Investigating protein-protein interactions (PPIs) holds great potential for therapeutic applications, since they mediate intricate cell signaling networks in physiological and disease states. However, their complex and multifaceted nature poses a major challenge for biochemistry and medicinal chemistry, thereby limiting the druggability of biological partners participating in PPIs. Molecular Dynamics (MD) provides a solid framework to study the reciprocal shaping of proteins’ interacting surfaces. Here, we review successful applications of MD-based methods developed in our group to predict interfacial areas involved in PPIs of pharmaceutical interest. We report two interesting examples of how structural, dynamic and energetic information can be combined into efficient strategies which, complemented by experiments, can lead to the design of new small molecules with promising activities against cancer and infections. Our advances in targeting key PPIs in angiogenic pathways and antigen-antibody recognition events will be discussed for their role in drug discovery and chemical biology. The existence of complex wirings in protein-protein interaction (PPI) networks finely modulates the inner working of the circuits at the basis of cell life. Their correct or incorrect regulation is naturally linked to the evolution of cells towards normal or diseases states. Being so important in disparate aspects of cellular functions, it comes as no surprise that PPIs have been the subject of intense studies over the last few years ,3,4,5,6.In general, PPIs represent a class of interactions of high complexity. Structural and biophysical studies have shown that the features of the regions involved in interactions with other partners are diverse and multifaceted: contact surfaces may be large compared to the ones involved in protein-small molecule interactions; they are often flat and lack the grooves and crevices which are engaged by small molecules, and finally, they can be highly dynamic to favor adaptation to alternative binding partners ,8. NonetFrom the experimental point of view, mutational studies have shown that limited subsets of interface residues actually contribute to the affinity between the binding partners. In the context of targeting interface plasticity, flexible peptides selected by high-throughput screening (HTS) methods (such as phage display or large library screenings) have shown the ability to outcompete the natural partner by adapting to the interaction surface ,11. SimiThese facts vividly portray a situation in which many aspects of protein-protein interactions have been investigated with success. Despite this sophistication and advancement, there is still no experimental technique that can predict at atomic level the determinants of what makes a protein surface an interacting one, or defines rules for the design of new molecular entities with applications in chemical biology or drug development. To tackle these problems, we have little choice but to turn to theoretical and computational approaches.Theoretical methods to predict interacting surfaces of a protein of known structure fall into three main classes: (a) statistical approaches, (b) structural techniques, and (c) molecular dynamics (MD)-based methods. Statistical approaches relate an amino acid sequence to known 3-D structures and known tendencies for specific sequence motifs to be localized within interaction areas. Nowadays, these methods are widely used also in combination with coevolution concepts . HoweverMD simulations represent a prime tool to characterize both the networks of interactions and the range of alternative states that can determine whether a protein surface may actually be an interacting one, and/or the dynamics of the processes of molecular recognition with binding partners ,17,18,19The availability of a general framework to design molecules that meet the specific structural/dynamical requirements to perturb a certain function is both a necessity and an opportunity towards innovative discovery of therapeutics and chemical tools. A full understanding of the roles of different sub-states of a molecular interacting system will allow a more rational design of the chemical probes we need to target a specific PPI; this can potentially translate into our ability to control the responses obtained by any system in which the interaction is involved.Building on these considerations, we built a pipeline for the design of small molecules mimics of peptides known to interrupt relevant PPIs in the control of angiogenesis, the process of vascular growth widely exploited by tumors to support their own development and diffusion. To proceed along these lines, we started from the experimentally characterized interactions between the protein Fibroblast Growth Factor-2 (FGF2) and peptides derived from two large extracellular multi-domain proteins known to interact with it, namely Thrombospondin-1 (TSP1) and Pentraxin-3 (PTX-3) ,24,25,26Although both proteins inhibit FGF-dependent angiogenic responses, in mechanisms related to tumor onset and development such inhibitory activity is not present and FGF2 is free to engage tyrosine kinase (TK) FGF Receptors (FGFR1-4). In presence of heparan sulphate proteoglycans (HSPGs), FGF2 binds the TKR subtypes to form HSPG/FGF/FGFR ternary complexes . ActivatIn this context, we started by examining the possibility of exploiting the dynamic cross-talk between FGF2 and a binding peptide in drug-candidate selection ,28. Our Kon of 19.7 ± 2.0 M−1·s−1 and a dissociation rate Koff of (5.5 ± 0.8) × 10−4 s−1, with a resulting dK of 28.0 μM. The peptide was located in the type III repeats of TSP1 [Upon focusing on the best binding sequences, SPR identified peptide DDDDDNDKIPDDRDN, labeled DD15, as the one with the highest affinity. Sensorgrams indicated a dose-dependent binding of DD15 to FGF-2, with an association rate  of TSP1 ,28  method with the AMBER force field and the Polak-Ribiere Conjugate Gradient (PRCG) minimization method . The minStatistical analyses of the trajectories were next used to identify the stereochemical requirements the peptide must satisfy to ensure a stable binding to FGF. This information was translated into a pharmacophore model used to screen the NCI2003 small molecule databases. Briefly, the model was created using the central structure of the most populated cluster for the DD15·FGF-2 complex as a template on which to cast the design. The relative distances, orientations  among the different groups of DD15, and the contacts (hydrophilic/hydrophobic) associated to the most persistent interactions with FGF-2 were retained as pharmacophoric determinants. The details of the procedure can be found in . The scrThe lead compounds inhibited the angiogenic activity of FGF-2 in vitro, and in the Chick Chorioallantoic Membrane (CAM) assay, in vivo. Importantly, the discovered leads showed inhibiting properties comparable to the ones of the full length TSP-1 protein domain, which they were discovered from, at the same time featuring drug-like properties.These results demonstrate the feasibility of integrating structure and dynamics to develop small molecule mimics of endogenous proteins as therapeutic agents ,28. It iThis work was one of the first instances in which simulations and experiments were combined to target a difficult PPI. The surface on FGF2 is indeed large, flat and highly charged, all factors that together conspire against the possibility to define a druggable surface. In subsequent developments, the most potent compound, sm27, was used as a template for a similarity-based screening of small molecule libraries, followed by docking calculations and experimental studies. This allowed selecting seven binaphthalenic compounds that bound FGF2, inhibiting its binding to both heparin sulfate proteoglycans and FGFR. The compounds suppressed FGF2 activity in ex vivo and in vitro models of angiogenesis, with improved potency over sm27. Comparative analysis of the selected hits, complemented by NMR and biochemical analysis of four newly synthesized phenylamino-substituted naphthalene derivatives, allowed identifying the minimal stereochemical requirements to improve the design of naphthalene sulfonates as FGF2 inhibitors ,33,34,352 , corresponding to the N-terminal amino acid sequence of PTX3 (100–104), was shown to act as a minimal anti-angiogenic FGF-binding peptide able to interfere with the formation of FGF/FGFR complexes [Next, we studied the interaction of a peptidic lead derived from the soluble pattern recognition receptor long-pentraxin 3 (PTX3) . Human Pomplexes . We starPharmacophore modeling of the interaction of ARPCA with FGF2 was next used for the identification of the first small molecule chemical (NSC12), which was shown to act as an orally active extracellular FGF trap with significant implications in cancer therapy. Indeed, in FGF-dependent murine and human tumor models, parenteral and oral delivery of NSC12 inhibits FGFR activation, tumor growth, angiogenesis and metastasis  Figure .Importantly, the characterization of a PPI by means of a minimal peptide led to the rational design of NSC12, which represents the first orally active small molecule ligand that can selectively prevent FGF2 from binding to FGFR and has interesting potential for anticancer drug development. Most interestingly, the two small FGF2-targeted molecules were predicted by computational approaches to bind different regions of FGF2. This fact was verified experimentally by competition experiments and NMR analyses ,33,34,35These results strongly support the validity of computational approaches to investigate hard-to-drug PPIs, showing the ability to recapitulate the determinants of the binding process involving large multi-domain proteins (TSP1 and PTX3) and their endogenous target, for drug design applications. Furthermore, the diversity of the generated chemotypes and their ability to target different interaction surfaces open up attracting perspectives for drug development and drug-combination strategies. As hard as they are to drug, protein-protein interaction regions offer, nonetheless, fresh opportunities for the discovery of molecules with therapeutic perspectives. This consideration may be particularly valid in the context of the development of strategies to tackle emerging pathogens or drug-resistant ones. Indeed, the spread of drug-resistance in pathogenic bacteria or the appearance of new viruses  have severely limited the therapeutic efficacy of routinely used antibiotics, posing one of the most serious threats in modern medicine. In most of these cases, rapid diagnosis and vaccination represent the best option for the treatment of emerging infectious diseases. In fact, rapid and effective diagnosis can help preventing the spread of these threats in an increasing part of the population, while directing patients towards the best therapeutic options. In the last few years, it has become increasingly clear that, in order to develop biomolecules with both diagnostic and vaccine application potential, it is crucial to identify antigens on the surface of bacteria that are capable of eliciting a strong immune response, which is usually achieved through the production of  antibodies (Abs). In terms of diagnostic applications, the ability of antigens to proficiently interact with Abs can be exploited to develop probes that can reveal circulating antibodies produced in response to a specific infection in patient serum, blood or plasma samples. In terms of vaccine development, reactive protein antigens can be exploited in formulations aimed to elicit protective responses against successive pathogenic challenges. Even if vaccines have traditionally suffered from slow routine studies, sometimes providing viable products well after the peak in the epidemics, the advent of ‘Reverse Vaccinology’ (RV) has revolutionized the field, introducing a whole new strategy of antigen selection ,39,40,41In chemical and physico-chemical terms, this comes down to identifying which regions in a protein antigen are the ones most