However, the inability of docking softwares to correctly score the occurred interactions between ligands and receptors is still a relevant issue. This study examined the Pearson’s correlation coefficient between the experimental monoamine oxidase-B (MAO-B) inhibitory activity of 44 novel coumarins and the obtained GOLD 5.3 docking scores. Subsequently, optimization of the docking protocol was carried out to achieve the best possible pairwise correlation. Numerous modifications in the docking settings such as alteration in the scoring functions, size of the grid space, presence of active waters, and side-chain flexibility were conducted. Furthermore, ensemble docking simulations into two superimposed complexes were performed. The model was validated with a test set. A significant Pearson’s correlation coefficient of 0.8217 was obtained for the latter. In the final stage of our work, we observed the major interactions between the top-scored ligands and the active site of 1S3B.

scholarly journals Using a partial atomic model from medium-resolution cryo-EM to solve a large crystal structure

2021 ◽  
Vol 77 (1) ◽  
pp. 11-18
Author(s):  
Montserrat Fàbrega-Ferrer ◽  
Ana Cuervo ◽  
Francisco J. Fernández ◽  
Cristina Machón ◽  
Rosa Pérez-Luque ◽  
...  
Keyword(s):  
Molecular Replacement ◽  
Large Crystal ◽  
Rotation Matrices ◽  
Cryo Electron Microscopy ◽  
Partial Models ◽  
Medium Resolution ◽  
Experimental Phasing ◽  
Crystallographic Structures ◽  
Direct Use ◽  
Rotation Function

Medium-resolution cryo-electron microscopy maps, in particular when they include a significant number of α-helices, may allow the building of partial models that are useful for molecular-replacement searches in large crystallographic structures when the structures of homologs are not available and experimental phasing has failed. Here, as an example, the solution of the structure of a bacteriophage portal using a partial 30% model built into a 7.8 Å resolution cryo-EM map is shown. Inspection of the self-rotation function allowed the correct oligomerization state to be determined, and density-modification procedures using rotation matrices and a mask based on the cryo-EM structure were critical for solving the structure. A workflow is described that may be applicable to similar cases and this strategy is compared with direct use of the cryo-EM map for molecular replacement.

scholarly journals Using Phaser and ensembles to improve the performance of SIMBAD

2020 ◽  
Vol 76 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Adam J. Simpkin ◽  
Felix Simkovic ◽  
Jens M. H. Thomas ◽  
Martin Savko ◽  
Andrey Lebedev ◽  
...  
Keyword(s):  
Model Identification ◽  
Structural Similarity ◽  
Test Cases ◽  
Target Sequence ◽  
Molecular Replacement ◽  
Search Models ◽  
Termination Criteria ◽  
Alternative Approach ◽  
The Relationship ◽  
Rotation Function

The conventional approach to search-model identification in molecular replacement (MR) is to screen a database of known structures using the target sequence. However, this strategy is not always effective, for example when the relationship between sequence and structural similarity fails or when the crystal contents are not those expected. An alternative approach is to identify suitable search models directly from the experimental data. SIMBAD is a sequence-independent MR pipeline that uses either a crystal lattice search or MR functions to directly locate suitable search models from databases. The previous version of SIMBAD used the fast AMoRe rotation-function search. Here, a new version of SIMBAD which makes use of Phaser and its likelihood scoring to improve the sensitivity of the pipeline is presented. It is shown that the additional compute time potentially required by the more sophisticated scoring is counterbalanced by the greater sensitivity, allowing more cases to trigger early-termination criteria, rather than running to completion. Using Phaser solved 17 out of 25 test cases in comparison to the ten solved with AMoRe, and it is shown that use of ensemble search models produces additional performance benefits.

Experiences with a new translation-function program

1987 ◽  
Vol 20 (6) ◽  
pp. 517-521 ◽  
Author(s):  
M. Fujinaga ◽  
R. J. Read
Keyword(s):  
Computer Program ◽  
Correlation Coefficient ◽  
Linear Correlation ◽  
Correct Position ◽  
Crystal Cell ◽  
Translation Function ◽  
Rotation Function ◽  
Oriented Molecule ◽  
Better Than

