Computational analysis of missense mutations causing Snyder-Robinson syndrome

Impaired polyamine metabolism causes behavioral and neuroanatomical defects in a mouse model of Snyder-Robinson syndrome

Snyder-Robinson syndrome (SRS) is a rare X-linked recessive disorder caused by a mutation in the SMS gene, which encodes spermine synthase, and aberrant polyamine metabolism. SRS is characterized by intellectual disability, thin habitus, seizure, low muscle tone/hypotonia and osteoporosis. Progress towards understanding and treating SRS requires a model that recapitulates human gene variants and disease presentations. Here, we evaluated molecular and neurological presentations in the G56S mouse model, which carries a missense mutation in the Sms gene. The lack of SMS protein in the G56S mice resulted in increased spermidine/spermine ratio, failure to thrive, short stature and reduced bone density. They showed impaired learning capacity, increased anxiety, reduced mobility and heightened fear responses, accompanied by reduced total and regional brain volumes. Furthermore, impaired mitochondrial oxidative phosphorylation was evident in G56S cerebral cortex, G56S fibroblasts and Sms-null hippocampal cells, indicating that SMS may serve as a future therapeutic target. Collectively, our study establishes the suitability of the G56S mice as a preclinical model for SRS and provides a set of molecular and functional outcome measures that can be used to evaluate therapeutic interventions for SRS.

Difluoromethylornithine rebalances aberrant polyamine ratios in Snyder-Robinson syndrome

Snyder-Robinson syndrome (SRS) results from mutations in spermine synthase (SMS), which converts the polyamine spermidine into spermine. Affecting primarily males, common manifestations of SRS include intellectual disability, osteoporosis, hypotonia, and seizures. Symptom management is the only treatment. Reduced SMS activity causes spermidine accumulation while spermine levels are reduced. The resulting exaggerated spermidine:spermine ratio is a biochemical hallmark of SRS that tends to correlate with symptom severity. Our studies aim to pharmacologically manipulate polyamine metabolism to correct this imbalance as a therapeutic strategy for SRS. Here we report the repurposing of 2-difluoromethylornithine (DFMO), an FDA-approved inhibitor of polyamine biosynthesis, in rebalancing spermidine:spermine ratios in SRS patient cells. Mechanistic in vitro studies demonstrate that, while reducing spermidine biosynthesis, DFMO also stimulates the conversion of spermidine into spermine in hypomorphic SMS cells and induces uptake of exogenous spermine, altogether reducing the aberrant ratios. In a Drosophila SRS model characterized by reduced lifespan, DFMO improves longevity. As nearly all SRS patient mutations are hypomorphic, these studies form a strong foundation for translational studies with significant therapeutic potential.

Assessing variants of uncertain significance implicated in hearing loss using a comprehensive deafness proteome

Hearing loss is the leading sensory deficit, affecting ~ 5% of the population. It exhibits remarkable heterogeneity across 223 genes with 6328 pathogenic missense variants, making deafness-specific expertise a prerequisite for ascribing phenotypic consequences to genetic variants. Deafness-implicated variants are curated in the Deafness Variation Database (DVD) after classification by a genetic hearing loss expert panel and thorough informatics pipeline. However, seventy percent of the 128,167 missense variants in the DVD are "variants of uncertain significance" (VUS) due to insufficient evidence for classification. Here, we use the deep learning protein prediction algorithm, AlphaFold2, to curate structures for all DVD genes. We refine these structures with global optimization and the AMOEBA force field and use DDGun3D to predict folding free energy differences (∆∆GFold) for all DVD missense variants. We find that 5772 VUSs have a large, destabilizing ∆∆GFold that is consistent with pathogenic variants. When also filtered for CADD scores (> 25.7), we determine 3456 VUSs are likely pathogenic at a probability of 99.0%. Of the 224 genes in the DVD, 166 genes (74%) exhibit one or more missense variants predicted to cause a pathogenic change in protein folding stability. The VUSs prioritized here affect 119 patients (~ 3% of cases) sequenced by the OtoSCOPE targeted panel. Approximately half of these patients previously received an inconclusive report, and reclassification of these VUSs as pathogenic provides a new genetic diagnosis for six patients.

An In Silico Functional Analysis of Non-Synonymous Single-Nucleotide Polymorphisms of Bovine CMAH Gene and Potential Implication in Pathogenesis

The sugar molecule N-glycolylneuraminic acid (Neu5Gc) is one of the most common sialic acids discovered in mammals. Cytidine monophospho-N-acetylneuraminic acid hydroxylase (CMAH) catalyses the conversion of N-acetylneuraminic acid (Neu5Ac) to Neu5Gc, and it is encoded by the CMAH gene. On the one hand, food metabolic incorporation of Neu5Gc has been linked to specific human diseases. On the other hand, Neu5Gc has been shown to be highly preferred by some pathogens linked to certain bovine diseases. We used various computational techniques to perform an in silico functional analysis of five non-synonymous single-nucleotide polymorphisms (nsSNPs) of the bovine CMAH (bCMAH) gene identified from the 1000 Bull Genomes sequence data. The c.1271C>T (P424L) nsSNP was predicted to be pathogenic based on the consensus result from different computational tools. The nsSNP was also predicted to be critical based on sequence conservation, stability, and post-translational modification site analysis. According to the molecular dynamic simulation and stability analysis, all variations promoted stability of the bCMAH protein, but mutation A210S significantly promoted CMAH stability. In conclusion, c.1271C>T (P424L) is expected to be the most harmful nsSNP among the five detected nsSNPs based on the overall studies. This research could pave the way for more research associating pathogenic nsSNPs in the bCMAH gene with diseases.

Difluoromethylornithine rebalances aberrant polyamine ratios in Snyder-Robinson syndrome: mechanism of action and therapeutic potential

Snyder-Robinson Syndrome (SRS) is caused by mutations in the spermine synthase (SMS) gene, the enzyme product of which converts the polyamine spermidine into spermine. Affecting primarily males, common manifestations of SRS include intellectual disability, osteoporosis, hypotonic musculature, and seizures, along with other more variable symptoms. Currently, medical management focuses on treating these symptoms without addressing the underlying molecular cause of the disease. Reduced SMS catalytic activity in cells of SRS patients causes the accumulation of spermidine, while spermine levels are reduced. The resulting exaggeration in spermidine-to-spermine ratio is a biochemical hallmark of SRS that tends to correlate with symptom severity in the patient. Our studies aim to pharmacologically manipulate polyamine metabolism to correct this polyamine imbalance and investigate the potential of this approach as a therapeutic strategy for affected individuals. Here we report the use of difluoromethylornithine (DFMO; eflornithine), an FDA-approved inhibitor of polyamine biosynthesis, in re-establishing normal spermidine-to-spermine ratios in SRS patient cells. Through mechanistic studies, we demonstrate that, while reducing spermidine biosynthesis, DFMO also stimulates the conversion of existing spermidine into spermine in cell lines with hypomorphic variants of SMS. Further, DFMO treatment induces a compensatory uptake of exogenous polyamines, including spermine and spermine mimetics, cooperatively reducing spermidine and increasing spermine levels. In a Drosophila SRS model characterized by reduced lifespan, adding DFMO to the feed extended lifespan. As nearly all known SRS patient mutations are hypomorphic, these studies form a foundation for future translational studies with significant therapeutic potential.