likely to be immunoreactive with Abs. Such regions are called epitopes. In other words, one should detect the parts of the antigen that have the highest tendency to bind Abs. In this context, the problem is a particular case-study of protein-protein interactions . To meetOur approach starts from the idea that recognition sites may correspond to localized regions on the surface with low-intensity energetic couplings with the folding core of the protein which antigen belongs to: such minimal coupling to the rest of the structure can in principle allow the regions to sustain the conformational changes necessary to adapt to a binding partner. Indeed, in many cases, PPI regions have been shown to be endowed with flexibility features. We thus set out to identify non-optimized, low-intensity energetic interaction networks in the protein structure isolated in solution and then to benchmark the results against antibody complexes. Interestingly, it was found that the method could successfully identify binding sites located on the protein surface that are accessible to putative binding partners.To identify localized surface regions with non-optimized interactions, we combined the analysis of internal protein energetics with the topological structural information obtainable from the contact matrix of either the crystal structure of the protein or the representative structure extracted from the MD trajectory .The analysis of energetics derives from the energy decomposition method (EDM) ,53,54,55N × N matrix (ijM) of average nonbonded interactions between pairs of residues is built first. This energy matrix is then simplified through eigenvalue decomposition. Analysis of the N components of the eigenvector associated with the lowest eigenvalue was shown to identify strong interaction centers. This map of pair interactions is subsequently analyzed in light of the topological information summarized by the contact matrix associated to a certain structure. The resulting filtered matrix can be used to identify local couplings characterized by energetic interactions of minimal intensities. In fact, while local low-energy couplings identify those sites in which interaction-networks are not energetically optimized, low-intensity couplings between distant residues in the structure are only a trivial consequence of the distance-dependence of energy functions.In the case of a protein of length N, the Local low-energy coupled regions can thus be considered as the “soft spots” needed to interact with potential binding partners (in contrast with the “hot spots” characterized by high coupling intensities). Given the low intensity constraints to the rest of the structure, these sub-structures would be characterized by dynamic properties that allow them to visit multiple conformations, a subset of which can be recognized by the antibody to form a complex ,47.Burkholderia pseudomallei (Bp), the etiological agent of melioidosis. The latter is a severe respiratory infection against which no rapid and efficient diagnostic method or vaccination strategy exists. Several immunoreactive proteins were identified through an RV strategy. The crystal structure of one of these antigens, OppA (Bp), was solved at 2.1 Å resolution and was the basis for MLCE analysis that returned three potential epitopes  approach in a predictive and design-oriented fashion. The first instance in which the method was applied focused on the discovery and design of reactive epitopes from the antigens of the bacterium epitopes . Once idBurkholderia antigen, namely BPSL2765, the approach was extended to the production of bactericidal antibodies. Based on the structure, MLCE, coupled to in vitro mass-spectrometry mapping, identified a sequence within the antigen that, when engineered as a synthetic peptide, was selectively immunorecognized to the same extent as the recombinant protein in sera from melioidosis patients. Next, the peptide was employed to elicit Abs that were subsequently tested in bacterial killing experiments and antibody-dependent agglutination tests. Importantly, the Abs produced against the designed synthetic peptide turned out to induce the killing of B. pseudomallei at levels higher than the Abs raised against the full length protein [Starting from the resolution of the structure of a second  protein  ) [MLCE was further applied to proteins constituent of the flagella of the bacterium. Flagella are used by the bacterium to move in the environment and are conceivably the first parts of the pathogen that come into contact with the host. MLCE epitope prediction was applied to lgK(Bp)) ,59, allolgK(Bp)) . TogetheBurkholderia infections in cystic fibrosis (CF) patients [In general, it is tempting to suggest that the possibility to predict the parts of a protein (antigens) endowed with antibody recognition/binding properties and the demonstration of their reactivity in the form of isolated peptides can open up new venues for diagnosis and treatment. In the case of diagnostics, for instance, multiple predicted binding sequences can be displayed on microarrays for medium-high throughput analysis of their interaction profiles: in a notable instance, predicted peptides were optimized for oriented display on microarray plates and proved to be efficient in the rapid diagnosis of patients . To mimipatients .The data described above indicate that it is becoming possible to apply rational methods to target difficult protein-protein interactions, both through small molecules and through the harnessing of the reactivity towards large biological molecules as antibodies. We suggest that these methods of drug and peptide design could be conceivably coupled to the design of polyvalent systems that allow the simultaneous binding of multiple ligands to a certain target, mimicking the types of interactions that are widespread in biology . The ava


It remains a significant challenge to define individual protein associations within networks where an individual protein can directly interact with other proteins and/or be part of large complexes, which contain functional modules. Here we demonstrate the topological scoring (TopS) algorithm for the analysis of quantitative proteomic datasets from affinity purifications. Data is analyzed in a parallel fashion where a prey protein is scored in an individual affinity purification by aggregating information from the entire dataset. Topological scores span a broad range of values indicating the enrichment of an individual protein in every bait protein purification. TopS is applied to interaction networks derived from human DNA repair proteins and yeast chromatin remodeling complexes. TopS highlights potential direct protein interactions and modules within complexes. TopS is a rapid method for the efficient and informative computational analysis of datasets, is complementary to existing analysis pipelines, and provides important insights into protein interaction networks. Inferring direct protein−protein interactions (PPIs) and modules in PPI networks remains a challenge. Here, the authors introduce an algorithm to infer potential direct PPIs from quantitative proteomic AP-MS data by identifying enriched interactions of each bait relative to the other baits. These studies typically consist of affinity purifications of different bait proteins analyzed using mass spectrometry (AP-MS) and utilize statistical tools to provide confidence that a prey protein is associated with a bait protein. Approaches like CompPASS5, QSPEC6, SAINT7, and SFINX8 largely yield statistical values, like a p value, to provide a confidence that two proteins are associating or are part of a protein complex. Within a protein interaction network, an individual protein may have multiple interactions, may be part of a large protein complex or complexes, which can be composed of important functional modules. For example, modularity is a hallmark of protein complexes involved in transcription and chromatin remodeling. Within this area of protein interaction networks, Mediator9, SAGA11, and SWI/SNF12 are just a few of the many complexes well known to have modules that carry out distinct functions. Determining these modules in these complexes has required years of study using biochemical, genetic, and proteomic methods12. In addition, within protein interaction networks and protein complexes, there are also direct protein interactions that are critical for biological functions. For example, in DNA repair, the formation of the Ku70-Ku80 (XRCC5-XRCC6) heterodimer is critical for recognition of DNA double strand breaks that occur during nonhomologous end joining13. Existing statistical tools struggle to gain insight into the behavior of an individual protein in a protein interaction network. Methods are needed to dig deeper into protein interaction datasets to determine direct protein interactions and to capture modularity within complexes.Many large- and medium-scale analyses of protein interaction networks exist for the study of protein complexes14. Here we describe a topological scoring (TopS) algorithm for the analysis of protein interaction networks derived from quantitative proteomic AP-MS datasets. TopS can be used by itself or in addition to existing tools8 to analyze and interpret datasets. Here, we apply TopS to a protein interaction network centered on human DNA repair proteins, to a previously published human polycomb complexome dataset4, and to yeast chromatin remodeling datasets from the INO80 and SWI/SNF complexes14. TopS yields insights into potential direct protein interactions and modularity within these networks. TopS is a simple and powerful algorithm based on the likelihood ratio method to infer the interaction preferences of proteins within a network consisting of reciprocal and nonreciprocal purifications. The TopS algorithm generates positive or negative values across a broad range for each prey/bait combination relative to the other AP-MS analyses in a dataset. TopS can differentiate between high-confidence interactions found with large positive values and lower confidence interactions found with negative values. TopS has the advantage that the scores it generates can be easily further integrated into additional computational workflows and clustering approaches.We have used approaches like deletion network analyses, network perturbation, and topological data analysis to determine the modularity in protein interaction networks and protein complexes themselvesQij of a protein i in a bait j, and the expected spectral count Eij in row i and column j . Each pp values or fold changes where the difference between the largest and the smallest values is relatively small, TopS generates a wide range of positive and negative scores that can easily differentiate high, medium, or low interaction preferences within the data. This is an advantage for proteomics data analysis since these scores not only reflect the interaction preference of proteins relative to others, but can now be directly integrated to further analyses such as clustering or network analysis in order to discover network organization. TopS is written in R and the platform is built with SHINY (https://shiny.rstudio.com/). It is easily implemented and includes correlations and clustering for bait/prey relationships.Unlike 16. To uncover the connectivity between proteins involved in these pathways, we selected 17 proteins that are part of different DNA repair mechanisms. In addition to the affinity purifications of known elements of the DNA repair pathways such as MSH2, MSH3, and MSH6 (involved in mismatch repair), RPA1, RPA2, and RPA3 (involved in nucleotide excision repair), XRCC5 and XRCC6 (involved in double strand break repair), SSBP1 and PARP117, we analyzed an additional seven proteins  with chromatin associated functions and some of which had been associated with DNA repair18. For example, we have previously demonstrated that WDR76 is a DNA damage response protein with strong associations with members of DNA repair pathways and the CBX proteins19. It is important to note that our objective