Modeling effects of human single nucleotide polymorphisms on protein-protein interactions

Mannose-binding lectin gene polymorphism in psoriasis and vitiligo: an observational study and computational analysis

Introduction

Psoriasis and vitiligo are inflammatory autoimmune skin disorders with remarkable genetic involvement. Mannose-binding lectin (MBL) represents a significant immune molecule with one of its gene variants strongly linked to autoimmune diseases. Therefore, in this study, we investigated the role of the MBL variant, rs1800450, in psoriasis and vitiligo disease susceptibility.

Methods

The study comprised performing in silico analysis, performing an observational study regarding psoriasis patients, and performing an observational study regarding vitiligo patients. Various in silico tools were used to investigate the impact of the selected mutation on the function, stability, post-translational modifications (PTMs), and secondary structures of the protein. In addition, a total of 489 subjects were enrolled in this study, including their demographic and clinicopathological data. Genotyping analysis was performed using real-time PCR for the single nucleotide polymorphism (SNP) rs1800450 on codon 54 of the MBL gene, utilizing TaqMan genotyping technology. In addition, implications of the studied variant on disease susceptibility and various clinicopathological data were analyzed.

Results

Computational analysis demonstrated the anticipated effects of the mutation on MBL protein. Furthermore, regarding the observational studies, rs1800450 SNP on codon 54 displayed comparable results in our population relative to global frequencies reported via the 1,000 Genomes Project. This SNP showed no significant association with either psoriasis or vitiligo disease risk in all genetic association models. Furthermore, rs1800450 SNP did not significantly correlate with any of the demographic or clinicopathological features of both psoriasis and vitiligo.

Discussion

Our findings highlighted that the rs1800450 SNP on the MBL2 gene has no role in the disease susceptibility to autoimmune skin diseases, such as psoriasis and vitiligo, among Egyptian patients. In addition, our analysis advocated the notion of the redundancy of MBL and revealed the lack of significant impact on both psoriasis and vitiligo disorders.

In Silico Prediction and Molecular Docking of SNPs in NRP1 Gene Associated with SARS-COV-2

Neuropilin-1 (NRP1) which is a main transmembrane cell surface receptor acts as a host cell mediator resulting in increasing the SARS-Cov-2 infectivity and also plays a role in neuronal development, angiogenesis and axonal outgrowth. The goal of this study is to estimate the impact of single nucleotide polymorphisms (SNPs) in the NRP1 gene on the function, structure and stabilization of protein as well as on the miRNA-mRNA binding regions using bioinformatical tools. It is also aimed to investigate the changes caused by SNPs in NRP1 on interactions with drug molecule and spike protein. The missense type of SNPs was analyzed using SIFT, PolyPhen-2, SNAP2, PROVEAN, Mutation Assessor, SNPs&GO, PhD-SNP, I-Mutant 3.0, MUpro, STRING, Project HOPE, ConSurf, and PolymiRTS. Docking analyses were conducted by AutoDock Vina program. As a result, a total of 733 missense SNPs were determined within the NRP1 gene and nine SNPs were specified as damaging to the protein. The modelling results showed that wild and mutant type amino acids had some different properties such as size, charge, and hydrophobicity. Additionally, their three-dimensional structures of protein were utilized for confirmation of these differences. After evaluating the results, nine polymorphisms rs141633354, rs142121081, rs145954532, rs200028992, rs200660300, rs369312020, rs370117610, rs370551432, rs370641686 were determined to be damaging on the structure and function of NRP1 protein and located in conserved regions. The results of molecular docking showed that the binding affinity values are nearly the same for wild-type and mutant structures support that the mutations carried out are not in the focus of the binding site, therefore the ligand does not affect the binding energy. It is expected that the results will be useful for future studies.

Identification of molecular signatures and molecular dynamics simulation of highly deleterious missense variants of key autophagy regulator beclin 1: a computational based approach

Beclin 1 is a key autophagy regulator that also plays significant roles in other intracellular processes such as vacuolar protein sorting. Beclin 1 protein functions as a scaffold in the formation of a multiprotein assemblage during autophagy. Beclin 1 is involved in various diseases such as cancers, neurodegenerative and autophagy-related disorders. In this study, we have used various in silico tools to scan beclin 1 at the molecular level to find its molecular signatures. We have predicted and analysed deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) of beclin 1 causing alterations in its structure and also affecting its interactions with other proteins. In total, twelve coding region deleterious variants were predicted using sequence-based tools and nine were predicted using various structure-based tools. The molecular dynamics (MD) simulations revealed an altered stability of the native structure due to the introduction of mutations. Destabilization of beclin 1 ECD domain was observed due to nsSNPs W300R and E302K. Beclin 1 deleterious nsSNPs were predicted to show significant effects on beclin 1 interactions with ATG14L1, UVRAG and VPS34 proteins and were also predicted to alter the protein-protein interface of beclin 1 complexes. Additionally, beclin 1 was predicted to have thirty-one potential phosphorylation and three ubiquitination sites. In conclusion, the molecular details of beclin 1 could help in the better understanding of its functioning. The study of nsSNPs and their effect on beclin 1 and its interactions might aid in understanding the basis of anomalies caused due to beclin 1 dysfunction.Communicated by Ramaswamy H. Sarma.

