The global PDB archive now contains more than 126,000 experimentally determined atomic level three-dimensional (3D) structures of biological macromolecules , all of which are freely accessible via the Internet. Knowledge of the 3D structure of the gene product can help in understanding its function and role in disease. Of particular interest in the PDB archive are proteins for which 3D structures of genetic variant proteins have been determined, thus revealing atomic-level structural differences caused by the variation at the DNA level. Herein, we present a systematic and qualitative analysis of such cases. We observe a wide range of structural and functional changes caused by single amino acid differences, including changes in enzyme activity, aggregation 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.The Protein Data Bank (PDB;  Although genome wide studies provide insights into the genetic basis of human disease, 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 found [With the ever-growing importance of genomics for human health, considerable efforts have been devoted to linking human phenotypes to genotypic variations at the nucleotide level and changes in 3D protein structure ,2. Genetbe found .One way to analyze large datasets of genetic variation is to use bioinformatics tools to filter the data . ComputaArguably, the most informative source of data that can explain what is causing a particular phenotype is the availability of a 3D experimentally-determined structure that contains atomic level insight into the consequences of a particular genomic variant. The RCSB Protein Data Bank (RCSB PDB)  enables Knowledge of the 3D structure of a gene product is beneficial in predicting and understanding both function and role in disease. However, most studies that analyze the relationship between point mutations and experimentally observed 3D protein structure published to date have been restricted to individual proteins or single diseases. There is a paucity of quantitative analyses of the consequences of SNVs on 3D protein structure going beyond the realm of prediction .The goal of this study is to improve our understanding of the relationship between point mutations and experimentally observed consequences in 3D. We identified a benchmark dataset of protein structures that contain well-characterized point 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 effects of SNVs on the structure, function, stability, and binding properties of proteins.The data set used in this paper is a semi-automatically derived and hand-curated collection of proteins, each of which possess an amino acid that has been changed by a SNV and 3D atomic coordinates are available in the PDB.To assemble this data set,via LS-SNP/PDB [We identified 2596 structures extant in the PDB for which non-synonymous SNV could be mapped -SNP/PDB . For eacFrom these 2596 structures, we selected only those structures for which the dbSNP mutation information matched information coming from UniProt and the 3D structure. For example, rs28933981, the change in dbSNP is T→M and in PDB: 1BZE, the sequence difference in the structure is also T→M, and this case was included in our dataset. In contrast, the dbSNP database entry for SNV rs128620185 reports R→H, but in the PDB archive (1BTK) the experimentally observed sequence difference is R→C. This case was excluded from our dataset, because it does not correspond to the reported R→H SNV.After filtering for database inconsistencies, we removed mappings of the same SNV to multiple PDB entries, ensuring that each SNV is only 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 not possible to do so, and the dataset contains 49 structures determined by NMR.  of a given SNV on a gene product, we systematically reviewed the available literature to identify experimentally verified functional effects. We also performed searches in several databases (see below). For each SNV, we extracted the following information from literature and from databases:position of the SNV on the 3D protein structure in the PDB (present on the surface vs. buried in the interior), estimated with BioJava surface accessibility calculations [The ulations .Loop vs. Alpha_helix vs. Beta_strand secondary structure, determined from secondary structure annotations obtained from PDB [Whether the amino acid substitution falls within from PDB ,43.effect or consequence does the SNV have on the protein?What Activity of a protein vs. its Stability vs. Binding vs. Assembly vs. Rearrangement . 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 understanding 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 [Rare, with the remainder classified as Common SNVs.We classified mutations, whether they affect t server ,44 and dt server . SNVs wiDatabases and servers used in this work were as follows:RCSB PDB—The RCSB Protein Data Bank [ata Bank ,43 is thLS-SNP/PDB–Is a web-tool for the annotation of human SNPs. It contains an automated pipeline that systematically maps human non-synonymous SNPs onto PDB structures [ructures .dbSNP—The Single Nucleotide Polymorphism Database (dbSNP) [ (dbSNP)  is an ar (dbSNP) .NHLBI Exome Sequencing Project (ESP) Exome Variant Server—Contains a large collection of well-phenotyped US populations [ulations ,46.PubMed—PubMed contains more than 23 million abstracts for biomedical literature from MEDLINE, life science journals, and online books [ne books .To enable deeper analysis of genetic variation in the context of protein sequence and 3D structures, we developed tools to facilitate mapping of any genetic location onto corresponding protein sequences and 3D protein structures . These tMapping tool from human genomic position to protein 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.doHuman Gene View—This genome browser supports navigation of the human genome and investigating the relationship between PDB archival entries and genes.Protein Feature 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.The following categories were used to classify the effects of SNVs at the level of the protein:Activity—The SNV causes increase, decrease, or complete loss of protein activity.Aggregation—The SNV renders the protein aggregation prone.Stability—The SNV causes a change in protein stability. It may make the protein susceptible to proteolytic cleavage, or cause a change in thermal inactivation temperature, or cause a change in the energy of stabilization of the protein. It can also lead to destabilization of a protein oligomer, loss of packing or hydrophobic interactions, or change a mode(s) of protein-protein interaction.Binding/Dissociation–The SNV leads to changes in affinity for a known binding partner, or alterations in association or dissociation kinetics. It can also cause structural changes in the binding site or affect specificity for a binding partner(s).Assembly—The SNV affects the oligomeric assembly properties of the protein.Rearrangement—The SNV causes local structural rearrangements  in the neighborhood of the amino acid change arising from the SNV.Surface and Buried, by 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  of the SNVs map to Loop regions compared to ~34% for Alpha_helix and ~14% for Beta_strand. This finding was not unexpected as amino acid changes in Loop regions can often be compensated for without affecting the structure and function of the protein, owing to the flexibility of these polypeptide chain 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 , and data regarding the structural consequences of the SNVs related changes are NOT available for 284 of 374 of SNVs (~75%). Nevertheless, the effects that have been described in the literature are often quite dramatic. By systematically reviewing relevant peer-reviewed literature, we determined that a broad range of possible effects could be attributed to a single residue change. To categorize these findings, we classified responses or consequences due to SNVs as follows: Examples for each response category are summarized below in A single residue mutation can have multiple effects on the protein structure and function. Thus, the consequences of a single SNV can affect more than one of the six categories represented in Arylsulfatase A (gene: ARSA) breaks down sulfatides. The Pro→Leu mutation (P428L) (rs28940893) mapping to amino acid 426 in the PDB structure yields an oligomerization defect (preferred mutant assembly 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 half-life  and metaDelta-aminolevulinic acid dehydratase (gene: ALAD) catalyzes an early step in tetrapyrrole biosynthesis . The PheIn the following section, we provide a summary of the results for each SNV response category, and discuss several examples in more detail.Beta_strand β19 with Alpha_helix α13. With the exception of the mutation site, the overall structure of the G526R mutant protein is almost identical to that of the wild type (Wildtype PDB: 2ZT5) enzyme . Although the G526R change does not disturb the positions of residues comprising the active site, the sidechain of the mutated residue (R526) interdicts access to the active site, thereby inactivating the enzyme [52 of 374 SNV related changes in our dataset (~14%) either increase or decrease protein activity. In some cases, SNVs lead to complete loss of function. For example, human glycyl-tRNA synthetase (mutant PDB: 2PMF) loses detectable enzymatic activity due to a G526R (rs137852646) mutation, which is causative of Charcot-Marie-Tooth disease . G526 ise enzyme  Fig 4)..Beta_strBeta_strand adjacent to dimer interface residues and near residues Leu106 and Ile113, which help to stabilize the dimer interface. Leu106 is part of a Greek key super secondary structural motif involved in capping one end of the β barrel. The aliphatic sidechain of Leu106 stabilizes the dimer interface by acting as a cork, which is stabilized by van der Waals interactions with Ala4 and Ile113 [28 of 374 SNVs related changes in our dataset (~6%) give rise to protein aggregation, which is a hallmark of some neurodegenerative diseases, e.g., Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's 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’s disease or amyotrophic lateral sclerosis (ALS), which is caused by instability of the Ala→Val (A4V) (rs121912442) mutant of human Cu, Zn superoxide dismutase (HSOD) (mutant PDB: 1N19) . Ala4 isd Ile113 . Locatiod Ile113 .58 of 374 SNV related changes in our