Partner and Localizer of BRCA2 or PALB2 is a typical tumor suppressor protein, that responds to DNA double stranded breaks through homologous recombination repair. Heterozygousmutations in PALB2 are known to contribute to the susceptibility of breast and ovarian cancer. However, there is no comprehensive study characterizing the structural and functionalimpacts of SNPs located in the PALB2 gene. Therefore, it is of interest to document a comprehensive analysis of coding and non-coding SNPs located at the PALB2 loci using in silicotools. The data for 1455 non-synonymous SNPs (nsSNPs) located in the PALB2 loci were retrieved from the dbSNP database. Comprehensive characterization of the SNPs using a combinationof in silico tools such as SIFT, PROVEAN, PolyPhen, PANTHER, PhD-SNP, Pmut, MutPred 2.0 and SNAP-2, identified 28 functionally important SNPs. Among these, 16 nsSNPs were furtherselected for structural analysis using conservation profile and protein stability. The most deleterious nsSNPs were documented within the WD40 domain of PALB2. A general outline ofthe structural consequences of each variant was developed using the HOPE project data. These 16 mutant structures were further modelled using SWISS Model and three most damaging mutantmodels  were identified. The non-coding SNPs in the 3' UTR region of the PALB2 gene were analyzed for altered miRNA target sites. The comprehensivecharacterization of the coding and non-coding SNPs in the PALB2 locus has provided a list of damaging SNPs with potential disease association. Further validation through geneticassociation study will reveal their clinical significance. PALB2, a 1186 amino acid residue protein, (the gene for PALB2 is located on chromosome 16p 12.2) is mainly responsible for the co-adjuvancy of BRCA1 and BRCA2 in the DNA damageresponse pathway .ImpairmThe overall flowchart of the major steps followed in this study is demonstrated via a generalized workflow .The nsSNPs of the human PALB2 were retrieved from NCBI  dbSNP database . This waFor distinguishing the deleterious nsSNPs from tolerated ones, total eight bioinformatics tools were implemented. These tools include: SIFT, PolyPhen 2.0, PROVEAN, PANTHER, SNAP2,PhD-SNP, SNPs and GO, and PMut. The rsIDs of the nsSNPs were provided as input in the SIFT (Sorting Intolerant from Tolerant) tool . NsSNPsProtein stability alterations due to SNPs were measured via two web-based tools. They were support vector machine-based tools named I-Mutant 2.0 and MUpro. Here, the protein sequencecollected from NCBI database was provided as input to predict the direction towards which the mutation causes the protein stability change, expressed in DDG value. Positive DDG valueindicates increased protein stability, and negative DDG value, protein destabilization . MUpro bConsurf was used to predict the conservancy of the residues at the SNP positions. The FASTA format of the protein sequence was provided as input in the Consurf tool and the outputwas provided in a score scheme starting from 1 to 9 with colour differences following Bayesian calculation method. Score 9 with deep purple colour represented highly conserved residue,whereas score 1 with deep blue colour represented a highly variable residue.Prediction of the secondary structure, solvent and surface accessibility of a protein was performed using the NetsurfP2.0 . The FASPosttranslational modification sites were predicted using ModPred. The input was FASTA format of PALB2 and result showed overall PTM sites available in the residue of this protein. DiffereIt is used to combine genetic and molecular data for interpreting g-score  ranging from 0 to 1, reasoning the probability of structural and functional alteration dueto nsSNP MutPred2.0 also yielded the p-value of every alteration depicting clinically significant variants . The scoThe amino acid variants located on the UTR regions and microRNA target sites were identified by utilizing Ensembl , RegulomEnsembl is a single point of access to an noted genomes for mainly vertebrate species . The amiPolymiRTS (Polymorphism in micro RNA Target Site) database was utilized for identification of DNA polymorphisms in miRNA target sites as well as seed regions .DNA varThe WD40 domain of PALB2 protein that has been reported in breast cancer patients was emphasized for structural analysis.HOPE (Have (y) Our Protein Explained), a web application tool, was utilized for analysis of the difference between wild type and mutant structures. It shows structural and functionalconsequences upon point mutations such as alterations in amino acid bindings, hydrophobicity changes, charge differences etc. . The FASHomology modelling and structural validation of the WD40 domain of PALB2 protein regarding the mutant residues were performed by Swiss Model .Upon suPROCHECK was used for the overall structural "quality" assessment of the mutant structures modelled by SWISS. PROCHECK generates Ramachandran plot which determines the backbone confirmationusing phi/psi dihedral angles . It reveThe PyMOL  molecular Graphics System, version 2.0 Schrodinger, LLC was implemented for RMSD (Root Mean SquareDeviation) score prediction. Superimposition followed by alignment between the wild type and mutant structures were executed. Since RMSD calculates the distance between residue pairsequally, the higher the RMSD value the greater the deviation of the mutant structure from the native one.TM-Align (Template Modeling -Align) optimizes residue to residue alignment in a sequence independent manner between two different structure of proteins for TM-score (Template ModellingScore) prediction . Tm-aligMutation 3D helps to visualize any cluster formation due to the provided amino acid substitutions causing somatic cancer mutations . Genesy was utilized in case of selected mutant models for deeper insightinto their properties. Alterations in hydrogen bond formation, hydrogen bond number was observed after superimposing the wild type and mutant structures. Also, changes in the interactionof the wild type residue and mutated residues with other amino acids were visualized with the help of this program.Total 11725 SNPs were found in Human PALB2 gene from dbSNP database of NCBI. Out of 11725 SNP, 1455 were non-synonymous, 592 were synonymous. The non-synonymous SNPs accounted for 13%of the total SNPs reported in human PALB2 gene.Eight different web-based tools SIFT, PolyPhen2.0, PROVEAN, SNAP2, PhD-SNP, PANTHER, SNPs and GO and PMut were utilized for the prediction of damaging and disease associated nsSNP(Supplementary Table 1).SIFT predicts the effects of nsSNPs. Among 1454 nsSNPs, 192 variants were found on SIFT server. Protein coding transcript, Protein ID (ENSP00000261584) was selected for analysis asthis transcript ID had the highest SIFT score. Out of 171 variants of this particular transcript, 59 variants were predicted to be deleterious and the remaining 112 as tolerant. Therest of the nsSNPs predictor programs further examined these 171 variants.Out of 171 variants, 60 amino acid variants were predicted to be probably damaging, 13 variants were predicted to be possibly damaging and the remaining were predicted as benign.43% of the total nsSNPs were identified for encouraging possible damaging impact with 57% having no significant effect on PALB2 protein.PROVEAN is used to identify functionally important nsSNPs. PROVEAN characterized 70 nsSNPs out of 171 as deleterious with a threshold value below -2.5. 