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Article

Multiple Bio-Computational Tools Emerge as Valid Approach in the Assessment of Apolipoproteins Pathogenicity Related Mutations

by
Giorgia Francesca Saraceno
and
Erika Cione
*
Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, 87036 Rende, Italy
*
Author to whom correspondence should be addressed.
BioMedInformatics 2025, 5(1), 16; https://doi.org/10.3390/biomedinformatics5010016
Submission received: 23 January 2025 / Revised: 4 March 2025 / Accepted: 18 March 2025 / Published: 20 March 2025
(This article belongs to the Section Computational Biology and Medicine)
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Abstract

:
Background: Critical studies have unwaveringly established the importance of peculiar single-nucleotide polymorphisms (SNPs) in apolipoproteins (Apos) genes as genetic risk factors for dyslipidemias and their related comorbidities. In this study, we employed in silico approaches to analyze mutations in Apos. Methods: A comprehensive set of computational tools was utilized. The tools for predictions derived from sequence analysis were: SIFT, PolyPhen-2, FATHMM and SNPs&GO; The tools for structure analysis were: mCSM, DynaMut2, MAESTROweb, and PremPS; for prediction of pathogenic potential were: MutPred2, and PhD-SNP; for profiling of aggregation propensity were: Camsol, and Aggrescan3D 2.0, and lastly, for residual frustration analysis, the Frustratometer was used. These approaches assess variant effects on protein structure, stability, and function. Results: We identified seventeen SNPs in total, twelve for ApoB, one for ApoC2, one for ApoC3, and three for ApoE, representing 70%, 6%, 6% and 18%, respectively. The pathogenity of ApoE, was highlighted in two SNPs the rs769452 with amino acid replacement L46P, and rs769455 with amino acid replacement R163C. The aggregation/solubility analysis revealed that the L46P leads to a decrease in ApoE aggregation. The R163C, showed a decrease in solubility in one of two tools used, resulting in destabilizing effects altering its solubility. Conclusions: The two mutations in ApoE studied with the in silico methodologies identified clinically significant genetic variants, highlighting the robustness of the integrated approach. The future direction of the research is to create a multiplex panel with the SNPs identified here in APOE and expanding to other proteins to have a panel genetic risk assessment and disease prediction in which ApoE correlates.

1. Introduction

Apolipoproteins (Apos) are essential proteins that associate with lipids to create lipoproteins. These complexes are essential for lipid metabolism, facilitating lipid transport through the lymphatic and circulatory systems [1]. The Apos protein family includes over twenty identified members playing also roles beyond lipid metabolism since their genetic and genomic phenotypes correlate to predispose humans to various disorders. A significant aspect of these genetic risks is attributed to Apos SNPs, which contribute to the functional diversity of Apos. These variations are closely linked to differences in disease susceptibility and progression, particularly in vascular conditions [1]. Polymorphisms in the Apo gene family are associated with various diseases. For example, ApoE genotypes are linked to circulating lipid levels, increased risk of coronary heart disease, and cardiovascular disease (CVD) [1,2,3]. Individuals with the ε4 homozygous genotype are at a higher risk of developing Alzheimer’s disease (AD) [4,5,6]. Variants in ApoA5 are associated with hypertriglyceridemia [7], while ApoA polymorphisms are connected to familial hypercholesterolemia [8]. Additionally, alterations in the ApoB gene are linked to an increased risk of ischemic heart disease [9]. In this scenario, the composition of Apos not only determines the cargo, processing, and transport efficiency of lipoproteins but also serves as a critical factor in disease risk assessment. By integrating the genetic and functional variability of Apos, this approach highlights their central role in disease heterogeneity and clinical assessment [10,11]. A particularly significant area of research focuses on single nucleotide variants that result in amino acid substitutions in proteins, known as missense mutations, which are associated with more than half of all known inherited diseases [12,13]. This study aims to investigate, using in silico tools, the impact of single nucleotide mutations on the stability of Apos.

