The L-2-hydroxyglutarate dehydrogenase (L2HGDH) gene encodes an important mitochondrial enzyme. However, its altered activity results in excessive levels of L-2-hydroxyglutarate, which results in diverse psychiatric features of intellectual disability. In the current study, we executed an in-silico analysis of all reported L2HGDH missense and nonsense variants in order to investigate their biological significance. Among the superimposed 3D models, the highest similarity index for a wild-type structure was shown by the mutant Glu336Lys (87.26%), while the lowest similarity index value was shown by Arg70* (10.00%). Three large active site pockets were determined using protein active site prediction, in which the 2nd largest pocket was shown to encompass the substrate L-2-hydroxyglutarate (L2HG) binding residues, i.e., 89Gln, 195Tyr, 402Ala, 403Gly and 404Val. Moreover, interactions of wild-type and mutant L2HGDH variants with the close functional interactor D2HGDH protein resulted in alterations in the position, number and nature of networking residues. We observed that the binding of L2HG with the L2HGDH enzyme is affected by the nature of the amino acid substitution, as well as the number and nature of bonds between the substrate and protein molecule, which are able to affect its biological activity. L-2-hydroxyglutaric aciduria  is a very rare genetic condition of the metabolism. It occurs due to null or reduced action of L-2-hydroxyglutarate dehydrogenase (L2HGDH), a mitochondrial enzyme that catalyzes the oxidation of L-2-hydroxyglutaric acid into α-ketoglutarate ,2,3. L2HThe phenotypical traits of the individuals affected with L2HGA are variable, and may comprise macrocephaly, cerebellar ataxia, developmental delay spasticity, variable range of intellectual disability, epilepsy, communication problems and difficulties in speech ,5,6,7,8.The inception of L2HGA is also different, e.g., it might be either congenital with severe seizures and intellectual disability or might occur in middle age with mild features, which is an issue that may result in a delayed diagnosis of L2HGA ,10,11. MEarly anamnesis and treatment may decelerate the development of the disorder. In this sense, due to the uncertainty regarding the different phenotypes of metabolism disorders, a complete neurological checkup must be a component of regular pediatric appointments ,2. RadioHijikata and his coworkers documented that a major structural location of autosomal recessive mutations is the buried region of a protein, while the mutations cause the destabilization of the protein structure . A BayesSo far, the Human Gene Mutation Database (HGMD) has registered 52 missense or nonsense variants in the L2HGDH gene, which are reported to disturb the activity of its enzyme and have been documented in different ethnicities around the world. Previously, in Pakistan just 2 mutations, c.C751T (p.Arg251*) and c.G178A (p.Gly60Arg), have been described ,20.The current study intended to evaluate the biological effects of all missense or nonsense mutations reported in the L2HGDH gene via protein modeling and docking.https://www.uniprot.org/uniprot/Q9H9P8, (accessed on 1 October 2021) [The data for all L2HGDH described variants were retrieved from the HGMD list , while ter 2021) . To preder 2021) . Among ter 2021) . To examer 2021) . We usedhttps://string-db.org, accessed on 1 October 2021) [www.imods.iqfr.csic.es (accessed on 10 January 2022); iMODS works on the normal mode analysis (NMA) principal. The results of the simulation were calculated in the form of an elastic network, covariance map, eigenvalues and the mobility of the complex using default basic settings.Protein–protein interactions for wild-type and all mutant L2HGDH variants with their close functional interactor D2HGDH protein were assessed using Cluspro . The cloer 2021) . Likewiser 2021) . The proer 2021) . ProteinAll predicted 3D structures of L2HGDH mutations  were supAmongst the mutated protein models, the maximum resemblance index of wild-type L2HGDH protein with mutant protein was presented by Glu336Lys (87.26%), whereas the lowermost resemblance index was presented by mutant Arg70*, which was 10.00% . A similThe top three largest active sites pockets were predicted in the wild-type L2HGDH protein using the CASTp tool. Among three largest predicted active site pockets, wild-type L2HGDH protein amino acids that were involved in binding with the substrate molecule L2HG were present in the second largest active site pocket. Wild-type protein amino acids interacting with the substrate were 89Gln, 195Tyr, 402Ala, 403Gly and 404Val. The complete explanation of wild-type L2HGDH protein residues and their positions, existing in these three largest active site pockets, are summarized in The active site pockets of the L2HGDH protein are illustrated in Protein–protein interactions were assessed between all mutant and wild-type L2HGDH proteins with their close functional interactor D2HGDH protein and notable alterations were observed in the interrelating sites of wild-type and all mutant L2HGDH proteins. The interactions showed that the wild-type L2HGDH protein interacted with the interactor D2HGDH protein through fourteen different amino acids, i.e., Asp194, Ser31, Arg16, Ser23, Gly13, Arg14, Thr346, Gln125, Lys292, Glu291, Arg33, Gly32, Arg35 and Cys27, via seventeen interactive hydrogen bonds. Nonetheless, among all L2HGDH mutant proteins, the greatest interaction was shown by Arg335*, which interacted with the D2HGDH protein through twenty-seven bonds  and twelve different amino acids. The lowest levels of interaction were shown by Ala406Val and Pro302Leu proteins, wherein both the mutants  interacted with D2HGDH through two hydrogen bonds involving two different amino acids (each). Illustrative images of all protein–protein interactions between wild-type and mutant L2HGDH proteins with their close functional interactor D2HGDH are presented in Protein–substrate docking was performed to better understand the interaction of L2HG with the wild-type and L2HDGH mutant proteins. Wild-type L2HGDH docked with L2HG through five hydrogen bonds via five amino acids, i.e., Gln89, Tyr195, Ala402, Gly403 and Val404. The highest levels of protein–substrate docking was present in Gly211Asp and Arg282Gln variants, wherein the mutant Gly211Asp protein docked with L2HG via seven bonds  and five different amino acids, while nine residues characterized by Van der Waals interactions with the substrate molecule were also observed.Mutant Arg282Gln interacted with L2HG through seven bonds (one hydrogen and one carbon–hydrogen) via four different residues, while Van der Waals interactions were also noted for six different amino acids.The lowest level of protein–substrate docking was noted in the Tyr301* variant. No protein–substrate docking was present between the mutant Tyr301* and substrate molecule, since it resulted in a short-truncated protein; nevertheless, nine different amino acids interacting through Van der Waals forces were observed around the substrate molecule.Every interacting amino acid of mutant L2HGDH proteins with L2HG was changed in comparison to the wild-type. Here, 2D illustrations of protein–substrate interactions amongst wild-type and mutant L2HGDH proteins by L2HG are presented in We calculated the index values of wild-type and mutant Glu336Lys variants, having the highest similarity index values, and mutant Arg70* variants, having the lowest similarity index values, with the wild-type L2HGDH protein and substrates of L2HG individually . Figure We also calculated the elastic network, which defines the pairs of atoms that are connected by springs. Each dot in the graph represents one spring between the corresponding pair of atoms. Dots are colored according to their stiffness, whereby the darker grays indicate stiffer springs and vice versa. The stiffness levels of the complexes are shown in L2HGDH (NM#024884.3) is a protein-coding gene and is positioned on the chromosome number 14. It comprises ten exons with an estimated size of 75 Kb, which transcribe five transcripts for L2HGDH proteins [The protein is composed of 463 amino acids with two domains, one of which is a mitochondrial targeting sequence (aa 1–50), while the other one is an FAD-dependent domain.L2HGDH is expressed in a variety of tissues such as the brain, muscles and testes. An important purpose of the enzyme is to be a catalyst of the oxidation reaction of L2HG into α2-ketoglutarate . The funPenderis et al. (2007) documented a dog model of aciduria in bull terriers, which were outbred. All animals displayed excessive urinary secretion of L2HG, whereas twelve dogs subjected to MRI imaging exhibited symmetric sections of hyperintensity similar to that observed in L2HGDA patients .Likewise, Ma et al. (2017) established a null mice model for a variety of physical features, i.e., excessive L2HG concentrations in various tissues, particularly in the brain and testes. Moreover, the model exhibited white matter weakening and massive gliosis, microglia-mediated neuroinflammation coupled with the development of oligodendrocyte precursor cells. Furthermore, L2HGDH defects in subsequent phases cause hippocampal neurogenesis and late-inception neurodegeneration .The precise frequency of L2HGDA OMIM#236792) has still not been identified, and roughly one hundred and forty-four incidents have occurred so far, with one hundred mutations of L2HGDH having been recorded in various ethnic groups worldwide 6792 has ,13. The Each L2HGDH mutation demonstrated variable outcomes in terms of the 3D shapes and attachments to the respective substrate, the L2HG.−1, in contrast with the roughly 100 kcal mol−1 of energy for covalent bonds formed between carbon and hydrogen.Normal L2HGDH protein attached to the substrate molecule through five hydrogen bonds, but we observed extra bonding in all mutant proteins, i.e., unfavorable bonds, carbon–hydrogen bonds and Van der Waal forces were produced around the protein–substrate complexes of mutant and substrate molecules. Hydrogen bond interactions are comparatively fragile interactions; nonetheless, they are vital for biological macro-molecule formation, i.e., for DNA and proteins. Wendler et al. (2010) documented that hydrogen bonds are comparatively less strong than covalent bonds . HydrogeThe bond distance for hydrogen bonds is slightly longer compared to covalent bonds; when measured from the hydrogen atom, the bond distances range between 1.5 and 2.6 Å, while the bond distances range between 2.4 and 3.5 Å among non-hydrogen atoms when held together through a hydrogen bond. A strong hydrogen bond is inclined to form an almost straight bond, i.e., a bond established amongst a hydrogen bond donor and a hydrogen bond acceptor through a hydrogen atom lies beside a straight line ,42,43.In the present study, we observed hydrogen bond interactions between wild-type L2HGDH proteins with their substrate, but in the case of the L2HGDH mutant protein, we observed the presence of covalent bonds (carbon–hydrogen bonding) along with some other non-specific bonds. Furthermore, all mutant proteins had Van der Waal interactions that may affect the activity of the L2HGD enzyme.−1/atom pair, although when two big molecules interact, a huge number of atoms come in contact by means of Van der Waals interactions, producing a net effect, which is summated over numerous atom pairs and can be significant.Although energies related with Van der Waals forces are small, normal interactions provide 0.5–1.0 kcal molRoth et al. documented that Van der Waals forces are involved in the contact of proteins with several different molecules directly or indirectly with their exteriors, but due to the intricacy of the protein structure, the degree of these consequences is usually considered based on their (protein) perfect structure, i.e., spheroids or spheres. The shape of the molecular has also been shown to affect the magnitude of the interactions. Regarding the interactions occurring in the spherical protein structure, an initial coarseness of the molecular surfaces led to lower standard interaction forces for protein–protein and protein-surface instances relative to interactions in which the protein was assessed as a sphere. The results found by Roth et al. indicated that a form of steric stabilization might be vital in protein interactions .In normal protein substrates, interactive hydrogen bonds were present, which were weaker as compared to the carbon–hydrogen bonds that were present in most of the mutant proteins. Moreover, many Van der Waals forces were also present in mutant protein–substrate interactions, which possibly interfered with protein–substrate interactions and affected the enzymatic activity of the L2HGDH enzyme, resulting in the disease phenotype.The current in silico study was carried out to verify the effects of all reported missense and nonsense L2HGDH mutations on interactions of L2HG with its protein by means of different modeling and docking techniques. From the results, we noticed that the type of amino acid replacement, the sum of the bonds and the presence of Van der Waal forces modify the binding and dissociation of the L-2-hydroxyglutaric acid molecule with the L2HGDH enzyme, thereby altering its action. Additionally, we noticed that the severity of damage to L2HGDH varies with the type of mutation. No similar study on L2HGDH gene has been documented so far, meaning this is the first in silico study on all reported L2HGDH mutations, which can provide a better understanding of the topic for researchers and scientists in the field of genetics and metabolic disorders.