dataset (~16%) lead to reduced protein stability. A SNV can affect the stability of the protein by making it susceptible to proteolysis or by changing the thermal inactivation temperature. To exemplify how a mutation can influence protein stability, we analyze the following 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 . It is a44 of 374 SNV related changes in our dataset (~12%) affect ligand or macromolecule binding properties 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 binding specificity. Structurally, a SNV can alter the binding site of the protein, which can in turn affect interactions with partner proteins, ligands, etc. The Lys→Arg (K117R) (rs104894227) substitution in HRAS (mutant PDB: 2QUZ) does not alter either intrinsic Ras GTPase activity or responsiveness to GTPase activating proteins, but instead causes constitutive activation of HRAS (and downstream targets) by markedly increasing the rate of GDP dissociation . This muThe Lys→Arg substitution at position 117 maps to the nucleotide-binding consensus sequence NKXD. In wild-type HRAS (Wildtype PDB: 2CE2), K117 stabilizes nucleotide binding when its aliphatic portion interacting with the base, while its terminal amino group interacts with ribose oxygen O4 of N85 and with a main chain segment  from the phosphate binding loop (P-loop). Destabi19 of 374 SNVs in our dataset (~5%) change the quaternary structure (oligomeric assembly) of a protein. Mutation of a buried Ile→Thr (I58T) (rs1141718) in the core of the four-helix bundle, which also forms an inter-subunit interface in human manganese superoxide dismutase or MnSOD (mutant PDB: 1VAR), reduces both protein assembly stability and activity. Native human MnSOD is a homotetramer, or more precisely a dimer of dimers. . The I58via interactions with the A1 domain.) In the experimentally determined structure of the mutant protein, a water molecule has insinuated its way into a cavity within the hydrophobic core of the protein, created by the substitution of Ile with the smaller Val sidechain [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  causes the blood clotting disorder von Willebrand disease. The mutation has a “Gain of Function” effect, producing a constitutively active form of vWF that binds platelets in the absence of shear forces . Ile546 idechain . The preidechain . The disidechain  Fig 8)  via intewww.rubegoldberg.com/about/).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 modest perturbation of one part of the machine would lead to big changes at the end of a complicated sequence of concerted interactions  (Mutant PDB: 1JQE) causes a change in temperature dependent specific activity of the protein, but is not known to cause disease [In the preceding examples, we highlighted 3D protein structural changes arising from SNVs thought to be causative of disease. Many single amino acid variants, however, have effects on macromolecule structure and function that are  disease ,59. In t disease . The idedisease causing or not disease causing, neither of which adequately describe the changes that are actually taking place. Available software tools predict that the T105I variant would have either 1) moderate impact, 2) ~40% chances of being a deleterious mutation, or 3) decreased thermal stability. In fact, the T105I mutation exhibits effects only at supra-physiologic temperatures. There is, therefore, a pressing need for more accurate software prediction tools.Most bioinformatics software tools would predict that the T105I variant is Unknown_Structural_Consequence and Unknown_Functional_Consequence, respectively. The SNVs that did not have information about the structural consequence  were grouped in the Unknown_Structural_Consequence category. If there is no information in the literature about the functional impact  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. One possible reason behind the high values in these two categories  could be ~70% of missense mutations are thought to be neutral [For the majority of SNVs represented in our dataset, we found no information about the structural or the functional changes caused by the SNV published in peer-reviewed literature. We grouped all these SNVs into For a small subset of the 374 PDB entries in our dataset, it was also possible to identify corresponding wild-type structures in the PDB archive. As of late November 2016, 143 PDB entries with SNV related mutations could be matched to a wild type counterpart in the PDB. The supplemental CSV file (S4 File) described in the Data Availability section contains a mapping of PDB IDs for both wild-type and mutant entries, where available.The various categories of SNV consequences enumerated above suffice to describe most observed SNVs. Nevertheless, there are several additional effects that warrant discussion.Change of Function —This SNV related change is associated with Alpha1-Antitrypsin Pittsburg, a fatal bleeding disorder [disorder . The Metdisorder  Fig 9A)Change ofGeneration of a mitochondrial targeting sequence —This mutation is associated with primary 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 [tion 170 ,62. The tion 170 . When thChanged DNA binding affinity, DNA bending, sex reversal –This mutation causes 46X,Y sex reversal. M64I (using the full-length hSRY sequence numbering) acts principally by reducing the amount of protein-induced DNA bending [One important question of human genetic studies is how the frequency with which a genetic variation can be found in a population is correlated with the risk for a disease. Genetic contributions to disease have been attributed A) to a large number of small-effect common variants across the entire allele frequency spectrum, B) a large number of large-effect rare variants, or C) some combination of genotypic, environmental, and epigenetic interactions ,66,67.With the growing adoption of next-generation sequencing technology, the frequency with which a particular variation can be found 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 consequence severity data.Rare SNV, otherwise as a Common 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 US, and dbSNP. Among the 374 SNVs we analyzed, 51% (191) were Common, 16% (61) were Rare, and for 33% (122) no frequency information was available, denoted No_Freq  < = 1%, we refer to it as a  No_Freq . In one Disease related SNVs appear to be more frequent in the Rare category. In addition, the diseases that have Common SNVs generally are much milder and unlikely to be life threatening prior to procreation . Variations in the No_Freq category have a large number of disease related SNVs and the frequency distribution is similar to the Rare category. We speculate that some of these SNVs are ultra rare SNPs, or the diseases caused by these variations are serious, so a much larger population size might be needed to establish frequencies reliably.The dataset compiled here contains a mix of large- and small- effect variants. Some of the most striking examples described in this manuscript are rare variations that have large effects on proteins. There are, however, also a large number of SNVs, for which no clear consequence on the 3D protein structure is known. Another possible model to explain these mutations is also the small-effect/common variant hypothesis mentioned above.qualitative description of possible changes, not a quantitative assessment. Notwithstanding the enormous growth in the PDB from 7 to more than 124,000 archival entries, it is simply not possible to provide an accurate account of the consequences of human genetic variation across the human proteome.The focus of this study are protein 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 Data Bank do not constitute a representative subset of all proteins. There is selection bias in the PDB in the sense that the availability of the 3D structure of a given protein depends critically on investigator scientific taste, funding trends, technical feasibility, and no small amount of luck at the bench. The data collected here provide important insights into possible structural and functional changes in proteins. But it must be stressed that our work provides a Single Nucleotide Variations (SNVs) represent the most common genetic variations observed in humans, accounting for about 90% of sequence differences . In thisdisease causing or not disease causing. A point mutation may not be causing a disease, but it can still have an effect on the structure and function of the protein. Consequences due to such point mutations often go undetected, as they do not result in a disease phenotype, although they do affect the protein and may perturb normal human physiology.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, most of the software methods that attempt to predict the consequence of SNVs, classify SNVs as either In addition to the examples described herein, it is easy to imagine that other consequences related to SNV changes will be found as more experimentally determined 3D structures become available and our understanding of protein structure-function relationships continues to grow. For example, the impact of genetic variation on protein-protein interactions is not well represented in the current dataset.A comprehensive understanding of three-dimensional structure, dynamics, and biophysics of wild-type and mutant proteins will be required to develop better tools that can make accurate predictions regarding the consequences of genetic changes manifested at the atomic level in protein gene products.S1 FigExperimental procedures for determining the PDB structures in the dataset of 374 SNVs. 325 SNVs have PDB coordinates determined by X-ray crystallography. 49 have solution NMR structures available in PDB.(PNG)Click here for additional data file.S2 FigDistribution of the resolution of the X-Ray crystallography PDB structures used for the dataset.(PNG)Click here for additional data file.S3 FigDistribution of the SNVs in the dataset, for which no structural and functional consequence was found in existing literature, based on structural position and secondary structure elements (left). Distribution based on structural position and secondary structure elements for SNVs with structural and functional consequence information (right).(PNG)Click here for additional data file.S1 FileA CSV file containing the annotated dataset that has been used for this manuscript.(CSV)Click here for additional data file.


