The identification of host / pathogen interactions is essential to both understanding the molecular biology of infection and developing rational intervention strategies to overcome disease. Alphaviruses, such as Sindbis virus, Chikungunya virus, and Venezuelan Equine Encephalitis virus are medically relevant positive-sense RNA viruses. As such, they must interface with the host machinery to complete their infectious lifecycles. Nonetheless, exhaustive RNA:Protein interaction discovery approaches have not been reported for any alphavirus species. Thus, the breadth and evolutionary conservation of host interactions on alphaviral RNA function remains a critical gap in the field. Herein we describe the application of the Cross-Link Assisted mRNP Purification (CLAMP) strategy to identify conserved alphaviral interactions. Through comparative analyses, conserved alphaviral host / pathogen interactions were identified. Approximately 100 unique host proteins were identified as a result of these analyses. Ontological assessments reveal enriched Molecular Functions and Biological Processes relevant to alphaviral infection. Specifically, as anticipated, Poly(A) RNA Binding proteins are significantly enriched in virus specific CLAMP data sets. Moreover, host proteins involved in the regulation of mRNA stability, proteasome mediated degradation, and a number of 14-3-3 proteins were identified. Importantly, these data expand the understanding of alphaviral host / pathogen interactions by identifying conserved interactants. Alphavirus are single stranded positive-sense RNA viruses, many of which represent significant concerns to public health amino]hexyl]-1H-thienoimidazole-4-pentanamide–Biotin) at room temperature for a period of 30 minutes. The preloaded resin was washed twice with 1xPBS to remove unbound HPDP-Biotin, followed by resuspension in RIPA buffer.The biotinylation and capture of the vRNAs was achieved via a 1 hour incubation at 16°C under agitation. After binding, the resin was collected via centrifugation, and the unbound materials were discarded. The resin was washed once with RIPA buffer, and three times with RIPA buffer supplemented with 1M Urea. After washing the resin was exchanged into 1xPBS supplemented with 1.0% SDS via two additional washes. The formaldehyde cross-linking was reversed via heating the resin in a minimal volume of 1xPBS supplemented with 1.0% SDS at 70°C for 1 hour. The supernatants were harvested via high-speed  centrifugation of the capture resin.To assess the viral RNA abundances and synthesis kinetics of SINV, CHIKV, and VEEV 293HEK cells were infected with the aforementioned alphaviruses at an MOI of 10 PFU/Cell. After an one hour adsorption period the cells were washed, and incubated under the CLAMP assay conditions described above foregoing any RNA:Protein cross-linking. At 0.5, 1, 2, 4, and 6 hours post infection total RNA was harvested from the cells using TRIzol, as per the manufacturer’s instructions. The total RNA was then reverse transcribed using primers specific for the genomic, subgenomic, and minus strand RNAs as previously described. In addition to the viral transcripts, the host RNAse P mRNA was primed during the reverse transcription reactions to as an endogenous control to enable the normalization of the qPCR assay. Absolute viral copy numbers were determined by comparing the Ct values of the strand specific cDNAs to standard curves of known abundance, as previously described .To determine the efficiency with which the 4SU-labeled viral RNA species are purified during the CLAMP assay, cross-linked RNA:Protein complexes were extracted, biotinylated and purified as described above. However, rather than releasing and harvesting the retained proteins for mass spectrometry analyses, the RNAs purified by the CLAMP process were extracted using TRIzol reagent after the reversal of the RNA:Protein cross-linking after elution of the purified cross-linked RNA:Protein complexes via the de-biotinylation of the viral RNAs via addition of 50mM DTT. The purified RNAs were then used as input materials for strand specific reverse transcription reactions, as described above. The abundances of the viral RNA species, and the host RNAse P mRNA, were then detected using qRT-PCR to determine which RNA species were purified during the CLAMP strategy. The individual purifications were compared relative to Input controls consisting of an equivalent volume of CLAMP extract, processed identically to that above with the exception that the streptavidin purification was skipped prior to TRIzol extraction.The preparation of the mass spectrometric samples was performed as follows. The CLAMP purified proteins were TCA precipitated and resuspended in 100mM Ammonium Bicarbonate solution supplemented with 8M Urea. The protein pellets were vortexed until completely resuspended before the addition of Dithiothreitol (DTT) to a final concentration of 10mM. The protein samples were incubated for a period of 1 hour at room temperature prior to alkylation. Protein alkylation was achieved via the addition of freshly prepared Iodoacetamide to a final concentration of 20mM, and a 1-hour incubation at room temperature in the dark. After the alkylation period the excess Iodoacetamide was quenched via the addition of DTT to a final concentration of 40mM, after which the samples were diluted with 100mM Ammonium Bicarbonate solution to reduce the concentration of Urea to <1M. A standardized quantity (1μg) of proteomics grade trypsin was added to each sample, and the proteins were digested at 37°C overnight to generate a peptides for MS.18 resin at a flow rate of 300 nl/min. A 2-hour gradient from a solution of 2% acetonitrile and 0.1% formic acid to 100% acetonitrile and 0.1% formic acid was used to resolve the peptide fragments. MS-MS peak lists were searched against the Swiss-Prot Homo sapiens database using Protein Prospector (v5.10.14). Carbidomethylation of cysteine residues was set as a fixed modification; and acetylation of the protein amino terminus, oxidation of methionine, and pyroglutamine formation of peptide N-terminal glutamines were set as variable modifications. A mass tolerance of 0.6 Daltons was used for precursor and fragment ions. Expectation values for the peptides was set to <0.05, but for proteins identified by a single peptide the expectation value was set to <0.01. All CLAMP mass spectrometry data sets may be found in the The tryptic libraries were analyzed by the Laboratory for Biological Mass Spectrometry at the Department of Chemistry of Indiana University- Bloomington. The peptide libraries were dried down and injected into an Eksigent HPLC apparatus coupled to an LTQ Velos mass spectrometer operating in “top eight” data-dependent tandem-MS (MS-MS) selection. The peptides were separated by a 75μm x 15cm column packed with CThe mass spectrometry data was used to generate lists of detected proteins for subsequent parsing to identify potential interactants. Interactants were identified through a series of comparative analyses. First, in order to be considered as an interactant a particular protein must have been detected in both independent data sets. Note that this is a relaxation of the previously reported criteria (the reasons for which are described below) . FinallyIt is important to directly state and acknowledge that the CLAMP assays described in this report were all conducted in parallel at the same time- including those previously reported in the literature as part of an independent report . In realHomo sapiens background list to identify enriched Molecular Function and Biological Process ontological categories. All categories enriched above expected (as determined by the Homo sapiens reference genome) were considered for statistical analysis. To ensure analytical rigor, a statistical threshold of <0.05 was maintained via the examination of Benjamini corrected p-Values.The lists of CLAMP-identified interactants for SINV, CHIKV, and VEEV were compared to those detected during the analyses of Mock infected samples to identify nonspecific interactants. Any CLAMP-interactant identified in Mock infected samples were removed from the final alphaviral lists prior to further analyses. Ontological analyses were performed using the Database for Annotation, Visualization and Integrated Discovery  , 37. BriTo identify common interactants amongst the tested alphavirus species the individual lists of CLAMP-identified interactants were sorted and compared to identify conserved Uniprot accession numbers. All possible two-pair combinations, namely- SINV/CHIKV; CHIKV/VEEV; and SINV/VEEV, were assessed. However, only the three-way comparison of SINV/CHIKV/VEEV is reported in detail in this manuscript. The paired comparisons, and their subsequent analyses, may be found in the Ontological analysis of the common alphaviral interactants was performed identically to that described in the previous subsection. In addition to the ontological enrichment analyses described above, STRING database analysis was used to identify Protein:Protein interactions amongst the common alphaviral CLAMP-interactants , 39. TheTo validate and confirm select RNA:Protein interactions 293HEK cells were infected with either SINV, CHIKV, or VEEV at an MOI of 10 PFU/Cell. The cells were treated identically to the conditions described in the CLAMP assay above, with the exception that the use of 4SU and Actinomycin D were omitted. After the solubilizing the cross-linked cells the whole cell lysates were centrifuged at 16,000xg for 5 minutes to remove insoluble materials. The clarified mixed supernatants were transferred to fresh microfuge tube, and to conserve reagents, equal volumes of SINV, CHIKV, and VEEV lysates were combined and processed together in batch form. To pre-block the batched lysates 50ul of Protein G Mag Beads (50% slurry in RIPA buffer) was added to a volume of 300ul lysate. After an hour incubation period at room temperature with gentle agitation the magnetic beads were collected, and the pre-blocked clarified batch lysates were transferred to a fresh microfuge tube. In parallel to the pre-blocking of the batched lysates, target specific antibodies were pre-bound to Protein G Mag beads. Briefly, per target 10ul of anti-hnRNP K , anti-hnRNP A1 , anti-ANP32a , or as a control anti-IL-1 (17H18L16 Invitrogen) antibodies were incubated with 50ul of Protein G Beads (50% slurry) were bound in a final volume of 100ul (diluted in RIPA buffer) for one hour at room temperature. Unbound antibodies were washed from the Protein G Mag beads, and 300ul of pre-blocked batch lysate was added to each of the pre-bound Protein G Mag beads. Binding was allowed to proceed at room temperature for one hour under gentle agitation, prior to the collection of the Mag beads and the removal and disposal of the supernatant. The RNA:Protein complexes were washed as described above, however Urea was omitted from the wash buffers to prevent the disruption of the antibody:protein complexes. After the final wash the beads were resuspended in 200ul of 1xPBS supplemented with 1.0% SDS, and the cross-linking was reversed via heating at 70°C for 1 hour. The immunoprecipitated RNAs were then extracted using TRIzol. The immunoprecipitated RNAs were then reverse transcribed with random hexamer to generate cDNAs, and assessed via qRT-PCR to detect the positive-sense viral RNAs of SINV, CHIKV, and VEEV, and the cellular 18S rRNA. Quantitative analysis of viral RNA recovery was achieved by comparing the relative quantities of the viral RNAs of each target specific immunoprecipitation with the negative control immunoprecipitations.A primary goal of these studies was to identify host-pathogen interactions that are conserved across several medically relevant alphaviruses. To this end, we utilized a previously described novel identification approach to detect host factors that associate with the viral RNAs during infection . BrieflyThe individual samples were then affinity purified to enrich the cross-linked RNA:Protein complexes. Specifically, the clarified lysates described above were incubated with streptavidin resin