Protein and mRNA levels correlate only moderately. The availability of proteogenomics data sets with protein and transcript measurements from matching samples is providing new opportunities to assess the degree to which protein levels in a system can be predicted from mRNA information. Here we examined the contributions of input features in protein abundance prediction models. Using large proteogenomics data from 8 cancer types within the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data set, we trained models to predict the abundance of over 13,000 proteins using matching transcriptome data from up to 958 tumor or normal adjacent tissue samples each, and compared predictive performances across algorithms, data set sizes, and input features. Over one-third of proteins  showed relatively poor predictability (elastic net r ≤ 0.3) from their cognate transcripts. Moreover, we found widespread occurrences where the abundance of a protein is considerably less well explained by its own cognate transcript level than that of one or more trans locus transcripts. The incorporation of additional trans-locus transcript abundance data as input features increasingly improved the ability to predict sample protein abundance. Transcripts that contribute to non-cognate protein abundance primarily involve those encoding known or predicted interaction partners of the protein of interest, including not only large multi-protein complexes as previously shown, but also small stable complexes in the proteome with only one or few stable interacting partners. Network analysis further shows a complex proteome-wide interdependency of protein abundance on the transcript levels of multiple interacting partners. The predictive model analysis here therefore supports that protein-protein interaction including in small protein complexes exert post-transcriptional influence on proteome compositions more broadly than previously recognized. Moreover, the results suggest mRNA and protein co-expression analysis may have utility for finding gene interactions and predicting expression changes in biological systems. The abundance of mRNA is often measured as a surrogate variable of protein levels, but how well the mRNA level of different genes correlate with their protein across samples remains incompletely understood. Here we trained machine learning models over large RNA sequencing and mass spectrometry data from up to 8 cancer types in the CPTAC data sets to evaluate how well protein level variances across samples can be predicted from their transcripts. Despite voluminous data, up to one-third of genes shows poor mRNA-protein correlation suggesting their protein abundance is not primarily regulated from cognate transcripts. The inclusion of mRNA level information from protein interaction partners into the prediction models substantially improved prediction performance for a subset of genes, suggesting their protein abundance may be primarily regulated post-transcriptionally through protein-protein interactions. Notably, these proteins involve not only subunits of large multi-protein complexes such as the ribosome as previously suspected, but many proteins that form stable interactions with one or few other partners, including the propionyl-CoA carboxylase, mitochondrial calcium uniporter, calcineurin, and others. The results add to emerging evidence of independent regulation of protein levels from their cognate transcripts and suggest avenues to improve the interpretation of transcriptomics data. This has been taken to indicate that while abundant proteins have abundant transcripts, transcript variance within a group of samples does not necessarily predict or signify corresponding protein changes  features, compared to 1 feature in the self transcript model. Incorporation of STRING features led to an average increase in test set correlation coefficients of 0.16 [IQR: 0.07–0.27]  compared to the size of the whole transcriptome feature set, the results indicate that a substantial portion of performance gains over the single-transcript feature set is already achievable from relatively few selected prior features.We next examined more closely the underlying causes behind the performance gains of protein predictions in the CORUM and STRING feature sets. To do so, we examined the genes whose protein prediction performance from transcripts increased substantially (Δr ≥ 0.25) after the incorporation of additional transcript features. In total, we observed 484 such proteins in the CORUM feature sets, and 3,272 proteins from the STRING data set, representing over 24% of all examined proteins. These numbers increase further when a less conservative Δr threshold of ≥ 0.15 is used , altogether suggesting non-cognate transcript contributions to protein level are common at the proteome level. Upon inspecting the model feature coefficients (elastic nets) and feature importance (random forests), we observed that a considerable portion of these proteins with improved prediction are associated with (1) poor contribution from the self-transcript, and (2) a substantial contribution from primarily a few non-cognate  transcripts coding for other proteins (see below). In other words, although model performance continued to increase with increased feature set sizes, the contributions of trans locus transcript features to overall prediction performance is unevenly distributed, and are therefore attributable to a few high contribution genes rather than a simple scaling with feature size. Notably, despite the STRING feature set being substantially larger than the CORUM feature set, substantial contributions from trans locus transcripts in the STRING feature set primarily involve transcripts encoding proteins that form part of the same CORUM complex as the protein of interest itself, suggesting that stable complex memberships play an outsized role in determining protein levels.Fig 3A). PCCB showed poor prediction by their own self-transcripts (r:–0.008) but experienced high gains in test set correlation upon incorporating additional transcript features in the CORUM (r: 0.483) and STRING (r: 0.692) models. CMC1 is an assembly factor that forms an early intermediate of the cytochrome c oxidase complex in the mitochondrion with 14 documented protein members in CORUM . Likewise, CMC1 showed only moderate prediction by their own self-transcripts  but experienced high gains in test set correlation upon incorporating additional transcript features in the CORUM (r: 0.615) and STRING (r: 0.564) models.To illustrate the poor predictive power of cognate transcripts on some proteins, we next interrogated a subset of proteins where a non-self transcript has an outsized effect on the protein level of the proteins in the CORUM and STRING feature sets: Propionyl-CoA Carboxylase Subunit Beta (PCCB), C-X9-C Motif Containing 1 (CMC1), Proteasome Assembly Chaperone 2 (PSMG2), SMCR8-C9orf72 Complex Subunit (SMCR8), Mitochondrial Calcium Uptake 2 (MICU2), and Protein Phosphatase 3 Regulatory Subunit B, Alpha (PPP3R1). PCCB forms the propionyl-CoA carboxylase complex with two protein members, which breaks down certain amino acids in the cell .We then considered the transcriptomics and proteomics data distributions of the features that had highest coefficients in the elastic net model from the CORUM feature set for PCCB and CMC1. For PCCB, most protein-level predictions are recovered when the feature set includes PCCA, which together with PCCB forms the stable propionyl-CoA carboxylase enzyme that consists of six copies of PCCB and six copies of PCCA each. There is a remarkably strong correlation with its propionyl-CoA carboxylase complex interacting partner PCCA at the protein level (r: 0.957), but this correlation is almost entirely absent at the transcript level (r: –0.059). This is partially explainable by the observation that PCCA transcript has a higher level of variability and is also strongly correlated with the PCCB protein level. The total protein level of PCCB is therefore primarily driven by PCCA rather than PCCB transcripts . MT-CO1 is the mitochondrial genome encoded subunit of complex IV that is thought to act as the nascent scaffold around which the complex is assembled. CMC1 binds to MT-CO1 during the formation of the early module known as MITRAC and is subsequently released during assembly [In the case of CMC1, we found that MT-CO1 has the highest contribution to CMC1 protein level  forms the heterodimer PAC1-PAC2 complex (2 protein members in CORUM) along with Proteasome Assembly Chaperone 1 (PSMG1), which binds with 20S proteasome precursors and acts as a scaffold to promote proteasome ring assembly while preventing aberrant dimerization of 20S proteasome α rings [. PSMG2 protein has a higher correlation with PSMG1 transcript (r: 0.472) than its own (r: 0.158). From GTEx v8 data, PSMG2 is expressed more highly than PSMG1 across tissues (TPM ~40 vs. 15) [Two other examples of small stable complexes with interdependent protein and mRNA correlation are shown in  α rings . PSMG2 a vs. 15) , which sipt r: 0.2 than it(C9orf72) and WD Repeat Domain 41 (WDR41) [Fig 5B). A previous study suggested C9orf72 may stabilize excess SMCR8 [In another example, Smith-Magenis Syndrome Chromosomal Region Candidate Gene 8 (SMCR8) forms the heterotrimer C9orf72-SMCR8-WDR41 complex (3 protein members in CORUM) together with the C9orf72-SMCR8 Complex Subunit  (WDR41) . C9orf72 (WDR41) . Mutatioss SMCR8 , providiS5 Fig shows two more examples concerning small complexes. The mitochondrial calcium uniporter complex (MCU complex) contains five protein members in CORUM . Regulatory subunits of the complex, including calcium uptake protein 2, mitochondrial (MICU2) sense calcium levels, regulating the uptake of calcium performed by the mitochondrial calcium uniporter (MCU) [S5A Fig).er (MCU) . Heteroler (MCU) . This is2+ binding regulatory subunit , with 2 isoforms also encoded by 2 separate genes (PPP3R1 and PPP3R2) [S5B Fig). Together, these cases illustrate a general observation––in cases where the protein abundance of a gene is more highly correlated to the transcript level of a binding partner than to its cognate transcript, there appears to be stronger co-expression between the gene and its abundance driver at the protein level but not at the transcript level.The final example highlighted here comes from the heterodimer calcineurin, which is made up of a catalytic subunit , with 3 isoforms encoded by 3 separate genes , and a Ca PPP3R2) . Calcine PPP3R2) . In the S6 Fig). In some examined cases including PSMG2, the transcript coding for the protein of action is more abundant than that of its regulating interacting partner, which is compatible with a scenario where simple stoichiometric constraints control the protein level of the supernumerary subunit. It is however less clear whether this scenario applies to other examined cases including CMC1 and MT-CO1, where the MT-CO1 transcript is much more abundant than CMC1 owing to the multiple copies of mitochondrial genome in the tissue. Hence, how MT-CO1 regulates CMC1 protein level is not explainable directly from transcript stoichiometry alone and awaits further mechanistic studies. Finally, we also examined feature importance in a single data set of a recent CPTAC study that showed the highest performance in the single feature model (LSCC) . Among the six highlighted proteins , five were best predicted by a non-self transcript, and five of the top trans locus predictors were conserved from the combined data set analysis with the exception of MT-CO1, which was not among the model features due to the number of shared