Structure‐based ligand design requires an exact description of the topology of molecular entities under scrutiny. IChem is a software package that reflects the many contributions of our research group in this area over the last decade. It facilitates and automates many tasks  usually left to the modeler's choice. It therefore permits the detection of molecular interactions between two molecules in a very precise and flexible manner. Moreover, IChem enables the conversion of intricate three‐dimensional (3D) molecular objects into simple representations  that facilitate knowledge acquisition at very high throughput. The toolkit is an ideal companion for setting up and performing many structure‐based design computations. IChem is a suite of tools consisting of about 50 000 lines of computer code written in C++, decomposed in nine modules, for detecting and comparing molecular objects  frequently manipulated in structure‐based computational chemistry Table . Herein Setting the scene: pdbconv. A reasonable start to any structure‐based design project is the retrieval of experimentally determined protein structures from the Protein Data Bank (PDB),pdbconv module of the IChem toolkit automates the preparation of ready‐to‐use protein–ligand structures. The process first assigns a specific class to each residue name  classifier to predict the structural druggability (or ligandability) of the inspected cavity. In a standard benchmarking exercise consisting of 113 cavities of known druggability, Volsite presented the highest accuracy when compared with state‐of‐the‐art tools.Shaper)Interestingly, the similarity of two Volsite cavities can be estimated by analogy to classical ligand similarity measurements, using a companion tool  of protein–ligand interactions. For example, the IFP module enables to list all protein–ligand interactions occurring in a complex and to output an interaction fingerprint as a bit string .GRIM presents several advantages over alternative knowledge‐based rescoring strategies: 1) it can be coupled to any docking algorithm, 2) it does not constrain ligand docking but rewards interaction patterns already present among PDB templates, 3) it takes advantage of ligands with similar binding modes and not necessarily similar chemical structures, 4) it can be applied in a target family‐biased pose selection process in which PDB templates from the same protein but also from similar targets can be used to store reference interaction patterns, and 5) it permits to directly quantify binding mode similarity between a predicted protein–ligand complex and any PDB template at a very high throughput.Detecting biologically relevant protein–protein interfaces: detectPPI. Protein–protein interfaces (PPIs) represent challenging but very promising targets for drug discovery.detectPPI module uses the general IChem functions  to detect the interface, identifies the corresponding IPAs and generate a fixed‐length property vector  by just specifying the PDB three‐letter code.Interestingly, this new IChem module can be used at a high throughout to detect biologically relevant PPIs at the PDB scale. Alternatively, the method can be used on‐line (http://bioinfo-pharma.u-strasbg.fr/labwebsite/download.html.In conclusion, IChem is a suite of software dedicated to the analysis and comparison of three‐dimensional molecular objects. It converts an intricate three‐dimensional information into much simpler fingerprints or graphs, thereby enabling high‐throughput comparisons and fueling machine learning models for predicting important features like protein–protein interfaces, druggable cavities, interaction patterns and binding poses. IChem is available for nonprofit academic research at The authors declare no conflict of interest.


Giardia are highly prevalent globally, and infect a wide range of vertebrate hosts including humans, with proliferation and pathology restricted to the small intestine. This narrow ecological specialization entailed extensive structural and functional adaptations during host-parasite co-evolution. An example is the streamlined mitosomal proteome with iron-sulphur protein maturation as the only biochemical pathway clearly associated with this organelle. Here, we applied techniques in microscopy and protein biochemistry to investigate the mitosomal membrane proteome in association to mitosome homeostasis. Live cell imaging revealed a highly immobilized array of 30–40 physically distinct mitosome organelles in trophozoites. We provide direct evidence for the single giardial dynamin-related protein as a contributor to mitosomal morphogenesis and homeostasis. To overcome inherent limitations that have hitherto severely hampered the characterization of these unique organelles we applied a novel interaction-based proteome discovery strategy using forward and reverse protein co-immunoprecipitation. This allowed generation of organelle proteome data strictly in a protein-protein interaction context. We built an initial Tom40-centered outer membrane interactome by co-immunoprecipitation experiments, identifying small GTPases, factors with dual mitosome and endoplasmic reticulum (ER) distribution, as well as novel matrix proteins. Through iterative expansion of this protein-protein interaction network, we were able to i) significantly extend this interaction-based mitosomal proteome to include other membrane-associated proteins with possible roles in mitosome morphogenesis and connection to other subcellular compartments, and ii) identify novel matrix proteins which may shed light on mitosome-associated metabolic functions other than Fe-S cluster biogenesis. Functional analysis also revealed conceptual conservation of protein translocation despite the massive divergence and reduction of protein import machinery in Giardia mitosomes.Protozoan parasites of the genus  Giardia lamblia are the most highly reduced MROs known and have proven difficult to investigate because of their extreme divergence and their unique biophysical properties. Here, we implemented a novel strategy aimed at systematic analysis of the organelle proteome by iterative expansion of a protein-protein interaction network. We combined serial forward and reverse co-immunoprecipitations with mass spectrometry analysis, data mining, and validation by subcellular localization and/or functional analysis to generate an interactome network centered on a giardial Tom40 homolog. This iterative ab initio proteome reconstruction provided protein-protein interaction data in addition to identifying novel organelle proteins and functions. Building on this data we generated information on organelle replication, mitosome morphogenesis and organelle dynamics in living cells.Organelles with endosymbiotic origin are present in virtually all extant eukaryotes and have undergone considerable remodeling during > 1 billion years of evolution. Highly diverged organelles such as mitosomes or plastids in some parasitic protozoa are the product of extensive secondary reduction. They are sufficiently unique to generate interest as targets for pharmacological intervention, in addition to providing a rich ground for evolutionary cell biologists. The so-called mitochondria-related organelles (MROs) comprise mitosomes and hydrogenosomes, with the former having lost any role in energy metabolism along with the organelle genome. The mitosomes of the intestinal pathogen  Furthermore, this dataset contained two axoneme-associated GASP-180 proteins (Gl50803_137716 and Gl50803_16745) [Co-IP with an HA-tagged variant of MOMTiP-1 yielded a large dataset of 221 exclusive hits  which integories . In addiproteins . SimilarTaken together, a first reverse co-IP analysis using the single-pass transmembrane MOMTiP-1 provided robust validation of the experimental approach used to identify mitosome membrane proteins, and has expanded the predicted mitosomal membrane and import machinery interactome to 22 proteins .GlTom40 are strong interaction partners. We analyzed the intersection of their respective datasets to identify common candidate interaction partners and identified 27 proteins with high reliability  and protein Gl50803_14939 (MOMTiP-3) as baits. MOMTiP-7 (Qb-SNARE 4), MOMTiP-4 and MOMTiP-3 were chosen because they were identified either exclusively or in both the GlTom40- and MOMTiP-1 co-IP datasets, suggesting they may reside in the mitosomal outer membrane and could thus serve as tools for a lateral and outward expansion of this compartment’s interactome. On the other hand, GIIscS was chosen to extend the mitosomal proteome inwards towards the organellar matrix. Correct mitosomal localization for all 4 HA-tagged variants had been previously confirmed by IFA  were detected in this dataset, in addition to several previously identified hypothetical mitosomal proteins   , suggestMOMTiP-7 presents dual localization,  as baits based on i) spectral counts with high stringency parameters in the GlTom40 co-IP dataset and ii) confirmed localization of epitope-tagged variants to mitosomes by IFA. All 24 localized mitosome proteins  were parsed according to molecular function and biological process (https://www.blast2go.com/). Metal ion, Fe-S, ATP, and protein binding were the major molecular functions associated with these proteins. Interestingly, other biological processes involving response to lipid and transmembrane transport were also identified with significant p-values. An additional 93 candidates annotated as hypothetical proteins  were analyzed using Blast2go . Ectopic expression of C-MOMTiP-1 showed a distinct cytosolic localization by IFA  or MOMTiP-3 [MOMTiP-3 was exclusively identified in the monicida , yieldedmonicida . These dMOMTiP-3  were conMOMTiP-3 ). IncideMOMTiP-3 .GlTom40 and MOMTiP-1 co-IP datasets.Co-IP data combined with imaging of tagged variants identified 6 proteins with dual localization at mitosomes and ER . ContactT. vaginalis [G. lamblia, possibly due to extensive sequence divergence. Thus, whether this function is preserved in Giardia mitosomes is not known however, organelle biogenesis would necessarily depend on ER-derived lipids which are transported to mitosomes either by carrier proteins or via membrane contact sites. The latter requires tethering complexes to facilitate phospholipid exchange between the two organelles. Given that MOMTiP-7 is predicted to be a SNARE, we explored the idea that this protein is part of a larger complex mediating ER-mitosome interaction. Co-IP of MOMTiP-7 specifically detected, in addition to outer membrane proteins such as GlTom40, MOMTiP-1 and MOMTiP-3, 3 hypothetical proteins, two of which, MOMTiP-8 and MOMTiP-5 (both predicted soluble proteins), localized both to the ER and to mitosomes. In addition, a domain in MOMTiP-8 has similarity to a yeast “Maintenance of mitochondrial morphology” protein 1 (Mmm1) of the ERMES complex. Moreover, HHpred analysis revealed a link between MOMTiP-5 and a beta barrel lipid binding protein MLN64  in H. sapiens