Assessing Variants of Uncertain Significance Implicated in Hearing Loss Using a Comprehensive Deafness Proteome

Hearing loss is the leading sensory deficit, affecting ~ 5% of the population. It exhibits remarkable heterogeneity across 223 genes with 6,328 pathogenic missense variants, making deafness-specific expertise a prerequisite for ascribing phenotypic consequences to genetic variants. Deafness-implicated variants are curated in the Deafness Variation Database (DVD) after classification by a genetic hearing loss expert panel and thorough informatics pipeline. However, seventy percent of the 128,167 missense variants in the DVD are "variants of uncertain significance" (VUS) due to insufficient evidence for classification. Here, we use the deep learning protein prediction algorithm, AlphaFold2, to curate structures for all DVD genes. We refine these structures with global optimization and the AMOEBA force field and use DDGun3D to predict folding free energy differences (∆∆G Fold ) for all DVD missense variants. We find that 5,772 VUSs have a large, destabilizing ∆∆G Fold that is consistent with pathogenic variants. When also filtered for CADD scores (> 25.7), we determine 3,456 VUSs are likely pathogenic at a probability of 99.0%. These VUSs affect 119 patients (~ 3% of cases) sequenced by the OtoSCOPE targeted panel. Approximately half of these patients previously received an inconclusive report, and reclassification of these VUSs as pathogenic provides a new genetic diagnosis for six patients.

An efficient method to predict protein thermostability in alanine mutation

The relationship between protein sequence and its thermodynamic stability is a critical aspect of computational protein design. In this work, we present a new theoretical method to calculate the free energy change (ΔΔG) resulting from a single-point amino acid mutation to alanine in a protein sequence. The method is derived based on physical interactions and is very efficient in estimating the free energy changes caused by a series of alanine mutations from just a single molecular dynamics (MD) trajectory. Numerical calculations are carried out on a total of 547 alanine mutations in 19 diverse proteins whose experimental results are available. The comparison between the experimental ΔΔG_exp and the calculated values shows a generally good correlation with a correlation coefficient of 0.67. Both the advantages and limitations of this method are discussed. This method provides an efficient and valuable tool for protein design and engineering.

Electrostatics in Computational Biophysics and Its Implications for Disease Effects

This review outlines the role of electrostatics in computational molecular biophysics and its implication in altering wild-type characteristics of biological macromolecules, and thus the contribution of electrostatics to disease mechanisms. The work is not intended to review existing computational approaches or to propose further developments. Instead, it summarizes the outcomes of relevant studies and provides a generalized classification of major mechanisms that involve electrostatic effects in both wild-type and mutant biological macromolecules. It emphasizes the complex role of electrostatics in molecular biophysics, such that the long range of electrostatic interactions causes them to dominate all other forces at distances larger than several Angstroms, while at the same time, the alteration of short-range wild-type electrostatic pairwise interactions can have pronounced effects as well. Because of this dual nature of electrostatic interactions, being dominant at long-range and being very specific at short-range, their implications for wild-type structure and function are quite pronounced. Therefore, any disruption of the complex electrostatic network of interactions may abolish wild-type functionality and could be the dominant factor contributing to pathogenicity. However, we also outline that due to the plasticity of biological macromolecules, the effect of amino acid mutation may be reduced, and thus a charge deletion or insertion may not necessarily be deleterious.

Fyn and Lyn gene polymorphisms impact the risk of thyroid cancer

Thyroid cancer is the most common malignancy of the endocrine glands, and during last couple of decades, its incidence has risen alarmingly, across the globe. Etiology of thyroid cancer is still debatable. There are a few worth mentioning risk factors which contribute to initiation of abnormalities in thyroid gland leading to cancer. Genetic instability is major risk factors in thyroid carcinogenesis. Among the genetic factors, the Src family of genes (Src, Yes1, Fyn and Lyn) have been implicated in many cancers but there is little data regarding the association of these (Src, Yes1, Fyn and Lyn) genes with thyroid carcinogenesis. Fyn and Lyn genes of Src family found engaged in proliferation, migration, invasion, angiogenesis, and metastasis in different cancers. This study was planned to examine the effect of Fyn and Lyn SNPs on thyroid cancer risk in Pakistani population in 500 patients and 500 controls. Three polymorphisms of Fyn gene (rs6916861, rs2182644 and rs12910) and three polymorphisms of Lyn gene (rs2668011, rs45587541 and rs45489500) were analyzed using Tetra-primer ARMS-PCR followed by DNA sequencing. SNP rs6916861 of Fyn gene mutant genotype (CC) showed statistically significant threefold increased risk of thyroid cancer (P < 0.0001). In case of rs2182644 of Fyn gene, mutant genotype (AA) indicated statistically significant 17-fold increased risk of thyroid cancer (P < 0.0001). Statistically significant threefold increased risk of thyroid cancer was observed in genotype AC (P < 0.0001) of Fyn gene polymorphism rs12910. In SNP rs2668011 of Lyn gene, TT genotype showed statistically significant threefold increased risk of thyroid cancer (P < 0.0001). In case of rs45587541 of Lyn gene, GA genotypes showed statistically significant 11-fold increased risk in thyroid cancer (P < 0.0001). Haplotype analysis revealed that AAATAG*, AGACAG*, AGCCAA*, AGCCAG*, CAATAG*, CGCCAG* and CGCCGA* haplotypes of Fyn and Lyn polymorphisms are associated with increased thyroid cancer risk. These results showed that genotypes and allele distribution of Fyn and Lyn are significantly linked with increased thyroid cancer risk and could be genetic adjuster for said disease.

Solvent accessibility of E1α and E1β residues with known missense mutations causing pyruvate dehydrogenase complex (PDC) deficiency: Impact on PDC-E1 structure and function

Pyruvate dehydrogenase complex deficiency is a major cause of primary lactic acidemia resulting in high morbidity and mortality, with limited therapeutic options. PDHA1 mutations are responsible for >82% of cases. The E1 component of PDC is a symmetric dimer of heterodimers (αβ/α'β') encoded by PDHA1 and PDHB. We measured solvent accessibility surface area (SASA), utilized nearest-neighbor analysis, incorporated sequence changes using mutagenesis tool in PyMOL, and performed molecular modeling with SWISS-MODEL, to investigate the impact of residues with disease-causing missense variants (DMVs) on E1 structure and function. We reviewed 166 and 13 genetically resolved cases due to PDHA1 and PDHB, respectively, from variant databases. We expanded on 102 E1α and 13 E1β nonduplicate DMVs. DMVs of E1α Arg112-Arg224 stretch (exons 5-7) and of E1α Arg residues constituted 40% and 39% of cases, respectively, with invariant Arg349 accounting for 22% of arginine replacements. SASA analysis showed that 86% and 84% of residues with nonduplicate DMVs of E1α and E1β, respectively, are solvent inaccessible ("buried"). Furthermore, 30% of E1α buried residues with DMVs are deleterious through perturbation of subunit-subunit interface contact (SSIC), with 73% located in the Arg112-Arg224 stretch. E1α Arg349 represented 74% of buried E1α Arg residues involved in SSIC. Structural perturbations resulting from residue replacements in some matched neighboring pairs of amino acids on different subunits involved in SSIC at 2.9-4.0 Å interatomic distance apart, exhibit similar clinical phenotype. Collectively, this work provides insight for future target-based advanced molecular modeling studies, with implications for development of novel therapeutics for specific recurrent DMVs of E1α.

