Efficient In Silico Saturation Mutagenesis of a Member of the Caspase Protease Family

Wiedemann-Steiner syndrome (WSS) is a neurodevelopmental disorder caused by de novo variants in KMT2A, which encodes a multi–domain histone methyltransferase. To gain insight into the currently unknown pathogenesis of WSS, we examined the spatial distribution of likely WSS–causing variants across the 15 different domains of KMT2A. Compared to variants in healthy controls, WSS variants exhibit a 64.1–fold overrepresentation within the CXXC domain – which mediates binding to unmethylated CpGs – suggesting a major role for this domain in mediating the phenotype. In contrast, we find no significant overrepresentation within the catalytic SET domain. Corroborating these results, we find that hippocampal neurons from Kmt2a–deficient mice demonstrate disrupted H3K4me1 preferentially at CpG-rich regions, but this has no systematic impact on gene expression. Motivated by these results, we combine accurate prediction of the CXXC domain structure by AlphaFold2 with prior biological knowledge to develop a classification scheme for missense variants in the CXXC domain. Our classifier achieved 96.0% positive and 92.3% negative predictive value on a hold–out test set. This classification performance enabled us to subsequently perform an in silico saturation mutagenesis and classify a total of 445 variants according to their functional effects. Our results yield a novel insight into the mechanistic basis of WSS and provide an example of how AlphaFold2 can contribute to the in silico characterization of variant effects with very high accuracy, establishing a paradigm potentially applicable to many other Mendelian disorders.

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In silico saturation mutagenesis and docking screening for the analysis of protein-ligand interaction: the Endothelial Protein C Receptor case study

BMC Bioinformatics ◽

10.1186/1471-2105-10-s12-s3 ◽

2009 ◽

Vol 10 (Suppl 12) ◽

pp. S3 ◽

Cited By ~ 16

Author(s):

Federica Chiappori ◽

Pasqualina D'Ursi ◽

Ivan Merelli ◽

Luciano Milanesi ◽

Ermanna Rovida

Keyword(s):

In Silico ◽

Protein C ◽

Saturation Mutagenesis ◽

Endothelial Protein C Receptor ◽

Ligand Interaction ◽

Protein Ligand Interaction

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NO● Represses the Oxygenation of Arachidonoyl PE by 15LOX/PEBP1: Mechanism and Role in Ferroptosis

International Journal of Molecular Sciences ◽

10.3390/ijms22105253 ◽

2021 ◽

Vol 22 (10) ◽

pp. 5253

Author(s):

Karolina Mikulska-Ruminska ◽

Tamil S. Anthonymuthu ◽

Anastasia Levkina ◽

Indira H. Shrivastava ◽

Oleksandr O. Kapralov ◽

...

Keyword(s):

In Silico ◽

Functional Significance ◽

Catalytic Site ◽

Saturation Mutagenesis ◽

M2 Macrophages ◽

Modeling And Simulations ◽

Biochemical System ◽

No Donor ◽

M1 Macrophages ◽

Biochemical Model

We recently discovered an anti-ferroptotic mechanism inherent to M1 macrophages whereby high levels of NO● suppressed ferroptosis via inhibition of hydroperoxy-eicosatetraenoyl-phosphatidylethanolamine (HpETE-PE) production by 15-lipoxygenase (15LOX) complexed with PE-binding protein 1 (PEBP1). However, the mechanism of NO● interference with 15LOX/PEBP1 activity remained unclear. Here, we use a biochemical model of recombinant 15LOX-2 complexed with PEBP1, LC-MS redox lipidomics, and structure-based modeling and simulations to uncover the mechanism through which NO● suppresses ETE-PE oxidation. Our study reveals that O2 and NO● use the same entry pores and channels connecting to 15LOX-2 catalytic site, resulting in a competition for the catalytic site. We identified residues that direct O2 and NO● to the catalytic site, as well as those stabilizing the esterified ETE-PE phospholipid tail. The functional significance of these residues is supported by in silico saturation mutagenesis. We detected nitrosylated PE species in a biochemical system consisting of 15LOX-2/PEBP1 and NO● donor and in RAW264.7 M2 macrophages treated with ferroptosis-inducer RSL3 in the presence of NO●, in further support of the ability of NO● to diffuse to, and react at, the 15LOX-2 catalytic site. The results provide first insights into the molecular mechanism of repression of the ferroptotic Hp-ETE-PE production by NO●.

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Accelerating in-silico saturation mutagenesis using compressed sensing

10.1101/2021.11.08.467498 ◽

2021 ◽

Author(s):

Jacob Schreiber ◽

Surag Nair ◽

Akshay Balsubramani ◽

Anshul Kundaje

Keyword(s):

Compressed Sensing ◽

In Silico ◽

Computational Genomics ◽

Saturation Mutagenesis ◽

Sequence Length ◽

Biological Sequences ◽

Constant Number ◽

Order Of Magnitude ◽

Speed Up ◽

The Difference

In-silico saturation mutagenesis (ISM) is a popular approach in computational genomics for calculating feature attributions on biological sequences that proceeds by systematically perturbing each position in a sequence and recording the difference in model output. However, this method can be slow because systematically perturbing each position requires performing a number of forward passes proportional to the length of the sequence being examined. In this work, we propose a modification of ISM that leverages the principles of compressed sensing to require only a constant number of forward passes, regardless of sequence length, when applied to models that contain operations with a limited receptive field, such as convolutions. Our method, named Yuzu, can reduce the time that ISM spends in convolution operations by several orders of magnitude and, consequently, Yuzu can speed up ISM on several commonly used architectures in genomics by over an order of magnitude. Notably, we found that Yuzu provides speedups that increase with the complexity of the convolution operation and the length of the sequence being analyzed, suggesting that Yuzu provides large benefits in realistic settings. We have made this tool available at https://github.com/kundajelab/yuzu.