which facilitates cholesterol transport to mitochondria [G. lamblia mitosomes possibly involved in generating ER—mitosome membrane contact sites  allowed for time-lapse experiments to follow individual organelles in a cell. However, we found no evidence for motility of organelles, neither in the CMC nor in PMs, even after prolonged observation (1.5 h). This is consistent with the lack of motor proteins such as kinesins and dyneins in any of the mitosomal protein interactomes we generated. Moreover, FRAP experiments revealed no exchange of GFP-tagged membrane proteins between organelles during the period of observation  indicative of an organelle replication defect. As one of the key players in the regulation of mitochondrial fission, DRPs are mechano-enzymes conserved from yeast to vertebrates [G. lamblia harbors a single dynamin homologue GlDRP shown to play a major role in this parasite’s endocytic pathway and stage conversion [GlDRP-K43E variant exhibited larger and fewer mitosomes, compared to cells expressing the wild type GlDRP variant functional conservation of mitochondrial and MRO fission [G. lamblia mitosome fission is functionally conserved is further substantiated by the identification of MOMTiP-6 which presents dual localization to mitosomes and the ER. HMMER-based predictions relate MOMTiP-6 to human mitochondrial fission protein  which participates in the recruitment of dynamin-related protein 1 (Drp1) to the mitochondrial surface for organelle fission [G. lamblia mitosomes retain at least the machinery for fission in which the mechano-enzyme GlDRP and outer mitosomal membrane elements such as MOMTiP-1 and 3 [We had previously shown that replication and inheritance of the CMC is coordinated in a cell cycle-dependent manner, whereas PMs divided stochastically . The lacervation , which ftebrates ,86,87,88nversion ,89,90. TP variant. This is fission ,92,93,94 fission ,96. The -1 and 3  play cenGlTom40-centered interactome network. Ultimately this strategy should allow building a combined proteome, which delineates the full complement of organelle proteins, peripherally associated factors, as well as interfaces with the ER and the cytoskeleton. Although this strategy requires numerous rounds of sequential co-IP and validation, it is highly informative because it produces interaction data in addition to identifying novel organelle proteins. Combined with testing of epitope-tagged variants of candidate proteins for organelle localization as a straightforward validation criterion, serial co-IPs allow for unambiguous definition of the organelle-specific proteome, as well as interfaces with other cellular structures. This strategy also led to the discovery of MOMTiP-1, a strong GlTom40 interaction partner which plays a role in mitosomal morphogenesis. Together with GlDRP (this work) and MOMTiP-3 , these S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S1 FigG. lamblia lines expressing (A) GlTom40, (B) MOMTiP-1, (C) MOMTiP-7, (D) GlIscS, (E) MOMTiP-4 and (F) MOMTiP-3, all used as HA-tagged baits in co-IP experiments , in combination with the endogenous mitosomal marker GlIscU . Nuclei were stained with DAPI . The central mitosome complex was clearly labelled by both fluorophores in all lines. Scale bar: 1μm.Immunofluorescence co-labelling and wide-field microscopy analysis of transgenic (TIF)Click here for additional data file.S2 FigWith increasing concentrations of formaldehyde (0–4.5%), immuno-detection (Western blot) of the Tom40-HA reporter shows a shift from the monomeric form to higher molecular weight complexes,. Molecular size (kDa) marker (M) bands are indicated on the left axis. A concentration of 2.25% formaldehyde (arrowhead) was later adopted for all subsequent forward and reverse co-IPs.(TIF)Click here for additional data file.S3 Fig(A) Data distribution pie chart for protein hits with either Blast2Go annotation (“B2G annotated”), or associated to gene ontology (GO) terms (“with GO mapping”) or to Blast annotation data (“with Blast hits”). (B) The top 20 GO terms distributed in the three root categories for “biological process” (BP), “molecular function” (MF) and “cellular component” (CC). (C) A direct count of GO terms associated to MF. (D) A direct count of GO terms associated to BP.(TIF)Click here for additional data file.S4 Fig(A) Data distribution pie chart for protein hits with either Blast2Go annotation (“B2G annotated”), or associated to gene ontology (GO) terms (“with GO mapping”) or to Blast annotation data (“with Blast hits”). (B) The top 20 GO terms distributed in the three root categories for “biological process” (BP), “molecular function” (MF) and “cellular component” (CC). (C) A direct count of GO terms associated to MF. (D) A direct count of GO terms associated to BP.(TIF)Click here for additional data file.S5 Fig(A) IFA and wide-field microscopy analysis of transgenic Giardia cells expressing C-MOMTiP-1 (in green), a truncated HA-tagged version of MOMTiP-1 consisting only of the predicted C-terminal domain (residues 31–133). C-MOMTiP-1 accumulates primarily in the cytosol. Nuclei are labelled with DAPI (in blue). Inset: DIC image. Scale bar: 1μm. (B) Venn diagram depicting the overlap of datasets derived from native co-IP of C-MOMTiP-1 and control WB cells.(TIF)Click here for additional data file.


Next-generation sequencing (NGS) technologies are providing genomic information for an increasing number of healthy individuals and patient populations. In the context of the large amount of generated genomic data that is being generated, understanding the effect of disease-related mutations at molecular level can contribute to close the gap between genotype and phenotype and thus improve prevention, diagnosis or treatment of a pathological condition. In order to fully characterize the effect of a pathological mutation and have useful information for prediction purposes, it is important first to identify whether the mutation is located at a protein-binding interface, and second to understand the effect on the binding affinity of the affected interaction/s. Computational methods, such as protein docking are currently used to complement experimental efforts and could help to build the human structural interactome. Here we have extended the original pyDockNIP method to predict the location of disease-associated nsSNPs at protein-protein interfaces, when there is no available structure for the protein-protein complex. We have applied this approach to the pathological interaction networks of six diseases with low structural data on PPIs. This approach can almost double the number of nsSNPs that can be characterized and identify edgetic effects in many nsSNPs that were previously unknown. This can help to annotate and interpret genomic data from large-scale population studies, and to achieve a better understanding of disease at molecular level. Next-generation sequencing (NGS) technologies have dramatically lowered gene sequencing costs, and are providing genomic information for an increasing number of healthy individuals and patient populations. To make the most of all these increasingly available genomic data, we need to understand better the link between the genetic information and the phenotype it produces . In thisAmong the different possible genetic variants found in human populations, non-synonymous single nucleotide polymorphisms (nsSNPs) are small changes in the DNA of an individual that result in a single aminoacid mutation at protein level. Understanding the effect of disease-related mutations at molecular level can contribute to close the gap between genotype and phenotype and thus improve prevention, diagnosis or treatment of a pathological condition. In a first approach, the impact of a mutation in the functional activity of a protein (e.g. an enzyme) can be described in structural terms, because it produces a change in protein folding and stability  or it afIn this scenario, it will be important to consider the impact of a given mutation in such networks. By gaining a system-wide perspective of protein functions, we can further study which subsets of PPIs are essential in regulating a particular biological process and how genetic variants such as non-synonymous single nucleotide polymorphisms (nsSNPs) affect these PPIs ,14. For In addition to improving our knowledge of disease at molecular level, understanding the role of pathological mutations in protein-protein interactions is also of paramount importance for predicting purposes. The functional prediction of nsSNPs that cause amino acid changes in proteins is difficult because they can modify the cellular behavior through different mechanisms, for instance by affecting protein stability, function, or interactions with other proteins and biomolecules , as we mThus, in order to fully characterize the effect of a pathological mutation at molecular level and have useful information for prediction purposes, it is important first to identify whether the mutation is located at a protein-binding interface, and second to understand the effect on the binding affinity of the affected interaction/s. However, the limited structural data on PPIs make all the above mentioned studies incomplete. The number of protein-protein complexes with their 3D structure deposited in the Protein Data Bank (PDB)  is very The general aim of this work is to show how docking-based computational approaches can help characterizing disease-related mutations in PPIs at interactomic scale, where the majority of protein-protein interfaces have no structural data. For this purpose, we focused our analysis on the protein-protein interaction networks of six disease phenotypes for which there was detailed structural information for most of the individual proteins within the network, but low structural coverage of the protein-protein interfaces see . Table 1We first analyzed the distribution of nsSNPs within the protein interaction networks of the six analyzed diseases see , consideThus, it remains to be seen whether having more structural data on the protein interactions for these six diseases analyzed here could improve the structural and functional characterization of known disease-related nsSNPs. The following section will explore computational ways to extend the structural characterization of protein interaction networks.The main goal of this work is to explore computational ways of characterizing pathological mutations possibly involved in protein-protein interactions for which there is no available structural data. We previously found that energy-based protein docking can be efficiently applied to identify interface and hot-spot residues in protein-protein complexes . BasicalAs an additional test, we applied the extended interface predictions to the structural interaction networks of six selected diseases, as above mentioned, containing 462 protein-protein interactions for which the complex structure is available or can be modelled based on a homologous template, which