41% nsSNPs were identified tohave deleterious effect on PALB2 protein.PANTHER calculates the functional effect caused by a potentially pathogenic or deleterious nsSNP on the protein. It showed the preservation time of 8 nsSNPs to be higher than 450 my(millions of years), 40 nsSNPs to be in between 200my to 400 my and the rest to be below 200my. Thus estimated 28% nsSNPs are probably deleterious and 72% are probably benign.SNAP2 speculates disease-inducing mutations caused by the amino acid variants. It predicted 71 amino acid variants to be damaging. So, 42% nsSNPs were responsible for possible damagingimpact on PALB2.PhD-SNP predicted 45 nsSNPs responsible to cause disease in humans. PhD-SNP provided an output with a score between 0 and 1, where, any nsSNP having score greater than 0.5 was consideredas pathogenic.SNPs and GO predicts whether a variant is disease inducing or not. It provided results estimating 21nsSNPs out of 171nsSNPs to be associated with insurgence of disease in humans.Thus, 12% nsSNPs were predicted to be deleterious with the rest having no noteworthy effect.PMut characterized 22nsSNPs to be pathological out of the given 171 mutations. Thus, 13% of the total nsSNPs were prone to giving rise to diseased state in humans. A Venn diagram isprepared distributing the nsSNPs having most damaging effect structurally-functionally according to the various in silico tools employed in this study .The mosShortlisted 29 nsSNPs were analyzed to investigate the change in protein stability. They were submitted to I-mutant 2.0 and MUpro. In both cases majority of 29 nsSNP showed destabilizedeffect on protein, with few increasing its stability. Among them, 22 nsSNPs belonged to C terminal domain of PALB2 protein, WD40. Conserved sequences are important in finding homologybetween different species and can also be used to identify the cause of genetic diseases. The evolutionary conservation pattern of amino acids or nucleotides in a protein was measuredby Consurf to discover the structurally and functionally important regions . From CoNetSurf P 2.0 analyzed the secondary structure of the above-mentioned residues, categorizing them as helix, strand or coiled. 50% of these residues were exposed and 50% were buried.NetSurf P2.0 further showed that among these residues, R34, L35, R37 and P735 were moderately disordered and S417 was highly disordered (Supplementary Table 2).Post translational Modification (PTM) sites were predicted via Modpred, GPS-SUMO, GPS- Palm, GPS-MSP, iGPS. PTMs with high or medium confidence were taken into analysis. Modpredpredicted position R34, R37 and Q921 as proteolytic cleavage sites in wild protein. No phosphorylation and methylation sites had been predicted by iGPS and GPS-MSP. GPS-SUMO and GPS-Palm identified position N1096 as a site of sumo interaction and C1060 as a site of palmitoylation.The results obtained from MutPred2.0 revealed that 19 nsSNPs out of 27 nsSNPs had g-score greater than 0.5 with a significant p-value. These substitutions contributed to a greaterrisk of structural and functional alterations with significant P-value. The amino acid variations which had g-score less than 0.5, alteration probability less than 20% and p -valuegreater or equal than 0.05 had been excluded from the list .From Ensembl, 110 variations have been found on the non-coding regions. Out of 110 variants, 17 of them proved to be functionally important by RegulomeDB. All of them returned thescore of 2(a-c) (Supplementary Table 3) where, lower the RegulomeDB score, greater the functionality. PolymiRTS outlined 3 nsSNPs rs180748355, rs185410736, rs189962793 that alter miRNAtarget site .For structural analysis, the WD40 domain of PALB2 protein was emphasized due to lack of protein structure availability in Protein Data Bank (PDB).The structural effects caused by amino acid mutations such as loss of hydrogen bond formation, hydrophobicity change, disruption of protein folding etc. were explored by HOPE. Thedeleterious nsSNPs situated within WD40 domain were submitted to HOPE. One of the most common structural variations were the interruption of interaction with POLH (DNA polymerase eta)and POLH DNA synthesis stimulation. Mutations at protein surface area such as D1125Y, G1021R and L939W had led to disrupt the interaction with neighboring ligand molecules. Moreover,mutation at 998, were found to be associated with breast cancer susceptibility. L939W reduces interaction with BRCA2, RAD51, XRCC3 and decreases double stranded DNA break initiatedhomologous recombination associated with breast cancer susceptibility .Swiss Modeler modelled 16 mutant structures. One template with GMQE  of 0.92 was selected to build these 11 models. GMQE score of 0.92 reflectedthe expected accuracy of model built with that alignment. The mutant models all had a score near to zero with a “thumbs-up” beside the QMEAN score suggesting they were of good quality. Additionally, all the models built by Swiss Modeler were validated by PROCHECK, a representative of Ramachandran Plot. Each of them had more than86% region in favored region. (Supplementary Table 4)Superimposition of wild type and mutant structures via PyMol, yields that model C891W had the highest RMSD value of 1.463 followed by several other models  with RMSD value of 1.459 and 1.458. The one with the lowest RMSD value of 1.266 was mutant model L947S. There were 3 other models , which possessed lower RMSD value close to 1.266. All the models had TM- score not greater than 0.99918. L939W, L1150R and Q921H models had the highest TM-score.Finally, based on RMSD value and TM- score prediction, 8 models  were selected to estimate cluster formation via Mutation3D (Supplementary Table 5). All the models submitted had greater MPQS  score from the minimum quality requirement threshold. Mutation 3D showed that 4 models  were involved in cluster formation. All 4 models had significant p- value of 0.000504 and were pictured as red cluster balls .Out of the 4 models previously mentioned, 3 mutant models  had been selected for further analysis via Discovery Studio Visualize as they provoke the moststructural alterations. The amino acid interactions at different positions are shown both in case of native structure and mutant structures .Upon muMost human non-synonymous single nucleotide polymorphisms (nsSNPs) represent genetic variations along with phenotypic differences. The goal of nsSNP research is to comprehend itsassociation with many complex human diseases in genetic level . PALB2 . BesidesIn this study, non- synonymous single nucleotide polymorphisms (nsSNPs) were characterized that alter PALB2 protein structure and functional activity. Out of initially screened 28deleterious nsSNPs, the most damaging nsSNPs were identified. Due to unavailability of whole protein structure of PALB2 protein in Protein Data Bank, only nsSNPs within WD40 domain(PDB code: 2W18) were emphasized for structural investigations. Lastly, 3 mutations were chosen to be most deleterious according to RMSD and TM score. Importance of PALB 2 investigatedfrom cBioportal, GTEx and GWAS portal also indicate the pathogenic effect of deleterious SNPs. Thus, this study provides us with a new region to look into diseases like breast cancerand investigate the proteins mechanism performing further polymorphism analysis studies.