Computational Studies of the Structural Basis of Human RPS19 Mutations Associated With Diamond-Blackfan Anemia

Diamond-Blackfan Anemia (DBA) is an inherited rare disease characterized with severe pure red cell aplasia, and it is caused by the defective ribosome biogenesis stemming from the impairment of ribosomal proteins. Among all DBA-associated ribosomal proteins, RPS19 affects most patients and carries most DBA mutations. Revealing how these mutations lead to the impairment of RPS19 is highly demanded for understanding the pathogenesis of DBA, but a systematic study is currently lacking. In this work, based on the complex structure of human ribosome, we comprehensively studied the structural basis of DBA mutations of RPS19 by using computational methods. Main structure elements and five conserved surface patches involved in RPS19-18S rRNA interaction were identified. We further revealed that DBA mutations would destabilize RPS19 through disrupting the hydrophobic core or breaking the helix, or perturb the RPS19-18S rRNA interaction through destroying hydrogen bonds, introducing steric hindrance effect, or altering surface electrostatic property at the interface. Moreover, we trained a machine-learning model to predict the pathogenicity of all possible RPS19 mutations. Our work has laid a foundation for revealing the pathogenesis of DBA from the structural perspective.

Analysis and Interpretation of the Impact of Missense Variants in Cancer

Large scale genome sequencing allowed the identification of a massive number of genetic variations, whose impact on human health is still unknown. In this review we analyze, by an in silico-based strategy, the impact of missense variants on cancer-related genes, whose effect on protein stability and function was experimentally determined. We collected a set of 164 variants from 11 proteins to analyze the impact of missense mutations at structural and functional levels, and to assess the performance of state-of-the-art methods (FoldX and Meta-SNP) for predicting protein stability change and pathogenicity. The result of our analysis shows that a combination of experimental data on protein stability and in silico pathogenicity predictions allowed the identification of a subset of variants with a high probability of having a deleterious phenotypic effect, as confirmed by the significant enrichment of the subset in variants annotated in the COSMIC database as putative cancer-driving variants. Our analysis suggests that the integration of experimental and computational approaches may contribute to evaluate the risk for complex disorders and develop more effective treatment strategies.

Digestive involvement in a severe form of Snyder-Robinson syndrome: Possible expansion of the phenotype

Snyder-Robinson syndrome (OMIM #309583) is a rare X-linked condition, caused by mutation in the SMS gene (MIM *300105), characterized by a wide spectrum of clinical signs including developmental delay, epilepsy, asthenic habitus, dysmorphism, osteopenia, and renal or genital anomalies. Here we describe two maternal half-brothers who both presented with severe neurodevelopmental delay, seizures, hearing loss, facial dysmorphism, renal and ophthalmologic anomalies, failure to thrive and premature death. A novel p.(Gly203Asp) variant was found at the hemizygous state in the two boys, and an elevated Spermidine/Spermine ratio confirmed the diagnosis of Snyder-Robinson syndrome. One of the brothers presented with gastrointestinal symptoms, with jejunal stenosis, enteral feeding intolerance, failure to thrive due to a dysfunctional gastrointestinal system, cholestasis and exocrine pancreatic insufficiency. Although more studies will be needed to understand its mechanisms, this observation lends further support to the possibility of severe digestive involvement in Snyder Robinson syndrome.

In silico analysis predicting effects of deleterious SNPs of human RASSF5 gene on its structure and functions

Ras association domain-containing protein 5 (RASSF5), one of the prospective biomarkers for tumors, generally plays a crucial role as a tumor suppressor. As deleterious effects can result from functional differences through SNPs, we sought to analyze the most deleterious SNPs of RASSF5 as well as predict the structural changes associated with the mutants that hamper the normal protein-protein interactions. We adopted both sequence and structure based approaches to analyze the SNPs of RASSF5 protein. We also analyzed the putative post translational modification sites as well as the altered protein-protein interactions that encompass various cascades of signals. Out of all the SNPs obtained from the NCBI database, only 25 were considered as highly deleterious by six in silico SNP prediction tools. Among them, upon analyzing the effect of these nsSNPs on the stability of the protein, we found 17 SNPs that decrease the stability. Significant deviation in the energy minimization score was observed in P350R, F321L, and R277W. Besides this, docking analysis confirmed that P350R, A319V, F321L, and R277W reduce the binding affinity of the protein with H-Ras, where P350R shows the most remarkable deviation. Protein-protein interaction analysis revealed that RASSF5 acts as a hub connecting two clusters consisting of 18 proteins and alteration in the RASSF5 may lead to disassociation of several signal cascades. Thus, based on these analyses, our study suggests that the reported functional SNPs may serve as potential targets for different proteomic studies, diagnosis and therapeutic interventions.

Bioinformatics classification of mutations in patients with Mucopolysaccharidosis IIIA

Mucopolysaccharidosis (MPS) IIIA, also known as Sanfilippo syndrome type A, is a severe, progressive disease that affects the central nervous system (CNS). MPS IIIA is inherited in an autosomal recessive manner and is caused by a deficiency in the lysosomal enzyme sulfamidase, which is required for the degradation of heparan sulfate. The sulfamidase is produced by the N-sulphoglucosamine sulphohydrolase (SGSH) gene. In MPS IIIA patients, the excess of lysosomal storage of heparan sulfate often leads to mental retardation, hyperactive behavior, and connective tissue impairments, which occur due to various known missense mutations in the SGSH, leading to protein dysfunction. In this study, we focused on three mutations (R74C, S66W, and R245H) based on in silico pathogenic, conservation, and stability prediction tool studies. The three mutations were further subjected to molecular dynamic simulation (MDS) analysis using GROMACS simulation software to observe the structural changes they induced, and all the mutants exhibited maximum deviation patterns compared with the native protein. Conformational changes were observed in the mutants based on various geometrical parameters, such as conformational stability, fluctuation, and compactness, followed by hydrogen bonding, physicochemical properties, principal component analysis (PCA), and salt bridge analyses, which further validated the underlying cause of the protein instability. Additionally, secondary structure and surrounding amino acid analyses further confirmed the above results indicating the loss of protein function in the mutants compared with the native protein. The present results reveal the effects of three mutations on the enzymatic activity of sulfamidase, providing a molecular explanation for the cause of the disease. Thus, this study allows for a better understanding of the effect of SGSH mutations through the use of various computational approaches in terms of both structure and functions and provides a platform for the development of therapeutic drugs and potential disease treatments.

Computational chemistry methods to investigate the effects caused by DNA variants linked with disease

Computational chemistry offers variety of tools to study properties of biological macromolecules. These tools vary in terms of levels of details from quantum mechanical treatment to numerous macroscopic approaches. Here, we provide a review of computational chemistry algorithms and tools for modeling the effects of genetic variations and their association with diseases. Particular emphasis is given on modeling the effects of missense mutations on stability, conformational dynamics, binding, hydrogen bond network, salt bridges, and pH-dependent properties of the corresponding macromolecules. It is outlined that the disease may be caused by alteration of one or several of above-mentioned biophysical characteristics, and a successful prediction of pathogenicity requires detailed analysis of how the alterations affect the function of involved macromolecules. The review provides a short list of most commonly used algorithms to predict the molecular effects of mutations as well.

