Network theory-based approaches provide valuable insights into the variations in global structural connectivity between different dynamical states of proteins. Our objective is to review network-based analyses to elucidate such variations, especially in the context of subtle conformational changes. We present technical details of the construction and analyses of protein structure networks, encompassing both the non-covalent connectivity and dynamics. We examine the selection of optimal criteria for connectivity based on the physical concept of percolation. We highlight the advantages of using side-chain-based network metrics in contrast to backbone measurements. As an illustrative example, we apply the described network approach to investigate the global conformational changes between the closed and partially open states of the SARS-CoV-2 spike protein. These conformational changes in the spike protein is crucial for coronavirus entry and fusion into human cells. Our analysis reveals global structural reorientations between the two states of the spike protein despite small changes between the two states at the backbone level. We also observe some differences at strategic locations in the structures, correlating with their functions, asserting the advantages of the side-chain network analysis. Finally, we present a view of allostery as a subtle synergistic-global change between the ligand and the receptor, the incorporation of which would enhance drug design strategies. The concept of allostery has evolved for more than half a century  simulations, providing equilibrium conformational ensembles, have offered extensive computational characterization of the conformational dynamics in proteins/protein complexes  viewing macromolecules, such as proteins, as one single connected entity, where perturbations can affect the conformations at the local or global level, and (2) considering the dynamically accessible conformations of proteins, and the interconversion of their populations under different conditions as a key to biological functions. Regarding the concept of viewing proteins as a single unit, the connections at the non-covalent level play an important role since these are pliable for minor perturbations that are encountered at normal physiological conditions, unlike the covalently stitched polymer chains.A number of approaches available in the literature (some of them referenced above) differ in how we view the protein structure as a single unit, connected through non-covalent interactions. One can focus on backbone connectivity alone, or connectivity at the level of side chains (explicit atoms or a representation through centroids) Greene, . There aThe identification of specific regions responsible for the overall perturbation and the reorganization of interactions to yield a different conformation in the landscape has received great attention. This is a crucial step in the process of making the connection between molecular events and protein functions. The methods range from direct analysis of the structures to ones developed based on the physical, mathematical, and engineering principles. Many such concepts are integrated together in computational programs to obtain critical biological insights  characterizing protein structures as a single entity connected by non-covalent interactions and (ii) integrating with conformational dynamics, for which several comprehensive reviews are available  and a partially open structure (PDB_ID: 6VYB) reveals small structural differences except that in the partially opened state the receptor-binding domain of one subunit swings outward as compared to the closed state . This isProtein Structure Network (PSN) Perspective Into Structural Organization, an overview of network theory in the context of protein side-chain networks, connectivity criteria for the protein backbone (PBN) and the side-chain (PScN) networks, the selection of optimal strength of interaction from percolation theory perspective, and the cluster identification from graph spectral analysis are presented. In the section Protein Structure Network (PScN) for Dynamically Accessible Conformational Ensembles, the method for integrating network analysis with dynamically stable conformational landscapes is introduced. Additionally, the differences between the closed and open trimeric states of the SARS-CoV-2 spike protein are elucidated through chosen network metrics. This is followed by a Summary and Outlook section.In the section The overall shape of protein structures at a molecular level is captured elegantly through secondary structures, such as helices, beta strands and sheets, and loops, formed by the backbone atoms of the polypeptide chain. Based on non-covalent interactions, the Ramachandran map characterized the allowed regions of the backbone torsion angles  and demonstrated that the allowed conformational space of the polypeptide chain mainly consists of compact secondary and super-secondary structures, stabilized by backbone hydrogen bonds . The choice of the network metric being used depends on the question of interest. Briefly, metrics such as hubs and clustering coefficient indicate the degree of a residue and its connectivity to neighboring residues. The percolation behavior of a network can be captured in terms of clusters and cliques/communities  and values within a distance of 4.5 Å  with respect to the maximum values (Ni and Nj) observed from a large dataset of high-resolution crystal structures of proteins provides a uniform basis of evaluation as shown in Equation 1 , Cα atoms are generally considered as the representative of nodes and a distance of about 6.5 Å or less  between any two sequentially non-adjacent residues are considered as an edge  can range from a value of zero to one. Values close to zero and one represent weak and strong side-chain connections, respectively. In general, strong connections can be related to nucleation centers formed by the interactions between the residue pairs such as oppositely charged, stacked aromatic residues, or polar residues involved in hydrogen bonds. The weak interactions, on the other hand, usually emerge from a smaller number of non-covalent interactions (nij) between pairs of hydrophobic amino acid residues. Generally, these interactions aid in bridging the strongly connected nucleation centers and in organizing the overall tertiary structure of the proteins. A PScN is constructed based on a user-defined value of Iij (termed Imin), and an edge is drawn when the calculated Iij between a pair of residues exceeds Imin.This expression allows us to weigh the strength of the interactions (edge weights) in a systematic manner, which can be uniformly applied to all protein structures. Edge weight (min) on the properties of PScN for protein structures, the concept of the largest connected subnetwork (cluster or cliques/communities) transition profile was established and has been applied to a wide range of biological problems  or the largest clique/community (LCli) in the protein structure network are measured as a function of network connectivity at various Imin values. Plotting the values of LClu or LCli as a function of Imin leads to a sigmoidal curve. The transition point of this sigmoidal curve is identified as the percolation transition point at which a giant connected cluster still permeates the protein structure network. Interaction strength (Imin) around this transition point balances the problems of identifying non-specific, weak interactions at smaller Imin values, and discontinuous, sparse network connectivity across the structure at high Imin values. From earlier studies, it is shown that generally the transition point occurs in the range of Imin values 0.2 to 0.4  for Dynamically Accessible Conformational Ensembles) corresponding to the closed (PDB_ID: 6VXX) and partially open structure (PDB_ID: 6VYB) of the spike protein (min (between 0.2 and 0.3), with the closed state exhibiting a plateau of the LClu consisting of about 2000 residues, whereas, in the partially open state, the plateau is around a 1,000-residue cluster. Structurally, this is reminiscent of the conformational variations between the two states, with more residues held together as the largest cluster in the closed state, in comparison with the partially open state in the transition region. Based on earlier studies, as well as this one, we infer that the results obtained from the interaction strengths around the percolation transition point, Imin value of 0.3, are more sensitive and also provide a global view of the structural connectivity in proteins. In the analysis described in subsequent sections, we have used an Imin value of 0.3 to generate the networks.In this study, we have analyzed the largest cluster percolation profile for the partially open and closed states of the SARS-CoV-2 spike protein. The plots of I protein . A notewWith an increase in availability of data in various fields, the advancement in the research area of large-complex network studies has moved in different directions, such as problem and data-driven approaches, development of efficient algorithms, and availability of computational packages Newman, . As we hHubs represent highly connected residues in the protein structure network, which essentially refer to the degree of a node. In some general networks, the degree of certain nodes can be very large  algorithm  can also provide insights into the percolation behavior of strongly connected components within a protein as a function of Imin  for Dynamically Accessible Conformational Ensembles, we will demonstrate the application of some of these parameters, by comparing the hubs and cliques/communities between the closed and partially open states of the SARS-CoV-2 spike protein, in their dynamical equilibrium states.n nodes, the Laplacian L is an n x n matrix that satisfies equation 2.The graph spectral methods based on analyzing eigenvalues and eigenvector components of the connectivity matrices are another approach that has been extensively used to analyze protein structure networks Hall, . Graph su, v) connected by an edge with weight wuv for some vector X in the space of nodes. It is shown that the Laplacian may be expressed in terms of the degree matrix D and the adjacency matrix A as equation 3  as a function of the nodes (residue details given in We have considered the example of the SARS-CoV-2 spike protein receptor-binding domain (RBD)  to demonIt should be noted that there are limitations of performing graph spectral calculations, such as on large datasets , as they are