Fitness Effects of Mutations: An Assessment of PROVEAN Predictions Using Mutation Accumulation Data

Guidelines for releasing a variant effect predictor

Computational methods for assessing the likely impacts of mutations, known as variant effect predictors (VEPs), are widely used in the assessment and interpretation of human genetic variation, as well as in other applications like protein engineering. Many different VEPs have been released to date, and there is tremendous variability in their underlying algorithms and outputs, and in the ways in which the methodologies and predictions are shared. This leads to considerable challenges for end users in knowing which VEPs to use and how to use them. Here, to address these issues, we provide guidelines and recommendations for the release of novel VEPs. Emphasising open-source availability, transparent methodologies, clear variant effect score interpretations, standardised scales, accessible predictions, and rigorous training data disclosure, we aim to improve the usability and interpretability of VEPs, and promote their integration into analysis and evaluation pipelines. We also provide a large, categorised list of currently available VEPs, aiming to facilitate the discovery and encourage the usage of novel methods within the scientific community.

A computational approach to analyzing the functional and structural impacts of Tripeptidyl-Peptidase 1 missense mutations in neuronal ceroid lipofuscinosis

Neuronal ceroid-lipofuscinosis (NCLs) are a group of severe neurodegenerative conditions, most likely present in infantile, late infantile, juvenile, and adult-onset forms. Their phenotypic characteristics comprise eyesight damage, reduced motor activity and cognitive function, and sometimes tend to die in the initial stage. In recent studies, NCLs have been categorized into at least 14 genetic collections (CLN1-14). CLN2 gene encodes Tripeptidyl peptidase 1 (TPP1), which affects late infantile-onset form. In this study, we retrieved a mutational dataset screening for TPP1 protein from various databases (ClinVar, UniProt, HGMD). Fifty-six missense mutants were enumerated with computational methods to perceive the significant mutants (G475R and G501C) and correlated with clinical and literature data. A structure-based screening method was initiated to understand protein-ligand interaction and dynamic simulation. The docking procedure was performed for the native (3EDY) and mutant (G473R and G501C) structures with Gemfibrozil (gem), which lowers the lipid level, decreases the triglycerides amount in the blood circulation, and controls hyperlipidemia. The Native had an interaction score of -5.57 kcal/mol, and the mutants had respective average binding scores of -6.24 (G473R) and - 5.17 (G501C) kcal/mol. Finally, molecular dynamics simulation showed that G473R and G501C mutants had better flexible and stable orientation in all trajectory analyses. Therefore, this work gives an extended understanding of both functional and structural levels of influence for the mutant form that leads to NCL disorder.

PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity

Protein mutations, especially those which occur in the binding site, play an important role in inter-individual drug response and may alter binding affinity and thus impact the drug's efficacy and side effects. Unfortunately, large-scale experimental screening of ligand-binding against protein variants is still time-consuming and expensive. Alternatively, in silico approaches can play a role in guiding those experiments. Methods ranging from computationally cheaper machine learning (ML) to the more expensive molecular dynamics have been applied to accurately predict the mutation effects. However, these effects have been mostly studied on limited and small datasets, while ideally a large dataset of binding affinity changes due to binding site mutations is needed. In this work, we used the PSnpBind database with six hundred thousand docking experiments to train a machine learning model predicting protein-ligand binding affinity for both wild-type proteins and their variants with a single-point mutation in the binding site. A numerical representation of the protein, binding site, mutation, and ligand information was encoded using 256 features, half of them were manually selected based on domain knowledge. A machine learning approach composed of two regression models is proposed, the first predicting wild-type protein-ligand binding affinity while the second predicting the mutated protein-ligand binding affinity. The best performing models reported an RMSE value within 0.5 [Formula: see text] 0.6 kcal/mol^-1 on an independent test set with an R² value of 0.87 [Formula: see text] 0.90. We report an improvement in the prediction performance compared to several reported models developed for protein-ligand binding affinity prediction. The obtained models can be used as a complementary method in early-stage drug discovery. They can be applied to rapidly obtain a better overview of the ligand binding affinity changes across protein variants carried by people in the population and narrow down the search space where more time-demanding methods can be used to identify potential leads that achieve a better affinity for all protein variants.

