Computational modeling of protein mutant stability: analysis and optimization of statistical potentials and structural features reveal insights into prediction model development

A large-scale screening identified in USH2A gene the P3272L founder pathogenic variant explaining familial Usher syndrome in Sardinia, Italy

BACKGROUND

Usher syndrome (USH) encompasses a group of disorders characterized by congenital sensorineural hearing loss (SNHL) and retinitis pigmentosa (RP). We described the clinical findings, natural history, and molecular analyses of USH patients identified during a large-scale screening to identify quantitative traits related to ocular disorders in the SardiNIA project cohort.

METHODS

We identified 3 USH-affected families out of a cohort of 6,148 healthy subjects. 9 subjects presented a pathological phenotype, with SNHL and RP. All patients and their family members underwent a complete ophthalmic examination including best-corrected visual acuity, slit-lamp biomicroscopy, fundoscopy, fundus autofluorescence, spectral-domain optical coherence tomography, and electrophysiological testing. Audiological evaluation was performed with a clinical audiometer. Genotyping was performed using several arrays integrated with whole genome sequence data providing approximately 22 million markers equally distributed for each subject analyzed. Molecular diagnostics focused on analysis of the following candidate genes: MYO7A, USH1C, CDH23, PCDH15, USH1G, CIB2, USH2A, GPR98, DFNB31, CLRN1, and PDZD7.

RESULTS

A single missense causal variant in USH2A gene was identified in homozygous status in all patients and in heterozygous status in unaffected parents. The presence of multiple homozygous patients with the same phenotypic severity of the syndromic form suggests that the Sardinian USH phenotype is the result of a founder effect on a specific pathogenic variant related haplotype. The frequency of heterozygotes in general Sardinian population is 1.89. Additionally, to provide new insights into the structure of usherin and the pathological mechanisms caused by small pathogenic in-frame variants, like p.Pro3272Leu, molecular dynamics simulations of native and mutant protein-protein and protein-ligand complexes were performed that predicted a destabilization of the protein with a decrease in the free energy change.

CONCLUSIONS

Our results suggest that our approach is effective for the genetic diagnosis of USH. Based on the heterozygous frequency, targeted screening of this variant in the general population and in families at risk or with familial USH can be suggested. This can lead to more accurate molecular diagnosis, better genetic counseling, and improved molecular epidemiology data that are critical for future intervention plans.

TRIAL REGISTRATION

We did not perform any health-related interventions for the participants.

Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors

Background

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).

Results

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.

Conclusions

VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications.

Availability

VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.

Gut microbial metalloproteins and its role in xenobiotics degradation and ROS scavenging

The gut microbial metalloenzymes play an important role in maintaining the balance between gut microbial ecosystem, human physiologically processes and immune system. The metals coordinated into active site contribute in various detoxification and defense strategies to avoid unfavourable environment and ensure bacterial survival in human gut. Metallo-β-lactamase is a potent degrader of antibiotics present in periplasmic space of both commensals and pathogenic bacteria. The resistance to anti-microbial agents developed in this enzyme is one of the global threats for human health. The organophosphorus eliminator, organophosphorus hydrolases have evolved over a course of time to hydrolyze toxic organophosphorus compounds and decrease its effect on human health. Further, the redox stress responders namely superoxide dismutase and catalase are key metalloenzymes in reducing both endogenous and exogenous oxidative stress. They hold a great importance for pathogens as they contribute in pathogenesis in human gut along with reduction of oxidative stress. The in-silico study on these enzymes reveals the importance of point mutation for the evolution of these enzymes in order to enhance their enzyme activity and stability. Various mutation studies were conducted to investigate the catalytic activity of these enzymes. By using the "directed evolution" method, the enzymes involved in detoxification and defense system can be engineered to produce new variants with enhance catalytic features, which may be used to predict the severity due to multi-drug resistance and degradation pattern of organophosphorus compounds in human gut.

