Global computational mutagenesis provides a critical stability framework in protein structures

In silico prediction of mutation sites for anthranilate synthase from Serratia marcesens to deregulate tryptophan feedback inhibition

Allosteric feedback inhibition of the committed step in amino acid biosynthetic pathways is a major concern for production of amino acids at industrial scale. Anthranilate synthase (AS) catalyzes the first reaction of tryptophan biosynthetic pathway found in microorganisms and is feedback inhibited by its own product i.e. tryptophan. Here, we identified new mutant sites in AS using computational mutagenesis approach. MD simulations (20 ns) followed by MMPBSA and per residue decomposition energy analysis identified seven amino acid residues with best binding affinity for tryptophan. All 19 mutant structures were generated for each identified amino acid residue followed by simulation to evaluate effect of mutation on protein stability. Later, molecular docking studies were employed to generate mutant-tryptophan complex and structures with binding energies (kcal/mol) much higher than wild-type AS were selected. Finally, two mutants i.e., S37W and S37H were identified on the basis of positive binding scores and loss of tryptophan binding inside pocket. Further, MD simulations run for 200 ns were performed over these mutant-tryptophan complexes followed by RMSD, RMSF, radius of gyration , solvent accessible surface area , intra-protein hydrogen bond numbers, principal component analysis, free energy landscape (FEL) and secondary structure analysis to rationale effect of mutations on stability of protein. Cross correlation analysis of mutant site amino acids (S37W) with key residues of catalytic site (G325, T326, H395 and G482) was done to evaluate the effect of mutations on catalytic site conformation. Current computational mutagenesis approach predicted two mutants S37W and S37H with proposed deregulated feedback inhibition by tryptophan and retained catalytic activity.Communicated by Ramaswamy H. Sarma.

Insight into de-regulation of amino acid feedback inhibition: a focus on structure analysis method

Regulation of amino acid's biosynthetic pathway is of significant importance to maintain homeostasis and cell functions. Amino acids regulate their biosynthetic pathway by end-product feedback inhibition of enzymes catalyzing committed steps of a pathway. Discovery of new feedback resistant enzyme variants to enhance industrial production of amino acids is a key objective in industrial biotechnology. Deregulation of feedback inhibition has been achieved for various enzymes using in vitro and in silico mutagenesis techniques. As enzyme's function, its substrate binding capacity, catalysis activity, regulation and stability are dependent on its structural characteristics, here, we provide detailed structural analysis of all feedback sensitive enzyme targets in amino acid biosynthetic pathways. Current review summarizes information regarding structural characteristics of various enzyme targets and effect of mutations on their structures and functions especially in terms of deregulation of feedback inhibition. Furthermore, applicability of various experimental as well as computational mutagenesis techniques to accomplish feedback resistance has also been discussed in detail to have an insight into various aspects of research work reported in this particular field of study.

Neuropathy target esterase activity predicts retinopathy among PNPLA6 disorders

Biallelic pathogenic variants in the PNPLA6 gene cause a broad spectrum of disorders leading to gait disturbance, visual impairment, anterior hypopituitarism, and hair anomalies. PNPLA6 encodes Neuropathy target esterase (NTE), yet the role of NTE dysfunction on affected tissues in the large spectrum of associated disease remains unclear. We present a clinical meta-analysis of a novel cohort of 23 new patients along with 95 reported individuals with PNPLA6 variants that implicate missense variants as a driver of disease pathogenesis. Measuring esterase activity of 46 disease-associated and 20 common variants observed across PNPLA6 -associated clinical diagnoses unambiguously reclassified 10 variants as likely pathogenic and 36 variants as pathogenic, establishing a robust functional assay for classifying PNPLA6 variants of unknown significance. Estimating the overall NTE activity of affected individuals revealed a striking inverse relationship between NTE activity and the presence of retinopathy and endocrinopathy. This phenomenon was recaptured in vivo in an allelic mouse series, where a similar NTE threshold for retinopathy exists. Thus, PNPLA6 disorders, previously considered allelic, are a continuous spectrum of pleiotropic phenotypes defined by an NTE genotype:activity:phenotype relationship. This relationship and the generation of a preclinical animal model pave the way for therapeutic trials, using NTE as a biomarker.