Deleterious Variation in Natural Populations and Implications for Conservation Genetics

Deleterious mutations decrease reproductive fitness and are ubiquitous in genomes. Given that many organisms face ongoing threats of extinction, there is interest in elucidating the impact of deleterious variation on extinction risk and optimizing management strategies accounting for such mutations. Quantifying deleterious variation and understanding the effects of population history on deleterious variation are complex endeavors because we do not know the strength of selection acting on each mutation. Further, the effect of demographic history on deleterious mutations depends on the strength of selection against the mutation and the degree of dominance. Here we clarify how deleterious variation can be quantified and studied in natural populations. We then discuss how different demographic factors, such as small population size, nonequilibrium population size changes, inbreeding, and gene flow, affect deleterious variation. Lastly, we provide guidance on studying deleterious variation in nonmodel populations of conservation concern.

An updated interactive database for 1692 genetic variants in coagulation factor IX provides detailed insights into hemophilia B

BACKGROUND

Genetic variants in coagulation factor IX (FIX) are associated with hemophilia B, a rare bleeding disease. F9 variants are widespread across the gene and were summarized in our FIX variant database introduced in 2013.

OBJECTIVES

We aimed to rationalize the molecular basis for 598 new F9 variants and 1645 new clinical cases, totaling 1692 F9 variants and 5358 related patient cases.

METHODS

New F9 variants were identified from publications and online resources, and compiled into a MySQL database for comparison with the human FIXa protein structure.

RESULTS

The new total of 1692 F9 variants correspond to 406 (88%) of the 461 FIX residues and now include 70 additional residues. They comprise 945 unique point variants, 281 deletions, 352 polymorphisms, 63 insertions, and 51 others. Most FIX variants were point variants, although their proportion (56%) has reduced compared to 2013 (73%); at the same time, the proportion of polymorphisms has increased from 5% to 21%. The 764 unique mild severity variants in the mature protein with known phenotypes include 74 (9.7%) quantitative type I variants and 116 (15.2%) predominantly qualitative type II variants. The remaining 574 variants types are unspecified. Inhibitors are associated with 152 hemophilia B cases out of 5358 patients (2.8%), an increase of 93 from the previous database.

CONCLUSION

The even distribution of the F9 variants revealed few mutational hotspots, and most variants were associated with small perturbations in the FIX protein structure. The updated database will assist clinicians and researchers in assessing treatments for patients with hemophilia B.

Genomic Underpinnings of Population Persistence in Isle Royale Moose

Island ecosystems provide natural laboratories to assess the impacts of isolation on population persistence. However, most studies of persistence have focused on a single species, without comparisons to other organisms they interact with in the ecosystem. The case study of moose and gray wolves on Isle Royale allows for a direct contrast of genetic variation in isolated populations that have experienced dramatically differing population trajectories over the past decade. Whereas the Isle Royale wolf population recently declined nearly to extinction due to severe inbreeding depression, the moose population has thrived and continues to persist, despite having low genetic diversity and being isolated for ∼120 years. Here, we examine the patterns of genomic variation underlying the continued persistence of the Isle Royale moose population. We document high levels of inbreeding in the population, roughly as high as the wolf population at the time of its decline. However, inbreeding in the moose population manifests in the form of intermediate-length runs of homozygosity suggestive of historical inbreeding and purging, contrasting with the long runs of homozygosity observed in the smaller wolf population. Using simulations, we confirm that substantial purging has likely occurred in the moose population. However, we also document notable increases in genetic load, which could eventually threaten population viability over the long term. Overall, our results demonstrate a complex relationship between inbreeding, genetic diversity, and population viability that highlights the use of genomic datasets and computational simulation tools for understanding the factors enabling persistence in isolated populations.