Glutamate dehydrogenase in "Liverworld"-A study in selected species to explore a key enzyme of plant primary metabolism in Marchantiophyta

In plants, glutamate dehydrogenase (GDH) is an ubiquitous enzyme that catalyzes the reversible amination of 2-oxoglutarate in glutamate. It contributes to both the amino acid homeostasis and the management of intracellular ammonium, and it is regarded as a key player at the junction of carbon and nitrogen assimilation pathways. To date, information about the GDH of terrestrial plants refers to a very few species only. We focused on selected species belonging to the division Marchantiophyta, providing the first panoramic overview of biochemical and functional features of GDH in liverworts. Native electrophoretic analyses showed an isoenzymatic profile less complex than what was reported for Arabidposis thaliana and other angiosperms: the presence of a single isoform corresponding to an α-homohexamer, differently prone to thermal inactivation on a species- and organ-basis, was found. Sequence analysis conducted on amino acid sequences confirmed a high similarity of GDH in modern liverworts with the GDH2 protein of A. thaliana, strengthening the hypothesis that the duplication event that gave origin to GDH1-homolog gene from GDH2 occurred after the evolutionary bifurcation that separated bryophytes and tracheophytes. Experiments conducted on Marchantia polymorpha and Calypogeia fissa grown in vitro and compared to A. thaliana demonstrated through in gel activity detection and monodimensional Western Blot that the aminating activity of GDH resulted in strongly enhanced responses to ammonium excess in liverworts as well, even if at a different extent compared to Arabidopsis and other vascular species. The comparative analysis by bi-dimensional Western Blot suggested that the regulation of the enzyme could be, at least partially, untied from the protein post-translational pattern. Finally, immuno-electron microscopy revealed that the GDH enzyme localizes at the subcellular level in both mitochondria and chloroplasts of parenchyma and is specifically associated to the endomembrane system in liverworts.

Protein Arginine Methyltransferase 5 (PRMT5) Mutations in Cancer Cells

Arginine methylation is a form of posttranslational modification that regulates many cellular functions such as development, DNA damage repair, inflammatory response, splicing, and signal transduction, among others. Protein arginine methyltransferase 5 (PRMT5) is one of nine identified methyltransferases, and it can methylate both histone and non-histone targets. It has pleiotropic functions, including recruitment of repair machinery to a chromosomal DNA double strand break (DSB) and coordinating the interplay between repair and checkpoint activation. Thus, PRMT5 has been actively studied as a cancer treatment target, and small molecule inhibitors of its enzymatic activity have already been developed. In this report, we analyzed all reported PRMT5 mutations appearing in cancer cells using data from the Catalogue of Somatic Mutations in Cancers (COSMIC). Our goal is to classify mutations as either drivers or passengers to understand which ones are likely to promote cellular transformation. Using gold standard artificial intelligence algorithms, we uncovered several key driver mutations in the active site of the enzyme (D306H, L315P, and N318K). In silico protein modeling shows that these mutations may affect the affinity of PRMT5 for S-adenosylmethionine (SAM), which is required as a methyl donor. Electrostatic analysis of the enzyme active site shows that one of these mutations creates a tunnel in the vicinity of the SAM binding site, which may allow interfering molecules to enter the enzyme active site and decrease its activity. We also identified several non-coding mutations that appear to affect PRMT5 splicing. Our analyses provide insights into the role of PRMT5 mutations in cancer cells. Additionally, since PRMT5 single molecule inhibitors have already been developed, this work may uncover future directions in how mutations can affect targeted inhibition.

Computational Exploration and Characterization of Potential Calcium Sensitizing Mutations in Cardiac Troponin C

Calcium-dependent heart muscle contraction is regulated by the cardiac troponin protein complex (cTn) and specifically by the N-terminal domain of its calcium binding subunit (cNTnC). cNTnC contains one calcium binding site (site II), and altered calcium binding in this site has been studied for decades. It has been previously shown that cNTnC mutants, which increase calcium sensitization may have therapeutic benefits, such as restoring cardiac muscle contractility and functionality post-myocardial infarction events. Here, we computationally characterized eight mutations for their potential effects on calcium binding affinity in site II of cNTnC. We utilized two distinct methods to estimate calcium binding: adaptive steered molecular dynamics (ASMD) and thermodynamic integration (TI). We observed a sensitizing trend for all mutations based on the employed ASMD methodology. The TI results showed excellent agreement with experimentally known calcium binding affinities in wild-type cNTnC. Based on the TI results, five mutants were predicted to increase calcium sensitivity in site II. This study presents an interesting comparison of the two computational methods, which have both been shown to be valuable tools in characterizing the impacts of calcium sensitivity in mutant cNTnC systems.