2. Materials and Methods

2.1. Data Retrieval and Web Resources

We searched the dbSNP database for missense mutations in several Apos, including ApoA types 1, 2, 4, and 5; ApoB, ApoC types1, 2, 3 and 4; ApoD, ApoE, ApoF, ApoH, ApoJ, ApoL types 1 and 2; ApoM, and ApoO, focusing on those with conflicting pathogenicity interpretations (cpi) in dbSNP [14], Ensembl [15], and the relevant PubMed literature. The cpi criterion was used to cluster bio-computational data on this topic. Various computational tools were employed to conduct diverse predictions and calculations, with detailed discussions provided in the subsequent sections. We employ a multifaceted approach that integrates predictions from various computational tools, including SIFT [16], PolyPhen-2 [17], FATHMM [18], SNPs&Go [19], mCSM [20], DynaMut2 [21], MAESTROweb [22], PremPS [23], MutPred2 [24], PhDSNP [25], Camsol [26], Aggrescan3D 2.0 [27] and Frustratometer [28]. The explanation of the scoring system for each tool is provided underneath of each table. Details for each tool are present in Supplementary Material in brief here after.

2.2. Predictions Derived from Sequence Analysis

  • Sorts Tolerant From Intolerant (SIFT): bioinformatics tool that predicts the potential impact of amino acid substitutions on protein function [16]. Advantage of SIFT, it allows the analysis of multiple variants, suitable for high-throughput genetic screening studies. A limitation of SIFT, however, is its dependence on multiple sequence alignments to assess the conservation of amino acid residues. When a protein lacks a sufficient number of homologous sequences, the algorithm may struggle to generate accurate predictions.
  • PolyPhen-2: bioinformatics tool that predicts the possible effects of amino acid substitutions on protein structure and function [17]. It integrates comparative and physical properties of amino acids substitutions disrupting the native conformation of the protein. It gathers relevant information, which improves the robustness of predictions. The limit is the indirect prediction of disease risk.
  • Functional Analysis Through Hidden Markov Models (FATHMM): bioinformatics tool used for functional impact of genetic variants prediction, particularly in coding regions of the genome [18]. The predictive accuracy depends heavily on the quality and size of the training dataset. For variants that are not well-represented in the training data, the predictions may be less reliable.
  • SNPs&GO: web-based tool that utilizes a support vector machine (SVM) to identify deleterious single-nucleotide polymorphisms that lead to amino acid substitutions [19]. The SVM classifier integrates protein sequence data, functional profiles, and gene ontology (GO) annotations to differentiate between disease-associated and neutral variants. It applies machine learning algorithms trained on various features derived from protein sequence and structure to assess the potential effects of SNPs. A score greater than 0.5 suggests that a given substitution is likely to be pathogenic.

2.3. Predictions Derived from Structure Analysis

  • Mutation Cutoff Scanning Matrix (mCSM): web-based tool used in structural bioinformatics to predict the effects of variants on protein stability [20]. It operates by evaluating single-point amino acid substitutions using a graph-based method. It can predict changes in protein stability. The limit is the indirect prediction of aminoacidic changes might affect the protein’s function or interactions, which may be critical in understanding disease mechanisms.
  • Dynamut2: tool developed to predict the effects of amino acid substitutions on protein stability [21]. DynaMut2 employs a combination of Normal Mode Analysis (NMA) and molecular dynamics, providing a more detailed assessment of the effects of mutations on protein stability compared to other tools that rely solely on structural data. While DynaMut2 assesses protein stability, it does not directly predict the functional consequences of mutations, such as changes in enzymatic activity or alterations in protein–protein interactions.
  • MAESTROweb: tool used to discern how variants impact protein stability, specifically focusing on the functional implications of genetic variations concerning human diseases [22]. By combining various computational tools, MAESTROweb provides insights into the consequences of variants on protein stability. While MAESTROweb assesses stability changes, the tool does not directly predict how mutations affect protein function, enzymatic activity, or molecular interactions.
  • PremPS: tool that evaluates the impact of amino acid substitutions in proteins by employing multiple approaches, such as multiple sequence alignment (MSA), protein structure analysis, and a deep learning model [23] providing more accurate predictions compared to methods based solely on sequence or structure. One limitation is that predictions may be less reliable if no experimental protein structure is available, and homologous models are used instead.