pre-complexed with HPDP-Biotin, a biotin moiety with a reactive sulfhydryl group. The unbound materials were removed following centrifugation, and the resin was washed several times with RIPA supplemented with urea. The purified complexes were released via heating in the presence of 1.0% SDS in 1xPBS. As previously published, this method resulted in the release of highly purified RNA:Protein, and accessory Protein:Protein complexes, without releasing nonspecifically biotinylated protein contaminants. The purified materials were precipitated via TCA treatment, zip-tip exchanged, trypsin digested, and assessed via mass spectrometry.Despite SINV, CHIKV, and VEEV having highly similar molecular life cycles, each virus is known to exhibit nuances in regards to their relative replication and growth kinetics. Therefore, to confirm that the CLAMP assay described above would not be influenced by differences in viral replication kinetics, we quantitatively assessed the accumulation of the SINV, CHIKV, and VEEV viral RNA species during the time period pertinent to the CLAMP assay. Briefly, total RNA was harvested from infected 293HEK cells at regular intervals post infection, and the genomic, subgenomic, and minus strand viral RNAs were detected using strand specific standard curve qRT-PCR. As shown in ms of the products were indicative of nonspecific amplification.As the overall viral RNA synthesis profiles were more or less equivalent amongst the three alphaviruses, we next sought to assess the efficiency to which the viral RNA species were purified during the CLAMP assay. To this end we performed the CLAMP assay identically to that described above for the capture of the interacting proteins, but focused instead on assessing the purified RNA species. As shown in Altogether these data indicate that the CLAMP assay conditions described in this study result in the assessment of the RNA:Protein complexes of the positive-sense RNAs of SINV, CHIKV, and VEEV. Furthermore, the interactions detected are likely to be skewed towards those of the subgenomic RNA, however complexes of the genomic RNAs are also likely to be represented in the CLAMP data sets.a priori the prior criteria has the potential to introduce bias by discriminating against small molecular weight, and hence, short in regards to primary sequence, proteins. Similarly, proteins that have peptide fragments outside the tolerance range of the mass spectrometer, or which poorly ionize, would skew the resulting data. Thus, we revised our initial criteria to eliminate, or reduce, bias during identification prior to the establishment of a quantitative data based triage. To this effect, we established new criteria for the assessment of CLAMP-identified host factors.Previously, we utilized a rigorous set of criteria designed to reduce or eliminate type-I errors during the analysis of SINV host pathogen interactions via CLAMP . In ordeIn order to eliminate unintentional bias, the CLAMP mass-spectrometric data were assessed using modified criteria. To eliminate any bias introduced against proteins which would inherently produce few peptides, to be considered a potential CLAMP-identified interactant an individual protein had to be observed in both biological replicates, regardless of the number of unique peptides observed. Collectively, this approach led, as shown in It should be noted that the SINV and Mock data sets analyzed in this manuscript are one in the same with those of our previous report; however, It should be noted that despite the SINV CLAMP assay being previously independently reported, all of the CLAMP assays reported in this study were all conducted at the same time in parallel. Thus, the previously published SINV and Control CLAMP assays are directly related to the CHIKV and VEEV CLAMP assays now reported here. The altering of the identification criteria, as described above, increased the number of CLAMP-identified specific interactants from 279 to 355, an increase of ~27%. Interestingly, however, analysis of the SINV CLAMP-identified specific interactants indicated that the newly included interactants spanned a wide range of molecular weights, with a mean of ~44kDa (+/- 35kDa). Nonetheless, if the newly restored CLAMP-identified interactants are assessed based on their relative protein abundance, it becomes apparent that relaxing the criteria has expanded the detection of lower abundance proteins . Thus, wAs described above, the CLAMP strategy leads to the identification of host factors that were associated with the vRNA during infection. Nonetheless, as demonstrated by the data in -23. Nonetheless, despite the high statistical significance associated with this enrichment, the overall fold-enrichment is lackluster (~3.7-fold) compared to other enriched Molecular Functions. The mild fold-enrichment value for this ontological group is more than likely due to the large proportion of host proteins which populate this particular group, resulting in a high expected value. The other highly statistically significant Molecular Function ontological groups include Protein Binding (GO:0005515), Threonine-Type Endopeptidase Activity (GO:0004298), and Unfolded Protein Binding (GO:0051082). Of the previously named Molecular Function ontological groups the Threonine-Type Endopeptidase Activity exhibited the highest fold enrichment relative to expected (~32.5-fold). Other Molecular Function ontological groups are comparatively highly enriched, albeit with low statistical significance, such as Pyruvate Dehydrogenase (NAD+) Activity (GO:0034604), and 3-Hydroxyacyl-CoA Dehydrogenase Activity (GO:0003857).As shown in Analyses of the SINV CLAMP-identified interactants in regards to Biological Process ontology also yields insight into the molecular interactions of the SINV viral RNAs during infection. Biological Process ontological groups that are greatly enriched and highly statistically significant include Regulation of Cellular Amino Acid Metabolic Process (GO:0006521), Regulation of mRNA Stability (GO:0043488), NIK/NF-kappaB Signaling (GO:0038061), and Antigen Processing and Presentation of Exogenous Peptide Antigen via MHC I, Tap-Dependent (GO:0002479).Similar analyses of the 256 CLAMP-identified CHIKV viral RNA interactants also reveals that several Molecular Function ontological groups are enriched . In factNonetheless, despite the high degree of congruence between the SINV and CHIKV Molecular Function ontological enrichments, the enriched Biological Process ontological groups diverged from SINV to a greater degree. In particular, the ordering of the Biological Process ontological groups, in regards to statistical significance, changed between the two analyses. Notable changes include the Regulation of mRNA Stability (GO:0043488) group dropping in rank from the second to the fourth most statistically enriched group.Ontological assessment of the CLAMP-identified VEEV interactants results in an overall profile similar to that described above for SINV and CHIKV. However, there are notable differences between the enriched VEEV ontological groups and those detailed above, . The MolThe Biological Process ontological groups that were enriched during the analyses of the VEEV CLAMP-identified interactants were also different from those identified for SINV and CHIKV. This is illustrated by the presence of the Biological Process ontological group Response to Reactive Oxygen Species (GO:0000302) amongst the top enriched groups. The primary exception to this generalization is that the Regulation of mRNA Stability (GO:0043488) group is still highly enriched. It should be noted that the statistical significance of the enriched VEEV Biological Process ontological groups are poorer in magnitude relative to those of SINV and CHIKV.While the above ontological analyses of the individual CLAMP-identified interactant data sets reveals the enrichment of particular biological processes for a given alphavirus, through comparative analyses common interactants and ontologically enriched groups may be identified. Despite an exhaustive comparative analysis process, for the sake of brevity only the comparative analysis of all three alphavirus species will be described in detail. Nonetheless, comparisons between the individual pairs of alphaviruses may be found in the Supplemental Data files  accompanAs shown in The enriched Molecular Function ontological groups of the common alphaviral interactants were Poly(A) RNA Binding (GO:0044822), Protein Binding (GO:0005515), Unfolded Protein Binding (GO:0051082) Threonine-Type Endopeptidase Activity (GO:0004298), and Cadherin Binding Involved in Cell-Cell Adhesion (GO:0098641). As with the previous ontological analyses of molecular function, the ontological group with the highest fold-enrichment relative to expected was Threonine-Type Endopeptidase Activity (GO:0004298).Biological Process ontological analyses indicates that the Protein Folding (GO:0006457), Chaperone-Mediated Protein Transport (GO:0072321), Viral Process (GO:0016032), Proteasome-Mediated Ubiquitin-Dependent Protein Catabolic Process (GO:0043161), Regulation of mRNA Stability (GO:0043488), Chaperone-Mediated Protein Folding (GO:0061077), and Response to Reactive Oxygen Species (GO:0000302) ontological groups were enriched to a statistically significant degree relative to background. Interestingly, the ontological group with the highest enrichment was the Chaperone-Mediated Protein Transport (GO:0072321), which while present in all data sets, was not a leading enrichment group in any of the individual analyses.Interaction mapping of the CLAMP-identified common interactants indicates significant interconnectivity. As shown in To verify that the CLAMP assay enables the detection and identification of conserved host / pathogen RNA:Protein interactions, we conducted a series of quantitative immunoprecipitations. As our lab has an avowed interest in the biological roles of host RNA-binding proteins on viral RNA functions during infection, we focused our efforts on the host hnRNP K, hnRNP A1, and ANP32a proteins. The host hnRNP K protein was selected as it has been a subject of previous study by the Hardy and Sokoloski research groups, and thus is a topic of ongoing research interest. Similarly, the hnRNP A1 protein was selected as it has been previously identified as an interactant for several alphaviruses, however has not yet been directly linked to either the viral RNAs of CHIKV or VEEV. Unlike the other two selected proteins, the host ANP32a protein, also known as PHAPI, is a truly novel putative interactant for SINV, CHIKV, and VEEV.As demonstrated by the data in As described above, CLAMP analysis has revealed a broad network of host / pathogen interactions for SINV, CHIKV, and VEEV. Ontological analyses of Molecular Function reveal highly significant enrichments of RNA-associated proteins for each tested alphavirus, confirming the capacity of the CLAMP strategy to lead the discovery of Protein:vRNA interactions. Furthermore, the nature of the cross-linking method used enables the identification of distal interactions via the formation of Protein:Protein interactions proximal to, or in partnership with, the Protein:vRNA interactions during infection. These secondary interactions are likely represented by the enrichment of proteins associated with non-RNA binding activities, including host proteases, ubiquitin related proteins, and host proteins central to mitochondrial activity.Comparative assessments of the alphaviral CLAMP-identified interactants yields several interesting insights into alphaviral host / pathogen interactions. Foremost, there are numerous conserved proteins associated with the Poly(A) RNA Binding Molecular Function ontological group, including several RNA-binding proteins. Specifically, a number of hnRNP proteins, such as hnRNP A1, E2 (PCBP2), K, A2B1, and AB, are readily identified as conserved interactants. These hnRNP proteins are largely responsible for the statistical enrichment of the Regulation of mRNA Stability Biological Process ontological group. Whether or not these proteins function in relation to the evasion of the host RNA decay