Analyzing Change in Protein Stability Associated with Single Point Deletions in a Newly Defined Protein Structure Database

Banerjee, Anupam; Levy, Yaakov; Mitra, Pralay

doi:10.1021/acs.jproteome.9b00048

Cited by 13 publications

(18 citation statements)

References 33 publications

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“…Additionally, while testing PROFOUND, we find that the PU classifiers report a precision of 92%, recall of 78%, and accuracy of 76.5% on the SPD dataset . In Table S6, we find that PROFOUND performs poorly with respect to the SPD-specific classifier (precision = 100%, recall = 99.2%, and accuracy = 99.4%).…”

Section: Resultsmentioning

confidence: 89%

“…Additionally, while testing PROFOUND, we find that the PU classifiers report a precision of 92%, recall of 78%, and accuracy of 76.5% on the SPD dataset. 23 In Table S6, we find that PROFOUND performs poorly with respect to the SPDspecific classifier (precision = 100%, recall = 99.2%, and accuracy = 99.4%). However, it is to be noted that PROFOUND is trained on MPD instances and many of its features are not applicable (not applicable features are considered as 0) to SPDs.…”

Section: Resultsmentioning

confidence: 97%

“…KD4i, using an inductive logic programming-based machine learning approach, predicts disease-causing non-frameshift (NFS) InDels . Despite the existence of a new dataset and classifiers that encode the foldability of proteins owing to single-point deletions (SPD) of amino acids, , a generic computational framework codifying the change in structural integrity due to multipoint deletions (MPDs) or contiguous deletion of more than one amino acid is missing. An additional limitation is the lack of experimental datasets listing changes in thermodynamic stability (or foldability) owing to MPDs in proteins.…”

Section: Introductionmentioning

confidence: 99%

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Estimating Change in Foldability Due to Multipoint Deletions in Protein Structures

Banerjee

Kumar

Ghosh

et al. 2020

J. Chem. Inf. Model.

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Insertions/deletions of amino acids in the protein backbone potentially result in altered structural/functional specifications. They can either contribute positively to the evolutionary process or can result in disease conditions. Despite being the second most prevalent form of protein modification, there are no databases or computational frameworks that delineate harmful multipoint deletions (MPD) from beneficial ones. We introduce a positive unlabeled learning-based prediction framework (PROFOUND) that utilizes fold-level attributes, environment-specific properties, and deletion site-specific properties to predict the change in foldability arising from such MPDs, both in the non-loop and loop regions of protein structures. In the absence of any protein structure dataset to study MPDs, we introduce a dataset with 153 MPD instances that lead to native-like folded structures and 7650 unlabeled MPD instances whose effect on the foldability of the corresponding proteins is unknown. PROFOUND on 10-fold cross-validation on our newly introduced dataset reports a recall of 82.2% (86.6%) and a fall out rate (FR) of 14.2% (20.6%), corresponding to MPDs in the protein loop (non-loop) region. The low FR suggests that the foldability in proteins subject to MPDs is not random and necessitates unique specifications of the deleted region. In addition, we find that additional evolutionary attributes contribute to higher recall and lower FR. The first of a kind foldability prediction system owing to MPD instances and the newly introduced dataset will potentially aid in novel protein engineering endeavors.

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Section: Resultsmentioning

confidence: 89%

Section: Resultsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimating Change in Foldability Due to Multipoint Deletions in Protein Structures

Banerjee

Kumar

Ghosh

et al. 2020

J. Chem. Inf. Model.

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“…For missense variants, it is well-established that many have neutral or slightly destabilizing effects, and only a fraction of the possible single amino acid changes are severely detrimental [66][67][68]. In contrast, there is far less data on protein stability for in-frame deletions, though existing studies on certain model proteins show that a number of both insertions and deletions are functional [69][70][71][72]. All three deletions in our initial dataset, however, are severely destabilized (Fig 1).…”

