Predicting protein–DNA binding free energy change upon missense mutations using modified MM/PBSA approach: SAMPDI webserver

Peng, Yunhui; Sun, Lexuan; Jia, Zhe; Li, Lin; Alexov, Emil

doi:10.1093/bioinformatics/btx698

Cited by 64 publications

(79 citation statements)

References 51 publications

(58 reference statements)

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“…Protein‐nucleic acids interactions are essential components of cellular interaction networks and frequently are implicated in human diseases . This motivated us to develop a method “single amino acid mutation binding free energy change of protein‐DNA interaction (SAMPDI)” method, that computes the change of the protein‐DNA/RNA binding free energy caused by mutations (Supporting Information Fig.…”

Section: Resultsmentioning

confidence: 99%

“…It provides fast and accurate predictions of the effects of single amino acid substitution on the binding free energy of protein‐DNA complex . This method utilizes modified molecular mechanics Poisson‐Boltzmann Surface Area (MM/PBSA) approach along with an additional set of knowledge‐based terms delivered from investigations of the physicochemical properties of protein‐DNA complexes.…”

Section: Resultsmentioning

confidence: 99%

“…Furthermore, various methods, software, and webservers were developed utilizing DelPhi. These resources include changes of folding (SAAMBE method) and binding (SAAFEC and SAMPDI) free energies due to mutations, predicting nonspecifically bound ions (BION method), DelPhiPKa method and webservers for predicting pKa's in proteins, RNAs and DNAs, and calculating the electrostatic forces between macromolecules (DelPhiForce) …”

Section: Introductionmentioning

confidence: 99%

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DelPhi Suite: New Developments and Review of Functionalities

Jia

Chakravorty

et al. 2019

J Comput Chem

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Electrostatic potential, energies, and forces affect virtually any process in molecular biology, however, computing these quantities is a difficult task due to irregularly shaped macromolecules and the presence of water. Here, we report a new edition of the popular software package DelPhi along with describing its functionalities. The new DelPhi is a C++ object‐oriented package supporting various levels of multiprocessing and memory distribution. It is demonstrated that multiprocessing results in significant improvement of computational time. Furthermore, for computations requiring large grid size (large macromolecular assemblages), the approach of memory distribution is shown to reduce the requirement of RAM and thus permitting large‐scale modeling to be done on Linux clusters with moderate architecture. The new release comes with new features, whose functionalities and applications are described as well. © 2019 The Authors. Journal of Computational Chemistry published by Wiley Periodicals, Inc.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DelPhi Suite: New Developments and Review of Functionalities

Jia

Chakravorty

et al. 2019

J Comput Chem

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“…Many efforts to computationally predict and model the effects of missense mutations on protein stability and protein-protein interactions have been made that help uncover the relationships between genotype and phenotype (19)(20)(21)(22)(23)(24)(25). However, predicting the impacts of mutations on protein-nucleic acid interactions has been more intractable and very few methods have been proposed that can do this (26)(27)(28)(29)(30). One of the reasons that has hindered the development of methods is due to the complexity of nucleic acid chemistry and binding that has limited the availability of high-quality data.…”

Section: Introductionmentioning

confidence: 99%

PremPRI: Predicting the Effects of Single Mutations on Protein-RNA Interactions

Zhang

Chen

et al. 2020

Preprint

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Protein-RNA interactions are crucial for many cellular processes, such as protein synthesis and regulation of gene expression. Missense mutations that alter protein-RNA interaction may contribute to the pathogenesis of many diseases. Here we introduce a new computational method PremPRI, which predicts the effects of single mutations occurring in RNA binding proteins on the protein-RNA interaction by calculating the binding affinity changes quantitatively. The multiple linear regression scoring function of PremPRI is composed of 11 sequence-and structure-based features, and is parameterized on 248 mutations from 50 protein-RNA complexes. Our model shows a good agreement between calculated and experimental values with Pearson correlation coefficient of 0.72 and the corresponding root-mean-square error of 0.76 kcal mol -1 , and outperforms three other available methods. PremPRI can be used for finding functionally important variants, understanding the molecular mechanisms underlying the effects, and designing new protein-RNA interaction inhibitors. PremPRI is freely available at

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“…The prominent role of nonpolar interactions in processes like the formation of protein aggregates in solvent, protein–drug binding, and membrane formation is well documented . Furthermore, binding free energy changes occurring due to mutations in proteins also conceive the important role played by the change in the surface area (SA) of the mutant sites in predicting the pathogenicity of the mutation . Besides binding, studies on folding and unfolding of proteins have signified the role of MV and MSA .…”

Section: Introductionmentioning

confidence: 99%

A grid‐based algorithm in conjunction with a gaussian‐based model of atoms for describing molecular geometry

2019

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A novel grid‐based method is presented, which in conjunction with a smooth Gaussian‐based model of atoms, is used to compute molecular volume (MV) and surface area (MSA). The MV and MSA are essential for computing nonpolar component of free energies. The objective of our grid‐based approach is to identify solute atom pairs that share overlapping volumes in space. Once completed, this information is used to construct a rooted tree using depth‐first method to yield the final volume and SA by using the formulations of the Gaussian model described by Grant and Pickup (J. Phys Chem, 1995, 99, 3503). The method is designed to function uninterruptedly with the grid‐based finite‐difference method implemented in Delphi, a popular and open‐source package used for solving the Poisson–Boltzmann equation (PBE). We demonstrate the time efficacy of the method while also validating its performance in terms of the effect of grid‐resolution, positioning of the solute within the grid‐map and accuracy in identification of overlapping atom pairs. We also explore and discuss different aspects of the Gaussian model with key emphasis on its physical meaningfulness. This development and its future release with the Delphi package are intended to provide a physically meaningful, fast, robust and comprehensive tool for MM/PBSA based free energy calculations. © 2019 Wiley Periodicals, Inc.

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Predicting protein–DNA binding free energy change upon missense mutations using modified MM/PBSA approach: SAMPDI webserver

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 64 publications

References 51 publications

DelPhi Suite: New Developments and Review of Functionalities

DelPhi Suite: New Developments and Review of Functionalities

PremPRI: Predicting the Effects of Single Mutations on Protein-RNA Interactions

A grid‐based algorithm in conjunction with a gaussian‐based model of atoms for describing molecular geometry

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