machinery is yet to be exhaustively characterized. Moreover, previous studies have indicated that the functions of the hnRNP proteins during alphaviral infection are highly complex, and may not be unified in respect to function across the members of the genus , 42. In Another notable cluster of conserved interactions centers around proteins associated with the host proteasome. Chemical inhibitors of the proteasome have been previously demonstrated to impact VEEV infection; however, the data shown above infer that other alphaviruses may be similarly affected by MG132 and Bortezomib . The preA further novel cluster of host proteins are the 14-3-3 adaptor proteins YWHAZ (14-3-3 zeta), YWHAB (beta), YWHAQ (tau), and YWHAG (gamma). Normal cellular functions of the 14-3-3 adaptor proteins involve their respective binding to cell signaling proteins, including kinases, phosphatases, and transmembrane receptor proteins, as reviewed by –46. FurtDespite causing notably different diseases, the alphaviruses assayed with the CLAMP method in these studies all share a similar underlying molecular life cycle. Thus, despite their capacity to result in clinically different illnesses, the targeting of a common interactant may have wide ranged therapeutic potential by disrupting or preventing the successful completion of the viral life cycle. Interfering with viral replication can be reasonably expected to alleviate viral disease regardless as to whether the infection resulted in encephalitic or arthritic disease.As demonstrated by our previous report and the data reported above, the CLAMP assay is a robust discovery approach capable of identifying novel host / pathogen interactions. A major strength of the CLAMP method is that it is highly adaptable, requiring only that the target viral transcript(s) be capable of being selectively labeled with the 4SU nucleotide analogue. As cellular transcription can be inhibited through a number of available small molecule inhibitors, any number of RNA viruses could be assessed using the CLAMP method. Similarly, any number of host cell types or systems could be assessed to identify host cell type or host species specific interactions. Complementary CLAMP assessments of individual viral strains, such as those which are virulent and avirulent, could also potentially reveal the existence of any differential host factor interactions. While we have not directly expanded the use of the CLAMP assay along these lines of study, the CLAMP methodology could be readily used in such manners to further define the molecular biology of viral infections. Thus, the future application of the CLAMP approach to other viruses and hosts will undoubtedly lead to the identification of previously undetected RNA:Protein interactions with important biological roles to infection.In addition to the flexibility of the CLAMP assay in regards to the virus and host, further adaptation of the assay could enable the assessment of RNA:Protein complexes with respect to time. Although not directly evaluated in this study, the CLAMP assay could be modified to assess whether or not temporal differences in RNA:Protein complexes were present by altering the timing and duration of the 4SU labeling period prior to cross-linking event. For instance, shortening the labeling period and altering the initiation of the cross-linking event relative to the labeling period in a serial manner (as per a standard pulse-chase experiment) would allow the sequential analysis of RNA:Protein interactions for a given population of viral RNAs with respect to time. Care should be taken when designing such experiments to ensure that any potential differences in viral RNA kinetics are accounted for. Similarly, the recovery of the viral RNAs should be assessed to ensure that appropriate comparisons are being made. These notions are also true when the CLAMP data of several interrelated viruses are comparatively assessed to identify common interactants, as is reported here. Significant differences in regards to viral RNA synthesis kinetics or recoveries could confound data interpretation and lead to the exclusion of potential host interactions. As demonstrated by the data reported in Figs While the clear focus of our CLAMP studies to date have been the identification of novel host / pathogen interactions, the CLAMP assay could also be utilized to probe the interactions of viral proteins with the viral RNAs during infection. For the alphaviruses these would expected to be the proteins of the viral replication machinery (consisting of nsPs 1 through 4), and the viral capsid proteins. Indeed elements of the viral replication machinery were identified during the CLAMP assays described above; however, as the rigorous assessment of the RNA synthetic complexes were not the focus of our studies they were ignored in our analyses. Nonetheless, the CLAMP assay could be utilized to directly probe the interactions of the viral RNAs with viral proteins.A notable caveat of this approach, and truly all discovery approaches, lies with the inherent limitations of protein detection via mass spectrometry. This is especially true for comparative analyses such as those described above, where the absence of a particular interactant from a data set may or may not be due to the genuine absence of the protein interaction, but also may be due to a lack of detection during mass spectrometry. These instances may arise from low target abundances, poor recoveries, and other intrinsic properties of the protein itself (such as poor ionization during mass spectrometry). Hence it is important that discovery approaches, such as the CLAMP assay, be recognized as tools to identify potential interactions, and not the rigorous exclusion of them.To conclude, comparative analysis of the host / pathogen interactions of SINV, CHIKV, and VEEV have identified a number of conserved interactants. While the functional aspects of these interactions are largely unknown, they represent novel targets for further biological exploration. Moreover, these studies underscore the importance of Protein:RNA discovery methods, and validate the robust potential of the CLAMP assay.S1 Dataset(ZIP)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.


Information on protein-protein interactions affected by mutations is very useful for understanding the biological effect of mutations and for developing treatments targeting the interactions. In this study, we developed a natural language processing (NLP) based machine learning approach for extracting such information from literature. Our aim is to identify journal abstracts or paragraphs in full-text articles that contain at least one occurrence of a protein-protein interaction (PPI) affected by a mutation.Our system makes use of latest NLP methods with a large number of engineered features including some based on pre-trained word embedding. Our final model achieved satisfactory performance in the Document Triage Task of the BioCreative VI Precision Medicine Track with highest recall and comparable F1-score.The performance of our method indicates that it is ideally suited for being combined with manual annotations. Our machine learning framework and engineered features will also be very helpful for other researchers to further improve this and other related biological text mining tasks using either traditional machine learning or deep learning based methods. Each cell of an organism contains a network of chemical reactions involving various types of molecules. The most important molecules within these networks are proteins which play a variety of roles within the cell: enzymes that catalyze chemical reactions, messengers to transmit signals to other cells, carriers of atoms and molecules within and between cells, and other roles some of which may still be unknown. Proteins rarely act alone, and they interact with one another or with other biomolecules in complex biological systems within the processes of life. There is a growing interest in seeking specific protein-protein interactions (PPIs) as drug targets, and though this presents a challenge previously considered insurmountable, there have already been some successes , 2. DisrA searchable database of information on protein-protein interactions affected by mutations (abbreviated as PPIAM in this paper) would be of great benefit to researchers for developing drugs targeting at PPIs, identifying novel biomarkers for a disease, and investigating other topics of potential relevance to precision medicine. Such information can also be very useful in automatic knowledge discovery  and inteA considerable number of computational methods have been developed to address a similar problem - extracting PPI information from literature –29. ThesIn 2004, BioCreative was initiated as an international, community-wide effort for evaluating text mining and information extraction methods applied to the biological and biochemical domains. BioCreative focuses on developing common standards and benchmark datasets for evaluating biological text mining systems. Friendly competitions are held among researchers developing systems to tackle well-defined problems. The systems are then evaluated based on gold standard datasets compiled and annotated by experts.the search . For each abstract, counts of the number of triplets with predicted probability As mentioned earlier, for each protein-mutation pair in an abstract we generated a dependency parse for its containing sentence and computed the shortest path in the dependency graph between the protein mention and the mutation-related word. Unigrams and bigrams along these shortest paths were included as features, with the direction of the path taken from the protein to the mutation related word. For example, Fig. The unigrams along this path are binds, PROT2, and fails. The bigrams are binds/PROT2 and PROT2/fails. Abstract level features are then given by the unigrams and bigrams along the shortest paths in all protein-mutation pairs in an abstract. As before, these unigrams and bigrams were TF-IDF vectorized. Stop words were not removed. We believe that they may carry important information within a shortest path.nsubj, nmod, and conj, with nsubj appearing twice. These were vectorized by count instead of TF-IDF. Since most dependency relations appear in a large majority of abstracts, the TF-IDF of almost all dependency relation terms is equal to or very near zero.Unigrams and bigrams of dependency relations along these shortest paths are also included. In the example shown in Fig. Frequencies of key terms along the shortest paths were also included as features. We used the number of protein mentions in all shortest paths divided by the total number of terms in all shortest paths, and similarly for interaction words and mutation related words. Note that we do not include the protein mention at the start nor the mutation word at the end as members of a shortest path.The terms in the shortest path between a protein and mutation word should contain information that may help decide if the mutation affects the protein. However, it may not contain information about whether the abstract describes an interaction containing that particular protein. Information describing an interaction may be in a different sentence. We attempted to capture such information with the following scheme.p predicted by our PPI extraction algorithm among all such triplets. If p > 0.5, we call  a positive pair, otherwise we call  a negative pair.Given a protein-mutation pair  in an abstract All terms within the shortest paths corresponding to positive pairs then had a nonsense string appended, and the terms in the shortest paths corresponding to negative pairs had a different nonsense string attached so that they could be distinguished. Unigram and bigram features were then extracted for these augmented terms. The same procedure was also carried out for unigrams and bigrams of dependency relations. A new collection of shortest path features was also calculated. It consists of counts of positive pairs with path lengths in the bins 1, 2, 3, 4, …, 11+ and counts of negative pairs with path lengths in the bins 1, 2, 3, 4, …, 11 + .Significant improvement was seen when incorporating features based on pre-trained word embeddings. These were added after the competition. Word embeddings are generally dense, relatively low dimensional vector representations of words. We used a collection of word embeddings trained by Pysallo et al. on what was at the time a complete collection of PubMed abstracts and PubMed Central articles . These wIn our initial experiments, we used the scikit-learn implementations of the random forest and linear SVM classifiers as baseline models , 74. TheIn our final model, we used a regularized gradient boosted trees classifier from the popular XGBoost library . This isp was chosen through cross-validation.Since gradient boosted trees do not tend to give accurate probability estimates, we used Platt scaling to calibrate these values to accurate probabilities . This in


