FoldX 5.0: working with RNA, small molecules and a new graphical interface

Delgado, Javier; Radusky, Leandro; Cianferoni, Damiano; Serrano, Luís

doi:10.1093/bioinformatics/btz184

Cited by 277 publications

(323 citation statements)

References 7 publications

(7 reference statements)

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“…77 The Calculation of contribution of each amino acid in a protein partner 78 was computed with MM-GBSA method implemented in the HawkDock 79 server [23]. Different 3D structures of hACE2, each comprising one of 80 identified variants, were modeled using the BuildModel module of FoldX5 [3]. 81 Because it is more adapted to predict the effect of punctual variations of 82 amino acids, we used DynaMut at this stage of analysis [17].…”

mentioning

confidence: 99%

Interaction of the spike protein RBD from SARS-CoV-2 with ACE2: similarity with SARS-CoV, hot-spot analysis and effect of the receptor polymorphism

Othman

Bouslama

Brandenburg

et al. 2020

Preprint

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AbstractThe spread of COVID-19 caused by the SARS-CoV-2 outbreak has been growing since its first identification in December 2019. The publishing of the first SARS-CoV-2 genome made a valuable source of data to study the details about its phylogeny, evolution, and interaction with the host. Protein-protein binding assays have confirmed that Angiotensin-converting enzyme 2 (ACE2) is more likely to be the cell receptor through which the virus invades the host cell. In the present work, we provide an insight into the interaction of the viral spike Receptor Binding Domain (RBD) from different coronavirus isolates with host ACE2 protein. By calculating the binding energy score between RBD and ACE2, we highlighted the putative jump in the affinity from a progenitor form of SARS-CoV-2 to the current virus responsible for COVID-19 outbreak. Our result was consistent with previously reported phylogenetic analysis and corroborates the opinion that the interface segment of the spike protein RBD might be acquired by SARS-CoV-2 via a complex evolutionary process rather than a progressive accumulation of mutations. We also highlighted the relevance of Q493 and P499 amino acid residues of SARS-CoV-2 RBD for binding to human ACE2 and maintaining the stability of the interface. Moreover, we show from the structural analysis that it is unlikely for the interface residues to be the result of genetic engineering. Finally, we studied the impact of eight different variants located at the interaction surface of ACE2, on the complex formation with SARS-CoV-2 RBD. We found that none of them is likely to disrupt the interaction with the viral RBD of SARS-CoV-2.

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confidence: 99%

Interaction of the spike protein RBD from SARS-CoV-2 with ACE2: similarity with SARS-CoV, hot-spot analysis and effect of the receptor polymorphism

Othman

Bouslama

Brandenburg

et al. 2020

Preprint

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“…Three dimensional structures of protein-RNA complexes were obtained from the Protein Data Bank (PDB) (34), and the biological assembly 1 of crystal structure or the first model of NMR was used as the initial wild-type structure. Then we used the BuildModel module of FoldX software package (20,35) to produce mutant structures. Next we applied the VMD program (36) to add missing heavy side-chain and hydrogen atoms to wild-type and mutant structures using the topology file of the CHARMM36 force field (37).…”

Section: Structural Optimization Protocolmentioning

confidence: 99%

“…26), PrabHot(29) and FoldX5.0(35), for predicting the effects of mutations on protein-RNA interactions. mCSM-NA calculates the changes in protein-nucleic acid binding affinity induced by single mutations using graph-based signatures.…”

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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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“…Evaluation of coding variants is usually divided into Loss of Function (frameshifts, direct splice site impact, and stop gain or loss) and missense. Many methods have been developed to estimate the effect of missense mutations based on sequence conservation properties (for example, SIFT (Kumar, Henikoff, & Ng, 2009), PolyPhen-2 (Adzhubei et al, 2010), SNPs3D profile (Yue & Moult, 2006), SNAP2 (Hecht, Bromberg, & Rost, 2015), and Evolutionary Action (Katsonis & Lichtarge, 2017)) and on protein stability, as estimated from protein structure (for example, SNPs3D stability (Yue, Li, & Moult, 2005), Rosetta (Park et al, 2016), and FoldX (Delgado, Radusky, Cianferoni, & Serrano, 2019;Schymkowitz et al, 2005)). Some methods also include functional information (for example, MutPred2 (Pejaver, Mooney, & Radivojac, 2017)).…”

Section: Introductionmentioning

confidence: 99%

Matching whole genomes to rare genetic disorders: Identification of potential causative variants using phenotype-weighted knowledge in the CAGI SickKids5 clinical genomes challenge

Pal

Kundu

Yuan

et al. 2019

Preprint

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Precise identification of causative variants from whole‐genome sequencing data, including both coding and noncoding variants, is challenging. The Critical Assessment of Genome Interpretation 5 SickKids clinical genome challenge provided an opportunity to assess our ability to extract such information. Participants in the challenge were required to match each of the 24 whole‐genome sequences to the correct phenotypic profile and to identify the disease class of each genome. These are all rare disease cases that have resisted genetic diagnosis in a state‐of‐the‐art pipeline. The patients have a range of eye, neurological, and connective‐tissue disorders. We used a gene‐centric approach to address this problem, assigning each gene a multiphenotype‐matching score. Mutations in the top‐scoring genes for each phenotype profile were ranked on a 6‐point scale of pathogenicity probability, resulting in an approximately equal number of top‐ranked coding and noncoding candidate variants overall. We were able to assign the correct disease class for 12 cases and the correct genome to a clinical profile for five cases. The challenge assessor found genes in three of these five cases as likely appropriate. In the postsubmission phase, after careful screening of the genes in the correct genome, we identified additional potential diagnostic variants, a high proportion of which are noncoding.

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FoldX 5.0: working with RNA, small molecules and a new graphical interface

Cited by 277 publications

References 7 publications

Interaction of the spike protein RBD from SARS-CoV-2 with ACE2: similarity with SARS-CoV, hot-spot analysis and effect of the receptor polymorphism

Interaction of the spike protein RBD from SARS-CoV-2 with ACE2: similarity with SARS-CoV, hot-spot analysis and effect of the receptor polymorphism

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

Matching whole genomes to rare genetic disorders: Identification of potential causative variants using phenotype-weighted knowledge in the CAGI SickKids5 clinical genomes challenge

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