Protein post-translational modifications: In silico prediction tools and molecular modeling

Audagnotto, Martina; Peraro, Matteo Dal

doi:10.1016/j.csbj.2017.03.004

Cited by 150 publications

(99 citation statements)

References 326 publications

(343 reference statements)

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“…However, the precise role of the viral protein phosphorylation (especially in Alphavirus) has not been reported yet. In this investigation, nsP2 was found to interact with the phosphorylation lip of p38, hence, in silico analysis was carried out using PTM prediction tools, GPS (group-based phosphorylation scoring method) (101,102) and NetPhos 3.1, to predict the target phosphorylation sites of nsP2 in a kinase specific manner (103). The GPS is a groupbased phosphorylation algorithm, which predicts kinase-specific phosphorylation sites among different host protein kinase groups according to specific sequence pattern (101,102).…”

Section: Discussionmentioning

confidence: 99%

“…The GPS is a groupbased phosphorylation algorithm, which predicts kinase-specific phosphorylation sites among different host protein kinase groups according to specific sequence pattern (101,102). Whereas, the NetPhos server is based on an artificial neuronal network (ANN) that allows the users to choose between generic predictions based on the given protein sequence or kinase-specific predictions (103,104). Out of several predictions, both the softwares predicted T5, S28, and S513 sites in CHIKV-nsP2 with a high probability of phosphorylation by p38 ( Supplementary Table S3).…”

Section: Discussionmentioning

confidence: 99%

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P38 and JNK Mitogen-Activated Protein Kinases Interact With Chikungunya Virus Non-structural Protein-2 and Regulate TNF Induction During Viral Infection in Macrophages

et al. 2019

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Chikungunya virus (CHIKV), a mosquito-borne Alphavirus, is endemic in different parts of the globe. The host macrophages are identified as the major cellular reservoirs of CHIKV during infection and this virus triggers robust TNF production in the host macrophages, which might be a key mediator of virus induced inflammation. However, the molecular mechanism underneath TNF induction is not understood yet. Accordingly, the Raw264.7 cells, a mouse macrophage cell line, were infected with CHIKV to address the above-mentioned question. It was observed that CHIKV induces both p38 and JNK phosphorylation in macrophages in a time-dependent manner and p-p38 inhibitor, SB203580 is effective in reducing infection even at lower concentration as compared to the p-JNK inhibitor, SP600125. However, inhibition of p-p38 and p-JNK decreased CHIKV induced TNF production in the host macrophages. Moreover, CHIKV induced macrophage derived TNF was found to facilitate TCR driven T cell activation. Additionally, it was noticed that the expressions of key transcription factors involved mainly in antiviral responses (p-IRF3) and TNF production (p-c-jun) were induced significantly in the CHIKV infected macrophages as compared to the corresponding mock cells. Further, it was demonstrated that CHIKV mediated TNF production in the macrophages is dependent on p38 and JNK MAPK pathways linking p-c-jun transcription factor. Interestingly, it was found that CHIKV nsP2 interacts with both p-p38 and p-JNK MAPKs in the macrophages. This observation was supported by the in silico protein-protein docking analysis which illustrates the specific amino acids responsible for the nsP2-MAPKs interactions. A strong polar interaction was predicted between Thr-180 (within the phosphorylation lip) of p38 and Gln-273 of nsP2, whereas, no such polar interaction was predicted for the phosphorylation lip of JNK which indicates the differential roles of p-p38 and p-JNK during CHIKV infection in the host macrophages. In summary, for the first time it has been shown that CHIKV triggers robust TNF production in the host macrophages via both p-p38 and p-JNK/p-c-jun pathways and the interaction of viral protein, nsP2 with these MAPKs during infection. Hence, this information might shed light in rationale-based drug designing strategies toward a possible control measure of CHIKV infection in future.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

P38 and JNK Mitogen-Activated Protein Kinases Interact With Chikungunya Virus Non-structural Protein-2 and Regulate TNF Induction During Viral Infection in Macrophages

et al. 2019

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“…They mainly differ in the types of PTM and/or organisms focused, in their learning protocols (support vector machine, random forest, neuronal network, etc. ), and in the set of descriptors extracted from the mining of the experimental data (Audagnotto and Dal Peraro 2017;Gianazza et al 2016). Few of them, used descriptors derived from structural data, such as prediction of secondary structures, disorder and accessible surface area (Lopez et al 2017;Lorenzo et al 2015), or from structural properties extracted from PDB (Torres et al 2016; Flexibility & PTMs 5 Wuyun et al 2016).…”

