Comprehensive in-silico prediction of damage associated SNPs in Human Prolidase gene

Identification and analysis of oncogenic non-synonymous single nucleotide polymorphisms in the human NRAS gene: An exclusive in silico study

BACKGROUND

N-ras protein is encoded by the NRAS gene and operates as GDP-GTP-controlled on/off switching. N-ras interacts with cellular signaling networks that regulate various cellular activities including cell proliferation and survival. The nonsynonymous single nucleotide polymorphism (nsSNPs)-mediated alteration can substantially disrupt the structure and activity of the corresponding protein. N-ras has been reported to be associated with numerous diseases including cancers due to the nsSNPs. A comprehensive study on the NRAS gene to unveil the potentially damaging and oncogenic nsSNPs is yet to be accomplished. Hence, this extensive in silico study is intended to identify the disease-associated, specifically oncogenic nsSNPs of the NRAS gene.

RESULTS

Out of 140 missense variants, 7 nsSNPs (I55R, G60E, G60R, Y64D, L79F, D119G, and V152F) were identified to be damaging utilizing 10 computational tools that works based on different algorithms with high accuracy. Among those, G60E, G60R, and D119G variants were further filtered considering their location in the highly conserved region and later identified as oncogenic variants. Interestingly, G60E and G60R variants were revealed to be particularly associated with lung adenocarcinoma, rhabdomyosarcoma, and prostate adenocarcinoma. Therefore, D119G could be subjected to detailed investigation for identifying its association with specific cancer.

CONCLUSION

This in silico study identified the deleterious and oncogenic missense variants of the human NRAS gene that could be utilized for designing further experimental investigation. The outcomes of this study would be worthwhile in future research for developing personalized medicine.

Next-generation sequencing of prolidase gene identifies novel and common variants associated with low prolidase in coronary artery ectasia

BACKGROUND

Decreased collagen biosynthesis and increased collagenolysis can cause ectasia progression in the arterial walls. Prolidase is a key enzyme in collagen synthesis; a decrease in prolidase activity or level may decrease collagen biosynthesis, which may contribute to ectasia formation. Considering that, the variations in PEPD gene encoding prolidase enzyme were evaluated by analyzing next-generation sequencing (NGS) for the first time together with known risk factors in coronary artery ectasia (CAE) patients.

METHODS

Molecular analysis of the PEPD gene was performed on genomic DNA by NGS in 76 CAE patients and 76 controls. The serum levels of prolidase were measured by the sandwich-ELISA technique.

RESULTS

Serum prolidase levels were significantly lower in CAE group compared to control group, and it was significantly lower in males than females in both groups (p < 0.001). On the other hand, elevated prolidase levels were observed in CAE patients in the presence of diabetes (p < 0.001), hypertension (p < 0.05) and hyperlipidemia (p < 0.05). Logistic regression analysis demonstrated that the low prolidase level (p < 0.001), hypertension (p < 0.02) and hyperlipidemia (p < 0.012) were significantly associated with increased CAE risk. We identified four missense mutations in the PEPD gene, namely G296S, T266A, P365L and S134C (novel) that could be associated with CAE. The pathogenicity of these mutations was predicted to be "damaging" for G296S, S134C and P365L, but "benign" for T266A. We also identified a novel 5'UTR variation (Chr19:34012748 G>A) in one patient who had a low prolidase level. In addition, rs17570 and rs1061338 common variations of the PEPD gene were associated with low prolidase levels in CAE patients, while rs17569 variation was associated with high prolidase levels in both CAE and controls (p < 0.05).

CONCLUSIONS

Our findings indicate that the low serum prolidase levels observed in CAE patients is significantly associated with PEPD gene variations. It was concluded that low serum prolidase level and associated PEPD mutations may be potential biomarkers for the diagnosis of CAE.