Accurate and efficient structure-based computational mutagenesis for modeling fluorescence levels of Aequorea victoria green fluorescent protein mutants

A computational mutagenesis technique was used to characterize the structural effects associated with over 46 000 single and multiple amino acid variants of Aequorea victoria green fluorescent protein (GFP), whose functional effects (fluorescence levels) were recently measured by experimental researchers. For each GFP mutant, the approach generated a single score reflecting the overall change in sequence-structure compatibility relative to native GFP, as well as a vector of environmental perturbation (EP) scores characterizing the impact at all GFP residue positions. A significant GFP structure–function relationship (P < 0.0001) was elucidated by comparing the sequence-structure compatibility scores with the functional data. Next, the computed vectors for GFP mutants were used to train predictive models of fluorescence by implementing random forest (RF) classification and tree regression machine learning algorithms. Classification performance reached 0.93 for sensitivity, 0.91 for precision and 0.90 for balanced accuracy, and regression models led to Pearson’s correlation as high as r = 0.83 between experimental and predicted GFP mutant fluorescence. An RF model trained on a subset of over 1000 experimental single residue GFP mutants with measured fluorescence was used for predicting the 3300 remaining unstudied single residue mutants, with results complementing known GFP biochemical and biophysical properties. In addition, models trained on the subset of experimental GFP mutants harboring multiple residue replacements successfully predicted fluorescence of the single residue GFP mutants. The models developed for this study were accurate and efficient, and their predictions outperformed those of several related state-of-the-art methods.

Immunoglobulin interface redesigning to enhance lebrikizumab mediated immunomodulation of IL-13 hyper-response

The overexpression of interleukin-13 (IL-13) leads to autoimmune and inflammatory diseases. These adverse responses can be neutralized by using lebrikizumab as a therapeutic monoclonal antibody (mAb). Herein, we have attempted to modulate the lebrikizumab mAb to enhance its binding affinity towards IL-13. The interface residues of the lebrikizumab-IL-13 complex were determined by the PyMOL and verified by the artificial neural network-based B-cell epitope prediction server (ABCpred server) and the Paratome web server. The Cologne University Protein Stability Analysis Tool (CUPSAT) web server based mutational approach was used to identify the stable and favorable interface mutations in the lebrikizumab. Only 40 mutations were selected to generate a single mutant library, and their binding affinity for IL-13 was analyzed by using the Z-Dock server. Based on high Z-score, mutants having a better affinity with IL-13 were selected to create a multi-mutant library. The multi-mutant library was again subjected to the Z-Dock server, and their binding affinity was determined. The highest-scoring ten mAb mutants were validated by using PatchDock and ClusPro servers. The best two potential mAb mutants were identified and subjected to molecular dynamics (MD) simulations to ensure its structural stability at the microscopic level. The changes in the different bonds as the effect of mutation were assessed by LigPlot + v2.1. The AllerTOP and ToxinPred web servers were used to analyze the non-allergic and nontoxic nature of the selected mutants. Therefore, these redesigned mAb could be used for potential treatment against IL-13 associated diseased conditions.Communicated by Ramaswamy H. Sarma.

VIPdb, a genetic Variant Impact Predictor Database

Genome sequencing identifies vast number of genetic variants. Predicting these variants' molecular and clinical effects is one of the preeminent challenges in human genetics. Accurate prediction of the impact of genetic variants improves our understanding of how genetic information is conveyed to molecular and cellular functions, and is an essential step towards precision medicine. Over one hundred tools/resources have been developed specifically for this purpose. We summarize these tools as well as their characteristics, in the genetic Variant Impact Predictor Database (VIPdb). This database will help researchers and clinicians explore appropriate tools, and inform the development of improved methods. VIPdb can be browsed and downloaded at https://genomeinterpretation.org/vipdb.

Integration of Random Forest Classifiers and Deep Convolutional Neural Networks for Classification and Biomolecular Modeling of Cancer Driver Mutations

Development of machine learning solutions for prediction of functional and clinical significance of cancer driver genes and mutations are paramount in modern biomedical research and have gained a significant momentum in a recent decade. In this work, we integrate different machine learning approaches, including tree based methods, random forest and gradient boosted tree (GBT) classifiers along with deep convolutional neural networks (CNN) for prediction of cancer driver mutations in the genomic datasets. The feasibility of CNN in using raw nucleotide sequences for classification of cancer driver mutations was initially explored by employing label encoding, one hot encoding, and embedding to preprocess the DNA information. These classifiers were benchmarked against their tree-based alternatives in order to evaluate the performance on a relative scale. We then integrated DNA-based scores generated by CNN with various categories of conservational, evolutionary and functional features into a generalized random forest classifier. The results of this study have demonstrated that CNN can learn high level features from genomic information that are complementary to the ensemble-based predictors often employed for classification of cancer mutations. By combining deep learning-generated score with only two main ensemble-based functional features, we can achieve a superior performance of various machine learning classifiers. Our findings have also suggested that synergy of nucleotide-based deep learning scores and integrated metrics derived from protein sequence conservation scores can allow for robust classification of cancer driver mutations with a limited number of highly informative features. Machine learning predictions are leveraged in molecular simulations, protein stability, and network-based analysis of cancer mutations in the protein kinase genes to obtain insights about molecular signatures of driver mutations and enhance the interpretability of cancer-specific classification models.