here was not to describe a human DNA repair protein interaction network. The proteins chosen for this small-scale study were of interest because of their potential relationship to the poorly characterized WDR76 protein19. Furthermore, these proteins were transiently overexpressed in HEK293 cells, and given the potential issues with transient overexpression20, the dataset would be expected to be noisy. We reasoned that using a noisy dataset would be an excellent test of the TopS method and its ability to extract meaningful biological information.We first tested TopS on a dataset generated from HaloTag proteins involved in human DNA repair. DNA repair mechanisms are complex, independent, interdependent, and have been extensively studied21 was used to quantify the prey proteins in each bait AP-MS. To eliminate potential nonspecific proteins, three negative controls were  analyzed from cells expressing the Halo tag alone. A total of 54 purifications were completed and 4509 prey proteins identified 22 and detected 118 protein complexes consisting of 230 proteins. Some of these 230 proteins were shared by multiple protein complexes. We systematically examined their TopS values and found that indeed proteins within complexes tended to associate with the same baits with high topological scores  have been reported by others in SPIN1 purifications2.We have previously demonstrated that topological data analysis (TDA)put Fig. . TDA in 27. The list of proteins with high TopS values were analyzed using the CRAPome web interface. We next removed proteins with high TopS values in our dataset that were present in a maximum of 10/411 controls (~2% of the negative controls) with a maximum spectral count of 15. Using this threshold, we created a reduced network and inspected these specific interactions 30, all others are previously undescribed high-confidence interactors. The reduced subnetwork presented in Fig. Many additional approaches may be used to further interpret the dataset. For example, we introduced another filter to the TopS associations by adding information from the CRAPome, which is a database of proteins known to be present in 411 negative controls for human affinity purificationsons Fig. . For simons Fig. . Most of30 suggested we look deeper into TopS values in our dataset. We sorted the TopS values for all the prey proteins for each bait protein  with TRIM28, and 5 wAn alternative approach to visualize positive and negative TopS scores is found in Supplementary Figure 31 to perform drug−gene association enrichment for the proteins with high TopS scores in our dataset. The enrichment analysis resulted in nine identified drug classes  reported in Hauri et al.4.One issue with such computational analyses is that the TopS algorithm may have been tailored to be particularly well-suited to the type of AP-MS datasets generated in our laboratory. To demonstrate the larger capabilities of the TopS algorithm, we reanalyzed an independently generated and previously published protein interaction network of the human polycomb complexometed Fig.  when com40. Other modules captured in this analysis included a CBX2, PCGF4, RING1, PHC1, and PHC2 module, an EED, EZH2, SUZ12, MTF2, and JARID2 module, which are both known modules within the polycomb system4, and a previously uncharacterized ADNP1, CHAP1, POGZ, and TIF1B module . TopS is complementary to many computational pipelines used to process quantitative AP-MS datasets43. TopS can be used in addition to any of these approaches to provide a different perspective on a dataset. The TopS platform can be easily implemented in addition to these approaches to further analyze such datasets. If quantitative values are provided for all the prey proteins in a given bait, TopS can be used to reanalyze the data, like the recent description of the human polycomb complexome4, for potential direct interactions and/or modules within protein complexes.The TopS platform has several important features. It is easy to implement. There are no parameters or assumptions of a probability distribution in our algorithm. The number of replicates can vary for the baits, where the replicates could be averaged by the user, without severely affecting the results. In addition, TopS is parameter free, distribution free, and independent of reference knowledge. TopS values can serve as a starting point or as a scaffold for other computational methods, visualization tools, and can be directly used in clustering and network analysis approaches. Lastly, multiple datasets can be integrated by merging their TopS scores. For straightforward usage by the community, TopS is implemented as a SHINY application . The following clones from the Kazusa DNA Research Institute  were used: Halo-WDR76 (FHC25370), Halo-XRCC5 (FHC07775), Halo-XRCC6 (FHC01518), Halo-RPA1 (FHC01462), Halo-RPA2 (FHC11655), Halo-RPA3 (FHC06678), Halo-MSH2 (FHC07773), Halo-MSH3 (FHC12698), Halo-MSH6 (FHC08173), Halo-CBX1 (FHC07438), Halo-CBX3 (FHC02188), Halo-CBX5 (FHC10519), Halo-CBX7 (FHC10535), Halo-CBX8 (FHC01705), Halo-PARP1 (FHC01012), Halo-SPIN1 (FHC10419), and Halo-SSBP1 (FHC07926). Flip-In HEK293T cells were purchased from Thermo Fisher Scientific  and authenticated using the ATCC  cell line authentication service. Cells are tested quarterly for mycoplasma and using the ATCC Mycoplasma detection kit (#30-1012K).44. Briefly, 300 μl of whole cell extract diluted with 700 μl Tris Buffered Saline (TBS) was used for purifying Halo-tagged bait complexes using Magne HaloTag magnetic affinity beads . The extracts were then incubated for 1 h at 4 °C with beads prepared from 100 μl bead slurry. The beads were then washed four times in buffer containing 50 mM Tris·HCl (pH 7.4), 137 mM NaCl, 2.7 mM KCl, and 0.05% NonidetP40. Bound proteins were eluted by incubating the beads for 2 h at 25 °C in 100 μl buffer containing 50 mM Tris·HCl (pH 8.0), 0.5 mM  Ethylenediaminetetraacetic acid (EDTA), 0.005 mM Dithiothreitol (DTT), and two units of AcTEV Protease . To remove any traces of affinity resin, the eluates were spun through Micro Bio-Spin columns . Samples were then processed and analyzed using label-free quantitative proteomic analyses using standard methods19. Briefly, after affinity purification of proteins using Halo affinity chromatography, samples were precipitated with trichloroacetic acid and centrifuged at 21,000 × g for 30 min at 4 °C. The resulting pellet was washed twice with acetone and resuspended in buffer containing 100 mM Tris·HCl (pH 8.5) and 8 M urea. The sample was treated with Tris(2-carboxylethyl)-phosphine hydrochloride to reduce disulfide bonds, chloroacetamide (to prevent bond reformation), and digested with endoproteinase Lys-C for 6 h at 37 °C. After dilution to 2 M urea with buffer, samples were digested overnight with trypsin. Digested samples were then analyzed via multidimensional protein identification technology (MudPIT) on linear ion trap mass spectrometers 19. RAW files were converted to the ms2 format using RAWDistiller v. 1.0, an in-house developed software. The ms2 files were subjected to database searching using SEQUEST (version 27 (rev.9))19. Tandem mass spectra of proteins purified were compared against 29,375 nonredundant human protein sequences obtained from the National Center for Biotechnology (2012-08-27 release). Randomized versions of each nonredundant protein entry were included in the databases to estimate the false discovery rates (FDR)19. All SEQUEST searches were performed without peptide end requirements and with a static modification of +57 Da added to cysteine residues to account for carboxamidomethylation, and dynamic searches of +16 Da for oxidized methionine. Spectra/peptide matches were filtered using DTASelect/CONTRAST45. In this dataset, spectrum/peptide matches only passed filtering if they were at least seven amino acids in length and fully tryptic. The DeltCn was required to be at least 0.08, with minimum XCorr values of 1.8 for singly, 2.0 for doubly, and 3.0 for triply charged spectra, and a maximum Sp rank of 10. Proteins that were subsets of others were removed using the parsimony option in DTASelect on the proteins detected after merging all runs. Proteins that were identified by the same set of peptides (including at least one peptide unique to such protein group to distinguish between isoforms) were grouped together, and one accession number was arbitrarily considered as representative of each protein group. Quantitation was performed using label-free spectral counting. The number of spectra identified for each protein was used for calculating the distributed normalized spectral abundance factors (dNSAF)21. NSAF v7 (an in-house developed software) was used to create the final report on all nonredundant proteins detected across the different runs, estimate FDR, and calculate their respective distributed Normalized Spectral Abundance Factor (dNSAF) values.Human proteins in the pFN21A plasmid with an N-terminal HaloTag were transiently transfected into HEK293T cells and whole cell extracts prepared and Halo affinity chromatography was performed on each independent whole cell lysateSince our approach aims to identify the enrichment of each protein in each bait relative to a collection of baits, overexpression of affinity-tagged bait proteins can diminish the interaction score. With this knowledge, we therefore selected to use a normalization method where the baits are estimated directly from the dataset.a is the number of columns, b is number of rows, Ti represents the bait total spectral counts, Tj is the row total spectral counts, and T.. is the total spectral counts in the matrix. Using this approach, the estimated values of the bait proteins are now close to their average spectral counts in all the AP-MS runs of the dataset.To adjust for baits enrichment, we used this approachQij is the observed spectral count in row i and column j; andNext, topological scores were calculated as follows. All the data that passed criteria from the QSPEC analysis was used as an input to the TopS determinations. We used a simple model to calculate a score for each prey–bait interaction as follows:The data are treated in the following manner: (1) For each column and row, the sum of spectral counts is calculated; 2) the total number of spectral counts in the dataset is determined; (3) a TopS value for each protein in a bait AP-MS run is determined as described in Eq.  are measured in multiple observations (e.g. baits/samples). In our case, the numerical values are distributed spectral counts. To make data input easier for the end user, we have defined the input file formats that include rows and columns annotation and numeric data. Any quantitative value, not only spectral counts, can therefore be utilized. TopS includes an example dataset for testing purposes: DNA repair dataset. TopS next generates an automatic output to make this type of analysis easier. MS Excel can be used to visualize the output and identify for example differences between samples or interactions between proteins and baits. Pearson correlation map, clustering analyses on initial numeric values , and TopS values are provided to the user as a pdf format output.24. Two types of parameters are needed to generate a topological analysis: First is a measurement of similarity, called metric, which measures the distance between two points in space (i.e. between rows in the data). Second are lenses, which are real valued functions on the data points. Lenses could come from statistics , from geometry , and machine learning . In the next step the data are partitioned. Lenses are used to create overlapping bins in the dataset, where the bins are preimages under the lens of an interval. Overlapping families of intervals are used to create overlapping bins in the data. Metrics are used with lenses to construct the output. There are two parameters used in defining the bins. One is resolution, which determines the number of bins; higher resolution means more bins. The second is gain, which determines the degree of overlap of the intervals. Once the bins are constructed, we perform a clustering step on each bin, using single linkage clustering with a fixed heuristic for the choice of the scale parameter. This gives a family of clusters within the data, which may overlap, and we will construct a network with one node for each such cluster, and we connect two nodes if the corresponding clusters contain a data point in common. Norm Correlation was used as a distance metric with two filter functions: Neighborhood lens 1 and Neighborhood lens 2. Resolution 30 and gain 3 were used to generate Fig. The input data for TDA are represented in a bait–prey matrix, with each column corresponding to purification of a bait protein and each row corresponding to a prey protein: values are TopS values for each protein. A network of nodes with edges between them is then created using the TDA approach based on Ayasdi platform Further information on experimental design is available in the https://github.com/WashburnLab/Topological-score-TopS.TopS is written using Shiny application (R package version 3.4.2) for R statistics software. TopS uses several packages, including gplots, devtools and gridExtra. TopS is freely available at Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Reporting Summary


