FoldX as Protein Engineering Tool: Better Than Random Based Approaches?

Buß, O.; Rudat, Jens; Ochsenreither, Katrin

doi:10.1016/j.csbj.2018.01.002

Cited by 210 publications

(153 citation statements)

References 141 publications

(202 reference statements)

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“…We identified a slightly negative binding Δ Δ G (average Δ Δ G =-0.37 kcal/mol) due to the combination of A184D (OPTN) and R37Q (LC3B) and a destabilization (average Δ Δ G =1.41 kcal/mol) induced by D16N(FUNDC1) and R70C (LC3B), respectively. In parallel, we estimate the changes in binding free energies also with another protocol based on Rosetta (Barlow et al, 2018) for a reciprocal control of the calculation results, as recently suggested for similar applications (Buß et al, 2018). The calculation suggests that only D16N(FUNDC1)-R70C(LC3B) has a destabilizing effect on binding affinity, whereas A184D(OPTN)-R37Q(LC3B) has neutral effects according to Rosetta scan.…”

Section: Local Effects Of the Mutations On Binding Of Lir Motifs And mentioning

confidence: 94%

The conformational and mutational landscape of the ubiquitin-like marker for the autophagosome formation in cancer

Fas

Kumar

Sora

et al. 2019

Preprint

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Autophagy is a cellular process to recycle damaged cellular components and its modulation can be exploited for disease treatments. A key autophagy player is a ubiquitin-like protein, LC3B. Compelling evidence attests the role of autophagy and LC3B in different cancer types. Many LC3B structures have been solved, but a comprehensive study, including dynamics, has not been yet undertaken. To address this knowledge gap, we assessed ten physical models for molecular dynamics for their capabilities to describe the structural ensemble of LC3B in solution using different metrics and comparison with NMR data. With the resulting LC3B ensembles, we characterized the impact of 26 missense mutations from Pan-Cancer studies with different approaches. Our findings shed light on driver or neutral mutations in LC3B, providing an atlas of its modifications in cancer. Our framework could be used to assess the pathogenicity of mutations by accounting for the different aspects of protein structure and function altered by mutational events.

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Section: Local Effects Of the Mutations On Binding Of Lir Motifs And mentioning

confidence: 94%

The conformational and mutational landscape of the ubiquitin-like marker for the autophagosome formation in cancer

Fas

Kumar

Sora

et al. 2019

Preprint

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“…In fact, 39 missense mutations in FUS occur in patients with the neurodegenerative diseases 40 amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration 8 . For instance, 41 a single ALS-associated mutation, G156E, facilitates a liquid-to-solid phase transition of 42 FUS into irreversible aggregates 16 . The importance of FUS in the life of cells, together 43 with the sensitivity of FUS assemblies to point mutations, raises the possibility that natural 44 selection must actively maintain the ability of FUS to form the liquid-droplet state.…”

Section: Body: 27mentioning

confidence: 99%

Natural selection has preserved and enhanced the phase-separation properties of FUS during 160 million years of mammalian evolution

Dasmeh

Wagner

2020

Preprint

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10Protein phase separation is essential for the self-assembly of non-membraneous 11 organelles. However, we know little about its ability to change in evolution. Here we 12 studied the evolution of the mammalian RNA binding protein FUS, a protein whose prion-13 like domain (PLD) is essential for the formation of stress granules through liquid-liquid 14 phase separation. Although the prion-like domain evolves three times as rapidly as the 15 remainder of FUS, it harbors absolutely conserved tyrosine residues that are crucial for 16phase separation. Ancestral reconstruction shows that the phosphorylation sites within 17 the PLD are subject to stabilizing selection. They toggle among a small number of amino 18 acid states. One exception to this pattern are primates, where the number of such 19 phosphosites has increased through positive selection. In addition, we find frequent 20 glutamine to proline changes that help maintain the unstructured state of FUS that is 21 necessary for phase separation. In summary, natural selection has stabilized the liquid-22 forming potential of FUS and minimized the propensity of cytotoxic liquid-to-solid phase 23 transitions during 160 million years of mammalian evolution. 24 25 avoids the pathological liquid-to-solid phase separation in FUS, just like it maintains 47 folding stability and reduces misfolding in proteins with structured domains 17 . 48To validate this hypothesis, we first identified 105 mammalian orthologs of FUS, 49 aligned them ( Figure 1A), and computed each residue's sequence entropy, a widely-used 50 measure of sequence divergence ( Figure 1B, see Supplementary Methods for a list of 51 sequences). The PLD domain, which is central for FUS phase separation, has the highest 52 sequence entropy of all FUS domains, with a median ~3-fold higher than that of the other 53 FUS domains ( Figure 1C; Wilcoxon rank-sum test, p=6.12 Î10 -12 ), and it shows that the 54 PLD domain evolves much faster than the rest of FUS. Nonetheless, tyrosine residues 55 within this domain are fully conserved, which indicates the essential role of these amino 56 acids for phase separation. 57Within the PLD, we observed two evolutionary hotspots, which are the regions S30 58 to S86, and A105 to Q147 (All site numbers and amino acids refer to human FUS). These 59 regions are subject to multiple substitutions that involve the amino acids glycine, serine, 60 alanine, threonine, asparagine, proline, and glutamine ( Figure 1D). By reconstructing 61 ancestral FUS proteins (Tables S1-S2, see Methods for details), we found that changes 62where the PLD sites toggle forth and back between G and S (113 changes), as well as 63 between A and T (34 changes), are especially prevalent ( Figures 1E-F). Together, these 64 changes account for ~60% of all changes in the evolution of the PLD. In addition, we 65 found 32 switches between serine and asparagine, and 20 switches between glutamine 66 and proline in these evolutionary hotspots (Tables S1-S2). 67 P in all residues that had experienced Q to P substitutions...

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“…In combination, these factors contribute to PyRosetta being more strongly correlated with the experimental data, as evidenced by its 0.44 coefficient of determination relative to MD+FoldX's 0.071 coefficient. Indeed, PyRosetta's correlation coefficient of 0.67 for β-lactamase is among the highest PyRosetta correlation coefficients for proteins published in the literature [69,70]. To complement our regression analysis, we additionally computed Spearman's rank correlation coefficients [71] for our Experiment vs. MD+FoldX and Experiment vs. PyRosetta data sets.…”

Section: Accuracy Of Single Mutant Predictionsmentioning

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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FoldX as Protein Engineering Tool: Better Than Random Based Approaches?

Cited by 210 publications

References 141 publications

The conformational and mutational landscape of the ubiquitin-like marker for the autophagosome formation in cancer

The conformational and mutational landscape of the ubiquitin-like marker for the autophagosome formation in cancer

Natural selection has preserved and enhanced the phase-separation properties of FUS during 160 million years of mammalian evolution

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

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