The ability to detect and characterize bacteria within a biological sample is crucial for the monitoring of infections and epidemics, as well as for the study of human health and its relationship with commensal microorganisms. To this aim, a commonly used technique is the 16S rRNA gene targeted sequencing. PCR-amplified 16S sequences derived from the sample of interest are usually clustered into the so-called Operational Taxonomic Units (OTUs) based on pairwise similarities. Then, representative OTU sequences are compared with reference (human-made) databases to derive their phylogeny and taxonomic classification. Here, we propose a new reference-free approach to define the phylogenetic distance between bacteria based on protein domains, which are the evolving units of proteins. We extract the protein domain profiles of 3368 bacterial genomes and we use an ecological approach to model their Relative Species Abundance distribution. Based on the model parameters, we then derive a new measurement of phylogenetic distance. Finally, we show that such model-based distance is capable of detecting differences between bacteria in cases in which the 16S rRNA-based method fails, providing a possibly complementary approach , which is particularly promising for the analysis of bacterial populations measured by shotgun sequencing. The accurate identification and classification of bacteria is essential for epidemiological surveillance, as well as for the study and understanding of specific ecosystems such as the human microbiome. Bacterial classification, however, is a complex problem, especially at the intraspecies level. Commonly used methods include the reconstruction of phylogenetic trees based on the comparison of specific molecular sequences (e.g. 16S rRNA gene) with reference databases, which contain annotated sequences of previously classified bacteria . This allows to generate the so-called taxonomic classification of the sampled bacteria.1. For instance, different strains can infect different hosts4, or even more extremely, some bacterial species can have both pathogenic and non-pathogenic isolates5. The 16S rRNA gene is commonly used for bacterial classification since random mutations of the 16S rRNA gene sequence serve as a proxy of evolutionary time6. However, bacterial classification based on 16S rRNA gene has low phylogenetic power at the intraspecies level, where functional diversification of strains is faster than random mutations of the 16S rRNA gene6. Various alternative approaches to estimate the bacterial phylogeny have hence been proposed9. Such methods mainly start from specific bacterial molecular sequences and either compute a distance matrix or model the substitution process through which the molecular sequences have evolved. In both cases, the appropriate choice of the specific molecular sequences to be investigated is crucial. The chosen sequences have to satisfy two conditions for the phylogenetic reconstruction to be successful: orthologs of sequences have to be shared by the tested bacteria and they have to contain a vertical phylogenetic signal. Approaches that use a single molecular sequence, such as 16S rRNA, may work well at higher taxonomic levels, but they are usually too generic to differentiate species or strains. A popular method which overcomes this problem while still controlling the phylogenetic noise is multilocus sequence analysis (MLSA)11, which simultaneously uses sequences of seven housekeeping genes. However, phylogenetic signals are not equally distributed across the genome and, while some classes of genes are expected to perform better for phylogenetic inference than others12, the choice of the specific set of genes to be used is still a matter of debate.The basic unit of classification in taxonomy is usually the species level. While such resolution may be sufficient for other domains of life, the intraspecies level of bacteria includes high amounts of genomic and phenot ypic variation, through which strains of bacteria belonging to the same species manifest highly different behaviours14. Studies on the conformation, function and evolution of proteins suggest to consider the protein as a molecule composed of a set of independent protein domains, which constitute its organizational units, so as its evolutionary components. The evolution of living organisms, or rather, the proteome evolution, is caused by the occurrence of random modifications of the genetic code and these events affect each protein domain independently. Protein domain duplication, mutation, recombination, relocation and horizontal transfer are in fact the main mechanisms that allow the outbreak of proteins with new functionalities, thereby contributing to the emergence of complexity17. As a matter of fact, the relationship between protein domain evolution and phylogeny has already been investigated in previous works, with findings suggesting a connection between the two. On the one hand the phylogeny of hundreds of organisms (including more than 100 bacteria) has been reconstructed satisfactorily  based on the presence or absence of protein domain superfamilies18, or based on specific patterns of protein domains19. On the other hand the evolutionary origin and history of protein domains has been reconstructed based on the taxonomic tree of organisms20.Here, we develop a new measurement of evolutionary distance derived from the ecological modeling of the protein domains that populate the bacterial genome and we propose to exploit such distance to deepen the characterization of bacteria at the intraspecies level. Our method does not rely on the selection of a specific set of genes or molecular sequences, but is based on the ecological modelling of the whole set of protein domains