PremPRI can be used for finding functionally important variants, understanding the molecular mechanisms, and designing new protein–RNA interaction inhibitors. Protein–RNA interactions are crucial for many cellular processes, such as protein synthesis and regulation of gene expression. Missense mutations that alter protein–RNA interaction may contribute to the pathogenesis of many diseases. Here, we introduce a new computational method PremPRI, which predicts the effects of single mutations occurring in RNA binding proteins on the protein–RNA interactions by calculating the binding affinity changes quantitatively. The multiple linear regression scoring function of PremPRI is composed of three sequence- and eight structure-based features, and is parameterized on 248 mutations from 50 protein–RNA complexes. Our model shows a good agreement between calculated and experimental values of binding affinity changes with a Pearson correlation coefficient of 0.72 and the corresponding root-mean-square error of 0.76 kcal·mol The interactions between protein and RNA are crucial for many cellular processes, such as protein synthesis and regulation of gene expression ,3,4,5,6.Many efforts have been made to computationally predict and model the effects of missense mutations on protein stability and protein–protein interactions ,30,31,32https://lilab.jysw.suda.edu.cn/research/PremPRI/.To address this need, we introduced a new computational method, PremPRI, for characterizing the effects of missense mutations on protein–RNA interactions by calculating the binding affinity changes quantitatively. PremPRI uses a novel multiple linear regression scoring function composed of 11 sequence- and structure-based features, and achieves significantly better performance than other predictors. PremPRI can be applied to many tasks, such as finding potential disease-causing and cancer driver missense mutations and understanding their molecular mechanisms, and designing inhibitors of protein–RNA interactions. PremPRI is freely available at p-value and importance for each feature are shown in The PremPRI model is built using a multiple linear regression algorithm and composed of eight structure- and three sequence-based features. The −1 and 1.00, respectively.The performance of PremPRI trained and tested on the S248 set is shown in PremPRI takes about four minutes to perform the calculation for a single mutation in a protein–RNA complex with ~330 residues and ~70 nucleotides, and requires 30 s for each additional mutation introduced in the same complex.Overfitting is one of the major problems in machine learning. In order to check whether our method has this issue, we performed three types of cross-validation . In CV1 and CV2, we randomly chose 50% and 80% of all mutations from the S248 dataset to train the model, respectively, and used the remaining mutations for blind testing; both procedures were repeated 100 times. In CV3, the model was trained and tested using nonoverlapping sets of protein–RNA complexes. Namely, we left one complex and its mutations out as the test set and trained the model using the rest of the complexes/mutations ; this process was repeated for each complex.−1, respectively  and 0.24  for FoldX and mCSM-NA, respectively , and the results are shown in We applied all three methods on the S248 dataset, and the correlation coefficients are 0.20 , predicted binding affinity change, and positive and negative signs correspond to the mutations decreasing and increasing binding affinity, respectively; Interface (yes/no), shows whether the mutation occurs at the protein–RNA binding interface. When a residue’s solvent-accessible surface area in the complex is lower than in the unbound partner, it is defined as located at the interface. Furthermore, for each mutation, PremPRI provides an interactive 3D viewer which shows the noncovalent interactions between the residue in the mutated site and its adjacent residues/nucleotides, generated by Arpeggio [For every single mutation in a protein–RNA complex, the PremPRI server provides: ΔΔG  . The bioam v2.12  and the am v2.12  were useam v2.12 . The flop-value < 0.01, t-test). The p-value and the importance of each feature are presented in  program .Closeness of the node of the mutated site in the residue interaction network. It is defined as:he other . The Cα he other . program . If the  program , we defistudy of  directlyThe multiple linear regression scoring function of PremPRI is composed of 11 features and each of them has a significant contribution to the quality of the model , Back Propagation Neural Network (BPNN), Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost), and the results shown in p-value < 0.01, t-test). RMSE  is the standard deviation of the residuals (prediction errors). The Hittner2003 test [Pearson correlation coefficient (R) and root mean square error (RMSE) are used to measure the agreement between experimentally-determined and predicted values of binding affinity change. All correlation coefficients presented in the paper are significantly different from zero  from others. True positive rate (TPR) and false-positive rate (FPR) is defined as TPR=TP/(TP + FN) and FPR=FP/(FP+TN) , respectively. In addition, the maximal Matthews correlation coefficient (MCC) value is reported for each method by calculating the MCC across a range of thresholds:Receiver Operating Characteristics and precision-recall analyses were performed to quantify the performance of different methods in distinguishing mutations highly decreasing binding affinity (


The formation of functionally versatile protein complexes underlies almost every biological process. The estimation of how fast these complexes can be formed has broad implications for unravelling the mechanism of biomolecular recognition. This kinetic property is traditionally quantified by association rates, which can be measured through various experimental techniques. To complement these time-consuming and labor-intensive approaches, we developed a coarse-grained simulation approach to study the physical processes of protein–protein association. We systematically calibrated our simulation method against a large-scale benchmark set. By combining a physics-based force field with a statistically-derived potential in the simulation, we found that the association rates of more than 80% of protein complexes can be correctly predicted within one order of magnitude relative to their experimental measurements. We further showed that a mixture of force fields derived from complementary sources was able to describe the process of protein–protein association with mechanistic details. For instance, we show that association of a protein complex contains multiple steps in which proteins continuously search their local binding orientations and form non-native-like intermediates through repeated dissociation and re-association. Moreover, with an ensemble of loosely bound encounter complexes observed around their native conformation, we suggest that the transition states of protein–protein association could be highly diverse on the structural level. Our study also supports the idea in which the association of a protein complex is driven by a “funnel-like” energy landscape. In summary, these results shed light on our understanding of how protein–protein recognition is kinetically modulated, and our coarse-grained simulation approach can serve as a useful addition to the existing experimental approaches that measure protein–protein association rates. In ae formed ,16. For e formed . The rece formed . Additioe formed . Using te formed . TherefoRelative to the current experimental techniques for measuring the rates of protein–protein interactions, such as surface plasma resonance (SPR)  and specWe have previously also developed a coarse-grained simulation approach to study protein–protein association . Positivhttps://life.bsc.es/pid/skempi2/. Combining these two sources, excluding the overlapping cases, we obtained a total of 108 entries.Our benchmark set contains a total number of 62 protein complexes. The experimental data of these complexes, including their atomic structures with the corresponding protein data bank (PDB) IDs, experimentally observed associations and ionic strength used in the measurements, are all publicly accessible. Detailed information about this benchmark set is summarized in 5 to 1.0 × 109 M−1s−1, in order to systematically test our simulations in a computationally feasible manner. We further removed the protein complexes with non-consistent experimental data of association rates that were collected from different studies. Most of these studies using SPR or IASP may suffer from a number of technical limitations. Moreover, the protein complexes with atypical association pathways or irregular binding interfaces were also eliminated. For example, the complex formed between transcriptional coactivator CBP/p300 and nuclear receptor p160 (PDB 1KBH) exists as a cooperatively folded helical heterodimer. The association of this type of complex cannot be simulated by our method. They are thus excluded from the study. Consequently, the number of protein complexes in the final benchmark was narrowed down from 108 to 62. Coarse-grained Monte Carlo simulations were carried out on all remaining protein complexes, as described in the next section.For these entries, we only consider the protein complexes with no more than 500 amino acids in total, and the experimentally measured rate constants within the range from 1.0 × 10The association of binding partners in a given protein complex is simulated by a previously developed kinetic Monte Carlo (KMC) algorithm ,65. The 3 steps, and each time step is 1ns, so that the total simulation time for each trajectory is 1μs.A new configuration for different binding partners in the simulation box is obtained after each step of diffusions. The total energy of intermolecular interactions is derived for the new configuration, based on the hybrid force field. The probability of accepting the diffusions is further calculated based on the metropolis criterion, by comparing the total energy of the old configuration before diffusions and the new configuration after diffusion . If the https://github.com/wulab-github/KassKMC. This package contains all the source codes used for simulation in the FORTRAN77 programming language and as an executable file. The parameter files which contain all the force constants in the statistical potential are included. The package also contains the list of 62 protein complexes in the benchmark set. The package further offers a demonstration example (PDB 2VLN) with both the protein structure in PDB format as an input file and an output file that contains the simulation results of 104 trajectories. The program work on a Linux platform and downloading is free for academic users.In order to calibrate the computational performance of the method, we also use the test system 2VLN as a specific example. It takes approximately 10 seconds to generate a trajectory of 100 ns on a regular Linux desktop. Based on previous literature, it has been shown that for a protein complex with normal size, it takes on average one hour to generate a typical 100 ns trajectory