It is of interest to document the molecular docking analysis of phytocompounds from Andrographis paniculata binding with protein NOTCH1 in the Notch-signaling pathway in the context of cancer. Hence, we document the binding features of neoandrographolide,14-deoxyandrographolide, androgapholide and andrograpanin with proteins in the notch-signaling pathway for further consideration. It remains one of the most dangerous malignancies and the second largest cause of cancer-related deaths in the world, considering the increasing the occurrence of gastric carcinoma ,2.AccorThe structures of the target receptor of human Notch-1 (PDB: 2VJ3), was downloaded from protein databank. Before using this structure for docking studies, the heteratoms, water molecules and co-crystallized ligands were removed and then it was saved as.pdbqtformat.Ten Andrographis paniculata chemical components have been obtained from the Pubchem database (Table 1 - see PDF). In this analysis, we used pdb coordinates for all hydrogen output formats. The charges were further repaired by inserting partial gasteiger chargesand then push the autodock. Then the structure of the compounds was opened on PyRx by clicking on Load Molecule and making ligand.Many docking algorithms becomes capable of constructing a wide range of possible structures, so they still need a means to score each structure to categories those of greatest interest. In the present study, the docking process was carried using PyRx 0.8 withthe Autodock Vina method using the Lamrkian genetic algorithm as the score function was completed ,15. PossMolecular Docking is an important tool for predicting ligand modes with the protein of known three-dimensional structures. In order to elucidate key structural characteristics and interactions, studies on binding modes are important and provide helpful evidenceto develop successful inhibitors. Protein molecular docking experiments, specifically the Notch-1 protein with active Andrographis paniculata compounds was carried out in the current study. (Table 2 - see PDF) displays the docking results. Examination of the resultsshows all the compounds have greater affinity for the preferred target protein Notch-1. These compounds can also be used successfully to control the Notch signaling pathway. The best four compounds were chosen based on these binding affinities. The main contributors to the receptor protein's stability tend to be hydrogen interactions. Thus, in current study, docking experiments have shown that interactions with hydrogen bonding have been mediated in each ligand-proteininteraction by particular amino acid residues. In specific, for most compounds, the amino acids SER-458, GLU-455, PHE-474, ASN-459, PRO-460 & GLN-462 alternately form the H bond. Four compounds had been picked out of 10 compounds and shown in (Table 2 - see PDF).Analyzing the docking outcome shows that several of the compounds exhibited score-based and interaction-based interaction with the target protein, and we observed that although these four compounds exhibited very strong activity against Notch-1 protein. Neoandrographolidehas found a very high energy binding value  across all the compounds, suggesting that it can bind successfully to Notch-1. As described above, neoandrographolide was observed to have the largest number of interactions. It formed the four hydrogenbond interaction via the amino acid residues SER-458, ASN-459, PRO-460 & GLN-462 at distances of 2.6, 2.0,2.4 & 2.1 respectively . The twoWe document the molecular binding features of neoandrographolide, 14-deoxyandrographolide, androgapholide and andrograpanin from Andrographis paniculata with proteins in the notch-signaling pathway for further consideration.


