ProThermDB: thermodynamic database for proteins and mutants revisited after 15 years

Nikam, Rahul; Kulandaisamy, A.; Harini, Kannan; Sharma, Divya; Gromiha, M. Michael

doi:10.1093/nar/gkaa1035

Cited by 153 publications

(128 citation statements)

References 40 publications

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“…Conversely, an agreement between expected and observed impact of mutations provides confidence in the accuracy of a structural model. We obtained two independent compilations of experimentally measured impacts of protein mutations on protein function: 1) a compilation of measured changes in stability upon mutations (Nikam et al, 2021;Xavier et al, 2021); and 2) a compilation of deep mutational scanning (DMS) experiments (Dunham and Beltrao, 2021) that can measure the outcome of any possible single point mutation on a large proportion of all protein positions.…”

Section: Application Of Af2 Models For Structure Based Variant Effect Predictionmentioning

confidence: 99%

A structural biology community assessment of AlphaFold 2 applications

Akdel

Pires

et al. 2021

Preprint

120

166

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Most proteins fold into 3D structures that determine how they function and orchestrate the biological processes of the cell. Recent developments in computational methods have led to protein structure predictions that have reached the accuracy of experimentally determined models. While this has been independently verified, the implementation of these methods across structural biology applications remains to be tested. Here, we evaluate the use of AlphaFold 2 (AF2) predictions in the study of characteristic structural elements; the impact of missense variants; function and ligand binding site predictions; modelling of interactions; and modelling of experimental structural data. For 11 proteomes, an average of 25% additional residues can be confidently modelled when compared to homology modelling, identifying structural features rarely seen in the PDB. AF2-based predictions of protein disorder and protein complexes surpass state-of-the-art tools and AF2 models can be used across diverse applications equally well compared to experimentally determined structures, when the confidence metrics are critically considered. In summary, we find that these advances are likely to have a transformative impact in structural biology and broader life science research.

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Section: Application Of Af2 Models For Structure Based Variant Effect Predictionmentioning

confidence: 99%

A structural biology community assessment of AlphaFold 2 applications

Akdel

Pires

et al. 2021

Preprint

120

166

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“…This particular study was possible because of a large, previously published dataset of * 150,000 phenotypes of the mutagenized variants of the target enzyme (Wu et al 2016), which was used as a training set for ML. Availability of databases such as ProTherm (Nikam et al 2021) and Meltome Atlas (Jarzab et al 2020) enable the development of algorithms for protein stability traits. Large publicly available genomics and structure datasets could also potentially be used to narrow down the sequence space for activity optimization.…”

Section: Roadblocks and Practical Considerations For Evolution-aided Metabolic Engineeringmentioning

confidence: 99%

Evolution-aided engineering of plant specialized metabolism

et al. 2021

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The evolution of new traits in living organisms occurs via the processes of mutation, recombination, genetic drift, and selection. These processes that have resulted in the immense biological diversity on our planet are also being employed in metabolic engineering to optimize enzymes and pathways, create new-to-nature reactions, and synthesize complex natural products in heterologous systems. In this review, we discuss two evolution-aided strategies for metabolic engineering-directed evolution, which improves upon existing genetic templates using the evolutionary process, and combinatorial pathway reconstruction, which brings together genes evolved in different organisms into a single heterologous host. We discuss the general principles of these strategies, describe the technologies involved and the molecular traits they influence, provide examples of their use, and discuss the roadblocks that need to be addressed for their wider adoption. A better understanding of these strategies can provide an impetus to research on gene function discovery and biochemical evolution, which is foundational for improved metabolic engineering. These evolution-aided approaches thus have a substantial potential for improving our understanding of plant metabolism in general, for enhancing the production of plant metabolites, and in sustainable agriculture.

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“…Prior work has shown that temperature optima for observed enzyme rates correlate well with organism TGrowth (r = 0.75, ), but enzyme temperature optima reflect a combination of rate and stability effects. To isolate stability, we surveyed the relationship between stability and TGrowth using the ProThermDB, a collection of experimental data of protein and mutant stability (Nikam et al, 2021). Across 433 wild-type variants present in this database, we observed a significant relationship between Tm and TGrowth (Figure 4A, R 2 = 0.43, p = 2 x 10 -54 ).…”

Section: Testing Stability Compensation Using Literature Datamentioning

confidence: 99%

“…Wild-type mutation type stability data were downloaded from ProThermDB (Nikam et al, 2021) with the following fields: protein information (entry, source, mutation, E.C. number), experimental conditions (pH, T, measure, method), thermodynamic parameters (Tm, state, reversibility), and literature (PubMed ID, key words, reference, author).…”

Section: Literature Stability Analysismentioning

confidence: 99%

Systematic investigation of the link between enzyme catalysis and cold adaptation

Stark

Bautista-Leung

Siegfried

et al. 2021

Preprint

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Cold temperature is prevalent across the biosphere and slows the rates of chemical reactions. Increased catalysis has been predicted to be a general adaptive trait of enzymes to reduced temperature, and this expectation has informed physical models for enzyme catalysis and influenced bioprospecting strategies. To broadly test rate as an adaptive trait to cold, we paired kinetic constants of 2223 enzyme reactions with their organism’s optimal growth temperature (TGrowth) and analyzed trends of rate as a function of TGrowth. These data do not support a prevalent increase in rate in cold adaptation. In the model enzyme ketosteroid isomerase (KSI), there was prior evidence for temperature adaptation from a change in an active site residue that results in a tradeoff between activity and stability. Here, we found that little of the overall rate variation for 20 KSI variants was accounted for by TGrowth. In contrast, and consistent with prior expectations, we observed a correlation between stability and TGrowth across 433 proteins. These results suggest that temperature exerts a weaker selection pressure on enzyme rate than stability and that evolutionary forces other than temperature are responsible for the majority of enzymatic rate variation.

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ProThermDB: thermodynamic database for proteins and mutants revisited after 15 years

Cited by 153 publications

References 40 publications

A structural biology community assessment of AlphaFold 2 applications

A structural biology community assessment of AlphaFold 2 applications

Evolution-aided engineering of plant specialized metabolism

Systematic investigation of the link between enzyme catalysis and cold adaptation

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