2.4. Prediction of Pathogenic Potential

  • MutPred2: web-based tool designed to predict the potential pathogenicity of amino acid substitutions. It does this by assessing the likelihood that a variant impacts protein function and structure [24]. MutPred2 provides insights into possible molecular mechanisms by which mutations can contribute to disease, such as altered protein–protein interactions, post-translational modifications, and structural changes. It evaluates both the structural and functional impact of amino acid substitutions, providing insights into potential disease mechanisms. While it generates hypotheses, experimental validation is necessary to confirm the predicted effects.
  • PhD-SNP: tool designed to predict the impact of single nucleotide polymorphisms (SNPs) on protein function and their association with disease [25]. PhD-SNP is generally accessible through web-based interfaces, facilitating its use by researchers without requiring advanced computational expertise. However, as with many predictive tools, there exists the potential for both false positives and false negatives.

2.5. Profiling of Aggregation Propensity and Residual Frustration Analysis

  • Camsol: tool method begins by using a linear combination of biophysical properties, to determine a solubility score for each individual residue [26]. The protein’s overall solubility score, derived from its intrinsic solubility profile, reflects the contributions of both poorly and highly soluble regions [29]. CamSol applies to diverse proteins, including those in disease and biotechnology, but excludes external factors like protein interactions or post-translational modifications.
  • Aggrescan3D 2.0: web-based tool for predicting aggregation-prone regions, assessing mutation effects, and comparing protein aggregation profiles [27]. It quantifies intrinsic aggregation propensities of amino acids, providing insights into residue-specific contributions [26,27]. While valuable, the model may not fully capture complex interactions or folding dynamics influencing aggregation in biological contexts.
  • Frustratometer: server was used to calculate individual and configurational residual indices for the structure of ApoE. This analysis provides valuable insights into a protein’s propensity to aggregate, which is useful for understanding how specific variants within the protein influence its tendency to form aggregates [28]. The frustration index might require careful interpretation; as high frustration does not necessarily correlate with aggregation in all contexts. Experimental validation is needed to confirm predictions.

3. Results

The Apos resulted positive in dbSNPs for pathogenicity were seventeen in total; in particular, ApoB counted twelve results, representing 70% of the total. The ApoC types 2 and 3 gave one result for each, represented in total 12%, and ApoE representing 18% which gave three results (Figure 1). The protein sequences of ApoB, ApoC2, ApoC3, and Pre-ApoE, were extracted via UniProt (Accession ID: P04114, P02655, P02656, P02649, respectively).
A comprehensive gathering of individual amino acid substitutions within ApoB, ApoC2, ApoC3 and ApoE, was conducted by accessing data from dbSNP and Ensembl. The three-dimensional structure of human ApoB, ApoC2, ApoC3 and pre-ApoE, was retrieved from the Alfaphold. For this latter, the variants reported here are in respect to the mature ApoE, amino acid (aa) replacement leucine (L) to proline (P) in position 46 indicated as L46P, and arginine (R) to cysteine (C) in the 163-position indicated as R163C. List of amino acids replacement resulted with pathogenicity are in the Supplementary Table S1. The functional impact of these mutations was predicted using sequence-based tools, including SIFT, PolyPhen-2, FATHMM, and SNP&GO are presented in Table 1. Among the mutations analyzed the L46P and R163C in ApoE were predicted to be damaging by three out of four tools. SNPs flagged as deleterious by three out of four sequence-based methods were subjected to further analysis. The prediction score reflects the overall response from the tools, with a value of zero indicative of not significant alterations in sequence, structure, or pathogenicity according to entirely tools employed. Based on these findings, both mutations (L46P and R163C) underwent further bio-computational analysis.
The two mutations in ApoE, L46P and R163C were tested using structure-based tools. The structure-based approach involved the followed tools, MAESTROweb, PremPS, mCSM and Dynamut2. They were used in order to evaluate single-point amino acid substitutions in ApoE, for stability/destability score prediction. The L46P and R163C resulted in destabilizing effects in 3 out of 4 tools and 4 out of 4 tools, respectively (Table 2).
Data extracted from DynaMut2 highlighted that in the wild-type structure, L46 forms a hydrogen bond with R43, and several polar interactions with Q42, R43, R50, and G49 (Figure 2, panel A). After substitution, Q42 interacts with P46, and the polar bonds are rearranged among proximal amino acids (Figure 2, panel B). Instead, in the wild type structure R163 is stabilized by polar interactions with K161, L167, and L159, and by hydrophobic interactions with T52 (Figure 2 panel C). While, in the mutated protein, C163 is stabilized by van der Waals forces and polar bonds with L167 and R160 (Figure 2, panel D).
Then, using the PhD-SNP and MutPred2 web servers, we were able to classify mutations based on their pathogenicity scores and delineate associated disease phenotypes. The tools reported the mutation L46P, correlated with the onset of several pathologies, with 1 out of 2 tools reported it as pathogenetic, as it is shown in Table 3.
In addition, aggregation propensity of the protein mutants L46P and R163C was assessed in comparison of the ApoE wild type. To analyze the solubility of the protein variants we utilized Camsol [26]. Furthermore, Aggrescan3D 2.0 [27] was used for aggregation propensity. In the Camsol analysis, scores lower than -1 indicate a tendency to promote aggregation, while scores above 1 suggest enhanced solubility. Similarly, Aggrescan3D 2.0 provides a total score, with more negative values corresponding to higher solubility. The results showed that the L46P mutation reduces the aggregation propensity of ApoE, enhancing its solubility. In contrast, the R163C mutation yielded mixed results; one of the tools indicated a decrease in solubility (Table 4).
Finally, we assess the degree of frustration of the mutated ApoE, L46P and R163C compared to the wild-type. The frustration in ApoE wild type is shown in Figure 3 panel A. with the contact map highlighting substantial changes in frustration patterns for both, L46P (Figure 3 panel B) and R163C (Figure 3 panel C). Specifically, the structure displayed moderate frustration at several sites across the proteins.