A mutation-induced drug resistance database (MdrDB)

Mutation-induced drug resistance is a significant challenge to the clinical treatment of many diseases, as structural changes in proteins can diminish drug efficacy. Understanding how mutations affect protein-ligand binding affinities is crucial for developing new drugs and therapies. However, the lack of a large-scale and high-quality database has hindered the research progresses in this area. To address this issue, we have developed MdrDB, a database that integrates data from seven publicly available datasets, which is the largest database of its kind. By integrating information on drug sensitivity and cell line mutations from Genomics of Drug Sensitivity in Cancer and DepMap, MdrDB has substantially expanded the existing drug resistance data. MdrDB is comprised of 100,537 samples of 240 proteins (which encompass 5119 total PDB structures), 2503 mutations, and 440 drugs. Each sample brings together 3D structures of wild type and mutant protein-ligand complexes, binding affinity changes upon mutation (ΔΔG), and biochemical features. Experimental results with MdrDB demonstrate its effectiveness in significantly enhancing the performance of commonly used machine learning models when predicting ΔΔG in three standard benchmarking scenarios. In conclusion, MdrDB is a comprehensive database that can advance the understanding of mutation-induced drug resistance, and accelerate the discovery of novel chemicals.

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.

Comprehensive deep mutational scanning reveals the pH induced stability and binding differences between SARS-CoV-2 spike RBD and human ACE2

The SARS-CoV-2 spike (S) glycoprotein with its mobile receptor-binding domain (RBD), binds to the human ACE2 receptor and thus facilitates virus entry through low-pH-endosomal pathways. The high degree of SARS-CoV-2 mutability has raised concern among scientists and medical professionals because it created doubt about the effectiveness of drugs and vaccinations designed specifically for COVID-19. In this study, we used computational saturation mutagenesis approach, including structure-based free energy calculations to analyse the effects of the missense mutations on the SARS-CoV-2 S-RBD stability and the S-RBD binding affinity with ACE2 at three different pH (pH 4.5, pH 6.5, and pH 7.4). A total of 3705 mutations in the S-RBD protein were analyzed, and we discovered that most of these mutations destabilize the RBD protein. Specifically, residues G404, G431, G447, A475, and G526 were important for RBD protein stability. In addition, RBD residues Y449, Y489, Y495, Q498, and N487 were critical for the RBD-ACE2 interaction. Next, we found that the distribution of the mean stability changes and mean binding energy changes of RBD due to mutations at both serological and endosomal pH correlated well, indicating the similar effects of mutations. Overall, this computational analysis is useful for understanding the effects of missense mutations in SARS-CoV-2 pathogenesis at different pH.Communicated by Ramaswamy H. Sarma.

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.

Predicting the effects of rare genetic variants on oncogenic signaling pathways: A computational analysis of HRAS protein function

The HRAS gene plays a crucial role in regulating essential cellular processes for life, and this gene's misregulation is linked to the development of various types of cancers. Nonsynonymous single nucleotide polymorphisms (nsSNPs) within the coding region of HRAS can cause detrimental mutations that disrupt wild-type protein function. In the current investigation, we have employed in-silico methodologies to anticipate the consequences of infrequent genetic variations on the functional properties of the HRAS protein. We have discovered a total of 50 nsSNPs, of which 23 were located in the exon region of the HRAS gene and denoting that they were expected to cause harm or be deleterious. Out of these 23, 10 nsSNPs ([G60V], [G60D], [R123P], [D38H], [I46T], [G115R], [R123G], [P11OL], [A59L], and [G13R]) were identified as having the most delterious effect based on results of SIFT analysis and PolyPhen2 scores ranging from 0.53 to 69. The DDG values -3.21 kcal/mol to 0.87 kcal/mol represent the free energy change associated with protein stability upon mutation. Interestingly, we identified that the three mutations (Y4C, T58I, and Y12E) were found to improve the structural stability of the protein. We performed molecular dynamics (MD) simulations to investigate the structural and dynamic effects of HRAS mutations. Our results showed that the stable model of HRAS had a significantly lower energy value of -18756 kj/mol compared to the initial model of -108915 kj/mol. The RMSD value for the wild-type complex was 4.40 Å, and the binding energies for the G60V, G60D, and D38H mutants were -107.09 kcal/mol, -109.42 kcal/mol, and -107.18 kcal/mol, respectively as compared to wild-type HRAS protein had -105.85 kcal/mol. The result of our investigation presents convincing corroboration for the potential functional significance of nsSNPs in augmenting HRAS expression and adding to the activation of malignant oncogenic signalling pathways.

HBD-2 variants and SARS-CoV-2: New insights into inter-individual susceptibility

Background

A deep understanding of the causes of liability to SARS-CoV-2 is essential to develop new diagnostic tests and therapeutics against this serious virus in order to overcome this pandemic completely. In the light of the discovered role of antimicrobial peptides [such as human b-defensin-2 (hBD-2) and cathelicidin LL-37] in the defense against SARS-CoV-2, it became important to identify the damaging missense mutations in the genes of these molecules and study their role in the pathogenesis of COVID-19.

Methods

We conducted a comprehensive analysis with multiple in silico approaches to identify the damaging missense SNPs for hBD-2 and LL-37; moreover, we applied docking methods and molecular dynamics analysis to study the impact of the filtered mutations.

Results

The comprehensive analysis reveals the presence of three damaging SNPs in hBD-2; these SNPs were predicted to decrease the stability of hBD-2 with a damaging impact on hBD-2 structure as well. G51D and C53G mutations were located in highly conserved positions and were associated with differences in the secondary structures of hBD-2. Docking-coupled molecular dynamics simulation analysis revealed compromised binding affinity for hBD-2 SNPs towards the SARS-CoV-2 spike domain. Different protein-protein binding profiles for hBD-2 SNPs, in relation to their native form, were guided through residue-wise levels and differential adopted conformation/orientation.