The development of a new translation-function program is reported. It is one that uses a linear correlation coefficient to determine the correct position of an oriented molecule in the crystal cell. The method has been implemented in a computer program called BRUTE. The program can also refine the orientation of the model and accept a set of atoms with fixed positions. Comparison of the correlation coefficient with other translation functions indicates that it is comparable to or slightly better than the rest. The most important feature of the program is its ability to adjust the orientation of the model. This allows for errors in the orientation obtained from the rotation function to be corrected.

scholarly journals Learning Protein-Ligand Binding Affinity with Atomic Environment Vectors

2020 ◽  
Author(s):  
Rocco Meli ◽  
Andrew Anighoro ◽  
Mike Bodkin ◽  
Garrett Morris ◽  
Philip Biggin
Keyword(s):  
Ligand Binding ◽  
Binding Affinity ◽  
Correlation Coefficient ◽  
Scoring Function ◽  
Scoring Functions ◽  
Autodock Vina ◽  
Pearson’S Correlation ◽  
Pearson’S Correlation Coefficient ◽  
Pearson's Correlation ◽  
Pearson's Correlation Coefficient

<div> <div> <div> <p>Scoring functions for the prediction of protein-ligand binding affinity have seen renewed interest in recent years when novel machine learning and deep learning methods started to consistently outperform classical scoring functions. Here we explore the use of atomic environment vectors (AEVs) and feed-forward neural networks, the building blocks of several neural network potentials, for the prediction of protein-ligand binding affinity. The AEV-based scoring function, which we term AEScore, is shown to perform as well or better than other state-of-the-art scoring functions on binding affinity prediction, with an RMSE of 1.22 pK units and a Pearson’s correlation coefficient of 0.83 for the CASF-2016 benchmark. However, AEScore does not perform as well in docking and virtual screening tasks. We therefore show that the model can be combined with the classical scoring function AutoDock Vina in the context of ∆-learning, where corrections to the AutoDock Vina scoring function are learned instead of the protein-ligand binding affinity itself. Combined with AutoDock Vina, ∆-AEScore has an RMSE of 1.32 pK units and a Pearson’s correlation coefficient of 0.80 on the CASF-2016 benchmark, while retaining the good docking and screening power of the underlying classical scoring function. </p> </div> </div> </div>

A note on the dynamic correlation coefficient.

1978 ◽  
Vol 63 (3) ◽  
pp. 329-337 ◽  
Author(s):  
Lawrence R. James ◽  
Christopher W. Hornick ◽  
Robert G. Demaree
Keyword(s):  
Correlation Coefficient ◽  
Dynamic Correlation

scholarly journals CFM-ID 3.0: Significantly Improved ESI-MS/MS Prediction and Compound Identification

Metabolites ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 72 ◽  
Author(s):  
Yannick Djoumbou-Feunang ◽  
Allison Pon ◽  
Naama Karu ◽  
Jiamin Zheng ◽  
Carin Li ◽  
...  
Keyword(s):  
Metabolite Identification ◽  
Scoring Functions ◽  
Compound Identification ◽  
Spectral Matching ◽  
Rule Based ◽  
Matching Algorithm ◽  
Esi Ms ◽  
Chemical Structures ◽  
Speed And Accuracy ◽  
New Scoring

Metabolite identification for untargeted metabolomics is often hampered by the lack of experimentally collected reference spectra from tandem mass spectrometry (MS/MS). To circumvent this problem, Competitive Fragmentation Modeling-ID (CFM-ID) was developed to accurately predict electrospray ionization-MS/MS (ESI-MS/MS) spectra from chemical structures and to aid in compound identification via MS/MS spectral matching. While earlier versions of CFM-ID performed very well, CFM-ID’s performance for predicting the MS/MS spectra of certain classes of compounds, including many lipids, was quite poor. Furthermore, CFM-ID’s compound identification capabilities were limited because it did not use experimentally available MS/MS spectra nor did it exploit metadata in its spectral matching algorithm. Here, we describe significant improvements to CFM-ID’s performance and speed. These include (1) the implementation of a rule-based fragmentation approach for lipid MS/MS spectral prediction, which greatly improves the speed and accuracy of CFM-ID; (2) the inclusion of experimental MS/MS spectra and other metadata to enhance CFM-ID’s compound identification abilities; (3) the development of new scoring functions that improves CFM-ID’s accuracy by 21.1%; and (4) the implementation of a chemical classification algorithm that correctly classifies unknown chemicals (based on their MS/MS spectra) in >80% of the cases. This improved version called CFM-ID 3.0 is freely available as a web server. Its source code is also accessible online.

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