observations required. Hence the observed trans locus predictors are conserved in a single data set model and are unlikely to be due to heterogeneity across data sets.To corroborate these observations, we used the Boruta algorithm against the random forest CORUM models and confirmed each of the principal non-cognate transcript features is retained. We further considered Shapley values as an explanation of random forest and gradient boosting models, and likewise found that the interacting protein transcripts formed the top contributors in explaining the abundance of the protein of interest in each of the six cases above . Thus, small protein complexes are widespread in the proteome and a large fraction of the proteome could be placed under complex post-transcriptional control that decouples protein levels from mRNA levels. Moreover, half of the proteins in the CORUM annotations belong to 2 or more distinct complexes (including intermediates and subcomplexes) which suggest further opportunities for more complex patterns of interdependent protein and mRNA levels.Taken together, these results exemplify widespread interdependency of protein levels on trans locus transcripts, involving not only large megadalton protein complexes but many small complexes involved in diverse biological processes in the cell. The CORUM feature set considered here alone contains over 2,700 human complexes derived from CORUM release 3.0 mappable to 3,689 proteins with distinct gene names, with a median of 3 proteins per complex (S6 Table) and the STRING (S7 Table) feature sets. The resulting network is partitioned into connected components of transcript-protein relationships. The subgraph that corresponds to the relationship between PCCA and PCCB as discussed above shows the PCCB node contributing (as transcript) to its own protein level weakly, and the PCCA node contributing strongly to the PCCB node (as protein) . The relationship of CMC1 with other cytochrome c oxidase complex subunits, as described above, is represented in a wheel and spokes pattern where multiple transcripts (orange nodes) contribute either positively or negatively to the CMC1 protein level, with MT-CO1 having the strongest positive contributions . Another member of the MITRAC module COA3 to which CMC1 binds likewise exerts a positive influence on the protein level of CMC1, consistent with the possibility that interdependent protein and transcript relationships may reflect information related to protein complex assembly sequences. Larger subgraphs represent instances where two or more protein target nodes are connected by shared transcript source nodes. Due to the method by which the graphs are generated, protein hubs (with high in-degrees) are more prevalent, but transcripts with high outflow activities are also seen that connect two or more protein nodes. A subgraph in Fig 6C predicts that the transcript levels of SH3KBP1 and CD2AP both contribute to the predicted protein levels of multiple proteins involved in the RICH1/AMOT polarity complex and the PI4K2A-WASH complex, and the protein level of WASHC1 is influenced by genes in the CCC-Wash  complex and the PI4K2A-WASH complex .We next examined whether the interdependency of proteins and trans locus transcripts may be used to infer potential novel regulatory drivers. To do so, we constructed graphical models to visualize the connections between each protein and the transcriptome features that contribute to its predicted level, followed by topological analysis to find hub nodes and extract network patterns. To limit scope, we generated graphs to proteins whose prediction improved in the CPTAC_8 data set following the inclusion of the CORUM or STRING features (Δr ≥ 0.25) A directed graph is then generated using a list of edges that connect feature variables (transcripts) to target variables (proteins), with the edge weights calculated using a function of the elastic net coefficients and random forest feature importance of the models for both the CORUM . Using topological analysis based on the hierarchical community detection framework (HiDeF) method [Fig 6E). We then extracted the hub nodes from the subgraph using the betweenness centrality algorithm, which revealed mitochondrial serine beta-lactamase-like protein (LACTB) to be an upstream gene that is highly connected within the subgraph and exerts a regulatory effect on both small and large mitochondrial ribosome subunit components. Feature importance analysis further suggests the LACTB transcript to have a negative impact on multiple ribosomal mitochondrial proteins . Of note, LACTB was previously identified as a new subunit (MRP-L56) of the mitochondrial ribosomal large subunit isolated from sucrose gradients [LACTB may also affect oxidative phosphorylation in differentiated cells by regulating mitochondrial ribosome biogenesis and protein synthesis, which may be validated experimentally.We highlight an instance where mRNA-protein relationships may be examined to generate new hypotheses. The mitochondrial ribosome is represented in a highly connected subgraph where the abundances of a majority of complex subunit proteins are each contingent upon multiple transcripts  method , we showradients . Subsequradients  that posradients  and thatradients . The phyradients . The anaS8Table) showed chromogranin A and B (CHGA and CHGB) as inward hubs  associated with secreted peptides including CST3, SERPINC1, MFGE8, SERPIND1, and others , which is consistent with the known roles of these acidic glycoproteins as the primary constituents of secretory granules that can regulate their rate of their formation [A similar hub node analysis from the largest subgraph from the STRING feature set  in the CORUM feature set and potentially more transient interactions documented in the STRING feature set. The common presence of small stable complexes in the proteome greatly expands the repertoire of proteins for which due considerations should be given when directly interpreting transcript level data as representing protein level information. Moreover, it has been suggested that promiscuous protein-protein interactions without established biological function may be a common occurrence during the co-evolution of functional protein-protein interactions . The occACTR3 and ARPC4 will be more effective in modulating ARPC3 protein levels than augmenting the expression of ARPC3 itself, which is readily testable by experimentation. More generally, the results here corroborate the importance of post-transcriptional regulation including protein degradation and turnover in modulating protein levels.The imperfect correlation between protein and mRNA points to orthogonal information that exists between transcript and protein regulations, which underpins the untapped potential for further multi-omics integration to derive new insights . The useSignificant differences in protein regulations likely occur in different cell and tissue types. The predictive models here are trained using publicly available CPTAC data from 8 cancer types, which contain transcriptome and proteome data from both tumor and normal adjacent tissue samples. In prior work, we found general concordance between protein and mRNA correlation in CPTAC samples vs. GTEx tissue proteomics data . HoweverWe performed the analysis using expression data from TCGA/CPTAC RNA sequencing experiments through the cptac Python API, which retrieves the final data tables from the flagship CPTAC papers of each individual cancer type . AlthougFinally, pitfalls must be considered when attempting to interpret predictive models in search of mechanistic insights. Feature importance in the predictive models represents correlation rather than causality, and hence interpretations can be confounded by independent confounders, multicollinearity, and reverse causality. In simple cases such as PCCB–PCCA, given the emphasis on prior feature selections that prioritize known protein-protein interaction partners, the logical interpretation would be to assume the hierarchical nature of gene regulations where transcript levels are more likely to affect protein abundance than the opposite, but this assumption becomes more tenuous as feature sets expand and the number of associations increase, and in cases such as transcription factor proteins whose abundance can affect transcript levels across samples. Future work in this area may employ more sophisticated causal inference methods to identify regulatory modalities.In summary, this study compared predictive models of protein levels using different transcript feature sets, and provided biological interpretations of the results by highlighting trans locus transcripts with substantial contributions to protein levels. The analysis here therefore reveals new details into the gene identity and modality of trans regulation of protein levels, and gives support to further development of prior transcript feature selection strategies to optimize protein prediction tasks. The results show that the transcript levels of protein-protein interaction partners can broadly influence protein abundance in a tissue, which has implications on the interpretations of transcriptomics data and on understanding the architecture of proteome composition regulations. With further refinement of feature selection and feature engineering methods and the availability of large data sets, we foresee that similar approaches to those shown here will provide valuable new insights into post-transcriptional mechanisms of protein regulations.S1 FigA. Box plots of test set normalized root mean square error (NRMSE) between the transcript-predicted and actual protein level for each protein are shown across five feature sets  and three algorithms . In each plot, x axis denotes the number of CPTAC data set used to train the models box: interquartile range; whiskers: +/– 1.5 IQR. B. As above, but for test set goodness-of-fit (R2).(PDF)Click here for additional data file.S2 FigA. Model performance in single cancer data sets. Box plots of test set correlation coefficients (r) between the transcript-predicted and actual protein level for each protein are shown across five feature sets  and three algorithms . In each plot, x axis denotes the CPTAC cancer type study used to train the models; box: interquartile range; whiskers: +/– 1.5 IQR. B. Model performance when the 8 data sets were combined in the order of decreasing single data set performance.(PDF)Click here for additional data file.S3 FigA–B. Scatterplot showing a significant linear relationship between the number of low-confidence interactors a protein has as annotated in STRING vs. the increase in test set correlation between predicted vs. actual protein levels in the Elastic Net models over the self-transcript feature set . C–D. As above, but for the CORUM feature set.