How Does The Structural Change Of The Dmd Protein Affect Its Function

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Open Access

Peer-reviewed

Research Article

Bear on of genetic variation on three dimensional structure and function of proteins

• Roshni Bhattacharya,
• Peter W. Rose,
• Stephen K. Burley,
• Andreas Prlić

x

• Published: March fifteen, 2017
• https://doi.org/10.1371/periodical.pone.0171355

Figures

Abstract

The Protein Information Banking company (PDB; http://wwpdb.org) was established in 1971 every bit the showtime open up admission digital data resource in biology with seven protein structures every bit its initial holdings. The global PDB annal now contains more than 126,000 experimentally determined diminutive level three-dimensional (3D) structures of biological macromolecules (proteins, Dna, RNA), all of which are freely attainable via the Internet. Knowledge of the 3D construction of the gene product can help in understanding its office and office in disease. Of particular interest in the PDB archive are proteins for which 3D structures of genetic variant proteins have been adamant, thus revealing atomic-level structural differences acquired by the variation at the Deoxyribonucleic acid level. Herein, we nowadays a systematic and qualitative analysis of such cases. We observe a wide range of structural and functional changes caused past unmarried amino acid differences, including changes in enzyme activity, assemblage propensity, structural stability, binding, and dissociation, some in the context of large assemblies. Structural comparison of wild type and mutated proteins, when both are available, provide insights into atomic-level structural differences caused by the genetic variation.

Citation: Bhattacharya R, Rose PW, Burley SK, Prlić A (2017) Impact of genetic variation on three dimensional structure and function of proteins. PLoS ONE 12(3): e0171355. https://doi.org/x.1371/journal.pone.0171355

Editor: Yang Zhang, University of Michigan, The states

Received: Baronial 23, 2016; Accepted: January 19, 2017; Published: March fifteen, 2017

Copyright: © 2017 Bhattacharya et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted apply, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The supplemental data described in this manuscript is freely available on GitHub https://github.com/rcsb/SnpsInPdb.

Funding: This work was supported past a National Science Foundation grant to the RCSB PDB (DBI-1338415; PI: Stephen G. Burley). The Protein Data Banking company archive is managed by the Worldwide Protein Data Bank organization, which consists of the RCSB Protein Information Bank, Protein Data Bank in Europe, Poly peptide Data Banking company Nihon, and BioMagResBank. The funders had no role in study design, information drove and analysis, determination to publish, or training of the manuscript.

Competing interests: The authors take declared that no competing interests exist.

1. Introduction

With the ever-growing importance of genomics for human health, considerable efforts take been devoted to linking homo phenotypes to genotypic variations at the nucleotide level and changes in 3D protein structure [one,2]. Genetic variation can cause changes in phenotype if expression levels are contradistinct or pre-mRNA splicing is affected. Sequence changes at the amino acrid level influence the shape, function, or binding properties of a given protein. Of particular involvement when analyzing genome-sequencing information are Unmarried Nucleotide Variations (SNVs). Near SNVs are neutral or take no effect on human health or embryonic evolution [three,4]. Certain SNVs, however, may exist useful for predicting individual responses to particular drugs, susceptibility to other exogenous factors such as ecology toxins, or risk of developing disease [4,5,6]. Identification of an SNV giving ascent to a phenotype is a challenging problem, owing to the complexity of human biology. Association studies are often used to identify the SNV (or SNVs) giving rise to complex phenotypes [7], relying on genetic variations amidst affected individuals to detect association of the variation with a trait (or phenotype). Such studies mostly concentrate on associations between point mutations and phenotypic traits or diseases [8]. Nonetheless, Genome Broad Association Studies (GWAS) require screening of large numbers of markers [9,10,11], and correlation of a given SNV with a particular phenotype does not per se testify causality. Although genome wide studies provide insights into the genetic basis of human illness, they have explained relatively little of the heritability of many complex traits. This shortcoming has raised the question of where the 'missing heritability' of complex diseases might be plant [12].

Ane way to analyze large datasets of genetic variation is to use bioinformatics tools to filter the information [ix]. Computational methods such every bit SIFT [13,14], Polyphen-2 [xv], or MAPP [16] allocate SNVs according to negative, neutral, or positive effects on the structure or role of the protein. Several algorithms even attempt to guess the change in the gratuitous free energy of stabilization of protein structure, due to single sequence changes, e.g., DUET [17], Mupro [eighteen], and I-Mutant2.0 [nineteen]. A method developed by Topham and colleagues, Site Directed Mutator (SDM), utilizes an arroyo analogous to the thermodynamic cycle [twenty,21]. Alternative analytic tools use sequence conservation of a particular amino acid within a protein family, or search for a distinct poly peptide structure feature to predict whether a substitution affects role, such as SIFT or Sorts Intolerant From Tolerant substitutions [thirteen,fourteen,21]. Other bioinformatics tools based on evolutionary principles that predict the event of coding variants on poly peptide office, including PANTHER [22,23], HMMER/LogR.Eastward-value [24], Condel [25], and several others [26,27,28,69]. Custom databases, including SAAP [27], PolyDoms [28], topoSNP [29], SNPeffect [30], SNPs3D [31], MutDB [32], FATHMM [33] and LS-SNP [34], provide links between SNVs and protein sequence/structure information and/or cellular processes such as localization, phosphorylation, and glycosylation. The National Library of Medicine NCBI supports the tranSNP tool, which permits display of the location of a SNV on the genome [35]. ENSEMBL offers the Variant Event Predictor [36]. The resources described above use one of the six popular training dataset enumerated in Table 1. Notwithstanding the composure of these and other approaches, there is always a question every bit to whether predictions therefrom can be relied on, considering at that place are numerous examples of discordance among single mutation prediction methods.