Identification of deleterious nsSNPs in human HGF gene: in silico approach

HGF is a protein that binds to the hepatocyte growth factor receptor to regulate cell growth, cell motility and morphogenesis in different cells and tissues. Several bioinformatics tools and in silico methods were used to identify most deleterious nsSNPs that might change the structure and function of HGF protein. The in silico tools such as SIFT, SNP&GO and PolyPhen2 were used to distinguish deleterious nsSNPs from neutral ones. Protein stability is analysed by I-Mutant, MUpro and iStable. The functional and structural effects are predicted by other tools like MutPred2, Maestro, DUET etc. Analysis of structure was performed by HOPE and Mutation3D. SWISS-MODEL. server, was used for wild type and mutant proteins 3-D Modelling. Gene-gene and protein-protein interaction were predicted by GeneMANIA and STRING, respectively. The wildtype HGF protein and these three variants were independently docked with their close interactor protein MET by the use of ClusPro. Our study suggested that out of 392 missense nsSNPs of the HGF gene, five nsSNPs (D358G, G648R, I550N, N175S and R220Q), are the most deleterious in HGF gene. Gene-gene interactions showed relation of HGF with other genes depicting its importance in several pathways and co-expressions. The protein-protein interacting network is composed of 11 nodes. Analysis of protein stability by different tools indicated that the five nsSNPS decreased the stability of the protein. Anyway these nsSNPs need a confirmation analysis by experimental investigation and GWAS studiesCommunicated by Ramaswamy H. Sarma.

An in silico analysis of rpoB mutations to affect Chlamydia trachomatis sensitivity to rifamycin

Background

Chlamydia trachomatis is an obligate intracellular gram-negative pathogen, responsible for diverse affections, mainly trachoma and sexually transmitted diseases. Antibiotics are the commonly used drugs to tackle chlamydiae infections. However, when overused or wrongly used this may lead to strains’ resistance to antibiotics, this phenomenon represents a real health problem worldwide. Numerous studies showed the association of Chlamydia trachomatis resistance with mutations in different genes; these mutations could have a deleterious or neutral impacts on the encoded proteins. The aim of this study is to perform an in silico analysis of C. trachomatis rpoB-encoded proteins using numerous bioinformatics tools and to identify the functional and structural-related effects of the mutations and consequently their impact on the bacteria sensitivity to antibiotics.

Results

The analysis revealed that the prediction of the damaging impact related to the mutations in rpoB-encoded proteins showed eight mutations: V136F, Q458K, V466A, A467T, H471N, H471Y, H471L, and I517M with big deleterious effects. Among them, six mutations, V136F, Q458K, V466A, A467T, H471N, and I517M, are located in a highly conserved regions decreasing the protein’s stability. Furthermore, the structures analysis showed that the mutations A467T, H471N, I517M, and V136F models had a high deviation compared to the wild type. Moreover, the prediction of protein-protein network indicated that rpoB wild type interacts strongly with 10 proteins of C. trachomatis, which are playing different roles at different levels.

Conclusion

As conclusion, the present study revealed that the changes observed in the encoded proteins can affect their functions and structures, in addition to their interactions with other proteins which impact the bacteria sensitivity to antibiotics. Consequently, the information revealed through this in silico analysis would be useful for deeper exploration to understand the mechanisms of C. trachomatis resistance and enable managing the infection to avoid its complications. We recommend further investigations and perform deeper experimental analysis with collaboration between bioinformaticians, physicians, biologists, pharmacists, and chemistry and biochemistry scientists.

ADGRL3 genomic variation implicated in neurogenesis and ADHD links functional effects to the incretin polypeptide GIP

Attention deficit/hyperactivity disorder (ADHD) is the most common childhood neurodevelopmental disorder. Single nucleotide polymorphisms (SNPs) in the Adhesion G Protein-Coupled Receptor L3 (ADGRL3) gene are associated with increased susceptibility to developing ADHD worldwide. However, the effect of ADGRL3 non-synonymous SNPs (nsSNPs) on the ADGRL3 protein function is vastly unknown. Using several bioinformatics tools to evaluate the impact of mutations, we found that nsSNPs rs35106420, rs61747658, and rs734644, previously reported to be associated and in linkage with ADHD in disparate populations from the world over, are predicted as pathogenic variants. Docking analysis of rs35106420, harbored in the ADGLR3-hormone receptor domain (HRM, a common extracellular domain of the secretin-like GPCRs family), showed that HRM interacts with the Glucose-dependent insulinotropic polypeptide (GIP), part of the incretin hormones family. GIP has been linked to the pathogenesis of diabetes mellitus, and our analyses suggest a potential link to ADHD. Overall, the comprehensive application of bioinformatics tools showed that functional mutations in the ADGLR3 gene disrupt the standard and wild ADGRL3 structure, most likely affecting its metabolic regulation. Further in vitro experiments are granted to evaluate these in silico predictions of the ADGRL3-GIP interaction and dissect the complexity underlying the development of ADHD.