computationally expensive. However, the method provides unique information which is difficult to obtain directly from other methods. For instance, information can be extracted not only for clusters within a protein but also on the interfaces between domains in a single protein or across subunits in multimeric proteins  simulations. The network properties that we described above for a single structure of proteins can also be extended to study the dynamically average properties of conformational ensembles. Depending on the objective, a judicious choice has to be made as to whether to get the averages from all the structural snapshots along the MD trajectory or from selected structures representing various local minima along the trajectory.Analyses of protein conformational ensembles have been facilitated by the development of multiple open-source program packages  Perspective Into Structural Organization, can be used to analyze the dynamically stable PScN. Using the dynamically stable matrix, PSN-Ensemble can compute structural hotspots  and analyze structural rigidity or flexibility   of SARS-CoV-2 spike protein in the closed and partially open states  from the long-timescale MD trajectories on the closed and partially open states of the SARS-CoV-2 spike protein (10 μs each)  and partially open (PDB_ID:6VYB) structures of the spike protein is ~0.5 Å. In addition, each subunit in the trimeric spike protein shows < ~0.5 Å root mean square deviation when compared to each other, either within or between the two conformational states . This indicates highly symmetric trimeric organization in the starting structures used for the long MD simulations, in terms of backbone superposition. The two states mainly differ in the conformations of the RBD in one subunit, with the rest of the domains relatively unchanged at the backbone level  Walls e. Subtle To compute the dynamically stable PScN, we extracted conformational snapshots every 100 ns from the two MD simulation trajectories of the spike protein in its closed and partially open states  and partially open states (74 unique hubs). The number and distribution of these dynamically stable unique hubs show large variations between corresponding subunits of the trimeric spike protein, in going from the closed to the partially open state . StrikinDepiction of these unique hubs on each subunit of the respective conformational states reveals structural rewiring in the entire SARS-CoV-2 spike protein as the RBD goes from the closed to the open conformation. Here, we discuss the differences observed in chain A . Communities capture the percolation of structural rigidity through the protein structure network. Together, comparison of cliques/communities reflects subtle conformational changes that alter regions of rigidity/flexibility in protein structural organization.Cliques represent a subset of residues within a protein structure network where each residue is connected to every other residue  in the percolation transition region, with the closed state being more stable than the open state, and (2) the differences in the network parameters such as hubs, cliques, and communities.Due to the global pandemic caused by the highly infectious COVID-19, we have chosen the SARS-CoV-2 spike protein as an example to illustrate the dynamic PScN perspective. We have investigated the molecular dynamics trajectories of the closed and partially open states of the trimeric spike protein that have been made available by D.E. Shaw Research  and the receptor. The mechanism of long-distance communication involves specific routes and subtle changes in the communication paths, in order to signal at a distance. Analysis of PScN reveals allosteric communication paths via side-chain interactions even without substantial backbone reorganization. A stimulus at the ligand-binding pocket may be transmitted to the desired destination through subtle reorganization of the side-chain interactions that are allowed in the equilibrium dynamical state. Comparison between the two states of the SARS-CoV-2 spike protein reveals significant changes in the hubs and cliques/communities in regions distant from the RBD. The global reorganization of the side-chain connectivity between the two states of the spike protein could also influence the communication paths within and across proteins. Thus, one can consider the conformational landscape as being made up of various side-chain network paths.Finally, in the context of treatment of infections, the antibodies and vaccines are produced in response to the global topology of the host protein or receptor. They complement the naturally evolved receptor more globally around the binding sites. The drugs, on the other hand, which are designed based mainly on the binding site information, may not be highly effective. As we have seen here, the binding site residues are held loosely or tightly by the residue clusters, firmly anchoring some of the interacting residues deep within the pocket. The drug-development strategies would benefit by incorporating the side-chain network connectivity information into their design, thus providing a rationale for incorporating the effects of variations in global structural connectivity in proteins.AA, AH, and VS worked on data collection, running the programs, and writing codes, participated in data analysis, and assisted in manuscript writing. MB, SmV, and SaV designed the article concept, analyzed the data, and wrote the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Much effort has been invested in the identification of protein-protein interactions using text mining and machine learning methods. The extraction of functional relationships between chemical compounds and proteins from literature has received much less attention, and no ready-to-use open-source software is so far available for this task.We created a new benchmark dataset of 2,613 sentences from abstracts containing annotations of proteins, small molecules, and their relationships. Two kernel methods were applied to classify these relationships as functional or non-functional, named shallow linguistic and all-paths graph kernel. Furthermore, the benefit of interaction verbs in sentences was evaluated.The cross-validation of the all-paths graph kernel  shows slightly better results than the shallow linguistic kernel  on our benchmark dataset. Both models achieve state-of-the-art performance in the research area of relation extraction. Furthermore, the combination of shallow linguistic and all-paths graph kernel could further increase the overall performance slightly. We used each of the two kernels to identify functional relationships in all PubMed abstracts (29 million) and provide the results, including recorded processing time.https://github.com/KerstenDoering/CPI-Pipeline.The software for the tested kernels, the benchmark, the processed 29 million PubMed abstracts, all evaluation scripts, as well as the scripts for processing the complete PubMed database are freely available at  Interactions of biomolecules are substantial for most cellular processes, involving metabolism, signaling, regulation, and proliferation . Small mDetailed information about compound-protein interactions is provided in several databases. ChEMBL annotates binding affinity and activity data of small molecules derived from diverse experiments . PDBbindAlthough STITCH and OntoGene deliver broadly beneficial text mining results, it is difficult to compare their approaches, because no exact statistical measures of their protein-compound interaction prediction methods are reported. Furthermore, no published gold standard corpus of annotated compound-protein interactions could be found to evaluate text mining methods for their detection.et al. compared 13 kernel methods for protein-protein interaction extraction on several text corpora. Out of these methods, the SL kernel and APG kernel consistently achieved very good results [Tikk  results . To deteBoth kernels have been successfully applied in different domains, including drug-drug interaction extraction , the extIf two biomolecules appear together in a text or a sentence, they are referred to as co-occurring. A comparably high number of such pairs of biomolecules can be used as a prediction method for functional relationship detection, e.g. between proteins or proteins and chemical compounds . This coSo far, it was unclear whether machine learning outperforms a rather naive baseline relying on co-occurrences (with or without interaction words) for the detection of functional compound-protein relations in texts. Especially for the identification of newly described interactions in texts, that were not described in any other data source, the annotation of a functional relation is challenging.In this publication, we evaluated the usability of two diverse machine learning kernels for the detection of functional and non-functional compound-protein relationships in texts, independent of additional descriptors, such as the frequency of co-occurrences of specific pairs. Although the ChemProt-benchmark for the training of the classification of functional compound-protein interactions into different groups  was published before , this beBoth kernels achieved a much better performance on the benchmark dataset than simply using the concept of co-occurrences. These findings imply that a relatively high classification threshold can be used to automatically identify and extract functional compound-protein interactions from publicly available literature with high precision.Chemical compounds are referred to as small molecules up to a molecular weight of about 1,000 Da, for which a synonym and a related ID are contained in the PubChem database . Similaret al. [PubChem synonyms were automatically annotated with the approach described in the manuscripts about the web services CIL  and prolet al. . Proteinet al. . SynonymThe complete compound-protein-interaction benchmark dataset (CPI-DS) was generated from the first 40,000 abstracts of all PubMed articles published in 2009, using PubMedPortable .All pairs of proteins and compounds co-occurring in a sentence are considered as potential functionally related or putative positive instances. Pairs with no functional relation were subsequently annotated as negative instances. Positive instances must be described as directly interacting, up- or down-regulating each other (directly or indirectly), part of each other, or as cofactors of the related proteins. If a named entity exists as a long-form synonym and an abbreviated form in brackets, both terms are