Molecular Mechanism of Cancer Susceptibility Associated with Fok1 Single Nucleotide Polymorphism of VDR in Relation to Breast Cancer

Breast cancer is the leading cause of death among women worldwide. It is a multi-factorial disease caused by genetic and environmental factors. Vitamin D has been hypothesized to lower the risk of breast cancer via the nuclear vitamin D receptor (VDR). Genetic variants of these vitamin D metabolizing genes may alter the bioavailability of vitamin D, and hence modulate the risk of breast cancer.

Effect of Specific Mutations in Cd300 Complexes Formation; Potential Implication of Cd300f in Multiple Sclerosis

Herein, we have used bioinformatics tools to predict five clusters defining ligand-binding sites on the extracellular domain of human CD300b receptor, presumably involved in the formation of both homodimers and heterodimers with other CD300 family members. Site-directed mutagenesis revealed residues glutamic acid 28 and glutamine 29 in cluster 5 to be necessary for the formation of CD300b complexes. Surprisingly, the disruption of cluster 2 and 4 reconstituted the binding capability lost by the mutation of residues glutamic acid 28 to alanine, glutamine 29 to alanine (E28A-Q29G). We identified a missense mutation arginine 33 to glutamine (R33Q) in CD300f by direct sequencing of exon 2 in peripheral blood samples from 50 patients with multiple sclerosis (MS). Levels of expression of CD300f were almost undetectable on monocytes from the patient bearing the R33Q mutation compared with healthy individuals. Whereas R33Q mutation had no effect in the formation of CD300f complexes, the inhibition of protein synthesis with cycloheximide indicated that CD300f R33Q is less stable than native CD300f. Finally, we report that the levels of expression of CD300f on the surface of classical and intermediate monocytes from MS patients are significantly lower when compared to the same cell populations in healthy individuals.

Universal distribution of mutational effects on protein stability, uncoupling of protein robustness from sequence evolution and distinct evolutionary modes of prokaryotic and eukaryotic proteins

Robustness to destabilizing effects of mutations is thought of as a key factor of protein evolution. The connections between two measures of robustness, the relative core size and the computationally estimated effect of mutations on protein stability (ΔΔG), protein abundance and the selection pressure on protein-coding genes (dN/dS) were analyzed for the organisms with a large number of available protein structures including four eukaryotes, two bacteria and one archaeon. The distribution of the effects of mutations in the core on protein stability is universal and indistinguishable in eukaryotes and bacteria, centered at slightly destabilizing amino acid replacements, and with a heavy tail of more strongly destabilizing replacements. The distribution of mutational effects in the hyperthermophilic archaeon Thermococcus gammatolerans is significantly shifted toward strongly destabilizing replacements which is indicative of stronger constraints that are imposed on proteins in hyperthermophiles. The median effect of mutations is strongly, positively correlated with the relative core size, in evidence of the congruence between the two measures of protein robustness. However, both measures show only limited correlations to the expression level and selection pressure on protein-coding genes. Thus, the degree of robustness reflected in the universal distribution of mutational effects appears to be a fundamental, ancient feature of globular protein folds whereas the observed variations are largely neutral and uncoupled from short term protein evolution. A weak anticorrelation between protein core size and selection pressure is observed only for surface residues in prokaryotes but a stronger anticorrelation is observed for all residues in eukaryotic proteins. This substantial difference between proteins of prokaryotes and eukaryotes is likely to stem from the demonstrable higher compactness of prokaryotic proteins.