Conclusions

The presented model paves the way for identifying patients prone to COVID-19 in a way that would guide the personalization of both the diagnostic and management protocols for this serious disease.

Candidate genes for marker-assisted selection for growth, carcass and meat quality traits in rabbits

Growth and meat production are the most relevant targets for animal breeders, there are strong relationships between animal growth regulation, body composition and meat quality. Therefore, it is essential to identify the genetic factors that are controlling growth, carcass, and meat quality traits and to explore the correlations between identified genes of those traits. Identification of candidate genes may shift rabbit breeding from classical to modern approaches, which offer great potential to accelerate genetic improvement plans, especially in developing countries. The current work reviews several genes and mutations affecting growth, carcass and meat quality traits. These candidate genes and mutations can be incorporated into MAS programs to improve rabbit breeds especially local breeds, provided that a reasonable proportion of trait additive genetic variance is explained by the significant marker. Furthermore, we highlighted the indispensable need for more researches investigating candidate genes for different traits.

Short Linear Motifs in Colorectal Cancer Interactome and Tumorigenesis

Colorectal tumorigenesis is driven by alterations in genes and proteins responsible for cancer initiation, progression, and invasion. This multistage process is based on a dense network of protein-protein interactions (PPIs) that become dysregulated as a result of changes in various cell signaling effectors. PPIs in signaling and regulatory networks are known to be mediated by short linear motifs (SLiMs), which are conserved contiguous regions of 3-10 amino acids within interacting protein domains. SLiMs are the minimum sequences required for modulating cellular PPI networks. Thus, several in silico approaches have been developed to predict and analyze SLiM-mediated PPIs. In this review, we focus on emerging evidence supporting a crucial role for SLiMs in driver pathways that are disrupted in colorectal cancer (CRC) tumorigenesis and related PPI network alterations. As a result, SLiMs, along with short peptides, are attracting the interest of researchers to devise small molecules amenable to be used as novel anti-CRC targeted therapies. Overall, the characterization of SLiMs mediating crucial PPIs in CRC may foster the development of more specific combined pharmacological approaches.

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.

Genome interpretation using in silico predictors of variant impact

Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.

The properties of human disease mutations at protein interfaces

The assembly of proteins into complexes and their interactions with other biomolecules are often vital for their biological function. While it is known that mutations at protein interfaces have a high potential to be damaging and cause human genetic disease, there has been relatively little consideration for how this varies between different types of interfaces. Here we investigate the properties of human pathogenic and putatively benign missense variants at homomeric (isologous and heterologous), heteromeric, DNA, RNA and other ligand interfaces, and at different regions in proteins with respect to those interfaces. We find that different types of interfaces vary greatly in their propensity to be associated with pathogenic mutations, with homomeric heterologous and DNA interfaces being particularly enriched in disease. We also find that residues that do not directly participate in an interface, but are close in three-dimensional space, show a significant disease enrichment. Finally, we observe that mutations at different types of interfaces tend to have distinct property changes when undergoing amino acid substitutions associated with disease, and that this is linked to substantial variability in their identification by computational variant effect predictors.

Nearly all proteins interact with other molecules as part of their biological function. For example, proteins can interact with other copies of the same type of protein, with different proteins, with DNA, or with small ligand molecules. Many mutations at protein interfaces, the regions of proteins that interact with other molecules, are known to cause human genetic disease. In this study, we first investigate how different types of protein interfaces have different tendencies to be associated with disease. We also show that the closer a mutation is to an interface, the more likely it is to cause disease. Finally, we study how mutations at different types of interfaces tend to be associated with different changes in amino acid properties, which appears to influence our ability to computationally predict the effects of mutations. Ultimately, we hope that consideration of protein interface properties will eventually improve our ability to identify new disease-causing mutations.

rs7041 and rs4588 Polymorphisms in Vitamin D Binding Protein Gene (VDBP) and the Risk of Diseases

The purpose of the study was to investigate the role of vitamin D binding protein (VDBP, DBP) and its polymorphism in the vitamin D pathway and human health. This narrative review shows the latest literature on the most popular diseases that have previously been linked to VDBP. Vitamin D plays a crucial role in human metabolism, controlling phosphorus and calcium homeostasis. Vitamin D binding protein bonds vitamin D and its metabolites and transports them to target tissues. The most common polymorphisms in the VDBP gene are rs4588 and rs7041, which are located in exon 11 in domain III of the VDBP gene. rs4588 and rs7041 may be correlated with differences not only in vitamin D status in serum but also with vitamin D metabolites. This review supports the role of single nucleotide polymorphisms (SNPs) in the VDBP gene and presents the latest data showing correlations between VDBP variants with important human diseases such as obesity, diabetes mellitus, tuberculosis, chronic obstructive pulmonary disease, and others. In this review, we aim to systematize the knowledge regarding the occurrence of diseases and their relationship with vitamin D deficiencies, which may be caused by polymorphisms in the VDBP gene. Further research is required on the possible influence of SNPs, modifications in the structure of the binding protein, and their influence on the organism. It is also important to mention that most studies do not have a specific time of year to measure accurate vitamin D metabolite levels, which can be misleading in conclusions due to the seasonal nature of vitamin D.

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.