Understanding transient protein interactions biochemically at the proteome scale remains a long-standing challenge. Current tools developed to study protein interactions in high-throughput measure stable protein complexes and provide binary readouts; they do not elucidate dynamic and weak protein interactions in a proteome. The majority of protein interactions are transient and cover a wide range of affinities. Nucleic acid programmable protein arrays (NAPPA) are self-assembling protein microarrays produced by freshly translating full-length proteins in situ on the array surface. Herein, we have coupled NAPPA to surface plasmon resonance imaging (SPRi) to produce a novel label-free platform that measures many protein interactions in real-time allowing the determination of the KDs and rate constants. The developed novel NAPPA-SPRi technique showed excellent ability to study protein-protein interactions of clinical mutants of p53 with its regulator MDM2. Furthermore, this method was employed to identify mutant p53 proteins insensitive to the drug nutlin-3, currently in clinical practice, which usually disrupts the p53-MDM2 interactions. Thus, significant differences in the interactions were observed for p53 mutants on the DNA binding domain , on the structural domain , on hydrophobic moieties in the DNA binding domain  and hot spot mutants , which signifies the importance of point mutations on the MDM2 interaction and nutlin3 effect, even in molecular locations related to other protein activities.  Systems biology and proteomics emerged as powerful tools for understanding protein interactions with different partners under various micro-environmental conditions. However, as this area is still evolving, generating a concrete understanding of the quantitative estimation of this dynamic behavior is still a daunting task. Such interactions often vary from stable-bonds, high to low-affinity complexes, and short-lived interchanges under different environmental conditions. The majority of protein-protein interactions are transient in nature involving an unstable intermediate complex which is difficult to capture using conventional methods ,2,3. TheThe majority of the conventional methods like co-immunoprecipitation (Co-IP), two-hybrid approaches, and tandem affinity purification (TAP) prior to mass spectrometry are more suitable to analyze the stable protein complexes; dynamic weak interactions do not survive long enough for detection. These techniques are employed in understanding moderate to HT studies, though the extrapolation of those data in order to resolve dynamic interaction that can be manipulated by extrinsic forces remains still a challenging area ,2,3,4. MProtein microarrays are an attractive emerging technology for understanding protein interactions. Interestingly, protein microarrays allow the simultaneous study of hundreds to thousands of proteins displayed in high spatial density in a microscopic format . As an oThe traditional manufacture of protein microarrays entails printing purified proteins onto a substrate, which requires the purification, storage, and manipulation of thousands of proteins. This is a time-consuming, tedious, costly process and often results in uneven display protein yields that span more than 3 orders of magnitude, most of which is low. Moreover, the manipulation and storage of the proteins may sometimes lead to loss of function. In our previous reports, we have addressed the yield and activity-related issues by employing a more advanced Nucleic Acid Programmable Protein Arrays (NAPPA) technology. Unlike conventional microarray, in NAPPA the process of printing of proteins is replaced with the simple process of spotting cDNAs that encode the proteins fused to a common binding tag. At the time of the experiment, these cDNAs are simultaneously transcribed/translated in situ with mammalian ribosomes and the expressed proteins are subsequently captured using the c-terminal epitope tag. Importantly, this approach eliminates the need for HT protein isolation, while allowing to achieve highly consistent levels of all protein types and ensures that all the proteins are produced in full-length and freshly synthesized ,7. Typically, the interactions on protein microarrays have been detected with fluorescently-labeled antibodies or tagged ligands after multiple buffer washes, thus recording steady-state binding only. However, most variants of protein microarrays are also compatible with label-free approaches, which are capable of measuring dynamic protein interactions. The benefit of coupling protein microarrays with a label-free, real-time technology is a tool with the immense power of measuring the dynamics of protein interactions against numerous proteins simultaneously.Surface plasmon resonance (SPR) has evolved as one of the most appealing methods for measuring various dynamic molecular interactions by detecting even minute changes in the localized refractive index induced by the binding of an analyte (test protein) molecule to an immobilized target . Changes in the SPR signal correspond to the dynamic interaction of the test protein with the immobilized target thus allowing the capture of real-time assessment of dynamic protein interactions ,9,10. MoA recent adaptation of standard SPR technology resulted in an SPR-based imaging (SPRi) tool that showed huge potential for the simultaneous measurement of thousands of biomolecular interactions ,10,11. IHerein, we report the development of a novel protein microarray-driven SPRi platform which is based on our previously developed NAPPA technology. Newly developed NAPPA-SPRi technology is specifically designed to make a facile comparison of the kinetic information on both the strong as well as weak interactions without the need of having to purify each target protein. Importantly, our method is capable of measuring those interactions at a scale that was not attainable earlier with previously developed techniques. In the current report, we have demonstrated our method in elucidating a mechanistic understanding of the dynamic interaction of p53 protein with MDM2 in the presence and absence of nutlin-3. Nutlin-3 is a potent antitumoral drug already in the clinic. Intracellular action of nutlin-3 as an inhibitor of p53-MDM2 binding is well characterized in the literature. Additionally, we have also described protein-protein interactions of known clinical mutants of p53 with its regulator MDM2. The TP53 gene is the most commonly mutated gene in cancer, as it is mutated in greater than 50% of all cancers. Greater than 95% of these mutations are missense mutations that alter the function and regulation of p53 . MutatioSurface plasmons require metallic surfaces, such as gold. We initially adapted our standard NAPPA chemistry for gold-coated microscope slides by functionalizing the gold with amino-PEG-thiols which resulted in the formation of a homogeneous self-assembled monolayer of gold on microscopic slides see . This apTo test whether binding to NAPPA expressed proteins could be detected by SPRi, an array was printed with plasmids coding for the human proteins geminin, Akt1, fos, p53, and jun along with the GST-E-coil tag at their C-termini and expressed in situ. Similarly, we further tested this configuration for