of a Brownian dynamic (BD) simulation on a regular Linux desktop ,70. ThisWe constructed a hybrid force field to describe the total intermolecular energy between two binding partners of a protein complex. As mentioned in the last section, this force field is used to guide the Monte Carlo simulations of protein complex association. It can be written as follows:Equation (1) consists of two terms: a physics-based potential and a statistics-based potential . The parthi and the thj residues at the binding interface of a protein complex, where iq gives the charge of residue i at the functional center of its side-chain, and 0ε is the vacuum electric permittivity. In order to capture the shielding effect between two residues, an effective dielectric coefficient, ξ is the Coulomb Debye length used to mimic the screening effect at different ion strengths. The second term of the physics-based potential is used to estimate the hydrophobic effect between proteins, in which iHP is the Kyte–Doolittle hydrophobic score for residue i with its corresponding type [αw is used to balance the hydrophobic and electrostatic interactions. Finally, the third term takes into account the excluded volume effect. The depth of the potentials ijε is a step function depending on the distance between two specific representative sites ijr. It equals 0 if the distance is larger than 0σ; otherwise, it equals 5kT. The parameter 0σ has constant values which depend on the type of distance between the two representative sites. In particular, like the values used in our previous study, it equals 3.8 Å between two Cα atoms, 2.8 Å between a Cα atom and a side-chain functional center and 2.2 Å between two side-chain functional centers, respectively.The first term is the Kim–Hummer model ,72, whicing type . The weiIn addition to the physics-based potential used in our original simulation method, we further introduced a statistics-based potential that was derived from analyzing the available protein complexes in the current structural database . In detaf(x) equals 1 when x is smaller than 0 and -1 when x is larger than 0. This function ensures that the negative energy parameters derived from the statistics are attractive, while the positive parameters are repulsive. On the other hand, the function g(x) equals -1 when x is smaller than 0 and 0 when x is larger than 0. In turn, scu determines the strength of the interaction between residue types i and j. The specific value of the parameter was derived from the statistics of structurally available protein complexes [The summation in Equation (3) is taken across all residue pairs at the interface between two binding partners of a protein complex, which is recognized if the inter-molecular distance between any atoms of their side-chains is less than 5.5 Angstrom. The distance between the functional centers of these residues is omplexes . In detai and type j, which form contact at the binding interfaces; the denominator in the equation represents the expectation number of pairs between residue type i and type j under the quasi-chemical approximation [iχ represents the mole fraction of residues with type i at the binding interfaces. Two residues are considered to form a contact if a pair of any atoms belonging to the side-chains of these residues is closer than the distance cut-off value (5.5 Angstrom). In order to obtain different values for the energy parameter, we counted all the observed numbers of corresponding residue pairs in Equation (4) through a large-scale structural library of protein complexes that was constructed based on the original 3did database [In Equation (4), ximation ,77, in wdatabase . The datdatabase .Finally, it is worth mentioning that in our simulation of protein–protein association, we only consider the intermolecular non-covalent interactions between two binding partners. We assume that the tertiary structure of each individual protein is well conserved during the process of association. In order to reach this goal, all the intramolecular degrees of freedom are fixed, and proteins undergo translational and rotational diffusions as rigid bodies in the simulation. As a result, it is not necessary to reflect the covalent bonds that are used to maintain protein tertiary structures in our model.4 trajectories were carried out by the kinetic Monte Carlo simulation algorithm  simulation in estimating the association rates of different protein complexes, we collected 62 protein complexes from previous literature as a benchmark set. The criteria of benchmark construction are described in the Methods. Detailed information about the benchmark set, including the PDB IDs, the index of two binding partners and their corresponding experimental values of association rates, is listed in minP and maxP in Equation (5) stand for the minimal and maximal values of association probabilities that were obtained from all the protein complexes in the benchmark. The parameter 5, which stands for the lowest value of experimentally measured association rates observed in the benchmark. The parameter N indicates how many orders of magnitudes for experimental association rates are considered in our benchmark. As described in the Methods, because we only considered protein complexes whose rate constants are within the range of 1.0 × 105 and 1.0 × 109 M−1s−1, the value of N equals 4 in Equation (5).The parameters 4 simulation trajectories, as illustrated in the previous paragraph. We compared the calculated association rates of all protein complexes with their corresponding experimental measurements. Consequently, the Pearson correlation coefficient (PCC) between these two datasets was obtained under different values of weight constant.The force field which was developed to delineate the intermolecular interaction in our kinetic Monte Carlo simulations combines a previously constructed physics-based potential with a newly added statistics-based potential. A weight constant, ω, is used to define the relative contributions of these two potentials, as shown in Equation (1). In order to explore the capability of this new hybrid force field in estimating the association rates of different protein complexes, we adjusted the value of this weight constant from 0 to 1. Only the physics-based potential is used in the simulations when the weight constant equals 0, while only the statistics-based potential is used when the weight equals 1. Given a specific value for the weight constant, the association rate was calculated for each protein complex in the benchmark based on the statistical analysis from its 10In detail, the variation between weight constant and calculated PCC is plotted in The common logarithm of the ratio between simulated and experimental association rates Based on the definition of 4 simulation trajectories, with a maximal duration of 1000 ns for each individual trajectory, we found that encounter complexes were successfully formed in 1819 of them. This indicates that the value of the association probability equals 0.1819. Using Equation (5), we further calculated the rate of association between proteins E9 DNase domain and Im9. The association rate of the complex equals 9.86 × 107 M−1s−1, which is close to the experimental measurement (1.0 × 108 M−1s−1) [We selected a protein complex from the benchmark as a specific example to characterize the dynamic mechanisms of association. In detail, the complex formed between the E9 DNase domain of colicin endonucleases and immunity protein Im9 (PDB id 2VLN) was selected as a test system . Among a M−1s−1) . The phyIn the first system, small fluctuations in total energy were observed at around 0 throughout the simulation trajectory, as shown in Taken together, the analysis of simulation details reveals the dynamic mechanism of association. We showed that the individual association pathway is a multistep process which involves continuously searching local binding configurations and repeatedly forming kinetic intermediates with dissociation and re-association. A similar phenomenon has also been observed in a previous study using all-atom molecular dynamic simulations ,34.3 final configurations were randomly selected from the 104 trajectories to avoid spatial crowding in the figure. The structures of protein E9 DNase domain in these configurations were aligned with the native configuration, while the centers of mass for the protein Im9 are represented by the grey points. In addition to the analysis of the individual association pathways, it is also helpful to compare results between different simulation trajectories. As a result, protein complex 2VLN was still used as a test system. We plotted the spatial distribution of the final configurations that were generated from all the simulation trajectories in order to obtain an overview of the structural similarity among all the successfully formed encounter complexes. This distribution is plotted in 3 selected trajectories. We further plotted the spatial distribution of these configurations in According to the association probability calculated for protein complex 2VLN, encounter complexes were successfully formed in about 200 final configurations out of 10In summary, the analysis of the overall simulation trajectories reveals, firstly, that association between proteins could be kinetically trapped into nonnative states, secondly, that the pathway towards the final formation of the encounter complex is a dynamic process consisting of local structural reorganization and, finally, that even the structures in the final ensemble of encounter complexes are highly diverse. These features are consistent with the observations in another previous study using all-atom molecular dynamic simulations ,34.4 trajectories. The correlation is shown in y-axis equals the intermolecular energy calculated based on this configuration, and the x-axis is its RMSD from the native structure of the protein complex. The group of trajectories in which two binding partners failed to form a contact are reflected by the tip in the top-right corner of the figure. The distribution below the tip, on the other hand, indicates a large variety of complexes formed between these two binding partners in the remaining trajectories. We found a strong positive correlation in this distribution. The calculated Pearson correlation coefficient equals 0.7. In particular, a large group of encounter complexes formed through the lowest intermolecular energy, as indicated by the red arrow in 4 simulation trajectories for all 62 protein complexes in the benchmark. The distribution of Pearson correlation coefficient values is plotted in In the last three trajectories plotted in In the last section of the Results, we explored the combination of different criteria for encounter complex formation. As described in the Methods, the number of intermolecular contacts recovered from the native structure was used as a single criterion to determine whether an encounter complex can be successfully formed along simulations. Here, in addition to the number of native contacts, we further considered four other criteria. The first criterion is that the percentage of native contacts must be above a cutoff value. The percentage is calculated by the ratio of the number of recovered native contacts to the total number of intermolecular contacts in the native complex. The second criterion is that the RMSD between the encounter complex and the native complex needs to be below a cutoff value. The third is the distance cutoff between the interfaces of two binding partners, while the last criterion is the energy cutoff calculated by Equation (1). The number of encounter complexes observed in all simulation trajectories strongly depends on the combinations and values of these different binding criteria. We enumerated all different combinations for these criteria. For each combination, a wide range of discretized values was adopted for different criteria. In detail, the range of native contact numbers was initially 0 to 10, with an interval of 1; the range of the native contact percentage was initially 0% to 10%, with an interval of 1%; the range of RMSD was initially 