Systemic effects of missense mutations on SARS-CoV-2 spike glycoprotein stability and receptor-binding affinity

The spike (S) glycoprotein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the binding to the permissive cells. The receptor-binding domain (RBD) of SARS-CoV-2 S protein directly interacts with the human angiotensin-converting enzyme 2 (ACE2) on the host cell membrane. In this study, we used computational saturation mutagenesis approaches, including structure-based energy calculations and sequence-based pathogenicity predictions, to quantify the systemic effects of missense mutations on SARS-CoV-2 S protein structure and function. A total of 18 354 mutations in S protein were analyzed, and we discovered that most of these mutations could destabilize the entire S protein and its RBD. Specifically, residues G431 and S514 in SARS-CoV-2 RBD are important for S protein stability. We analyzed 384 experimentally verified S missense variations and revealed that the dominant pandemic form, D614G, can stabilize the entire S protein. Moreover, many mutations in N-linked glycosylation sites can increase the stability of the S protein. In addition, we investigated 3705 mutations in SARS-CoV-2 RBD and 11 324 mutations in human ACE2 and found that SARS-CoV-2 neighbor residues G496 and F497 and ACE2 residues D355 and Y41 are critical for the RBD-ACE2 interaction. The findings comprehensively provide potential target sites in the development of drugs and vaccines against COVID-19.

Computational analysis of hereditary spastic paraplegia mutations in the kinesin motor domains of KIF1A and KIF5A

Hereditary spastic paraplegias (HSPs) are a genetically heterogeneous collection of neurodegenerative disorders categorized by progressive lower-limb spasticity and frailty. The complex HSP forms are characterized by various neurological features including progressive spastic weakness, urinary sphincter dysfunction, extra pyramidal signs and intellectual disability (ID). The kinesin superfamily proteins (KIFs) are microtubule-dependent molecular motors involved in intracellular transport. Kinesins directionally transport membrane vesicles, protein complexes, and mRNAs along neurites, thus playing important roles in neuronal development and function. Recent genetic studies have identified kinesin mutations in patients with HSPs. In this study, we used the computational approaches to investigate the 40 missense mutations associated with HSP and ID in KIF1A and KIF5A. We performed homology modeling to construct the structures of kinesin–microtubule binding domain and kinesin–tubulin complex. We applied structure-based energy calculation methods to determine the effects of missense mutations on protein stability and protein–protein interaction. The results revealed that the most of disease-causing mutations could change the folding free energy of kinesin motor domain and the binding free energy of kinesin–tubulin complex. We found that E253K associated with ID in KIF1A decrease the protein stability of kinesin motor domains. We showed that the HSP mutations located in kinesin–tubulin complex interface, such as K253N and R280C in KIF5A, can destabilize the kinesin–tubulin complex. The computational analysis provides useful information for understanding the roles of kinesin mutations in the development of ID and HSPs.

PremPRI: Predicting the Effects of Missense Mutations on Protein-RNA Interactions

Protein–RNA interactions are crucial for many cellular processes, such as protein synthesis and regulation of gene expression. Missense mutations that alter protein–RNA interaction may contribute to the pathogenesis of many diseases. Here, we introduce a new computational method PremPRI, which predicts the effects of single mutations occurring in RNA binding proteins on the protein–RNA interactions by calculating the binding affinity changes quantitatively. The multiple linear regression scoring function of PremPRI is composed of three sequence- and eight structure-based features, and is parameterized on 248 mutations from 50 protein–RNA complexes. Our model shows a good agreement between calculated and experimental values of binding affinity changes with a Pearson correlation coefficient of 0.72 and the corresponding root-mean-square error of 0.76 kcal·mol⁻¹, outperforming three other available methods. PremPRI can be used for finding functionally important variants, understanding the molecular mechanisms, and designing new protein–RNA interaction inhibitors.

Ab-initio binding of barnase–barstar with DelPhiForce steered Molecular Dynamics (DFMD) approach

Receptor–ligand interactions are involved in various biological processes, therefore understanding the binding mechanism and ability to predict the binding mode are essential for many biological investigations. While many computational methods exist to predict the 3D structure of the corresponding complex provided the knowledge of the monomers, here we use the newly developed DelPhiForce steered Molecular Dynamics (DFMD) approach to model the binding of barstar to barnase to demonstrate that first-principles methods are also capable of modeling the binding. Essential component of DFMD approach is enhancing the role of long-range electrostatic interactions to provide guiding force of the monomers toward their correct binding orientation and position. Thus, it is demonstrated that the DFMD can successfully dock barstar to barnase even if the initial positions and orientations of both are completely different from the correct ones. Thus, the electrostatics provides orientational guidance along with pulling force to deliver the ligand in close proximity to the receptor.

Impact of IL-17F 7488T/C Functional Polymorphism on Progressive Rheumatoid Arthritis: Novel Insight from the Molecular Dynamic Simulations

Resorption of bones and cartilage coupled with structural changes in the inflamed joints are the major hallmark of rheumatoid arthritis (RA). Genetic polymorphisms in pro-inflammatory interleukins (ILs) appear to play an important role in the susceptibility towards progressive RA. We therefore aimed to investigate the association of single nucleotide polymorphisms (SNP), present in the hotspot coding/promoter regions of IL-6, -17 and -18, with RA susceptibility or severity in a larger study cohort from Pakistan together with finding clues as to how a functional SNP impacts the predisposition towards RA. TaqMan SNP genotyping approach was first used to assess IL-6 (rs1800795), IL-17 F (rs763780), IL-17A (rs2275913), and IL-18 (rs1946518) polymorphisms in 310 subjects (150 RA and 160 control). Molecular dynamic simulations (MDS) of wild- and mutant-type IL-17A with corresponding receptor were thereafter performed using AMBER-16; Chimera 1.13 was used for analyses. Our results showed the association of two SNPs, namely IL-6 - 174 G/C [allelic (OR = 0.960, 95% CI = 0.929-0.992, p = .009)] and IL-17 F 7488 T/C [allelic (OR = 0.907, 95%CI = 0.861-0.954, p = .000)] with increased RA risk in Pakistani subjects. When mapped, IL-17 F 7488 T/C was found involved in His₁₆₁→Arg₁₆₁ change near the C-terminus of IL-17 F. Comparative MDS revealed enhanced stability of the mutant hence advocating a potential role of IL-17F functional SNP in RA susceptibility and/or severity. This study provides a novel structural insight for SNP-derived functional mutation and its overall impact on binding with heterotrimeric receptor complex of IL-17 receptor thereby opening new avenues for understanding the biochemical basis of the disease.