Section: In Context Of Protein Function This Diversity Leads To Coopmentioning

confidence: 99%

Investigation of the impact of PTMs on the protein backbone conformation

et al. 2019

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Post-Translational Modifications (PTMs) are known to play a critical role in the regulation of the protein functions. Their impact on protein structures, and their link to disorder regions have already been spotted on the past decade. Nonetheless, the high diversity of PTMs types, and the multiple schemes of protein modifications (multiple PTMs, of different types, at different time, etc) make difficult the direct confrontation of PTM annotations and protein structures data.We so analyzed the impact of the residue modifications on the protein structures at local level. Thanks to a dedicated structure database, namely PTM-SD, a large screen of PTMs have been done and analyze at a local protein conformation levels using the structural alphabet Protein Blocks (PBs). We investigated the relation between PTMs and the backbone conformation of modified residues, of their local environment, and at the level of the complete protein structure. The two main PTM types (N-glycosylation and phosphorylation) have been studied in non-redundant datasets, and then, 4 different proteins were focused, covering 3 types of PTMs: N-glycosylation in renin endopeptidase and liver carboxylesterase, phosphorylation in cyclin-dependent kinase 2 (CDK2), and methylation in actin. We observed that PTMs could either stabilize or destabilize the backbone structure, at a local and global scale, and that these effects depend on the PTM types.

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“…Seeing which aspects of recent, cutting-edge kinetic models of β-lactamase [82] can be used to predict fitness would be a next intriguing step. Sufficient progress has also been made toward modeling post-translational modifications that one can readily imagine the incorporation of these effects into high-throughput computational methodologies for mutants in the very near future [83]. The computational modeling of protein quality control and transcriptional and translational dynamics, however, remain in their infancy owing to the difficulty of experimentally determining the strengths and frequencies of the protein-protein and protein-nucleic acid interactions involved which can be used to parameterize reaction network models [84].…”

Section: Resultsmentioning

confidence: 99%

Predicting the Viability of Beta-Lactamase: How Folding and Binding Free Energies Correlate with Beta-Lactamase Fitness

Yang

Naik

Patel

et al. 2020

Preprint

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One of the long-standing holy grails of molecular evolution has been the ability to predict an organism's fitness directly from its genotype. With such predictive abilities in hand, researchers would be able to more accurately forecast how organisms will evolve and how proteins with novel functions could be engineered, leading to revolutionary advances in medicine and biotechnology. In this work, we assemble the largest reported set of experimental TEM-1 β-lactamase folding free energies and use this data in conjunction with previously acquired fitness data and computational free energy predictions to determine how much of the fitness of β-lactamase can be directly predicted by thermodynamic folding and binding free energies. We focus upon β-lactamase because of its long history as a model enzyme and its central role in antibiotic resistance. Based upon a set of 21 β-lactamase single and double mutants expressly designed to influence protein folding, we first demonstrate that modeling software such as FoldX and PyRosetta designed to compute folding free energies can meaningfully, although not perfectly, predict the experimental folding free energies of single mutants. Interestingly, while these techniques also yield sensible double mutant free energies, we show that they do so for the wrong physical reasons. We then go on to assess how well both experimental and computational folding free energies explain single mutant fitness. We find that folding free energies account for, at most, 24% of the variance in β-lactamase fitness values according to linear models and, somewhat surprisingly, complementing folding free energies with computationally-predicted binding free energies of residues near the active site only increases the folding-only figure by a few percent. This strongly suggests that the majority of β-lactamase's fitness is controlled by factors other than free energies. Overall, our results shed a bright light on April 15, 2020 1/26to what extent the community is justified in using thermodynamic measures to infer protein fitness as well as how applicable modern computational techniques for predicting free energies will be to the large data sets of multiply-mutated proteins forthcoming.April 15, 2020 2/26 1 Please note that PyRosetta and AutoDock Vina utilize a combination of empirical and physical free energy contributions by weighting physically-inspired terms based upon fits to larger data sets. They are therefore not strictly empirical free energy function techniques.

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Protein post-translational modifications: In silico prediction tools and molecular modeling

Cited by 150 publications

References 326 publications

P38 and JNK Mitogen-Activated Protein Kinases Interact With Chikungunya Virus Non-structural Protein-2 and Regulate TNF Induction During Viral Infection in Macrophages

P38 and JNK Mitogen-Activated Protein Kinases Interact With Chikungunya Virus Non-structural Protein-2 and Regulate TNF Induction During Viral Infection in Macrophages

Investigation of the impact of PTMs on the protein backbone conformation

Predicting the Viability of Beta-Lactamase: How Folding and Binding Free Energies Correlate with Beta-Lactamase Fitness

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