Arguably, the most informative source of information that tin explain what is causing a item phenotype is the availability of a 3D experimentally-adamant structure that contains atomic level insight into the consequences of a detail genomic variant. The RCSB Protein Data Depository financial institution (RCSB PDB) [35] enables open admission to the Protein Information Bank annal of experimental structures of biological macromolecules without limitations on usage. The PDB is one of the most widely used digital data resources in biology and biomedicine worldwide. The RCSB PDB provides deposition, note, query, analysis and visualization tools, and educational resources for use with the PDB archive [43]. All of the 3D macromolecular structure data in the PDB were obtained by one of iii experimental methods: X-ray Diffraction (~89%), solution Nuclear Magnetic Resonance (NMR) (~10%), or Electron Microscopy (<ane%). PDB structures provide diminutive level detail with which to analyze the structural effects of non-synonymous coding SNVs.

Knowledge of the 3D structure of a factor product is beneficial in predicting and understanding both office and part in disease. Still, well-nigh studies that analyze the human relationship between point mutations and experimentally observed 3D protein structure published to date take been restricted to individual proteins or single diseases. At that place is a paucity of quantitative analyses of the consequences of SNVs on 3D protein structure going beyond the realm of prediction [44].

The goal of this study is to improve our understanding of the relationship between bespeak mutations and experimentally observed consequences in 3D. We identified a benchmark dataset of poly peptide structures that contain well-characterized indicate mutations for which 3D atomic coordinates are available from the PDB. We manually analyzed 374 human protein structures and SNVs. Herein, we present a detailed overview about the observed furnishings of SNVs on the structure, function, stability, and binding properties of proteins.

ii. Methodology

two.i. Structure of the dataset

The data set used in this newspaper is a semi-automatically derived and manus-curated collection of proteins, each of which possess an amino acrid that has been changed past a SNV and 3D atomic coordinates are available in the PDB.

To assemble this data prepare,

1. We identified 2596 structures extant in the PDB for which non-synonymous SNV could be mapped via LS-SNP/PDB [34]. For each PDB entry, the amino acrid sequence of the crystallized protein experimentally observed in 3D differs from the corresponding UniProt sequence at the position of the variation.
2. From these 2596 structures, we selected but those structures for which the dbSNP mutation data matched information coming from UniProt and the 3D structure. For example, rs28933981, the alter in dbSNP is T→Thousand and in PDB: 1BZE, the sequence deviation in the structure is as well T→K, and this case was included in our dataset. In contrast, the dbSNP database entry for SNV rs128620185 reports R→H, but in the PDB annal (1BTK) the experimentally observed sequence difference is R→C. This case was excluded from our dataset, considering it does not correspond to the reported R→H SNV.
3. After filtering for database inconsistencies, nosotros removed mappings of the same SNV to multiple PDB entries, ensuring that each SNV is merely represented once. When multiple PDB entries with the same mutation are available, preference was been given to structures determined by X-ray crystallography. In a few cases it was non possible to practise so, and the dataset contains 49 structures determined by NMR. (see supplemental files S1 and S2 Figs).

This rigorous procedure yielded a final benchmark dataset of 374 unique homo SNVs, each corresponding to a different PDB entry for which 3D atomic level coordinates are available. When filtering by protein sequence identity, the dataset contains 334 unique PDB structures, documenting that nosotros accepted some limited back-up when constructing the dataset. Each of the 374 SNVs are described in contained experiments, and all such cases were retained in the dataset. See supplemental file S1 File for the complete dataset.

2.ii. Transmission annotation of SNVs

To enumerate the consequence(s) of a given SNV on a cistron production, nosotros systematically reviewed the available literature to identify experimentally verified functional effects. We too performed searches in several databases (see below). For each SNV, we extracted the post-obit information from literature and from databases:

1. The position of the SNV on the 3D protein structure in the PDB (nowadays on the surface vs. buried in the interior), estimated with BioJava surface accessibility calculations [70].
2. Whether the amino acid substitution falls within Loop vs. Alpha_helix vs. Beta_strand secondary structure, determined from secondary structure annotations obtained from PDB [35,43].
3. What outcome or issue does the SNV accept on the poly peptide?

Nosotros classified mutations, whether they affect Activity of a protein vs. its Stability vs. Binding vs. Associates vs. Rearrangement (local conformational changes). The 374 PDB structures, which reflect the consequences of a particular SNV in this dataset, may contain other point mutations. Such differences may be neutral or the result of intentional mutations to aid in crystallization, etc. The dataset used herein contains only literature described and phenotype causative SNVs that have been linked to structural change(s) at the level of the protein. In many cases, these proteins were deliberately crystallized with a view to agreement the structural consequences of the sequence variation. To determine the frequency with which a SNV occurs in a population, we consulted the NHLBI Exome Sequencing Project (ESP) Exome Variant server [43,44] and dbSNP [34]. SNVs with Modest Allele Frequency (MAF, referring to the frequency at which the least mutual allele occurs in a given population) at < 1% are considered Rare, with the remainder classified as Mutual SNVs.