(PDF)Click here for additional data file.S4 FigA. MICU2, B. PPP3R1. For each protein, the transcript-trained prediction of protein level is plotted on the x axis and the actual protein level is plotted on the y axis. Blue: train set, brown: test set. Columns denote the transcript feature set used to train the model. The number of features used to train the model in each feature set is shown inside each plot. r: Correlation coefficient.Two additional highlighted proteins with substantial predictability from transcriptome data upon the inclusion of additional features are shown: (PDF)Click here for additional data file.S5 FigA. MICU2 protein level is predicted by MCU transcript but not its own transcript. B. PPP3R1 protein level is explained by PPP3CA transcript level but not its own transcript. Substantial correlations across transcripts and proteins (≥ 0.4) are bolded.Two examples of proteins whose abundance is better explained by another transcript are shown. (PDF)Click here for additional data file.S6 FigA. random forest and B. gradient boosting model output from the CPTAC_8 CORUM feature set. The SHAP values of top transcript features and their impact on model output are shown for each of six proteins highlighted in the text (from top to bottom): PCCB, CMC1, PSMG2, SMCR8, MICU2, PPP3R1.SHAP interpretation of feature importance in (PDF)Click here for additional data file.S7 FigConsistent with the analysis of the combined CPTAC_8 data set, five of the six proteins are not best predicted by their cognate transcripts and all but one (MT-CO1) of the top trans locus transcripts are preserved, which was not among the examined candidate features. Only test set data points are shown.(PDF)Click here for additional data file.S8 FigNames of select complexes are labeled. The majority of complexes are small with a median of 3 proteins per complex.(PDF)Click here for additional data file.S9 FigThe hierarchical network diagram shows Chromogranin A and B (CHGA/CHGB) (magenta) and their first-degree inward flow neighbors (green) in the largest subgraph constructed from the STRING data set. The protein level of CHGA and CHGB is associated with the transcript levels of multiple secreted peptide coding genes, consistent with these proteins serve to regulate gene expression in secretion pathways.(PDF)Click here for additional data file.S1 Table(TSV)Click here for additional data file.S2 Table(TSV)Click here for additional data file.S3 Table(TSV)Click here for additional data file.S4 Table(TSV)Click here for additional data file.S5 Table(TSV)Click here for additional data file.S6 Table(CSV)Click here for additional data file.S7 Table(CSV)Click here for additional data file.S8 Table(TXT)Click here for additional data file.


Protein–protein interactions are widespread in biological systems and play an important role in cell biology. Since traditional laboratory-based methods have some drawbacks, such as time-consuming, money-consuming, etc., a large number of methods based on deep learning have emerged. However, these methods do not take into account the long-distance dependency information between each two amino acids in sequence. In addition, most existing models based on graph neural networks only aggregate the first-order neighbors in protein–protein interaction (PPI) network. Although multi-order neighbor information can be aggregated by increasing the number of layers of neural network, it is easy to cause over-fitting. So, it is necessary to design a network that can capture long distance dependency information between amino acids in the sequence and can directly capture multi-order neighbor information in protein–protein interaction network.In this study, we propose a multi-hop neural network (LDMGNN) model combining long distance dependency information to predict the multi-label protein–protein interactions. In the LDMGNN model, we design the protein amino acid sequence encoding (PAASE) module with the multi-head self-attention Transformer block to extract the features of amino acid sequences by calculating the interdependence between every two amino acids. And expand the receptive field in space by constructing a two-hop protein–protein interaction (THPPI) network. We combine PPI network and THPPI network with amino acid sequence features respectively, then input them into two identical GIN blocks at the same time to obtain two embeddings. Next, the two embeddings are fused and input to the classifier for predict multi-label protein–protein interactions. Compared with other state-of-the-art methods, LDMGNN shows the best performance on both the SHS27K and SHS148k datasets. Ablation experiments show that the PAASE module and the construction of THPPI network are feasible and effective.In general terms, our proposed LDMGNN model has achieved satisfactory results in the prediction of multi-label protein–protein interactions. Protein takes one of the most common molecules in organisms. It is the material basis of life activities and participates in various biological processes in organisms . Most viIn pace with the rapid development of high-throughput technology, a number of laboratory-based traditional methods have been used to predict PPIs, such as mass spectrometric protein complex identification (MS-PCI) , yeast tDeep learning, by virtue of its powerful feature learning ability, has been valued by various fields and is rapidly evolving, no exception in the bioinformatics field, where it is ingeniously applied to probe some problems in bioinformatics, such as protein–protein interactions prediction tasks. Sun et al.  applied It should be noted that all the methods mentioned above only take amino acid sequence features as input. They don’t consider the interactions between proteins , which mTherefore, Yang et al.  proposedThese models have improved the accuracy of PPIs prediction. However, they only considered the information of two proteins directly interacting in PPI network, ignoring the information of protein–protein pairs indirectly interacting. In this way, the information captured by these model are incomplete. Studies , 28 haveWe use the Transformer module with a multi-head self-attention mechanism to capture the long-distance dependency information between each two amino acids in sequence.Based on the two-hop concept, we construct a THPPI network based on PPI network, which can capture the information between indirectly interacting proteins and thus increase the receptive field in space.The experimental results show that our method exhibit good performance.In this paper, we propose a novel LDMGNN model to implement the multi-label prediction task of protein–protein interactions. This model mainly aims to solve the two problems mentioned above. The first problem is that the existing methods ignore the long-distance dependence information between amino acids in the sequence, and the second is that the existing methods do not fully consider the interaction among indirect connected protein nodes. To solve the first problem, we use the Transformer with a multi-head self-attention mechanism to capture the correlation between every two amino acids in the sequence. For the second problem, we construct a two-hop protein–protein interaction (THPPI) network based on the PPI network to enhance graph representation learning. Overall, our main contributions:In the following, the datasets, experimental parameter settings, evaluation metrics, baselines, experimental results and analysis used in our experiment will be introduced.In this paper, we use two common datasets, i.e., SHS27k and SHS148k, to evaluate our method. These two datasets contain a lot of PPIs information and amino acid sequence information. They were randomly selected by  accordinWe regard all known PPIs as positive samples, and negative samples of the same size are randomly selected from unknown interactions. In our experiment, the positive and negative sample rate is 1:1. Specifically, in the SHS27k dataset, the number of negative samples is 7624. In the SHS148k dataset, the number of negative samples is 44,488. At the same time, inspired by , in ordeP and PPI set I , construct a protein–protein interaction network N . Firstly, a protein is randomly selected from the protein set P as the root node t, the degree of the root node N.Given the protein set Our experiment is performed on an NVIDIA GTX 3090 GPU with a PyTorch deep learning framework. We choose the Adam algorithm  as the oMicro-F1 will emphasize the common labels in the datasets, which is not easy to be affected by small samples or large samples, so that each sample has the same importance 34] and LHIN2Vec [HIN2Vec : A repreSDNE [SDNE : A strucLPI-DLDN [LPI-DLDN : A deep LPI-deepGBDT [deepGBDT : A multiDTI-CDF [DTI-CDF :A cascadPIPR [PIPR : An end-GAT [GAT : A new nGNN-PPI [GNN-PPI : A graphOur experiment is inspired by GNN-PPI [When compare with the models based on deep learning, we construct the same architecture as them. We input the SHS27k dataset and SHS148k dataset into the model, and change the output from the original two classification to multi-label classification. These deep learning models are as follows. GNN-PPI . HoweverAs shown in Table p value ) is less than 0.05, it indicates that there is a significant difference between the two samples.To verify whether the performance of our proposed LDMGNN model is statistically significantly different from these 11 baseline models, we conducted a paired samples t-test using SPSS software. The related results are shown in Tables entclass1pt{minimap value (Sig.) is 0.001 (< 0.05), which indicates that the two samples of LDMGNN and SVM have significant correlation, and is strongly correlated. Similarly, it can be seen that the correlations of the last 10 pairs of samples are 0.917, 0.920, 0.916, 0.953, 0.938, 0.929, 0.943, 0.916, 0.938 and 0.965, respectively . And the corresponding significance level p value  of the last 10 paired samples are 0.010, 0.009, 0.010, 0.003, 0.006, 0.007, 0.005, 0.010, 0.006 and 0.002 . Obviously, these 10 paired samples are all significantly correlated. Therefore, it is meaningful to perform a paired samples t-test on these 11 pairs of paired samples.As shown in Table p values ) of the top 10 pairs of samples are 0.001, 0.005, 0.000, 0.000, 0.009, 0.002, 0.001, 0.002, 0.015 and 0.011, respectively, all of which are less than 0.05. It shows that there are significant differences between the first 10 pairs of samples. The p values ) of the 11th pair of samples is 0.094, which is greater than 0.05. As for the 11th pair of samples, we consider that the extreme imbalance of the data may be a factor in this situation. Further, from the perspective of reality, we believe that LDMGNN model has certain practical significance. Lv et al. [From the Table v et al.  studied In order to verify the importance and effectiveness of each module in this study for the prediction model, we conduct an ablation study by deleting or replacing each module in this study. We use GNN-PPI as the baseline for PPIs prediction, which processes amino acid sequences using RNN and aggregates only first-order neighbor information. −PMHGE represents the removal of PMHGE from the LDMGNN model and, unlike baseline GNN-PPI, and uses the Transformer with a multi-head self-attention mechanism to learn the amino acid interdependency in the sequence. -PAASE represents the deletion of the PAASE module from the LDMGNN model. Unlike the baseline GNN-PPI, we construct a THPPI network and simultaneously aggregates first-order and second-order neighbor information, increasing the spatial receptive field in the model. LDMGNN is our proposed model. Compared with the baseline GNN-PPI, our LDMGNN not only captures the long-distance dependency information in the sequence but also increases the spatial receptive field in space. We still use Micro-F1 as the evaluation metric.As can be seen from Table When the model only increases the spatial receptive field of network, for the SHS27k dataset, the current model increases by k to determine the k-hop network to be constructed. The experimental results are shown in Table k is 2.When constructing a multi-hop PPI network to increase the receptive field, we conducted experiments on different k is set to 2, the accuracy of the model obtained in each partition scheme on the two datasets is higher than that when K is set to 1. This indicates that it is necessary to construct a multi-hop PPI network to increase