Interactions between protein molecules are essential for the assembly, function, and regulation of proteins. The contact region between two protein molecules in a protein complex is usually complementary in shape for both molecules and the area of the contact region can be used to estimate the binding strength between two molecules. Although the area is a value calculated from the three-dimensional surface, it cannot represent the three-dimensional shape of the surface. Therefore, we propose an original concept of two-dimensional contact area which provides further information such as the ruggedness of the contact region. We present a novel algorithm for calculating the binding direction between two molecules in a protein complex, and then suggest a method to compute the two-dimensional flattened area of the contact region between two molecules based on the binding direction. Proteins are involved in almost all biological processes, such as gene expression, cell proliferation and cell signaling. Most proteins work as a multiprotein complex or together with partner proteins and rarely function alone. Interactions between protein molecules are essential for the function of the proteins, including multimeric assembly like cytoskeleton and viral capsid. Proteins form a transient complex like signal transducing proteins or a permanent complex as a certain multi-subunit enzyme. In such protein–protein interactions, the contact region between the two protein molecules is referred to as the binding surface of the proteins. An analysis of the protein binding surfaces could provide an understanding of functions and action mechanisms of the proteins in the complex, while also offering information for the dynamics of the complexes and development of drugs that can interfere with these interactions ,5,6,7,8.Since analyzing the features of the contact regions between two protein molecules is very important for various usages, many research attempts have already been made ,14,15,16In a natural state, a protein assembly can exist under certain conditions and yet dissociate under other conditions. It depends on the binding strength of the proteins and possible modifications of the binding surface. Protein–protein interactions can be stabilized mostly by ionic interactions, hydrogen-bond, and hydrophobic interaction. The binding strength may be higher with a greater binding surface area as more atoms participate in the interaction between the two molecules. In particular, a hydrophobic interaction is the most important factor for protein binding in an aquatic solution as it removes the water molecules surrounding the hydrophobic regions of the proteins. Thus, when two proteins are combined, the water molecules surrounding the proteins are reduced according to the binding area, which means the binding area can represent the strength of the protein–protein interaction. Therefore, many programs have been developed to calculate the binding area for solvation. However, the solvation area is a two-dimensional value, and cannot present the three-dimensional shape of the binding surface whether it is flat or has ruggedness.Lee and Richards  introduci = 1,2, where Wodak and Janin  suggesteRichmond  proposedFor the binding specificity in protein–protein interactions, two major factors are complementary shape and charges of the binding area between two proteins. To evaluate or predict the binding interface of one protein to its real or potential partners, the ruggedness, which is a numerical value describing the degree of prominence and depression on the binding surface, is an important factor. It puts weight, on shape rather than charge, for evaluating binding specificity.When we imagine an island, the two-dimensional area in a map helps to estimate the size of the island whether it has a big mountain or not. If we apply this concept to a binding interface of a protein, two-dimensional flatted area for the interface can present a size for the interface other than the solvation area. Additionally, by dividing the solvation area value with flatted area of the binding surface, we can obtain numerical value describing the degree of prominence or depression on the binding surface. Thus, it is necessary to define a sea level for the proteins in a complex representing the flattened binding interface.According to the ‘collision theory’ explanation of chemical reaction rates, the impact between reactants in the right direction with sufficient energy leads to a successful reaction . For theThis paper presents a novel approach for estimating the interaction between two proteins in a protein complex from coordinate files containing three-dimensional protein structures, which are deposited in Protein Data Bank. First, the binding direction This paper is organized as follows. The PSIMAP algorithm uses the Euclidean distance between two protein structures to check their interaction ,10,21. TDafas et al.  improvedp-value. However, this approach is not consistently effective as the training genomic data does not contain clear information. Hence, Nye et al. [j] = (DA[i][j] - DB[i][j])*NormFact; // Figure 5e  else distMap[i][j] = ∞;return distMap; Endz-axis by applying rigid-body rotation and translation for both molecules After the binding direction for ecessary a,b. The  and MB. c. Thereahttp://www.opengl.org). When three-dimensional objects are rendered using the OpenGL library, a depth buffer is used to store the minimum distance information of the rendered objects from the view plane. For example, when rendering a protein molecule using a depth buffer, each screen pixel should render a particular part of the molecule. When a pixel corresponds to several parts, the part closest to the view plane is rendered. Thus, a depth buffer keeps the minimum distance value from the view plane to the object for each pixel. The depth value is computed by selecting the closest part to the projection plane, and implemented using hardware, so the computation speed is very fast and the resolution easily adjusted by rendering the object larger or smaller.A depth buffer is used for hidden surface removal in the OpenGL graphics library  b. Howeve  c. After from one d. MeanwhA and MB e. The acContactArea shows the procedure for computing the sectional area between t, we consider the pixels in the algorithm DistMap that show the distance as smaller than or equal to t as in the contact region. The pixels with a distance value that satisfies this condition are counted, and the area of the contact region is then computed by multiplying the unit area for each pixel. The unit area for a pixel u is computed using the ratio between the window size of the screen coordinate and the width and height of the three-dimensional view volume (The depth values of interest are restricted to the contact region of w volume f.Given a protein complex in a crystal that has a binding direction coincident with the −z axis a, the seshown in b. The deshown in c, where The difference between the depth values of Function ContactArea(/* t: threshold for meaningful distance *//* u: unit area for one pixel *//* Begin For each distMap[i][j] begin if distMap[i][j] ≤tthen     End Return EndIn the previous section, the binding direction was determined based on the best approach for two proteins to bind with each other. The perpendicular plane to the binding direction was used to view the binding interface and describe the binding surface in a two-dimensional way. Based on the perpendicular plane, a two-dimensional sectional binding area of the proteins was calculated for the viewpoint of the binding direction. This sectional area can also be used to analyze the protein–protein interaction, where the shape of the binding surface can be very diverse.Similar to two molecules in an enzyme-substrate interaction or ligand-receptor binding, two proteins in a complex have a binding specificity and this specificity comes from the reciprocal shape of their binding surfaces, like a lock-and-key. Thus, the prominence and depression patterns found on the surface of one protein are the opposite patterns found on its binding partner. Of course, the specificity also includes additional components, such as the pairing of opposite electro potentials, where a negatively charged region is matched with a positively charged region, while neutral regions provide hydrophobic interactions. In some protein–protein interactions, the binding region can be quite flat. In this case, the specificity is mainly derived from the potentials of the surface or the proteins could be promiscuous. The flatness of the binding surfaces can be estimated by comparing the two-dimensional sectional area with the three-dimensional surface area of the binding interface, thereby revealing the degree of convexity or concaveness of the binding surfaces.To obtain the coordinates of protein–protein complexes, 32 crystal structures were randomly selected from a protein data bank. The binding direction of the proteins in the 32 complexes were then determined and the binding interfaces were analyzed . The secThe sectional area can also be divided according to the distance between the surfaces of two proteins. Different distances suggest different binding characteristics. In the case of hydrogen bonding, the distance between the surfaces of an atom from each protein has a negative value as the two atoms share a pair of electrons. Meanwhile, if there is a van der Waals interaction with close contact, the distance will be less than 1  or 5 A˚ . As exped 3∼5 A˚ . The disxibility .The contact region between two protein molecules in a protein complex is usually complementary in shape for each molecule. If one protein has prominence in the region, there will be a depression in the counter part of the other protein. Thus, the flatness or ruggedness of the contact region would be similar for those two protein molecules. Although computing the area of the contact region is useful for estimating the binding strength between the molecules, it is not effective for presenting the ruggedness of the region. A flattened base area of the contact region was necessary to evaluate the three-dimensional ruggedness by comparing with the contact area. A criterion for protein binding direction was required to define the flattened area. Therefore, this paper presented a method to compute the binding direction between two molecules and then measure the two-dimensional contact area based on the binding direction. A novel concept of the sectional contact area between two protein molecules in a protein complex was also introduced, which is useful for evaluating the properties of the protein–protein interaction, such as the degree of prominence and depression on the binding surface, and determining the area depending on the characteristics of the interaction.