Databases and servers used in this work were equally follows:

• RCSB PDB—The RCSB Protein Data Bank [35,43] is the Us regional data center for the Worldwide Protein Data Bank (wwpdb.org), which manages the unmarried global PDB archival repository of experimental 3D structural data of biological macromolecules.
• LS-SNP/PDB–Is a web-tool for the annotation of human SNPs. Information technology contains an automatic pipeline that systematically maps man non-synonymous SNPs onto PDB structures [34].
• dbSNP—The Single Nucleotide Polymorphism Database (dbSNP) [36] is an annal for genetic variation inside and across dissimilar species developed and hosted by National Center for Biotechnology Information (NCBI) in collaboration with National Human Genome Enquiry Institute (NHGRI). The database contains information about SNPs, short deletion and insertional polymorphisms (indels/DIPs), microsatellite markers and short tandem repeats (STRs), multi nucleotide Polymorphisms (MNPs), heterozygous Sequences, and named variants [36].
• NHLBI Exome Sequencing Project (ESP) Exome Variant Server—Contains a big collection of well-phenotyped Us populations [45,46].
• PubMed—PubMed contains more than than 23 1000000 abstracts for biomedical literature from MEDLINE, life science journals, and online books [47].

2.2.1. Software tools for mapping of genetic variation to protein sequence and 3D structure.

To enable deeper analysis of genetic variation in the context of protein sequence and 3D structures, we developed tools to facilitate mapping of whatsoever genetic location onto corresponding protein sequences and 3D poly peptide structures [43]. These tools are available from the RCSB PDB website [71] and were used to verify the integrity of the benchmark data assembled for this study.

• Mapping tool from homo genomic position to poly peptide sequence and 3D structure—This tool allows to map coordinates from the human reference assemblies versions 37, or 38 (as provided by the Genome Reference Consortium) to the correct UniProt isoforms and 3D structures. http://www.rcsb.org/pdb/chromosome.do
• Human Gene View—This genome browser supports navigation of the human genome and investigating the relationship between PDB archival entries and genes.
• Protein Characteristic View—Provides a rich graphical summary of protein sequence features, including identification of genomic positions mapped to protein sequences.
• 3D Viewer—The PV (Protein Viewer) enables highlighting of genomic positions mapped to protein structures in 3D.

ii.three. Categories for assigning effects of SNV

The post-obit categories were used to allocate the effects of SNVs at the level of the protein:

1. Action—The SNV causes increase, decrease, or consummate loss of protein activity.
2. Aggregation—The SNV renders the protein aggregation prone.
3. Stability—The SNV causes a change in protein stability. It may make the protein susceptible to proteolytic cleavage, or cause a alter in thermal inactivation temperature, or cause a alter in the energy of stabilization of the protein. It can also atomic number 82 to destabilization of a poly peptide oligomer, loss of packing or hydrophobic interactions, or alter a mode(s) of poly peptide-protein interaction.
4. Binding/Dissociation–The SNV leads to changes in affinity for a known bounden partner, or alterations in association or dissociation kinetics. Information technology tin can also crusade structural changes in the binding site or affect specificity for a binding partner(s).
5. Assembly—The SNV affects the oligomeric assembly properties of the protein.
6. Rearrangement—The SNV causes local structural rearrangements (conformational changes) in the neighborhood of the amino acid alter arising from the SNV.

3. Results and discussion

3.1. Location of SNVs within 3D structures

We offset investigated whether it is possible to identify patterns apropos sites at which indicate mutations occur. Specifically, we determined the position amino acid change caused past the SNV inside the 3D structure available from the PDB. Structural locations of the SNVs were and so manually categorized into 2 master groups: Surface and Buried, past analyzing the biological assembly (3D oligomeric structure) of the protein. We observed that 79% of the SNVs (297 of 374) lie on the protein surface and the remaining 21% (77 of 374) were buried in the interior of the protein (Fig 1A). For reference, surface and buried residues comprise 71% and 29%, respectively, for all residues in all of the structures in the dataset.

Fig 1. Distribution of SNVs based on structural position.

A. Distribution of the SNVs in our benchmark dataset based on structural position (Piechart). At that place are 2 broad categories, Surface (residue) or Buried (rest). B. Distribution of the SNVs in the dataset based on secondary structural location (Bar graph). The ii broad categories, Surface (rest) or Cached (remainder) are further categorized into Loop, Alpha_helix and Beta_strand based on the secondary structural element to which the SNV maps.

https://doi.org/x.1371/journal.pone.0171355.g001

Surface and Buried categories were further subcategorized into Loop, Alpha_helix and Beta_strand co-ordinate to the secondary structural context of each SNV related alter within the corresponding PDB structure. Considering the secondary structures, the expected distribution in our dataset is 46% Alpha_helix, 24% Beta_strand and thirty% Loop regions.

In the Surface category, it was observed that 52% (155 out of 297) of the SNVs map to Loop regions compared to ~34% for Alpha_helix and ~xiv% for Beta_strand. This finding was not unexpected as amino acid changes in Loop regions tin can often be compensated for without affecting the structure and office of the poly peptide, owing to the flexibility of these polypeptide concatenation segments. In contrast, for the Buried category, ~42% of the SNVs map to Alpha_helix vs. ~31% in Beta_strand vs. ~27% in Loop regions (Fig 1B). Thus, the SNVs in the Surface category have a higher likelihood of being found in Loop regions when compared to the Buried category, wherein SNVs related changes are more likely to be constitute in Alpha_helix and Beta_strand. Like distributions based on structural position and secondary structural elements were observed when comparing the SNVs with unknown structural and functional upshot and SNVs with structural and functional result information (run across S3 Fig). Representative examples are illustrated in Fig ii.