Insights into the binding specificity of wild type and mutated wheat germ agglutinin towards Neu5Acα(2-3)Gal: a study by in silico mutations and molecular dynamics simulations

Wheat germ agglutinin (WGA) is a plant lectin, which specifically recognizes the sugars NeuNAc and GlcNAc. Mutated WGA with enhanced binding specificity can be used as biomarkers for cancer. In silico mutations are performed at the active site of WGA to enhance the binding specificity towards sialylglycans, and molecular dynamics simulations of 20 ns are carried out for wild type and mutated WGAs (WGA1, WGA2, and WGA3) in complex with sialylgalactose to examine the change in binding specificity. MD simulations reveal the change in binding specificity of wild type and mutated WGAs towards sialylgalactose and bound conformational flexibility of sialylgalactose. The mutated polar amino acid residues Asn114 (S114N), Lys118 (G118K), and Arg118 (G118R) make direct and water mediated hydrogen bonds and hydrophobic interactions with sialylgalactose. An analysis of possible hydrogen bonds, hydrophobic interactions, total pair wise interaction energy between active site residues and sialylgalactose and MM-PBSA free energy calculation reveals the plausible binding modes and the role of water in stabilizing different binding modes. An interesting observation is that the binding specificity of mutated WGAs (cyborg lectin) towards sialylgalactose is found to be higher in double point mutation (WGA3). One of the substituted residues Arg118 plays a crucial role in sugar binding. Based on the interactions and energy calculations, it is concluded that the order of binding specificity of WGAs towards sialylgalactose is WGA3 > WGA1 > WGA2 > WGA. On comparing with the wild type, double point mutated WGA (WGA3) exhibits increased specificity towards sialylgalactose, and thus, it can be effectively used in targeted drug delivery and as biological cell marker in cancer therapeutics.

Structural insight into the binding interactions of modeled structure of Arabidopsis thaliana urease with urea: an in silico study

Urease (EC 3.5.1.5., urea amidohydrolase) catalyzes the hydrolysis of urea to ammonia and carbon dioxide. Urease is present to a greater abundance in plants and plays significant role related to nitrogen recycling from urea. But little is known about the structure and function of the urease derived from the Arabidopsis thaliana, the model system of choice for research in plant biology. In this study, a three-dimensional structural model of A. thaliana urease was constructed using computer-aided molecular modeling technique. The characteristic structural features of the modeled structure were then studied using atomistic molecular dynamics simulation. It was observed that the modeled structure was stable and regions between residues index (50-80, 500-700) to be significantly flexible. From the docking studies, we detected the possible binding interactions of modeled urease with urea. Ala399, Ile675, Thr398, and Thr679 residues of A. thaliana urease were observed to be significantly involved in binding with the substrate urea. We also compared the docking studies of ureases from other sources such as Canavalia ensiformis, Helicobacter pylori, and Bacillus pasteurii. In addition, we carried out mutation analysis to find the highly mutable amino acid residues of modeled A. thaliana urease. In this particular study, we observed Met485, Tyr510, Ser786, Val426, and Lys765 to be highly mutable amino acids. These results are significant for the mutagenesis analysis. As a whole, this study expounds the salient structural features as well the binding interactions of the modeled structure of A. thaliana urease.

Effect of serine phosphorylation and Ser25 phospho-mimicking mutations on nuclear localisation and ligand interactions of annexin A2

Annexin A2 (AnxA2) interacts with numerous ligands, including calcium, lipids, mRNAs and intracellular and extracellular proteins. Different post-translational modifications participate in the discrimination of the functions of AnxA2 by modulating its ligand interactions. Here, phospho-mimicking mutants (AnxA2-S25E and AnxA2-S25D) were employed to investigate the effects of Ser25 phosphorylation on the structure and function of AnxA2 by using AnxA2-S25A as a control. The overall α-helical structure of AnxA2 is not affected by the mutations, since the thermal stabilities and aggregation tendencies of the mutants differ only slightly from the wild-type (wt) protein. Unlike wt AnxA2, all mutants bind the anxA2 3' untranslated region and β-γ-G-actin with high affinity in a Ca(2+)-independent manner. AnxA2-S25E is not targeted to the nucleus in transfected PC12 cells. In vitro phosphorylation of AnxA2 by protein kinase C increases its affinity to mRNA and inhibits its nuclear localisation, in accordance with the data obtained with the phospho-mimicking mutants. Ca(2+)-dependent binding of wt AnxA2 to phosphatidylinositol, phosphatidylinositol-3-phosphate, phosphatidylinositol-4-phosphate and phosphatidylinositol-5-phosphate, as well as weaker but still Ca(2+)-dependent binding to phosphatidylserine and phosphatidylinositol-3,5-bisphosphate, was demonstrated by a protein-lipid overlay assay, whereas binding of AnxA2 to these lipids, as well as its binding to liposomes, is inhibited by the Ser25 mutations. Thus, introduction of a modification (mutation or phosphorylation) at Ser25 appears to induce a conformational change leading to increased accessibility of the mRNA- and G-actin-binding sites in domain IV independent of Ca(2+) levels, while the Ca(2+)-dependent binding of AnxA2 to phospholipids is attenuated.