MutaBind2: Predicting the Impacts of Single and Multiple Mutations on Protein-Protein Interactions

Missense mutations may affect proteostasis by destabilizing or over-stabilizing protein complexes and changing the pathway flux. Predicting the effects of stabilizing mutations on protein-protein interactions is notoriously difficult because existing experimental sets are skewed toward mutations reducing protein-protein binding affinity and many computational methods fail to correctly evaluate their effects. To address this issue, we developed a method MutaBind2, which estimates the impacts of single as well as multiple mutations on protein-protein interactions. MutaBind2 employs only seven features, and the most important of them describe interactions of proteins with the solvent, evolutionary conservation of the site, and thermodynamic stability of the complex and each monomer. This approach shows a distinct improvement especially in evaluating the effects of mutations increasing binding affinity. MutaBind2 can be used for finding disease driver mutations, designing stable protein complexes, and discovering new protein-protein interaction inhibitors.

Computing the Pathogenicity of Wilson's Disease ATP7B Mutations: Implications for Disease Prevalence

Genetic variations in the gene encoding the copper-transport protein ATP7B are the primary cause of Wilson's disease. Controversially, clinical prevalence seems much smaller than the prevalence estimated by genetic screening tools, causing fear that many people are undiagnosed, although early diagnosis and treatment is essential. To address this issue, we benchmarked 16 state-of-the-art computational disease-prediction methods against established data of missense ATP7B mutations. Our results show that the quality of the methods varies widely. We show the importance of optimizing the threshold of the methods used to distinguish pathogenic from nonpathogenic mutations against data of clinically confirmed pathogenic and nonpathogenic mutations. We find that most methods use thresholds that predict too many ATP7B mutations to be pathogenic. Thus, our findings explain the current controversy on Wilson's disease prevalence because meta-analysis and text search methods include many computational estimates that lead to higher disease prevalence than clinically observed. As proteins and diseases differ widely, a one-size-fits-all threshold cannot distinguish pathogenic and nonpathogenic mutations efficiently, as shown here. We also show that amino acid changes with small evolutionary substitution probability, mainly due to amino acid volume, are more associated with the disease, implying a pathological effect on the conformational state of the protein, which could affect copper transport or adenosine triphosphate recognition and hydrolysis. These findings may be a first step toward a more quantitative genotype-phenotype relationship of Wilson's disease.

Insights into the Effects of Cancer Associated Mutations at the UPF2 and ATP-Binding Sites of NMD Master Regulator: UPF1

Nonsense-mediated mRNA decay (NMD) is a quality control mechanism that recognizes post-transcriptionally abnormal transcripts and mediates their degradation. The master regulator of NMD is UPF1, an enzyme with intrinsic ATPase and helicase activities. The cancer genomic sequencing data has identified frequently mutated residues in the CH-domain and ATP-binding site of UPF1. In silico screening of UPF1 stability change as a function over 41 cancer mutations has identified five variants with significant effects: K164R, R253W, T499M, E637K, and E833K. To explore the effects of these mutations on the associated energy landscape of UPF1, molecular dynamics simulations (MDS) were performed. MDS identified stable H-bonds between residues S152, S203, S205, Q230/R703, and UPF2/AMPPNP, and suggest that phosphorylation of Serine residues may control UPF1-UPF2 binding. Moreover, the alleles K164R and R253W in the CH-domain improved UPF1-UPF2 binding. In addition, E637K and E833K alleles exhibited improved UPF1-AMPPNP binding compared to the T499M variant; the lower binding is predicted from hindrance caused by the side-chain of T499M to the docking of the tri-phosphate moiety (AMPPNP) into the substrate site. The dynamics of wild-type/mutant systems highlights the flexible nature of the ATP-binding region in UPF1. These insights can facilitate the development of drug discovery strategies for manipulating NMD signaling in cell systems using chemical tools.

Modeling Electrostatic Force in Protein-Protein Recognition

Electrostatic interactions are important for understanding molecular interactions, since they are long-range interactions and can guide binding partners to their correct binding positions. To investigate the role of electrostatic forces in molecular recognition, we calculated electrostatic forces between binding partners separated at various distances. The investigation was done on a large set of 275 protein complexes using recently developed DelPhiForce tool and in parallel, evaluating the total electrostatic force via electrostatic association energy. To accomplish the goal, we developed a method to find an appropriate direction to move one chain of protein complex away from its bound position and then calculate the corresponding electrostatic force as a function of separation distance. It is demonstrated that at large distances between the partners, the electrostatic force (magnitude and direction) is consistent among the protocols used and the main factors contributing to it are the net charge of the partners and their interfaces. However, at short distances, where partners form specific pair-wise interactions or de-solvation penalty becomes significant, the outcome depends on the precise balance of these factors. Based on the electrostatic force profile (force as a function of distance), we group the cases into four distinctive categories, among which the most intriguing is the case termed "soft landing." In this case, the electrostatic force at large distances is favorable assisting the partners to come together, while at short distance it opposes binding, and thus slows down the approach of the partners toward their physical binding.