Fig ii. SNV consequences map to various locations within protein structures.

A) PDB: 1AZV, SNV: rs121912431 (G37R) is present on the surface of the protein in the highlighted Loop segment, where it causes the neurological disease Lou Gehrig'due south disease. B) PDB: 1J04, SNV: rs121908529 (G170R) is present on the surface of the protein in the highlighted Alpha_helix, where it causes hereditary kidney stone disease primary hyperoxaluria type one. C) PDB: 3S5E, SNV: rs138471431 (W155R) is present on the surface of the poly peptide in the highlighted Beta_sheet, where information technology causes the neurodegenerative affliction Friedreich's ataxia. D) PDB: 2V7A, SNV rs121913459 (T315I) is nowadays in the ATP-binding domain and causes resistance to the drug imatinib in patients with chronic myelogenous leukemia.

https://doi.org/10.1371/periodical.pone.0171355.g002

3.two. Consequences of SNVs related changes

By systematically reviewing relevant peer-reviewed literature, nosotros determined that a broad range of possible furnishings could exist attributed to a single residue alter. To categorize these findings, we classified responses or consequences due to SNVs as follows: Activeness, Assemblage, Stability, Binding, Assembly, and Rearrangement (Section 3.3, Tabular array 2). However, the level of detail with which each of the SNVs related changes have been experimentally characterized varies. For example, functional assays have only been performed for a relatively small number of cases. Information pertaining to functional consequences of the mutation are NOT readily available in the literature for 249 of 374 SNVs (~66%), and information regarding the structural consequences of the SNVs related changes are NOT available for 284 of 374 of SNVs (~75%). Nevertheless, the effects that take been described in the literature are often quite dramatic. Table 3 provides examples of what we do know most the 374 SNV cases comprising our dataset.

Table 2. Outcome of SNVs on protein structure and function for a dataset of 374 SNVs for which experimentally obtained atomic level data for the variation is available in the Protein Information Banking company.

Each SNV can be scored for multiple categories.

https://doi.org/ten.1371/journal.pone.0171355.t002

Examples for each response category are summarized below in Tabular array 3.

A unmarried residue mutation can have multiple effects on the protein construction and function. Thus, the consequences of a single SNV tin touch on more than than one of the six categories represented in Tabular array 3. Two informative instance studies are discussed beneath:

Arylsulfatase A (gene: ARSA) breaks down sulfatides. The Pro→Leu mutation (P428L) (rs28940893) mapping to amino acid 426 in the PDB construction yields an oligomerization defect (preferred mutant associates is dimer instead of octamer as for wild-type (Wildtype PDB: 1AUK)) that increases the susceptibility of the protein to degradation by lysosomal cysteine proteinases, leading to severe reduction in one-half-life [48] and metachromatic leukodystrophy [48]. Therefore, this SNV related alter affects both Stability and the poly peptide Associates (Fig 3A).

Fig three. SNVs that affect both protein construction and office.

A) The P428L mutant form of Arylsulfatase A adopts an atypical dimeric configuration (instead of the normal octamer), which reduces protein half-life. B) The F12L mutant course of Delta-aminolevulinic acid dehydratase assembles as a hexamer (instead of the normal octamer), which shifts the pH optimum of the enzyme from pH vii→pH ix.

https://doi.org/10.1371/journal.pone.0171355.g003

Delta-aminolevulinic acid dehydratase (gene: ALAD) catalyzes an early stride in tetrapyrrole biosynthesis [49]. The Phe→Leu mutation (F12L) (rs121912984) causes ALAD Porphyria, a rare autosomal recessive disease. Despite of being located far from active site residues 199 and 252 (21.7 and 24.0Å, respectively) this variant changes the preferred protein assembly from octamer to hexamer. In addition, the optimal pH for enzyme activity is shifted from pH 7 (wild-type) to pH ix in the mutant. The mutant enzyme is barely active under physiological conditions [49]. This SNV was, therefore, categorized every bit an SNV that affects both enzymatic Activeness and the protein Assembly (Wildtype PDB: 1E51) (Fig 3B).

In the following section, we provide a summary of the results for each SNV response category, and talk over several examples in more particular.

3.2.1. Activity.

52 of 374 SNV related changes in our dataset (~14%) either increase or subtract poly peptide activity. In some cases, SNVs lead to consummate loss of part. For example, man glycyl-tRNA synthetase (mutant PDB: 2PMF) loses detectable enzymatic activity due to a G526R (rs137852646) mutation, which is causative of Charcot-Marie-Molar disease [l]. G526 is an evolutionarily conserved residue located in the midst of motif 3 that connects Beta_strand β19 with Alpha_helix α13. With the exception of the mutation site, the overall structure of the G526R mutant poly peptide is nigh identical to that of the wild blazon (Wildtype PDB: 2ZT5) enzyme (alpha-Carbon atomic position root-mean-foursquare deviation = 0.8Å). Although the G526R change does not disturb the positions of residues comprising the active site, the sidechain of the mutated remainder (R526) interdicts admission to the active site, thereby inactivating the enzyme [fifty] (Fig 4).

Fig 4. SNV related change that affects enzymatic activity.