involved 353 proteins with available structure (or a reliable homology-based model). Some of the proteins in this dataset had more than one binding partner, so we considered as interface residues those that are involved in any of the possible interactions. As a consequence, 44% of the surface protein residues were observed to be located at a protein-protein interface . Then coWe then tested the docking-based extended interface predictions on all the nsSNPs found in the structural interaction networks of the six selected diseases. We applied this methodology to the identification of interface nsSNPs, and the predictive success rates were similar to those of the interface predictions . As an aThe above described protein interaction networks for the six selected diseases contained 1,472 interactions for which there is no available structure. They involved as many as 3,323 nsSNPs that could not be structurally mapped in such interactions. Some of these nsSNPs might have been considered in the previous analysis of the structural interaction network dataset, simply because they were involved in other complexes with available structure, but they still lacked information for all the other interactions with no available structure. In 1,055 of these interactions, the interacting subunits had available structure or could be easily modelled by homology, which made them suitable for docking calculations. In total, there were 583 proteins with structure or easily modelled by homology . We ran Then, we combined the results of the structural dataset with the modelled interactions for the protein interaction networks of the six selected diseases. In this way, we had structural or modelled data for 1,517 protein-protein interactions, involving proteins that harbored a total of 2,786 nsSNPs. They contained 1,438 disease-related, 899 polymorphisms and 449 unclassified nsSNPs. Among the characterized disease-related nsSNPs, 44% were buried, 32% were located at interfaces, and 24% at non-interacting regions . AccordiInterestingly, the disease-related nsSNPs that are estimated to be interacting hot-spots according to the docking-based predictions (interface residues with NIP > 0.2) show an O/E ratio of 1.05, and a clear preference over the non-interacting regions (OR 1.68), similar to that previously reported for interface core disease nsSNPs vs. non-interacting regions (OR 1.72 ).For the interaction networks of the six selected diseases, on top of the 183 disease-related nsSNPs that could be structurally mapped at protein-protein interfaces, we found 283 additional disease-related nsSNPs that were predicted to be at an interface based on the docking models. We should note that some of the nsSNPs residues predicted as interface from the docking models could have been already defined as interface from the complex structures, because they were involved in other interactions for which there was available structure, and this is why last column in We used our interface prediction method to extend the characterization of nsSNPs in other protein interaction networks. A recent comprehensive study on pathological mutations involved in cancer and RASopathies in proteins of the RAS/MAPK pathway showed that around 20% of the structurally-mapped pathological mutations were predicted to have a direct effect on protein-protein or domain-domain interfaces . HoweverThe complete interaction network involved a total of 236 proteins, 234 of them with available structure, and 482 protein-protein interactions . We performed 1,893 docking calculations on those protein interactions with no available structure, in order to identify the interface and hot-spot residues. From the 208 pathological mutations that were unexplained in the mentioned study , we founStructural characterization of nsSNPs and their involvement in protein-protein interfaces is a starting point to understand complex diseases, for which databases like dSysMap  are valuhumsavar) and disorder genes (e.g. OMIM) that are not always fully consistent. Indeed, the gene map file used here from OMIM had 3,012 disease phenotypes, while the version of humsavar used in this work has 2,727 phenotypes with assigned nsSNPs. This means that there could be protein coding genes associated to a disease phenotype, which do not have any described nsSNP. An example of this was the phenotype MCI  [MIM: 608446]. This phenotype is not considered in databases like dSySmap, because all coding protein genes that have been reportedly associated to the disease harbor mutations for other diseases, and thus no nsSNPs can be found associated with this MIM code in humsavar . The involvement of different nsSNPs causing other diseases in the protein-protein interfaces of this interaction network is indicative of a complex genotype-to-phenotype relationship, which is probably masking the nsSNPs linked to this specific MCI phenotype.The first difficulty we encountered in this analysis was the availability of data. The task of finding all the coding protein genes to construct the protein interaction network of a complex disorder is not trivial, as there are different sources of data for nsSNPs  [MIM:535000], a rare mitochondrial disease, not a single LHON nsSNP could be structurally mapped at a protein-protein interface, since there were no available structures for the protein-protein interactions associated to this disease . Fortunately, we were able to model most of the protein complexes of the LHON interaction network by means of computational docking. In this way, we identified 4 LHON disease-related nsSNPs at protein-protein interfaces, involving 3 proteins  that are part of the respiratory chain . One of Crosstalk in cellular pathways provides the cell with a robust network of interactions to respond to stimulus. The description of these pathway crosstalk events at molecular level and the mutations that may affect them would open multiple applications in biomedicine, from understanding the homeostatic response of a given drug in a particular population to discovering new personalized scenarios for drug repurposing . A recenIn the case of RASopathies, where several of the network nodes are important interaction hubs, a given disease-associated nsSNP at the interface region might have an edgetic effect by affecting certain specific pathways but not others. We found that many of the disease-related nsSNPs that could not be explained in a previous study  were locWe have presented here a procedure to improve the characterization of genomic variants involved in protein-protein interactions, especially in cases with low or limited structural information on the binding complexes. The application of interface and hotspot predictions based on docking simulations can extend the structural knowledge of protein-protein interfaces and estimate the role of nsSNPs regarding the interaction with other proteins. We have applied this to selected protein interaction networks for disease in which little structural data for the protein complexes were available. This approach can almost double the number of nsSNPs that can be characterized and identify edgetic effect of many nsSNPs that were previously unknown. In summary, this procedure overcome current limitations of complex structures and can help to understand the structural and functional role of genomic variants involved in protein-protein interactions, and their edgetic effect on specific protein interaction networks within a given disease. Future research will focus on improvement the structural modeling of protein interfaces by novel scoring methodologies, integration with template-based docking, or optimized flexibility treatment. The will help to annotate and interpret genomic data from large-scale population studies, and to achieve a better understanding of disease at molecular level.Genes associated with different diseases were obtained from the OMIM database . Thus, 2In this work, we selected six disease phenotypes for which there were detailed structural information for most of the individual proteins within the network, but low structural coverage of the protein-protein interfaces : Hyper-Ihumsavar.txt file  and mapped to the corresponding protein structure  disease-associated, when the mutation is known to cause a disorder; ii) polymorphism, when the mutation is believed to be a neutral mutation; and iii) unclassified, when the mutation is detected in one or few patients, but showed low statistical significance due the limited size of the sample.The nsSNPs variants for each gene in the protein structural interaction networks associated to the six selected disease phenotypes were obtained from the tructure . For thiResidues in the protein structures were classified as core, interface and non-interacting surface according to the available structural data on the known protein-protein complexes . Core reThe observed/expected (O/E) ratios for the distribution of nsSNPs in the above mentioned protein regions  were calculated as the observed fraction of nsSNPs found in each protein region over the fraction of nsSNPs expected by chance in each protein region. The latter was estimated from the fraction of total residues found in each protein region in all analyzed proteins.i rather that in a region j was computed as an odds ratio (OR), as previously described [Pi is the probability of observing a nsSNP of a given type in protein region i, and is computed as:ni is the number of nsSNPs of a given type observed in protein region i, and Ni is the total number of residues in protein region i in all the analyzed proteins. The statistical significance of the OR values were estimated by a two-tailed P-value, as previously described [The preference of a nsSNP for being in a given protein region escribed :ORij=Piescribed , using tWe have developed a new version of the pyDockNIP method for predicting interface residues in a given protein-protein complex, as follows. Docking simulations were run with FTDock  to generThe protein-protein docking benchmark 4.0  was usedWe used our interface prediction method to extend a previous study  on 956 RS1 Table(PDF)Click here for additional data file.S2 Table(TXT)Click here for additional data file.S3 TableAll of them have available structure or homology-based model in Interactome3D.(TXT)Click here for additional data file.S4 TableStructural coverage according to Interactome3D .(CSV)Click here for additional data file.S5 TableStructural information according to Interactome3D , or docking. NS: no structure for the subunits.(CSV)Click here for additional data file.S6 Tablehumsavar, with their location in the protein according to the real complex structures (Interactome3D) or to the docking predictions (pyDockNIP extended). Many of the nsSNPs are characterized based on both structural and docking data, because they are involved in interactions with available structure and in other ones with no available structure.They are based on (CSV)Click here for additional data file.