non-antibody-based protein-protein interactions by examining the binding of the transcription factor fos (query) to its partner jun , thus effects in the direct binding pocket are not considered. It is useful to compare the effects of point mutations on the p53-MDM2 interaction first in the absence and then in the presence of nutlin-3 . If no sFrom all available structural data ,59,60,61Interestingly, several nutlin-3 sensitive mutations reside in this interface, and the effects of nutlin-3 on the mutant Arg280Thr as well as the mutant Asp281Asn could reflect structural changes in this protein-protein interface, with Arg280Thr exhibiting a strong effect compared to an intermediate effect on Asp281Asn. In this case, MDM2 binding to Asp281Asn is somewhat perturbed (reduced), possibly indicating an overall destabilization of the mutant protein. Among the nutlin-3 resistant mutants, Arg273Leu represents an interesting case: the interaction with MDM2 is unperturbed  was <0.3% of the KD value. There was more variability in the rate measurements, with median SEs ranging from 0.3–25% of the measured values. This method is ideal for determining relative quantitative values amongst numerous proteins. Refinement of these values to achieve precise and accurate absolute values currently still requires more detailed studies in lower throughput. Yet, there are numerous applications that would benefit from an early and rapid assessment of relative binding affinities amongst many candidates. This represents a facile method for the HT study of transient interactions. Applications include assessing a query’s interaction with a group of proteins of interest, such as members of a particular pathway or family, or it can be applied in a more detailed study of various relevant mutant or modified forms of a single protein, as demonstrated here. It will be useful to apply this approach to characterize the structure-activity relationships of proteins, providing an adjunct tool in the quest for more personalized approaches. These kinetic data will enhance our understanding of protein function at an accelerated pace. However, improved informatics methods and tools will be needed to manage the analysis and interpretation of the voluminous data generated by this approach.www.dnasu.org (accessed on 10 October 2012)), ),  were culNanocapture gold activated slides (25 × 76 × 1 mm slide covered with a thin layer of gold 47.5 nm) were obtained from Plexera (USA). The gold surface was functionalized by a 1 mM solution of amino-PEG-thiols (HS-(CH2)11-EG6-NH2 supplied by Prochimia (Poland) to generate a homogeneous self-assembled monolayer onto the gold surface. The SAM preparation was performed in a tight box filled with ethanol. Gold slides were placed on a metal rack and after amino-PEG-thiol treatment, the coverslip was placed. The humidifier chamber was closed and after 16 h of incubation, the slides were washed with ethanol and dried using filtered air see . Gold-SPg for 30 min. Precipitated DNA was then washed with 80% ethanol and allowed to air dry. K-coil peptide  was purchased from New England Peptides   . The masThe master mix plate and the SF10 gold slides  were loaded to a Qarray2 robotic microarray spotter  configured to use 48 pins that produced 300 μm features. Microarrays were printed by pin-spotting master mix solution on NanoCapture Gold SPR-functionalized slides. The relative humidity was maintained at 60% during the printing. The printing program was set to print arrays in 12 rows × 12 columns and each feature was printed in duplicate. The master mix was also printed on gold slides  and amino silane  functionalized glass slides. After printing was completed, the arrays were stored in a dry container with silica packets. The printed gold arrays were washed with 1× PBS for 15 min with gentle agitation, followed by a brief washing step with deionized water for 1 min. The array surface was blocked with a solution of sulfate-dextran  for 1 h at RT with gentle agitation and followed by 5 min wash with deionized water. The arrays were dried under a stream of filtered compressed air. The in vitro transcription and translation step was performed as previously described . BrieflyPrior to the SPRi experiments, we performed a few QC experiments to ensure the printing quality as well as protein expression on arrays. The printed arrays were blocked with superblock for 1 h on the rocking shaker. The PicoGreen  staining was performed by applying 150 μL of 1:600 diluted PicoGreen . To detect protein expression universally on the array, the slides were incubated with superblock for 1 h. The mouse anti-GST antibody with a final solution of 10 μg/mL  was applied on slides and incubated for 1 h at RT and a coverslip was placed. For detection of specific proteins, the slides were incubated with anti-p53 (DO-1) monoclonal antibody, anti-c-fos, and anti-jun antibodies . After 1 h of incubation with primary antibody the slides were rinsed with superblock for 5 min × 3. The HRP linked secondary antibody with a final concentration of 10 μg/mL  was applied on slides and incubated for 1 h at RT and a coverslip was placed. After 1 h of incubation, the slides were rinsed with 1× PBS for 5 min in triplicate. The signal on arrays was developed by adding 600 μL of the tyramide signal amplification reagent  for 10 min and a coverslip  was applied. The slides were rinsed with de-ionized water, dried using compressed air, and scanned with a ProScanArray HT scanner . The array images were quantified using Micro Vigene software version 2.9.9.2 . The SPRi analysis was performed on a ProteomicProcessor Instrument . The 1× PBS and 1% Tween 20 was degassed  and used as SPR running buffer. n = 1.3347 RIU) and 2× PBS (n = 1.4465 RIU) into the flow cell and recording the signal intensity at each point. The typical calibration factor was 2 camera units/micro refractive index unit (μRIU). Using a titration of glycerol solutions, the sensitivity of the instrument was determined as described by Campbell et al. [2 and <50 fg/spot, was determined. In this set of experiments, the Region of Interest (ROIs) was defined by SPRit software as per the manufacturer’s instructions .The rate of data acquisition was set to 10 Hz. An elliptical region of interest (10 pixels height × 16 pixels width) was defined for each spot. Six background regions of interest of 10 × 16 pixels were defined at the top, middle, and bottom areas of the array. A simple two-point calibration was obtained by injecting 1× PBS n = 1.337 RIU andAll the in situ expressed proteins contain GST-E-coil tag. The expressed protein was captured on the array due to the heterodimer interaction of E-coil with K-coil. The SPRi instrument was set up as previously described. The flow chamber was assembled onto the blocked slides and 5 mL of running buffer was injected with a flow rate of 10 μL/s. Once the baseline was stabilized, the minimal angle was set up. In this set of experiments, the minimal angle was approximately 20 ± 1 mm. At this angle, the ROIs were adjusted again in order to simultaneously measure the resonance units change of 144 spots. The detection of in situ expressed proteins was performed by anti-GST antibody . After 250 s, 500 μL of anti-GST antibody solution (1:50 dilution in running