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fastISM: Performant in-silico saturation mutagenesis for convolutional neural networks

10.1101/2020.10.13.337147 ◽

2020 ◽

Author(s):

Surag Nair ◽

Avanti Shrikumar ◽

Anshul Kundaje

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

In Silico ◽

De Novo ◽

Input Sequence ◽

Saturation Mutagenesis ◽

Single Mutation ◽

Limited Region ◽

Link Type ◽

Intermediate Layers

AbstractDeep learning models such as convolutional neural networks are able to accurately map biological sequences to associated functional readouts and properties by learning predictive de novo representations. In-silico saturation mutagenesis (ISM) is a popular feature attribution technique for inferring contributions of all characters in an input sequence to the model’s predicted output. The main drawback of ISM is its runtime, as it involves multiple forward propagations of all possible mutations of each character in the input sequence through the trained model to predict the effects on the output. We present fastISM, an algorithm that speeds up ISM by a factor of over 10x for commonly used convolutional neural network architectures. fastISM is based on the observations that the majority of computation in ISM is spent in convolutional layers, and a single mutation only disrupts a limited region of intermediate layers, rendering most computation redundant. fastISM reduces the gap between backpropagation-based feature attribution methods and ISM. It far surpasses the runtime of backpropagation-based methods on multi-output architectures, making it feasible to run ISM on a large number of sequences. An easy-to-use Keras/TensorFlow 2 implementation of fastISM is available at https://github.com/kundajelab/fastISM, and a hands-on tutorial at https://colab.research.google.com/github/kundajelab/fastISM/blob/master/notebooks/colab/DeepSEA.ipynb.

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Computational Saturation Mutagenesis to predict structural consequences of systematic mutations on protein stability and rifampin interactions in the β subunit of RNA polymerase inMycobacterium leprae

10.1101/654095 ◽

2019 ◽

Author(s):

Sundeep Chaitanya Vedithi ◽

Carlos H. M. Rodrigues ◽

Stephanie Portelli ◽

Marcin J. Skwark ◽

Madhusmita Das ◽

...

Keyword(s):

Drug Resistance ◽

Rna Polymerase ◽

In Silico ◽

Growth Patterns ◽

Diagnostic Methods ◽

Saturation Mutagenesis ◽

Mode Analysis ◽

Relative Solvent Accessibility ◽

Β Subunit ◽

Rifampin Resistance

ABSTRACTIn contrast to the situation with tuberculosis, rifampin resistance in leprosy may remain undetected due to the lack of rapid and effective diagnostic methods. A quick and reliable method is essential to determine the impacts of emerging detrimental mutations. The functional consequences of missense mutations within the β-subunit of RNA polymerase inMycobacterium leprae(M. leprae) contribute to phenotypic rifampin resistance outcomes in leprosy. Here we reportin-silicosaturation mutagenesis of all residues in the β-subunit of RNA polymerase to all other 19 amino acid types and predict their impacts on overall thermodynamic stability, on interactions at subunit interfaces, and on β-subunit-RNA and rifampin affinities using state-of-the-art structure, sequence and normal mode analysis-based methods. A total of 21,394 mutations were analysed, and it was noted that mutations in the conserved residues that line the active-site cleft show largely destabilizing effects, resulting in increased relative solvent accessibility and concomitant decrease in depth of the mutant residues. The mutations at residues S437, G459, H451, P489, K884 and H1035 are identified as extremely detrimental as they induce highly destabilizing effects on the overall stability, nucleic acid and rifampin affinities. Destabilizing effects were predicted for all the experimentally identified rifampin-resistant mutations inM. lepraeindicating that this model can be used as a surveillance tool to monitor emerging detrimental mutations conferring rifampin resistance in leprosy.AUTHOR SUMMARYEmergence of primary and secondary drug resistance to rifampin in leprosy is a growing concern and poses threat to the leprosy control and elimination measures globally. In the absence of an effectivein-vitrosystem to detect and monitor phenotypic rifampin resistance in leprosy, most of the diagnosis relies on detecting mutations in the drug resistance determining regions of therpoBgene that encodes the β subunit of RNA polymerase inM. leprae. Few labs in the world perform mouse food pad propagation ofM. lepraein the presence of drugs (rifampin) to determine growth patterns and confirm resistance, however the duration of these methods lasts from 8 to 12 months making them impractical for diagnosis. Understanding molecular mechanisms of drug resistance is vital to associating mutations to clinical resistance outcomes in leprosy. Here we propose anin-silicosaturation mutagenesis approach to comprehensively elucidate the structural implications of any mutations that exist or can arise in the β subunit of RNA polymerase inM. leprae. Most of the predicted mutations may not occur inM. lepraedue to fitness costs but the information thus generated by this approach help decipher the impacts of mutations across the structure and conversely enable identification of stable regions in the protein that are least impacted by mutations (mutation coolspots) which can be a choice for small molecule binding and structure guided drug discovery.

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