considered as individual entities. The result is a corpus of 2,613 sentences containing at least one compound and protein name (CPI-pair).et al. [For further manual annotation, all sentences were transferred to an HTML form. Verbs that belong to a list of defined interaction verbs, defined by Senger et al.  and whicTo analyze how much specific verbs, enclosed by a compound and protein name, affect the precision of functional relationships, we further differentiated between sentences with or without this structure. et al. developed this kernel to perform relation extraction from biomedical literature [et al. adapted the LIBSVM package to compute the distance to the hyperplane, which allowed us to calculate an area under the curve value.Giuliano terature . It is dterature . Tikk etn = 3. These n-grams are implemented using the bag-of-words approach. The method counts the number of occurrences of every word in a sentence including punctuation, but excludes the candidate entities. The patterns are computed regarding the phrase structures before-between, between, and between-after the considered entities.The global context kernel works on unsorted patterns of words up to a length of w = 3 are considered in two separated kernels, which are summed up for each relationship instance.The local context kernel considers tokens with their part-of-speech tags, capitalisation, punctuation, and numerals , 27. TheThe APG kernel is based on dependency graph representations of sentences, which are gained from dependency trees . In geneA graph can be represented in an adjacency matrix. The entries in this matrix determine the weights of the connecting edges. A multiplication of the matrix with itself returns a new matrix with all summed weights of path length two.All possible paths of all lengths can be calculated by computing the powers of the matrix. Matrix addition of all these matrices results in a final adjacency matrix, which consists of the summed weights of all possible paths . Paths oAll labels are represented as a feature vector. The feature vector is encoded for every vertex, containing the value 1 for labels that are presented within this particular node. This results in a label allocation matrix.et al. sums up all weighted paths with all presented labels [A feature matrix as defined by Gärtner d labels . This cad labels . The simd labels .In the implementation used here , 31, theWe considered co-occurrences as a simple approach to calculate the baseline in the way that every appearance of a compound and a protein in a sentence is classified as a functional relationship , taking into account the number of all true functional relationships.The evaluation was calculated by document-wise 10-fold cross-validation, as an instance-wise cross-validation leads to overoptimistic performance estimates . Each coSubsequently, the kernels were applied solely to sentences which contain an interaction verb and sentences containing no interaction verb. The analyses compare each of the three baselines with the kernel method results.To analyze if the combination of both kernels yields a higher precision and F1 score than each individual kernel, we combined them by applying majority voting, with the definition that only those relations were classified as functional for which both kernels predicted a functional relation. As we are considering the benchmark dataset, the values for recall, specificity, precision, accuracy, and F1 score could be calculated for the new majority outcome. Based on what is known from the AUC analysis, we can identify the classification threshold for which each of the two kernels reaches the same precision as resulting from the majority voting with default thresholds. By recalculating the above mentioned parameters for each kernel with the new classification threshold, we can compare the single kernel performance with the majority voting outcome.We applied both kernels on all PubMed abstracts before 2019, including titles. For the annotation of proteins and small molecules the PubTator web service was applied , 35. TheProcessing these annotations with PubMedPortable  in combiWithin all sentences, a total number of 5,562 CPI-pairs was curated and separated into 2,931 functionally related (positive instances) and 2,631 non-functionally related CPI-pairs (negative instances). Considering the prediction approach of co-occurrences, this results in a precision  of 52.7% and an F1 score of 69.0% .All parameter combinations in the range 1-3 for n-gram and window size of the SL kernel were evaluated. The selection of n-gram 3 and window size 1 shows the best AUC value and the highest precision in comparison to all other models. In general, a lower value of window size leads to a higher precision and a lower recall .We evaluated the APG kernel using the same cross-validation splits as for the SL kernel. Results shown in In general, the APG kernel achieved slightly better results than the SL kernel in terms of the resulting AUC value , which iLarge-scale dataset application).The complete cross-validation procedure for all parameter settings (including linguistic preprocessing) required almost 5.3 h for the APG kernel and about 34 min for the SL kernel on an Intel Core i5-3570 (4x 3.40GHz). For the APG kernel, a substantial amount of the time is required for dependency parsing. This aspect has to be considered within the scenario of applying a selected model to all PubMed articles  for each individual kernel and the appropriate classification threshold was derived from the AUC analysis of each kernel. The resulting F1 score was slightly lower for the APG kernel (73.9%) and considerably lower for the SL kernel (69.4%). This indicates that the combination of both kernels by majority voting leads to a small improvement of the performance .chi-squared test. Both characteristic features are not independent from each other (p<0.01). The fraction of sentences containing an interaction verb is higher in the functionally related CPI-pairs  and pairs of compounds and proteins that do not show this structure, i.e. no interaction verb enclosed (CPI-DS_NIV). For both datasets (CPI-DS_IV and CPI-DS_NIV), the parameter selection n-gram 3 and window size 1 shows the highest area under the curve value . Again, The kernels have been successfully applied to all PubMed titles and abstracts that were published before July 2019, comprising about 29M references to biomedical articles. The dataset consists of more than 120M sentences, with around 16M containing at least one compound-protein pair. The SL and APG kernels were already applied in different domains, e.g. protein-protein interactions, drug-drug interactions, and neuroanatomical statements. The approach presented herein is focusing on the extraction of functional compound-protein relationships from literature. A benchmark dataset was developed to evaluate both kernels with a range of parameters. This corpus consists of 2,613 sentences, manually annotated with 5,562 compound-protein relationships after automatic named entity recognition. Both kernels are dependent on successfully recognizing protein and compound names in texts. Considering PubMed articles, reliable annotations of these entities are provided by PubTator , and pubCross-validation results with an AUC value of around 82% for the SL kernel and 84% for the APG kernel represent a remarkable performance within the research area of relation extraction.Considering the filtering of specific interaction verbs, the SL kernel performance is almost the same, while the APG kernel performs slightly better on sentences with an interaction verb. This seems to be intuitive because the APG kernel uses a dependency graph structure and the SL kernel evaluates word neighborhoods. However, since the filtering by interaction verbs does not yield a clearly better precision for both datasets and kernels, this approach can be ignored for the development of an automatic classification method.The combination of both kernels could slightly increase the overall performance of the classification compared to the single APG kernel. Since both kernels are quite different regarding their classification approach, their combination is supposed to result in a high robustness of the predictions. For fully automatic methods the classification threshold for both kernels can be adjusted to a relatively high precision. The models for predicting functional relationships between compounds and proteins can then be considered e.g. as a filter to decrease the number of sentences a user has to read for the identification of specific relationships.The selected procedure of training a model with the SL and APG kernels might also come into question for the identification of other types of relationships, such as gene-disease or compound-compound relationships.Recent work on biomedical relation classification using deep learning approaches outperformed the overall results of kernel methods for related problems –43. We a


Highly coordinated water molecules are frequently an integral part of protein-protein and protein-ligand interfaces. We introduce an updated energy model that efficiently captures the energetic effects of these ordered water molecules on the surfaces of proteins. A two-stage method is developed in which polar groups arranged in geometries suitable for water placement are first identified, then a modified Monte Carlo simulation allows highly coordinated waters to be placed on the surface of a protein while simultaneously sampling amino acid side chain orientations. This “semi-explicit” water model is implemented in Rosetta and is suitable for both structure prediction and protein design. We show that our new approach and energy model yield significant improvements in native structure recovery of protein-protein and protein-ligand docking discrimination tests. Well-coordinated water molecules—those forming multiple hydrogen bonds with nearby polar groups—play an important role in the structure of biomolecular systems, yet the effect of these waters is often not considered in molecular energy computations. In this paper, we describe a method to efficiently consider these water molecules both implicitly and explicitly at the interfaces formed by two polar molecules. In computations related to determining how a protein interacts with binding partners, we show that the use of this new method significantly improves results. Future application of this approach may improve the design of new protein and small molecule drugs. PLOS Computational Biology Methods paper.This is a Water plays a significant