the receptive field of the network in space. However, with the increase of k, the performance of the model on the two datasets shows a decreasing trend. When k is 3, the model acquires much less accuracy in each of the partition methods on both datasets than when k is 2. Indeed, the accuracy obtained when k is 3 is less in all cases than when k is 1. This indicates that the simple construction of a multi-hop PPI network is not the best. As the receptive field gradually increases, the model gradually tends to be over-fitting. Therefore, in this study, k is selected as 2 when we construct the multi-hop PPI network. It further shows that it is effective and reasonable for us to aggregate the first-order and second-order neighbor information at the same time and appropriately increase the receptive field of the network.It can be seen from Table To test how more negative samples will affect the performance of the model, we increase the number of negative samples while keeping the number of positive samples constant. At this time, the positive: negative sample rate in the datasets will change. Specifically, for the SHS27k dataset, we randomly selected three negative samples from proteins pairs with unknown interactions, and the number of these three negative samples were 22,872, 38,120 and 76,240, respectively. We took the protein pairs that are known interactions as positive samples, and the number is 7624. Thus we can obtain three different positive:negative sample rates, which are 1:3, 1:5 and 1:10. Similarly, for the SHS148k dataset, we randomly selected three negative samples from protein pairs of unknown interactions, with numbers of 133,464, 222,440, and 444,880, respectively. We also took known interacting protein pairs as positive samples with a number of 44,488, resulting in three different positive:negative sample rates, which are 1 : 3, 1 : 5, and 1 : 10, respectively. We still choose three partition schemes to divide the test set, namely random, BFS and DFS, and our test set accounts for The experimental results are shown in Table Obviously, we can see that when we add more negative samples, the performance of the model will decrease significantly. This is because when the number of negative samples exceeds the number of positive samples, it will affect the correct judgment of the model on the positive samples, so that the classifier can not capture the features of the positive samples well. Therefore, the imbalance between positive and negative samples will negatively affect the performance of the model.Next, we will introduce why we use the Transformer with a multi-head self-attention mechanism to capture amino acid sequence information and why we construct a THPPI network to aggregate two-hop neighbor information.It is acknowledged that the Transformer  was firsMeanwhile, we constrcut a THPPI network since there may be meaningful information between two indirectly interacting nodes. Exactly, the competitive inhibition  in biochIn this study, we propose the LDMGNN model to predict multi-label protein–protein interactions. LDMGNN first captures the potential features of amino acid sequences through the PAASE module and then concatenates this information with the topological information of the initial PPI network and the topological information of the THPPI network respectively. Then, they are respectively input into the graph neural network, and the two obtained feature matrices are addition by element-wise as the final embedding of protein and protein pair. Finally, this embedding is fed into a classifier for predicting protein–protein interactions.We carry out a series of experiments, and in the SHS27k dataset and SHS148k dataset, our model shows better performance than the existing model. Furthermore, we perform an ablation experiment to verify that each module in the model is indispensable and that the parameters in the experiment are reasonable and effective. This indicates that the Transformer with a multi-head self-attention mechanism can successfully capture the long-distance dependence information in amino acid sequences. The spatial receptive field of the network can be increased by aggregating the first-order and second-order neighbor information simultaneously. In conclusion, the LDMGNN model can comprehensively learn the feature information between protein pairs and has a good potential in PPI prediction.This section introduces the proposed multi-label PPI prediction approach, which is an end-to-end representation learning model. Given the representation of protein amino acid sequence, the adjacency matrix of PPI network and the adjacency matrix of THPPI network, we try to predict the labels between protein–protein pairs. The representation of protein amino acid sequence input here is processed into a numerical vector by . In thisM, and define the amino acid sequence S of a protein, which consists of amino acids in varying proportions as P is the set of proteins and denoted as I is the set of protein–protein interactions, defined as We represent the set of amino acids as p is protein pairs with known interaction, L. We define the label set of PPIs as F is used to predict the labels The task of this multi-label classification is to learn a model The framework of the proposed LDMGNN model is shown in Fig. In the process of protein encoding, we can regard this process as two parts. These two parts are trained together in an end-to-end manner. One is protein amino acid sequence encoding (PAASE), which is to capture the protein feature based on amino acid sequence, which we call sequence feature. The second is protein multi-hop graph encoding (PMHGE), which can be regarded as composed of two branches. One branch uses a GIN block to capture the first embedding of protein, and the other branch uses a GIN block to capture the second embedding of protein.This module is used to capture the protein feature based on amino acid sequence. Predefined feature appearing in modules were observed by  through As shown in Fig. The function of this block is to learn the interdependence between each two amino acids in the amino acid sequence by calculating the correlation coefficient between them. This can not only capture the local information of amino acids in the sequence, but also capture the long-distance dependency information between amino acids.Because of the potential for multiple types of interactions between each pair of amino acids, we apply multi-head self-attention mechanism to each amino acid of each protein amino acid sequence. Then we extract the low dimensional feature embedding of each amino acid by computing the correlation of different kinds between each pair of amino acids. As shown in Fig. h types of correlations for each pair of amino acids h types of each amino acid Since there are In the process of protein multi-hop graph encoding, we use two GIN blocks to obtain two embeddings of protein. The input of the first GIN block is the original PPI network and the sequence feature, and the output is the first embedding A of the original graph i and j correspond to values greater than 0, indicating that there is two-hop relationship between them on the original graph. Similarly, in the THPPI network Through the THPPI graph network, the GIN block can learn new interactions between two proteins for the purpose of augmented graph representation. We construct the THPPI network through the PPI network. Specifically, we generate the adjacency matrix L represents the number of proteins and 256 represents the number of features of each protein. This feature matrix is obtained by concatenating structural information of the PPI network with amino acid sequence information. After two linear layers, two ReLU activation layers and normalization layers, the protein embedding was obtained.The two GIN blocks in Fig. Graph neural networks , 48–50 hk iterations, the structure information in the k-hop neighborhood can be captured. And the new feature vector p can be expressed by the following mathematical formula:N(p) is the set of all neighbor nodes of node p. We choose vector sum as the aggregation function and multi-layer perceptrons (MLP) as the update function.Similar to graph neural networks, the neighbor aggregation mechanism is the core of GIN. We iteratively update the feature of each node by aggregating feature of its neighbors. After kth iterations, node p obtains the feature vector p, and Then, for the original PPI network kth iterations, node p obtains the feature vector p, and p. However, on the graph p. Finally, we can obtain the embedding of Similarly, for the THPPI network This operation can well integrate the embedding k, respectively.We input the final embedding


Accurate and efficient prediction of protein-ligand interactions has been a long-lasting dream of practitioners in drug discovery. The insufficient treatment of hydration is widely recognized to be a major limitation for accurate protein-ligand scoring. Using an integration of molecular dynamics simulations on thousands of protein structures with novel big-data analytics based on convolutional neural networks and deep Taylor decomposition, we consistently identify here three different patterns of hydration to be essential for protein-ligand interactions. In addition to desolvation and water-mediated interactions, the formation of enthalpically favorable networks of first-shell water molecules around solvent-exposed ligand moieties is identified to be essential for protein-ligand binding. Despite being currently neglected in drug discovery, this hydration phenomenon could lead to new avenues in optimizing the free energy of ligand binding. Application of deep neural networks incorporating hydration to docking provides 89% accuracy in binding pose ranking, an essential step for rational structure-based drug design. Accurate prediction of protein-ligand binding is limited by our ability to model solvation. Here, in addition to protein and ligand information, density maps for hydration site occupancy are used to train a convolutional neural network in order to predict binding poses more accurately compared to common scoring functions. Several groups utilized CNNs to generalize the modeling of protein-ligand interactions in context of scoring functions6. In these approaches, the structural information of a protein-ligand complex is provided to the CNN as three-dimensional (3D) images representing the density of different atom types. The architecture of CNNs allows them to learn from these data important local features of protein-ligand interactions, without limitation to pre-defined scoring functions that typically rely on two-body interaction models. Current CNN models are focused on modeling the direct interactions between protein and ligand in the bound state, but neglect critical contributions of protein-ligand binding, in particular the solvation and desolvation of protein and ligand upon binding.Recently, convolutional neural networks (CNNs) have been widely applied to different fields in computational chemistry such as QSAR, virtual screening, de novo molecular design, quantum chemistry, prediction of molecular properties, and materials design8. In structure-based drug design, ligands are optimized to replace energetically unfavorable water molecules, particularly ordered water molecules in hydrophobic moieties. This desolvation of free energy is often an essential driving force for strong protein-ligand association  or molecular-dynamics (MD)-based simulation methods, such as WaterMAP16 or WATsite14. Considering the excess enthalpy and entropy of the water molecules in the binding site that are