buffer) was injected at a constant flow rate of 3 μL/s. At the end of the injection, a running buffer was applied for 1000 s at 10 μL/s. The specific detection of in situ expressed proteins  was performed by anti-p53 , anti-jun , anti-fos  antibodies. After 250 s, 500 μL of each antibody solution (1:50 dilution in running buffer) was injected at a constant flow rate of 3 μL/s. At the end of the injection, a running buffer was applied for 1000 s at 10 μL/s. To test p53-MDM2 and Jun-Fos interaction studies using SPRi, in situ protein expression and instrument set-up were performed as described above. After which, the flow chamber was assembled onto the gold chips and 5 mL of running buffer was injected with a flow rate of 10 μL/s. Once the baseline was stabilized, the minimal angle was set up. In this set of experiments, the minimal angle was approximately 20 ± 1 mm. At this angle, the ROIs were adjusted again in order to simultaneously measure the resonance units change of all the 144 spots printed on the array. The purified proteins Fos  and Mdm2  were diluted in running buffer at a final concentration of 20 μg/mL. This solution was injected at a constant flow of 3 μL/s, and at the end of the injection, a running buffer was applied for 1000 s at flow rate of 10 μL/s. To test the effect of nutlin-3 on p53-MDM2 interaction using SPRi, in situ protein expression and SPRi instrument set-up was performed as described above. After which, the flow chamber was assembled onto the gold chips and 5 mL of running buffer was injected with a flow rate of 10 μL/s. Once the baseline was stabilized, the minimal angle was set up. In this set of experiments, the minimal angle was approximately 20 ± 1 mm. At this angle, the ROIs were adjusted again in order to simultaneously measure the resonance units change of 144 spots. At a constant flow (3 μL/s) a solution of pure Mdm2  and 900 nM of nutlin-3  was injected in running buffer. At the end of the injection, running buffer was applied for 1000 s at 10 μL/s. −1s−1, or koff ≥ 1 s−1) or were affected by experimental processes such as bubbles, zone variation, etc., which could not be measured accurately, were eliminated from the statistical analysis.The large amount of raw binding data produced by the SPRi was analyzed by Scrubber 2.0 software. The raw SPRi data was produced by SPRit software , which annotated the p53 mutant array features, converted camera units into refractive index units, and performed background referencing to remove bulk refractive index effects. The SPRit software allowed the measurement of binding signal responses at various time points in units of refractive index. The data exported from SPRit software was transferred to CLAMP XP software to make it compatible with Scrubber 2.0 analysis. By using Scrubber software, the data were prepared for the global analysis by subtracting the average of the response over 20 s prior to the query protein injection and zeroing the time of injection in each experiment. The corrected binding data was then analyzed by direct curve fitting to a simple biomolecular interaction mechanism (1:1). These were fit to the association and dissociation phase sensor data in all the experiments. The error space for each of the parameters was assessed using statistical profiling (Rmax < 0.001). Each feature  was printed in duplicate on each array, and the experiment was performed in triplicate. In the case of wt p53, binding parameters from MDM2 titration studies were also included in the data processing . TherefoIf the overall computation of the values for a mutant fell outside instrument limits, no numerical result was reported. Instead, the value was listed as “<1 nM” or “>1 µM”, as appropriate. In all cases, we examined the sensorgrams to ensure that the binding curves were consistent with either “tight binding” or “weak binding” as implied by the out-of-range value reported. The statistical analysis was performed by MathLab v 7.8 (2009) software considering a normal distribution of each data set  per feature and per assay using median absolute deviation for a robust measure of statistical dispersion of the obtained data excluding values  outside the criteria (average ± 2 SD). Affinity constant (KD) was established as koff/kon values.Our results described a method readily adaptable to a high-throughput format to measure the real-time interaction of a query protein with many full-length target proteins, without having the necessity to produce and purify the target proteins. This protein microarray fabrication method showed huge potential to be implemented for multiplexed SPRi biosensing measurements in both clinical and research applications.One of the key advantages of our method is indicated by its ability to study full-length proteins, which has been challenging by conventional methods. For that, a novel NAPPA chemistry has been developed and optimized in order to adapt multiplex in situ protein expression suitable for SPRi. This novel chemistry is based on K-coil/E-coil interaction which allows capturing the in situ expressed proteins with high efficiency (high-affinity similar to streptavidin-biotin) and very close to the sensing surface, due to the small size of the tag which might increase the sensitivity. Interestingly, this chemistry is stable enough for use in affinity purification in protein chromatography . In addiFurthermore, as the values of binding parameters obtained by SPRi are comparable with the ones obtained by conventional SPR, this novel method could be a suitable approach for determining relative quantitative binding parameters values amongst numerous proteins in a high-throughput format. There are numerous applications that would directly benefit from an early and rapid assessment of relative binding affinities amongst many candidates. However, in order to generate a more precise and acute understanding, these values should be subjected to further refinement by conducting extensive studies on lower throughput systems.


The abundance of already known information on protein interactions, mechanism, and tertiary structure provides the possible means to understand these changes rapidly, though a careful and systematic integration of these diverse datasets is first needed. For this purpose, we developed Mechnetor, a tool that allows users to quickly explore and visualize integrated mechanistic data for proteins or interactions of interest. Central to the system is a careful cataloguing of diverse sources of protein interaction mechanism, and an efficient means to visualize interactions between relevant and/or known protein regions. The result is a finer resolution interaction network that provides more immediate clues as to points of intervention or mechanistic understanding. Users can import protein, interactions, genetic variants or post-translational modifications and see these data in the best known mechanistic context. We demonstrate the tool with topical examples in human genetic diseases and cancer genomics. The tool is freely available at:   High-throughput sequencing technologies permit the identification of thousands of genetic variants in healthy and diseased individuals . HoweverBecause this wealth of information is scattered across numerous databases and the literature, gathering