role in biomolecular structure. The hydrophobic effect drives the collapse of proteins into their general shape while highly coordinated water molecules (water molecules making multiple water-protein hydrogen bonds) on the surface of a protein may confer specific conformations to nearby polar groups. Furthermore, water plays a key role in biomolecular recognition: when a ligand binds its host in an aqueous environment, it must displace water molecules on the surface and energetically compensate for the lost interactions. CoordinSimulations of proteins in explicit solvent have been successful in predicting folded conformations as well In this paper, we describe the development of general methods for capturing the energetic effects of explicit solvent, but with the computational efficiency of an implicit solvent model. Our intent is that this energy model is better at discriminating the correct binding modes of protein-protein and protein-ligand complexes. These new methods include: 1.) a new energy function that implicitly captures the energetics of protein and coordinated-water interactions and 2.) a conformational sampling approach that efficiently samples protein and explicit water conformations simultaneously. We show that these methods enable us to predict water positions accurately, as well as improving our ability to discriminate native protein-protein and protein-ligand interfaces from non-native decoy conformations.Methods, is briefly presented here. We have developed two complimentary approaches for capturing coordinated-water energetics. We hypothesize that more accurately modeled interface waters will lead to better discrimination of correct binding modes from incorrect (decoy) binding modes. First, Rosetta-ICO (Implicit Consideration of cOordinated water), implicitly captures pairs of polar groups arranged such that a theoretical “bridging” water molecule may form favorable hydrogen bonds to stabilize the interaction. This calculation is efficient but ignores multi-body interactions that may favor, for example, waters coordinated by >2 hydrogen bond donors or acceptors. While this implicit water model is more accurate than our prior model, which did not consider these water molecules at all, modeling a subset of waters explicitly should further improve model accuracy. Therefore, we have also developed Rosetta-ECO (Explicit Consideration of cOordinated water), in which Rosetta’s Monte Carlo (MC) simulation is augmented with moves to add or remove explicit solvent molecules from bulk. By sampling water orientations at sites where predicted bridging waters overlap , we simultaneously sample protein side chain conformations and water placements. A baseline rotamer recovery error of 9.73 ± 0.13% was obtained using the REF2015 energy function for the 7040 flexible side chains of the test set. A marginal improvement is made with Rosetta-ICO, reducing error to 9.52 ± 0.04%. Inclusion of explicit water molecules in this test fails to further decrease the overall rotamer recovery error beyond the improvements observed with Rosetta-ICO, with a Rosetta-ECO error of 9.59 ± 0.15%, while predicting ~19 explicit water molecules per protein-protein interface. For reference, side chain packing tests that use “native” water molecules  achieves a rotamer side chain recovery error of 8.36 ± 0.04%, while random perturbation of these waters suggest a placement tolerance of less than 0.8 Å  and protein-ligand interfaces. In these tests, which were not used at all in parameter training, the binding free energies for a number of near-native and incorrect (decoy) docking conformations of each complex are computed with the aim of discriminating the correct binding poses from the decoys. PPI decoys were sampled using a combination of Zdock and Rosen score,” which coRosetta-ICO to the baseline results, with the discrimination score increasing from 0.63 to 0.74. Rosetta-ECO further improves this discrimination score to 0.79. We also consider the “success rate,” the time the lowest-energy conformation is within 2.0 Å of native: the ECO model enables successful prediction of a near-native conformation in 8 additional cases out of the set of 53, a ~15% improvement. This comes at a modest increase in computational cost, with an average 1.25- and 2.59-fold increase in runtime for ICO and ECO, respectively.In protein-protein docking discrimination tests with binding modes that broadly sample conformational space, signifiRosetta-ECO improves the discrimination score for 38 of 53 cases, adding 13.4 water molecules to the average bound state and 15.0 water molecules to the average unbound state. These average improvements remain statistically significant. Looking at one such case , we see that while all three energy models correctly predict a near-native conformation, the “energy gap” between native and non-native conformations is improved under Rosetta-ECO  in 7 additional cases out of the 46. These results indicate that both Rosetta-ICO and ECO help discriminate distant decoys from native conformations when compared to the REF2015 energy model, with the inclusion of explicit water modeling in ECO conferring the largest benefit. This also comes at only a modest increase in run time: about 10% increased time for ICO, and about 52% increased computation time for ECO.For protein-ligand docking discrimination tests, Rosetta-ECO provides a nearly across-the-board improvement in native discrimination compared to the baseline calculations. The individual energy distributions for PDB ID 1X8X (tyrosyl t-RNA synthase / tyrosine) in REF2015 and Rosetta-ICO incorrectly favor a decoy 6.6 Å from native. Rosetta-ECO’s explicit waters dramatically alter the binding energy landscape, improving the discrimination score from 0.27 to 0.89, and energetically favoring a structure only 0.43 Å from native. The ECO model predicts two water molecules that bridge the carboxyl group of the tyrosine ligand to interactions with an arginine side chain and a backbone nitrogen group  a fundamentally different task than other approaches: simultaneously predicting both side chain geometry and coordinating waters. Nevertheless, our native water recovery numbers are encouraging when compared to a fixed-structure approach such as 3D-RISM, where results are similar when both methods are applied to the same PPI data set using the same recovery criteria.While the precision and recall reported by our explicit method might seem low, this is due to several factors. First, we are using a very strict recovery tolerance  builds upon the existing implicit water model used in Rosetta to not only account for the energy of desolvating protein functional groups, but to additionally energetically favor conformations that are suitable to accommodate bridging waters. The second model (Rosetta-ECO) places well-coordinated water molecules on the surface or at interfaces of biomolecules based largely on statistics from high-resolution experimental data.Two new biomolecular solvation methods are introduced here. The first  is the isotropic solvation term between atoms i and j , and D0len, D0angle, S0len, and S0angle are parameters that are optimized during energy function evaluation, with final values of 0.5 Å, 4.33 Å, 1.61 Å, and 2.69 Å, respectively. Since a single polar atom may have multiple putative water binding sites, we take the minimum distance between all water sites corresponding to atoms i and j (the first term of the equation). Overall, the two terms in the equation characterize the overlap between potential water sites and the angle formed between polar groups that potentially coordinate a bridging water molecule.Here, ERosetta-ECO model to include fully modeled water molecules at possible hydration sites that can help filter out conformations with poor coordination geometry. Additionally, because this two-body energy term is only dependent upon the configuration of pairs of protein polar groups, it can be used in all Monte Carlo minimization methods used in Rosetta Upper right: Distribution of waters about a non-polar reference [log(ALAHB1_CB1)] Lower left: The sum of the upper two figures: the statistical potential for histidine Lower right: Final, modified histidine potential filtered for noise and second solvation shell effects.(PNG)Click here for additional data file.S15 FigUpper right: distance and angle of all waters measured (grey) and those used for statistical placement about the polar group (purple). Bottom left: Angle and dihedral distribution with histogram projections in upper left (angle) and lower right .(PNG)Click here for additional data file.S16 FigThe crystallographic water positions used for statistical placement of potential solvation sites about C = O backbone polar groups are shown here in red, with the k-means cluster centroids (k = 10) illustrated in yellow. Two views of these data are shown about an arbitrary alanine residue.(PNG)Click here for additional data file.S17 FigCrystallographic water molecules in our benchmark set were randomly perturbed 0.0 to 1.6 Å and the interface residues were repacked in Rosetta. Data points represent the average of three independent runs with 95% confidence interval error bars. The baseline of packing the interfaces without any water molecules (REF2015 score function) is shown as a dashed grey line with 95% confidence intervals from three runs shaded in light grey.(PNG)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S1 TextRosetta-ICO and ECO force fields.Final parameters used in (DOCX)Click here for additional data file.S2 TextDetails on datasets used for water recovery and docking discrimination tests, including GOLD docking protocol.(DOCX)Click here for additional data file.S3 TextIncludes information about CAPRI Target 47, details on the derivation of low-resolution statistical water potential, and more information on the force field parameter training tasks.(DOCX)Click here for additional data file.S1 ScriptRosetta-ECO).RosettaScripts XML file used for protein-protein / protein-ligand interface scoring with explicit water molecules ((DOCX)Click here for additional data file. 30 Jan 2020Dear Dr. DiMaio,Thank you very much for submitting your manuscript "Efficient consideration of coordinated water molecules improves computational protein-protein and protein-ligand docking discrimination" for consideration at PLOS Computational Biology.As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.We cannot make any decision about publication until we have seen the revised manuscript and your response to the reviewers' comments. Your revised manuscript is also likely to be sent to reviewers for further evaluation.When you are ready to resubmit, please upload the following:[1] A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).Important additional instructions are given below your reviewer comments.Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts.Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.Sincerely,Björn WallnerAssociate EditorPLOS Computational BiologyMona SinghMethods EditorPLOS Computational Biology***********************Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: Most protein structure prediction, docking and design methods use implicit solvent for efficiency, but it is well known that specific waters can appear in crystallographic interfaces and folds. One way to keep the efficiency of implicit models is to include only a few explicit waters in a hybrid model, but this approach proved too slow in prior attempts. Here, this paper presents two new methods to model coordinated waters in biomolecular structures. The first, ICO, is still implicit but examines overlap of virtual water placement sites. The second, ECO, is semi-explicit and places three-atom waters using hash tables and Monte Carlo approaches to keep it fast. The results show that it is still quite difficult to place crystallographic waters (under 18% recovered with under 18% precision), but nevertheless this reduces side-chain placement error modestly. More importantly, it clearly improves discrimination of protein-protein and protein-ligand docking poses upon refinement, showing that, as has been conjectured, the lack of explicit waters at interfaces is one of the limiting factors in docking. The paper marks a significant advance in methods and should be published. I have a few small points on the main paper, and several clarifications needed in the methods. My long list of methods questions is to help understand this calculation and is not an indication of shortcomings of the main work.Main text / major comments:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4582081/ for details; at CAPRI meetings, Lensink has even offered to provide the evaluation scripts as needed.1. The CAPRI challenge included a water-placement target a few years ago. It would be interesting to see how these methods could perform on that target, and it would provide a clear metric of these methods against a large set of methods in the field. See 2. Please add a couple more sentences to the main text describing the main hypotheses and concepts behind the two models.3. In Figure 1A-D, the coordinate system is not clear. What are we looking at here?4. Binding energy: are the separated components repacked or relaxed? I think not from the methods, please specify (line 470).  Also are waters in the separated components placed all over the monomers or just at the binding interface? It was nice that the method buries about the same number of waters at interfaces as Janin counted in crystals; can you also compare the waters at the separated interfaces and are they also like Janin or another reference of structured waters?5. It seems like the docking is done with a rigid protein backbone. Please confirm and make sure that is clear in the paper. Or, if there is backbone motion, describe and explain its impact on the results.Methods questions:6. Table 2: which rmsd measure is used? Lrmsd? Irmsd? Atom selection? Please define in methods.7. Line 176: In the benchmark set, the 53 protein-protein interfaces are categorized as rigid, moderate or flexible backbone targets. Which were chosen? Is Rosetta-ICO and Rosetta-ECO more or less helpful on any of these categories?8. Line 177: As per line 466 and comment 4 above, use “interface energy”9. Line 339: Add units on D0 and S0. I suggest replacing S0 with S0^2 in the formula so that both parameters can have dimensions of length. This would help make it physically interpretable.10. Line 335: Why was the form of G chosen? It seems like it might be very sharp at the minimum. Add a plot with the final values of D0 and S0 and units.11. What is the reference energy for the lk-bridge term? As atoms move away, the energy goes to infinity! How is this handled?12. Line 340-341: I get how the second term targets for distance, but the first term seems to look to superpose the two water molecules. I guess ‘angle’ comes out of that, but I suggest rephrasing this sentence.13. Line 343: Please add a table of all the new parameters, to compare with the one in Alford et al and show the differences from the old set. Also, in the text, please give the nominal values for the new fitted parameters S0 and D0.14. Line 346: I’m not sure what ‘quite reasonable’ means. Change to state the fraction of time that a donor or acceptor violates its number of physically reasonable bonds.15. Line 348: citation doesn’t seem right.16. Line 355: it took me a long time to figure out what was meant by treating the ‘water asymmetrically’. Maybe add another example (in addition to the serine example) with the water atoms as an example. If I’m following correctly, the water will not even have a pseudo-torsion energy, correct?17. Line 355: what is M? Please define with final value and units. Also I think the sign might be wrong here.18. E_ref is a poor name for the energy of releasing a water to the bulk because this symbol is already used in Rosetta many times. How about E_bulk or E_water_release or E_WS for water/entropy…anything that is more directly tied to its meaning here.19. I wasn’t sure the “lk” suffix is useful on the bridge energy term name. Sure, it’s based on the Lazaridis-Karplus base model, but this is a whole new water approach. Is this just so it sorts in an alphabetical list near the other solvation terms? Maybe beyond the scope here, but maybe all solvation terms should be renamed E_solv_*, where * is lk or otherwise.20. Is the lk_ball model used in ICO or ECO?21. Line 391, what is the capital W about? Should that be a script W to denote the set of waters placed?22. Line 391, replace d with a norm function as in the earlier equation.23. Line 394 add a small picture; I presume the angle is between w, a and b?24. Line 397 give the final numbers for sigma and K with units.25. Line 410, why not nitrogen backbone H-bonding sites too? Are side chain sites enumerated only from oxygens, not nitrogens?26. Paragraph starting line 411, I don’t quite get the meaning and how this calculation is done. If rot_i1 clashes with rot_j1 but does not clash with rot_j2, what happens? When you detect overlaps in line 414 it sounds like it’s about sc-sc overlaps, but line 417 seems to talk about w-sc overlaps too? Please clarify as this hash table approach seems to be a key to speeding the calculation relative to the older solvated rotamers approaches. What’s in the hash table? Water positions?27. For calculating ‘dwell time,’ is that only at the low temperature? After an equilibration period each time?28. For the ECO monte carlo, please specify the MC move set and the fractional probability of calling each move type and the sizes of the moves (e.g. translations and rotations). The fold tree seems important too…do the waters move with one or both docking partners? Maybe all molecules  tie to a virtual origin point to move independently?29. Line 466: Binding energies imply a transition from the unbound form of receptors and ligands to the bound form of the complex. The energy calculations described involve the energy of bound conformations of receptors and ligands as they form the binding complex, and would be better defined as the “interface” (or interaction) energyGrammatical edits1. In all the equations, some terms are xyz vectors and some are scalars. Consider using boldface for the xyz vectors to make them easier to interpret.2. Line 142, add “two” before “predicted water sites”3. Line 128: “implemented”4. Line 224: change “scores” to “discrimination scores”? Also in Supp. Figure captions.5. Line 319: not only account for desolvation penalties, but “also” energetically reward ….6. Line 355 I think “replaced” should be “was replaced”?7. Line 405: “conformations”8. Line 414 “challenge”9. Line 424 add “kcal/mol” units to RT10. Line 459 capitalize “Placevent”11. Line 444 should probably cite the separate Weng paper that just presents the docking benchmarkReviewer #2: The manuscript by Pavlovicz et al. focuses on prediction of water molecules in protein-protein and protein-ligand complexes by implementing new methods in the Rosetta program. An impressive and original feature of the approach is that water positions and side chain rotamers are sampled at the same time, whereas most previous approaches have used a rigid protein. The authors also evaluate the performance of predictions of protein-protein and protein-ligand complexes. The research topic is relevant and important – waters are important for molecular recognition and accurate positioning of water should improve the performance of scoring functions. The results are mixed. In some cases, an improvement is observed with the waters included, in other cases it has no effect. I think that further analysis of the results is needed. I recommend that the following changes are made to the manuscript:(1) Page 3: The authors write that their approach yields “superior results” at the end of the introduction. Compared to what?https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0032036, https://pubs.acs.org/doi/10.1021/ci200150p) that are likely as fast or faster than the presented one, 76-97% of waters in crystal structures are predicted. These studies focus on proteins (not protein-protein complexes) and likely use rigid side chains, but can that explain the substantially lower performance of the method presented in this work? Or is there some other difference in the assessments (I think there might be some)? I recommend that the authors validate their method in the same way as these previous studies (e.g. with static side chains and same cutoffs) and comment on potential differences in the results. This analysis should not only include protein-protein complexes, but also protein-ligand complexes. This will give the reader a better idea of the performance.(2) Page 4: I was surprised to read that only 17.7% of the native water molecules were predicted by the method for protein-protein interfaces. With previous methods (e.g. (3) Page 7: The authors compare they results to 3D-RISM, which gives better results, but is much slower (20-fold). However, there are many very fast methods too, which have not been mentioned (see previous comment). The relative performance of the methods and their advantages/disadvantages should also be mentioned in the discussion.