within the bacterial genome. Protein domains are segments of proteins and are defined as substructures produced by any contiguous part of a polypeptide chain that can fold independently of the rest of the protein into a compact and stable structureTaking an ecological approach, we here focus on the dynamic processes that led to the current configuration of protein domains, assuming that this could highlight important aspects of the evolution of genomes, proteins, and organisms. The proposed phylogeny will then be based on the evolutionary model of protein domains, incorporating the parameters estimated from such model.22.In the past, various models of evolution have been proposed for protein domains, each linked to a particular distribution of the protein domains frequencies. This distribution is what in ecology is called Relative Species Abundance distribution (RSA). It is a measure of the biodiversity of an ecosystem, in our case the set of protein domain families that populate the genome, and can be used to fit real data in order to verify the validity of the underlying ecological hypothesis. Among the proposed ecological models for protein domains, the birth-death-innovation one stands out. The RSA distribution predicted by this model is a Pareto distribution and it fits the RSA of a wide range of genome-associated quantities well, including protein domains21 in which both demographic and environmental noise are added to the model. Environmental stochasticity is due to environmental changes which act simultaneously on all individuals while demographic stochasticity reflects the reproduction differences among individuals within a population. A suitable generalized model was developed by S. Engen and R. Lande in 199624, who proposed that the RSA distribution could be a Poisson Log-Normal or a Negative Binomial, depending on the assumptions.Here, we consider a generalization of the birth-death-innovation model24, we make the neutral ecological equivalence assumption that the evolutionary dynamics is the same for all the protein domain families. In this way, we will not need to introduce protein domain family-specific indices for the evolutionary rates in the following equations25. As in Engen and Lande’s papers, we also assume that protein domain families enter the protein domain community (i.e. the genome) at times generated by an inhomogeneous Poisson process  with rate X(t) at time f and let p(t) be the probability that it has not gone extinct. Then, Engen and Lande showed that the abundances x of the protein domain families in the community are generated by an inhomogeneous Poisson process with ratex still indicates the number of individuals within a protein domain family , r is the constant growth rate (b is the birth rate and d is the death rate), g(x) is a density regulation function, B(t) is a standard Brownian motion, so that Following Engen and Lande’s derivationIf protein domain families evolve independently from each other, then Engen and Lande showed that their abundances are generated by an inhomogeneous Poisson process with rate 28Engen and Lande showed that if we consider the Gompertzian density regulation function24If instead, we consider a linear density regulation functionFinally, if we consider the limit of Eq.  in whichHere, we aim to model the evolution of protein domains within the genome using Engen and Lande’s theoretical framework. Specifically, modeling the protein domain RSA of thousands of bacterial genomes, we intend to provide a novel phylogenetic characterization of bacteria.r is constant; (ii) both environmental and demographic noise are included; iii) density regulation function is either linear or Gompertzian. The growth rate r can be interpreted as the rate by which a protein domain family gets a new copy inside the genome. This unifies two different events, the birth and death of protein domains which correspond to protein domain duplication and protein domain inactivation/deletion. Regarding the two noise terms (g(x), is to keep the abundance of protein domain family from diverging. This is usually a consequence of the limited availability of resources in the community, and it can be related to the limited size of the genome (and consequently of the proteome) in the contest of protein domains29.We hypothesize that new protein domain families are generated in the genome at times specified by an inhomogeneous Poisson process and that different families are mutually independent. Within the protein domain families, we assume that the evolutionary dynamics can be described by a stochastic birth-death process in which: (i) the growth rate The modeling of protein domain RSA within bacterial genomes will allow us to derive a novel measurement of phylogenetic distance. Specifically, we will show that the Poisson Log-Normal distribution [Eq. ] is the The protein domain RSA distributions of 3368 bacterial genomes were obtained as detailed in the "30, in Three evolutionary hypotheses were tested by fitting the empirical RSAs with the Log-Series [Eq. ], the Nementclass2pt{minimThe examination of the Poisson Log-Normal scale  was used as a measurement of agreement between different clustering solutionsAs expected by their intrinsic relationship, taxonomy and 16S rRNA phylogeny show high agreement . This could be attributed to the smaller size of the phylogenetic tree. However, the species with similar phylogenetic tree size still show differences in correlation , suggesting that the RSA-based distance matrix between the strains of Xanthomonas citri carries stronger phylogenetic signal. Comparing 6 observed species with the randomly sampled subsets of 100 bacteria, we can analogously conclude that the RSA-captured phylogenetic signal is stronger within