7 I

7.1 Talking Glossary: Insertion mutation (1.25 min)

genome.gov/genetics-glossary/Insertion

Abstract: “Insertion is a type of mutation involving the addition of genetic material. An insertion mutation can be small, involving a single extra DNA base pair, or large, involving a piece of a chromosome.”

Image: https://www.genome.gov/sites/default/files/tg/en/illustration/insertion.jpg

Audio: https://www.genome.gov/sites/default/files/tg/en/narration/insertion.mp3

Transcript “Insertion really means that something has been stuck in there. And again, as a geneticist, when we think of an insertion, we think of a piece of DNA, and that can be small or large, being stuck in at a place where it really doesn’t belong. So an insertion of just one base pair could lead to something that we call a frameshift. It shifts the reading of the three-base pair code and by that can throw off the entire protein, and by that can lead, for example, to a birth defect. Insertion can be larger, that, for example, there is an insertion of three base pairs, and then it will not throw off the frame, or it will not lead to a frameshift, and potentially is less harmful than having the insertion of just one base pair. And of course you can have an insertion of huge pieces of DNA. They can be so large that you could actually see it on the chromosome analysis, where all of the smaller insertions you would see only by sequencing the stretch of DNA.”

Maximilian Muenke, M.D.

For an interview with Dr. Muenke, see: https://www.genome.gov/player/wyo8AF_3nz8/PL1ay9ko4A8sk0o9O-YhseFHzbU2I2HQQp

7.2 Intrinscially disordered protein

Adapted from Wikipedia https://en.wikipedia.org/wiki/Intrinsically_disordered_proteins

An intrinsically disordered protein (IDP) is a protein that lacks a fixed or ordered three-dimensional structure (2, 3, 4) typically in the absence of its macromolecular interaction partners, such as other proteins or RNA. IDPs range from fully unstructured to partially structured and include random coil, molten globule-like aggregates, or flexible linkers in large multi-domain proteins. They are sometimes considered as a separate class of proteins along with globular, fibrous and membrane proteins (5).

The discovery of IDPs offers support against the idea that three-dimensional structures of proteins must be fixed to accomplish their biological functions. The dogma of rigid protein structure has been questioned due to the increasing evidence of dynamics being necessary for the protein machines. Despite their lack of stable structure, IDPs are a very large and functionally important class of proteins. Many IDPs can adopt a fixed three-dimensional structure after binding to other macromolecules. Overall, IDPs are different from structured proteins in many ways and tend to have distinctive function, structure, sequence, interactions, evolution and regulation (6).

7.2.1 Abundance

It is now generally accepted that proteins exist as an ensemble of similar structures with some regions more constrained than others. IDPs occupy the extreme end of this spectrum of flexibility.

Bioinformatic predictions indicated that intrinsic disorder is more common in genomes and proteomes than in known structures in the protein database. Based on DISOPRED2 prediction, long (>30 residue) disordered segments occur in 2.0% of archaean, 4.2% of eubacterial and 33.0% of eukaryotic proteins (10) including certain disease-related proteins (11).

7.2.2 Disorder annotation

Intrinsic disorder can be either annotated from experimental information or predicted with specialized software. Disorder prediction algorithms can predict Intrinsic Disorder (ID) propensity with high accuracy (approaching around 80%) based on primary sequence composition, similarity to unassigned segments in protein x-ray datasets, flexible regions in NMR studies and physico-chemical properties of amino acids.

7.2.3 Disorder databases

Databases have been established to annotate protein sequences with intrinsic disorder information. The DisProt database contains a collection of manually curated protein segments which have been experimentally determined to be disordered. MobiDB is a database combining experimentally curated disorder annotations (e.g. from DisProt) with data derived from missing residues in X-ray crystallographic structures and flexible regions in NMR structures.

7.2.4 Predicting IDPs by sequence

Separating disordered from ordered proteins is essential for disorder prediction. One of the first steps to find a factor that distinguishes IDPs from non-IDPs is to specify biases within the amino acid composition. The hydrophilic, charged amino acids (A, R, G, Q, S, P, E and K) have been characterized as disorder-promoting amino acids, while order-promoting amino acids (W, C, F, I, Y, V, L, and N) are hydrophobic and uncharged. The remaining amino acids (H, M, T and D) are ambiguous, found in both ordered and unstructured regions (2). A more recent analysis ranked amino acids by their propensity to form disordered regions as follows (order promoting to disorder promoting): W, F, Y, I, M, L, V, N, C, T, A, G, R, D, H, Q, K, S, E, P (43).

This information is the basis of most sequence-based predictors. Regions with little to no secondary structure, also known as NORS (NO Regular Secondary structure) regions (44) and low-complexity regions can easily be detected. However, not all disordered proteins contain such low complexity sequences.

7.2.5 Prediction methods

Determining disordered regions from lab methods is very costly and time-consuming. Due to the variable nature of IDPs, only certain aspects of their structure can be detected, so that a full characterization requires a large number of different methods and experiments. This further increases the expense of IDP determination. In order to overcome this obstacle, computer-based methods are created for predicting protein structure and function. It is one of the main goals of bioinformatics to derive knowledge by prediction. Predictors for IDP function are also being developed, but mainly use structural information such as linear motif sites (4, 45). There are different approaches for predicting IDP structure, such as neural networks or matrix calculations, based on different structural and/or biophysical properties.

Many computational methods exploit sequence information to predict whether a protein is disordered (46). Notable examples of such software include IUPRED and Disopred. Different methods may use different definitions of disorder. Meta-predictors show a new concept, combining different primary predictors to create a more competent and exact predictor.

Due to the different approaches of predicting disordered proteins, estimating their relative accuracy is fairly difficult. For example, neural networks are often trained on different datasets. The disorder prediction category is a part of biannual CASP experiment that is designed to test methods according accuracy in finding regions with missing 3D structure (marked in PDB files as REMARK465, missing electron densities in X-ray structures).

7.2.6 Disorder and disease

This section optional

Intrinsically unstructured proteins have been implicated in a number of diseases (47). Aggregation of misfolded proteins is the cause of many synucleinopathies and toxicity as those proteins start binding to each other randomly and can lead to cancer or cardiovascular diseases. Thereby, misfolding can happen spontaneously because millions of copies of proteins are made during the lifetime of an organism. The aggregation of the intrinsically unstructured protein α-synuclein is thought to be responsible. The structural flexibility of this protein together with its susceptibility to modification in the cell leads to misfolding and aggregation. Genetics, oxidative and nitrative stress as well as mitochondrial impairment impact the structural flexibility of the unstructured α-synuclein protein and associated disease mechanisms (48). Many key tumour suppressors have large intrinsically unstructured regions, for example p53 and BRCA1. These regions of the proteins are responsible for mediating many of their interactions. Taking the cell’s native defense mechanisms as a model drugs can be developed, trying to block the place of noxious substrates and inhibiting them, and thus counteracting the disease (49).

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