With the development of next generation sequencing techniques, it is fast and cheap to determine protein sequences but relatively slow and expensive to extract useful information from protein sequences because of limitations of traditional biological experimental techniques. Protein function prediction has been a long standing challenge to fill the gap between the huge amount of protein sequences and the known function. In this paper, we propose a novel method to convert the protein function problem into a language translation problem by the new proposed protein sequence language “ProLan” to the protein function language “GOLan”, and build a neural machine translation model based on recurrent neural networks to translate “ProLan” language to “GOLan” language. We blindly tested our method by attending the latest third Critical Assessment of Function Annotation (CAFA 3) in 2016, and also evaluate the performance of our methods on selected proteins whose function was released after CAFA competition. The good performance on the training and testing datasets demonstrates that our new proposed method is a promising direction for protein function prediction. In summary, we first time propose a method which converts the protein function prediction problem to a language translation problem and applies a neural machine translation model for protein function prediction. With the wide application of next generation sequencing techniques, it takes a short time with low cost to generate millions of protein sequences . UnderstProtein function prediction is usually treated as a multi-label classification problem. Researchers have tried different computation methods in the last few decades for this problem ,10,11,12The first and the most widely used method for protein function prediction is the Basic Local Alignment Search Tool (BLAST)  used to The second includes network based methods. Most methods in this category use protein–protein interaction networks for protein function prediction based on the assumption that interacted proteins share similar functions ,21,22,23The third is other information based methods. Some methods use protein structure or microarray gene expression data ,26,27,28The most challenging method is to directly use protein sequence without searching any database or any other resource for protein function prediction. For this kind of methods, the machine learning techniques are usually used to predict protein function from scratch. Currently most machine learning methods use features generated from the protein sequences for model training, and use that model to classify a number of function categories for an input sequence ,34. The The latest state-of-the-art machine learning method—deep learning which uses multiple layers representation and abstraction of data has drastically improved the accuracy of speech-recognition, visual object recognition, language translation, protein structure prediction, etc. ,46,47,48http://biofunctionprediction.org/) , [3–6], and [3–7] respectively), and no significant improvement has been found in our experiment. More details of our experiments are in We ranked all k-mers based on its frequency in training dataset, and the frequency of k-mers ranked at 1000th is 47,802, 7167, 1232, 650, 565 for each k of three, four, five, six, and seven respectively. We did not evaluate performance of k-mers with k larger than five, because low frequency in training dataset for these large k could be less reliable. We have also tried to add six-mers and seven-mers  terms for a protein sequence. The GO terms are represented by GO term ID using a seven digit number , and these GO terms are used to form a directed acyclic tree structure based on the relationship between each other . There are three trees to represent the GO terms in each of the three biological categories . The traditional GO term ID does not considered the relationship between each GO term . Moreover, it needs seven digits to describe a GO term. In this paper, we describe a new way to encode GO terms, and consider the encoded GO terms as a new language to describe functions of proteins.We do a depth-first search (DFS) on Biological Process, Cellular Component, and Molecular Function tree respectively, and assign the 26-base Alphabet number to each GO term as a new ID (Alphabet ID). For example, GO:0008150 is the root of Biological Process (BP) tree, and there are 28,678 GO terms in BP tree, so GO:0008150 is the number 28,678th node to be visited by DFS, and we can convert 10-base number 28,678 to 26-base Alphabet ID: BQKZ. In theory, a four letter Alphabet ID can represent base See .In the previous sections, we convert protein sequences into a new language based on the protein “word” generated from UniProtKB database; we call it the “ProLan” language. Also, the functions of each protein are converted into a new language based on the Alphabet ID, we let it be the “GOLan” language See . We now There are in total 420,127 different protein “words” in “ProLan”, and 25,160 different Alphabet IDs in “GOLan”. In order to simplify the training and decoding complexity with this big “ProLan” and “GOLan” data, we use sampled softmax  based onThe buckets are used to improve the efficiency of RNN. For example, let’s assume there are three protein “words” in a ProLan sentence and four outputs in a GOLan sentence, then the length for input (L1) is three, and the length for output (L2) is four. Even though the input length L1 can fit the first bucket , the output length L2 is larger than the output limit three for the first bucket. In this case, we finally use the second bucket , and add special symbol PAD to fill the rest so that the input length L1 would be 109, and output length L2 would be four. It should be mentioned here that the maximum number of outputs is 10, based on the bucketing and padding technique, which means we can predict a maximum of 10 GO term functions for each protein sequence. Some proteins may contain more than 10 functions and in order to solve this problem, we add an extended NMT model. For this extended NMT model, we still keep the same “ProLan” language, and change a little bit of the “GOLan” language. For the GO terms of the protein in the training dataset, we add all descendants of each of the GO terms in the directed acyclic tree to extend the sentence in “GOLan” language. The same NMT framework is used except the following buckets are used instead:In this extended NMT model, the maximum number of function prediction could be 60, which is big enough for most proteins. As we could imagine, the new added descendants could be less accurate especially when the descendant is much deeper from the real GO term in the tree structure. A decreased rate is added to each new added descendant GO term, so that the one far away from the original true GO term would have lower score.Finally, the ProLanGO model is a combination of the NMT and extended NMT model by giving different weights for each model based on their performance on training data.The prediction part is straightforward, for each protein sequence, we first convert it to a “ProLan” sentence, and then applied the trained neural machine translation model to translate the “ProLan” sentences to “GOLan” sentences and extended “GOLan” sentences. Finally, we apply the ProLanGO model to combine these predicted “GOLan” sentences and convert these “GOLan” sentences back to GO terms by a simple mapping.In order to benchmark our neural machine translation model for protein function prediction, we create a benchmark method which applies simple probability theory to make the protein function prediction. Let x be the fragments vectors from the “ProLan” space, and y be the Alphabet ID for all possible protein functions. Here, x is a vector [xP achieves better performance compared to the NMT and extended NMT model on both recall and precision, and similar performance are achieved compared to the probability based model. We also compare with four other state-of-the-art protein function prediction methods, and our methods show better performance than the SEQ score from SMISS model, but there is still a big gap between our method and the top homologous based method. There are 332 proteins selected by us for the evaluation, but more proteins with real functions would be released later , and the official evaluation result from CAFA will also be released. We could rigorously evaluate our method at that time and compare our method with other protein function prediction methods.In general, we propose a new method for protein function prediction. We compare our method with four state-of-the-art protein function prediction methods. The good performance of our model ProLanGO demonstrates the potential application for protein function prediction problem. There are a lot of improvements that can be done in future. For example, the cross-validation can be included in the training part to improve the reliability of the ProLanGO model. Also, in the Prolan language, we use the frequency of k-mers while the k in the range of three and five is used, the accuracy could be improved by a larger k. In addition, instead of using the frequency of k-mers, a more biologically fragment could be used to divide the protein sequences, such as using a fragment of binding site for a protein. For the bucketing technique, different sizes for each bucket could be tested to additionally improve the performance. The GO terms predicted by our model could be ranked by using other information. Finally, it will be very interesting to see how the order of these k-mers improve the performance.