The complete loss of function of the SMS gene results in a severe form of Snyder-Robinson syndrome

Snyder-Robinson syndrome (SRS) is an X-linked syndromic intellectual disability condition caused by variants in the spermine synthase gene (SMS). The syndrome is characterized by facial dysmorphism, thin body build, kyphoscoliosis, osteoporosis, hypotonia, developmental delay and associated neurological features (seizures, unsteady gait, abnormal speech). Until now, only missense variants with a functionally characterized partial loss of function (LoF) have been described. Here we describe the first complete LoF variant, Met303Lysfs*, in a male patient with a severe form of Snyder-Robinson syndrome. He presented with multiple malformations and severly delayed development, and died at 4 months of age. Functional in vitro assays showed a complete absence of functional SMS protein. Taken together, our findings and those of previously reported patients confirm that pathogenic variants of SMS are indeed LoF and that there might exist a genotype-phenotype correlation between the type of variant and the severity of the syndrome.

DFMD: Fast and Effective DelPhiForce Steered Molecular Dynamics Approach to Model Ligand Approach Toward a Receptor: Application to Spermine Synthase Enzyme

Here we report a novel approach, the DelPhiForce Molecular Dynamics (DFMD) method, for steered molecular dynamics simulations to model receptor-ligand association involving charged species. The main purpose of developing DFMD is to simulate ligand's trajectory toward the receptor and thus to predict the "entrance" of the binding pocket and conformational changes associated with the binding. We demonstrate that the DFMD is superior compared with molecular dynamics simulations applying standard cut-offs, provides correct binding forces, allows for modeling the ligand approach at long distances and thus guides the ligand toward the correct binding spot, and it is very fast (frequently the binding is completed in <1 ns). The DFMD is applied to model the binding of two ligands to a receptor (spermine synthase) and it is demonstrated that it guides the ligands toward the corresponding pockets despite of the initial ligand's position with respect to the receptor. Predicted conformational changes and the order of ligand binding are experimentally verified.

A novel pathogenic missense variant in CNNM4 underlying Jalili syndrome: Insights from molecular dynamics simulations

Background

Jalili syndrome (JS) is a rare cone‐rod dystrophy (CRD) associated with amelogenesis imperfecta (AI). The first clinical presentation of JS patients was published in 1988 by Jalili and Smith. Pathogenic mutations in the Cyclin and CBS Domain Divalent Metal Cation Transport Mediator 4 (CNNM4) magnesium transporter protein have been reported as the leading cause of this anomaly.

Methods

In the present study, a clinical and genetic investigation was performed in a consanguineous family of Pakistani origin, showing characteristic features of JS. Sanger sequencing was successfully used to identify the causative variant in CNNM4. Molecular dynamics (MD) simulations were performed to study the effect of amino acid change over CNNM4 protein.

Results

Sequence analysis of CNNM4 revealed a novel missense variant (c.1220G>T, p.Arg407Leu) in exon‐1 encoding cystathionine‐β‐synthase (CBS) domain. To comprehend the mutational consequences in the structure, the mutant p.Arg407Leu was modeled together with a previously reported variant (c.1484C>T, p.Thr495Ile) in the same domain. Additionally, docking analysis deciphered the binding mode of the adenosine triphosphate (ATP) cofactor. Furthermore, 60ns MD simulations were carried out on wild type (p.Arg407/p.Thr495) and mutants (p.Arg407Leu/p.Thr495Ile) to understand the structural and energetic changes in protein structure and its dynamic behavior. An evident conformational shift of ATP in the binding site was observed in simulated mutants disrupting the native ATP‐binding mode.

Conclusion

The novel identified variant in CNNM4 is the first report from the Pakistani population. Overall, the study is valuable and may give a novel insight into metal transport in visual function and biomineralization.

Myosin Va and spermine synthase: partners in exosome transport

A recent paper in Bioscience Reports (BSR20182189) describes the discovery of an interaction between the motor protein myosin Va and the metabolic enzyme spermine synthase. Myosin Va is a molecular motor which plays a key role in vesicle transport. Mutations in the gene which encodes this protein are associated with Griscelli syndrome type 1 and the ‘dilute’ phenotype in animals. Spermine synthase catalyzes the conversion of spermidine to spermine. This largely cytoplasmic enzyme can also be localized to the soluble fraction in exosomes. Mutations in the spermine synthase gene are associated with Snyder Robinson mental retardation syndrome. The interaction between the two proteins was detected using the yeast two hybrid method and verified by microscale thermophoresis of recombinant proteins. Knockdown of the MYO5A gene reduced the expression of mRNA coding for spermine synthase. The amount of this transcript was also reduced in cells derived from a patient with Griscelli syndrome type 1. This suggests that, in addition to a direct physical interaction between the two proteins, myosin Va also modulates the transcription of the spermine synthase gene. The mechanism for this modulation is currently unknown. These findings have implications for Griscelli syndrome type 1 and Snyder Robinson mental retardation syndrome. They also suggest that interactions between myosin Va and soluble exosome proteins such as spermine synthase may be important in the mechanism of exosome transport.

Long-range effect of a single mutation in spermine synthase

Spermine synthase (SpmSyn) is an enzyme critical for maintaining the balance of spermine/spermidine in the cell. The amino acid sequence of SpmSyn is highly conserved among the species. Most of the mutations found in the human population are shown to be causing Snyder–Robinson syndrome, a severe mental disorder, while not so many are neutral. This is intriguing since SpmSyn is a relatively large protein and less than 10% of its amino acids are directly involved in the catalysis. Here, we demonstrated that a mutation (G191S) at a site far away from the active pocket affects the active site dynamics and thus the functionality of SpmSyn. This suggests that SpmSyn functionality is regulated by networks of interacting residues and thus expands the functional and structural importance beyond the amino acids directly involved in the catalysis. Comparing the calculated effects of G191S and a nine-residue deletion shown to decrease SpmSyn activity [Wu H, Min J, Zeng H, McCloskey DE, Ikeguchi Y, Loppnau P, Michael AJ, Pegg AE, Plotnikov AN, Crystal structure of human spermine synthase: Implications of substrate binding and catalytic mechanism, J Biol Chem 283:16135–16146, 2008], we predict that G191S mutation also decreases SpmSyn activity and may be causing disease.

Computational Approaches to Prioritize Cancer Driver Missense Mutations

Cancer is a complex disease that is driven by genetic alterations. There has been a rapid development of genome-wide techniques during the last decade along with a significant lowering of the cost of gene sequencing, which has generated widely available cancer genomic data. However, the interpretation of genomic data and the prediction of the association of genetic variations with cancer and disease phenotypes still requires significant improvement. Missense mutations, which can render proteins non-functional and provide a selective growth advantage to cancer cells, are frequently detected in cancer. Effects caused by missense mutations can be pinpointed by in silico modeling, which makes it more feasible to find a treatment and reverse the effect. Specific human phenotypes are largely determined by stability, activity, and interactions between proteins and other biomolecules that work together to execute specific cellular functions. Therefore, analysis of missense mutations’ effects on proteins and their complexes would provide important clues for identifying functionally important missense mutations, understanding the molecular mechanisms of cancer progression and facilitating treatment and prevention. Herein, we summarize the major computational approaches and tools that provide not only the classification of missense mutations as cancer drivers or passengers but also the molecular mechanisms induced by driver mutations. This review focuses on the discussion of annotation and prediction methods based on structural and biophysical data, analysis of somatic cancer missense mutations in 3D structures of proteins and their complexes, predictions of the effects of missense mutations on protein stability, protein-protein and protein-nucleic acid interactions, and assessment of conformational changes in protein conformations induced by mutations.

