Identification of discriminative gene-level and protein-level features associated with pathogenic gain-of-function and loss-of-function variants

Bayrak, Çiğdem Sevim; Stein, David; Jain, Aayushee; Chaudhary, Kumardeep; Nadkarni, Girish N.; Vleck, Tielman Van; Puel, Anne; Boisson–Dupuis, Stéphanie; Okada, Satoshi; Stenson, Peter D.; Cooper, David Neil; Schlessinger, Avner; Itan, Yuval

doi:10.1016/j.ajhg.2021.10.007

Cited by 33 publications

(50 citation statements)

References 99 publications

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“…Moreover, for simplicity, we assume that all pathogenic missense mutations in the same gene are associated with the same molecular mechanism, which is clearly not true for all genes. Importantly, however, we were able to reproduce our results using a recent variant-level dataset, annotated with GOF and LOF mechanism assignments 40 , thus supporting the validity of our gene-level approach. However, currently available variant-level datasets do not cover DN variants on a large-scale.…”

Section: Discussionsupporting

confidence: 78%

“…To validate our generalised variant disease mechanism annotation approach, we took advantage of a recently published dataset containing variant-level GOF vs LOF mechanism assignments from Bayrak et al 40 , derived through a natural language processing model applied to available literature. Using their HGMD 41 missense mutation annotations as a foundation, we derived a structural FoldX score dataset based on predicted AlphaFold 42 monomer models (see ‘Methods’), as many of the proteins in this dataset lacked experimentally determined structures.…”

Section: Resultsmentioning

confidence: 99%

“…For external validation we derived an independent structural dataset, based on variant-level GOF and LOF disease mechanism annotation by Bayrak et al 40 . The HGMD GOF and LOF label data was downloaded from https://itanlab.shinyapps.io/goflof/ on 2021.10.25.…”

Section: Methodsmentioning

confidence: 99%

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Loss-of-function, gain-of-function and dominant-negative mutations have profoundly different effects on protein structure: implications for variant effect prediction

Gerasimavicius

Livesey

Marsh

2021

Preprint

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Most known pathogenic mutations occur in protein-coding regions of DNA and change the way proteins are made. Taking protein structure into account has therefore provided great insight into the molecular mechanisms underlying human genetic disease. While there has been much focus on how mutations can disrupt protein structure and thus cause a loss of function (LOF), alternative mechanisms, specifically dominant-negative (DN) and gain-of-function (GOF) effects, are less understood. Here, we have investigated the protein-level effects of pathogenic missense mutations associated with different molecular mechanisms. We observe striking differences between recessive vs dominant, and LOF vs non-LOF mutations, with dominant, non-LOF disease mutations having much milder effects on protein structure, and DN mutations being highly enriched at protein interfaces. We also find that nearly all computational variant effect predictors underperform on non-LOF mutations, even those based solely on sequence conservation. However, we do find that non-LOF mutations could potentially be identified by their tendency to cluster in space. Overall, our work suggests that many pathogenic mutations that act via DN and GOF mutations are likely being missed by current variant prioritisation strategies, but that there is considerable scope to improve computational predictions through consideration of molecular disease mechanisms.

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Section: Discussionsupporting

confidence: 78%

Section: Resultsmentioning

confidence: 99%

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Loss-of-function, gain-of-function and dominant-negative mutations have profoundly different effects on protein structure: implications for variant effect prediction

Gerasimavicius

Livesey

Marsh

2021

Preprint

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“…We checked if any of the mutations corresponded to gain of function (GOF)/loss of function (LOF) from the GOF/LOF database [ 70 ] based on the Human Gene Mutation database [ 71 ] and MAVEDB [ 72 ]. GOF and cancer mutations are usually clustered together in 3D space [ 73–75 ].…”

Section: Resultsmentioning

confidence: 99%

Characterizing and explaining the impact of disease-associated mutations in proteins without known structures or structural homologs

Sen

Anishchenko

Bordin

et al. 2022

Briefings in Bioinformatics

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Mutations in human proteins lead to diseases. The structure of these proteins can help understand the mechanism of such diseases and develop therapeutics against them. With improved deep learning techniques, such as RoseTTAFold and AlphaFold, we can predict the structure of proteins even in the absence of structural homologs. We modeled and extracted the domains from 553 disease-associated human proteins without known protein structures or close homologs in the Protein Databank. We noticed that the model quality was higher and the Root mean square deviation (RMSD) lower between AlphaFold and RoseTTAFold models for domains that could be assigned to CATH families as compared to those which could only be assigned to Pfam families of unknown structure or could not be assigned to either. We predicted ligand-binding sites, protein–protein interfaces and conserved residues in these predicted structures. We then explored whether the disease-associated missense mutations were in the proximity of these predicted functional sites, whether they destabilized the protein structure based on ddG calculations or whether they were predicted to be pathogenic. We could explain 80% of these disease-associated mutations based on proximity to functional sites, structural destabilization or pathogenicity. When compared to polymorphisms, a larger percentage of disease-associated missense mutations were buried, closer to predicted functional sites, predicted as destabilizing and pathogenic. Usage of models from the two state-of-the-art techniques provide better confidence in our predictions, and we explain 93 additional mutations based on RoseTTAFold models which could not be explained based solely on AlphaFold models.

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“…We checked if any of the mutations corresponded to gain of function (GOF)/loss of function (LOF) from GOF/LOF database [70] based on Human Gene Mutation database [71] and MAVEDB [72]. GOF/cancer mutations are usually clustered together in 3D space [73–75].…”

Section: Resultsmentioning

confidence: 99%

Characterizing and explaining impact of disease-associated mutations in proteins without known structures or structural homologues

Sen

Anishchenko

Bordin

et al. 2021

Preprint

View full text Add to dashboard Cite

Mutations in human proteins lead to diseases. The structure of these proteins can help understand the mechanism of such diseases and develop therapeutics against them. With improved deep learning techniques such as RoseTTAFold and AlphaFold, we can predict the structure of these proteins even in the absence of structural homologues. We modeled and extracted the domains from 553 disease-associated human proteins. We noticed that the model quality was higher and the RMSD lower between AlphaFold and RoseTTAFold models for domains that could be assigned to CATH families as compared to those which could be assigned to Pfam families of unknown structure or could not be assigned to either. We predicted ligand-binding sites, protein-protein interfaces, conserved residues and destabilising effects caused by residue mutations in these predicted structures. We then explored whether the disease-associated mutations were in the proximity of these predicted functional sites or if they destabilized the protein structure based on ddG calculations. We could explain 80% of these disease-associated mutations based on proximity to functional sites or structural destabilization. Usage of models from the two state-of-the-art techniques provide better confidence in our predictions, and we explain 93 additional mutations based on RoseTTAFold models which could not be explained based solely on AlphaFold models.

show abstract

Identification of discriminative gene-level and protein-level features associated with pathogenic gain-of-function and loss-of-function variants

Cited by 33 publications

References 99 publications

Loss-of-function, gain-of-function and dominant-negative mutations have profoundly different effects on protein structure: implications for variant effect prediction

Loss-of-function, gain-of-function and dominant-negative mutations have profoundly different effects on protein structure: implications for variant effect prediction

Characterizing and explaining the impact of disease-associated mutations in proteins without known structures or structural homologs

Characterizing and explaining impact of disease-associated mutations in proteins without known structures or structural homologues

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