Protein Biochemistry and Molecular Modeling of the Intra-Melanosomal Domain of Human Recombinant Tyrp2 Protein and OCA8-Related Mutant Variants

Tyrosinase-related protein 2 (Tyrp2) is involved in the melanogenesis pathway, catalyzing the tautomerization of dopachrome to 5,6-dihydroxyindole-2-carboxylic acid (DHICA). Recently, a new type of albinism was discovered with disease-causing mutations in the TYRP2 gene. Here, for the first time, we characterized the intra-melanosomal protein domain of Tyrp2 (residues 1-474) and missense variants C40S and C61W, which mimic the alterations found in genetic studies. Recombinant proteins were produced in the Trichoplusia Ni (Ti. Ni) larvae, purified by a combination of immobilized metal affinity (IMAC) and gel-filtration (GF) chromatography, and biochemically characterized. The mutants showed the protein expression in the lysates such as the wild type; however, undetectable protein yield after two steps of purification exhibited their misfolding and instability. In addition, the misfolding effect of the mutations was confirmed computationally using homology modeling and molecular docking. Together, experiments in vitro and computer simulations indicated the critical role of the Cys-rich domain in the Tyrp2 protein stability. The results are consistent with molecular modeling, global computational mutagenesis, and clinical data, proving the significance of genetic alterations in cysteine residues, which could cause oculocutaneous albinism type 8.

Tyrp1 Mutant Variants Associated with OCA3: Computational Characterization of Protein Stability and Ligand Binding

Oculocutaneous albinism type 3 (OCA3) is an autosomal recessive disorder caused by mutations in the TYRP1 gene. Tyrosinase-related protein 1 (Tyrp1) is involved in eumelanin synthesis, catalyzing the oxidation of 5,6-dihydroxyindole-2-carboxylic acid oxidase (DHICA) to 5,6-indolequinone-2-carboxylic acid (IQCA). Here, for the first time, four OCA3-causing mutations of Tyrp1, C30R, H215Y, D308N, and R326H, were investigated computationally to understand Tyrp1 protein stability and catalytic activity. Using the Tyrp1 crystal structure (PDB:5M8L), global mutagenesis was conducted to evaluate mutant protein stability. Consistent with the foldability parameter, C30R and H215Y should exhibit greater instability, and two other mutants, D308N and R326H, are expected to keep a native conformation. SDS-PAGE and Western blot analysis of the purified recombinant proteins confirmed that the foldability parameter correctly predicted the effect of mutations critical for protein stability. Further, the mutant variant structures were built and simulated for 100 ns to generate free energy landscapes and perform docking experiments. Free energy landscapes formed by Y362, N378, and T391 indicate that the binding clefts of C30R and H215Y mutants are larger than the wild-type Tyrp1. In docking simulations, the hydrogen bond and salt bridge interactions that stabilize DHICA in the active site remain similar among Tyrp1, D308N, and R326H. However, the strengths of these interactions and stability of the docked ligand may decrease proportionally to mutation severity due to the larger and less well-defined natures of the binding clefts in mutants. Mutational perturbations in mutants that are not unfolded may result in allosteric alterations to the active site, reducing the stability of protein-ligand interactions.

bCNN-Methylpred: Feature-Based Prediction of RNA Sequence Modification Using Branch Convolutional Neural Network

RNA modification is vital to various cellular and biological processes. Among the existing RNA modifications, N⁶-methyladenosine (m6A) is considered the most important modification owing to its involvement in many biological processes. The prediction of m6A sites is crucial because it can provide a better understanding of their functional mechanisms. In this regard, although experimental methods are useful, they are time consuming. Previously, researchers have attempted to predict m6A sites using computational methods to overcome the limitations of experimental methods. Some of these approaches are based on classical machine-learning techniques that rely on handcrafted features and require domain knowledge, whereas other methods are based on deep learning. However, both methods lack robustness and yield low accuracy. Hence, we develop a branch-based convolutional neural network and a novel RNA sequence representation. The proposed network automatically extracts features from each branch of the designated inputs. Subsequently, these features are concatenated in the feature space to predict the m6A sites. Finally, we conduct experiments using four different species. The proposed approach outperforms existing state-of-the-art methods, achieving accuracies of 94.91%, 94.28%, 88.46%, and 94.8% for the H. sapiens, M. musculus, S. cerevisiae, and A. thaliana datasets, respectively.