replaced by a ligand, the contribution of desolvation to the binding free energy can in principle be computed18. For a direct inclusion of explicit desolvation into standard scoring procedures, grid-based adaptations of the inhomogeneous solvation theory (IST)20, such as GIST21, were utilized. Even with such accurate desolvation estimation, the docking performance did not significantly nor consistently improve for most protein targets22. Reasons for these results include the lack of consistent scoring function design and optimization, including the explicit desolvation term , inaccuracies in the other interaction terms, and the neglect of water-mediated interactions.To explicitly and more precisely model solvation effects of individual water molecules, a spectrum of various computational approaches have been developed to identify the likely position of water molecules in binding sites and to evaluate their energetic stabilityHere we present to the best of our knowledge the first attempt to include explicit hydration information into CNN-based scoring, titled DeepWATsite Fig. , which e23  methodology based on deep Taylor decomposition23 Fig. b24,25.26. In short, water molecules were initially placed around a protein surface using 3D-RISM site-distribution function29 and GAsol30 to optimize the initial water network. Using this initial water placement, WATsite then performs explicit water MD simulations of each protein and computes explicit water-occupancy and free-energy profiles of each hydration site (=high water-occupancy spot) in the binding site.Hydration site data was generated for several thousand protein systems using the new GPU-accelerated version of WATsite , these images are considered as input to the subsequent CNN models Fig. b, c. TheFirst, the CNN models were trained on a rather small set of 377 protein systems and validated on a much larger test set of 2046 systems demonstrating the universality of the generated models. These models are based on 25 poses per protein-ligand system, the typical output from docking, here using Smina. Systems for training and test set were randonly selected from PDBbind. The test set displays high diversity in ligand chemistry and protein structure. It includes structures from 369 protein families based on PFAM classification ), one with positive and negative enthalpy as well as entropy information about hydration ), and one without any hydration information (named CNN(P + L)) (see Methods).Figure Whereas Vina identifies native poses in 61%, 77%, and 85% of all systems in the top 1-, top 3-, and top 5-ranked poses, respectively, CNN(P + L) significantly outperforms Vina in identifying native poses. CNN(P + L) displays an improvement of 9%, 10%, and 7% compared to Vina scoring in the top 1-, top 3-, and top 5-ranked poses, respectively.Including hydration information into the CNN model further improves the prediction quality significantly. The occupancy hydration model improves the prediction of Vina by 16%, 15%, and 11%, the enthalpy/entropy model by 14%, 14%, and 11% (not shown in figure) at the top 1, top 3, and top 5 levels, respectively. If the search algorithm is able to provide a native-like pose, DeepWATsite can rank this pose within the top 5-ranked poses in almost all cases. Combining occupancy, enthalpy and entropy layers in a model did not show any further prediction quality improvements. To limit the number of input layers, all subsequent models were generated with the hydration information provided only in the form of an occupancy layer.31. Applying the model to the identical test set of 2046 systems resulted in only moderate enhancement in prediction performance from 77% to 82% in the top 1 rank  . Those RMSD deviations are largely due to the presence of solvent-exposed ligand moieties in alternative positions  by comparing the experimental electron density to the expected density for the alternative predicted ligand pose34. Using this analysis, several systems were identified where the top pose, although not in line with the original PDB structure, was consistent with the experimental electron density, for example, EphB4 kinase domain inhibitor complex  was added as additional criterion to the loss function, measuring how many of the native contacts were conserved in a binding pose of consideration.To overcome the limitations of RMSD as the sole success criterion, in particular for solvent-exposed ligands, we made two changes to the loss function and performance evaluation. First, atoms with high solvent exposure are removed from the RMSD calculations (see Methods). Second, the contact mode score (CMS)39 forcefield was utilized, which incorporates anisotropic atomic charge distributions (see Methods). To incorporate the anisotropic charge distribution in a form of 3D images compatible with the atom-type densities, first the interactions of a positive and negative probe with the ligand and protein were computed separately. The maxima of those interactions were identified. Additional input channels representing the interactions with the two probes were included in the model by placing Gaussian distribution functions on the centers of the extrema.In order to better model interactions, such as halogen bonding, the extended electron distribution (XED)31. The model performance on the test set was enhanced considerably to the previous models with 89% of native-like predictions in the top 1   were combined and subsequently split into three diverse groups of approximately equal size (see Methods). Three-group cross-validation was performed on this set with almost equal prediction performance for the left out data. A native-like pose was identified at top-1 rank with 91%, 90%, and 88% for the three cross-validation models, respectively.43. With respect to machine learning scoring functions, the same training and test set was applied on a variety of different machine learning architectures. A concise summary of methods and results of this study is found in Supplementary Note In this manuscript, only one classical scoring function was used for comparison. The reason for this selection are recent comparison studies on standard scoring functions that revealed overall convergence of those classical scoring functions in correctly predicting binding posesWhile the pose ranking performance of DeepWATsite is significantly improved compared to standard protein-ligand-based CNN, it is critical to understand how the inclusion of hydration information caused the quality increase. Furthermore, the utility of DeepWATsite and CNN-based scoring in general as drug-design tools critically depends on the human interpretability of the model. Thus, we aimed to generate a detailed analysis method for interpreting the CNN models and for identifying critical features for protein-ligand binding. This could guide the rational optimization of molecules with a new level of accuracy and detail compared to standard docking approaches.44. An adaptation of the initial method was recently tested on the study of protein-ligand interaction prediction45. The initial LRP, however, allowed for the mixture of positive and negative relevance values that results in artifacts such as large positive or negative values distant to the binding site.Despite impressive performances of CNNs in many applications related to image recognition, the models act typically as “black boxes” with a lack of interpretability. This “black-box” character is the result of the underlying nonlinear structure of the neural network. LRP is a recent method that aims to map the outcome of a model back to the input layers23 by modifying a fork of Gnina46. Deep Taylor decomposition is a method to explain the individual predictions of deep neural networks in terms of the input layers. It functions by performing a layer-by-layer backward pass on the network starting from the classification between native  and decoy pose  using the following rules: According to the new LRP analysis, this positive defined classifier outcome in node j in output layer N (=classification as active pose for given input x), named relevance In this paper, we implemented a new LRP methodology for interpreting generic multilayer neural networks based on the deep Taylor decomposition methodFurthermore, the relevance in each layer measures the contribution of each node to the decision that the pose is native with zero meaning no contribution. Therefore, all relevance values should be positive or zeroi of layer N − 1 estimates the contribution of this node to the positive outcome on node j (=native pose) in the output layer N. The relevance values from layer N − 1 are subsequently propagated to layer N − 2 using Taylor decomposition on each node i of layer N − 2 with respect to its overall relevance contribution of layer N − 1. Considering that the output value of layer N and the output of each ReLU node in the convolutational layers is non-negative, the following z+ rule can be derived for the relevance back-propagation23xi is the activation of node i in layer K − 1, wij the weight of connection between node i in layer K − 1, j in layer K, and bj the bias in layer K.The Taylor expansion at each node LRP analysis identifies the binding site residues and ligand groups critical for protein-ligand interactions in the native binding mode. It further highlights the water density most crucial for ligand binding. For example, Fig. Co-localized with this central region are highly relevant water molecules Fig. d demonstThe LRP analysis of all studied targets identified relevant water density for the correct prediction of protein-ligand complexes, highlighting the importance of desolvation of free-energy contributions and water-mediated interactions for protein-ligand association. For example, in the complex of CP-81,282 with endothiapepsin, a water molecule mediating critical interactions between protein and ligand Fig. c is idenIn thermolysin complexed with (2-sulfanyl-3-phenylpropanoyl)-Phe-Tyr, the LRP analysis reveals that the coordination of the ligand to the zinc ion stabilized by three histidine residues is essential for ligand binding Fig. d. Releva24 recently studied this particular water network around thermolysin inhibitors. They were able to interpret surprising thermodynamic structure–activity relationships with the stability or partial rupture of this network cage around the solvent-exposed ligand moieties. The DeepWATsite CNN model identifies the importance of this water network without any specific user input besides binding pose and WATsite information.Using a combination of X-ray crystallography, ITC measurements and simulations, Klebe and co-workersl-homocysteine, water density with negative enthalpy and high relevance is identified around the sulfur-containing moiety of the ligand solvation in modeling protein-ligand interactions. In this article, we presented the first deep-learning approach to model protein-ligand interactions incorporating hydration information. The significant improvement of DeepWATsite for pose ranking compared to standard CNN models or empirical scoring functions highlights the utility of this approach for a more precise modeling of protein-ligand interactions.The drastic reduction in failure rate to predict the correct binding pose at top rank from around 40% in standard docking using Vina to around 10% using the optimized DeepWATsite will have broad impact to the drug-design process. Even without experimental protein-ligand complex structure, rational structure-based lead optimization will be made possible without the significant ambiguity of current docking-based approaches. Furthermore, the