In silico profiling of deleterious amino acid substitutions of potential pathological importance in haemophlia A and haemophlia B

A graph-based machine learning framework identifies critical properties of FVIII that lead to hemophilia A

Introduction: Blood coagulation is an essential process to cease bleeding in humans and other species. This mechanism is characterized by a molecular cascade of more than a dozen components activated after an injury to a blood vessel. In this process, the coagulation factor VIII (FVIII) is a master regulator, enhancing the activity of other components by thousands of times. In this sense, it is unsurprising that even single amino acid substitutions result in hemophilia A (HA)-a disease marked by uncontrolled bleeding and that leaves patients at permanent risk of hemorrhagic complications. Methods: Despite recent advances in the diagnosis and treatment of HA, the precise role of each residue of the FVIII protein remains unclear. In this study, we developed a graph-based machine learning framework that explores in detail the network formed by the residues of the FVIII protein, where each residue is a node, and two nodes are connected if they are in close proximity on the FVIII 3D structure. Results: Using this system, we identified the properties that lead to severe and mild forms of the disease. Finally, in an effort to advance the development of novel recombinant therapeutic FVIII proteins, we adapted our framework to predict the activity and expression of more than 300 in vitro alanine mutations, once more observing a close agreement between the in silico and the in vitro results. Discussion: Together, the results derived from this study demonstrate how graph-based classifiers can leverage the diagnostic and treatment of a rare disease.

Protein residue network analysis reveals fundamental properties of the human coagulation factor VIII

Hemophilia A is an X-linked inherited blood coagulation disorder caused by the production and circulation of defective coagulation factor VIII protein. People living with this condition receive either prophylaxis or on-demand treatment, and approximately 30% of patients develop inhibitor antibodies, a serious complication that limits treatment options. Although previous studies performed targeted mutations to identify important residues of FVIII, a detailed understanding of the role of each amino acid and their neighboring residues is still lacking. Here, we addressed this issue by creating a residue interaction network (RIN) where the nodes are the FVIII residues, and two nodes are connected if their corresponding residues are in close proximity in the FVIII protein structure. We studied the characteristics of all residues in this network and found important properties related to disease severity, interaction to other proteins and structural stability. Importantly, we found that the RIN-derived properties were in close agreement with in vitro and clinical reports, corroborating the observation that the patterns derived from this detailed map of the FVIII protein architecture accurately capture the biological properties of FVIII.

Prediction of hemophilia A severity using a small-input machine-learning framework

Hemophilia A is a relatively rare hereditary coagulation disorder caused by a defective F8 gene resulting in a dysfunctional Factor VIII protein (FVIII). This condition impairs the coagulation cascade, and if left untreated, it causes permanent joint damage and poses a risk of fatal intracranial hemorrhage in case of traumatic events. To develop prophylactic therapies with longer half-lives and that do not trigger the development of inhibitory antibodies, it is essential to have a deep understanding of the structure of the FVIII protein. In this study, we explored alternative ways of representing the FVIII protein structure and designed a machine-learning framework to improve the understanding of the relationship between the protein structure and the disease severity. We verified a close agreement between in silico, in vitro and clinical data. Finally, we predicted the severity of all possible mutations in the FVIII structure – including those not yet reported in the medical literature. We identified several hotspots in the FVIII structure where mutations are likely to induce detrimental effects to its activity. The combination of protein structure analysis and machine learning is a powerful approach to predict and understand the effects of mutations on the disease outcome.

Predicting the functional consequences of genetic variants in co-stimulatory ligand B7-1 using in-silico approaches

The purpose of this research is to identify and characterize deleterious genetic variants in the co-stimulatory ligand B7-1, also known as the human cluster of differentiation CD80 marker. The B7-1 ligand and the major histocompatibility complex class II (MHC II) molecules are the main determinants that provide B-cells the required competency to act as antigen presenting cells. For this, participation of both MHC class II molecules and CD80 is required. The interaction of the CD80 ligand with CD28 on the surface 7 of T_H cells plays a key role in the activation of TH cells and progression of B cells through the S phase, hence, leading to their proliferation in mitosis. A set of 2313 genetic variants in the B7-1 ligand have been mapped and retrieved from dbSNP database. Subsequently, 150 non-synonymous single nucleotide polymorphisms (nsSNPs) were mapped and subjected to the sequence and structural homology based predictions, which were further analyzed for protein stability and the disease phenotypes. Finally, we identified 7 potentially damaging nsSNPs in the B7-1 ligand that may affect its interaction with the cognitive receptor CD28, hence, may also interfere with TH cell activation and B cell proliferation. We propose that subsequent experimental analyses (stability, expression and interactions) on these proteins can provide a deep understanding about the effect of these variants on the structure and function of CD80.