4. Discussion

APOs are a group of proteins crucial for lipid metabolism, as they are key participants in various transport pathways essential for physiological processes. In humans, several APOs with distinct roles have been identified, including ApoA1 [30], ApoA2 [31], ApoA4 [32], ApoA5 [33], ApoB [34], ApoC1 [35], ApoC2 [36], ApoC3 [37], ApoC4 [38], ApoD [39], ApoE [40], ApoF [41], ApoH [42], ApoJ [43], ApoL1 [44], ApoL2 [45], ApoM [46], ApoO [47]. ApoA and ApoD are integral to the High-Density Lipoprotein (HDL) transport system, with ApoA being the predominant component in plasma. ApoB, on the other hand, is essential for the Low-Density Lipoprotein (LDL) transport system. Similarly, ApoC is primarily associated with Very Low-Density Lipoprotein (VLDL), while ApoE serves as the principal apolipoprotein in chylomicrons. ApoE exists in three common isoforms E2, E3, and E4 exhibiting functional differences and linked to several diseases, including AD [48,49], type III hyperlipoproteinemia [50,51], atherosclerosis [52], cognitive decline [53], telomere shortening [54]. Several molecular mechanisms through which missense mutations contribute to the onset of diseases have been identified [55]. Notably, protein solubility is critical for its functionality, as insoluble regions are susceptible to aggregation [56], which can promote the progression of different diseases [57]. Additionally, protein aggregation is recognized as a risk factor in various disorders [58], as is the destabilization caused by mutations [59]. Dysfunction of APOs has been extensively studied, with specific SNPs being associated with various clinical conditions. In this study, dbSNP was used to identify human SNPs on APOs with conflicting interpretations of pathogenicity. Seventeen SNPs were identified in dbSNPs for ApoB, C2, C3 and E. For this latter, the pre-ApoE sequence was extracted and SNPs in the mature form were shifted of 18-residue, which represent the signal peptide at the beginning of the sequence in the immature form. Here, we referred to the mature ApoE sequence. We identified two mutations with high score confidence SNPs on ApoE, rs769452 (L46P) and rs769455 (R163C). This latter was found to have consistently destabilizing effects on the protein structure, as indicated by all structural prediction tools employed, including mCSM, MAESTROweb, PremPS, and DynaMut2. It was highlighted that in the wild type structure, L46 forms a hydrogen bond with R43 and several polar interactions with Q42, R43, R50, and G49. In the mutant form P46, interacts with Q42, and the polar bonds are rearranged among proximal amino acids to P46. Instead, in the wild-type structure, R163 is stabilized by polar interactions with K161, L167, and L159, and by hydrophobic interactions with T52. In the mutated protein, C163 is stabilized by van der Waals forces and polar bonds with L167 and R160. ApoE, when not associated with lipid, exists as a mixture of monomers, dimers, and tetramers, which is the prevalent species [60] affecting its function in biological micromilieu. Of note, ApoE4 is the prevalent risk factor for LOAD even in the Italian population, as recently analyzed by our group [61]. In a study by Nemergut and co-workers, the authors focused on the peculiar aminoacidic change in ApoE4 in respect to ApoE3, which differs only by the replacement of a cysteine to arginine in 112 position in the pre-ApoE, which correspond to 130 in the mature form. They found that this amino acidic substitution in ApoE4 induces a long-distance (>15 Å) conformational change forming a V-shaped dimeric unit, which, in turn, is more prone to aggregation respect to the ApoE3 structure [62]. To analyze the solubility of the protein variants and their potential aggregation propensity, we utilized Camsol [26] and Aggrescan3D 2.0 [27]. The solubility and aggregation analyses conducted revealed that the L46P mutation results in an increase in solubility, with both tools indicating it as more soluble. In contrast, the R163C mutation is associated with a decrease in solubility according to one of the two tools. The findings emphasize the potential of in silico methodologies to identify high-score confidence deleterious mutations of ApoE, offering a powerful tool for guiding future experimental and clinical research. The computational framework employed in this study is broadly applicable to other genes and proteins beyond apolipoproteins. It has been successfully applied to proteins involved in autism [63], cancer [64], acute myeloid leukemia [65], Huntington disease [66] demonstrates the potential of this computational platform in identifying mutations with destabilizing effects. Our in silico study highlighted the pathogenicity of both mutations L46P and R163C which, in turn, were found already in a clinical setting, putting in evidence the robustness of integrating multiple bio-computational approaches. As highlighted in the literature, L46P is strongly linked to an increased risk of late-onset Alzheimer’s disease (LOAD) [67] and reduced plasma ApoE levels [68], which are further associated with a higher risk of the disease [69,70]. Additionally, the CT genotype of R163C has been associated with the development of obstructive sleep apnea in children [71].