0 to 30Å, with an interval of 3Å; the range of interface distance cutoff was initially 0 to 10Å, with an interval of 1Å; and the range of binding energy cutoff was initially 0 to -30kT, with an interval of 3kT. For all possible combinations and values, we calculated association probabilities for each protein complex in the benchmark. Consequently, we found that by selecting the optimal combinations and values of these binding criteria, we were able to achieve a higher correlation between experimental and simulated association rates than using individual binding criterion. One example is shown in We further found that this correlation also depends on the values of the adopted binding criteria. For instance, in The versatile functions of protein complexes in cells strongly depend on how quickly the building blocks in these complexes can be assembled together ,81,82. T5M−1s−1 [Previous works showed that the association rate constant of forming transient complexes purely via unbiased diffusions in which the diffusion coefficients of individual proteins can be calculated by methods such as Hydropro  is ~105M5M−1s−1 ,84,85. TN are experimental constants observed in the benchmark. The association probabilities iP were derived from the first-principle simulation, while their upper and lower limits minP and maxP were fixed variables, as long as all the association probabilities were derived. Therefore, there is no issue of overfitting when using Equation (5) to compare our simulation results with experimental data. However, it is possible that this empirical formula could bring some artifacts. For instance, the probability of association is time dependent. It will approach a value of 1 when given an extremely long simulation time, but it will approach 0 with an extremely short simulation time. This effect, fortunately, can be potentially canceled out by calculating the relative difference of association probability from its maximal and minimal values, as shown in the equation. Moreover, the length of simulation duration in our study is carefully determined to obtain an appropriate range of association probability, while the same value was applied to all protein complexes in the benchmark. Additionally, there is another limitation in Equation (5). It only takes into account the association rates across four orders of magnitude, as the range considered in the benchmark set. Although we adopted this limited range of rate constant values in the equation, it can still be performed to study protein associations whose rates are beyond this range. For instance, if the simulated probability for one protein complex is smaller than the minimal probability in Equation (5), this will lead to the result that the calculated association rate is lower than 105M−1s−1. On the other hand, if the simulated probability for one protein complex is larger than the maximal probability in Equation (5), this will lead to the result that the calculated association rate is higher than 109M−1s−1.In our previous study, the association rates were derived directly from the calculated association probability and related simulation parameters, such as volume and length of association time. However, the potential used to describe the intermolecular interactions in the simulation is simplified and thus not accurate enough, so the energy landscape for guiding association is not funnel-like. As a result, one problem in estimating the association rates in our previous study is that the association with high experimental rate constants tends to be underestimated, while the association with low experimental rate constants tends to be overestimated. To tackle this issue, in this study, we attempt to use an empirical formula (as shown in Equation (5)) to minimize the effect of the simplification of force field. It is worth mentioning that this equation is only applied to rescaled simulation results. There is no free parameter which can be adjusted to fit against the experimental data. The parameters


Eukaryotic cells contain numerous components, which are known as subcellular compartments or subcellular organelles. Proteins must be sorted to proper subcellular compartments to carry out their molecular functions. Mis-localized proteins are related to various cancers. Identifying mis-localized proteins is important in understanding the pathology of cancers and in developing therapies. However, experimental methods, which are used to determine protein subcellular locations, are always costly and time-consuming. We tried to identify cancer-related mis-localized proteins in three different cancers using computational approaches. By integrating gene expression profiles and dynamic protein-protein interaction networks, we established DPPN-SVM (Dynamic Protein-Protein Network with Support Vector Machine), a predictive model using the SVM classifier with diffusion kernels. With this predictive model, we identified a number of mis-localized proteins. Since we introduced the dynamic protein-protein network, which has never been considered in existing works, our model is capable of identifying more mis-localized proteins than existing studies. As far as we know, this is the first study to incorporate dynamic protein-protein interaction network in identifying mis-localized proteins in cancers. Eukaryotic cell is the most basic structural and functional unit of eukaryotic living creatures. Every cell contains numerous more basic components named subcellular compartments or subcellular organelles . AccordiProteins, which are translated in cytosol or rough ER (Endoplasmic Reticulum), must be transported to proper compartments during or after the translations to perform their biological functions . This prWith the help of automatic image processing and understanding technology, the first comprehensive human protein localization map was finally established . HoweverDuring the last twenty years, hundreds of works have been done in predicting protein subcellular locations using various types of information at various levels of cellular structure in various species . For exaThis is because almost all existing studies of this kind utilize only the static information as the input data. For example, most of the existing methods tried to extract informative features from the primary sequence of proteins, while the mutations and the SNPs were not taken into considerations. For another example, some of the existing methods make use of the gene ontology annotations, as well as the functional domain composition of proteins . There iSeveral existing methods are designed to find the alternative protein subcellular locations in different cellular conditions. PROLocalizer makes use of sequence mutations to detect mis-localized protein in diseases , 2011. LIn these existing works, the information to distinguish different cellular conditions comes from two sources, one is the mutations and SNPs, while the other is the differential gene expressions. Although the gene mutation and SNP information is useful, it is not easy to utilize them in sequence based features. On the contrary, many gene expression datasets have been deposited in the NCBI GEO (Gene Expression Omnibus) database , which hAlthough state-of-the-arts methods, which applied gene expression profiles and PPI networks to predict mis-localized proteins in cancers, have achieved success in several specific types of cancers, it should be noted that these methods have two common issues.First, all state-of-the-arts methods used identical PPI network structures in both the disease and non-disease conditions. This is the result of lacking PPI network data in specific disease conditions. However, if a protein is mis-localized in the disease condition, its interacting proteins must be changed, as the physical distances between the mis-localized protein and the other proteins are changed. Therefore, the topological structure of the PPI network in the disease condition must not be identical to the non-disease condition.Second, as the topological structure of the PPI network should be changed in the disease condition, the difference of the topological structure of the PPI network should be utilized to predict mis-localized proteins.In this work, we tried to solve the above two issues by building a model named DPPN-SVM (Dynamic Protein-Protein Network with Support Vector Machine). We made changes to the PPI network in the non-disease condition according to the changes of co-expression scores in disease condition to establish an adjusted PPI network in the disease condition. We applied the ECC (edge clustering coefficient), which has already been applied in predicting essential proteins and protein subcellular locations , to extrWe downloaded our PPI data from the BioGRID database version 3.5.179 . To consWe obtained reviewed human protein records from the UniProt database , which iAfter the mapping procedure, we transferred the GO (Gene Ontology) annotations in cellular component ontology category from the UniProt records to the BioGRID proteins. We chose the following 12 subcellular locations, including Cell cortex(GO:0005938), Cytosol(GO:0005829), Actin cytoskeleton(GO:0015629), Golgi apparatus(GO:0005794), Endoplasmic reticulum(GO:0005783), Nucleolus(GO:0005730), Peroxisome(GO:0005777), Mitochondrion(GO:0005739), Lysosome(GO:0005764), Centrosome(GO:0005813), Nucleus(GO:0005634), and Plasma membrane(GO:0005886). When the GO annotations were transferred from the Uniprot records to the BioGRID proteins, we choose to transfer only those GO terms with experimental evidences. This is achieved by choosing only those terms with evidence code IDA (Inferred from Direct Assay) or HDA (Inferred from High Throughput Direct Assay). We have 6461 BioGRID proteins that were experimentally annotated with at least one of the above 12 subcellular locations.Among the 6461 annotated BioGRID proteins, there were 4112 proteins with only one subcellular location, 1731 proteins with two locations, 503 proteins with three locations, 98 proteins with four locations, 15 proteins with five locations and 2 proteins with six locations. The average multiplicity degree of the dataset was 1.48. The breakdown of the dataset for different location multiplicity is illustrated in Since one protein may have more than one subcellular locations, it is necessary to introduce the virtual locative protein concept . In the κ (κ > 1) subcellular locations was split into κ virtual locative proteins. Each of the κ virtual locative proteins has one and only one of the κ subcellular locations. For example, if a protein pi has two subcellular locations l1 and l2, we split the protein pi into two different virtual proteins, located at l1 and l2, respectively.Every protein with The virtual locative proteins inherited the properties of the original real proteins, including all PPI connections and gene expression profiles. Since the virtual locative proteins have different subcellular locations, we assumed that there is no PPI between the virtual locative proteins that are generated from the same real protein.The original 6461 proteins with experimentally annotated subcellular locations are split into 9562 virtual locative proteins, resulting in a multiplicity degree of 1.48. Therefore, the number of proteins that are mapped between UniProt and BioGRID increased to 19420, which is about 120% of the original. The number of PPI in the network increased to 601693, which is about 200% of the original. u-th and the v-th proteins, the ECC can be defined as follows:Edge clustering coefficient was originally developed in analyzing social networks . It has uη,v is the ECC between the u-th and the v-th proteins, zu,v the number of triangles that involve the edge between the u-th and the v-th proteins, and du and dv the degree of the u-th and the v-th proteins, respectively.where u-th and the v-th proteins. We set uη,v = 0 in the case that the denominator is degraded to zero.The denominator in Eq (1) represents the possible most number of triangles that may involve the In order to apply machine learning techniques to graph-like structures, diffusion kernel was proposed to capture the long-range relationships between vertices induced by the local structure of a graph . The difG be a simple graph. Its Laplacian matrix can be defined as:Let A is the adjacency matrix of the graph, and D the degree matrix. The matrix D can be defined as:where di is the degree of the i-th vertex in the graph. The diffusion kernel matrix K(τ) is given by:where τ is a constant parameter, exp the matrix exponential function. It can be easily shown that the K(τ) is a valid kernel function.where Three cancer-related gene expression profile datasets were obtained from the NCBI GEO database. These datasets are from studies on acute myeloid leukemia, breast cancer and hepatitis carcinoma, respectively. The datasets include GSE9476 , GSE27567  and GSE121248 . All gene expression datasets were retrieved using the Affymetrix platforms . We usedxi,u be the u-th protein expression values of the i-th sample, n the number of samples in a group. We define the sample-wise centered expression vector Xu as follows:Let T is the transpose operator for matrix, andwhere u-th protein and the v-th protein as the follows:We now defined the pair-wise PCC (Pearson Correlation Coefficient) between the uρ,v is the PCC between the u-th and the v-th proteins.where The PCC was used to quantify the coherent extent of two proteins in terms of gene expressions. Regardless to whether two proteins have physical interactions, their PCC was calculated as above.