Section: Disease-associated Flcn Variants Display Reduced Steady-state Levels Due To Proteasomal Degradationmentioning

confidence: 76%

Folliculin variants linked to Birt-Hogg-Dubé syndrome are targeted for proteasomal degradation

et al. 2020

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Germline mutations in the folliculin (FLCN) tumor suppressor gene are linked to Birt-Hogg-Dubé (BHD) syndrome, a dominantly inherited genetic disease characterized by predisposition to fibrofolliculomas, lung cysts, and renal cancer. Most BHD-linked FLCN variants include large deletions and splice site aberrations predicted to cause loss of function. The mechanisms by which missense variants and short in-frame deletions in FLCN trigger disease are unknown. Here, we present an integrated computational and experimental study that reveals that the majority of such disease-causing FLCN variants cause loss of function due to proteasomal degradation of the encoded FLCN protein, rather than directly ablating FLCN function. Accordingly, several different single-site FLCN variants are present at strongly reduced levels in cells. In line with our finding that FLCN variants are protein quality control targets, several are also highly insoluble and fail to associate with the FLCN-binding partners FNIP1 and FNIP2. The lack of FLCN binding leads to rapid proteasomal degradation of FNIP1 and FNIP2. Half of the tested FLCN variants are mislocalized in cells, and one variant (ΔE510) forms perinuclear protein aggregates. A yeast-based stability screen revealed that the deubiquitylating enzyme Ubp15/USP7 and molecular chaperones regulate the turnover of the FLCN variants. Lowering the temperature led to a stabilization of two FLCN missense proteins, and for one (R362C), function was re-established at low temperature. In conclusion, we propose that most BHD-linked FLCN missense variants and small in-frame deletions operate by causing misfolding and degradation of the FLCN protein, and that stabilization and resulting restoration of function may hold therapeutic potential of certain disease-linked variants. Our computational saturation scan encompassing both missense variants and single site deletions in FLCN may allow classification of rare FLCN variants of uncertain clinical significance.

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“…In addition, we tested the possibility of emerging single-point deleted variants of the spike protein. We engineer the spike protein by performing single-point deletion within 452-478 residue stretch using an existing method developed by our group (16). However, the study indicates it is on an average with the 50% probability that one single-point deleted variants will assume a folded structure and may emerge as new VoCs or VoIs in future.…”

Section: Resultsmentioning

confidence: 99%

Genome surveillance of SARS-CoV-2 variants and their role in pathogenesis focusing on second wave of COVID-19 in India

Sarkar

Banerjee

Saha

et al. 2022

Preprint

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India had witnessed unprecedented surge in SARS-CoV-2 infections and its dire consequences during the second wave of COVID-19, but the detailed report of the epidemiological based spatiotemporal incidences of the disease is missing. Here in, we have applied various statistical methods like correlation, hierarchical clustering to know the pattern of pathogenesis of the circulating VoCs. B.1.617.1 (Kappa) was the predominant VoC during the early phase of second wave. Delta (B.1.617.2) or Delta-like (AY.x) VoC constitutes majority (>90.17) of the cases during the peak of second wave. The correlation plot showed Delta/Delta-like lineage is inversely correlated with other lineages including B.1.617.1 (kappa), B.1.1.7, B.1, B.1.36.29 and B.1.36. Delta/Delta-like surge coincided with second wave whereas all other lineages (B.1.617.1, B.1.36.29, etc.) occurred during the prior phase of the second wave. The spatiotemporal analysis showed that most of the Indian states were affected during the peak of the second wave due to delta surge and fall under the same cluster. The second cluster populated mostly by north-eastern states and islands of India were minimally affected. The presence of signature mutations (T478K, D950N, E156G) along with L452K, D614G and P681R within the spike protein of Delta or Delta-like might cause elevation in host cell attachment, increased transmission and altered antigenicity which in due course of time has replaced the other circulating variants.. The timely assessment of new VoCs will provide a rationale for updating the diagnostic, vaccine development by medical industries and decision making by various agencies including government, educational institutions, and corporate industries.

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Analyzing Change in Protein Stability Associated with Single Point Deletions in a Newly Defined Protein Structure Database

Cited by 13 publications

References 33 publications

Estimating Change in Foldability Due to Multipoint Deletions in Protein Structures

Estimating Change in Foldability Due to Multipoint Deletions in Protein Structures

Folliculin variants linked to Birt-Hogg-Dubé syndrome are targeted for proteasomal degradation

Genome surveillance of SARS-CoV-2 variants and their role in pathogenesis focusing on second wave of COVID-19 in India

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