Semi-transparent, solvent-attainable surface representation of the AMP binding site. For the wild-blazon structure (PDB: 2ZT5) AMP is bound in the active site (atom type color coded stick figure), while in the mutant structure (PDB: 2PMF) AMP binding is blocked by projection of the arginine sidechain (red) into the active site, thereby blocking substrate ATP binding and inactivating the enzyme.

https://doi.org/10.1371/journal.pone.0171355.g004

three.two.two. Aggregation.

28 of 374 SNVs related changes in our dataset (~vi%) requite ascension to protein aggregation, which is a hallmark of some neurodegenerative diseases, e.g., Alzheimer's disease (Ad), Parkinson's disease (PD), Huntington'south disease (HD), amyotrophic lateral sclerosis (ALS), and prion diseases. To exemplify how a single point mutation can induce aggregation, we consider the case of Lou Gehrig'southward disease or amyotrophic lateral sclerosis (ALS), which is caused past instability of the Ala→Val (A4V) (rs121912442) mutant of human being Cu, Zn superoxide dismutase (HSOD) (mutant PDB: 1N19) [51]. Ala4 is located within a Beta_strand next to dimer interface residues and almost residues Leu106 and Ile113, which assist to stabilize the dimer interface. Leu106 is part of a Greek cardinal super secondary structural motif involved in capping one stop of the β barrel. The aliphatic sidechain of Leu106 stabilizes the dimer interface by acting equally a cork, which is stabilized past van der Waals interactions with Ala4 and Ile113 [51]. Locations of the sidechains of residues Phe20, Ile113, Leu106, and Ile15 are shifted due to the A4V mutation. This mutation also causes displacement of Leu106 at the one end of the β barrel. Enzymatic activity of the mutant protein is ~50% that of the wild-type (Wildtype PDB: 4FF9). Another issue of the destabilized A4V mutation is that it facilitates formation of HSOD-containing aggregates, which are believed to exist toxic to motor neurons and causative of illness [51].

3.two.3. Stability.

58 of 374 SNV related changes in our dataset (~sixteen%) lead to reduced poly peptide stability. A SNV tin can affect the stability of the poly peptide past making it susceptible to proteolysis or by changing the thermal inactivation temperature. To exemplify how a mutation can influence poly peptide stability, we analyze the post-obit case:

DJ-1 (mutant PDB: 2RK4) is a small conserved protein (189 amino acids), whose absence or inactivation leads to rare forms of familial Parkinsonism in humans [52]. Information technology is also a Ras-dependent oncogene and has been associated with several types of cancers [53]. The Met→Ile (M26I) mutation (rs74315351) decreases thermal stability and enhances formation of DJ-ane aggregates [54]. M26 (Wildtype PDB: 1P5F) is a conserved residue, located in the hydrophobic cadre of the protein. Although M26 lies near the dimer interface, it does non direct participate in intermolecular poly peptide-protein interactions across the dimer interface. The M26I mutation introduces a β-branched amino acid (isoleucine) into the tightly packed hydrophobic core of the DJ-1 monomer. The steric clash between I26 and the sidechain of I31 displaces the residues slightly and causes loss of optimal packing contacts in the interior of the protein resulting in lower stability [54] (Fig v).

Fig 5. SNV that affects protein structure stability.

Disease causing mutation site in protein DJ-i. The wild-type structure (PDB: 1P5F) is depicted in light-green and the variant (PDB: 2RK4) in red. M26 is a conserved residue in Alpha_helix A located within the hydrophobic core of the protein. The steric clash betwixt I26 and the sidechain of I31 results in a ~0.7 Å deportation of I31 abroad from I26, resulting in loss of favorable packing contacts involving M26.

https://doi.org/ten.1371/journal.pone.0171355.g005

iii.ii.4. Bounden.

44 of 374 SNV related changes in our dataset (~12%) affect ligand or macromolecule binding backdrop of the protein. A SNV can change the affinity of binding to partners, such as activators, repressors, or substrates. Such changes can also affect the kinetics of interactions with partners or alter bounden specificity. Structurally, a SNV can alter the binding site of the poly peptide, which can in turn affect interactions with partner proteins, ligands, etc. The Lys→Arg (K117R) (rs104894227) exchange in HRAS (mutant PDB: 2QUZ) does not alter either intrinsic Ras GTPase activeness or responsiveness to GTPase activating proteins, but instead causes constitutive activation of HRAS (and downstream targets) past markedly increasing the charge per unit of Gross domestic product dissociation [55]. This mutant HRAS protein activates the RAF/MEK/ERK signaling cascade, leading to growth factor independent cellular proliferation. Although lysine and arginine are both positively charged amino acids, fifty-fifty this conservative substitution results in constitutive activation of HRAS [55]. Clinically, the K117R modify in HRAS leads to abiding and unchecked cell division causing Costello Syndrome [55], which is a rare genetic disorder affecting many parts of the torso.

The Lys→Arg substitution at position 117 maps to the nucleotide-binding consensus sequence NKXD. In wild-blazon HRAS (Wildtype PDB: 2CE2), K117 stabilizes nucleotide bounden when its aliphatic portion interacting with the base, while its terminal amino group interacts with ribose oxygen O4 of N85 and with a primary concatenation segment (Gly13, CO) from the phosphate binding loop (P-loop)[55]. Destabilization of nucleotide bounden is a event of subtle rearrangements due to introduction of a larger sidechain capable of making boosted polar interactions [55]. (Fig half-dozen)

Fig vi. Close-up view of the nucleotide-bounden region of Lys117Arg.