Accurate annotation of protein functions is still a big challenge for understanding life in the post-genomic era. Many computational methods based on protein-protein interaction (PPI) networks have been proposed to predict the function of proteins. However, the precision of these predictions still needs to be improved, due to the incompletion and noise in PPI networks. Integrating network topology and biological information could improve the accuracy of protein function prediction and may also lead to the discovery of multiple interaction types between proteins. Current algorithms generate a single network, which is archived using a weighted sum of all types of protein interactions.The influences of different types of interactions on the prediction of protein functions are not the same. To address this, we construct multilayer protein networks (MPN) by integrating PPI networks, the domain of proteins, and information on protein complexes. In the MPN, there is more than one type of connections between pairwise proteins. Different types of connections reflect different roles and importance in protein function prediction. Based on the MPN, we propose a new protein function prediction method, named function prediction based on multilayer protein networks (FP-MPN). Given an un-annotated protein, the FP-MPN method visits each layer of the MPN in turn and generates a set of candidate neighbors with known functions. A set of predicted functions for the testing protein is then formed and all of these functions are scored and sorted. Each layer plays different importance on the prediction of protein functions. A number of top-ranking functions are selected to annotate the unknown protein.Saccharomyces cerevisiae protein data than other function prediction methods previously used. The proposed FP-MPN method takes different roles of connections in protein function prediction into account to reduce the artificial noise by introducing biological information.The method proposed in this paper was a better predictor when used on The online version of this article (doi:10.1186/s40246-016-0087-x) contains supplementary material, which is available to authorized users. The accurate annotation of protein functions is the key to understanding life at the molecular level and has great biomedical and pharmaceutical implications. Due to high-throughput biological technologies, a large number of protein sequences  are avaiThe past decade has witnessed a rapid development of computational methods for predicting protein functions from PPI datasets. A neighbor counting (NC) method proposed by Schwikowski et al.  predicteTaking both high noise in PPI data and insufficient number of available annotated proteins into account, some researchers have tried to improve the prediction performance by incorporating other heterogeneous data sources. Cozzetto et al.  proposedIn conclusion, many computational methods that integrate heterogeneous data for predicting protein (or gene) functions have been suggested. Most of these techniques follow the same basic paradigm: firstly, they generate various functional association networks by analyzing implicit information of shared functions of proteins from different data sources. Then these individual networks are combined into a composite and highly reliable network through a weighted sum. The weight of each individual network represents the contribution of the corresponding data source to the function prediction. A correct setting of these weights is thought to be the key to designing an effective function prediction method. In general, the weights adjustment of individual networks is mainly influenced by human experience and statistical analysis. The major drawback of how each network is weighted is that it varies between different datasets. Furthermore, functions of proteins are diverse and some of them only occur under specific conditions. Different functional association networks play different roles and have varying importance in function prediction. Combining a heterogeneous data source into a single weighted network could obscure the inherent nature of the protein function.Saccharomyces cerevisiae. Compared to several previously reported protein function prediction algorithms, FP-MPN achieved a greater degree of accuracy in predicting protein function. The experimental results demonstrate that this method, which distinguishes different types of connections in function prediction, is more robust and effective than those methods combining multiple interactions, and that FP-MPN is a good example of this.To address these difficulties, we construct a multilayer protein network which integrates PPI network topology, domain information, and protein complexes. Additionally, we propose an efficient protein function annotation method, named FP-MPN (function prediction based on multilayer protein networks). FP-MPN takes into account the varying influences by multiple connections in the prediction of protein function. Given an un-annotated protein, FP-MPN generates candidate functions by examining multilayer networks systematically in turn. The performance of FP-MPN was tested on the well-studied species of Cross-validation is a widely used method to evaluate the performance of protein function prediction algorithms. The proteins in the PPI network are partitioned into two subsets, the training set and the testing set. Functions are removed from the part of proteins in the PPI network artificially. These proteins consist of the testing set and the rest proteins form the training set. Functions of proteins in the testing set are predicted, using functional information of proteins in the training set. Finally, the comparing results of predicted functions with actual functions are used to evaluate the performance of protein function prediction algorithms. The cross-validation methods can be classified into two categories: leave-one-out cross-validation and leave-percent-out cross-validation. The leave-one-out cross-validation method puts one protein into the testing set and the remaining proteins into the training set, while the leave-percent-out cross-validation method randomly selects a percentage of proteins as the testing set and then puts other proteins into the training set. Each function of proteins in the testing set is assigned with a probability, according to the functions of proteins in the training set. Then a number of top-ranking functions are selected to annotate the protein with unknown functions. The quality of prediction depends on the matching results of predicted functions with actual ones. There are two widely used criteria to measure the predicted results. The one is Precision which measures the percentage of predicted functions that match the known functions. The other is Recall which measures the fraction of known functions that are matched by the predicted ones. They can be calculated as follows:F-measure, as the harmonic mean of precision and recall, is another measure to evaluate the performance of a method synthetically, which is calculated as follows:where TP (true positive) is the number of predicted functions matched by known functions. FP  is the number of predicted functions that are not matched by known functions. FN  is the number of known functions that are not matched by predicted functions. Selecting more functions can improve the recall, but it may lead to the reduction of precision. 1, tp2, …, tpn}, KF = {kf11, kf12,…, kfij, …, kfnm} is a list of known function sets of TP, KFi = {kfi1, kfi2,…, kfil} is a known function set of the protein tpi. PF = {pf11, pf12,…, pfij, …, pfnm’} is a list of predicted function sets of TP, PFi = {pfi1, pfi2,…, pfil’} is a predicted function set of the protein tpi. The coverage rate is then defined asAt the same time, the coverage rate (CR)  is also Some methods try to reconstruct more reliable networks by integrating PPI networks and biological information, in order to reduce the impact of random noise on predicting performance. There exist complex and diverse relationships between proteins as demonstrated after integrating biological information. For example, proteins can interact with each other through physical interactions which can be identified by biological experiments, co-expression based on time course gene expression data , 23, or v, its gene expression at n different times is denoted as a variate:Gen(v) = {T, T, …, T}, T denotes the expression level of gene v at the time point i. Generally, the Pearson correlation coefficient [Figure fficient  is used fficient  of one).) = {Tv, , TThe analysis of this experiment suggests that existing methods have two deficiencies. Different biological data sources  often describe protein properties in different ways and have different correlations with different GO terms. Combining multiple biological data into a single network can not only enhance the matching accuracy  to a certain extent but also introduce a lot of noise functions and reduce predicting accuracy . As a result, the comprehensive performance improvement is not apparent. Current methods set different weights for heterogeneous data based on the quality of data sources in order to integrate them into a single network. Setting the weighting system for multiple biological data is the key to ensuring the accuracy of protein function prediction. These optimal weighting methods rely on empirical analysis and have differences between datasets. Furthermore, these weighting methods may also lead to the inconsistency of these prediction algorithms.In conclusion, it is inappropriate to combine multiple interactions or connections between two proteins, as they often occur under different conditions and play different roles in protein function prediction. In this paper, we describe a multilayer protein network developed by integrating PPI network topology and heterogeneous data. In the constructed network, a pair of proteins has more than one connection which is connected through multiple links. Based on the multilayer protein network, we propose a new method for predicting protein functions, named FP-MPN.The complex network is a hot, new research area as a result