Deciphering the structure, function, and mechanism of lysine acetyltransferase cGNAT2 in cyanobacteria

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Abstract

Lysine acetylation is a conserved regulatory posttranslational protein modification that is performed by lysine acetyltransferases (KATs). By catalyzing the transfer of acetyl groups to substrate proteins, KATs play critical regulatory roles in all domains of life; however, no KATs have yet been identified in cyanobacteria. Here, we tested all predicted KATs in the cyanobacterium Synechococcus sp. PCC 7002 (Syn7002) and demonstrated that A1596, which we named cyanobacterial Gcn5-related N-acetyltransferase (cGNAT2), can catalyze lysine acetylation in vivo and in vitro. Eight amino acid residues were identified as the key residues in the putative active site of cGNAT2, as indicated by structural simulation and site-directed mutagenesis. The loss of cGNAT2 altered both growth and photosynthetic electron transport in Syn7002. In addition, quantitative analysis of the lysine acetylome identified 548 endogenous substrates of cGNAT2 in Syn7002. We further demonstrated that cGNAT2 can acetylate NAD(P)H dehydrogenase J (NdhJ) in vivo and in vitro, with the inability to acetylate K89 residues, thus decreasing NdhJ activity and affecting both growth and electron transport in Syn7002. In summary, this study identified a KAT in cyanobacteria and revealed that cGNAT2 regulates growth and photosynthesis in Syn7002 through an acetylation-mediated mechanism.

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Highly accurate protein structure prediction with AlphaFold

John Jumper, Richard Evans, Alexander Pritzel … (2021)

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

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MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

Jürgen Cox, Matthias Mann (2008)

Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.

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The Perseus computational platform for comprehensive analysis of (prote)omics data.

Stefka Tyanova, Tikira Temu, Pavel Sinitcyn … (2016)

A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.