The mutated residual R117 is stabilized by interactions with the P-loop (Gly13, main-chain CO) and additional interaction with Asn85. Thus the mutated residue causes destabilization of nucleotide binding owing to loss of a direct contact with the ligand. Mutated PDB: 2QUZ(blue) and wild-type PDB: 2CE2 (pink).

https://doi.org/ten.1371/journal.pone.0171355.g006

iii.2.5. Assembly.

19 of 374 SNVs in our dataset (~5%) modify the 4th structure (oligomeric assembly) of a poly peptide. Mutation of a buried Ile→Thr (I58T) (rs1141718) in the core of the 4-helix bundle, which also forms an inter-subunit interface in human manganese superoxide dismutase or MnSOD (mutant PDB: 1VAR), reduces both poly peptide assembly stability and activity. Native human MnSOD is a homotetramer, or more precisely a dimer of dimers. [56]. The I58T mutant course of MnSOD is a dimer, as judged by analytical gel filtration [56]. The native Ile 58 sidechain resides in the dimer-dimer interface, where it helps stabilize the normal tetrameric state of the enzyme (Wildtype PDB: 1MSD). The mutation would innovate a smaller sidechain, Thr58, into the dimer-dimer interface, where a packing defect cavity would be predicted to arise. Hence, disruption of the dimer-dimer interface alters the dimer-tetramer equilibrium, favoring dimer. which may be associated with Amylotrophic Lateral Sclerosis [56] (Fig 7). As predicted from the decrease in thermal stability, the mutant MnSOD is compromised at normal body temperatures. Rapid inactivation of Ile58Thr MnSOD at the elevated temperatures (similar during fever and inflammation) would increase superoxide-mediated oxidative damage and peradventure contribute to onset of the diseases.

Fig vii. In manganese superoxide dismutase, a SNV can impact protein associates.

The wild type associates state is tetrameric (left, but due to the mutation mapping to the dimer-dimer interface (in red), the tetrameric structure is non observed in solution (right).

https://doi.org/10.1371/journal.pone.0171355.g007

3.2.6. Rearrangement.

25 of 374 SNV related changes in our dataset (~7%) cause significant conformational changes in the vicinity of the mutated residue. The Ile→Val mutation (I546V) (rs61749389) in von Willebrand factor (vWF, mutant PDB: 1IJK) causes the blood clotting disorder von Willebrand disease. The mutation has a "Gain of Function" effect, producing a constitutively agile form of vWF that binds platelets in the absence of shear forces [57]. Ile546 lies buried in the hydrophobic core of the protein, close to the A1 domain. (N.B.: vWF binds to the glycoprotein lb or Gplb receptor on platelets via interactions with the A1 domain.) In the experimentally determined structure of the mutant protein, a water molecule has insinuated its way into a crenel within the hydrophobic cadre of the protein, created past the substitution of Ile with the smaller Val sidechain [57]. The presence of the water molecule affects the construction of the A1 domain, which in plow potentiates GpIb binding [57]. The disease-causing mechanism is propagation of conformational changes from the hydrophobic core of the poly peptide to its surface, where Gplb binding is enhanced [57] (Fig 8) (Wildtype PDB: 1OAK).

Fig viii. von Willebrand factor (wild-type: green PDBID 1OAK; I546V mutant PDB: 1IJK) with the location of I546V mutation highlighted.

Substitution of Ile with Val at position 546 creates a cavity in the hydrophobic cadre of the I546V mutant structure, which is occupied by a water molecule (denoted by +). The resulting structure perturbation is transmitted through the interior of the poly peptide affecting the locations of the sidechains of Y565, His563, and D560. Collectively, these changes affect Gplb bounden, giving ascension to von Willebrand'south disease.

https://doi.org/10.1371/journal.pone.0171355.g008

Such processes have likened to "Rube Goldberg" machines, which were depicted by the Pulitzer Prize winning cartoonist Rube Goldberg. The cartoonist "invented" fictional machines, in which he imagined that a small perturbation of i office of the machine would lead to large changes at the end of a complicated sequence of concerted interactions (www.rubegoldberg.com/almost/).

3.2.7. SNVs not implicated in disease.

In the preceding examples, we highlighted 3D poly peptide structural changes arising from SNVs thought to be causative of disease. Many single amino acid variants, notwithstanding, have effects on macromolecule structure and function that are Non associated with disease. For example, the T105I variant in Histamine N-Methyltransferase (HNMT) (Mutant PDB: 1JQE) causes a change in temperature dependent specific activeness of the protein, merely is not known to cause illness [58,59]. In this case, the Ile 105 variant but has significant furnishings on catalysis at supra-physiologic temperatures (i.e., producing thermal instability at ~50°C), which are incompatible with human life [58]. The identity of the amino acid at position 105 has significant furnishings on active site structure and dynamics. When visualized in 3D, Ile 105 is seen to make more contacts with other residues in the hydrophobic cadre than does Thr 105 (Wildtype PDB: 2AOT). Altered packing causes structural rearrangement the polypeptide chain, but does non appear to contribute to illness [59].

Well-nigh bioinformatics software tools would predict that the T105I variant is illness causing or not illness causing, neither of which adequately describe the changes that are actually taking place. Bachelor software tools predict that the T105I variant would have either one) moderate affect, 2) ~forty% chances of being a deleterious mutation, or 3) decreased thermal stability. In fact, the T105I mutation exhibits effects but at supra-physiologic temperatures. There is, therefore, a pressing need for more accurate software prediction tools.