BioStruct-Africa: empowering Africa-based scientists through structural biology knowledge transfer and mentoring - recent advances and future perspectives

Being able to visualize biology at the molecular level is essential for our understanding of the world. A structural biology approach reveals the molecular basis of disease processes and can guide the design of new drugs as well as aid in the optimization of existing medicines. However, due to the lack of a synchrotron light source, adequate infrastructure, skilled persons and incentives for scientists in addition to limited financial support, the majority of countries across the African continent do not conduct structural biology research. Nevertheless, with technological advances such as robotic protein crystallization and remote data collection capabilities offered by many synchrotron light sources, X-ray crystallography is now potentially accessible to Africa-based scientists. This leap in technology led to the establishment in 2017 of BioStruct-Africa, a non-profit organization (Swedish corporate ID: 802509-6689) whose core aim is capacity building for African students and researchers in the field of structural biology with a focus on prevalent diseases in the African continent. The team is mainly composed of, but not limited to, a group of structural biologists from the African diaspora. The members of BioStruct-Africa have taken up the mantle to serve as a catalyst in order to facilitate the information and technology transfer to those with the greatest desire and need within Africa. BioStruct-Africa achieves this by organizing workshops onsite at our partner universities and institutions based in Africa, followed by post-hoc online mentoring of participants to ensure sustainable capacity building. The workshops provide a theoretical background on protein crystallography, hands-on practical experience in protein crystallization, crystal harvesting and cryo-cooling, live remote data collection on a synchrotron beamline, but most importantly the links to drive further collaboration through research. Capacity building for Africa-based researchers in structural biology is crucial to win the fight against the neglected tropical diseases, e.g. ascariasis, hookworm, trichuriasis, lymphatic filariasis, active trachoma, loiasis, yellow fever, leprosy, rabies, sleeping sickness, onchocerciasis, schistosomiasis, etc., that constitute significant health, social and economic burdens to the continent. BioStruct-Africa aims to build local and national expertise that will have direct benefits for healthcare within the continent.

Dissecting in silico Mutation Prediction of Variants in African Genomes: Challenges and Perspectives

Genomic medicine is set to drastically improve clinical care globally due to high throughput technologies which enable speedy in silico detection and analysis of clinically relevant mutations. However, the variability in the in silico prediction methods and categorization of functionally relevant genetic variants can pose specific challenges in some populations. In silico mutation prediction tools could lead to high rates of false positive/negative results, particularly in African genomes that harbor the highest genetic diversity and that are disproportionately underrepresented in public databases and reference panels. These issues are particularly relevant with the recent increase in initiatives, such as the Human Heredity and Health (H3Africa), that are generating huge amounts of genomic sequence data in the absence of policies to guide genomic researchers to return results of variants in so-called actionable genes to research participants. This report (i) provides an inventory of publicly available Whole Exome/Genome data from Africa which could help improve reference panels and explore the frequency of pathogenic variants in actionable genes and related challenges, (ii) reviews available in silico prediction mutation tools and the criteria for categorization of pathogenicity of novel variants, and (iii) proposes recommendations for analyzing pathogenic variants in African genomes for their use in research and clinical practice. In conclusion, this work proposes criteria to define mutation pathogenicity and actionability in human genetic research and clinical practice in Africa and recommends setting up an African expert panel to oversee the proposed criteria.

Structural Perspective on Revealing and Altering Molecular Functions of Genetic Variants Linked with Diseases

Structural information of biological macromolecules is crucial and necessary to deliver predictions about the effects of mutations-whether polymorphic or deleterious (i.e., disease causing), wherein, thermodynamic parameters, namely, folding and binding free energies potentially serve as effective biomarkers. It may be emphasized that the effect of a mutation depends on various factors, including the type of protein (globular, membrane or intrinsically disordered protein) and the structural context in which it occurs. Such information may positively aid drug-design. Furthermore, due to the intrinsic plasticity of proteins, even mutations involving radical change of the structural and physico⁻chemical properties of the amino acids (native vs. mutant) can still have minimal effects on protein thermodynamics. However, if a mutation causes significant perturbation by either folding or binding free energies, it is quite likely to be deleterious. Mitigating such effects is a promising alternative to the traditional approaches of designing inhibitors. This can be done by structure-based in silico screening of small molecules for which binding to the dysfunctional protein restores its wild type thermodynamics. In this review we emphasize the effects of mutations on two important biophysical properties, stability and binding affinity, and how structures can be used for structure-based drug design to mitigate the effects of disease-causing variants on the above biophysical properties.