the species. In the following, we discuss the results obtained for the 6 selected bacterial species in more details.To evaluate the intraspecies composition obtained from the RSA-based clustering, we selected the subset of species for which at least 10 different strains were present in our data (48 species). Among them, we selected the species where hierarchical clustering showed a clear separation of clusters (including outliers) and for which published literature characterizing at least some of the strains was available (6 out of 48 species). For these 6 species, we again assessed the robustness and stability of RSA phylogenies, as detailed in the "Xanthomonas citri subsp. citri (XCC) and Chlamydia pneumoniae (Cpn) are two species whose subspecies can infect different hosts. Here we show that the RSA-based method correctly discriminates such subspecies even when their divergence is not detected comparing the 16S rRNA gene sequences.Xanthomonas citri subsp. citri (XCC) is a causal agent of citrus canker type A, a bacterial disease affecting different plants from the genus Citrus. While citrus canker A infects most citrus species, two of its variants, A* and C. aurantifolia) and alemow (C. macrophylla)2. Our data set includes 17 strains of XCC pathotype A and 5 strains of XCC pathotype 2. RSA-based clustering of the 22 XCC strains identifies two separated clusters , this is an obligate intercellular parasite which is widespread in human population and causes acute respiratory disease. Besides humans, different animal species can be infected with Chlamydia pneumoniae. Our data set includes 9 strains which infect humans (Homo sapiens) and 1 strain isolated from koala (Phascolarctos cinereus). RSA-based clustering clearly separates such isolate from the group of highly similar human isolates . As many endosymbionts, Buchnera aphidicola underwent the process of genome reduction as an adaptation to the host-associated lifestyle and has a genome with length 38. An intermediate step of this process is pseudogenization. For this reason, we expect the number of pseudogenes within a genome to be related to its evolutionary state and strains with similar numbers of pseudogenes to be phylogenetically similar. Among the 13 strains of Buchnera aphidicola available in our data set, we observe a high variability in the number of pseudogenes, with 12 strains having between 7 and 63, and one strain , in particular, we were able to find bibliographic information at the strain level which provides a biological explanation to our findings.For several species in our data set, the RSA-based phylogenic tree indicates the presence of outlier strains  to the detected metagenomes. While taxonomic classification allows to recognize known bacteria and their phenotype, de novo clustering potentially allows to obtain a higher resolution of the bacterial population at the intraspecies level, unaffected by the possible bias of human-made classifications.The 23 to describe the evolution of protein domains, considering them as ‘species’ that populate the genome. Based on such modeling, we derived a new phylogenetic distance that can be computed fitting the protein domains Relative Species Abundance distribution (RSA). We showed that clustering the bacterial genomes based on 16S rRNA or exploiting our model allows to capture different features in terms of both genome composition  and bacterial phenotype (e.g. infected host). Our results hence open to the possibility of developing an algorithm in which both pieces of information are exploited to obtain an overall best inference of bacterial phylogeny. This will hopefully be part of future works.Here, we proposed to measure the phylogenetic distance between bacteria, based on their genomic evolving units: protein domains. Specifically, we exploited the ecological modeling of ecosystems proposed by Engen and Lande40. Indeed, 16S rRNA sequencing is a powerful method to detect and compare metagenomes. However, it is limited by being based on the measurement of a single gene. On the other hand, information on the whole metagenome can be derived when using shotgun sequencing40. In terms of phylogeny, 16S rRNA sequencing detects only metagenomic divergences which directly affect the 16S rRNA gene, and its ability to discriminate metagenomes at the intraspecies level is therefore limited6. Our RSA-based method, on the other hand, provides a global measurement based on the whole proteome. As highlighted by our results, these two approaches are hence somehow complementary, being the RSA-based method more sensitive to evolutionary processes that affect the metagenome globally, possibly leaving the 16S rRNA gene unaffected, and the 16S rRNA-based method being instead more sensitive when such gene is involved, such as in the case of Buchnera aphidicola. Overall, our results show that the RSA-based approach is a promising method that could be exploited to study the metagenomic composition of samples, in particular when shotgun sequencing is performed and the whole metagenome could be reconstructed, though experimental validation would be required to definitely assess the efficacy of our approach.The proposed approach is especially promising when samples are analysed by means of shotgun sequencing, rather than 16S rRNA sequencing41. Draft genome sequences were discarded and only the higher quality fully circular genome sequences were retained. GeneBank files containing genome sequences and existing annotations were retrieved from the NCBI database and imported into the Semantic Annotation Platform for Prokaryotes42 using the EMBL/GBK to RDF SAPP module. De novo identification of genetic elements  was performed using Prodigal (2.6)43 with codon table 11, which is recommanded when