(4) Page 9: The improvement of docking performance for Rosetta-ECO is very encouraging. However, the low prediction accuracy for waters (page 4) makes me question if the results are “right for the right reason”. The authors should quantify if the top-ranked complexes for which an improvement is observed also has accurately placed waters compared those observed in the crystal structure.(5) Related to the previous question, in Figure 3C-D: Can you compare to the crystal structure water positions and side chain conformations in this case? How many of the water predictions are confirmed? Inspection of 1X8X revealed a phosphate close to the ligand.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Computational Biologydata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: No: Some parameter values requested to add in the methods. (software is available)Reviewer #2: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool, Data Requirements:http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: Reproducibility:http://journals.plos.org/compbiol/s/submission-guidelines#loc-materials-and-methodsTo enhance the reproducibility of your results, PLOS recommends that you deposit laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions, please see  3 Apr 2020Attachmentreviewer_comments.pdfSubmitted filename: Click here for additional data file. 8 Jun 2020Dear Dr. DiMaio,Thank you very much for submitting your manuscript "Efficient consideration of coordinated water molecules improves computational protein-protein and protein-ligand docking discrimination" for consideration at PLOS Computational Biology. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.Please prepare and submit your revised manuscript within 30 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. When you are ready to resubmit, please upload the following:[1] A letter containing a detailed list of your responses to all review comments, and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).Important additional instructions are given below your reviewer comments.Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.Sincerely,Björn WallnerAssociate EditorPLOS Computational BiologyJason PapinEditor-in-ChiefPLOS Computational Biology***********************ploscompbiol@plos.org immediately:A link appears below if there are any accompanying review attachments. If you believe any reviews to be missing, please contact [LINK]Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: Thank you for the CAPRI target analysis. This is quite nice. I think you should include it in the paper, maybe even summarize the result in the abstract, because it places your work within the context of the field. At a minimum, put the text in your reviewer response in the Supplement.All other concerns are well addressed in the text. Thank you for being so careful with the theory, methodological details, and presentation.The method is very exciting and is sure to be used by many, teaching us new things about the role of specific waters in structures.Reviewer #2: The authors have addressed most of my questions, and only a minor addition is required.I am puzzled about why the authors think that comparison to simplified methods  is not so relevant. I think the authors should extend the clarifying section about this in the discussion and explicitly mention the results (e.g. % reproduced water) achieved by such methods. The difference is interesting for the reader and may influence the choice of method to apply in applications. The authors could provide some advice on this also based on the results.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Computational Biologydata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article  digital diagnostic tool, Data Requirements:http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. 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All press must be co-ordinated with PLOS.Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. Best regards,Björn WallnerAssociate EditorPLOS Computational BiologyMona SinghMethods EditorPLOS Computational Biology***********************************************************Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: Thank you for including the CAPRI analysis in the paper, it will be a nice reference for others.Reviewer #2: My points have been addressed and I recommend acceptance.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Computational Biologydata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: Yes**********what does this mean?). 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Beside socio-economic issues, coronavirus pandemic COVID-19, the infectious disease caused by the newly discovered coronavirus SARS-CoV-2, has caused a deep impact in the scientific community, that has considerably increased its effort to discover the infection strategies of the new virus. Among the extensive and crucial research that has been carried out in the last months, the analysis of the virus-host relationship plays an important role in drug discovery. Virus-host protein-protein interactions are the active agents in virus replication, and the analysis of virus-host protein-protein interaction networks is fundamental to the study of the virus-host relationship.We have adapted and implemented a recent integer linear programming model for protein-protein interaction network alignment to virus-host networks, and obtained a consensus alignment of the SARS-CoV-1 and SARS-CoV-2 virus-host protein-protein interaction networks. Despite the lack of shared human proteins in these virus-host networks, and the low number of preserved virus-host interactions, the consensus alignment revealed aligned human proteins that share a function related to viral infection, as well as human proteins of high functional similarity that interact with SARS-CoV-1 and SARS-CoV-2 proteins, whose alignment would preserve these virus-host interactions. The present outbreak of a coronavirus-associated acute respiratory disease, the COVID-19 pandemic, has forced the scientific community to rapidly analyze the virus-host relationships of the new coronavirus (SARS-CoV-2) human infection. Thus, in less than a month, several databases as  have beeCoronaviridae Study Group (CSG) of the International Committee on Taxonomy of Viruses […] has assessed the placement of the human pathogen, tentatively named 2019-nCoV, within the Coronaviridae. Based on phylogeny, taxonomy and established practice, the CSG recognizes this virus as forming a sister clade to the prototype human and bat severe acute respiratory syndrome coronaviruses (SARS-CoVs) of the species Severe acute respiratory syndrome-related coronavirus, and designates it as SARS-CoV-2.The Therefore, the closest known human pathogen to SARS-CoV-2 is the coronavirus SARS-CoV that appeared in 2003 , also camodus operandi of every viral infection is through the interaction between viral proteins and host proteins, in order to use the host cells to replicate. In this line of research, virus-host protein-protein interaction networks, a particular form of protein-protein interaction networks, have become appropriate to analyze virus-host relationships, and information on well-known and studied virus-host protein-protein interaction networks can be carried over to new ones by way of protein-protein interaction network comparison and alignment. See  is the confidence score of the interaction {i, j} ∈ E, and the weight of an entry bkℓ ∈  is the confidence score of the interaction {k, ℓ} ∈ E′. Let also S = (sik) be a similarity matrix between the nodes of the two networks, with each sik ∈  the similarity score of i ∈ U∪V and k ∈ U′ ∪ V′.Let xik for each i ∈ U ∪ V and each k ∈ U′ ∪ V′, where xik = 1 if node i of the first network is aligned with node k of the second network, and xik = 0 otherwise. Then, an alignment of two virus-host protein-protein interaction networks G =  and G′ =  is represented by the binary matrix X = (xik). Let us also define an integer variable yik for each i ∈ U∪V and each k ∈ U′ ∪ V′, where each integer variable yik is intended to representi ∈ U ∪ V and k ∈ U′ ∪ V′. In this way, if xik = 0, yik = 0, and if xik = 1, yik is the weight of those edges incident to node i in G that are preserved by the alignment.Let us define a binary variable Then, the goal of the integer linear programming model is to maximizeλ is a parameter, with 0 ≤ λ ≤ 1, to control the balance between protein similarity scores and protein-protein interaction weights: only node scores are considered when λ = 1, and only edge scores are taken into account when λ = 0.where It is easy to see that this integer linear programming formulation is equivalent to the integer quadratic programming formulation of the network alignment problem given in . In facti, j)∈E and ∈E′ such that node i is aligned with node k and node j is aligned with node ℓ.The objective function comes from the PathBLAST  idea tham = |U| + |V| and n = |U′| + |V′|. The resulting integer linear programming formulation of the virus-host protein-protein network alignment problem has O(mn) binary variables, integer variables, and constraints.Let There are 130 interactions between 29 SARS-CoV-1 proteins and 109 human proteins in the March 2020 release of the VirHostNet database  in releaWe have obtained the amino acid sequences for the SARS-CoV-1 and human proteins from UniProt/SwissProt (122 sequences), UniProt/TrEMBL (2 sequences), and NCBI RefSeq (14 sequences), and for the SARS-CoV-2 and human proteins from UniProt/ SwissProt (332 sequences) and from the Supplementary material in  as impleλ = 0.5 in both cases, and took the consensus between them as the alignment of the two virus-host protein-protein interaction networks.The alignment of the virus-host protein-protein interaction networks of SARS-CoV-1 and SARS-CoV-2 was computed with AMPL version 2018.10.22  and GuroProtein similarity can be assessed by comparing the annotated Gene Ontology (GO) terms for the proteins along three classifications: the molecular function ontology (MFO), the biological process ontology (BPO), and the cellular component ontology (CCO). We considered the host proteins that interact with the viral proteins in the consensus alignment, for each of the two networks. For these human proteins we obtained their GO term annotations using GOnet , and meaWe can observe that the four structural proteins in one network were aligned with the corresponding protein in the other network. Also, most