Exploring amino acid functions in a deep mutational landscape.
Ontology highlight
ABSTRACT:
SUBMITTER: Dunham AS
PROVIDER: S-EPMC8297461 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The recent technological advances underlying the screening of large combinatorial libraries in high-throughput mutational scans deepen our understanding of adaptive protein evolution and boost its applications in protein design. Nevertheless, the large number of possible genotypes requires suitable computational methods for data analysis, the prediction of mutational effects, and the generation of optimized sequences. We describe a computational method that, trained on sequencing samples from multiple rounds of a screening experiment, provides a model of the genotype-fitness relationship. We tested the method on five large-scale mutational scans, yielding accurate predictions of the mutational effects on fitness. The inferred fitness landscape is robust to experimental and sampling noise and exhibits high generalization power in terms of broader sequence space exploration and higher fitness variant predictions. We investigate the role of epistasis and show that the inferred model provides structural information about the 3D contacts in the molecular fold.
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Project description:BackgroundEvaluating the impact of amino acid variants has been a critical challenge for studying protein function and interpreting genomic data. High-throughput experimental methods like deep mutational scanning (DMS) can measure the effect of large numbers of variants in a target protein, but because DMS studies have not been performed on all proteins, researchers also model DMS data computationally to estimate variant impacts by predictors.ResultsIn this study, we extended a linear regression-based predictor to explore whether incorporating data from alanine scanning (AS), a widely used low-throughput mutagenesis method, would improve prediction results. To evaluate our model, we collected 146 AS datasets, mapping to 54 DMS datasets across 22 distinct proteins.ConclusionsWe show that improved model performance depends on the compatibility of the DMS and AS assays, and the scale of improvement is closely related to the correlation between DMS and AS results.
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Project description:The serine protease inhibitor (SERPIN) plasminogen activator inhibitor-1 (PAI-1) is a key regulator of the fibrinolytic system, inhibiting the serine proteases tissue- and urokinase-type plasminogen activator (tPA and uPA, respectively). Missense variants render PAI-1 non-functional through misfolding, leading to its turnover as a protease substrate, or to a more rapid transition to the latent/inactive state. Deep mutational scanning was performed to evaluate the impact of amino acid sequence variation on PAI-1 inhibition of uPA using an M13 filamentous phage display system. Error prone PCR was used to construct a mutagenized PAI-1 library encompassing ~ 70% of potential single amino acid substitutions. The relative effects of 27% of all possible missense variants on PAI-1 inhibition of uPA were determined using high-throughput DNA sequencing. 826 missense variants demonstrated conserved inhibitory activity while 1137 resulted in loss of PAI-1 inhibitory function. The least evolutionarily conserved regions of PAI-1 were also identified as being the most tolerant of missense mutations. The results of this screen confirm previous low-throughput mutational studies, including those of the reactive center loop. These data provide a powerful resource for explaining structure-function relationships for PAI-1 and for the interpretation of human genomic sequence variants.
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Project description:BACKGROUND: Nonsynonymous mutations in the coding regions of human genes are responsible for phenotypic differences between humans and for susceptibility to genetic disease. Computational methods were recently used to predict deleterious effects of nonsynonymous human mutations and polymorphisms. Here we focus on understanding the amino-acid mutation spectrum of human genetic disease. We compare the disease spectrum to the spectra of mutual amino-acid mutation frequencies, non-disease polymorphisms in human genes, and substitutions fixed between species. RESULTS: We find that the disease spectrum correlates well with the amino-acid mutation frequencies based on the genetic code. Normalized by the mutation frequencies, the spectrum can be rationalized in terms of chemical similarities between amino acids. The disease spectrum is almost identical for membrane and non-membrane proteins. Mutations at arginine and glycine residues are together responsible for about 30% of genetic diseases, whereas random mutations at tryptophan and cysteine have the highest probability of causing disease. CONCLUSIONS: The overall disease spectrum mainly reflects the mutability of the genetic code. We corroborate earlier results that the probability of a nonsynonymous mutation causing a genetic disease increases monotonically with an increase in the degree of evolutionary conservation of the mutation site and a decrease in the solvent-accessibility of the site; opposite trends are observed for non-disease polymorphisms. We estimate that the rate of nonsynonymous mutations with a negative impact on human health is less than one per diploid genome per generation.