of the increased use of networks in various fields, such as mathematics, social science, and life science. The features of many real-life complex networks are that they are small-world  and scale-free . In reality, connections among nodes in complex networks are diversified. For instance, in social networks, people can contact each other via emails, telephones or MSN, etc., and hence make up a complex network with multi-links. Similarly, in biological networks there are diverse links among proteins via co-expression or co-annotation of the proteins. Multilayer networks are more complex than those with single link.G = , where V = {v1, v2,…, vn} represents a set of proteins, the edge set E = {Me1, Me2,…, Mem} consists of edges of L different types representing different relations. That is, Mei = {ei1, ei2,…, eiL} (0 < i < =m), eij (0 < j < =L) represents the ith connection in the jth layer of G. We can view the multilayer network as a graph with vector valued edge information, i.e., the adjacency matrix A consists of elements Aij, who are themselves L dimensional vectors: Aij = {Aij(1), Aij(2),…, Aij(L)}. An alternative way to approach the problem is to view the multi-graph as a collection of L, N × N adjacency matrices {A(1), A(2),…, A(L)}, each corresponding to one type of relation. Figure We consider a multilayer network Functions are often performed by proteins physically interacting with each other, located within the same complex, or by having similar structures. A protein consists of one or more domains which have independent functions. There may be discrepancies within domain combinations among different proteins and it is of great significance to recognize these. In this paper, we develop a multilayer network by integrating the PPI network, protein domain information, and protein complexes. The multilayer network consists of three layers, which include the physical interaction layer (PIL), sharing domain layer (SDL), and sharing complex layer (SCL). The physical interaction layer is derived from original PPI networks. On the SDL, two proteins are physically connected if there is at least one domain common to both of them. On the SCL, each node represents a protein and two nodes are physically connected if they are contained in a common complex. Our previous research on protein complex prediction  and esseMethods of Zhang and DCS successfully integrated domain information and PPI networks, improving the performance of protein function prediction. The two methods rely on the same principle, which is to implement function prediction by way of computing similarities between the two proteins. The two methods differ in that the method described by Zhang only computes similarity through the domain information of the protein itself, while the DCS method expands on the extra domain information of the neighbors surrounding it. The two methods are all based on the computing similarity of the combination formula. However, they have the problem of being highly complex to program. To balance the pros and cons of the two methods, this study has set up the weighting computational formula aiming at the interaction of shared domain as follows:Di and Dj are sets of distinct domain types of vi and vj, respectively.where vi and vj on the SCL can be calculated as follow:In a similar way, the weight of sharing complexes between Ci and Cj are the sets of protein complexes that contained vi and vj, respectively, and Ci∩Cj denotes the set of common protein complexes.where vi and vj, the weight of edge  on the PIL is defined as follows:As for the weight of connections on the PIL, we suggest that the weight of an interaction can be reflected by the number of common neighbors between the proteins. Here we use a variant of edge clustering coefficient (ECC)  to calcuNi and Nj are sets consisting of all neighbors of vi and vj, respectively.where G = , where V = {v1, v2,…, vn}, E = {Me1, Me2,…, Mem}. Mei = {ei1, ei2, ei3} (0 < i < =m), eij (0 < j < =3) represents the ith connection in the jth layer of G.Figure Based on the weighted multilayer protein network, we propose a new method for protein functional prediction, named FP-MPN. How to deal with the multilayer networks is the first problem to be addressed. Current algorithms combine different connections into a single connection when dealing with these complex biological networks. In reality, it is inappropriate to combine multiple connections between two proteins, as they often occur under different conditions and play different roles in protein function prediction. The influences of different types of interactions in protein function prediction are not the same. Combining different interactions into a single event can lead to false positive results. So, it is necessary to deal with multilayer networks in another way.The different connections among proteins may have different impacts on function prediction. To address this, FP-MPN visits each layer of the multilayer network in turn to generate candidate functions. Each layer has different contribution to predict ion of functions for an un-annotated protein. The FP-MPN algorithm operates in two stages, pre-processing data and predicting functions.A =  n×n×m, where n is the number of proteins and m is the number of types of interconnections. If node i is connected to node j by the kth type link, ai,j,k is equal to 1; otherwise, it equals 0. Figure A, we can get a new tensor A(1), which is calculated as follows:To assign functions of proteins in the testing of a set of probabilities, pre-processing of the multilayer protein network is required. The constructed multilayer protein network can be represented as a tensor i of the tensor A(1), Therefore, for each row A(1), Given that the proteins interact with each other under different conditions or stimuli in order to perform different functions, FP-MPN generates predicted functions across all layers. While the importance of each layer to the prediction is not the same. We assign different importance coefficient (IC) for each layer of the MPN. For the ith layer, its IC value can be calculated as follow:The second stage of FP-MPN is predicting functions for un-annotated proteins. The FP-MPN method visits each layer of the corresponding multilayer network of the tensor The final score of a predicted function is the weighed sum of scores achieved from all layers. The IC value of a layer is used to present the weight. The layer accessed firstly has higher IC value than that rest of the layers. For this reason, the set up access sequence of each layer in the MPN is critical for the FP-MPN method. This paper addresses the problem of the impact of each layer on the accuracy of function predictions using statistical analysis. More detailed statistical results can be found in Table F-measure to evaluate the significance of each layer for function prediction. The original PPI network consisted of 5093 proteins with 24,743 interactions. For the PIL, SDL, and SCL, there are 13,871, 23,749, and 7337 connections, respectively. Using PIL, there are 2388 proteins, which had at least one neighbor. The number of nodes with neighbors on the SDL and SCL is 2972 and 1494, respectively. From Table F-measure among the three layers. In addition, 73.83 % (1103/1494 = 73.83 %) of proteins with neighbors on the SCL have been annotated as at least one function. While the proportion of PIL and SDL is 53.35 % (1274/2388 = 53.35 %) and 40.88 % (1215/2972 = 40.88 %), respectively. The SDL gets the second highest F-measure and Recall after SCL among all the layers. Thus, we assigned the highest access sequence to SCL, the second highest priority to SCL, and the lowest order to PIL.In this experiment, we used the NC  method ou with unknown function, to generate candidate functions. Starting from the layer in MPN which has the highest access sequence, the FP-MPN method creates a functions list PF. These lists of functions are derived from neighbors of the testing protein u. Assume that P = {p1, p2,…, pn} is a set of neighbors of the protein u on the first layer, F = {f1, f2,…, fm} is a set of functions of all these proteins in P. The score of a certain function fj in F can be calculated by the following formula:The second stage of FP-MPN consists of two major steps. The first step is to search its neighbors in the MPN for a particular protein W represents the weight of the connection between u and pi. If pi contains function fj, then tij = 1, otherwise tij = 0. Then, the FP-MPN enters the next layer of MPN and continues to predict functions. If a function has been predicted on previous layers, its score is accumulated. This process is repeated for the next layer etc., until all the layers are traversed. For a predicted function f, its final score is the weighed sum of scores on all layers and can be calculated as follow:where L is the number of layers, IC(i) is the IC value of the ith layer, and S(fi) is the score of function f on the ith layer calculated using Equation (10). From Equation (9), it is not difficult to deduce the formula f) is less than 1 and can be used as a probability of the function f. Figure A generated by the FP-MPN method. In this example, FP-MPN predicts functions f3 and f4 according to its neighbors on the SCL. FP-MPN computes the scores of f3 and f4 on the SCL by Equation (10), which is 1 and 1, respectively. Then, FP-MPN enters the SDL and continues to generate functions. The candidate function set of A’s neighbors on SDL consists of {f1, f2, f3, f4}. The score of f1, f2, f3, f4 on the SDL is 0.28, 0.28, 0.72, and 0.72, respectively. In a similar way, FP-MPN records the functions {f1, f2, f3, f4, f5} on the PIL. Scores of the five functions are the same that is 0.5. According to Equation (11), the final score of f3 can be calculated as follow:where f1, f2, f4, f5 is 0.1325, 0.1325, 0.7425, and 0.0625, respectively.The final score of N of the ranked functions for the protein with unknown function. This is a key factor which influences the performance of the function prediction algorithm. Existing methods for function selection are mainly implemented in two ways: one is represented by the methods of Zhang [The last step of