MutateX: an automated pipeline for in silico saturation mutagenesis of protein structures and structural ensembles

Mutational biases favor complexity increases in protein interaction networks after gene duplication

Biological systems can gain complexity over time. While some of these transitions are likely driven by natural selection, the extent to which they occur without providing an adaptive benefit is unknown. At the molecular level, one example is heteromeric complexes replacing homomeric ones following gene duplication. Here, we build a biophysical model and simulate the evolution of homodimers and heterodimers following gene duplication using distributions of mutational effects inferred from available protein structures. We keep the specific activity of each dimer identical, so their concentrations drift neutrally without new functions. We show that for more than 60% of tested dimer structures, the relative concentration of the heteromer increases over time due to mutational biases that favor the heterodimer. However, allowing mutational effects on synthesis rates and differences in the specific activity of homo- and heterodimers can limit or reverse the observed bias toward heterodimers. Our results show that the accumulation of more complex protein quaternary structures is likely under neutral evolution, and that natural selection would be needed to reverse this tendency.

Abatement of the binding of human hexokinase II enzyme monomers by in-silico method with the design of inhibitory peptides

The hexokinase II enzyme is bound to the (VDAC1) channel in the form of a dimer and prevents the release of cell death factors from mitochondria to the cytoplasm. Studies have shown that blocking the binding of hexokinase II enzyme to (VDAC1) led to the initiation of apoptosis in cancer cells. No peptide has been designed so far to inhibit hexokinase II. The aim of this study was to inhibit the dimerization of enzyme subunits in order to inhibition the formation of (VDAC1) and the hexokinase II complex. In this study, the molecular dynamics simulation of the enzyme in monomer and dimer states was investigated in terms of RMSF, RMSD and radius of gyration. The following process involves extracting and designing variable-length peptides from the interacting segments of enzyme monomers. Using molecular dynamics simulation, the stability of the peptide was determined in terms of RMSD. Molecular docking was used to investigate the interaction between the designed peptides. Finally, the inhibitory effect of peptides on subunit association was measured using dynamic light scattering (DLS) technique. Our results showed that the designed peptides, which mimic common amino acids in dimerization, interrupt the bona fide form of the enzyme subunits. The result of this study provides a new way to disrupt the assembly process and thereby decreased the function of the hexokinase II.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-024-00201-8.

Understanding large scale sequencing datasets through changes to protein folding

The expansion of high-quality, low-cost sequencing has created an enormous opportunity to understand how genetic variants alter cellular behaviour in disease. The high diversity of mutations observed has however drawn a spotlight onto the need for predictive modelling of mutational effects on phenotype from variants of uncertain significance. This is particularly important in the clinic due to the potential value in guiding clinical diagnosis and patient treatment. Recent computational modelling has highlighted the importance of mutation induced protein misfolding as a common mechanism for loss of protein or domain function, aided by developments in methods that make large computational screens tractable. Here we review recent applications of this approach to different genes, and how they have enabled and supported subsequent studies. We further discuss developments in the approach and the role for the approach in light of increasingly high throughput experimental approaches.

Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks

The vast amount of available sequencing data allows the scientific community to explore different genetic alterations that may drive cancer or favor cancer progression. Software developers have proposed a myriad of predictive tools, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tools. However, such limitations force researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases daily, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartments, mutational burden or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive cancer driver tools. Furthermore, we introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving towards integrative frameworks.

Genomics 2 Proteins portal: A resource and discovery tool for linking genetic screening outputs to protein sequences and structures

Recent advances in AI-based methods have revolutionized the field of structural biology. Concomitantly, high-throughput sequencing and functional genomics technologies have enabled the detection and generation of variants at an unprecedented scale. However, efficient tools and resources are needed to link these two disparate data types - to "map" variants onto protein structures, to better understand how the variation causes disease and thereby design therapeutics. Here we present the Genomics 2 Proteins Portal (G2P; g2p.broadinstitute.org/): a human proteome-wide resource that maps 19,996,443 genetic variants onto 42,413 protein sequences and 77,923 structures, with a comprehensive set of structural and functional features. Additionally, the G2P portal generalizes the capability of linking genomics to proteins beyond databases by allowing users to interactively upload protein residue-wise annotations (variants, scores, etc.) as well as the protein structure to establish the connection. The portal serves as an easy-to-use discovery tool for researchers and scientists to hypothesize the structure-function relationship between natural or synthetic variations and their molecular phenotype.