of the non-structural proteins and half of the accessory proteins in one network were aligned with the corresponding protein in the other network. On the other hand, for each pair of aligned viral proteins, the highlighted proteins in the same column of a viral protein are the human proteins it interacts with. For instance, human proteins O00303 and Q9BYF1 interact with the SARS-CoV-1 spike protein P59594, while human protein Q7Z5G4 interacts with the SARS-CoV-2 spike protein P0DTC2. As can be seen in these tables, most of the aligned proteins have a cellular component ontology score above 0.700. This means that, despite the low number of conserved interactions, the aligned proteins share their cellular location. For instance, those human proteins that interact with the spike protein in SARS-CoV-1 are aligned with human proteins that interact with the membrane protein in SARS-CoV-2. However, some biological process ontology scores between aligned human proteins are very low. This can be explained by the lack of biological process ontology GO term annotation for one of the two interacting proteins.edge correctness score, which measures the ratio of conserved edges in a given alignment. Edge correctness assume that one of the aims of the alignment is to find similar regions between the two aligned networks, in terms of network topology. In the context of protein-protein interaction networks, it is also assumed that two proteins interact when they together carry out some biological function. For virus-host protein-protein interaction networks, viral proteins interact with host proteins to perturb the intracellular networks of their hosts to their advantage, and many virus-host interactions occur at the level of physical protein-protein interactions [With respect to molecular function ontology, it is remarkable that we obtained high scores for aligning proteins that interact with structurally different viral proteins. Indeed, one of the measures used to test the correctness of a protein-protein interaction network alignment is the ractions . This meThe question then arises, when a viral protein in one network is aligned with a viral protein in another network, should the host proteins that interact with one viral protein be aligned with those host proteins that interact with the other viral protein? Clearly the answer is yes, when the viral-host protein-protein interaction is a similar infectious process stage. Therefore, aligned virus-host interactions must entail conserved stages in the infectious process. However, non-conserved edges do not necessarily imply incorrect alignments. Indeed, when we analyze in more depth the functional description  of the aThe molecular function ontology score of these proteins is 1.000. Human protein O14920 interacts with viral protein P59596 (membrane) of SARS-CoV-1, which is aligned with protein P0DTC5 (membrane) of SARS-CoV-2. On the other hand, human protein Q9UHD2 interacts with viral protein P0DTD1-PRO_0000449630 (orf9b) of SARS-CoV-2. However, the functional description of the aligned human proteins reflects correctness of the consensus alignment:O14920: Serine kinase that plays an essential role in the NF-kappa-B signaling pathway which is activated by multiple stimuli such as inflammatory cytokines, bacterial or viral products.Q9UHD2: Serine/threonine kinase that plays an essential role in regulating inflammatory responses to foreign agents. Following activation of toll-like receptors by viral or bacterial components, associates with TRAF3 and TANK and phosphorylates interferon regulatory factors (IRFs) IRF3 and IRF7 as well as DDX3X.The molecular function ontology score of these proteins is 0.850. Human protein Q9NR30 interacts with viral protein P0DTC9 (nucleocapsid) of SARS-CoV-2, which is aligned with protein P59595 (nucleocapsid) of SARS-CoV-1. On the other hand, human protein Q92499 interacts with viral protein P0C6X7-PRO_0000037320 (proofreading exoribonuclease in replicase polyprotein 1ab) of SARS-CoV-1. The functional description of the aligned human proteins is:Q92499: Helicase required for Coronavirus IBV replication. Antiviral defense.Q9NR30: Component of a multi-helicase-TICAM1 complex that acts as a cytoplasmic sensor of viral double-stranded RNA (dsRNA) and plays a role in the activation of a cascade of antiviral responses including the induction of proinflammatory cytokines via the adapter molecule TICAM1.The molecular function ontology score of these proteins is 0.850. Both are GTP-binding proteins. Human protein P49703 interacts with viral protein NP_828865 (nsp7) of SARS-CoV-1. Human protein P62330 interacts with viral protein P0DTD1-PRO0000449632 (nsp15) of SARS-CoV-2, which, as reported in [P49703: Small GTP-binding protein which cycles between an inactive GDP-bound and an active GTP-bound form, and the rate of cycling is regulated by guanine nucleotide exchange factors (GEF) and GTPase-activating proteins (GAP).P62330: GTP-binding protein involved in protein trafficking that regulates endocytic recycling and cytoskeleton remodeling. Activation is generally mediated by a guanine exchange factor (GEF), while inactivation through hydrolysis of bound GTP is catalyzed by a GTPase activating protein (GAP).Therefore, it is not clear whether edge preservation should always be required in a correct alignment of virus-host protein-protein interaction networks. To reinforce this idea, we considered the functional similarity of all pairs of human proteins whose alignment would preserve edges, given the consensus alignment of 24 viral proteins. For each pair of aligned viral proteins  we considered the biological process, the cell component, and the molecular function ontology scores of all pairs of human proteins that interact with the aligned viral proteins . The cellular component ontology score is above 0.800 for most of the aligned human proteins, but the highest molecular function ontology score is 0.852, while it is 1.000 in the consensus alignment, and the highest biological process ontology score of the aligned human proteins is 0.670, while it is 0.863 in the consensus alignment.Nevertheless, some of these pairs of human proteins whose alignment would preserve virus-host interactions do show high functional similarity scores, and it could be interesting to further study their role in the viral mechanism of host infection. Coronaviridae family, do not follow the same detailed mechanism of host infection. We believe that further research on these aligned human proteins with high molecular function ontology scores, will help to elucidate the viral mechanism of infection and replication that is necessary to accomplish the goal of antiviral drug or vaccine discovery.We observed that, based on current knowledge, SARS-CoV-1 and SARS-CoV-2 share only 6 human proteins in their virus-host protein-protein interaction networks. On the one hand, aligned viral proteins in the consensus alignment obtained with our method show a sequence similarity of over 75% on the average, and most of the SARS-CoV-1 proteins are aligned with SARS-CoV-2 proteins that belong to the same category  in the genome organization of the viruses. On the other hand, the proposed alignment method does not preserve the virus-host interactions. This suggests that these viruses, despite their classification as human pathogens within the S1 File(PDF)Click here for additional data file.S2 File(GZ)Click here for additional data file.S3 File(GZ)Click here for additional data file.S4 File(GZ)Click here for additional data file.S5 File(GZ)Click here for additional data file.S6 File(MOD)Click here for additional data file.


Rattus norvegicus, or the rat, has been widely used as animal models for a diversity of human diseases in the last 150 years. The rat, as a disease model, has the advantage of relatively large body size and highly similar physiology to humans. In drug discovery, rat models are routinely used in drug efficacy and toxicity assessments. To facilitate molecular pharmacology studies in rats, we present the predicted rat interactome database (PRID), which is a database of high-quality predicted functional gene interactions with balanced sensitivity and specificity. PRID integrates functional gene association data from 10 public databases and infers 305 939 putative functional associations, which are expected to include 13.02% of all rat protein interactions, and 52.59% of these function associations may represent protein interactions. This set of functional interactions may not only facilitate hypothesis formulation in molecular mechanism studies, but also serve as a reference interactome for users to perform gene set linkage analysis (GSLA), which is a web-based tool to infer the potential functional impacts of a set of changed genes observed in transcriptomics analyses. In a case study, we show that GSLA based on PRID may provide more precise and informative annotations for investigators to understand the physiological mechanisms underlying a phenotype and lead investigators to testable hypotheses for further studies. Widely used functional annotation tools such as Gene Ontology (GO) analysis, and Database for Annotation, Visualization and Integrated Discovery (DAVID) did not provide similar insights.Database URL: http://rat.biomedtzc.cn The rat was the first mammalian species domesticated in scientific research in the mid-1800s and soon became the most widely used biomedical model for >150 years , 3. The Over the last few years, the development of omics approaches in rats has provided novel insights into the molecular mechanisms of human diseases and has promoted the development of successful precision medicine , 6. CompTo address these challenges, the existing approaches mostly rely on enrichment analysis to derive high-level biological sense from the omics data. These approaches evaluate whether the observed set of changed genes (SCG) between two physiological statuses are enriched or clustered in a typical biological process. To date, some enrichment-based gene set annotation tools have been developed and widely used, including gene ontology , Kyoto EIn this study, we present the high-quality functional association gene network called the PRID for rats and its associated GSLA web tool in terms of their functional impacts at the biological process level. PRID integrates 6 types of evidence from 10 public databases with the date before 2018 to infer the functional associations between genes. The inference accuracy of PRID is evaluated using experimentally confirmed protein–protein interactions that were recently