Vitamin D receptor (VDR) non-synonymous single nucleotide polymorphisms (nsSNPs) affect the calcitriol drug response - A theoretical insight

Pharmacogenetics and pharmacogenomics have become presumptive with advancements in next-generation sequencing technology. In complex diseases, distinguishing the feasibility of pathogenic and neutral disease-causing variants is a time consuming and expensive process. Recent drug research and development processes mainly rely on the relationship between the genotype and phenotype through Single nucleotide polymorphisms (SNPs). The SNPs play an indispensable role in elucidating the individual's vulnerability to disease and drug response. The understanding of the interplay between these leads to the establishment of personalized medicine. In order to address this issue, we developed a computational pipeline of vitamin D receptor (VDR) for SNP centered study by application of elegant molecular docking and molecular dynamics simulation approaches. In a few SNPs the volume of the binding cavities has increased in mutant structures when compared to the wild type, indicating a weakening in interaction (699.1 ų in wild type Vs. 738.8 in Leu230Val, 820.7 ų in Arg247Leu). This also differently reflected in the H-bond interactions and binding free energies -169.93 kcal/mol (wild type) Vs -156.43 kcal/mol (R154W), -105.49 kcal/mol (R274L) in Leu230Val and Arg247Leu respectively. Although we could not find noteworthy changes in the binding free energies and binding pocket in the remaining mutations, the H-bond interactions made these SNPs deleterious. Thus, we further analyzed the H-bond interactions and distances using molecular dynamics (MD) simulation studies.

An Integrated Computational Framework to Assess the Mutational Landscape of α-L-Iduronidase IDUA Gene

Mucopolysaccharidosis type I is a lysosomal genetic disorder caused due to the deficiency of the α-L-iduronidase enzyme (IDUA). Mutations associated with IDUA lead to mild to severe forms of diseases characterized by different clinical features. In the present study, we first performed a comprehensive analysis using various in silico prediction tools to screen and prioritize the missense mutations or nonsynonymous SNPs (nsSNPs) associated with IDUA. Subsequently, statistical analysis was empowered to examine the predictive ability and accuracy of the in silico prediction tool results supporting the disease phenotype ranging from mild to severe. Till date, no study has been carried out in IDUA in analyzing the impact of the nsSNPs at the structural level. In this context with the aid of pathogenic and stability prediction in silico tools, we identified nsSNPs R89Q, R89W, and P533R to be most deleterious and disease-causing having impact on the function of the protein. Extensive molecular dynamics analysis was performed using Gromacs to understand the deleterious nature of the mutants. Variations observed between the trajectory files of native and mutants R89Q, R89W, and P533R using Gromacs utilities enabled us to measure the adverse effects on the protein and could be the underlying reasons for the disease pathogenesis. These findings may be helpful in understanding the genotype-phenotype relationship and molecular basis of the disease to design drugs for better treatment. J. Cell. Biochem. 119: 555-565, 2018. © 2017 Wiley Periodicals, Inc.

Unveiling the Pathogenic Molecular Mechanisms of the Most Common Variant (p.K329E) in Medium-Chain Acyl-CoA Dehydrogenase Deficiency by in Vitro and in Silico Approaches

Medium-chain acyl-CoA dehydrogenase deficiency (MCADD) is the most common genetic disorder affecting the mitochondrial fatty acid β-oxidation pathway. The mature and functional form of human MCAD (hMCAD) is a homotetramer assembled as a dimer of dimers (monomers A/B and C/D). Each monomer binds a FAD cofactor, necessary for the enzyme's activity. The most frequent mutation in MCADD results from the substitution of a lysine with a glutamate in position 304 of mature hMCAD (p.K329E in the precursor protein). Here, we combined in vitro and in silico approaches to assess the impact of the p.K329E mutation on the protein's structure and function. Our in silico results demonstrated for the first time that the p.K329E mutation, despite lying at the dimer-dimer interface and being deeply buried inside the tetrameric core, seems to affect the tetramer surface, especially the β-domain that forms part of the catalytic pocket wall. Additionally, the molecular dynamics data indicate a stronger impact of the mutation on the protein's motions in dimer A/B, while dimer C/D remains similar to the wild type. For dimer A/B, severe disruptions in the architecture of the pockets and in the FAD and octanoyl-CoA binding affinities were also observed. The presence of unaffected pockets (C/D) in the in silico studies may explain the decreased enzymatic activity determined for the variant protein (46% residual activity). Moreover, the in silico structural changes observed for the p.K329E variant protein provide an explanation for the structural instability observed experimentally, namely, the disturbed oligomeric profile, thermal stability, and conformational flexibility, with respect to the wild-type.

SAAMBE: Webserver to Predict the Charge of Binding Free Energy Caused by Amino Acids Mutations

Predicting the effect of amino acid substitutions on protein–protein affinity (typically evaluated via the change of protein binding free energy) is important for both understanding the disease-causing mechanism of missense mutations and guiding protein engineering. In addition, researchers are also interested in understanding which energy components are mostly affected by the mutation and how the mutation affects the overall structure of the corresponding protein. Here we report a webserver, the Single Amino Acid Mutation based change in Binding free Energy (SAAMBE) webserver, which addresses the demand for tools for predicting the change of protein binding free energy. SAAMBE is an easy to use webserver, which only requires that a coordinate file be inputted and the user is provided with various, but easy to navigate, options. The user specifies the mutation position, wild type residue and type of mutation to be made. The server predicts the binding free energy change, the changes of the corresponding energy components and provides the energy minimized 3D structure of the wild type and mutant proteins for download. The SAAMBE protocol performance was tested by benchmarking the predictions against over 1300 experimentally determined changes of binding free energy and a Pearson correlation coefficient of 0.62 was obtained. How the predictions can be used for discriminating disease-causing from harmless mutations is discussed. The webserver can be accessed via http://compbio.clemson.edu/saambe_webserver/.

SAAFEC: Predicting the Effect of Single Point Mutations on Protein Folding Free Energy Using a Knowledge-Modified MM/PBSA Approach

Folding free energy is an important biophysical characteristic of proteins that reflects the overall stability of the 3D structure of macromolecules. Changes in the amino acid sequence, naturally occurring or made in vitro, may affect the stability of the corresponding protein and thus could be associated with disease. Several approaches that predict the changes of the folding free energy caused by mutations have been proposed, but there is no method that is clearly superior to the others. The optimal goal is not only to accurately predict the folding free energy changes, but also to characterize the structural changes induced by mutations and the physical nature of the predicted folding free energy changes. Here we report a new method to predict the Single Amino Acid Folding free Energy Changes (SAAFEC) based on a knowledge-modified Molecular Mechanics Poisson-Boltzmann (MM/PBSA) approach. The method is comprised of two main components: a MM/PBSA component and a set of knowledge based terms delivered from a statistical study of the biophysical characteristics of proteins. The predictor utilizes a multiple linear regression model with weighted coefficients of various terms optimized against a set of experimental data. The aforementioned approach yields a correlation coefficient of 0.65 when benchmarked against 983 cases from 42 proteins in the ProTherm database. Availability: the webserver can be accessed via http://compbio.clemson.edu/SAAFEC/.