Biochemical and cellular studies of three human 3-phosphoglycerate dehydrogenase variants responsible for pathological reduced L-serine levels

In the brain, the non-essential amino acid L-serine is produced through the phosphorylated pathway (PP) starting from the glycolytic intermediate 3-phosphoglycerate: among the different roles played by this amino acid, it can be converted into D-serine and glycine, the two main co-agonists of NMDA receptors. In humans, the enzymes of the PP, namely phosphoglycerate dehydrogenase (hPHGDH, which catalyzes the first and rate-limiting step of this pathway), 3-phosphoserine aminotransferase, and 3-phosphoserine phosphatase are likely organized in the cytosol as a metabolic assembly (a "serinosome"). The hPHGDH deficiency is a pathological condition biochemically characterized by reduced levels of L-serine in plasma and cerebrospinal fluid and clinically identified by severe neurological impairment. Here, three single-point variants responsible for hPHGDH deficiency and Neu-Laxova syndrome have been studied. Their biochemical characterization shows that V261M, V425M, and V490M substitutions alter either the kinetic (both maximal activity and Km for 3-phosphoglycerate in the physiological direction) and the structural properties (secondary, tertiary, and quaternary structure, favoring aggregation) of hPHGDH. All the three variants have been successfully ectopically expressed in U251 cells, thus the pathological effect is not due to hindered expression level. At the cellular level, mistargeting and aggregation phenomena have been observed in cells transiently expressing the pathological protein variants, as well as a reduced L-serine cellular level. Previous studies demonstrated that the pharmacological supplementation of L-serine in hPHGDH deficiencies could ameliorate some of the related symptoms: our results now suggest the use of additional and alternative therapeutic approaches.

Deep generative models of LDLR protein structure to predict variant pathogenicity

The complex structure and function of low density lipoprotein receptor (LDLR) makes classification of protein-coding missense variants challenging. Deep generative models, including Evolutionary model of Variant Effect (EVE), Evolutionary Scale Modeling (ESM), and AlphaFold 2 (AF2), have enabled significant progress in the prediction of protein structure and function. ESM and EVE directly estimate the likelihood of a variant sequence but are purely data-driven and challenging to interpret. AF2 predicts LDLR structures, but variant effects are explicitly modeled by estimating changes in stability. We tested the effectiveness of these models for predicting variant pathogenicity compared to established methods. AF2 produced two distinct conformations based on a novel hinge mechanism. Within ESM's hidden space, benign and pathogenic variants had different distributions. In EVE, these distributions were similar. EVE and ESM were comparable to Polyphen-2, SIFT, REVEL, and Primate AI for predicting binary classifications in ClinVar. However, they were more strongly correlated with experimental measures of LDL uptake. AF2 poorly performed in these tasks. Using the UK Biobank to compare association with clinical phenotypes, ESM and EVE were more strongly associated with serum LDL-C than Polyphen-2. ESM was able to identify variants with more extreme LDL-C levels than EVE and had a significantly stronger association with atherosclerotic cardiovascular disease. In conclusion, AF2 predicted LDLR structures do not accurately model variant pathogenicity. ESM and EVE are competitive with prior scoring methods for prediction based on binary classifications in ClinVar but are superior based on correlations with experimental assays and clinical phenotypes.