and integrating the data can be difficult and time-consuming. Effective data visualization is equally important as it allows a more rapid synthesis of diverse information into a coherent mechanism ,19. ResoThere are many methods to assess the impact of variants on protein function, for example, based on sequence, phylogenetic and/or structural information , a new web tool that sits between the above resources. For sets of proteins, interactions and/or variants, Mechnetor quickly integrates diverse mechanistic data sources  and constructs an interactive network for intuitive visualization of protein mechanisms. Proteins are represented as linear arrangements of domains, motifs and other functional elements, which permits the display of interactions between the relevant/known protein regions. The result is a finer resolution interaction network that enhances mechanistic interpretations of biological processes and variants of interest.Here, we present Mechnetor  and other relevant sequence features (from UniProt), as well as interactions between those proteins (PPIs) and their elements (DDIs and DMIs), interactions predicted from 3D structures, and interactions or associations extracted from annotations. This process relies on an underlying database where data from diverse sources are carefully integrated, by matching different formats and descriptors, ensuring that information can be retrieved efficiently Figure .The collected data are then used to create an interactive mechanistic network that can include variants/PTMs mapped into their corresponding protein positions, and is presented to the user together with extensive interactivity options to facilitate an intuitive exploration Figure . AdditioP-value that indicates the probability of randomly observing the particular pair in the interactome of the organism considered.The Mechnetor network view shows detailed mechanistic information for every submitted protein. Proteins are represented as linear diagrams  of functional elements Table . Edges iUsers can click on any protein element or interaction to display a box with more information and links to original data sources Figure . The conThe interactive network is especially designed to investigate mechanistic details for only a handful of proteins at the time. The ‘hairball’ effect is an intrinsic problem of network visualization and we do not recommend  networks involving >20 proteins. The user will be warned if the input is too big, and the network will contain only a subset of proteins. However, the table will still contain all relevant data gathered for the complete input protein set.SCNN1B (UniprotKB: P51168) constitutes the β subunit of the heterotrimeric epithelial sodium channel ENaC, located mostly in high resistance epithelia cells in vertebrates, and involved in maintaining homeostasis and regulating blood pressure (ASC family domain [Pfam accession: PF00858]). Thus, they are more likely deleterious and result in decreased channel activity . Using the preloaded cancer missense variant dataset from COSMIC (GSK3B (UniProtKB: P49841) phosphorylation sites (recognised by MOD_GSK3_1 motifs [ELM accession: ELME000053]) targeted by these variants . Only a few, including the two within the cancer hotspot, actually correspond with known phosphosites supported with experimental evidence (obtained from UniProt and PhosphositePlus). Moreover, these phosphorylations are required for the recognition of CTNNB1 by BTRC (UniProtKB: Q9Y297), a component of E3 ubiquitin-protein ligase complex, through a diphospho-dependent degron DEG_SCF_TRCP1_1 (ELM accession: ELME000269) that interacts with its WD40 β-propeller. Therefore, these variants ultimately prevent ubiquitination of CTNNB1 and its subsequent degradation, which can then translocate to the nucleus and continuously promote transcription of its target genes  .All data required by Mechnetor were obtained from publicly available data sources and stored in our PostgreSQL database after some pre-processing and integration, that ensures information can be quickly retrieved and displayed by the tool.Z score, P-value) how well the sequences fit on any identified interface.Protein names, identifiers, gene, sequences, and multiple other annotations  are obtained from UniProt . All othmechnetor.russelllab.org/help.Data will be periodically updated. Current data versions can always be consulted at: elm.eu.org/interactiondomains) only lists motif classes and their interaction domains but, actually, not all motifs that interact with the same domain type can interact with the same proteins. For example, ELM has >30 motif classes that interact with the ‘protein kinase domain’ (Pfam identifier: PF00069), which in the human proteome is present in hundreds of proteins. However, most of these motif classes are only recognized by the kinase domains of very particular and different protein kinases, e.g. the ELM motif MOD_NEK2_1 is the specific phosphorylation site of the Serine/Threonine-protein kinase NEK2, while DOC_MAPK_gen_1 is the docking motif of members of the MAP kinase family (MAPKs). Furthermore, some motifs are exclusively located in certain proteins, such as LIG_PEX14_1 which mediates the interaction between PEX5 and PEX14; or are exclusive to certain taxa, like LIG_PAM2_2, which is a variant of the PABP (Poly-adenylate binding protein)-interacting motif specific for animals.The source file of ELM interaction domains data  to those pairs where the observed frequency was zero. In addition, the observed frequency of every domain–domain, but also domain-motif pair, is also reported as a P-value for every DDI and DMI. It represents the probability of finding the particular pair in the interaction dataset and thus can be used to estimate their significance.To infer interactions between protein domains, we use the method first described by Sprinzak and Margalit in 2001  for the Despite the immense volume of data generated by sequencing efforts, its impact on the advancement of medical knowledge and the development of patient-tailored treatments has been limited by our still narrow ability to interpret the molecular consequences of coding variants. This task necessarily requires the simultaneous analysis of diverse protein data, which often implies consulting several data sources and applying computational approaches to take further advantage of them.Mechnetor facilitates this by performing a systematic and fast integration of diverse protein data and presenting it to the user in an interactive and intuitive way. One of our priorities was to make this tool very user-friendly so, in essence, Mechnetor can be used by simply entering a pair of proteins and/or protein variants, clicking the submit button, and getting mechanistic ideas in a few seconds. Further possibilities include studying larger datasets of interacting protein pairs by directly downloading the integrated data for local analysis. We will update Mechnetor data regularly, and we plan on supporting more organisms, as well as extending some of its functionalities.mechnetor.russelllab.org. The source code is available at https://github.com/JCGonzS/mechnetor.Mechnetor is a web server freely accessible without login requirement at gkab399_Supplemental_FilesClick here for additional data file.