5. Conclusions

The mutations rs769455 (R163C) and rs769452 (L46P) in ApoE studied with the in silico methodologies identified clinically significant genetic variants, highlighting the robustness of the integrated approach. The future direction of the research is to create a multiplex panel with the SNPs identified here in APOE and expand to other proteins to have a panel genetic risk assessment and disease prediction in which ApoE correlates.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biomedinformatics5010016/s1, Table S1: List of amino acid replacements in all APOs, including the mutation name, amino acid substitution notation, wild-type amino acid, mutation position, and the mutated amino acid.

Author Contributions

Conceptualization, E.C. and G.F.S.; methodology, E.C.; software, G.F.S.; validation, E.C. and G.F.S.; formal analysis, G.F.S.; investigation, G.F.S. and E.C.; resources, G.F.S.; data curation, G.F.S.; writing—original draft preparation, G.F.S.; writing—review and editing, G.F.S. and E.C.; visualization, G.F.S. and E.C.; supervision, E.C.; project administration, E.C.; funding acquisition, E.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by PNRR Project: H23C22000370006-T4Y S5G4PP1 to E.C.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. The pie chart displays the distribution of missense SNPs with conflicting interpretations of pathogenicity in dbSNP across apolipoprotein genes. The percentages are color-coded as follows: forest green represents the percentage of SNPs in the Apolipoprotein E (ApoE) gene, emerald green indicates the percentage in the Apolipoprotein C3 (ApoC3) gene, sage green corresponds to the Apolipoprotein C2 (ApoC2) gene, and mint green highlights the Apolipoprotein B (ApoB) gene.
Figure 1. The pie chart displays the distribution of missense SNPs with conflicting interpretations of pathogenicity in dbSNP across apolipoprotein genes. The percentages are color-coded as follows: forest green represents the percentage of SNPs in the Apolipoprotein E (ApoE) gene, emerald green indicates the percentage in the Apolipoprotein C3 (ApoC3) gene, sage green corresponds to the Apolipoprotein C2 (ApoC2) gene, and mint green highlights the Apolipoprotein B (ApoB) gene.
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Figure 2. Representation of intramolecular interactions by DynaMut. (A) wild type ApoE L46; (B) mutated P46; (C) wild type ApoE R163; (D) mutated C163. Green represents hydrophobic interactions; orange shows polar interactions and pink shows clash. As shown in panel (B), the L46P mutation creates a clash between the Pro46 and Gln42 amino acids, resulting in instability. On the other hand, as shown in panel (A), in the wild type, Leu46 is stabilized by Gln42 through the formation of a polar bond. As shown in panel (D), Cys163 is stabilized by the presence of a greater number of polar bonds compared to the wild type shown in panel (C), while hydrophobic interactions and the clash with Gln59 disappear.
Figure 2. Representation of intramolecular interactions by DynaMut. (A) wild type ApoE L46; (B) mutated P46; (C) wild type ApoE R163; (D) mutated C163. Green represents hydrophobic interactions; orange shows polar interactions and pink shows clash. As shown in panel (B), the L46P mutation creates a clash between the Pro46 and Gln42 amino acids, resulting in instability. On the other hand, as shown in panel (A), in the wild type, Leu46 is stabilized by Gln42 through the formation of a polar bond. As shown in panel (D), Cys163 is stabilized by the presence of a greater number of polar bonds compared to the wild type shown in panel (C), while hydrophobic interactions and the clash with Gln59 disappear.