predicted pose can be directly used for subsequent free-energy calculations without the uncertainty of considering several alternative docking poses. Thus, the additional WATsite simulation  is more than well invested considering the simulation lengths of free-energy calculations.47.It should be noted that the method described here is a re-scoring method, that is, docking is carried out initially with Smina and poses are re-scored using the CNN, which take into consideration the solvation grids. Ligand flexibility is thus fully included on a dihedral level as part of the sampling part of Smina docking. As it is common practice in scoring-docking evaluations, the current study uses re-docking into the fixed protein structure. This is consistent with the fact that the water grids are computed using MD simulations with restrained protein to ensure statistical stability of the thermodynamic values. In the future, we plan to incorporate protein flexibility by generating grids for an ensemble of different protein conformations as discussed in a recent publication by our groupOf similar importance as the pose ranking quality is the new level of interpretability of the underlying CNN models using the LRP analysis in our study. Several selected examples highlight that the model and its analysis can identify residues and ligand functional groups critical for protein-ligand binding. Furthermore, different types of hydration effects essential for ligand binding were identified, including desolvation, water-mediated interactions, and the formation of stable networks of water molecules around exposed ligand moieties.25 Our analysis reveals that this ignored facet of solvation is more widespread in protein-ligand complexes than currently recognized.This information can be valuable in various contexts. For example, the identification of important residues and ligand atoms for interaction can guide the rational modification of a ligand without relying on an imprecise energetic analysis based on simplistic scoring functions that neglects the environmental context of interactions. The identification of critical water-mediated interactions can guide the selection of water molecules to be included during ligand design or during docking. The analysis is also able to reveal enthalpically favorable water networks on the boundary of binding sites that are currently neglected by most drug-design projects, but can be important for the observed structure–activity relationships as recently highlighted.For validating and benchmarking the accelerated WATsite version, HIV-1 protease (PDB: 1PHX) was used as protein system. In addition to HIV-1 protease, dihydrofolate reductase was used to measure the efficiency of the new implementation. This system allows for direct comparison with other MD programs such as Amber, Charmm, or OpenMM, where it has been used as standard benchmark.48. In short, hydrogen positions, bond orders, and protonation states of histidines, glutamic, and aspartic acids, and side-chain conformations of asparagine and glutamine side chains were optimized using the default protocol.For training and validation of two different sets of CNN models 2423 and 14,740 protein-ligand structures from PDBbind were used, respectively  and entropic (ΔShs) change of transferring a water molecule from the bulk solvent to the hydration site of the protein binding site. The output files from the modified OpenMM-WATsite code containing the individual water-interaction energies were used in this step.A detailed description of the WATsite methodology can be found in previous publications21, an alternative grid-based analysis was implemented into WATsite. Similar to hydration site prediction, a 3D grid was placed over the user-defined binding site. The occupancy of water molecules was distributed onto the 3D grid using all snapshots generated throughout the production run of the MD simulation. In contrast to standard WATsite, the occupancy is not clustered into hydration sites, but every grid point with larger than twice the bulk occupancy is considered as a “pseudo-hydration site” and any water molecule within 1 Å radius throughout the MD trajectory is considered to contribute to it. Only the oxygen atom of a water molecule is considered as criterion for inclusion. The desolvation enthalpy and entropy of the “pseudo-hydration site” is calculated similarly as in the original hydration site analysis using the contribution of any water molecule within 1 Å radius throughout the MD trajectory.The previous implementations of WATsite predicted hydration sites representing the cluster centers of high solvent density regions (see previous section). This localized representation of water occupancy is not well suited as input for CNN, which requires 3D grid data. Inspired by GIST26.In order to achieve fast convergence of the thermodynamic profile of hydration data, in particular for water molecules in enclosed binding sites, we utilized our recently developed protocol that uses GAsol, which relies on 3D-RISM, for initial water placementSee Supplementary iFor speed-up of the MD simulation protocol, the protein was truncated beyond a threshold radius around any ligand atom. Different threshold radii or cutoffs  were tested. We analyzed the impact of such truncations by comparing the WATsite predictions to those of the full protein system. For all truncated systems, a python script is used to add capping acetyl (ACE) and amide (NME) groups to the break points in the protein sequence. The protein structures were then solvated in an orthorhombic box of water molecules. A minimum distance of 10 Å between any protein atom and the faces of the box was chosen. Hydration site and grid-based analysis were performed using 20 ns MD simulations of HIV-1 protease truncated at different cutoff distances around the center of the binding site. For the grid-based analysis, the values of the overlap coefficient (OC) (Eq. ).6\documSee Supplementary 5, the co-crystalized ligand was extracted and re-docked using Smina49 with the AutoDock Vina scoring function50. Docking was performed into rigid protein structures with all water molecules stripped. Protonation states for ligand and protein were determined using OpenBabel51. The “autobox_ligand” and “autobox_add” switches were used to define a docking box that extends the dimensions of the co-crystalized ligand by 12 Å in all directions. Exhaustiveness of 40 (default: 8) was used for more extensive sampling of poses. Twenty-five poses for each system were generated and classified based on their RMSD to the X-ray binding pose. Poses with an RMSD  <2 Å are considered native poses, other poses as decoy poses. As the focus is on the development of an improved scoring function, only systems with at least one native-like pose (RMSD  <2 Å to native binding pose) were selected for subsequent CNN modeling.Protein–ligand complexes were randomly selected from the PDBbind database for pose prediction with DeepWATsite. Similar to the work of Ragoza et al.49. The density distribution of each protein or ligand grid was computed using the following piece-wise continuous function5Ri is the van der Waals radius of atom-type i and r is the distance between atom center and grid point at which the atom density was computed. Each 3D grid covered a volume of 24 × 24 × 24 Å3 using a grid spacing of 0.5 Å. The data were provided as MolGrid input layers to Gnina5 with Caffe as the deep-learning framework52.Protein and ligand atoms were modeled as density on a 3D grid encompassing the binding site of the protein. These data were provided to the CNN model in the form of 34 different 3D grids as input channels representing the density of 34 distinct atom types (16 protein and 18 ligand types). The atom-type classification was based on Smina atom-type rulesOccupancy and thermodynamic values generated by WATsite on 3D grids encompassing the binding site were provided as additional input channels to the CNN model. We developed two different CNN models including hydration information. One model was constructed using only hydration occupancy in addition to protein and ligand density. This model is purely based on density information of protein, ligand, and water molecules.The other model, in contrast, utilizes energetic values of hydration in addition to protein and ligand density. Only enthalpy and entropy were used as input, as the free energy is the sum of the two. To allow for normalized input between 0 and 1, a hyperbolic tangent function was used to scale the thermodynamic values. Whereas the entropy values were always positive with respect to bulk solvent entropy, the enthalpy values can assume positive and negative values. Thus, we separated the enthalpy data into two different input channels, one for positive enthalpy values and one for negative values. The latter were taken as absolute values before normalization by the hyperbolic tangent function.Thus, in total 35 and 37 input channels are provided to the two CNN models in the form of 3D grids, respectively. CNN models using only the 34 protein and ligand input channels were generated for comparison, to validate the importance of solvation information in the CNN modeling.In addition to the input layer, the CNN architecture contained three convolutional layers with rectified linear unit (ReLU) activations coupled with intermittent max pooling layers, one fully connected layer and a final softmax layer, which mapped a probability for the two output options of the classifier model, native (RMSD to x-ray pose  <2 Å) or decoy pose  of the input images (3D grids). In a second model, the training set was expanded to 12,694 systems from the general set of PDBBind5. A distance matrix was computed between the proteins according to their sequence and ligand similarity. The latter was computed between ligands according to their SMILES fingerprints53. Clustering was done for all systems where a system is added to a group if the sequence distance is <0.5 or ligand similarity is >0.9. Three balanced groups (=folds) were generated where large groups were iteratively assigned to the fold with fewest systems. The PDB ids for the different folds are provided in the accompanying file Supplementary_Data_2.txt.After verifying that the model can predicted the binding poses for a diverse set of protein families, we decided to use even more challenging and robust tests in form of cross-validation. Cross-validation was performed similar to Ragoza et al54, interaction-based accuracy classification55, RSR34, and GARD 56. In this manuscript, we decided to use, in addition to standard RMSD, a modified RMSD excluding solvent-exposed groups and the CMS38 to measure the accuracy of docking poses.Due to its simplicity and ease of implementation, the RMSD criterion for binding mode prediction is typically used to discuss docking methods and applications. However, the use of RMSD as sole success criterion can be problematic. For example, small or highly symmetrical ligands are tending to have low RMSD values even if randomly placed in a small active site. On the contrary, large RMSD values are more frequently observed for large, flexible ligands even if most protein-ligand contacts are conserved. These large RMSD values are often caused by solvent-exposed moieties that are not important for protein-ligand binding. To overcome these limitations of RMSD as criterion for docking success, several alternative methods have been postulated for pose evaluations, such as relative displacement errorRMSD modified by solvent exposure: A modified RMSD calculation was designed, in which portions of the ligands that