3.3. Paucity of structural and functional information for SNVs

For the majority of SNVs represented in our dataset, we found no information almost the structural or the functional changes caused by the SNV published in peer-reviewed literature. We grouped all these SNVs into Unknown_Structural_Consequence and Unknown_Functional_Consequence, respectively. The SNVs that did not have information about the structural result (e.one thousand., conformational changes due to the mutation) were grouped in the Unknown_Structural_Consequence category. If there is no data in the literature nearly the functional bear on (east.thou., affecting the activity or binding) we grouped the SNVs under Unknown_Functional_Consequence. For these SNVs no experimental data is available on the effect. Thus, the SNVs whose influence on the structure and function of the protein is not known fall into this category. 1 possible reason behind the high values in these ii categories (249 SNVs in Unknown_Functional_Consequence and 284 SNVs in Unknown_Structural_Consequence) could be ~lxx% of missense mutations are idea to be neutral [4]. For reference, 9 of the 374 SNVs well characterized at the protein level accept experimental evidence confirming a neutral SNV. We think it likely that most of the 249 or 284 SNVs could also have neutral effect but experimental prove is required to make any such conclusions.

For a pocket-size subset of the 374 PDB entries in our dataset, it was as well possible to identify respective wild-type structures in the PDB archive. Every bit of late Nov 2016, 143 PDB entries with SNV related mutations could be matched to a wild type analogue in the PDB. The supplemental CSV file (S4 File) described in the Information Availability department contains a mapping of PDB IDs for both wild-blazon and mutant entries, where available.

iii.iv. Special cases

The various categories of SNV consequences enumerated above suffice to depict most observed SNVs. Nevertheless, there are several additional effects that warrant discussion.

• Change of Function (PDB: 1OPH, SNV ID: rs121912713, Mutation: M358R, Wildtype PDB: 2QUG)—This SNV related change is associated with Alpha1-Antitrypsin Pittsburg, a fatal bleeding disorder [lx]. The Met→Arg mutation at position 358 converts alpha1-antitrypsin, an elastase inhibitor, into a thrombin inhibitor. The active site surfaces of elastase and thrombin are sufficiently like so that wild-type alpha1-antitrypsin Met358 binds to the active site of elastase (which is specific for methionine at the cleavage site) and mutant alpha1-antitrypsin Arg358 binds to the active site of thrombin (which is specific for arginine or lysine at the cleavage site) [60] (Fig 9A).
• Generation of a mitochondrial targeting sequence (PDB: 1J04, SNV ID: rs121908529, Mutation: G170R, Wildtype PDB: 1H0C)—This mutation is associated with principal hyperoxaluria type 1 autosomal recessive kidney-stone disease, which is caused by peroxisome-to-mitochondrion mistargeting of the liver specific enzyme alanine glyoxylate aminotransferase (AGT). AGT mistargeting occurs in the context of a common polymorphism (P11L) combined with the disease-specific Gly→Arg mutation at position 170 [61,62]. The polymorphism generates a cryptic mitochondrial targeting sequence [63]. When the G170R mutation is present, AGT no longer forms a stable dimer, and the resulting enzyme monomer is able to cross the mitochondrial membrane (Fig 9B). The illness phenotype is caused by depletion of the enzyme within the peroxisome.
• Changed DNA bounden affinity, DNA bending, sex reversal (PDB: 1J47, SNV ID: rs104894969, Mutation: M9I, Wildtype PDB: 1J47)–This mutation causes 46X,Y sexual activity reversal. M64I (using the total-length hSRY sequence numbering) acts principally by reducing the corporeality of protein-induced Dna bending [64]. Deoxyribonucleic acid-binding affinity for the mutant protein is reduced by, at most, a factor of three relative to that of wild-type; however, the credible Deoxyribonucleic acid bend angle induced by M9I protein bounden is ~20° less for that measured for the wild-type protein-DNA complex [64]. Fifty-fifty this relatively modest change in bending angle tin take significant effects on longer-range interactions among other proteins spring near SRY recognition site (Fig 9C) [64].

Fig 9. Examples of special cases.

A) PDB: 1OPH. The highlighted residual in red represents the mutation (M358R) site. Due to this mutation, alpha1-antitrypsin loses its function as an elastase inhibitor, retains its function as a trypsin inhibitor, and gains a function every bit a thrombin inhibitor. B) PDB: 1J04. The 2 highlighted regions represent the ii polymorphisms that deed synergistically. The highlighted region in green represents P11L polymorphism in AGT whereas the highlighted region in ruby represents the affliction-specific G170R mutation. C) PDB: 1J47. The highlighted red residue represents the M64I in the full-length hSRY sequence, which corresponds to M9I in the given construct and affects the extent of DNA bending.

https://doi.org/10.1371/periodical.pone.0171355.g009

iii.5. Frequency in population

One important question of human being genetic studies is how the frequency with which a genetic variation tin exist constitute in a population is correlated with the risk for a disease. Genetic contributions to disease have been attributed A) to a big number of pocket-size-effect common variants across the unabridged allele frequency spectrum, B) a large number of big-effect rare variants, or C) some combination of genotypic, ecology, and epigenetic interactions [65,66,67].

With the growing adoption of next-generation sequencing technology, the frequency with which a particular variation can be plant in a population is being determined for an increasing number of SNVs. In this context, we examined the known population frequencies of the 374 SNVs in our dataset, and correlated observed frequencies with upshot severity information.