the second stage is to rank functions according to their scores and select a top of Zhang  and DCS of Zhang , which cu and v is defined as follows [The first column in Table  follows :12\documFu and Fv is the function set of proteins u and v, respectively. The second column in Table where N of these functions can be selected to annotate the testing protein u, where N is the number of functions of the protein most closely associated with u. In this paper, we used the highest weight of a pair of proteins to evaluate the close degree of all their layers. We limited the number of predicted functions to be less than or equal to that of the annotated GO terms in the protein with highest weight to u. Algorithm FP-MPN illustrates the overall framework to predict protein functions based on multilayer protein networks.All functions are sorted in descending order according to their scores. The top S. cerevisiae (yeast) PPI networks are widely used in the research of network-based function prediction methods, because the species of yeast has been well characterized by knockout experiments and is the most complete and convincible. Here, we also adopt the yeast PPI network to test our method. We have applied our method and four other competing algorithms by integrating network topological features, domain information, and protein complexes data: Zhang [The a: Zhang , DCS [16a: Zhang  on DIP da: Zhang . DSCP isa: Zhang . The GO a: Zhang , includia: Zhang , which cThe access sequence of each layer in the MPN plays an important role in the performance of the proposed FP-MPN method. In this paper, the priority of each layer was determined using statistical analysis. Different schemes were used to sequence layers of the MPN and then compare these results to verify the effectiveness of the FP-MPN method. Table F-measure and coverage rate (CR) of the various algorithms.A representative set of function prediction algorithms was run: FP-MPN, Zhang, DCS, DSCP, and PON, and their performance was examined using the leave-one-out cross-validation method. In the DIP PPI network, 2870, 1592, and 2427 proteins from a total of 5017 proteins were annotated by BP, MF, and CC, respectively. We analyzed the overall prediction performance of FP-MPN on these annotated proteins, as well as four other methods. The results are shown in Table F-measure of FP-MPN is 95.95, 38.98, 21.51, and 200 % higher than Zhang, DCS, DSCP, and PON, respectively. After integrating protein complexes and domains, DSCP improves the performance compared to DCS. FP-MPN outperforms DSCP, including the F-measure and coverage rate. When looking at MF, the performances of these five methods are better. The F-measure of FP-MPN is 67.47, 20.61, 7.75, and 152.73 % higher than the results using the methods of Zhang, DCS, DSCP, and PON, respectively. As for CC, the F-measure of FP-MPN is 128.93, 46.91, 23.9, and 215.38 % higher than the results using the methods of Zhang, DCS, DSCP, and PON, respectively. Compared to BP and MF, FP-MPN had a higher F-measure growth rate compared to other methods.In Table K functions of each prediction method. When examining the methods of Zhang, DCS, and DSCP, the top M (M < =K) proteins which had the highest similarity value were selected and the top K functions from the function list as a predictor of functions was listed in descending order according to the maximum value of protein similarity . As for the FP-MPN and PON methods, the top K GO terms are chosen to assign functional properties to the unknown proteins (K ranges from 1 to 50). The areas under the curve (AUC) for FP-MPN and other methods are used to compare their performance. AUC is considered to be a standard method to assess the accuracy of predictive distribution models. From Fig. A comprehensive comparison of the performances of these five methods was undertaken using a Precision-Recall (PR) curve to evaluate the global performance of every method in terms of the different strategies of function selection adopted by the five prediction methods. The same number of functions was chosen for each method, i.e., the top K functions are selected as its predicted ones, and TP and FP values are calculated according to its known functions. The TP and FP values of all testing proteins are added to calculated TP and FP pairs. Selecting different values of K (ranging from 1 to 50), a FP/TP curve can be generated with different TP and FP pairs, as shown in Fig. The number of incorrect predicted functions when matching a function correctly using these methods was determined. For each testing protein, the top The performance of FP-MPN was tested using leave-one-out validation. Experimental results demonstrate improvements when predicting protein functions by the FP-MPN method compared to competing methods. However, in practical applications, there are much more proteins without annotations, instead of one unknown protein. In this section, we will use the leave-percent-out cross-validation method to verify the effectiveness of FP-MPN on PPI networks that have less functional information. Tenfold cross-validation is a widely used leave-percent-out cross-validation, which is used in this paper. The tenfold cross-validation requires the entire set of examples to be divided into ten equal sets randomly. Nine of the ten parts are used for training, and one part is used for testing. This is repeated ten times, each time using another testing set. We evaluate the performance of each method using area under precision-recall (PR) curve. Figure To further analyze the differences between the FP-MPN and other methods, we selected 12 testing proteins and predicted their functions using the five methods. Table In addition, we continued to look for sources of functions predicted by various methods. For the protein YGL100W, the functions set predicted by the method of Zhang consists of GO:0000723, GO:0006348, GO:0006355, and GO:0051568, which were derived from the protein YAR003W. In this study, YAR003W is regarded as having the most similar domain to YGL100W among all the proteins. Unfortunately, these predicted functions are mismatched by the real functions of YGL100W. As for DCS and DSCP, the protein YCL039W is considered to be the most similar in domain to YGL100W than the other known proteins. Similarly, the predicted functions of GO:0043161, which were derived from YCL039W, created errors in predicted functions for YAR003W. Predicted functions by PON were GO:0000001, GO:0000002, GO:0000027, GO:0000055, GO:0000082, GO:0000086, GO:0000122, and GO:0000209, which were derived from YBR234C, YJL112W, YKL021C, YDR267C, YDR364C, YFL009W, YLR055C, and YIL046W, respectively. All of these proteins have at least one domain with YGL100W. So, we can draw a conclusion that we cannot predict functions for the protein YGL100W based on domain information only. Our FP-MPN predicts ten functions, in which eight are matched and two are mismatched. These matched functions were derived from protein YDL116W, which is located in the transcription factor TFIID complex with the YGL100W protein. FP-MPN successfully matched eight functions for the protein YGL100W, with the help of protein complexes information. The results suggest that complexes information improves the accuracy of protein function prediction. However, protein complexes data is also used in the DSCP methods, which has a different predictor results compared to that of FP-MPN. This could be due to the difference in how the data is used between the two methods. For the protein YNL262W, the methods of Zhang, DCS, and DSCP created the same function lists, consisting of GO:0006273, GO:0000084, and GO:0006270. These three functions are derived from the protein YNL102W, which has common domains with the protein YNL262W. In the predicted functions list, only GO:0006273 is correct as a function for the protein YNL102W. Compared to the methods of Zhang, DCS, and DSCP, PON can identify two other correct functions GO:0006273 and GO:0006289 from another protein YDL102W, which shares domains with the protein YNL102W. The result suggests that annotating proteins according to multiple known proteins is more reliable than predicting functions from a single protein. Besides the three matched functions identified by other methods, FP-MPN identifies a new correct function GO:0006298. In this example, FP-MPN predicts more matched functions compared to other methods, due to the domain and complexes information being used. This phenomenon suggests that proper use of multiple heterogeneous biological data can effectively improve the performance of function prediction algorithms. The analysis for the rest of the ten proteins described above is consistent with that of YGL100W and YNL262W.To compare the efficiency of these methods, we ran FP-MPN and competing methods under the same conditions and looked at their running time. All methods in this paper were run on a notebook computer with Inter(R) Core(TM) i5-4300M 2.6 GHz CPU and 4 GB RAM. Figure Different types of interactions or connections play different roles in protein function prediction. Combining multiple interactions or connections between two proteins could reduce the impact of false negatives and increase the number of correct predicted functions. However, there appears to be more false functions identified compared to positive functions, thus the overall performance of function prediction would not be improved greatly. In this paper, multilayer protein networks (MPN) are constructed based on topological characteristics, protein domain information, and protein complex information, with each layer given various priorities. Based on the constructed networks, we proposed a new method, named FP-MPN, to predict the functions of a particular protein. The proposed method is based around visiting each layer of the MPN in turn and forming a set of candidate neighbors with known functions. The set of predicted functions is then formed and all of these functions are scored and sorted. Each layer contributes differently to the predicted functions in the un-annotated protein. The experimental results indicate that it is an effective method to predict protein functions.