A frequent splicing mutation and novel missense mutations color the updated mutational spectrum of classic galactosemia in Portugal

Classic galactosemia is an autosomal recessive disorder caused by deficient galactose-1-phosphate uridylyltransferase (GALT) activity. Patients develop symptoms in the neonatal period, which can be ameliorated by dietary restriction of galactose. Many patients develop long-term complications, with a broad range of clinical symptoms whose pathophysiology is poorly understood. The high allelic heterogeneity of GALT gene that characterizes this disorder is thought to play a determinant role in biochemical and clinical phenotypes. We aimed to characterize the mutational spectrum of GALT deficiency in Portugal and to assess potential genotype-phenotype correlations. Direct sequencing of the GALT gene and in silico analyses were employed to evaluate the impact of uncharacterized mutations upon GALT functionality. Molecular characterization of 42 galactosemic Portuguese patients revealed a mutational spectrum comprising 14 nucleotide substitutions: ten missense, two nonsense and two putative splicing mutations. Sixteen different genotypic combinations were detected, half of the patients being p.Q188R homozygotes. Notably, the second most frequent variation is a splicing mutation. In silico predictions complemented by a close-up on the mutations in the protein structure suggest that uncharacterized missense mutations have cumulative point effects on protein stability, oligomeric state, or substrate binding. One splicing mutation is predicted to cause an alternative splicing event. This study reinforces the difficulty in establishing a genotype-phenotype correlation in classic galactosemia, a monogenic disease whose complex pathogenesis and clinical features emphasize the need to expand the knowledge on this "cloudy" disorder.

Molecular characterization and in silico analysis of naturally occurring TEM beta-lactamase variants among pathogenic Enterobacteriaceae infecting Indian patients

Cephalosporin resistance, particularly due to bla(TEM) encoded β-lactamases, among Enterobacteriaceae is, though, an increasing public health problem in India; their circulating genetic variants remain unknown. The present study deals with determination of bla(TEM) variants among 134 pathogenic Enterobacteriaceae of Indian origin. Their resistance profile against 3rd generation cephalosporins was determined. The presence of bla(TEM) variants among the bacterial plasmids was characterized by PCR followed by sequencing. Intergenic relations among the variants was determined by phylogenetic analysis. bla(TEM) protein were modeled by Modeller9v5 and verified. The catalytic pockets were characterized, and their interaction with cephalosporins was analyzed using AutoDock tools. More than 87% of isolates showed cephalosporin resistance with ESBL production among 57.8% of Escherichia coli and 50.6% of klebsiella pneumoniae. bla(TEM-1) (84.21%), bla(TEM-1) like (3.94%), bla(TEM-33) (3.94%), bla(TEM-116) (3.94%), bla(TEM-169) (3.94%), and bla(TEM-190) (7.89%) were detected in 76 isolates. Four variants, namely, bla(TEM-1) like, bla(TEM-33), bla(TEM-169), and bla(TEM-190), coexisted in 3 isolates. The largest catalytic pocket of bla(TEM-33) explained its expanded activity towards β-lactam-β-lactamase inhibitor combinations. Molecular docking indicated differential resistance pattern of bla(TEM) variants.

Analyzing effects of naturally occurring missense mutations

Single-point mutation in genome, for example, single-nucleotide polymorphism (SNP) or rare genetic mutation, is the change of a single nucleotide for another in the genome sequence. Some of them will produce an amino acid substitution in the corresponding protein sequence (missense mutations); others will not. This paper focuses on genetic mutations resulting in a change in the amino acid sequence of the corresponding protein and how to assess their effects on protein wild-type characteristics. The existing methods and approaches for predicting the effects of mutation on protein stability, structure, and dynamics are outlined and discussed with respect to their underlying principles. Available resources, either as stand-alone applications or webservers, are pointed out as well. It is emphasized that understanding the molecular mechanisms behind these effects due to these missense mutations is of critical importance for detecting disease-causing mutations. The paper provides several examples of the application of 3D structure-based methods to model the effects of protein stability and protein-protein interactions caused by missense mutations as well.