Birt-Hogg-Dubé syndrome: a large single family cohort

Background

Birt-Hogg-Dubé (BHD) syndrome is an autosomal dominant condition characterized by dermatologic lesions, pulmonary manifestations, and renal tumors. The syndrome arises from germline mutations in the folliculin (FLCN) gene. We present findings from the single largest family BHD cohort described to date. Primary objectives were to characterize cystic lung changes on computed tomography (CT) chest scanning and identify features that stratify patients at higher risk of pneumothorax. Secondary objectives entailed description of the following: type and natural history of BHD-associated pneumothorax, pulmonary function characteristics, and relationship between cystic lung changes and pulmonary function.

Methods

The study was a retrospective chart review for a case series of a single family. Over 70 family members of a proband with documented BHD were identified, 68 of which consented to genetic testing. All those with confirmed BHD were offered a clinical assessment by the Medical Genetics and Pulmonary services which included a history, physical exam, complete pulmonary function tests, and computed tomography (CT) scan of the chest and abdomen.

Results

Thirty-six individuals had a heterozygous mutation in the FLCN gene (c.59delT). Of these, 100 % (28/28) had pulmonary cysts, 41 % (13/32) had spontaneous pneumothoraces, 26 % (8/31) had kidney cysts, 3 % (1/31) had renal tumors, and 53 % (18/34) had dermatologic manifestations. Recurrent pneumothoraces were common (40 %). Cyst size (OR 3.23, 95 % CI 1.35–7.73) and extent of lower lung zone disease (OR 6.43, 95 % CI 1.41–29.2) were the only findings associated with pneumothorax. The size or extent of cystic disease did not correlate with lung function results.

Conclusions

This is the largest single family cohort of patients with BHD syndrome documented to date. We found that all individuals had pulmonary cysts, pneumothoraces were common, and cyst size and lower lobe predominant disease were associated with pneumothorax. Lung function was generally preserved and not affected by a high cyst burden.

The Bioinformatics Report of Mutation Outcome on NADPH Flavin Oxidoreductase Protein Sequence in Clinical Isolates of H. pylori

frxA gene has been implicated in the metronidazole nitro reduction by H. pylori. Alternatively, frxA is expected to contribute to the protection of urease and to the in vivo survival of H. pylori. The aim of present study is to report the mutation effects on the frxA protein sequence in clinical isolates of H. pylori in our community. Metronidazole resistance was proven in 27 of 48 isolates. glmM and frxA genes were used for molecular confirmation of H. pylori isolates. The primer set for detection of whole sequence of frxA gene for the effect of mutation on protein sequence was used. DNA and protein sequence evaluation and analysis were done by blast, Clustal Omega, and T COFFEE programs. Then, FrxA protein sequences from six metronidazole-resistant clinical isolates were analyzed by web-based bioinformatics tools. The result of six metronidazole-resistant clinical isolates in comparison with strain 26695 showed ten missense mutations. The result with the STRING program revealed that no change was seen after alterations in these sequences. According to consensus data involving four methods, residue substitutions at 40, 13, and 141 increase the stability of protein sequence after mutation, while other alterations decrease. Residue substitutions at 40, 43, 141, 138, 169, and 179 are deleterious, while, V7I, Q10R, V34I, and V96I alterations are neutral. As FrxA contribute to survival of bacterium and in regard to the effect of mutations on protein function, it might affect the survival and bacterium phenotype and it need to be studied more. Also, none of the stability prediction tool is perfect; iStable is the best predictor method among all methods.

Computational analysis of prolyl hydroxylase domain-containing protein 2 (PHD2) mutations promoting polycythemia insurgence in humans

Idiopathic erythrocytosis is a rare disease characterized by an increase in red blood cell mass due to mutations in proteins of the oxygen-sensing pathway, such as prolyl hydroxylase 2 (PHD2). Here, we present a bioinformatics investigation of the pathological effect of twelve PHD2 mutations related to polycythemia insurgence. We show that few mutations impair the PHD2 catalytic site, while most localize to non-enzymatic regions. We also found that most mutations do not overlap the substrate recognition site, suggesting a novel PHD2 binding interface. After a structural analysis of both binding partners, we suggest that this novel interface is responsible for PHD2 interaction with the LIMD1 tumor suppressor.

Investigating the linkage between disease-causing amino acid variants and their effect on protein stability and binding

Single amino acid variations (SAV) occurring in human population result in natural differences between individuals or cause diseases. It is well understood that the molecular effect of SAV can be manifested as changes of the wild type characteristics of the corresponding protein, among which are the protein stability and protein interactions. Typically the effect of SAV on protein stability and interactions was assessed via the changes of the wild type folding and binding free energies. However, in terms of SAV affecting protein functionally and disease susceptibility, one wants to know to what extend the wild type function is perturbed by the SAV. Here it is demonstrated that relative, rather than the absolute, change of the folding and binding free energy serves as a good indicator for SAV association with disease. Using HumVar as a source for disease-causing SAV and experimentally determined free energy changes from ProTherm and SKEMPI databases, correlation coefficients (CC) between the disease index (Pd) and relative folding (Ppr,f) and binding (Ppr,b) probability indexes, respectively, was achieved. The obtained CCs demonstrated the applicability of the proposed approach and it served as good indicator for SAV association with disease.

Revealing the Effects of Missense Mutations Causing Snyder-Robinson Syndrome on the Stability and Dimerization of Spermine Synthase

Missense mutations in spermine synthase (SpmSyn) protein have been shown to cause the Snyder-Robinson syndrome (SRS). Depending on the location within the structure of SpmSyn and type of amino acid substitution, different mechanisms resulting in SRS were proposed. Here we focus on naturally occurring amino acid substitutions causing SRS, which are situated away from the active center of SpmSyn and thus are not directly involved in the catalysis. Two of the mutations, M35R and P112L, are reported for the first time in this study. It is demonstrated, both experimentally and computationally, that for such mutations the major effect resulting in dysfunctional SpmSyn is the destabilization of the protein. In vitro experiments indicated either no presence or very little amount of the mutant SpmSyn in patient cells. In silico modeling predicted that all studied mutations in this work destabilize SpmSyn and some of them abolish homo-dimer formation. Since dimerization and structural stability are equally important for the wild type function of SpmSyn, it is proposed that the SRS caused by mutations occurring in the N-domain of SpmSyn is a result of dysfunctional mutant proteins being partially unfolded and degraded by the proteomic machinery of the cell or being unable to form a homo-dimer.

Mutations in the KDM5C ARID Domain and Their Plausible Association with Syndromic Claes-Jensen-Type Disease

Mutations in KDM5C gene are linked to X-linked mental retardation, the syndromic Claes-Jensen-type disease. This study focuses on non-synonymous mutations in the KDM5C ARID domain and evaluates the effects of two disease-associated missense mutations (A77T and D87G) and three not-yet-classified missense mutations (R108W, N142S, and R179H). We predict the ARID domain’s folding and binding free energy changes due to mutations, and also study the effects of mutations on protein dynamics. Our computational results indicate that A77T and D87G mutants have minimal effect on the KDM5C ARID domain stability and DNA binding. In parallel, the change in the free energy unfolding caused by the mutants A77T and D87G were experimentally measured by urea-induced unfolding experiments and were shown to be similar to the in silico predictions. The evolutionary conservation analysis shows that the disease-associated mutations are located in a highly-conserved part of the ARID structure (N-terminal domain), indicating their importance for the KDM5C function. N-terminal residues’ high conservation suggests that either the ARID domain utilizes the N-terminal to interact with other KDM5C domains or the N-terminal is involved in some yet unknown function. The analysis indicates that, among the non-classified mutations, R108W is possibly a disease-associated mutation, while N142S and R179H are probably harmless.