Genotyping and In Silico Analysis of Delmarva (DMV/1639) Infectious Bronchitis Virus (IBV) Spike 1 (S1) Glycoprotein

Genetic diversity and evolution of infectious bronchitis virus (IBV) are mainly impacted by mutations in the spike 1 (S1) gene. This study focused on whole genome sequencing of an IBV isolate (IBV/Ck/Can/2558004), which represents strains highly prevalent in Canadian commercial poultry, especially concerning features related to its S1 gene and protein sequences. Based on the phylogeny of the S1 gene, IBV/Ck/Can/2558004 belongs to the GI-17 lineage. According to S1 gene and protein pairwise alignment, IBV/Ck/Can/2558004 had 99.44–99.63% and 98.88–99.25% nucleotide (nt) and deduced amino acid (aa) identities, respectively, with five Canadian Delmarva (DMV/1639) IBVs isolated in 2019, and it also shared 96.63–97.69% and 94.78–97.20% nt and aa similarities with US DMV/1639 IBVs isolated in 2011 and 2019, respectively. Further homology analysis of aa sequences showed the existence of some aa substitutions in the hypervariable regions (HVRs) of the S1 protein of IBV/Ck/Can/2558004 compared to US DMV/1639 isolates; most of these variant aa residues have been subjected to positive selection pressure. Predictive analysis of potential N-glycosylation and phosphorylation motifs showed either loss or acquisition in the S1 glycoprotein of IBV/Ck/Can/2558004 compared to S1 of US DMV/1639 IBV. Furthermore, bioinformatic analysis showed some of the aa changes within the S1 protein of IBV/Ck/Can/2558004 have been predicted to impact the function and structure of the S1 protein, potentially leading to a lower binding affinity of the S1 protein to its relevant ligand (sialic acid). In conclusion, these findings revealed that the DMV/1639 IBV isolates are under continuous evolution among Canadian poultry.

Exploring the Structural and Functional Effects of Nonsynonymous SNPs in the Human Serotonin Transporter Gene Through In Silico Approaches

The sodium-dependent serotonin transporter SLC6A4 (solute carrier family 6 member 4) gene encodes an intrinsic membrane protein that transmits the serotonin neurotransmitter from synaptic clefts into presynaptic neurons. The product of the SLC6A4 gene is related to the regulation of mood and social behavior, sleep, appetite, memory, digestion, and sexual desire. This protein is a target for antidepressant and psychostimulant drugs, thus prolonged neurotransmitter signaling remains blocked. In this study, the functional consequences of nsSNPs in the human SLC6A4 gene were explored through computational tools: PhD-SNP, SIFT, Align GVGD, PROVEAN, PMut, nsSNP Analyzer, SNPs&GO, SNAP2, PolyPhen2, and PANTHER to identify the most deleterious and damaging nsSNPs. Then the mutant protein stabilities were assessed using I-Mutant, MUpro, and MutPred2; amino acid conservation using ConSurf, and posttranslational modification analysis using MusiteDEEP and PROSPER. Furthermore, the 3-dimensional (3D) model of the mutated proteins was predicted and validated using SPARKS-X, Verify3D, and PROCHECK. The protein–ligand binding sites were analyzed using the COACH meta-server. Results from this study predicted that T192M, G342E, R607C, W282S, R104C, P131L, P156L, and N351S were the most structurally and functionally significant nsSNPs in the human SLC6A4 gene. Arg607 and Pro156 were the predicted sites for posttranslational modifications, and Thr192 and Try282 were the ligand-binding sites in the human SLC6A4 gene. The analyzed data also suggested that R104C, P131L, P156L, T192M, G342E, and W282S mutants might affect the binding of sodium ions with this protein. Taken together, this study provided important information on structurally and functionally important nsSNPs of the human SLC6A4 gene for further experimental validation. In the future, these damaging nsSNPs of the SLC6A4 gene have the potential to be evaluated as prognostic biomarkers for SLC6A4-related disorder diagnosis and research.