Pseudotyped Viruses for Phlebovirus

Rift Valley fever virus (RVFV) is a member of the Phlebovirus genus, one of the 20 genera in the Phenuiviridae family. RVFV causes disease in animals and humans and is transmitted by sandflies or ticks. However, research into RVFV is limited by the requirement for biosafety level 3 (BSL-3) containment. Pseudotyped virus overcomes this limitation as it can be handled in a BSL-2 environment. Pseudotyped RVFV possesses an identical envelope protein structure to that of the authentic virus, simulating the same process of receptor binding and membrane fusion to host cells. Pseudotyped phleboviruses are therefore useful tools to study the infection mechanism of these viruses and for the screening of inhibitory drugs and the development of therapeutic monoclonal antibodies.

Mutations in GCK May Lead to MODY2 by Reducing Glycogen Synthesis

Dysfunction of glucokinase (GCK) caused by mutations in the GCK gene is the main cause of maturity-onset diabetes of the young type-2 (MODY2, also known as GCK-MODY), which is usually present in adolescence or young adulthood. MODY2 is characterized by mild, stable fasting hyperglycemia that presents at birth, usually 5.4-8.3 mmol L^-1 , and rarely develops complications from diabetes. The treatment of MODY2 prefers a manageable diet rather than the use of insulin. Previous studies have identified GCK mutations only by online software prediction or enzyme kinetic analysis and thermolability assays which are complicated to be conducted. In this study, six mutations in the GCK gene, including four novel mutations and two mutations that are previously reported, are identified. All the six locations are highly conserved according to the sequencing alignment. Moreover, missense mutations are strongly predicted to be pathogenic using online programs. Functional studies show that mutations in GCK mutation do not affect insulin secretion but affect glycogen synthesis. These findings demonstrate that GCK mutations decrease glycogen synthesis, which leads to hyperglycemia in MODY2. Meanwhile, this study provides a new perspective and methods for identifying pathogenic mutations in GCK.

Recent insights into the evolution of mutation rates in yeast

Mutation is the origin of all genetic variation, good and bad. The mutation process can evolve in response to mutations, positive or negative selection, and genetic drift, but how these forces contribute to mutation-rate variation is an unsolved problem at the heart of genetics research. Mutations can be challenging to measure, but genome sequencing and other tools have allowed for the collection of larger and more detailed datasets, particularly in the yeast-model system. We review key hypotheses for the evolution of mutation rates and describe recent advances in understanding variation in mutational properties within and among yeast species. The multidimensional spectrum of mutations is increasingly recognized as holding valuable clues about how this important process evolves.

Eph and Ephrin Variants in Malaysian Neural Tube Defect Families

Neural tube defects (NTDs) are common birth defects with a complex genetic etiology. Mouse genetic models have indicated a number of candidate genes, of which functional mutations in some have been found in human NTDs, usually in a heterozygous state. This study focuses on Ephs-ephrins as candidate genes of interest owing to growing evidence of the role of this gene family during neural tube closure in mouse models. Eph-ephrin genes were analyzed in 31 Malaysian individuals comprising seven individuals with sporadic spina bifida, 13 parents, one twin-sibling and 10 unrelated controls. Whole exome sequencing analysis and bioinformatic analysis were performed to identify variants in 22 known Eph-ephrin genes. We reported that three out of seven spina bifida probands and three out of thirteen family members carried a variant in either EPHA2 (rs147977279), EPHB6 (rs780569137) or EFNB1 (rs772228172). Analysis of public databases shows that these variants are rare. In exome datasets of the probands and parents of the probands with Eph-ephrin variants, the genotypes of spina bifida-related genes were compared to investigate the probability of the gene–gene interaction in relation to environmental risk factors. We report the presence of Eph-ephrin gene variants that are prevalent in a small cohort of spina bifida patients in Malaysian families.