Investigating the Inhibitory Effect of Wortmannin in the Hotspot Mutation at Codon 1047 of PIK3CA Kinase Domain: A Molecular Docking and Molecular Dynamics Approach

Oncogenic mutations in phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha (PIK3CA) are the most frequently reported in association with various forms of cancer. Several studies have reported the significance of hotspot mutations in a catalytic subunit of PIK3CA in association with breast cancer. Mutations are frequently observed in the highly conserved region of the kinase domain (797-1068 amino acids) of PIK3CA are activating or gain-of-function mutations. Mutation in codon 1047 occurs in the C-terminal region of the kinase domain with histidine (H) replaced by arginine (R), lysine (L), and tyrosine (Y). Pathogenicity and protein stability predictors PhD-SNP, Align GVGD, HANSA, iStable, and MUpro classified H1047R as highly deleterious when compared to H1047L and H1047Y. To explore the inhibitory activity of Wortmannin toward PIK3CA, the three-dimensional structure of the mutant protein was determined using homology modeling followed by molecular docking and molecular dynamics analysis. Docking studies were performed for the three mutants and native with Wortmannin to measure the differences in their binding pattern. Comparative docking study revealed that H1047R-Wortmannin complex has a higher number of hydrogen bonds as well as the best binding affinity next to the native protein. Furthermore, 100 ns molecular dynamics simulation was initiated with the docked complexes to understand the various changes induced by the mutation. Though Wortmannin was found to nullify the effect of H1047R over the protein, further studies are required for designing a better compound. As SNPs are major genetic variations observed in disease condition, personalized medicine would provide enhanced drug therapy.

ZC4H2, an XLID gene, is required for the generation of a specific subset of CNS interneurons

Miles–Carpenter syndrome (MCS) was described in 1991 as an XLID syndrome with fingertip arches and contractures and mapped to proximal Xq. Patients had microcephaly, short stature, mild spasticity, thoracic scoliosis, hyperextendable MCP joints, rocker-bottom feet, hyperextended elbows and knees. A mutation, p.L66H, in ZC4H2, was identified in a XLID re-sequencing project. Additional screening of linked families and next generation sequencing of XLID families identified three ZC4H2 mutations: p.R18K, p.R213W and p.V75in15aa. The families shared some relevant clinical features. In silico modeling of the mutant proteins indicated all alterations would destabilize the protein. Knockout mutations in zc4h2 were created in zebrafish and homozygous mutant larvae exhibited abnormal swimming, increased twitching, defective eye movement and pectoral fin contractures. Because several of the behavioral defects were consistent with hyperactivity, we examined the underlying neuronal defects and found that sensory neurons and motoneurons appeared normal. However, we observed a striking reduction in GABAergic interneurons. Analysis of cell-type-specific markers showed a specific loss of V2 interneurons in the brain and spinal cord, likely arising from mis-specification of neural progenitors. Injected human wt ZC4H2 rescued the mutant phenotype. Mutant zebrafish injected with human p.L66H or p.R213W mRNA failed to be rescued, while the p.R18K mRNA was able to rescue the interneuron defect. Our findings clearly support ZC4H2 as a novel XLID gene with a required function in interneuron development. Loss of function of ZC4H2 thus likely results in altered connectivity of many brain and spinal circuits.

On human disease-causing amino acid variants: statistical study of sequence and structural patterns

Statistical analysis was carried out on large set of naturally occurring human amino acid variations, and it was demonstrated that there is a preference for some amino acid substitutions to be associated with diseases. At an amino acid sequence level, it was shown that the disease-causing variants frequently involve drastic changes in amino acid physicochemical properties of proteins such as charge, hydrophobicity, and geometry. Structural analysis of variants involved in diseases and being frequently observed in human population showed similar trends: disease-causing variants tend to cause more changes in hydrogen bond network and salt bridges as compared with harmless amino acid mutations. Analysis of thermodynamics data reported in the literature, both experimental and computational, indicated that disease-causing variants tend to destabilize proteins and their interactions, which prompted us to investigate the effects of amino acid mutations on large databases of experimentally measured energy changes in unrelated proteins. Although the experimental datasets were linked neither to diseases nor exclusory to human proteins, the observed trends were the same: amino acid mutations tend to destabilize proteins and their interactions. Having in mind that structural and thermodynamics properties are interrelated, it is pointed out that any large change in any of them is anticipated to cause a disease.

Structural and physico-chemical effects of disease and non-disease nsSNPs on proteins

This review emphasizes the effects of naturally occurring mutations on structural features and physico-chemical properties of proteins. The basic protein characteristics considered are stability, dynamics, and the binding of proteins and methods for assessing effects of mutations on these macromolecular characteristics are briefly outlined. It is emphasized that the above entities mostly reflect global characteristics of considered macromolecules, while given mutations may alter the local structural features such as salt bridges and hydrogen bonds without affecting the global ones. Furthermore, it is pointed out that disease-causing mutations frequently involve a drastic change of amino acid physico-chemical properties such as charge, hydrophobicity, and geometry, and are less surface exposed than polymorphic mutations.

The impact of active site protonation on substrate ring conformation in Melanocarpus albomyces cellobiohydrolase Cel7B

Understanding how the protonation state of active site residues impacts the enzyme's structure and substrate conformation is important for improving the efficiency and economic viability of the degradation of cellulosic materials as feedstock for liquid fuel and value-added chemicals.

Chronic Beryllium Disease: revealing the role of beryllium ion and small peptides binding to HLA-DP2

Chronic Beryllium (Be) Disease (CBD) is a granulomatous disorder that predominantly affects the lung. The CBD is caused by Be exposure of individuals carrying the HLA-DP2 protein of the major histocompatibility complex class II (MHCII). While the involvement of Be in the development of CBD is obvious and the binding site and the sequence of Be and peptide binding were recently experimentally revealed [1], the interplay between induced conformational changes and the changes of the peptide binding affinity in presence of Be were not investigated. Here we carry out in silico modeling and predict the Be binding to be within the acidic pocket (Glu26, Glu68 and Glu69) present on the HLA-DP2 protein in accordance with the experimental work [1]. In addition, the modeling indicates that the Be ion binds to the HLA-DP2 before the corresponding peptide is able to bind to it. Further analysis of the MD generated trajectories reveals that in the presence of the Be ion in the binding pocket of HLA-DP2, all the different types of peptides induce very similar conformational changes, but their binding affinities are quite different. Since these conformational changes are distinctly different from the changes caused by peptides normally found in the cell in the absence of Be, it can be speculated that CBD can be caused by any peptide in presence of Be ion. However, the affinities of peptides for Be loaded HLA-DP2 were found to depend of their amino acid composition and the peptides carrying acidic group at positions 4 and 7 are among the strongest binders. Thus, it is proposed that CBD is caused by the exposure of Be of an individual carrying the HLA-DP2*0201 allele and that the binding of Be to HLA-DP2 protein alters the conformational and ionization properties of HLA-DP2 such that the binding of a peptide triggers a wrong signaling cascade.