Evolution and Diversification of FRUITFULL Genes in Solanaceae

Ecologically and economically important fleshy edible fruits have evolved from dry fruit numerous times during angiosperm diversification. However, the molecular mechanisms that underlie these shifts are unknown. In the Solanaceae there has been a major shift to fleshy fruits in the subfamily Solanoideae. Evidence suggests that an ortholog of FRUITFULL (FUL), a transcription factor that regulates cell proliferation and limits the dehiscence zone in the silique of Arabidopsis, plays a similar role in dry-fruited Solanaceae. However, studies have shown that FUL orthologs have taken on new functions in fleshy fruit development, including regulating elements of tomato ripening such as pigment accumulation. FUL belongs to the core eudicot euFUL clade of the angiosperm AP1/FUL gene lineage. The euFUL genes fall into two paralogous clades, euFULI and euFULII. While most core eudicots have one gene in each clade, Solanaceae have two: FUL1 and FUL2 in the former, and MBP10 and MBP20 in the latter. We characterized the evolution of the euFUL genes to identify changes that might be correlated with the origin of fleshy fruit in Solanaceae. Our analyses revealed that the Solanaceae FUL1 and FUL2 clades probably originated through an early whole genome multiplication event. By contrast, the data suggest that the MBP10 and MBP20 clades are the result of a later tandem duplication event. MBP10 is expressed at weak to moderate levels, and its atypical short first intron lacks putative transcription factor binding sites, indicating possible pseudogenization. Consistent with this, our analyses show that MBP10 is evolving at a faster rate compared to MBP20. Our analyses found that Solanaceae euFUL gene duplications, evolutionary rates, and changes in protein residues and expression patterns are not correlated with the shift in fruit type. This suggests deeper analyses are needed to identify the mechanism underlying the change in FUL ortholog function.

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.

Hot-spot analysis for drug discovery targeting protein-protein interactions

INTRODUCTION

Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions. Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions. Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.

Electrostatic Interactions in Protein Structure, Folding, Binding, and Condensation

Charged and polar groups, through forming ion pairs, hydrogen bonds, and other less specific electrostatic interactions, impart important properties to proteins. Modulation of the charges on the amino acids, e.g., by pH and by phosphorylation and dephosphorylation, have significant effects such as protein denaturation and switch-like response of signal transduction networks. This review aims to present a unifying theme among the various effects of protein charges and polar groups. Simple models will be used to illustrate basic ideas about electrostatic interactions in proteins, and these ideas in turn will be used to elucidate the roles of electrostatic interactions in protein structure, folding, binding, condensation, and related biological functions. In particular, we will examine how charged side chains are spatially distributed in various types of proteins and how electrostatic interactions affect thermodynamic and kinetic properties of proteins. Our hope is to capture both important historical developments and recent experimental and theoretical advances in quantifying electrostatic contributions of proteins.

Docking-based modeling of protein-protein interfaces for extensive structural and functional characterization of missense mutations

Next-generation sequencing (NGS) technologies are providing genomic information for an increasing number of healthy individuals and patient populations. In the context of the large amount of generated genomic data that is being generated, understanding the effect of disease-related mutations at molecular level can contribute to close the gap between genotype and phenotype and thus improve prevention, diagnosis or treatment of a pathological condition. In order to fully characterize the effect of a pathological mutation and have useful information for prediction purposes, it is important first to identify whether the mutation is located at a protein-binding interface, and second to understand the effect on the binding affinity of the affected interaction/s. Computational methods, such as protein docking are currently used to complement experimental efforts and could help to build the human structural interactome. Here we have extended the original pyDockNIP method to predict the location of disease-associated nsSNPs at protein-protein interfaces, when there is no available structure for the protein-protein complex. We have applied this approach to the pathological interaction networks of six diseases with low structural data on PPIs. This approach can almost double the number of nsSNPs that can be characterized and identify edgetic effects in many nsSNPs that were previously unknown. This can help to annotate and interpret genomic data from large-scale population studies, and to achieve a better understanding of disease at molecular level.

Decoding disease-causing mechanisms of missense mutations from supramolecular structures

The inheritance modes of pathogenic missense mutations are known to be highly associated with protein structures; recessive mutations are mainly observed in the buried region of protein structures, whereas dominant mutations are significantly enriched in the interfaces of molecular interactions. However, the differences in phenotypic impacts among various dominant mutations observed in individuals are not fully understood. In the present study, the functional effects of pathogenic missense mutations on three-dimensional macromolecular complex structures were explored in terms of dominant mutation types, namely, haploinsufficiency, dominant-negative, or toxic gain-of-function. The major types of dominant mutation were significantly associated with the different types of molecular interactions, such as protein-DNA, homo-oligomerization, or intramolecular domain-domain interactions, affected by mutations. The dominant-negative mutations were biased toward molecular interfaces for cognate protein or DNA. The haploinsufficiency mutations were enriched on the DNA interfaces. The gain-of-function mutations were localized to domain-domain interfaces. Our results demonstrate a novel use of macromolecular complex structures for predicting the disease-causing mechanisms through inheritance modes.

Screening of nucleotide variations in genomic sequences encoding charged protein regions in the human genome

Background

Studying genetic variation distribution in proteins containing charged regions, called charge clusters (CCs), is of great interest to unravel their functional role. Charge clusters are 20 to 75 residue segments with high net positive charge, high net negative charge, or high total charge relative to the overall charge composition of the protein. We previously developed a bioinformatics tool (FCCP) to detect charge clusters in proteomes and scanned the human proteome for the occurrence of CCs. In this paper we investigate the genetic variations in the human proteins harbouring CCs.

Results

We studied the coding regions of 317 positively charged clusters and 1020 negatively charged ones previously detected in human proteins. Results revealed that coding parts of CCs are richer in sequence variants than their corresponding genes, full mRNAs, and exonic + intronic sequences and that these variants are predominately rare (Minor allele frequency < 0.005). Furthermore, variants occurring in the coding parts of positively charged regions of proteins are more often pathogenic than those occurring in negatively charged ones. Classification of variants according to their types showed that substitution is the major type followed by Indels (Insertions-deletions). Concerning substitutions, it was found that within clusters of both charges, the charged amino acids were the greatest loser groups whereas polar residues were the greatest gainers.