JAK1 gene plays a critical role in signalling. Malfunction of JAK1 is linked to numerous human diseases ranging from chronic inflammation to cancers and autoimmune diseases.Genetic variations in JAK1 exhibit deleterious effects on gene function leading to the deregulation of signalling pathways. A comprehensive list of nsSNPs potentially affectingthe structure and function of JAK1 gene is not available. We report 3 deleterious nsSNPs  in the coding region of JAK1 with predicted structure and functionlinking to diseases. However, further studies are needed to validate this preliminary observation. The Janus kinases (JAKs) are one of ten recognized families of non-receptor protein tyrosine kinases that constitute a novel type of signal transduction pathway activated in response to cytokines and interferons. They govern many essential cellular functions such as survival, growth, development, apoptosis and immune regulation [1]. Activation of JAK in response to ligand involves receptor dimerization and phosphorylation creating docking sites for signal transducers and activators of transcription (STATs). Thus, activation of STAT proteins plays an important role in JAK kinase function. Mammals have four members of this family: JAK1, JAK2, JAK3 and Tyrosine Kinase 2 (TYK2). Each member possesses a C-terminal protein tyrosine kinase domain, an adjacent kinase-related domain and five further domains extending towards the N terminus which have amino acid similarity between members of this family [2]. In humans, the JAK1 gene is located on chromosome 1p31.3. JAK1 plays a significant role in lymphoid cell precursor proliferation, survival and differentiation. JAK1 somatic mutations occur in individuals with acute lymphoblastic leukemia (ALL) [3]. JAK1 loss of function results in a deficit in the production of mature B lymphocytes due to a block in differentiation at the pro–B/pre–B cell transition step [4]. JAK1 gene mutations are hypothesised as initial molecular defects in human cancer and autoimmune diseases [5]. Two different heterozygous mutations in the JAK1 gene can contribute to complete loss of the protein in several different prostate cancer cell lines [6].Understanding the human genetic variation is one of the major challenges to analyse the differences in susceptibility to diseases and designing individualized therapeutic treatments. It was estimated that 90% of human genetic variations were caused by single nucleotide polymorphisms (SNPs). SNPs are often neutral while some of them contribute to disease predisposition by modifying protein function. The non-synonymous single nucleotide polymorphisms (nsSNPs) trigger genomic disparities in the gene coding regions forming about half of all genetic changes related to human inherited diseases. The nsSNPs are responsible for amino acid substitutions resulting in functional variations of proteins in humans. Functional diversity may have deleterious or neutral effects on protein structure or function. Damaging effects might include destabilization of protein structure and dynamics, altering gene regulation, affecting protein charge, geometry, hydrophobicity, translation and protein-protein interactions [7].Numerous efforts have been implemented to illustrate the deleterious effects of nsSNPs on the stability of proteins linking to its function. Computer aided prediction tools have been widely applied to identify the effect of deleterious SNPs in candidate genes based on the biochemical severity of the amino acid substitution towards phenotype implications [8]. The functional consequences of most of the nsSNPs in JAK1 are still unknown at the structural level. Therefore, it is of interest to investigate the nsSNPs in the JAK1 gene and the effect on the protein structure and consequent function using prediction models. The deleterious nsSNPs were further analysed for protein stability linking to altered molecular function using computer aided known data enabled prediction tools.Data on SNPs in human JAK1 gene was collected from the dbSNP-NCBI (http://www.ncbi.nlm.nih.gov/SNP/). The FASTA format of the protein sequence of JAK1 gene was obtained from the UniProtKB database (http://www.uniprot.org/uniprot/).SIFT (https://sift.bii.a-star.edu.sg/) is a predicting tool for analysing the effects of an amino acid substitution on protein function. SIFT is used to the human variant databases to distinguish mutations (deleterious) involved in disease from neutral polymorphisms.The rsIDs of each nsSNP of JAK1 gene were submitted as a query [9].SNAP2 (https://www.rostlab.org/services/SNAP/) is a program to predict the functional effects of nsSNPs by differentiating between effective and neutral variants. The FASTA format of protein sequences is used as an input for SNAP2 [10].PolyPhen-2 (Polymorphism phenotyping) (http://genetics.bwh. harvard.edu/pph2/) scores the impact of amino acid substitutions on the stability and function of proteins. The prediction output is classified as probably damaging, possibly damaging, and benign with specificity and sensitivity values for a mutation [11].PROVEAN  (http://provean.jcvi.org/index.php) predicts the functional impact of protein sequence variations as 'deleterious' or 'neutral' by assessing the single amino acid substitutions.FASTA format data with substitutions predicted by the SIFT server is used as an input [12].SNPs and GO (http://snps.biofold.org/snps-and-go/snps-and-go.html), PhD-SNP (Predictor of Human Deleterious Single Nucleotide Polymorphisms) and PANTHER  assigns nsSNP to diseases. The FASTA format of the protein sequence is used as an input [13].This server also provides the output result for additional two servers such as PhD-SNP [14] and PANTHER [15] algorithms.I-Mutant 3.0 (http://gpcr2.biocomp.unibo.it/cgi/predictors/I-Mutant3.0/I-Mutant3.0.cgi) predicts protein stability changes by single point mutation. It peculates the value of the free energy stability change by nsSNPs using protein structure or sequence data [16].The FASTA sequence of JAK1 protein was submitted to NetSurfP (http://www.cbs.dtu.dk/services/NetSurfP/) to predict secondary structure, surface, and solvent accessibility of amino acids. The outcome shows buried (low accessibility), partially buried (moderate accessibility) and exposed (high accessibility) region in protein structure [17].HOPE (Have (y) Our Protein Explained) (https://www3.cmbi.umcn.nl/hope) hypothesizes the effects of the mutation on the 3D structure and the corresponding function. HOPE collects information from data on the 3D coordinates of the protein from PDB, sequence annotations from the UniProt database and DAS prediction [18].A dataset of SNPs in human JAK1 gene (gene ID: 3716) was retrieved from the dbSNP database. A total of 52315 SNPs were found in human JAK1 gene and 1957 of them were reported in the exon region of the gene. Among them 483 (473 missense and 10 nonsense) were non-synonymous SNPs, which contribute to only 0.92% of all SNPs known in human JAK1 gene. Different computational algorithms such as SIFT,SNAP2, Polyphen-2,PROVEAN,SNPs and GO, PhD-SNP and PANTHER were used for finding the deleterious nsSNPs. SIFT was used for the preliminary screening and identification of functionally significant nsSNPs.SIFT classifies missense variant as tolerated  or deleterious (less than 0.05). SIFT identified 30 substitutions as tolerated and 20 as deleterious from 483 nsSNPs (Table 1).It should be noted that the rest of the rsIDs were not found by SIFT [9].SNAP2 and PolyPhen-2 were used to support the data obtained from SIFT. SNAP2 illustrates the effects of specific mutation altering the native protein function with expected accuracy. SNAP2 gives a score  [10].The results from SNAP2 shows that out of 50 nsSNPs 15 variants (30%) was significant while the rest (70%) show no effect (Table 1). PolyPhen-2 hypothesizes the probable effect of amino acid substitution on structure of protein and function.Variants are classified as probably damaging (probabilistic score0.85 to 1.0), possibly damaging (probabilistic score0.15 to 1.0), and benign (0.0 to 0.15) with specificity and sensitivity values [11]. Among 50 nsSNPs, 22(44%) nsSNPs were probably damaging, 8(16%) as possibly damaging and the remaining (40%) as benign by PolyPhen-2 (Table 1).The prediction accuracy using SIFT, SNAP2 and PolyPhen-2 scores was further validated using PROVEAN. The functional effects of protein sequence variations are illustrated using PROVEAN. The predicted values of amino acid substitutions are above -2.5 are neutral and those below or equal to -2.5 are considered as deleterious [12].Among 50 nsSNPs, 34 amino acid substitutions (68%) were predicted to be neutral (score is above-2.5) and the remaining 16(32%) were having score below or equal -2.5 are assigned to be associated with diseases (Table 1).SNPs and GO [13], PhD-SNP [14] and PANTHER [15] provide another layer of refinement in nsSNPs characterization. They help predict the impact of mutation on the protein function to conclude disease association. Out of 50 nsSNPs, PhD-SNP and SNPs and GO predicted 17(34%) and 14(28%) nsSNPs respectively to be disease associated and the remaining as neutral. PANTHER predicted 16(32%) nsSNPs as disease-prone and 23(46%) as neutral without data for the remaining nsSNPs (Table 1). The most deleterious nsSNPs was confirmed by concordance. It should be noted that mutants were predicted as deleterious by sequence data aided by SVM models. Thus, a total of 3 variants with F78L (rs200161963), Q644R (rs374267637) and S646F (rs151047872) were found to be deleterious by the above methods.The selected 3 nsSNPs were analysed by I-mutant 3.0 and NetsurfP to reveal the effect of mutation on protein stability. Thus, the variants were grouped based on free energy change value into positive and negative DDG value groups. Positive DDG value (DDG>0) leads to increased stability whereas negative DDG value (DDG<0) indicates decreased stability with high reliability [16].The variants S646F and Q644R with negative DDG values are considered less stable and F78L with positive DDG value are referred more stable (Table 2).NetsurfP calculates protein solvent accessibility and assigns secondary structures [17]. The variants S646F, F78L and Q644R were assessed for solvent accessibility and stability. Among these 3 deleterious nsSNPs, 2 variants (S646Fand Q644R) and their respective wild variants were exposed to the surface whereas F78L and its respective wild variant were buried (Table 2). It is known that polar side chains tend to be exposed to the solvent whereas hydrophobic residues tend to be buried in the interior