Rational design of small-molecule stabilizers of spermine synthase dimer by virtual screening and free energy-based approach

Snyder-Robinson Syndrome (SRS) is a rare mental retardation disorder which is caused by the malfunctioning of an enzyme, the spermine synthase (SMS), which functions as a homo-dimer. The malfunctioning of SMS in SRS patients is associated with several identified missense mutations that occur away from the active site. This investigation deals with a particular SRS-causing mutation, the G56S mutation, which was shown computationally and experimentally to destabilize the SMS homo-dimer and thus to abolish SMS enzymatic activity. As a proof-of-concept, we explore the possibility to restore the enzymatic activity of the malfunctioning SMS mutant G56S by stabilizing the dimer through small molecule binding at the mutant homo-dimer interface. For this purpose, we designed an in silico protocol that couples virtual screening and a free binding energy-based approach to identify potential small-molecule binders on the destabilized G56S dimer, with the goal to stabilize it and thus to increase SMS G56S mutant activity. The protocol resulted in extensive list of plausible stabilizers, among which we selected and tested 51 compounds experimentally for their capability to increase SMS G56S mutant enzymatic activity. In silico analysis of the experimentally identified stabilizers suggested five distinctive chemical scaffolds. This investigation suggests that druggable pockets exist in the vicinity of the mutation sites at protein-protein interfaces which can be used to alter the disease-causing effects by small molecule binding. The identified chemical scaffolds are drug-like and can serve as original starting points for development of lead molecules to further rescue the disease-causing effects of the Snyder-Robinson syndrome for which no efficient treatment exists up to now.

Advances in Human Biology: Combining Genetics and Molecular Biophysics to Pave the Way for Personalized Diagnostics and Medicine

Advances in several biology-oriented initiatives such as genome sequencing and structural genomics, along with the progress made through traditional biological and biochemical research, have opened up a unique opportunity to better understand the molecular effects of human diseases. Human DNA can vary significantly from person to person and determines an individual’s physical characteristics and their susceptibility to diseases. Armed with an individual’s DNA sequence, researchers and physicians can check for defects known to be associated with certain diseases by utilizing various databases. However, for unclassified DNA mutations or in order to reveal molecular mechanism behind the effects, the mutations have to be mapped onto the corresponding networks and macromolecular structures and then analyzed to reveal their effect on the wild type properties of biological processes involved. Predicting the effect of DNA mutations on individual’s health is typically referred to as personalized or companion diagnostics. Furthermore, once the molecular mechanism of the mutations is revealed, the patient should be given drugs which are the most appropriate for the individual genome, referred to as pharmacogenomics. Altogether, the shift in focus in medicine towards more genomic-oriented practices is the foundation of personalized medicine. The progress made in these rapidly developing fields is outlined.

Integrating in silico prediction methods, molecular docking, and molecular dynamics simulation to predict the impact of ALK missense mutations in structural perspective

Over the past decade, advancements in next generation sequencing technology have placed personalized genomic medicine upon horizon. Understanding the likelihood of disease causing mutations in complex diseases as pathogenic or neutral remains as a major task and even impossible in the structural context because of its time consuming and expensive experiments. Among the various diseases causing mutations, single nucleotide polymorphisms (SNPs) play a vital role in defining individual's susceptibility to disease and drug response. Understanding the genotype-phenotype relationship through SNPs is the first and most important step in drug research and development. Detailed understanding of the effect of SNPs on patient drug response is a key factor in the establishment of personalized medicine. In this paper, we represent a computational pipeline in anaplastic lymphoma kinase (ALK) for SNP-centred study by the application of in silico prediction methods, molecular docking, and molecular dynamics simulation approaches. Combination of computational methods provides a way in understanding the impact of deleterious mutations in altering the protein drug targets and eventually leading to variable patient's drug response. We hope this rapid and cost effective pipeline will also serve as a bridge to connect the clinicians and in silico resources in tailoring treatments to the patients' specific genotype.

Predicting the Impact of Missense Mutations on Protein-Protein Binding Affinity

The crucial prerequisite for proper biological function is the protein's ability to establish highly selective interactions with macromolecular partners. A missense mutation that alters the protein binding affinity may cause significant perturbations or complete abolishment of the function, potentially leading to diseases. The availability of computational methods to evaluate the impact of mutations on protein-protein binding is critical for a wide range of biomedical applications. Here, we report an efficient computational approach for predicting the effect of single and multiple missense mutations on protein-protein binding affinity. It is based on a well-tested simulation protocol for structure minimization, modified MM-PBSA and statistical scoring energy functions with parameters optimized on experimental sets of several thousands of mutations. Our simulation protocol yields very good agreement between predicted and experimental values with Pearson correlation coefficients of 0.69 and 0.63 and root-mean-square errors of 1.20 and 1.90 kcal mol^-1 for single and multiple mutations, respectively. Compared with other available methods, our approach achieves high speed and prediction accuracy and can be applied to large datasets generated by modern genomics initiatives. In addition, we report a crucial role of water model and the polar solvation energy in estimating the changes in binding affinity. Our analysis also reveals that prediction accuracy and effect of mutations on binding strongly depends on the type of mutation and its location in a protein complex.

Utilizing multiple in silico analyses to identify putative causal SCN5A variants in Brugada syndrome

Brugada syndrome (BrS) is an inheritable sudden cardiac death disease mainly caused by SCN5A mutations. Traditional approaches can be costly and time-consuming if all candidate variants need to be validated through in vitro studies. Therefore, we developed a new approach by combining multiple in silico analyses to predict functional and structural changes of candidate SCN5A variants in BrS before conducting in vitro studies. Five SCN5A non-synonymous variants (1651G>A, 1776C>G, 1673A>G, 3269C>T and 3578G>A) were identified in 14 BrS patients using direct DNA sequencing. Several bioinformatics algorithms were applied and predicted that 1651G>A (A551T) and 1776C>G (N592K) were high-risk SCN5A variants (odds ratio 59.59 and 23.93). The results were validated by Mass spectrometry and in vitro electrophysiological assays. We concluded that integrating sequence-based information and secondary protein structures elements may help select highly potential variants in BrS before conducting time-consuming electrophysiological studies and two novel SCN5A mutations were validated.

In silico profiling and structural insights of missense mutations in RET protein kinase domain by molecular dynamics and docking approach

A major challenge remaining in drug design efforts towards protein kinase is due to the development of drug resistance initiated by the missense mutations in the kinase catalytic domain. Gain or loss of function mutations in the REarranged during Transfection (RET) tyrosine kinase gene have been associated with the development of a wide range of human associated cancers and Hirschsprung's disease. However, to what extent these mutations might affect bio-molecular functions remains unclear. In this article, the functionally significant mutations in RET were screened with the aid of various sequence and structure based in silico prediction methods. We mapped the deleterious mutants, modelled mutant proteins and deciphered the impact of mutations on drug binding mechanisms in the RET crystal structure of PDB ID: with the potential inhibitor vandetanib by docking analysis. Furthermore, molecular dynamics simulations were undertaken to understand the mechanistic action of cancer associated mutations in altering the protein kinase structure, dynamics, and stability. According to our results, the overall effect of V804M, M918T and S922Y were destabilizing and mostly alter the electrostatic component of the binding energy. Specifically, the mutation of gatekeeper residue valine 804 present in the ATP binding pocket affects the protein stability and confers resistance to the drug vandetanib, which was consistent with previously published experimental results. Overall, our findings may provide useful structural insights for in-depth understanding of the molecular mechanism underlying RET mutation and developing effective drugs.