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Figure 3. Residual frustration maps for ApoE. (A) Wild type; (B) L46P; (C) R163C. The upper panels shown the density around 5A sphere in percentage. The lower panels shown the local frustration density of 5A sphere as residue-residue contact level. In green is indicated minimally frustrated, in gray neutral, in red highly frustrated and in black (only in the lower panels) the total frustration behavior. The circles highlight the regions of change in terms of molecular frustration as demonstrated by the wild type variant (panel (A)), L46P (panel (B)), and R163C (panel (C)). Changes in areas of frustration may indicate that new energetic configurations could prevail, potentially altering the biological function of the protein.
Figure 3. Residual frustration maps for ApoE. (A) Wild type; (B) L46P; (C) R163C. The upper panels shown the density around 5A sphere in percentage. The lower panels shown the local frustration density of 5A sphere as residue-residue contact level. In green is indicated minimally frustrated, in gray neutral, in red highly frustrated and in black (only in the lower panels) the total frustration behavior. The circles highlight the regions of change in terms of molecular frustration as demonstrated by the wild type variant (panel (A)), L46P (panel (B)), and R163C (panel (C)). Changes in areas of frustration may indicate that new energetic configurations could prevail, potentially altering the biological function of the protein.
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Table 1. Prediction Score using sequence-based tools of mutations, indicated by amino acid (aa) replacement, SNP identity (rs) in the apolipoproteins. In bolt the apolipoproteins with high prediction.
Table 1. Prediction Score using sequence-based tools of mutations, indicated by amino acid (aa) replacement, SNP identity (rs) in the apolipoproteins. In bolt the apolipoproteins with high prediction.
Geneaa ReplacementSNPs ID (rs)SIFT Class 1 PolyPhen-2 2 FATHMM 3SNPs&GO 4 Prediction Score
ApoBT191Ars1318040190ToleratedBenignToleratedNeutral0/4
ApoBF299Vrs72653060ToleratedProbably DamagingToleratedNeutral1/4
ApoBN902Krs1801700DeleteriousPossibly DamagingToleratedNeutral2/4
ApoBR1128Hrs12713843DeleteriousPossibly DamagingToleratedNeutral2/4
ApoBS1203Grs78875649ToleratedPossibly DamagingToleratedNeutral1/4
ApoBG2540Vrs571626569ToleratedPossibly DamagingToleratedNeutral1/4
ApoBE2566Krs1801696DeleteriousBenignToleratedNeutral1/4
ApoBN2785Hrs2163204DeleteriousBenignToleratedNeutral1/4
ApoBP2821Lrs72653095ToleratedProbably DamagingToleratedNeutral1/4
ApoBY3295Hrs186299244ToleratedProbably DamagingToleratedNeutral1/4
ApoBR3500Wrs144467873ToleratedProbably DamagingDamagingNeutral2/4
ApoBR4385Hrs533755016DamagingBenignToleratedNeutral1/4
ApoC2K41Trs120074114Tolerated BenignTolerated Neutral0/4
ApoC3A43Trs147210663ToleratedProbably DamagingDamagingNeutral2/4
ApoEL46Prs769452ToleratedProbably DamagingDamagingDisease3/4
ApoEC130Rrs429358ToleratedBenignToleratedDisease1/4
ApoER163Crs769455DamagingProbably DamagingToleratedDisease3/4
1 SIFT score ranges from 0 to 1. A variant is classified as “damaging” if the score is ≤0.05 and “tolerated” if it is >0.05. 2 PolyPhen-2 classifies variants into “benign”, “possibly damaging”, and “probably damaging.” A “possibly damaging” variant may affect protein function, while a “probably damaging” variant has a high likelihood of impacting protein function. 3 FATHMM score ranges from 0 to 1. Variants with a score >0.5 are classified as “damaging”, while those with a score ≤0.5 are predicted to be “tolerated.” 4 SNPs&GO provides a score ranging from 0 to 1. A variant is classified as “disease-related” if the score is ≥0.5 and “neutral” if it is <0.5.