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Project description:Tumor heterogeneity results in more than 50% of hypermutated cancers failing to respond to standard immunotherapy. There are numerous challenges in terms of drug resistance, therapeutic strategies, and biomarkers in immunotherapy. In this study, we analyzed primary tumor samples from 533 cancer patients with six different cancer types using deep targeted sequencing and gene expression data from 78 colorectal cancer patients, whereby driver mutations, mutational signatures, tumor-associated neoantigens, and molecular cancer evolution were investigated. Driver mutations, including RET, CBL, and DDR2 gene mutations, were identified in the hypermutated cancers. Most hypermutated endometrial and pancreatic cancer patients carry genetic mutations in EGFR, FBXW7, and PIK3CA that are linked to immunotherapy resistance, while hypermutated head and neck cancer patients carry genetic mutations associated with better treatment responses, such as ATM and BRRCA2 mutations. APOBEC (apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like) and DNA repair defects are mutational drivers that are signatures for hypermutated cancer. Cancer driver mutations and other mutational signatures are associated with sensitivity or resistance to immunotherapy, representing potential genetic markers in hypermutated cancers. Using computational prediction, we identified NF1 p.T700I and NOTCH1 p.V2153M as tumor-associated neoantigens, representing potential therapeutic targets for immunotherapy. Sequential mutations were used to predict hypermutated cancers based on genomic evolution. Using a logistic model, we achieved an area under the curve (AUC) = 0.93, accuracy = 0.93, and sensitivity = 0.81 in the testing set. The sequential patterns were distinct among the six cancer types, and the sequential mutation order of MSH2 and the coexisting BRAF genetic mutations influenced the hypermutated phenotype. The TP53~MLH1 and NOTCH1~TET2 sequential mutations impacted colorectal cancer survival (p-value = 0.027 and 0.0001, respectively) by reducing the expression of PTPRCAP (p-value = 1.06 × 10-6) and NOS2 (p-value = 7.57 × 10-7) in immunity. Sequential mutations are significant for hypermutated cancers, which are characterized by mutational heterogeneity. In addition to driver mutations and mutational signatures, sequential mutations in cancer evolution can impact hypermutated cancers. They characterize potential responses or predictive markers for hypermutated cancers. These data can also be used to develop hypermutation-associated drug targets and elucidate the evolutionary biology of cancer survival. In this study, we conducted a comprehensive analysis of mutational patterns, including sequential mutations, and identified useful markers and therapeutic targets in hypermutated cancer patients.
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Project description:Ilv5p is a bifunctional yeast mitochondrial enzyme required for branched chain amino acid biosynthesis and for the stability of mitochondrial DNA (mtDNA) and its parsing into nucleoids. The latter occurs when the general amino acid control (GAC) pathway is activated. We have isolated ilv5 mutants that lack either the enzymatic (a(-)D(+)) or the mtDNA stability function (a(+)D(-)) of the protein. The affected residues in these two mutant classes cluster differently when mapped to the 3-D structure of the spinach ortholog of Ilv5p. a(-)D(+) mutations map to conserved internal domains known to be important for substrate and cofactor binding, whereas the a(+)D(-) mutations map to a C-terminal region on the surface of the protein. The a(+)D(-) mutants also have a temperature-sensitive phenotype when grown on a glycerol medium, which correlates with their degree of mtDNA instability. Analysis of an a(+)D(-) mutant with a strong mtDNA instability phenotype shows that it is also unable to parse mtDNA into nucleoids when activated by the GAC pathway. Finally, the wild-type Escherichia coli ortholog of Ilv5p behaves like a(+)D(-) mutants when expressed and targeted to mitochondria in ilv5Delta yeast cells, suggesting that yeast Ilv5p acquired its mtDNA function after the endosymbiotic event.
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Project description:Drug efflux is a common resistance mechanism found in bacteria and cancer cells, but studies providing comprehensive functional insights are scarce. In this study, we performed deep mutational scanning (DMS) on the bacterial ABC transporter EfrCD to determine the drug efflux activity profile of more than 1,430 single variants. These systematic measurements revealed that the introduction of negative charges at different locations within the large substrate binding pocket results in strongly increased efflux activity toward positively charged ethidium, whereas additional aromatic residues did not display the same effect. Data analysis in the context of an inward-facing cryogenic electron microscopy structure of EfrCD uncovered a high-affinity binding site, which releases bound drugs through a peristaltic transport mechanism as the transporter transits to its outward-facing conformation. Finally, we identified substitutions resulting in rapid Hoechst influx without affecting the efflux activity for ethidium and daunorubicin. Hence, single mutations can convert EfrCD into a drug-specific ABC importer.
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Project description:BackgroundThe number of applications of deep learning algorithms in bioinformatics is increasing as they usually achieve superior performance over classical approaches, especially, when bigger training datasets are available. In deep learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleotides in bioinformatics, are generally represented as a continuous vector through an embedding matrix. Recently, learning this embedding matrix directly from the data as part of the continuous iteration of the model to optimize the target prediction - a process called 'end-to-end learning' - has led to state-of-the-art results in many fields. Although usage of embeddings is well described in the bioinformatics literature, the potential of end-to-end learning for single amino acids, as compared to more classical manually-curated encoding strategies, has not been systematically addressed. To this end, we compared classical encoding matrices, namely one-hot, VHSE8 and BLOSUM62, to end-to-end learning of amino acid embeddings for two different prediction tasks using three widely used architectures, namely recurrent neural networks (RNN), convolutional neural networks (CNN), and the hybrid CNN-RNN.ResultsBy using different deep learning architectures, we show that end-to-end learning is on par with classical encodings for embeddings of the same dimension even when limited training data is available, and might allow for a reduction in the embedding dimension without performance loss, which is critical when deploying the models to devices with limited computational capacities. We found that the embedding dimension is a major factor in controlling the model performance. Surprisingly, we observed that deep learning models are capable of learning from random vectors of appropriate dimension.ConclusionOur study shows that end-to-end learning is a flexible and powerful method for amino acid encoding. Further, due to the flexibility of deep learning systems, amino acid encoding schemes should be benchmarked against random vectors of the same dimension to disentangle the information content provided by the encoding scheme from the distinguishability effect provided by the scheme.
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