Structural modeling of human AKAP3 protein and in silico analysis of single nucleotide polymorphisms associated with sperm motility

AKAP3 is a member of the A-kinase anchoring proteins and it is a constituent of the sperm fibrous sheath. AKAP3 is needed for the formation of sperm flagellum structure, sperm motility, and male fertility. This study aims to model the AKAP3 tertiary structure and identify the probable impact of four mutations characterized in infertile men on the AKAP3 structure. The T464S, I500T, E525K, and I661T substitutions were analyzed using in silico methods. The secondary structure and three-dimensional model of AKAP3 were determined using PSI-BLAST based secondary structure prediction and Robetta servers. The TM-score was used to quantitatively measure the structural similarities between native and mutated models. All of the desired substitutions were classified as benign. I-Mutant results showed all of the substitutions decreased AKAP3 stability; however, the I500T and I661T were more effective. Superposition and secondary structure comparisons between native and mutants showed no dramatic deviations. Our study provided an appropriate model for AKAP3. Destabilization of AKAP3 caused by these substitutions did not appear to induce structural disturbances. As AKAP3 is involved in male infertility, providing more structural insights and the impact of mutations that cause protein functional diversity could elucidate the etiology of male fertility problems at molecular level.

Evaluating the Effect of 3'-UTR Variants in DICER1 and DROSHA on Their Tissue-Specific Expression by miRNA Target Prediction

Untranslated gene regions (UTRs) play an important role in controlling gene expression. 3'-UTRs are primarily targeted by microRNA (miRNA) molecules that form complex gene regulatory networks. Cancer genomes are replete with non-coding mutations, many of which are connected to changes in tumor gene expression that accompany the development of cancer and are associated with resistance to therapy. Therefore, variants that occurred in 3'-UTR under cancer progression should be analysed to predict their phenotypic effect on gene expression, e.g., by evaluating their impact on miRNA target sites. Here, we analyze 3'-UTR variants in DICER1 and DROSHA genes in the context of myelodysplastic syndrome (MDS) development. The key features of this analysis include an assessment of both "canonical" and "non-canonical" types of mRNA-miRNA binding and tissue-specific profiling of miRNA interactions with wild-type and mutated genes. As a result, we obtained a list of DICER1 and DROSHA variants likely altering the miRNA sites and, therefore, potentially leading to the observed tissue-specific gene downregulation. All identified variants have low population frequency consistent with their potential association with pathology progression.

In silico screening and exploration into phenotypic alterations of deleterious oncogenic single nucleotide polymorphisms in HSPB1 gene

A small heat shock protein, HSP27, encoded by HSPB1 gene strongly favors survival, proliferation and metastasis of cancer cells and its expression is dependent on post-translational modifications like phosphorylation. This study performed an extensive in silico screening of 20 deleterious non-synonymous SNPs in the coding region of HSPB1 gene, among which four were identified to be cancer associated. The SNP variant I181S introduced a new phosphorylation site in position 181, which might elevate the protein's activation potential. Emergence of other post-translational modifications was also observed in SNP variants: L144P and E130K.Significant conformational changes were observed in I181S, L144P and E130K SNP variants with respect to wild-type HSP27. These SNPs appear in one among 10⁵ individuals, making them more susceptible towards cancer. This study would therefore, instigate development of novel biomarkers for cancer risk detection and would provide a detailed understanding towards varied cancer susceptibility of human population.

Prolidase - A protein with many faces

Prolidase is a metal-dependent peptidase specialized in the cleavage of dipeptides containing proline or hydroxyproline on their C-termini. Prolidase homologues are found in all kingdoms of life. The importance of prolidase in human health is underlined by a rare hereditary syndrome referred to as Prolidase Deficiency. A growing number of studies highlight the importance of prolidase in various other human conditions, including cancer. Some recent studies link prolidase's activity-independent regulatory role to tumorigenesis. Furthermore, the enzyme or engineered variants have some applications in biotechnology. In this short review, we aim to highlight different aspects of the protein the importance of which is increasingly recognized over the last years.

Predicting the most deleterious missense nsSNPs of the protein isoforms of the human HLA-G gene and in silico evaluation of their structural and functional consequences

BACKGROUND

The Human Leukocyte Antigen G (HLA-G) protein is an immune tolerogenic molecule with 7 isoforms. The change of expression level and some polymorphisms of the HLA-G gene are involved in various pathologies. Therefore, this study aimed to predict the most deleterious missense non-synonymous single nucleotide polymorphisms (nsSNPs) in HLA-G isoforms via in silico analyses and to examine structural and functional effects of the predicted nsSNPs on HLA-G isoforms.

