Pathogenic genetic variants from highly connected cancer susceptibility genes confer the loss of structural stability

Reza, Mahjerin Nasrin; Ferdous, Nadim; Emon, Md. Tabassum Hossain; Islam, Md. Shariful; Mohiuddin, A. K. M.; Hossain, Mohammad Uzzal

doi:10.1038/s41598-021-98547-y

Cited by 14 publications

(11 citation statements)

References 85 publications

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“…Understanding the impact of non-synonymous (or missense) DNA variations leading to the disruption or the enhancement of the protein function was shown to be fundamental for describing the molecular mechanisms of several human diseases [ 6–9 ]. Specific protein stability perturbations have already been associated with pathogenic missense variants [ 8 , 10–12 ]. Variations were shown to contribute to the loss of function in haploinsufficient genes [ 13 ] and to modulate drug resistance in several diseases [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset

Pancotti

Benevenuta

Birolo

et al. 2022

Briefings in Bioinformatics

102

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Predicting the difference in thermodynamic stability between protein variants is crucial for protein design and understanding the genotype-phenotype relationships. So far, several computational tools have been created to address this task. Nevertheless, most of them have been trained or optimized on the same and ‘all’ available data, making a fair comparison unfeasible. Here, we introduce a novel dataset, collected and manually cleaned from the latest version of the ThermoMutDB database, consisting of 669 variants not included in the most widely used training datasets. The prediction performance and the ability to satisfy the antisymmetry property by considering both direct and reverse variants were evaluated across 21 different tools. The Pearson correlations of the tested tools were in the ranges of 0.21–0.5 and 0–0.45 for the direct and reverse variants, respectively. When both direct and reverse variants are considered, the antisymmetric methods perform better achieving a Pearson correlation in the range of 0.51–0.62. The tested methods seem relatively insensitive to the physiological conditions, performing well also on the variants measured with more extreme pH and temperature values. A common issue with all the tested methods is the compression of the $\Delta \Delta G$ predictions toward zero. Furthermore, the thermodynamic stability of the most significantly stabilizing variants was found to be more challenging to predict. This study is the most extensive comparisons of prediction methods using an entirely novel set of variants never tested before.

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Section: Introductionmentioning

confidence: 99%

Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset

Pancotti

Benevenuta

Birolo

et al. 2022

Briefings in Bioinformatics

102

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“…parameters that we previously used [42,43]. The topologies of proteins and the ligands were generated using the 'pdb2gmx' script and the PRODRG server, respectively [44].…”

Section: Plos Onementioning

confidence: 99%

Mpropred: A machine learning (ML) driven Web-App for bioactivity prediction of SARS-CoV-2 main protease (Mpro) antagonists

et al. 2023

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The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic emerged in 2019 and still requiring treatments with fast clinical translatability. Frequent occurrence of mutations in spike glycoprotein of SARS-CoV-2 led the consideration of an alternative therapeutic target to combat the ongoing pandemic. The main protease (Mpro) is such an attractive drug target due to its importance in maturating several polyproteins during the replication process. In the present study, we used a classification structure–activity relationship (CSAR) model to find substructures that leads to to anti-Mpro activities among 758 non-redundant compounds. A set of 12 fingerprints were used to describe Mpro inhibitors, and the random forest approach was used to build prediction models from 100 distinct data splits. The data set’s modelability (MODI index) was found to be robust, with a value of 0.79 above the 0.65 threshold. The accuracy (89%), sensitivity (89%), specificity (73%), and Matthews correlation coefficient (79%) used to calculate the prediction performance, was also found to be statistically robust. An extensive analysis of the top significant descriptors unveiled the significance of methyl side chains, aromatic ring and halogen groups for Mpro inhibition. Finally, the predictive model is made publicly accessible as a web-app named Mpropred in order to allow users to predict the bioactivity of compounds against SARS-CoV-2 Mpro. Later, CMNPD, a marine compound database was screened by our app to predict bioactivity of all the compounds and results revealed significant correlation with their binding affinity to Mpro. Molecular dynamics (MD) simulation and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA) analysis showed improved properties of the complexes. Thus, the knowledge and web-app shown herein can be used to develop more effective and specific inhibitors against the SARS-CoV-2 Mpro. The web-app can be accessed from https://share.streamlit.io/nadimfrds/mpropred/Mpropred_app.py.

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“…Other systems use the same protocol settings. 33 We have optimized the entire system to avoid an inadequate system structure. MD simulations were performed using the PMEMD.…”

Section: Molecular Dynamics (Md) Simulationmentioning

confidence: 99%

Discovery of novel tubulin polymerization inhibitors by utilizing 3D-QSAR, molecular docking and molecular dynamics simulation

Zhao

Zhang

et al. 2022

New J. Chem.

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Pathogenic genetic variants from highly connected cancer susceptibility genes confer the loss of structural stability

Cited by 14 publications

References 85 publications

Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset

Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset

Mpropred: A machine learning (ML) driven Web-App for bioactivity prediction of SARS-CoV-2 main protease (Mpro) antagonists

Discovery of novel tubulin polymerization inhibitors by utilizing 3D-QSAR, molecular docking and molecular dynamics simulation

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