Bioinformatic analysis of pathogenic missense mutations of activin receptor like kinase 1 ectodomain

Activin A receptor, type II-like kinase 1 (also called ALK1), is a serine-threonine kinase predominantly expressed on endothelial cells surface. Mutations in its ACVRL1 encoding gene (12q11-14) cause type 2 Hereditary Haemorrhagic Telangiectasia (HHT2), an autosomal dominant multisystem vascular dysplasia. The study of the structural effects of mutations is crucial to understand their pathogenic mechanism. However, while an X-ray structure of ALK1 intracellular domain has recently become available (PDB ID: 3MY0), structure determination of ALK1 ectodomain (ALK1_EC) has been elusive so far. We here describe the building of a homology model for ALK1_EC, followed by an extensive bioinformatic analysis, based on a set of 38 methods, of the effect of missense mutations at the sequence and structural level. ALK1_EC potential interaction mode with its ligand BMP9 was then predicted combining modelling and docking data. The calculated model of the ALK1_EC allowed mapping and a preliminary characterization of HHT2 associated mutations. Major structural changes and loss of stability of the protein were predicted for several mutations, while others were found to interfere mainly with binding to BMP9 or other interactors, like Endoglin (CD105), whose encoding ENG gene (9q34) mutations are known to cause type 1 HHT. This study gives a preliminary insight into the potential structure of ALK1_EC and into the structural effects of HHT2 associated mutations, which can be useful to predict the potential effect of each single mutation, to devise new biological experiments and to interpret the biological significance of new mutations, private mutations, or non-synonymous polymorphisms.

Computed structures of point deletion mutants and their enzymatic activities

Point deletions in enzymes can vary in effect from negligible to complete loss of activity; however, these effects are not generally predictable. Deletions are widely observed in nature and often result in diseases such as cancer, cystic fibrosis, or osteogenesis imperfecta. Here, we have developed an algorithm to model the perturbed structures of deletion mutants with the ultimate goal of predicting their activities. The algorithm works by deleting the specified residue from the wild-type structure, creating a gap that is closed using a combination of local and global moves that change the backbone torsion angles of the protein structure. On a set of five proteins for which both wild-type and deletion mutant x-ray crystal structures are available, the algorithm produces deep, narrow energy funnels within 1.5 Å of the crystal structure for the deletion mutants. To assess the ability of our algorithm to predict activity from the predicted structures, we tested the correlation of experimental activity with several measures of the predicted structure ensemble using a set of 45 point deletions from ricin. Estimates incorporating likely prevalence of active and inactive deletion sites suggest that activity can be predicted correctly over 60% of the time from the active site root-mean squared deviation of the lowest energy predicted structures. The predictions are stronger than simple sequence organization measures, but more fundamental work is required in structure prediction and enzyme activity determination to allow consistent prediction of activity.

Cataloguing functionally relevant polymorphisms in gene DNA ligase I: a computational approach

A computational approach for identifying functionally relevant SNPs in gene LIG1 has been proposed. LIG1 is a crucial gene which is involved in excision repair pathways and mutations in this gene may lead to increase sensitivity towards DNA damaging agents. A total of 792 SNPs were reported to be associated with gene LIG1 in dbSNP. Different web server namely SIFT, PolyPhen, CUPSAT, FASTSNP, MAPPER and dbSMR were used to identify potentially functional SNPs in gene LIG1. SIFT, PolyPhen and CUPSAT servers predicted eleven nsSNPs to be intolerant, thirteen nsSNP to be damaging and two nsSNPs have the potential to destabilize protein structure. The nsSNP rs11666150 was predicted to be damaging by all three servers and its mutant structure showed significant increase in overall energy. FASTSNP predicted twenty SNPs to be present in splicing modifier binding sites while rSNP module from MAPPER server predicted nine SNPs to influence the binding of transcription factors. The results from the study may provide vital clues in establishing affect of polymorphism on phenotype and in elucidating drug response.

Computational predictions of the mutant behavior of AraC

An algorithm implemented in Rosetta correctly predicts the folding capabilities of the 17-residue N-terminal arm of the AraC gene regulatory protein when arabinose is bound to the protein and the dramatically different structure of this arm when arabinose is absent. The transcriptional activity of 43 mutant AraC proteins with alterations in the arm sequences was measured in vivo and compared with their predicted folding properties. Seventeen of the mutants possessed regulatory properties that could be directly compared with folding predictions. Sixteen of the 17 mutants were correctly predicted. The algorithm predicts that the N-terminal arm sequences of AraC homologs fold to the Escherichia coli AraC arm structure. In contrast, it predicts that random sequences of the same length and many partially randomized E. coli arm sequences do not fold to the E. coli arm structure. The high level of success shows that relatively "simple" computational methods can in some cases predict the behavior of mutant proteins with good reliability.

