Distribution of disease-causing germline mutations in coiled-coils implies an important role of their N-terminal region

Kálmán, Zsófia; Mészáros, Bálint; Gáspári, Zoltán; Dobson, László

doi:10.1038/s41598-020-74354-9

Cited by 4 publications

(3 citation statements)

References 59 publications

(62 reference statements)

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“…The N‐terminal coiled‐coil domain 1 binds dynein and dynactin, the C‐terminal coiled‐coil domain 3 binds cargo, and the coiled‐coil domain 2 interacts with kinesin‐1 (Urnavicius et al, 2015). According to MARCOIL and PCOILS prediction algorithms (Delorenzi & Speed, 2002; Gruber et al, 2006), Glu515Lys in the coiled‐coil 2 domain changes the polarity of an amino acid in the “c” position of the a–g heptad building block of the coiled coil (Kalman et al, 2020). We postulate that substitution of a positively charged Lys for the outward facing negatively charged Glu515 destabilizes interaction of the BICD2 with kinesin‐1 and impedes transport of vesicles to microtubular plus ends, that is, cellular periphery (Grigoriev et al, 2007; Splinter et al, 2010).…”

Section: Discussionmentioning

confidence: 99%

An infant with congenital respiratory insufficiency and diaphragmatic paralysis: A novel BICD2 phenotype?

Chin

Huynh

Ashkani

et al. 2021

American J of Med Genetics Pt A

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Monoallelic pathogenic variants in BICD2 are associated with autosomal dominant Spinal Muscular Atrophy Lower Extremity Predominant 2A and 2B (SMALED2A, SMALED2B). As part of the cellular vesicular transport, complex BICD2 facilitates the flow of constitutive secretory cargoes from the trans-Golgi network, and its dysfunction results in motor neuron loss. The reported phenotypes among patients with SMALED2A and SMALED2B range from a congenital onset disorder of respiratory insufficiency, arthrogryposis, and proximal or distal limb weakness to an adult-onset disorder of limb weakness and contractures. We report an infant with congenital respiratory insufficiency requiring mechanical ventilation, congenital diaphragmatic paralysis, decreased lung volume, and single finger camptodactyly. The infant displayed appropriate antigravity limb movements but had radiological, electrophysiological, and histopathological evidence of myopathy. Exome sequencing and longread whole-genome sequencing detected a novel de novo BICD2 variant (NM_001003800.1:c.[1543G>A]; [=]). This is predicted to encode p.(Glu515Lys); p.Glu515 is located in the coiled-coil 2 mutation hotspot. We hypothesize that this novel phenotype of diaphragmatic paralysis without clear appendicular muscle weakness and contractures of large joints is a presentation of BICD2-related disease.

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Section: Discussionmentioning

confidence: 99%

An infant with congenital respiratory insufficiency and diaphragmatic paralysis: A novel BICD2 phenotype?

Chin

Huynh

Ashkani

et al. 2021

American J of Med Genetics Pt A

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“…Prediction of protein stability change (∆∆G) upon mutation is one of the most important unsolved problems of structural bioinformatics (Toplak et al, 2021;Kalman et al, 2020;Wang et al, 2020;Pancotti et al, 2022;Pak & Ivankov, 2022). The recent success of AlphaFold in predicting 3D protein structure at near-to-experimental accuracy showed the perspectives of deep learning techniques for solving biological problems (Jumper et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

New mega dataset combined with deep neural network makes a progress in predicting impact of mutation on protein stability

Pak

Dovidchenko

Sharma

et al. 2023

Preprint

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Prediction of protein stability change due to single mutation is important for biotechnology, medicine, and our understanding of physics underlying protein folding. Despite the recent tremendous success in 3D protein structure prediction, the apparently simpler problem of predicting the effect of mutations on protein stability has been hampered by the low amount of experimental data. With the recent high-throughput measurements of mutational effects in 'mega' experiment for ~850,000 mutations [Tsuboyama et al., bioRxiv, 2022] it becomes possible to apply the state-of-the-art deep learning methods. Here we explore the ability of ESM2 deep neural network architecture with added Light Attention mechanism to predict the change of protein stability due to single mutations. The resulting method ABYSSAL predicts well the data from the 'mega' experiment (Pearson correlation 0.85) while the prediction of DDG values from previous experiments is more modest (Pearson correlation 0.50). ABYSSAL also shows a perfect satisfaction of the antisymmetry property. The ABYSSAL training demonstrated that the dataset should contain around ~100,000 data points for taking advantage of the state-of-the-art deep learning methods. Overall, our study shows great perspectives for developing the deep learning DDG predictors.

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“…Protein stability is a fundamental property of the protein native three-dimensional (3D) structure. Accurate prediction of folding free energy change upon amino acid mutation facilitates enzyme redesign [1], pathogenicity assessment [2], and optimization of thermostability [3] as well as characterizes our understanding of protein folding principles. Experimental measurements of protein stability change upon mutation are laborious and time-consuming, making computational prediction highly important.…”

Section: Introductionmentioning

confidence: 99%

Best templates outperform homology models in predicting the impact of mutations on protein stability

Pak

Ivankov

2021

Preprint

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Motivation: Prediction of protein stability change upon mutation (∆∆G) is crucial for facilitating protein engineering and understanding of protein folding principles. Robust prediction of protein folding free energy change requires the knowledge of protein three-dimensional (3D) structure. Unfortunately, protein 3D structure is not always available. In this case, one can still predict the protein stability change by constructing a homology model of the protein; however, the accuracy of homology model-based ∆∆G predictions is unknown. The perspectives of using 3D structures of the best templates are also unclear. Results: To investigate these questions, we used the most popular and accurate publicly available tools: FoldX for stability change prediction and I-Tasser for homology modeling. We found that both homology models and best templates worsen the ∆∆G prediction, with best templates performing 1.5 times better than homology models. For AlphaFold models, we also found that the best templates seem to outperform protein models. Our findings imply using the 3D structures of the best templates for ∆∆G prediction if the 3D protein structure is unavailable.

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Distribution of disease-causing germline mutations in coiled-coils implies an important role of their N-terminal region

Cited by 4 publications

References 59 publications

An infant with congenital respiratory insufficiency and diaphragmatic paralysis: A novel BICD2 phenotype?

An infant with congenital respiratory insufficiency and diaphragmatic paralysis: A novel BICD2 phenotype?

New mega dataset combined with deep neural network makes a progress in predicting impact of mutation on protein stability

Best templates outperform homology models in predicting the impact of mutations on protein stability

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