A number of structure-informed genomic and cross-species  interactomes will be described, and the unique information they provide will be illustrated with a number of examples. Tissue- and tumor-specific interactomes have also been developed through computational strategies that exploit patient information and through genetic interactions available from increasingly sensitive screens. Strategies to integrate structural information with these alternate data sources will be described. Finally, efforts to link protein structure space with chemical compound space offer novel sources of information in drug design, off-target identification, and the identification of targets for compounds found to be effective in phenotypic screens.Systems biology is a data-heavy field that focuses on systems-wide depictions of biological phenomena necessarily sacrificing a detailed characterization of individual components. As an example, genome-wide protein interaction networks are widely used in systems biology and continuously extended and refined as new sources of evidence become available. Despite the vast amount of information about individual protein structures and protein complexes that has accumulated in the past 50 years in the Protein Data Bank, the data, computational tools, and language of structural biology are not an integral part of systems biology. However, increasing effort has been devoted to this integration, and the related literature is reviewed here. Relationships between proteins that are detected  The growth of protein structure information has stimulated a parallel growth in computational tools that predict protein structure and function. These tools provide fundamental insights into the physical principles that underlie the behavior of biological macromolecules. For example, molecular dynamics simulations allow realistic descriptions of conformational heterogeneity; Poisson–Boltzmann calculations have revealed how electrostatic interactions play a central role in biological functions; and the forces that determine the stability of the native folded state are now well understood. Advances such as these have been transformative and are part of the language and intellectual foundation of modern structural biology.A illustrates the structural alignment of four protein domains where BLAST fails to detect any sequence relationship between them. Figure 1B shows the experimentally determined complex between the pleckstrin homology (PH) domain from phospholipase C-gamma-2 (yellow) and the small GTPase Rac2 (gray). Structural alignment of the Ezrin F3 lobe (red) with the PH domain produces a model for the complex between Ezrin and Rac2 (red–gray). Similarly, Figure 1C shows the experimentally determined complex between the PH domain from mouse Beta-II spectrin (green) and inositol 1,4,5-trisphosphate (sticks). Structural alignment of the Tiam-2 PH domain (blue) with the Beta-II spectrin PH domain produces a model for the complex between Tiam-2 and inositol 1,4,5-trisphosphate (blue and sticks). These examples provide the basis of many of the methods highlighted later that, as will be described, enable the use of structural information on a genomic scale.A parallel set of computational methods falls under the rubric of “structural genomics,” which includes the goal of structurally characterizing enough members of sequence families so as to enable the construction of homology models for the others. A key development has been the computational identification of geometric relationships among protein structures. Since structural similarity can identify functional relationships even in the absence of statistically significant sequence similarity, structural alignment has become a powerful tool to detect evolutionary relationships between proteins that cannot be detected from sequence alone. We have used the term Structural Blast  to implyFigure 1The Protein Data Bank (PDB)  stands aThere are areas of biomedical research where protein structure is still underutilized. Specifically, cellular systems biology, with its heavy emphasis on the study of pathways and networks, has made only limited use of 3D information. In networks, PPIs are typically described as nodes (proteins) connected by edges (interactions), without reference to the structures of the proteins involved or the nature of the interactions. With 20,000 human protein coding genes and potentially millions of PPIs, it is not possible to obtain experimental structures for every node and edge in the interactome. Computational methods to interrogate these interactions can complement the available experimental evidence, enabling more meaningful insights from systems biology approaches.This article summarizes some of the advances in structural systems biology and points to strategies through which structural information can be integrated with the vast quantities of data emerging from high-throughput (HT) genomic technologies and patient records . Databa BioGRID , HURI  (Context-specific associations can be derived from methods based on the correlation of gene profiles across many conditions (atments) , 20. Theatments) , 22; theatments)  and the atments)  for phenatments) ; and Genatments) . The Calatments) . As willatments) .e.g., PPI prediction can involve (a) predicting the structure of known complexes given the structures of interacting monomers; (b) predicting whether and how two proteins interact given their structures, which requires building a model of the putative complex and then scoring it; (c) predicting whether two proteins interact given their sequence, which can be accomplished either by purely sequence-based methods, that is, sequence relationships to proteins in known complexes, or through some combination of methods (a) and (b). There are two main computational approaches for method (a): docking and template-based modeling. Docking methods , 29 are The Interactome3D server was an early resource for the prediction of the structures of protein complexes for different organisms . The curInteractome INSIDER  also buiA, where models of the proteins for human CUL5 (yellow) and DCUN1D5 (green) are superimposed on chains of the PDB complex between the yeast proteins for CDC53 (brown) and DCN1 (purple). In this case, the template complex was identified because the proteins for human CUL5 and yeast CDC53 are structural neighbors as are the proteins for human DCUN1D5 and yeast DCN1.The Predicting Protein-Protein Interactions (PrePPI) algorithm is fundamentally different from Interactome3D and Interactome INSIDER in that it makes structure-informed predictions of whether two proteins interact independent of whether they appear in experimental databases , 36. FurFigure 2B) is transferred directly from the template interface (C). PrePPI scoring is based on the quality of the structural alignment of each individual protein to its template and on features of the alignment of query residues to interfacial residues in the template  database . P-HIPSTet al. , anchored on any oncoprotein of interest  and downstream effectors (blue) of a protein of interest (rose) and regulatory interactions, such as feedback loops, among them (green dotted line). They further account for tumor specificity since they are based on the analysis of molecular profile data from patient samples corresponding to different TCGA tumor types . The SigMap generated for lung adenocarcinoma recapitulated published KRas biology and identified novel KRas-associated proteins whose genes were experimentally validated as synthetic lethal with KRASmut in 3D spheroid models derived from primary lung cancer cells   . While patments) , 73, genatments) , 74. FigSystems pharmacology approaches typically aim to leverage network topology to elucidate drug mechanism of action, discover new targets, and design combination therapies . This havia network analysis, it is then necessary to find a compound that effectively and specifically inhibits that target. Or, if a particular drug is found to be effective in a phenotypic screen, in many cases, it will be necessary to identify the actual target(s). Furthermore, although drug repurposing has yielded important discoveries, the continuing exploration of chemical space is clearly of great importance.While the intersection of network analysis and phenotypic screens has proved powerful, systems-level implementation of traditional drug discovery tools is necessary for maximum impact. For example, if a new target is identified e.g., . Nu. Nue.g.,e.g., . The Sime.g., . ML is pe.g., .The most common current uses of protein structure are in ligand docking and lead optimization, and significant advances continue to be made in both these technologies. For example, flexible docking helps escape the constraint of using rigid protein structures , and neuC). The resulting ligand–protein interaction model can then be scored by enumerating the physiochemical features of the predicted binding site. An alternative approach is to search for regions in potential target proteins that structurally align to binding pockets in PDB complexes ..C). The Structural alignment is a way to explore protein structure space, whereas chemical similarity searches enable the exploration of chemical space. A number of efforts to combine the two have been described , 96 wherthe critical resource that enables the integration of structural biology with systems biology. We are now in a position to create and probe tissue- and disease-specific structure–informed protein interaction networks and similar networks that describe pathogen infection. The integration of structure in these networks is the only way to gain mechanistic insights and to link these networks to drug discovery tools, which themselves are undergoing rapid evolution. As the information available in the PDB grows, the ways in which that information can be used to carry out systems-wide analysis of biological processes will grow as well.We have highlighted a daunting array of genomic technologies and databases that have emerged in the past few years and that offer the possibility of transforming both basic and translational biomedical research. Given the proper tools, we have argued that the strategy of exploiting the information available in the PDB can make this database The authors declare that they have no conflicts of interest with the contents of this article.