In Silico Study of Correlation between Missense Variations of F8 Gene and Inhibitor Formation in Severe Hemophilia A

Objective:

Deleterious substitutions of the F8 gene are responsible for causing hemophilia A, which is an inherited bleeding disorder resulting from reduced or absent activity of the coagulant protein factor VIII (FVIII). The most important complication in treatment is inhibitor development toward therapeutic factor VIII. In this study, we aimed to analyze the effects of deleterious substitutions in the F8 gene upon protein structure and function.

Materials and Methods:

All tests were conducted by computational methods from the CHAMP (CDC Hemophilia A Mutation Project) database. We performed an in silico analysis of deleterious variations using five software programs, Sift, PolyPhen-2, Align-GVGD, KD4v, and MutationTaster, in order to analyze the correlation between variation and the disease. We also studied the correlation between these variations and inhibitor formation.

Results:

Our analysis showed that these in silico tools are coherent and that there are more variations in the A than the C domains. Moreover, we noticed that there are more deleterious variations than neutral variations in each of the A and C domains. We also found that 13.51% of the patients suffered from a severe form of hemophilia A and that carriers of missense variations developed inhibitors. Also, for the first time, we determined that variation nature is not associated with inhibitor formation. Furthermore, this analysis showed that the risk of developing inhibitors increases when the variation causes a change of amino acid class.

Conclusion:

This study will help to correctly associate variations with inhibitor development and aid in early characterization of novel variants.

Personalized approaches to the treatment of hemophilia A and B

The recognition that individuals respond differently to the same medication is not new and dates almost to the founding of western medicine. In the last century it came to be recognized that genetic factors influence the heterogeneity of individual responses to medications with respect to both toxicity and effectiveness. Nonetheless, it has been challenging to integrate pharmacogenetic approaches in the routine practice of medicine as the identification of biomarkers is difficult due to the inherent complexity of biological systems. Here, we present potential applications of pharmacogenetics in managing hemophilia A and B. We discuss how predicting and circumventing immunogenicity, an important impediment to treating hemophilia patients, particularly lends itself to a pharmacogenetic approach. In addition, we discuss new trends toward personalizing the management of hemophilia in clinical settings.

CoagVDb: a comprehensive database for coagulation factors and their associated SAPs

The current state of the art in medical genetics is to identify and classify the functional (deleterious) or non-functional (neutral) single amino acid substitutions (SAPs), also known as non-synonymous SNPs (nsSNPs). The primary goal is to elucidate the mechanisms through which functional SAPs exert their effects, and ultimately interrogating this information for association with complex phenotypes. This work focuses on coagulation factors involved in the coagulation cascade pathway which plays a vital role in the maintenance of homeostasis in the human system. We developed an integrated coagulation variation database, CoagVDb, which makes use of the biological information from various public databases such as NCBI, OMIM, UniProt, PDB and SAPs (rsIDs/variant). CoagVDb enriched with computational prediction scores classify SAPs as either deleterious or tolerated. Also, various other properties are incorporated such as amino acid composition, secondary structure elements, solvent accessibility, ordered/disordered regions, conservation, and the presence of disulfide bonds. This specialized database provides integration of various prediction scores from different computational methods along with gene, protein, and disease information. We hope our database will act as a useful reference resource for hematologists to reveal protein structure–function relationship and disease genotype–phenotype correlation.

In silico analyses of missense mutations in coagulation factor VIII: identification of severity determinants of haemophilia A

Factor VIII (FVIII) mutations cause haemophilia A (HA), an X-linked recessive coagulation disorder. Over 1000 missense mutations in FVIII are known and they lead to variable clinical phenotypes (severe, moderate and mild). The exact molecular basis of this phenotypic heterogeneity by FVIII missense mutations is elusive to date. In this study, we aimed to identify the severity determinants that cause phenotypic heterogeneity of HA. We compiled and curated a data set of 766 missense mutations from the repertoire of missense mutations in FVIII. We analysed these mutations by computational programs (e.g. Swiss-PdbViewer) and different mutation analysis servers (e.g. SIFT, PROVEAN, CUPSAT, PolyPhen2, MutPred); and various sequence- and structure-based parameters were assessed for any significant distribution bias among different HA phenotypes. Our analyses suggest that 'mutations in evolutionary conserved residues', 'mutations in buried residues', mutation-induced 'steric clash' and 'surface electrostatic potential alteration' act as risk factors towards severe HA. We have developed a grading system for FVIII mutations combining the severity determinants, and the grading pattern correlates with HA phenotype. This study will help to correctly associate the HA phenotype with a mutation and aid early characterization of novel variants.