Table 2. Prediction Score using structure-based tools indicated as amino acid (aa) replacement, in ApoE.
Table 2. Prediction Score using structure-based tools indicated as amino acid (aa) replacement, in ApoE.
aa ReplacementMAESTROweb 1PremPS 2mCSM 3DynaMut2 4Prediction Score
L46PDestabilizingDestabilizingDestabilizingStabilizing3/4
ΔΔG−0.01 kcal/mol0.25 kcal/mol−0.554 kcal/mol0.33 kcal/mol
R163CDestabilizingDestabilizingDestabilizingDestabilizing4/4
ΔΔG−0.672 kcal/mol0.71 kcal/mol−1.565 kcal/mol−1.28 kcal/mol
1 MAESTROweb score below 0 indicates a predicted destabilization of the protein due to the amino acid substitutions. 2 PremPS positive ΔΔG (kcal/mol) score suggests destabilization, a negative score indicates stabilization. 3 mCSM: ΔΔG (kcal/mol) score below 0 suggests that the mutation is likely to significantly affect protein structure. 4 DynaMut2 positive score indicates that the mutation increases protein stability, while a negative score suggests reduced stability.
Table 3. Prediction Score of pathogenic potential of mutations in ApoE indicated as amino acid (aa) replacement.
Table 3. Prediction Score of pathogenic potential of mutations in ApoE indicated as amino acid (aa) replacement.
aa ReplacementPhD-SNP 1 Mutpred 2 Pathogenicity Score
L46PDiseaseNeutral1/2
R163CNeutralNeutral0/2
1 PhDSNP classifies mutations into diseases related (desired output set to 0) and neutral polymorphism (desired output set to 1). 2 MutPred2 output includes a general score (g), which represents the probability that the amino acid substitution is pathogenic. When interpreted as a probability, a threshold score of 0.50 indicates potential pathogenicity.
Table 4. Prediction of solubility and aggregations of mutated ApoE indicated as amino acid (aa) replacement comparing to protein wild type.
Table 4. Prediction of solubility and aggregations of mutated ApoE indicated as amino acid (aa) replacement comparing to protein wild type.
aa ReplacementCamsol 1 Aggrescan3D 2.0 2
wild type1.819611−401.4035
L46P1.841586−408.2955
R163C1.731944−405.0661
1 Camsol scores below −1 indicate a propensity for promoting protein aggregation, whereas scores exceeding +1 are associated with an increased likelihood of aggregation. 2 Aggrescan3D 2.0 generates a total score, where more negative values correlate with higher protein solubility, and vice versa.
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Saraceno, G.F.; Cione, E. Multiple Bio-Computational Tools Emerge as Valid Approach in the Assessment of Apolipoproteins Pathogenicity Related Mutations. BioMedInformatics 2025, 5, 16. https://doi.org/10.3390/biomedinformatics5010016

AMA Style

Saraceno GF, Cione E. Multiple Bio-Computational Tools Emerge as Valid Approach in the Assessment of Apolipoproteins Pathogenicity Related Mutations. BioMedInformatics. 2025; 5(1):16. https://doi.org/10.3390/biomedinformatics5010016

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Saraceno, Giorgia Francesca, and Erika Cione. 2025. "Multiple Bio-Computational Tools Emerge as Valid Approach in the Assessment of Apolipoproteins Pathogenicity Related Mutations" BioMedInformatics 5, no. 1: 16. https://doi.org/10.3390/biomedinformatics5010016

APA Style

Saraceno, G. F., & Cione, E. (2025). Multiple Bio-Computational Tools Emerge as Valid Approach in the Assessment of Apolipoproteins Pathogenicity Related Mutations. BioMedInformatics, 5(1), 16. https://doi.org/10.3390/biomedinformatics5010016

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