In general, variations are identified as polymorphisms, if they are observed in >1% of the population. If a SNV has a Minor Allele Frequency (MAF) < = 1%, we refer to it as a Rare SNV, otherwise as a Mutual SNP. Population frequency data was obtained from the NHLBI Exome Sequencing Project (ESP) Exome Variant Server, which provides data on more than 200,000 individuals in the U.s., and dbSNP. Amongst the 374 SNVs we analyzed, 51% (191) were Mutual, 16% (61) were Rare, and for 33% (122) no frequency information was available, denoted No_Freq (Fig 10). In one case data was discordant between yard genomes and ESP. In this example the data was taken from ESP. We further partition this data, based on the severity of the SNV. Where SNVs are associated with a affliction, we categorized them as Affliction causing. SNVs that associated with the gamble of developing a disease are grouped under Risk. Finally, nether Other/No effect nosotros identified SNVs that accept a neutral upshot, or for which no disease related information was available.

Fig 10. Frequency distribution of the SNVs.

Bar graph indicates distribution of SNVs as Other/No effect (either neutral or does not cause a disease), Disease causing and associated with the Hazard of developing a disease inside each frequency category.

https://doi.org/ten.1371/periodical.pone.0171355.g010

Disease related SNVs appear to be more frequent in the Rare category. In addition, the diseases that take Common SNVs generally are much milder and unlikely to exist life threatening prior to procreation (such as asthma, or diabetes). Variations in the No_Freq category accept a big number of illness related SNVs and the frequency distribution is similar to the Rare category. Nosotros speculate that some of these SNVs are ultra rare SNPs, or the diseases acquired past these variations are serious, so a much larger population size might be needed to plant frequencies reliably.

The dataset compiled here contains a mix of large- and small- effect variants. Some of the near striking examples described in this manuscript are rare variations that take big effects on proteins. In that location are, however, likewise a large number of SNVs, for which no articulate consequence on the 3D protein structure is known. Some other possible model to explain these mutations is as well the small-upshot/common variant hypothesis mentioned above.

4. Conclusion

The focus of this study are poly peptide structures in the PDB archive for which 3D structures of genetic variant proteins have been determined. In this context, it is important to note that the contents of the Protein Information Banking company do non constitute a representative subset of all proteins. There is option bias in the PDB in the sense that the availability of the 3D construction of a given protein depends critically on investigator scientific gustatory modality, funding trends, technical feasibility, and no small amount of luck at the bench. The data collected hither provide of import insights into possible structural and functional changes in proteins. Merely information technology must exist stressed that our work provides a qualitative clarification of possible changes, not a quantitative assessment. Notwithstanding the enormous growth in the PDB from vii to more 124,000 archival entries, information technology is simply not possible to provide an accurate account of the consequences of human genetic variation across the homo proteome.

Single Nucleotide Variations (SNVs) correspond the most common genetic variations observed in humans, accounting for about 90% of sequence differences [68]. In this written report, we analyzed the structural and functional effects of single amino acid changes in proteins owing to SNVs. Our analyses of a relatively small-scale dataset of just 374 SNVs underscores the challenges inherent in attempting to understand the consequences of a particular genetic variation at the level of the encoded protein.

Specifically, our results document that the range of possible SNV effects at the protein level are significantly greater than currently assumed by existing software prediction methods, and that correct prediction of consequences remains a significant challenge. In general, virtually of the software methods that attempt to predict the consequence of SNVs, classify SNVs every bit either illness causing or not disease causing. A signal mutation may not be causing a disease, merely it tin can nonetheless have an result on the structure and office of the protein. Consequences due to such point mutations ofttimes go undetected, equally they do not result in a disease phenotype, although they practice affect the protein and may perturb normal human physiology.

In addition to the examples described herein, it is easy to imagine that other consequences related to SNV changes volition be institute every bit more experimentally determined 3D structures go available and our understanding of poly peptide structure-part relationships continues to grow. For instance, the impact of genetic variation on protein-protein interactions is not well represented in the current dataset.

A comprehensive understanding of iii-dimensional construction, dynamics, and biophysics of wild-type and mutant proteins will be required to develop better tools that tin make accurate predictions regarding the consequences of genetic changes manifested at the atomic level in poly peptide cistron products.

Supporting information

S1 Fig. Experimental procedures.

Experimental procedures for determining the PDB structures in the dataset of 374 SNVs. 325 SNVs have PDB coordinates adamant past X-ray crystallography. 49 have solution NMR structures available in PDB.

https://doi.org/10.1371/journal.pone.0171355.s001

(PNG)

S3 Fig. Distribution of SNVs.

Distribution of the SNVs in the dataset, for which no structural and functional outcome was found in existing literature, based on structural position and secondary construction elements (left). Distribution based on structural position and secondary structure elements for SNVs with structural and functional effect information (correct).

https://doi.org/10.1371/journal.pone.0171355.s003

(PNG)

Acknowledgments

We thank Hagen Tilgner, Lilia Iakoucheva, and Roser Corominas for useful discussions, Sajani Swamy for feedback on the manuscript.

Writer Contributions

1. Conceptualization: AP.
2. Data curation: RB.
3. Formal analysis: RB.
4. Funding conquering: SKB.
5. Investigation: RB.
6. Methodology: RB PWR SKB AP.
7. Software: AP RB.
8. Supervision: AP.
9. Validation: RB PWR SKB AP.
10. Writing – original draft: RB AP.
11. Writing – review & editing: PWR SKB.

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Source: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0171355

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