Computational analysis of five neurodegenerative diseases reveals shared and specific genetic loci

Neurodegenerative diseases (ND) are heterogeneous disorders of the central nervous system that share a chronic and selective process of neuronal cell death. A computational approach to investigate shared genetic and specific loci was applied to 5 different ND: Amyotrophic lateral sclerosis (ALS), Alzheimer's disease (AD), Parkinson's disease (PD), Multiple sclerosis (MS), and Lewy body dementia (LBD). The datasets were analyzed separately, and then we compared the obtained results. For this purpose, we applied a genetic correlation analysis to genome-wide association datasets and revealed different genetic correlations with several human traits and diseases. In addition, a clumping analysis was carried out to identify SNPs genetically associated with each disease. We found 27 SNPs in AD, 6 SNPs in ALS, 10 SNPs in PD, 17 SNPs in MS, and 3 SNPs in LBD. Most of them are located in non-coding regions, with the exception of 5 SNPs on which a protein structure and stability prediction was performed to verify their impact on disease. Furthermore, an analysis of the differentially expressed miRNAs of the 5 examined pathologies was performed to reveal regulatory mechanisms that could involve genes associated with selected SNPs. In conclusion, the results obtained constitute an important step toward the discovery of diagnostic biomarkers and a better understanding of the diseases.

TRAP1 S-nitrosylation as a model of population-shift mechanism to study the effects of nitric oxide on redox-sensitive oncoproteins

S-nitrosylation is a post-translational modification in which nitric oxide (NO) binds to the thiol group of cysteine, generating an S-nitrosothiol (SNO) adduct. S-nitrosylation has different physiological roles, and its alteration has also been linked to a growing list of pathologies, including cancer. SNO can affect the function and stability of different proteins, such as the mitochondrial chaperone TRAP1. Interestingly, the SNO site (C501) of TRAP1 is in the proximity of another cysteine (C527). This feature suggests that the S-nitrosylated C501 could engage in a disulfide bridge with C527 in TRAP1, resembling the well-known ability of S-nitrosylated cysteines to resolve in disulfide bridge with vicinal cysteines. We used enhanced sampling simulations and in-vitro biochemical assays to address the structural mechanisms induced by TRAP1 S-nitrosylation. We showed that the SNO site induces conformational changes in the proximal cysteine and favors conformations suitable for disulfide bridge formation. We explored 4172 known S-nitrosylated proteins using high-throughput structural analyses. Furthermore, we used a coarse-grained model for 44 protein targets to account for protein flexibility. This resulted in the identification of up to 1248 proximal cysteines, which could sense the redox state of the SNO site, opening new perspectives on the biological effects of redox switches. In addition, we devised two bioinformatic workflows ( https://github.com/ELELAB/SNO_investigation_pipelines ) to identify proximal or vicinal cysteines for a SNO site with accompanying structural annotations. Finally, we analyzed mutations in tumor suppressors or oncogenes in connection with the conformational switch induced by S-nitrosylation. We classified the variants as neutral, stabilizing, or destabilizing for the propensity to be S-nitrosylated and undergo the population-shift mechanism. The methods applied here provide a comprehensive toolkit for future high-throughput studies of new protein candidates, variant classification, and a rich data source for the research community in the NO field.

Epistasis between promoter activity and coding mutations shapes gene evolvability

The evolution of protein-coding genes proceeds as mutations act on two main dimensions: regulation of transcription level and the coding sequence. The extent and impact of the connection between these two dimensions are largely unknown because they have generally been studied independently. By measuring the fitness effects of all possible mutations on a protein complex at various levels of promoter activity, we show that promoter activity at the optimal level for the wild-type protein masks the effects of both deleterious and beneficial coding mutations. Mutations that are deleterious at low activity but masked at optimal activity are slightly destabilizing for individual subunits and binding interfaces. Coding mutations that increase protein abundance are beneficial at low expression but could potentially incur a cost at high promoter activity. We thereby demonstrate that promoter activity in interaction with protein properties can dictate which coding mutations are beneficial, neutral, or deleterious.