reported with the date after 2018. The current version of PRID includes 305 939 gene associations, which include 302 693 predicted functional associations and 3246 experimentally reported interactions. These 302 693 functional associations are expected to cover ∼13.02% protein–protein interactions of rats. Approximately 52.59% of these functional associations are expected to represent protein–protein interactions. We provide a web interface for PRID so that users can search the functional associations of their interested genes. We also provide the web interface for the PRID-based GSLA tool for users to interpret the collective functional impact of multiple simultaneously changed genes. Finally, a case study is presented to illustrate the use of PRID/GSLA.To build the interaction prediction model, we selected six types of evidence for the suggestion of functional associations between genes by support vector machine (SVM) algorithm. Six types of indirect evidence were collected from 7 public databases before the year 2018, including 17 353 expression profiles (Coxpresdb), 434 836 gene annotations (GOC), 55 462 domain interactions (IDDI and Pfam), 15 922 subcellular gene localizations (Compartments), 20 120 phylogenetic profiles (DIOPT) and 14 807 homologs interaction in other species (Inparanoid).Protein–protein interactions are considered to be one type of strong functional association. Here, we retrieved and integrated 6251 experimental reported protein–protein interactions of rats from three public databases, including the Rat Genome Database (RGD) , BioGridFrom these 6 types of functional association evidence, 36 feature values were computed to describe the functional associations between genes, including 3 shared annotation features, 2 co-expression features, 4 co-localization features, 23 domain interaction features, 3 phylogenetic profile features and 1 homologous interaction feature  11, 12), 1211, 1P-value cut-off reported in PANTHER, we counted (i) how many terms were predicted by PANTHER (N); (ii) how many PANTHER predicted terms are among the 376 904 known annotations or their more specific terms (X) and (iii) how many of the 113 105 new terms have themselves or their more specific terms predicted by PANTHER (Y). The precision and recall are calculated as follows:PRID was inferred from data that were released before 1 January 2018. We selected 11 810 genes from the GO database with new annotations added after 2018. These 11 810 genes have a total of 376 904 annotations, 113 105 of which were newly updated since 2018. These genes and their new annotations (4891) were used to evaluate the quality of the interaction networks. For each of the 11 810 target genes, we first found all first-degree neighbors of a target gene in an interactome to create a gene set. This gene set was analyzed by Protein ANalysis THrough Evolutionary Relationships (PANTHER) to find enriched annotation terms For each P-value < 0.05 (The microarray data GSE80339 was retrieved from the Gene Expression Omnibus (GEO) database . In the e < 0.05 .Six types of evidence were selected for the prediction of interactions between rat genes, each suggesting a specific type of functional association , 22. We From these 6 types of indirect evidence, 36 feature values were computed to characterize the strength of functional associations between rat genes . ActuallProtein–protein interactions are one type of strong functional association. Here, we preferred to use protein interactions as positive examples in model training. Experimentally reported protein–protein interactions in rats were integrated from three databases, which included RGD , BioGRIDThe functional association prediction model was built using the libSVM package, which is an integrated software package for support vector classification , 31. In We used the soft-margin Gaussian kernel SVM algorithm to train the prediction model. A 5-fold cross-validation was implemented to optimize the kernel width parameter σ and soft-margin parameter C. With the optimized σ and C, all the training data were used to train the prediction model, which was then validated by the protein–protein interactions (published after 31 December 2017) and randomly generated negative examples (positive:negative = 1:100). The final optimized model showed a total of 302 693 functional associations with a sensitivity of 13.02% and a specificity of 99.98%. For comparison, we also evaluated how well the predicted interaction in MIST and STRING covered these new interactions. The results are shown in Applying this model to all rat gene pairs produced 302 693 inferred functional associations. These inferred functional interactions together with the 3246 known protein interactions make the PRID dataset, which consists of 305 939 interactions. Based on the inferred 305 939 functional associations, we were also curious about the proportion of protein–protein interactions covered by the predicted functional interactome. Therefore, we solved the following equation:6. Based on the estimated interactome size (1.22 × 106) and the estimated sensitivity , the predicted interactions in PRID is expected to include 159 618 true protein interactions. Therefore, 52.59% of the PRID functional interactions (159 618 out of 302 693) are expected to represent protein interactions.In the equation, To assess the quality of our predicted functional association network of PRID, we evaluated how well it connects functional associated genes and compared its quality with five other existing rat interactomes, including Mentha , MIST 33, STRINGBecause PRID was inferred based on data available before 2018 (31 December 2017), we collected 11 810 genes from the GO database (up to 1 August 2018) for which new annotations were added after 31 December 2017. These genes contained a total of 376 904 annotations, 113 105 of which were newly reported after 2018. Based on these genes and their annotations, we compared the performance of six interactomes by PANTHER to infer new GO biological processes. To evaluate the overall accuracy of the new annotation prediction, we used the precision–recall curve in this study. Precision measured the proportion of PANTHER reported annotations that were correct with the known annotations , while recall measured the proportion of PANTHER reported annotations that were covered with the newly added 113 105 annotations. In reality, the number of PANTHER reported annotations will change if we alter the significance cut-off in PANTHER. A loose cut-off in PANTHER will report more annotations but with a higher false positive rate. In contrast, a strict cut-off in PANTHER will give fewer new annotations but with higher precision. In general, a loose cut-off will lead to higher recall, and a strict cut-off will lead to lower recall. Therefore, the precision–recall curve has the advantage of displaying the precision and recall rates at different cut-offs.Figure We developed a user-friendly interface of PRID, which supports two search options: a single gene search and a multiple gene search Figure A. The diP < 0.001 (Q2).The GSLA web tool was first developed based on the predicted Arabidopsis interactome resource (PAIR) to functionally interpret SCG in Arabidopsis . It evalP) are able to adjust  is upregulated in low oxygen conditions  or hypoxic (0.5% oxygen) conditions   . They reypoxic 0.% oxygen SE80339) . Notablyypoxic 0.% oxygen SE80339) , which aSE80339) . Some otSE80339) , 46. Furypoxic 0.% oxygen st cells . In thisst cells , GO ontost cells , in contst cells . TherefoRattus norvegicus. A comprehensive and accurate reference interactome of rats may facilitate hypothesis formulation in molecular pharmacology studies. To date, many rat interactome databases are available. Some of these databases integrate the experimentally reported protein–protein interactions, such as RGD (6), the highest coverage of protein interactions is the RGD database (up to 1.08%), which represents 7.89% of the rat protein interactome. Actually, the coverage of the RGD database will be lower than 1.08% due to false positive experimental interactions. Therefore, researchers can only obtain limited support from experimental interactions for molecular mechanism analysis. In contrast, databases with predicted interactions may cover a high proportion of the true rat protein interactome. For example, STRING presents 6 624 008 rat gene associations and is estimated to cover as high as 14.83% of the rat interactome. However, these associations cover many types of reported associations (not only protein interactions), and only ∼0.86% of these associations may represent protein interactions. In our new assessment of functional prediction, both the experimentally reported interactomes and the predicted interactomes did not perform as well as the PRID interactome, which showed a balanced coverage and reliability . In conclusion, PRID complements existing rat interactomes as a high-quality network for functional gene association analysis.As a powerful disease model organism, many efforts have been made to build a molecular interaction1 network of h as RGD , BioGridh as RGD  and IntAh as RGD . Others h as RGD , STRING h as RGD  and HitPh as RGD . In geneThe high quality of functional associations in PRID enables the GSLA approach to interpret the SCG in rats. To report significant functional associations between two gene sets, GSLA estimates the density of functional gene interactions between the component genes in two gene sets. Therefore, for the successful application of this strategy, a high-quality reference interactome with balanced coverage and accuracy was required. As discussed above, previous interactomes were unable to meet this need. If we used other interactomes, GSLA could not produce an interpretation of functional impacts as useful as those produced by PRID (data not shown). This was the same case when we assessed our previously developed high-quality functional interactome with other existing databases for humans and Arabidopsis , 13.Our developed PRID/GSLA system extends the capability of current enrichment-based tools to interpret the potential functional impacts from the observed rat SCG and summarize the SCG into known biological processes, especially when no established biological concept can describe the observed SCG, as mentioned in the introduction. Moreover, the functional association resource provided in PRID is a useful reference for researchers for interpreting the molecular mechanisms of their genes of interest.baaa086_SuppClick here for additional data file.