Homology modeling and global computational mutagenesis of human myosin VIIa

Usher syndrome type 1B (USH1B) is a genetic disorder caused by mutations in the unconventional Myosin VIIa (MYO7A) protein. USH1B is characterized by hearing loss due to abnormalities in the inner ear and vision loss due to retinitis pigmentosa. Here, we present the model of human MYO7A homodimer, built using homology modeling, and refined using 5 ns molecular dynamics in water. Global computational mutagenesis was applied to evaluate the effect of missense mutations that are critical for maintaining protein structure and stability of MYO7A in inherited eye disease. We found that 43.26% (77 out of 178 in HGMD) and 41.9% (221 out of 528 in ClinVar) of the disease-related missense mutations were associated with higher protein structure destabilizing effects. Overall, most mutations destabilizing the MYO7A protein were found to associate with USH1 and USH1B. Particularly, motor domain and MyTH4 domains were found to be most susceptible to mutations causing the USH1B phenotype. Our work contributes to the understanding of inherited disease from the atomic level of protein structure and analysis of the impact of genetic mutations on protein stability and genotype-to-phenotype relationships in human disease.

DNC4mC-Deep: Identification and Analysis of DNA N4-Methylcytosine Sites Based on Different Encoding Schemes By Using Deep Learning

N4-methylcytosine as one kind of modification of DNA has a critical role which alters genetic performance such as protein interactions, conformation, stability in DNA as well as the regulation of gene expression same cell developmental and genomic imprinting. Some different 4mC site identifiers have been proposed for various species. Herein, we proposed a computational model, DNC4mC-Deep, including six encoding techniques plus a deep learning model to predict 4mC sites in the genome of F. vesca, R. chinensis, and Cross-species dataset. It was demonstrated by the 10-fold cross-validation test to get superior performance. The DNC4mC-Deep obtained 0.829 and 0.929 of MCC on F. vesca and R. chinensis training dataset, respectively, and 0.814 on cross-species. This means the proposed method outperforms the state-of-the-art predictors at least 0.284 and 0.265 on F. vesca and R. chinensis training dataset in turn. Furthermore, the DNC4mC-Deep achieved 0.635 and 0.565 of MCC on F. vesca and R. chinensis independent dataset, respectively, and 0.562 on cross-species which shows it can achieve the best performance to predict 4mC sites as compared to the state-of-the-art predictor.

Global computational mutagenesis of domain structures associated with inherited eye disease

Multidomain proteins account for 70% of the eukaryotic proteome. In genetic disease, multidomain proteins are often affected by numerous mutations, but the effects of these mutations on protein stability and their roles in genetic disease are not well understood. Here, we analyzed protein globular domains to understand how genetic mutations affect the stability of multidomain proteins in inherited disease. In total, 291 domain atomic structures from nine multidomain proteins were modeled by homology, equilibrated using molecular dynamics in water, and subjected to global computational mutagenesis. The domains were separated into 7 groups based on protein fold homology. Mutation propensities within each group of domains were then averaged to select residues critical for domain fold stability. The consensus derived from the sequence alignment shows that the critical residues determined by global mutagenesis are conserved within each group. From this analysis, we concluded that 80% of known disease-related genetic variants are associated with critical residues and are expected to have significant destabilizing effects on domain structure. Our work provides an in silico quantification of protein stability and could help to analyze the complex relationship among missense mutations, multidomain protein stability, and disease phenotypes in inherited eye disease.