GSNOR deficiency promotes tumor growth via FAK1 S-nitrosylation

Nitric oxide (NO) production in the tumor microenvironment is a common element in cancer. S-nitrosylation, the post-translational modification of cysteines by NO, is emerging as a key transduction mechanism sustaining tumorigenesis. However, most oncoproteins that are regulated by S-nitrosylation are still unknown. Here we show that S-nitrosoglutathione reductase (GSNOR), the enzyme that deactivates S-nitrosylation, is hypo-expressed in several human malignancies. Using multiple tumor models, we demonstrate that GSNOR deficiency induces S-nitrosylation of focal adhesion kinase 1 (FAK1) at C658. This event enhances FAK1 autophosphorylation and sustains tumorigenicity by providing cancer cells with the ability to survive in suspension (evade anoikis). In line with these results, GSNOR-deficient tumor models are highly susceptible to treatment with FAK1 inhibitors. Altogether, our findings advance our understanding of the oncogenic role of S-nitrosylation, define GSNOR as a tumor suppressor, and point to GSNOR hypo-expression as a therapeutically exploitable vulnerability in cancer.

RosettaDDGPrediction for high-throughput mutational scans: From stability to binding

Reliable prediction of free energy changes upon amino acid substitutions (ΔΔGs) is crucial to investigate their impact on protein stability and protein-protein interaction. Advances in experimental mutational scans allow high-throughput studies thanks to multiplex techniques. On the other hand, genomics initiatives provide a large amount of data on disease-related variants that can benefit from analyses with structure-based methods. Therefore, the computational field should keep the same pace and provide new tools for fast and accurate high-throughput ΔΔG calculations. In this context, the Rosetta modeling suite implements effective approaches to predict folding/unfolding ΔΔGs in a protein monomer upon amino acid substitutions and calculate the changes in binding free energy in protein complexes. However, their application can be challenging to users without extensive experience with Rosetta. Furthermore, Rosetta protocols for ΔΔG prediction are designed considering one variant at a time, making the setup of high-throughput screenings cumbersome. For these reasons, we devised RosettaDDGPrediction, a customizable Python wrapper designed to run free energy calculations on a set of amino acid substitutions using Rosetta protocols with little intervention from the user. Moreover, RosettaDDGPrediction assists with checking completed runs and aggregates raw data for multiple variants, as well as generates publication-ready graphics. We showed the potential of the tool in four case studies, including variants of uncertain significance in childhood cancer, proteins with known experimental unfolding ΔΔGs values, interactions between target proteins and disordered motifs, and phosphomimetics. RosettaDDGPrediction is available, free of charge and under GNU General Public License v3.0, at https://github.com/ELELAB/RosettaDDGPrediction.

The Cancermuts software package for the prioritization of missense cancer variants: a case study of AMBRA1 in melanoma

Cancer genomics and cancer mutation databases have made an available wealth of information about missense mutations found in cancer patient samples. Contextualizing by means of annotation and predicting the effect of amino acid change help identify which ones are more likely to have a pathogenic impact. Those can be validated by means of experimental approaches that assess the impact of protein mutations on the cellular functions or their tumorigenic potential. Here, we propose the integrative bioinformatic approach Cancermuts, implemented as a Python package. Cancermuts is able to gather known missense cancer mutations from databases such as cBioPortal and COSMIC, and annotate them with the pathogenicity score REVEL as well as information on their source. It is also able to add annotations about the protein context these mutations are found in, such as post-translational modification sites, structured/unstructured regions, presence of short linear motifs, and more. We applied Cancermuts to the intrinsically disordered protein AMBRA1, a key regulator of many cellular processes frequently deregulated in cancer. By these means, we classified mutations of AMBRA1 in melanoma, where AMBRA1 is highly mutated and displays a tumor-suppressive role. Next, based on REVEL score, position along the sequence, and their local context, we applied cellular and molecular approaches to validate the predicted pathogenicity of a subset of mutations in an in vitro melanoma model. By doing so, we have identified two AMBRA1 mutations which show enhanced tumorigenic potential and are worth further investigation, highlighting the usefulness of the tool. Cancermuts can be used on any protein targets starting from minimal information, and it is available at https://www.github.com/ELELAB/cancermuts as free software.