of the protein [19]. The stability of proteins increases with the area of water-accessible hydrophobic surface reduces [20]. It should be noted that the presence of non-polar residues on the surface may reduce stability in the S646F mutant. The leucine side chains cluster together within proteins to stabilize the protein structure through hydrophobic effect. The substitution of phenylalanine by leucine increases protein stability in F78L. The substitution of the polar uncharged residue with a basic charged residue increases the hydrophilicity of surface area in Q644R.The 3D structure of protein plays a vital role in unveiling the molecular mechanisms leading to a disease. The evaluation of 3D structure of the mutant protein using HOPE [18] was completed. The mutants S464F  and Q644R  were larger than the wild type and they are situated on the surface of the protein. The F78L mutant was smaller in size and it is located within a domain as annotated in UniProt as FERM (Table 3).The wild-type Q644R is NEUTRAL and the mutant is positively charged. The hydrophobicity of the mutated residue in S464F is more than the wild-type. It is noted in F78L that the mutated residue is located near a highly conserved region. The mutant Q644R shows a substitution of glutamine (polar uncharged) to arginine (basic charged). The mutation of a neutral amino acid with a positively charged residue clashes with the neighboring residues. The large size of the mutated residue on the surface of the protein affects the interaction with other molecules. The combined effect of mutations alters the protein function. Phenylalanine (aromatic) is substituted by leucine  in F78L. The mutated residue is within FERM and it is crucial for binding with other molecules. The variant with changed properties disrupts the domain with abolishment of function. Further the mutation also results in reduced contact due smaller residue size. The presence of the variant F78L in the highly conserved region is damaging. The substitution of serine (polar uncharged) by phenylalanine (aromatic) in S646F is interesting. This mutation also results in increased size and hydrophobicity of the residue. Residue interacts with different molecules on the surface of the protein. Therefore, its size and location along with changed hydrophilicity has asignificant effect in the structural disruption of the protein leading to altered function.JAK1 plays a crucial role in regulating diverse signalling cascades. Increasing evidence has suggested that functional SNPs of JAK1 gene is linked to inflammation and cancer. Several nsSNPs of JAK1 gene are known. However, the functional consequences of the nsSNPs in JAK1 as a result of corresponding structural changes remain unknown. Moreover, discriminating deleterious nsSNPs with potential effects on disease susceptibility from tolerated variants is a major challenge. We report the identification of 3 potentially deleterious nsSNPs  in the coding region of JAK1 gene. Further studies are needed to validate this preliminary observation


Binding sites are the pockets of proteins that can bind drugs; the discovery of these pockets is a critical step in drug design. With the help of computers, protein pockets prediction can save manpower and financial resources.In this paper, a novel protein descriptor for the prediction of binding sites is proposed. Information on non-bonded interactions in the three-dimensional structure of a protein is captured by a combination of geometry-based and energy-based methods. Moreover, due to the rapid development of deep learning, all binding features are extracted to generate three-dimensional grids that are fed into a convolution neural network. Two datasets were introduced into the experiment. The sc-PDB dataset was used for descriptor extraction and binding site prediction, and the PDBbind dataset was used only for testing and verification of the generalization of the method. The comparison with previous methods shows that the proposed descriptor is effective in predicting the binding sites.A new protein descriptor is proposed for the prediction of the drug binding sites of proteins. This method combines the three-dimensional structure of a protein and non-bonded interactions with small molecules to involve important factors influencing the formation of binding site. Analysis of the experiments indicates that the descriptor is robust for site prediction.The online version of this article (10.1186/s12859-019-3058-0) contains supplementary material, which is available to authorized users. A new drug needs to go through multiple stages before entering the market, including the discovery of new drug compounds , clinical research, marketing, and tracking. The discovery of lead compounds is the most important and time-consuming of these stages. The traditional method involves chemical experiments in the laboratory and reactions of various small molecules with a target protein; then, the binding site for a small molecule in the complex is used as a protein pocket. This approach requires considerable manpower and time. Currently, computer-aided drug design has gradually become a new trend because of the skyrocketing costs of drug development . Virtualcsc [The computational methods of site prediction can be divided into sequence-based, energy-based and geometry-based methods. Initially, the binding sites are usually predicted by using the three-dimensional geometric structure of a protein by searching for the cavities and pockets; this is called the geometry-based method. Laskowski  proposedcsc , which lcsc  also usecsc , PASS [1csc , and Poccsc , used thCH3 probe to calculate the non-bonded interaction; a clustering algorithm was implemented to cluster the final energy distribution to predict the potential pockets. PocketFinder [The energy-based method used in flexible docking estimates the energy of each position of a protein through a probe and predicts the binding site by the distribution of the energy values. In 1984, Goodford  used proetFinder  used a tetFinder  and VardetFinder . MoreoveetFinder . The LenetFinder  is the mSequence-based methods typically focus on the sequence of a protein, and the results of site prediction include the residues with binding activity. For example, Schelling et al. proposed a method to predict active residues from the evolutionary couplings and sequence variation . Kumar pIn addition, there are certain algorithms that combine some of the above methods, such as ConCavity  and FINDIn recent years, new deep learning techniques have been used in drug discovery and development, opening a new door to computational decision making in pharmaceutical science .For examfactors that influence the formation of the binding sites in a protein and must be suitable for the neural network input. Similar to DeepSite, we have built a grid-based multi-channel descriptor that can more accurately describe a protein. The experimental results show that a model built with this descriptor is more accurate.Construction of a robust protein descriptor is a critical step in the prediction of binding sites using machine learning methods and especially deep learning methods. Appropriate protein descriptor needs to reflect the In this work, a multi-channel molecular descriptor for the prediction of protein drug binding sites is proposed, and appropriate super parameters are obtained in the experiments. The descriptor is more accurate than other methods of site prediction.In certain pocket prediction methods, such as PocketPicker , the hitTo better identify the contribution of various channel factors to the prediction of protein binding sites, the performance of various channels was tested, including the full-channel model and other four single-channel models . A total of 3000 proteins were randomly selected for training; 1000 proteins were selected for validation and 1000 proteins were selected for testing. The data set used is available in the Additional file The data of Fig. step=4 for the sampling step so that the size of Eps is set as step+1=5 for DBSCAN to ensure that at least two sampling blocks close to each other are grouped. The Minpts parameters were set as Minpts=i to observe the clustering performance; 5000 randomly selected proteins  algoritpts=ii=1,,3…,10 topts=ii=1,,3…,10 toMinpts is set to 7, the performance of the clustering is the best. An increase in Minpts is associated with gradual worsening of the performance. The value reflects the lowest number of the sampling blocks clustered into a group, and a large value means that more blocks should be clustered into a group. High number sampling blocks need to be clustered into a class at increased values, which ultimately leads to unsatisfactory results. A smaller value means that the number of the sampling blocks in each class can be very small resulting in too many classifications and inaccurate predictions.The data of Fig. CSC) were compared. For the training of the proposed method and DeepSite, the same training and validation sets were used. For more accurate analysis, 5000 randomly selected proteins  in the sc-PDB dataset and subsequently used to predict the binding sites of proteins in the PDBbind dataset. We removed the proteins that appeared in the training and validation sets of sc-PDB and finally randomly select 1000 new proteins in the PDBbind dataset; the dataset used in this experiment is available in the Additional file Computer-aided drug design has become popular, and the utilization of deep learning to predict the drug binding sites has gradually become a focus because of full use of the existing data resources and full exploitation of the information contained in the data.Construction of appropriate protein descriptor is the first and foremost problem to be solved while using deep learning, especially the convolutional neural network, to predict the drug binding sites in a protein. To address this problem, a four-channel grid protein descriptor was constructed by analyzing the factors influencing the formation of the binding sites.The constructed protein descriptor consists of four channels. First, shape is an important factor influencing the formation of the binding sites. A simplified version of LIGSITE is constructed for the first channel to describe the cavities on the protein surface. Second, hydrogen bonds and van der Waals forces play important roles in the binding between a protein and a ligand, and the L-J potential is used to construct the hydrogen bond energy grid and the van der Waals force grid. In addition, the Coulomb force is important for site prediction. The partial charges of the