Sequence and structure signatures of cancer mutation hotspots in protein kinases

Protein kinases are the most common protein domains implicated in cancer, where somatically acquired mutations are known to be functionally linked to a variety of cancers. Resequencing studies of protein kinase coding regions have emphasized the importance of sequence and structure determinants of cancer-causing kinase mutations in understanding of the mutation-dependent activation process. We have developed an integrated bioinformatics resource, which consolidated and mapped all currently available information on genetic modifications in protein kinase genes with sequence, structure and functional data. The integration of diverse data types provided a convenient framework for kinome-wide study of sequence-based and structure-based signatures of cancer mutations. The database-driven analysis has revealed a differential enrichment of SNPs categories in functional regions of the kinase domain, demonstrating that a significant number of cancer mutations could fall at structurally equivalent positions (mutational hotspots) within the catalytic core. We have also found that structurally conserved mutational hotspots can be shared by multiple kinase genes and are often enriched by cancer driver mutations with high oncogenic activity. Structural modeling and energetic analysis of the mutational hotspots have suggested a common molecular mechanism of kinase activation by cancer mutations, and have allowed to reconcile the experimental data. According to a proposed mechanism, structural effect of kinase mutations with a high oncogenic potential may manifest in a significant destabilization of the autoinhibited kinase form, which is likely to drive tumorigenesis at some level. Structure-based functional annotation and prediction of cancer mutation effects in protein kinases can facilitate an understanding of the mutation-dependent activation process and inform experimental studies exploring molecular pathology of tumorigenesis.

Computational modeling of structurally conserved cancer mutations in the RET and MET kinases: the impact on protein structure, dynamics, and stability

Structural and biochemical characterization of protein kinases that confer oncogene addiction and harbor a large number of disease-associated mutations, including RET and MET kinases, have provided insights into molecular mechanisms associated with the protein kinase activation in human cancer. In this article, structural modeling, molecular dynamics, and free energy simulations of a structurally conserved mutational hotspot, shared by M918T in RET and M1250T in MET kinases, are undertaken to quantify the molecular mechanism of activation and the functional role of cancer mutations in altering protein kinase structure, dynamics, and stability. The mechanistic basis of the activating RET and MET cancer mutations may be driven by an appreciable free energy destabilization of the inactive kinase state in the mutational forms. According to our results, the locally enhanced mobility of the cancer mutants and a higher conformational entropy are counterbalanced by a larger enthalpy loss and result in the decreased thermodynamic stability. The computed protein stability differences between the wild-type and cancer kinase mutants are consistent with circular dichroism spectroscopy and differential scanning calorimetry experiments. These results support the molecular mechanism of activation, which causes a detrimental imbalance in the dynamic equilibrium shifted toward the active form of the enzyme. Furthermore, computer simulations of the inhibitor binding with the oncogenic and drug-resistant RET mutations have also provided a plausible molecular rationale for the observed differences in the inhibition profiles, which is consistent with the experimental data. Finally, structural mapping of RET and MET cancer mutations and the computed protein stability changes suggest a similar mechanism of activation, whereby the cancer mutations which display the higher oncogenic activity tend to have the greatest destabilization effect on the inactive kinase structure.

Robust prediction of mutation-induced protein stability change by property encoding of amino acids

Current methods of predicting mutation-induced protein stability change are imprecise. Machine learning methods have been introduced for this prediction recently; however, the available experimental data used for training these predictors are biased. Abundant data are available for several frequently occurring amino acid substitutions, whereas only limited data have been accumulated for some other mutation types. Generally, current statistical models do not account for this bias toward the commoner amino acids during the encoding process and are thus less effective in making predictions on less frequently occurring mutations. In this paper, we propose a method based on support vector machines and property encoding of amino acids. The predictor we constructed outperforms other methods on the same data sets and is more robust with poor training data. The prediction accuracy for mutations with no training data exceeded 80%. This advantage is critical for practical application, where the prediction could be applied for any type of mutations. Further analysis demonstrates our model relies on biological significant features to make predictions. To overcome the drawbacks of classifying mutations into 'stabilizing' and 'destabilizing' ones, a three-class classification of mutations was also discussed, where our method obtained an overall accuracy of 79.1%.