are highly exposed to the solvent are ignored in the calculation of RMSD. To compute the modified RMSD, the ligand is split into a set of rigid fragments  was computed. Fragments with SASA ratio >0.25 are considered solvent exposed and were excluded from the RMSD computation. Using Schrodinger RMSD calculation tool, the ASL query language was utilized to compute the modified RMSD, excluding the solvent-exposed fragment. The modified RMSD was used to classify the poses either as near native or as decoy.Contact mode score: RMSD (and the modified RMSD) is used as classification measure between native-like and decoy poses, but is not useful in a regression model to quantify remaining native contacts. Thus, poses with a significant number of native contacts but >2 Å RMSD enforce low CNN scores similar to poses without any native contacts.38. CMS ranges between 0 and 1 and can be intuitively used as a loss function for a regression model, here in the form of the pseudo-Huber loss function. Based on general observations, we consider a pose to be incorrect if the CMS value is <0.4438. Those low CMS values might reflect a pose without significant overlap with the native pose. Those non-overlapping poses should therefore not be used in the quantification of the quality of the interactions. In our case, we considered those poses as “undefined” and a hinge loss is used so that the contact score prediction loss is only incurred on the low-contact score poses if the contact score is predicted to be higher than the cutoff value of 0.44.To increase the robustness of the model by positively considering native interactions during CNN model training, CMS is used in addition to RMSD. CMS reflects the similarity of the protein-ligand interactions of a docking pose relative to the native structure (Supplementary Fig. 39. XED provides a practical method to account for anisotropic and directional electrostatic interactions, such as halogen bonding or hydrogen bonding. In contrast to ab initio methods, it avoids prohibitive computational costs and difficulties in calculation setup for large molecules. To include XEDs compatible with the other input layers, which use Gaussian density functions, the interactions of a positive and a negative probe with the ligand are computed, and the maxima of those interactions are determined (Supplementary Fig. To model interactions, such as halogen bonding, which require representation of anisotropic electron distributions, the XED forcefield was chosenSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4


Host-pathogen interactions (HPIs) affect and involve multiple mechanisms in both the pathogen and the host. Pathogen interactions disrupt homeostasis in host cells, with their toxins interfering with host mechanisms, resulting in infections, diseases, and disorders, extending from AIDS and COVID-19, to cancer. Studies of the three-dimensional (3D) structures of host-pathogen complexes aim to understand how pathogens interact with their hosts. They also aim to contribute to the development of rational therapeutics, as well as preventive measures. However, structural studies are fraught with challenges toward these aims. This review describes the state-of-the-art in protein-protein interactions (PPIs) between the host and pathogens from the structural standpoint. It discusses computational aspects of predicting these PPIs, including machine learning (ML) and artificial intelligence (AI)-driven, and overviews available computational methods and their challenges. It concludes with examples of how theoretical computational approaches can result in a therapeutic agent with a potential of being used in the clinics, as well as future directions. These pathogens have evolved to adapt to their environment including multiple routes to infect a host. HPIs provide the pathogen a means to enter and dysregulate host cells . HPIs doPathogen invasion can cause pathologies in the host body, including cancer. A pathogen infection can lead to cancer through several mechanisms. Pathogen-induced senescence and chronic infection both contribute to and increase cancer susceptibility . Pro-infTo develop strategy and treatment options requires understanding of pathogen entry into the host cell. Most pathogen infections begin with a physical interaction between pathogens/pathogen-released molecules and components of the host cells , 10. IntIn this review, we explain the importance of HP PPIs from a structural point of view. We establish the relationship between pathogens and cancer and how HP PPIs play a crucial role in this association. We outline known HP PPI mechanisms and discuss how different therapeutic approaches can be applied to target them. We also discuss the current landscape of computation-guided prediction of HP PPIs, including available datasets, web servers, and tools, as well as the gaps and drawbacks in these approaches. We demonstrate how predicting and better understanding of HP PPIs can help cancer research with several case studies. We conclude by discussing lessons to be learnt from these approaches and future directions.2Proteins do not act alone, and more than 80% of all proteins in the cell interact with other molecules to execute their function , 29. TheHP PPIs, like any other PPIs, require knowledge of the pathogen and host protein architectures and repertoires, their evolutionary mechanisms, and information on relevant biological data sources. Pathogens exhibit vast differences in genomic composition, evolutionary patterns, and protein function when compared to the relatively well-conserved processes found in cellular organisms. These pathogen variations are considered when studying HP PPIs . HP PPI Pathogens can interact with the host through different mediators, such as proteins, metabolites, and nucleic acids , 4. Pathvia an existing entry mechanism established by exogenous interactions or for small metabolites, diffusion. These entry mechanisms can be studied using three different approaches  ng sites . HPID3.0ng sites . There ang sites . The HCVng sites –97. VirHng sites . Noticeang sites , 93. Somvia the same interface  interactions are not well-covered in available PPI databases. Stable PPIs typically use large interfaces, whereas transient PPIs use short linear peptides making the prediction more difficult . Also, bnterface . Furthernterface .Advanced computational techniques such as ML and deep learning (DL) suffer from different types of drawbacks. A large amount of data is needed for model training. A model might not be able to learn the rules because only a few thousand structures of complexes are available across the databases. Data imbalance is a challenge in any learning process. Third, there is a requirement for high-quality experimental data because a model cannot afford to learn from low-quality data .HP PPI-focused algorithms and databases have their own set of challenges. There is still a lack of domain knowledge for some viruses with genomes larger than 20 kb, and many of their proteins have neither an assigned domain nor a known function . The facOverall, CAPPs need rigorous validation because they are error prone. Although there are many protein-protein docking programs, there are few ways to systematically assess the predicted PPI complexes . Interna4The precise molecular mechanisms by which pathogens rewire the host pathways to trigger malignant transformation remain unknown, despite the abundance of data on their role in cancer. Pathogens can have an impact at any stage of the multi-step process of carcinogenesis. Here, we discuss some examples from the literature documenting how insight into HP PPI mechanisms can help cancer and pathogen-driven cancer research.Frisan  explainsIn brief, the first impacts of a pathogen on a host cell are the production of reactive oxygen species, DNA damage, imbalances in the activity of cellular pathways including but not limited to MAPK and β-catenin activation, and p53 degradation. These responses are followed by an inflammatory state, immunosuppression, and the impairment of DNA damage repair mechanisms. These lead to genomic instability, uncontrolled cell growth, and proliferation, which are some of the hallmarks of cancer. People living with HIV are more likely to develop the following cancers: Kaposi’s Sarcoma, non- Hodgkin’s lymphoma, and cervical cancer (in women), and as the HIV-positive population ages, the incidences of colon, breast, and prostate cancer also increase . FurtherThe Epstein-Barr virus is an oncovirus that secretes the lytic-cycle protein BARF1 to undermine host immunity by interacting with human cytokine CSF1 . Guven-Min silico screening for the 9 HPV-16 protein, they gathered and examined 96 FDA-approved antiviral drugs. They were able to correctly predict 57 pairs of antiviral-HPV protein interactions out of 864 pairs of antiviral-HPV protein associations made up of the combination of 9 HPV-16 proteins and 96 antiviral drugs. One of the drugs they identified as a potential anti-HPV treatment is docosanol, which was predicted to interact with HPV-16 protein E7. Docosanol is an FDA-approved anti-EBV drug. Interestingly, a recent clinical case report claimed that HPV infection were successfully treated using Docosanol, curcumin, and other medications in combination (Lin et al.  adapted bination . For theThese studies, which have been compiled from a variety of sources, demonstrate the wide range of computational approaches to identify new HPIs and cellular mechanisms that are affected by pathogen infections. They can help in screening novel or repurposed drug compounds against HPI complexes, and in producing drug candidates. A framework that summarizes the benefits of these studies is shown in 5Since PPIs are crucial for cell function, studies of protein interactions are essential to comprehend how biological systems operate. Aberrant, pathologic HPIs can influence downstream target genes, resulting in a variety of diseases, including cancer.HPI interface identification with high accuracy has many applications in computer-aided rational drug design. Although computational prediction of protein interfaces has made significant strides in recent years, there is still much room for improvement and innovation. The bottlenecks and drawbacks can be divided into two main categories: shortcomings of computational approaches and challenges due to the nature and physicochemical characteristics of the HPI. Traditional protein docking algorithms can work for complexes consisting of two partners. Increasing the number of proteins in the complex challenges these methods. In protein-protein docking, deeper pockets on the protein surface as well as the presence of hot spot residues, diversify the HPI and may, or may not, assist in accurate prediction . An intrIn summary, even though it is still not possible to predict HPI interactions with ‘perfect’ accuracy, computational methods can predict the most likely interaction models based on the input data. These interactions can act as the basis for further experimental studies. When combined, information on gene expression and protein interactions are expected to increase the confidence in the HPI and the corresponding HPI network. Recent advancements are also paving the way for the generation of networks for identifying HPI and signal transduction pathways playing role in cancer, all toward rational drug design.EO: Conceptualization, writing-original draft, writing-review and editing. RN: Supervision, conceptualization, writing-original draft, writing-review and editing. All authors contributed to the article and approved the submitted version.