RESULTS

Out of 301 reported SNPs in dbSNP, 35 missense SNPs in isoform 1, 35 missense SNPs in isoform 5, 8 missense SNPs in all membrane-bound HLA-G isoforms and 8 missense SNPs in all soluble HLA-G isoforms were predicted as deleterious by all eight servers (SIFT, PROVEAN, PolyPhen-2, I-Mutant 3.0, SNPs&GO, PhD-SNP, SNAP2, and MUpro). The Structural and functional effects of the predicted nsSNPs on HLA-G isoforms were determined by MutPred2 and HOPE servers, respectively. Consurf analyses showed that the majority of the predicted nsSNPs occur in conserved sites. I-TASSER and Chimera were used for modeling of the predicted nsSNPs. rs182801644 and rs771111444 were related to creating functional patterns in 5'UTR. 5 SNPs in 3'UTR of the HLA-G gene were predicted to affect the miRNA target sites. Kaplan-Meier analysis showed the HLA-G deregulation can serve as a prognostic marker for some cancers.

CONCLUSIONS

The implementation of in silico SNP prioritization methods provides a great framework for the recognition of functional SNPs. The results obtained from the current study would be called laboratory investigations.

Prediction of potential deleterious nonsynonymous single nucleotide polymorphisms of HIF1A gene: A computational approach

Hypoxia-inducible factor-1α (HIF-1α) is the oxygen sensitive subunit of HIF1 transcription factor. Its variations is associated with several diseases including different type of cancer, cardiovascular diseases, and liver and kidney failure. Despite all the investigations carried out on the single nucleotide polymorphisms (SNPs) of HIF1A gene and diseases, there are many uncharacterized nonsynonymous SNPs of this gene, which might have damaging effect on the protein function. Therefore, it is worthwhile to analyze these potential damaging nsSNPs, using different bioinformatics tools before launching large population studies. The objective of the present study was to predict the possible deleterious nsSNPs of HIF1A gene and their effects on the function and structure of HIF-1alpha protein, using different bioinformatics tools. Various prediction servers were used including SIFT, PROVEAN, PolyPhen-2, PANTHER, phD-SNP, SNP-GO, I-Mutant 2.0, Fathmm, SNPeffect 4.0, Mutation taster, CADD and RAMPAGE in a stepwise approach. After analyzing all 454 missense variants of the HIF1A gene using the abovementioned tools, we reported 11 variants with a significant impact on the function or structure of HIF-1α protein. Furthermore, among these variants only S274 P was predicted as stability enhancing variant with effect on protein function by increasing its stability. Although there are many advantages for computational analysis, the results has to be confirmed by experimental investigations.

Molecular Epidemiology of Myroides odoratimimus in Nosocomial Catheter-Related Infection at a General Hospital in China

Purpose

Catheter-related infection (CRI) is one of the most frequent causes of hospitalizations for immunocompromised patients. A major challenge is the increased prevalence of Myroides odoratimimus. The purpose of the present study was to evaluate the clinical features and molecular characteristics of M. odoratimimus collected from a general hospital in Shanghai, China.

Patients and Methods

From July 2015 to August 2016, a total of 22 isolates of M. odoratimimus were collected from inpatients respectively from the biliary and pancreatic surgery (6/22) and the urology department (16/22). Clonal relatedness among the isolates was assessed using pulsed-field gel electrophoresis (PFGE) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Moreover, the antimicrobial susceptibility tests were carried out using the Clinical and Laboratory Standards Institute (CLSI) broth microdilution method. The presence of antibiotic resistance genes was screened using the polymerase chain reaction (PCR) assay. Additionally, protein structure prediction was analyzed using PSIPRED and RaptorX.

Results

PFGE differentiated these isolates into six possibly related clones from two different departments obtained during a distinct period, indicating clonal dissemination in the two departments. We compared the dendrograms of M. odoratimimus isolates obtained by MALDI-TOF MS with those obtained by PFGE and found that the coincidence rate between them was only 68.2%. All the M. odoratimimus isolates were highly resistant to most available antibiotics, including carbapenems. Furthermore, chromosome-encoded β-lactamases MUS-1 was confirmed by PCR in 6 of 22 Myroides odoratimimus isolates. Herein, we also reported a novel variant of bla_MUS-1 in the remaining 16 isolates, which encodes MUS-3 protein at position 60 (Valine to Alanine), differing from the structure of MUS-1.

Conclusion

The opportunistic and extensively antibiotic-resistant Myroides odoratimimus has a small range of epidemics in these two different departments. Clinicians should be aware that M. odoratimimus may induce a severe nosocomial outbreak of catheter-related infections, particularly in immunocompromised patients.