Structure-function analysis of the SHOC2-MRAS-PP1C holophosphatase complex

Receptor tyrosine kinase (RTK)-RAS signalling through the downstream mitogen-activated protein kinase (MAPK) cascade regulates cell proliferation and survival. The SHOC2-MRAS-PP1C holophosphatase complex functions as a key regulator of RTK-RAS signalling by removing an inhibitory phosphorylation event on the RAF family of proteins to potentiate MAPK signalling1. SHOC2 forms a ternary complex with MRAS and PP1C, and human germline gain-of-function mutations in this complex result in congenital RASopathy syndromes2-5. However, the structure and assembly of this complex are poorly understood. Here we use cryo-electron microscopy to resolve the structure of the SHOC2-MRAS-PP1C complex. We define the biophysical principles of holoenzyme interactions, elucidate the assembly order of the complex, and systematically interrogate the functional consequence of nearly all of the possible missense variants of SHOC2 through deep mutational scanning. We show that SHOC2 binds PP1C and MRAS through the concave surface of the leucine-rich repeat region and further engages PP1C through the N-terminal disordered region that contains a cryptic RVXF motif. Complex formation is initially mediated by interactions between SHOC2 and PP1C and is stabilized by the binding of GTP-loaded MRAS. These observations explain how mutant versions of SHOC2 in RASopathies and cancer stabilize the interactions of complex members to enhance holophosphatase activity. Together, this integrative structure-function model comprehensively defines key binding interactions within the SHOC2-MRAS-PP1C holophosphatase complex and will inform therapeutic development .

Microbial engineering for the production and application of phytases to the treatment of the toxic pollutants: A review

Phytases are a group of digestive enzymes which are commonly used as feed enzymes. These enzymes are used exogenously in the feeds of monogastric animals thereby it improves the digestibility of phosphorous and thus reduces the negative impact of inorganic P excretion on the environment. Even though these enzymes are widely distributed in many life forms, microorganisms are the most preferred and potential source of phytase. Despite the extensive availability of the phytase-producing microbial consortia, only a few microorganisms have been known to be exploited at industrial level. The high costs of the enzyme along with the incapability to survive high temperatures followed by the poor storage stability are noted to be the bottleneck in the commercialization of enzymes. For this reason, besides the conventional fermentation approaches, the applicability of cloning, expression studies and genetic engineering has been implemented for the past few years to accomplish the abovesaid benefits. The site-directed mutagenesis as well as knocking out have also validated their prominent role in microbe-based phytase production with enhanced levels. The present review provides detailed information on recent insights on the modification of phytases through heterologous expression and protein engineering to make thermostable and protease-resistant phytases.

Directed Evolution of (R)-2-Hydroxyglutarate Dehydrogenase Improves 2-Oxoadipate Reduction by 2 Orders of Magnitude

Pathway engineering is commonly employed to improve the production of various metabolites but may incur in bottlenecks due to the low catalytic activity of a particular reaction step. The reduction of 2-oxoadipate to (R)-2-hydroxyadipate is a key reaction in metabolic pathways that exploit 2-oxoadipate conversion via α-reduction to produce adipic acid, an industrially important platform chemical. Here, we engineered (R)-2-hydroxyglutarate dehydrogenase from Acidaminococcus fermentans (Hgdh) with the aim of improving 2-oxoadipate reduction. Using a combination of computational analysis, saturation mutagenesis, and random mutagenesis, three mutant variants with a 100-fold higher catalytic efficiency were obtained. As revealed by rational analysis of the mutations found in the variants, this improvement could be ascribed to a general synergistic effect where mutation A206V played a key role since it boosted the enzyme’s activity by 4.8-fold. The Hgdh variants with increased activity toward 2-oxoadipate generated within this study pave the way for the bio-based production of adipic acid.