θ, we term the case sample set as θ1, while the control sample set as θ0.Given a specific disease status θ0. The PCC between the u-th and the v-th proteins in non-disease states can be noted as uρ,v(θ0). Similarly, we can compute the ECC for each interaction as Eq(1). The ECC between the u-th and the v-th proteins in non-disease states can be noted as uη,v(θ0).We can compute the PCC for all pairs of proteins as Eq(7) using only the samples in A(θ0) be the adjacency matrix of the PPI network in non-disease states, which can be defined as follows:Let The Laplacian matrix in non-disease state can be defined as:D(θ0) is the degree matrix that is computed using Eq(3).where L(θ0), we can create the diffusion kernel matrix K using Eq(4). This kernel matrix is used in an SVM model to predict protein subcellular locations in the non-disease state. Since we took the multi-label scenario into the consideration, we employed the libSVM package  be the probability score that the u-th protein localize to the k-th subcellular location. The libSVM package ensures thatLet m is the number of all possible subcellular locations.where pu,k(θ0) of different subcellular locations varies a lot. Therefore, we defined the following adjusted probability score, qu,k(θ0), which is for the u-th protein and the k-th subcellular location:Due to the imbalanced dataset, the ranges of whereu-th protein localize to the k-th subcellular location if the following condition is satisfied:With all above definitions, the u-th protein using Eq(13), can be denoted as a set Su(θ0).where α is a real number parameter between 0 and 1. The subcellular locations, which are predicted for the θ1. However, to amplify the differences between disease and non-disease status, we altered the topology of the PPI network before all computations in disease status. This is different to all existing works in predicting mis-localized proteins in diseases.For the disease state, all above computation can be performed on u-th protein and the v-th protein, we first compute the PCC in θ1, which can be noted as uρ,v(θ1). We define the disease status difference of PCC as follows:For the We define two threshold parameters as follows:h is the average value of all hu,v, and σ the standard deviation of all hu,v.where u-th protein and the v-th protein are two interacting proteins in non-disease status, the interaction would be removed, if hu,v < t– is satisfied. Similarly, if the u-th protein and the v-th protein are two non-interacting proteins in non-disease status, the interaction between them should be established, if hu,v > t+ is satisfied.If the Su(θ1) according to the Eq(8) to Eq(13) using the updated PPI network and gene expression samples in θ1. It should be noted that the uη,v(θ1) may be different to uη,v(θ0), as the topology of the PPI network is altered in the disease state.After altering the topology of the PPI network as above, we compute the Su(θ1) and Su(θ0), we can identify whether the subcellular locations of the u-th protein were altered in the disease state. However, this method cannot quantify how likely a protein would be mis-localized in the disease state. Therefore, we developed the following method to quantify the mis-localized proteins, which we termed as the mis-localization scores.By comparing the u-th protein in the k-th subcellular location of disease θ can be defined as follows:For each disease, we compute the differences of adjusted probability scores between the disease and non-disease states. The mis-localization score of the uφ,k(θ) indicates the extent that the u-th protein would localize to or move from the k-th subcellular location. For each protein, we define the following two boundaries:The uφ(θ)] and inf[uφ(θ)] in descending and ascending orders, respectively. The top-ranked proteins within a fixed proportion of the entire list are considered as mis-localized proteins. The proportion is fixed as 0.1% in this work.We sorted the proteins according to the sup[In this study, we used 10-fold cross-validation to evaluate the prediction performance of our method in the non-disease state. Four statistics, including aiming (AIM), coverage (CVR), multi-label accuracy (mlACC), absolute-true rate (ATR) were applied to measure the prediction performances . These sSu(θ0) is the set of predicted protein subcellular locations of the u-th protein in the non-disease state, Su the set of experimental protein subcellular locations, b the number of proteins, | .| the cardinal operator in set theory, andwhere Since we have introduced the virtual locative proteins in our work, we also applied single-label performance measures. Five statistics, including sensitivity (Sen), specificity (Spe), virtual-locative accuracy (vlAcc), positive-predictive value (PPV) and Matthew’s Correlation Coefficients (MCC) are applied in our work. These statistics can be defined as follows:TP, TN, FP and FN are the numbers of true positives, true negatives, false positives, and false negatives in the cross-validation, respectively.where τ in computing the diffusion kernel was searched from 0.1 to 2.0 with step 0.1. The parameter α in Eq(13) was searched from 0.1 to 0.3 with a step of 0.1. τ = 1.1 and α = 0.3 in our works.We used a grid search strategy to find the parameter combination of τ and α that optimize the 10-fold cross validation performances in the non-disease state. The parameter We used 10-fold cross-validation to evaluate the prediction performances in non-disease state. It should be noted that our method is designed to find out the alteration of protein subcellular locations, rather than the exact subcellular locations in non-disease state. Therefore, we choose to compare our method to Liu and Hu’s method . Since wTo make further performance assessment, we choose to compare the multi-label performance of our method to the Hum-mPLoc 3.0, which was developed by using gene ontology information. Since our method does not rely on the gene ontology annotations, which has been proved to have superior performances in predicting protein subcellular locations, it should be noted that the Hum-mPLoc 3.0  has intrSince Hum-mPLoc 3.0 does not use identical subcellular locations annotations as our method, we choose to compare the overlapped locations. To achieve a fair enough comparison, we compose a testing dataset of 3842 proteins. All these proteins are with at least one overlapped subcellular location. This testing dataset was fed into the Hum-mPLoc 3.0 and our method in non-disease state. The overall multi-label performances were compared in We applied our method on three different type of cancers, including leukemia, breast cancer and hepatitis carcinoma. A comprehensive list of all proteins with the mis-localization scores can be found in supplementary data. In supplementary data, In acute myeloid leukemia, we used 25 cases and 38 controls. Our prediction showed that protein SETBP1 mis-localized to ER in cancer cells, as its localization score in ER increased from 0.083 to 0.633 with a mis-localization score +658.04%, while its localization score in nucleus dropped from 0.226 to 0.036 with the mis-localization score −83.94%. A recent study have suggested a direct involvement of SETBP1 in leukemia development . We predFor breast cancer, we used 51 cases and 31 controls. We made a prediction that the protein B7H1 mis-localized from plasma membrane and to nucleus, as its localization score in plasma membrane dropped from 0.243 to 0.105 with a mis-localization score −56.76%, while its localization score in nucleolus increased from 0.023 to 0.092 with the mis-localization score +290.65%. This consists with the record in literature . Our metFor hepatitis carcinoma, we used 70 cases and 37 controls. The protein S100A11 was reported to have very weak nuclear expression in adenocarcinomas , while oUsing our method, we identified some proteins that may mis-localize from or to a specific location. Some of them have been verified by existing studies. But most of the predicted proteins have not been verified. Due to our limited resources, we cannot perform experimental validations. This may be considered as a future work. It should also be noted that, there is still no database for mis-localized proteins. The information regarding the mis-localized proteins is still scattered in many literatures. Establishing such kind of database is a valuable yet impacting work, which is also in our consideration as a future work in this research topic. Since mis-localized proteins are of great significance on revealing the mechanism of diseases, we believe that it is valuable to establish a database to summarize and store relevant discoveries in future.Computational prediction of proteins subcellular locations has been studied for over twenty years. However, computationally detecting disease-related mis-localized proteins was rarely discussed. By integrating gene expression profiles and protein-protein interaction networks, we developed a computational approach, DPPN-SVM, to detect mis-localized proteins in various cancers. The results indicated that our method can successfully identify cancer-related or mis-localized proteins that has been reported in various literatures. Comparing to existing studies, our method not only provide a comparable or better prediction performance in non-disease state, but also further amplify the differentially expressed gene information by introducing the dynamic PPI network and the SVM classifiers with diffusion kernels. The prediction results of our method provide candidate proteins as spatial cancer markers, while the method of our work gives a new way to explore the spatial distribution of proteins within a cell.https://github.com/brown-2/mis_localization.Publicly available datasets were analyzed in this study. This data can be found here: G-PL collected data, process the data, implement the algorithm, performed most of the experiments, and analyzed the results. P-FD designed and directed the study, proposed the algorithm, analyzed the results, and wrote the manuscript. Z-AS and H-YL performed part of the experiments. TL analyzed the results, and participated in writing the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