SNPeffect: identifying functional roles of SNPs using metabolic networks

Genetic sources of phenotypic variation have been a focus of plant studies aimed at improving agricultural yield and understanding adaptive processes. Genome-wide association studies identify the genetic background behind a trait by examining associations between phenotypes and single-nucleotide polymorphisms (SNPs). Although such studies are common, biological interpretation of the results remains a challenge; especially due to the confounding nature of population structure and the systematic biases thus introduced. Here, we propose a complementary analysis (SNPeffect) that offers putative genotype-to-phenotype mechanistic interpretations by integrating biochemical knowledge encoded in metabolic models. SNPeffect is used to explain differential growth rate and metabolite accumulation in A. thaliana and P. trichocarpa accessions as the outcome of SNPs in enzyme-coding genes. To this end, we also constructed a genome-scale metabolic model for Populus trichocarpa, the first for a perennial woody tree. As expected, our results indicate that growth is a complex polygenic trait governed by carbon and energy partitioning. The predicted set of functional SNPs in both species are associated with experimentally characterized growth-determining genes and also suggest putative ones. Functional SNPs were found in pathways such as amino acid metabolism, nucleotide biosynthesis, and cellulose and lignin biosynthesis, in line with breeding strategies that target pathways governing carbon and energy partition.

Clinical Genetics of Prolidase Deficiency: An Updated Review

Prolidase is a ubiquitous enzyme that plays a major role in the metabolism of proline-rich proteins. Prolidase deficiency is a rare autosomal recessive inborn metabolic and multisystemic disease, characterized by a protean association of symptoms, namely intellectual disability, recurrent infections, splenomegaly, skin lesions, auto-immune disorders and cytopenia. To our knowledge, no published review has assembled the different clinical data and research studies over prolidase deficiency. The aim of this study is to summarize the actual state of the art from the descriptions of all the patients with a molecular diagnosis of prolidase deficiency reported to date regarding the clinical, biological, histopathological features, therapeutic options and functional studies.

In silico approach to the analysis of SNPs in the human APAF1 gene

The apoptotic protease activating factor 1 (APAF1) gene encodes a cytoplasmic protein that initiates apoptosis and is a crucial factor in the mitochondria-dependent death pathway. APAF1 is implicated in many pathways such as apoptosis, neurodegenerative diseases, and cancer. The purpose of this study was to predict deleterious/damaging SNPs in the APAF1 gene viain silicoanalysis. To this end, APAF1 missense SNPs were obtained from the NCBI dbSNP database. In silico analysis of the missense SNPs was carried out by using publicly available online software tools. The stabilization and three-dimensional modeling of mutant proteins were also determined by using the I-Mutant 2.0 and Project HOPE webservers, respectively. In total, 772 missense SNPs were found in the APAF1 gene from the NCBI dbSNP database, 18 SNPs of which were demonstrated to be deleterious or damaging. Of those, 13 SNPs had a decreasing effect on protein stability, while the other 5 SNPs had an increasing effect. Based on the modeling results, some dissimilarities of mutant type amino acids from wild-type amino acids such as size, charge, and hydrophobicity were revealed. The SNPs predicted to be deleterious in this study might be used in the selection of target SNPs for genotyping in disease association studies. Therefore, we could suggest that the present study could pave the way for future experimental studies.

Extracting Complementary Insights from Molecular Phenotypes for Prioritization of Disease-Associated Mutations

Rapid advances in next-generation sequencing technology have resulted in an explosion of whole-exome/genome sequencing data, providing an unprecedented opportunity to identify disease- and trait-associated variants in humans on a large scale. To date, the long-standing paradigm has leveraged fitness-based approximations to translate this ever-expanding sequencing data into causal insights in disease. However, while this approach robustly identifies variants under evolutionary constraint, it fails to provide molecular insights. Moreover, complex disease phenomena often violate standard assumptions of a direct organismal phenotype to overall fitness effect relationship. Here we discuss the potential of a molecular phenotype-oriented paradigm to uniquely identify candidate disease-causing mutations from the human genetic background. By providing a direct connection between single nucleotide mutations and observable organismal and cellular phenotypes associated with disease, we suggest that molecular phenotypes can readily incorporate alongside established fitness-based methodologies to provide complementary insights to the functional impact of human mutations. Lastly, we discuss how integrated approaches between molecular phenotypes and fitness-based perspectives facilitate new insights into the molecular mechanisms underlying disease-associated mutations while also providing a platform for improved interpretation of epistasis in human disease.