Conclusions

Our findings highlight the prominent features of the human charged regions from the DNA up to the protein sequence which might provide potential clues to improve the current understanding of those charged regions and their implication in the emergence of diseases.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-4000-3) contains supplementary material, which is available to authorized users.

DelPhiForce, a tool for electrostatic force calculations: Applications to macromolecular binding

Long-range electrostatic forces play an important role in molecular biology, particularly in macromolecular interactions. However, calculating the electrostatic forces for irregularly shaped molecules immersed in water is a difficult task. Here, we report a new tool, DelPhiForce, which is a tool in the DelPhi package that calculates and visualizes the electrostatic forces in biomolecular systems. In parallel, the DelPhi algorithm for modeling electrostatic potential at user-defined positions has been enhanced to include triquadratic and tricubic interpolation methods. The tricubic interpolation method has been tested against analytical solutions and it has been demonstrated that the corresponding errors are negligibly small at resolution 4 grids/Å. The DelPhiForce is further applied in the study of forces acting between partners of three protein-protein complexes. It has been demonstrated that electrostatic forces play a dual role by steering binding partners (so that the partners recognize their native interfaces) and exerting an electrostatic torque (if the mutual orientations of the partners are not native-like). The output of DelPhiForce is in a format that VMD can read and visualize, and provides additional options for analysis of protein-protein binding. DelPhiForce is available for download from the DelPhi webpage at http://compbio.clemson.edu/downloadDir/delphiforce.tar.gz © 2017 Wiley Periodicals, Inc.

Annotating Mutational Effects on Proteins and Protein Interactions: Designing Novel and Revisiting Existing Protocols

In this review we describe a protocol to annotate the effects of missense mutations on proteins, their functions, stability, and binding. For this purpose we present a collection of the most comprehensive databases which store different types of sequencing data on missense mutations, we discuss their relationships, possible intersections, and unique features. Next, we suggest an annotation workflow using the state-of-the art methods and highlight their usability, advantages, and limitations for different cases. Finally, we address a particularly difficult problem of deciphering the molecular mechanisms of mutations on proteins and protein complexes to understand the origins and mechanisms of diseases.

A diversity oriented synthesis of natural product inspired molecular libraries

Diversity oriented synthesis of natural product inspired compounds from S-tryptophan methyl ester.

Exploring Protein-Protein Interactions as Drug Targets for Anti-cancer Therapy with In Silico Workflows

We describe a computational protocol to aid the design of small molecule and peptide drugs that target protein-protein interactions, particularly for anti-cancer therapy. To achieve this goal, we explore multiple strategies, including finding binding hot spots, incorporating chemical similarity and bioactivity data, and sampling similar binding sites from homologous protein complexes. We demonstrate how to combine existing interdisciplinary resources with examples of semi-automated workflows. Finally, we discuss several major problems, including the occurrence of drug-resistant mutations, drug promiscuity, and the design of dual-effect inhibitors.

Convert your favorite protein modeling program into a mutation predictor: "MODICT"

Background

Predict whether a mutation is deleterious based on the custom 3D model of a protein.

Results

We have developed modict, a mutation prediction tool which is based on per residue rmsd (root mean square deviation) values of superimposed 3D protein models. Our mathematical algorithm was tested for 42 described mutations in multiple genes including renin (REN), beta-tubulin (TUBB2B), biotinidase (BTD), sphingomyelin phosphodiesterase-1 (SMPD1), phenylalanine hydroxylase (PAH) and medium chain Acyl-Coa dehydrogenase (ACADM). Moreover, modict scores corresponded to experimentally verified residual enzyme activities in mutated biotinidase, phenylalanine hydroxylase and medium chain Acyl-CoA dehydrogenase. Several commercially available prediction algorithms were tested and results were compared. The modictperl package and the manual can be downloaded from https://github.com/IbrahimTanyalcin/MODICT.

Conclusions

We show here that modict is capable tool for mutation effect prediction at the protein level, using superimposed 3D protein models instead of sequence based algorithms used by polyphen and sift.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1286-0) contains supplementary material, which is available to authorized users.

Purification of Ovine Respiratory Complex I Results in a Highly Active and Stable Preparation

NADH-ubiquinone oxidoreductase (complex I) is the largest (∼1 MDa) and the least characterized complex of the mitochondrial electron transport chain. Because of the ease of sample availability, previous work has focused almost exclusively on bovine complex I. However, only medium resolution structural analyses of this complex have been reported. Working with other mammalian complex I homologues is a potential approach for overcoming these limitations. Due to the inherent difficulty of expressing large membrane protein complexes, screening of complex I homologues is limited to large mammals reared for human consumption. The high sequence identity among these available sources may preclude the benefits of screening. Here, we report the characterization of complex I purified from Ovis aries (ovine) heart mitochondria. All 44 unique subunits of the intact complex were identified by mass spectrometry. We identified differences in the subunit composition of subcomplexes of ovine complex I as compared with bovine, suggesting differential stability of inter-subunit interactions within the complex. Furthermore, the 42-kDa subunit, which is easily lost from the bovine enzyme, remains tightly bound to ovine complex I. Additionally, we developed a novel purification protocol for highly active and stable mitochondrial complex I using the branched-chain detergent lauryl maltose neopentyl glycol. Our data demonstrate that, although closely related, significant differences exist between the biochemical properties of complex I prepared from ovine and bovine mitochondria and that ovine complex I represents a suitable alternative target for further structural studies.

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/.