In silico investigation of the ATP7B gene: insights from functional prediction of non-synonymous substitution to protein structure

ATP7B is a copper-transporting ATPase that plays a key role in the regulation of copper homeostasis. Mutations in the ATP7B gene are causative for Wilson's disease, and recent reports have suggested that genetic variants are associated with susceptibility to Alzheimer's disease. Unfortunately, it is difficult to profile experimentally novel genetic variants in the ATP7B gene, because the human protein X-ray structure is not yet entirely understood. In order to investigate ATP7B non-synonymous substitutions, we used an in silico amino acid sequence-based approach. Specifically, we analyzed 337 ATP7B non-synonymous substitutions, which included Wilson's disease-causing mutations (DVs) and non Wilson's disease-causing variants (NDVs), with an algorithm that estimated a combined probability (cPdel) of an amino acidic change to be deleterious for the protein function. This approach appeared to reliably indentify the probability of DVs and NDVs to be deleterious and to profile still unknown gene variants. Specifically, after analyzing ATP7B protein domains with the cPdel method, we found results in line with the predicted-modeled domains and some new suggestions. In conclusion, a functional survey of amino acid changes in the ATP7B protein is provided herein, and we suggest that this bioinformatic method can furnish information about novel ATP7B mutations. Furthermore, the same approach can be applied to other uncharacterized proteins.

A gene-specific method for predicting hemophilia-causing point mutations

A fundamental goal of medical genetics is the accurate prediction of genotype-phenotype correlations. As an approach to develop more accurate in silico tools for prediction of disease-causing mutations of structural proteins, we present a gene- and disease-specific prediction tool based on a large systematic analysis of missense mutations from hemophilia A (HA) patients. Our HA-specific prediction tool, HApredictor, showed disease prediction accuracy comparable to other publicly available prediction software. In contrast to those methods, its performance is not limited to non-synonymous mutations. Given the role of synonymous mutations in disease and drug codon optimization, we propose that utilizing a gene- and disease-specific method can be highly useful to make functional predictions possible even for synonymous mutations. Incorporating computational metrics at both nucleotide and amino acid levels along with multiple protein sequence/structure alignment significantly improved the predictive performance of our tool. HApredictor is freely available for download at http://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/HA_Predict/index.htm.

Functional diversity of the glutathione peroxidase gene family among human populations: implications for genetic predisposition to disease and drug response

Aim: To analyze the human genetic variation of glutathione peroxidases (GPX), estimating the functional differences among human populations and suggesting interethnic differences in predisposition to disease and drug response. Materials & methods: Using 1000 Genomes Project data, we analyzed 723 GPX variants in 1092 individuals belonging to 14 populations. Combining functional prediction analyses of coding and noncoding variants, we developed a method to estimate haplotype functionality. Results: GPX rare variants have a higher functional impact than common variants. The frequency among Asian patients of haplotypes associated with normal functionality is significantly higher for GPX1 and lower for GPX3 than for non-Asian patients; no adaptation signals in GPX1 and GPX3 were found in Asian patients. Conclusion: GPX1 and GPX3 differences may be associated with alterations in antioxidant capacity and redox regulation, which suggests diverse susceptibility to complex disease and diverse response to relevant drugs in Asians compared with individuals with other ethnic origins.

Original submitted 7 February 2013; Revision submitted 16 May 2013

In silico discrimination of nsSNPs in hTERT gene by means of local DNA sequence context and regularity

Understanding and predicting the significance of novel genetic variants revealed by DNA sequencing is a major challenge to integrate and interpret in medical genetics with medical practice. Recent studies have afforded significant advances in characterization and predicting the association of single nucleotide polymorphisms in human TERT with various disorders, but the results remain inconclusive. In this context, a comparative study between disease causing and novel mutations in hTERT gene was performed computationally. Out of 59 missense mutations, five variants were predicted to be less stable with the most deleterious effect on hTERT gene by in silico tools, in which two mutations (L584W and M970T) were not previously reported to be involved in any of the human disorders. To get insight into the structural and functional impact due to the mutation, docking study and interaction analysis was performed followed by 6 ns molecular dynamics simulation. These results may provide new perspectives for the targeted drug discovery in the coming future.