protein atoms are used to construct the grid of this channel.A refined convolutional neural network is introduced for training. The experiments show that the constructed multi-channel grid descriptor is more accurate and robust in predicting the protein binding sites.Computer-aided drug design can accelerate drug development, and the prediction of the binding sites is a crucial step in computer-aided drug design. After analyzing the problems and drawbacks of the geometry-based and energy-based methods, we combined these two methods to construct a protein descriptor, which is adapted to deep learning specifically for the detection of the protein drug binding sites. The three-dimensional structure of the protein and the non-bonded interactions that influence the formation of the binding sites are introduced to construct the descriptor. Experiments were conducted to compare the accuracy of the proposed method with the previous methods. The experimental results show that the proposed descriptor is more accurate in predicting the binding sites. We have carried out the generalization experiment on other datasets using the trained model, and the results show the generalization ability of the descriptor. It is possible that other factors may influence the formation of the binding sites, including hydrophobicity etc. If these factors can be described in other channels of the grid voxel, the accuracy may be further improved. Our future work will focus on improving the descriptor based on these factors.It is obvious that the geometry-based approaches take protein shape into consideration and look for gaps or cavities on the surface of a protein. The energy-based methods take into account the potential energy factors influencing the formation of the binding sites, such as hydrogen bonds, van der Waals forces and electric potential energy. Thus, a combination of the two methods may have better performance. In addition, application of the deep learning method for protein binding site prediction requires that the protein descriptor is suited for the model input. Fortunately, grid-based approaches can solve this problem, since the grid voxels of the proteins are similar to the pixels of images, and the multi-attribute channel grid is analogous to the RGB channel image. Importantly, the conventional neural network used in image processing is also suitable for the protein multi-channel grid. However, the input of the network needs to be changed from a 2D image to a 3D grid. Based on these considerations, various factors influencing the formation of a protein pocket are introduced in the proposed descriptor. Geometry and energy-based methods are combined to construct a multi-channel protein descriptor utilizing the grid voxel. The constructed protein descriptor is a 4-channel grid, which consists of a shape channel, a van der Waals potential energy channel, a hydrogen bond potential energy channel, and an electric potential energy channel.Initially, a bounding box of a protein is constructed and an 8Å buffer is added to the surroundings of the box. The protein bounding box is subsequently divided into a grid of 1Å ×1Å ×1Å voxels. The final grid is processed to obtain four channels of the descriptor as described below.Structure is an important factor influencing pocket formation, and it is the focus of the original studies to the prediction of binding sites. In this channel, the LIGSITE method is slightly improved. A protein is mapped into a 3D grid, and a grid voxel becomes a part of the protein if it is within an atomic van der Waals radius of any protein atom; otherwise, it is considered to belong to the solvent. Then, the grid is scanned in x, y, z axes and four cubic diagonal directions. Thus, a protein grid is scanned in seven directions with a step of 1Å. If a scanning line experiences a protein-solvent-protein (PSP) situation during the scanning, the voxels contained in the intermediate solvent are marked as the PSP voxels. Each grid voxel value is increased by one when it undergoes a PSP event in a direction. This means that the minimum value of a voxel is 0 (the voxel has not experienced any PSP events in any direction) and the maximum value of a voxel is 7 . Thus, the higher value of a voxel corresponds to a higher probability of it belonging to a cavity. The representation of the improved LIGSITE method is shown in Fig. CH3, which is a functional group commonly found in the drug molecules. The 12-6 Lennard-Jones equation[Van der Waals force is a common intermolecular force and an important factor for the binding of a protein and a molecule. Detailed analysis of the van der Waals force field around a protein can improve the accuracy of prediction of the pocket position. The construction of this grid channel uses the ideas of the energy-based methods. The probe is placed at various grid positions; then, the van der Waals forces between the protein and the probe are calculated to obtain the van der Waals force energy distribution. The probe used here is − equation was usedwhere: i and j are the atoms of −CH3 and protein, respectively, ε is the depth of the potential well and r0 is the distance when the potential reaches its minimum. To calculate the potential for two particles, including atom i and atom j, the same procedure as Amber in Autodock [r0=ri+rj. Finally, the sum of the potential voxel_value=EVDW between the probe atoms and protein atoms is set as the value of the grid voxel when the probe is placed at this grid. The process is shown in Fig. Here, Autodock  is used,The calculation of the potential of the entire grid of proteins is a time-consuming process. To alleviate this computation pressure, a cut-off radius of 8Å was set. When the distance between a pair of atoms is over 8Å, their force will be ignored. The cut-off radius setting was also applied in the following two channels.OH) probe. The hydrogen atom can act as a hydrogen bond acceptor and a donor, and is a common functional group in drug molecules. To calculate the hydrogen bond potential, the 12-10 Lennard-Jones equation was used, similar to the ff86 force field in Amber [Hydrogen bonding is a non-bonded interaction stronger than the van der Waals force; it cannot be ignored when a molecule binds to a protein. Here, we used an approach similar to that used in the case of the van der Waals potential (channel 2) and calculated the hydrogen bond potential with the hydrogen  for the training while maintaining accuracy; the 2Å setting will produce an area with a side length of 20Å (16Å + 2Å + 2Å = 20Å). The process is shown in Fig. After obtaining the positive samples, the protein bounding box was sampled by 16Å ×16Å ×16Å block in steps of 4Å; if a sample box is not within the binding site area, it is marked as a negative sample. Finally, to reduce the serious imbalance of the sample ratios, the negative samples were sampled down to 64 samples; finally 128 sample blocks were used for each protein. The sampling process is shown in Fig. The deep learning framework was implemented using Keras . The neuIt should be noted that the calculated voxel values of each channel  are in different ranges and thus need to be normalized before training. However, large number of sampling blocks was obtained. After the experiments, we found that the mapping of the arctangent function had a good normalization performance and enabled quick convergence of the model weights. All voxel values can be mapped to  according to the equation: Eps  and MinPts (the minimum number of points required to form a dense region). In the experiment, we set Eps=step_size+1 thus increasing the sampling step size by 1. The performance of various Minpts values was compared in the experiment.Once the model is obtained, it can be used to predict the binding sites of a new protein. In the prediction process, the sampling step size is set to 4Å, and finally, the predicted value of each 16Å ×16Å ×16Å block of the protein is obtained; the value corresponds to the probability that each sampling block belongs to a site. Clustering analysis of the prediction results is required to predict multiple binding sites. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering method is used, which can divide the area into the clusters with sufficiently high density and can find clusters of arbitrary shapes in the space of noise . The finThe output of the model is the probability that a sample block belongs to the binding site; the output value ranges from 0 to 1. Therefore, it is necessary to set a threshold to indicate whether a sample is positive. Here, the threshold is set to 0.5, because the majority of the value of the positive predictions are close to 1 and the values of the negative predictions are close to 0. After the threshold screening and DBSCAN clustering, the sample blocks can be divided into multiple potential binding site regions, and the geometric center of all sampling blocks in the same cluster is calculated as the center of the pocket. At the same time, each pocket is scored based on the average predicted block probability values of each pocket (sampling blocks that are clustered to a single class) calculated as the score of a pocket.Additional file 1Protein list for the experiment with various channels. This file contains randomly selected training proteins for the experiment with various channels. All proteins come from the sc-PDB database, 3000 for training, 1000 for validation and 1000 for testing. (CSV 81 kb)Additional file 2Protein list for the experiment with various DBSCAN parameters. This file contains randomly selected training proteins for the experiment with various DBSCAN parameters. All proteins come from the sc-PDB database, 3000 for training, 1000 for validation and 1000 for testing. (CSV 81 kb)Additional file 3Protein list for the experiment on sc-PDB using various methods. This file contains randomly selected proteins for the experiment with various methods. They are used in the 5-fold cross-validation experiments. A total of 5000 proteins were randomly selected from the sc-PDB database are used in the experiment, with an average of 1000 proteins per fold. (CSV 68.4 kb)Additional file 4Protein list for the experiment on PDBbind. This file contains randomly selected proteins for the experiment on PDBbind. A total of 5000 proteins were used including 3000 proteins from the sc-PDB database constitute the training set, 1000 proteins from the sc-PDB database constitute the validation set, 1000 proteins from the PDBbind database constitute the test set. All proteins are randomly selected, and the three sets of proteins do not intercept. (CSV 79.1 kb)