In the field of protein engineering and biotechnology, the discovery and characterization of structural patterns is highly relevant as these patterns can give fundamental insights into protein-ligand interaction and protein function. This paper presents GSP4PDB, a bioinformatics web tool that enables the user to visualize, search and explore protein-ligand structural patterns within the entire Protein Data Bank.We introduce the notion of graph-based structural pattern (GSP) as an abstract model for representing protein-ligand interactions. A GSP is a graph where the nodes represent entities of the protein-ligand complex (amino acids and ligands) and the edges represent structural relationships (e.g. distances ligand - amino acid). The novel feature of GSP4PDB is a simple and intuitive graphical interface where the user can “draw” a GSP and execute its search in a relational database containing the structural data of each PDB entry. The results of the search are displayed using the same graph-based representation of the pattern. The user can further explore and analyse the results using a wide range of filters, or download their related information for external post-processing and analysis.GSP4PDB is a user-friendly and efficient application to search and discover new patterns of protein-ligand interaction. In the context of protein engineering and biotechnology, structural patterns are three-dimensional structures that occur in biological molecules, such as proteins or nucleid acid, and are key to understand their functionality . The disWe concentrate our interest on structural patterns representing protein-ligand interactions . LigandsThe study of the specific interaction of a protein with a ligand is an active research field because of the implications this has in the overall understanding of the structure and function of proteins, and in particular in the fast-growing area of rational drug design . ParticuThe importance of structural patterns can be exemplified by the Zinc finger motif that is widely found in DNA binding proteins including many eukaryotic transcription factors , 11. Alt2−4CX12HX 2−6H. Note that both notations are based on primary structure alone, so they are restricted to express sequence patterns (i.e. a sequence of amino acid symbols).To the best of our knowledge, there is no standard way to represent and search protein-ligand structural patterns (like the Zinc Finger motif) in structure databases. One way to represent patterns is to use a textual format. For instance, PROSITE defines the PA line notation  which alThere are only limited tools available to search and analyse these patterns in the sequence and structural databases. They rely on the input of simple numerical values and the results are usually represented as tables or statistical charts such as histograms. Therefore, the searches are complicated and cumbersome to visualise making the exploration of structural pattern difficult and non-intuitive. Different types of visual representations used by current tools and systems are described in the section about Related Work.Considering the problems identified above, we propose a graph-based model for representing structural patterns. Specifically, any protein-ligand structural pattern can be described as a graph whose nodes describe amino acids or ligands, and the edges represent their relationships. Based on this model we have developed GSP4PDB, a web application that enables the non-expert user to design, search and analyse protein-ligand structural patterns inside the Protein Data Bank (PDB) .Proteins are structurally complex and functionally sophisticated molecules, whose existence is essential to all forms of life with their wide ranging roles in all organisms .primary structure refers to the sequence of amino acids, which are linked by peptide bonds to form polypeptide chains. Polypeptide chains can fold into regular structures such as the alpha helices and beta sheets. These substructures, stabilized by regular H-bonding between the main chain atoms, conforms the secondary structure of the protein. Tertiary structure refers to the full three-dimensional organization of one polypeptide chain. Finally, if a particular protein is formed by more than one polypeptide chain, the complete structure is designated as the quaternary structure . In order to find the specific range for each distance, we can make use of filters. This allows us to define the ranges shown in example (d). Note that both patterns (c) and (d) have 630 hits. Finally, we can use any-amino-acid-nodes to search for specific sub-sequences inside a gap. For instance, assume we are interested in exploring subsequences of large 2 between the two histidines. This can be done by replacing the third gap by two any-amino-acid-nodes, as shown in example (e). The four results of this latest pattern can be further explored in a visual way or by using the corresponding filters. In both cases, we discovered that the subsequences ILE-ARG and LYS-ASP occur only once while GLU-ILE twice.Note that all the gaps in pattern (b) have a range X, i.e. one or more amino acids in the gap. Following the results presented in , where tGSP4PDB filters allow to group results and calculate summarized data. Table Importantly, GSP4PDB can be used to identify the functions of proteins that have not been annotated previously. Our analysis Table  showed tRecall that the GSP4PDB database contains CATH information as a complement to the data obtained from PDB. Table GSP4PDB also facilitates the analysis of the interaction distances between the ligand and the different amino acids that make up the pattern, through the analysis of the data that are downloaded from the JSON file. An example of this is shown in Table This analysis clearly shows that class 2.30.170.10, which corresponds to a ribosomal protein, is not a genuine Zn2+ binding as all distances are much larger than expected. If the user were interested in accurate average bond distances the search can easily be repeated with a smaller range such as distances between 1.5 and 3Å.In order to illustrate the use of GSP4PDB to represent and search different types of protein-ligand structural patterns, we selected six classes of zinc fingers  defined by the main graph pattern. For instance, the first sub-pattern describes the structure C-X(2)-C-X(12)-H-X(2)-H. For each pattern, we group and show the number of PDBs, classifications, organisms, CATH codes and average distances. A number in parentheses is used to indicate the number of results for each specific value .This structural bioinformatic search can be utilized as a starting point for further functional analysis of the sub-groups identified.In this section we review related approaches, discuss the general use of graph theory in bioinformatics and discuss the advantages of the software presented here.We reviewed several tools related to visualize protein structures. Due to space restrictions we just mention a small number of tools and system. The following articles present reviews and comparisons of visualization tools: –33.There are tools like JSMol , 3DMol , JSME 3, LigPlotFigure Our search and analysis of the related tools and systems, allowed us to verify that the graph-based representation of GSP4PDB is a novel approach. However, it is important to mention that most of the current approaches for visualizing protein structures share a common foundation: a graph-based structure.In terms of interfaces for searching protein-ligand interactions, there are four basic types: form-based, text-based, sequence-based and molecular-based. A form-based interface  consistsIn contrast to the current approaches, the graph-based query interface provided by GSP4PDB allows the user to create different types of structural patterns in a simple way (by doing drag-and-drop). Note that the notions of any-amino and any-ligand allows to explore and discover new kinds of structural patterns.Graphs are omnipresent in our lives and have been increasingly used in a variety of application domains. In our context, graphs are a natural way of representing biological networks, and graph theoretical concepts are useful for the description and analysis of interactions and relationships in biological systems .Different classes of graphs can be used to model different levels of abstraction and knowledge. For example, graphs have been used to represent protein-protein interaction networks  and cellProtein data modeled as graphs is been supported by the development of graph database systems. These systems enable efficient storage and processing of the encoded biological relationships, and can offer great speedups over relational databases . For insNext we discuss the advantages of GSP4PDB in terms of the following features: multi-purpose, usability, efficiency, availability, maintainability, interoperability and multi-Platform.Multi-purpose GSP4PDB allows the visualization, search and exploration of common structural patterns in protein-ligand interactions. Additionally, it facilitates the discovery of complex patterns that could be linked to the use or interaction with drugs or molecules of biotechnological interest. This information can be particularly important in the area of protein design and the creation of new enzymes.Usability GSP4PDB provides a very simple and intuitive graph-based visual interface to represent a protein-ligand interaction. As discussed before, a graph is a powerful abstraction to represent the relationships between biological entities. The usability of GSP4PDB was evaluated by researchers and students of the bioinformatics department at Universidad de Talca (Chile).Efficiency GSP4PDB allows fast and massive search of the structural characteristics in all the PDB data base facilitating the mining of data in three-dimensional information, something that is complex and expensive to do by traditional methods. The efficiency of the systems is given by the denormalized design of the database and the use of indexes. We believe that the efficiency could be improved with the use of a graph database system .Availability GSP4PDB is hosted in a Lightsail virtual private server of Amazon Web Services (AWS). Hence, the system inherits AWS features like reliability, high-performance, persistent SSD-based block storage, load balancing, data protection and network stability.Maintainability The installation of GSP4PDB in a web server is very simple and does not have special requirements. The most costly step (in terms of time) is the creation of the database due to the pre-processing of the entire PDB dataset. After the first data loading stage, the updating process is very simple thanks to the features of gsp4pdb-parser.Interoperability A protein-ligand pattern can be stored as a JSON file, and can be loaded in the future. The results are shown by using either a traditional tabular representation or a graphical mode view (following a graph-based representation). The results can be exported as a text file with the list of protein IDs, or as a structured JSON file containing the encoding of the graph-based representation of each solution.Multi-platform GSP4PDB is distributed as a web application and has been tested in all current browsers (see the section about Availability and requirements). The system does not require special plugins in the front-end, although requires PostgreSQL database system in the back-end.This paper presents GSP4PDB, a novel tool which allows to search protein-ligand interactions by using a simple and intuitive graphical representation based on graphs. Here, we describe the design and implementation of the three main elements, pre-processing, data storage and web interfaces. Furthermore, our case studies on the Zinc finger motifs demonstrate how new functionalities can be discovered for proteins with hitherto unknown function.As future work we expect to extend the notion of protein-ligand structural patterns to support filters and advanced relationships . Additionally, we will explore the use of big data technologies for storing and query PDB data. Particularly, we expect to use graph-based technologies like Giraph, a graph processing framework built on top of Apache Hadoop.Project name: GSP4PDBProject home page: http://gdblab.com/gsp4pdb/gsp4pdb2/Hardware (host): Lightsail virtual private server, 2 Core Processor, 8 GB Memory, 160 GB SSD Disk, 5 TB TransferOperating system(s): Platform independent (Web)Programming language: PHPCompatible Web navigators: Chrome 71.0.3578.98, Internet Explorer 11, Opera 58.0.3135.47, Firefox 64.0.2, Safari 12.0.2License: Academic Free License (AFL)