dealing with bacterial genomes. Dedicated SPARQL queries were built to extract all proteins and their sequences from the RDF triplestore used by SAPP to store the intermediate results. InterProScan44 was used to identify protein domains in the corresponding sequences. Due to the high number of distinct protein sequences to be analyzed, the SURFsara GRID infrastructure was used45 to concurrently analyze the sequences. Dedicated SPARQL queries were used to retrieve all the identified domains and assign them to the originating protein and bacterial genome. Finally, the matrix generating module from SAPP was used to generate a matrix containing the number of instances of the detected domain (domain abundance) for each of the studied genomes and for each of the identified protein domains. Overall 3370 bacterial genomes were analyzed and 13,934 distinct domains were identified.The genome sequences of 3370 bacteria were downloaded from the NCBI database46. Data were modelled with a truncated Poisson Log-Normal, a truncated Log-Series and a truncated Negative Binomial distribution, so that to test and compare different ecological hypothesis. In all cases, truncation was performed to exclude the zero abundance class, that is not observable in empirical data. Two bacterial genomes which couldn’t be fitted due to extreme RSA distributions are 30 and R-squared were computed to assess the models performances and for model selection.Protein domains RSAs were fitted with the Maximum Likelihood Estimation method implemented in R “sads” package v.0.4.2Fitting truncated Poisson Log-Normal distribution to protein domain RSA, we obtained estimates 47 to retrieve the 16S rRNA reference sequences of the bacterial species for which protein domain data were available. For 48 bacteria, the 16S rRNA sequence was not present, so we considered only the remaining 3320 bacteria for the following analysis. Since the same bacterial genome can have multiple different copies of 16S rRNA gene, as 16S rRNA distance between a pair of bacteria we used the mean pairwise distance between all pairs of 16S rRNA sequences within two genomes48. The alignment of 16S rRNA sequences and calculation of distances was performed with mothur32 “pairwise.seqs” function with default parameters.In order to calculate phylogenetic distances based on 16S rRNA gene, we used the silva database49. Since this method is based on Euclidean distance, it couldn’t be applied to 16S rRNA distance matrix. To get clustering solutions for RSA and 16S rRNA, we cut the hierarchical tree fixing the number of clusters to the number of taxa at the selected taxonomic level. Finally, we used the NMI score as a measure of clustering agreement between the three approaches: RSA, 16S and predefined taxonomy. To calculate the baseline for the NMI score, we compared the taxonomy with clustering solutions based on simulations. For the fixed number of clusters and data points, each simulation assigned random cluster to each data point. Purity of a RSA cluster with respect to taxonomy is calculated as the ratio between the size of the cluster’s most abundant taxonomic group and the cluster size. The total purity of the RSA clustering solution is a weighted average of its clusters’ purities where weight is a cluster size.For each taxonomic level, we considered only bacteria for which the classification was known and which belonged to a taxonomic group with at least 10 members. As a result, we analysed 3270, 3148, 3032, 2714, 2139 and 161 bacteria at phylum, class, order, family, genus and species level, respectively. They belonged to 14, 20, 48, 63, 54 and 48 different taxonomic groups. Hierarchical clustering was performed on both RSA and 16S rRNA distance matrices. For RSA clustering we used Ward’s minimum variance method while for 16S rRNA we used average linkage method. The Ward’s method was used to minimize the total in-cluster variance50 was used. For every subsampling proportion (0.1–0.9), 100 bacteria were randomly chosen from 3368 available bacteria and a random subsample of protein domains was taken from the remaining protein domains (which were present in at least one of the chosen bacteria). The correlation between two 100-leaf dendrograms  was calculated. The procedure was repeated for 50 times, resulting in 50 correlation measurements for every subsampling proportion. Additionally, robustness and stability of phylogeny was assessed for 6 bacterial species . For every bacterial species and for different subsampling proportions (from 0.1 to 0.9), with 100 repeats, a subsample of protein domains was randomly selected and subsample-based phylogeny of the species was recontructed. Correlations between the full-data dendrogram and the 100 subsample-based dendrograms were calculated. Subsequently, the mean and standard deviation of correlation was obtained for every species and every subsampling proportion.To assess the robustness and stability of RSA-based phylogeny, regarding the choice of protein domains, we randomly selected subsamples of protein domains and we compared the obtained subsample-based results with the one obtained when all protein domains were used. Specifically, we subsampled protein domains in various proportions (ranging from 0.1 to 0.9), then we fitted the subsampled protein domain RSA distribution with Poisson LogNormal distribution and we used the estimated parameters to calculate the phylogeny of the examined bacteria, following the same methodology used when considering all protein domains. The obtained subsample-based phylogeny  was then compared to the full-data-based phylogeny calculating the correlation between dendrograms. Two different correlation measurements were used, cophenetic correlation and correlation based on common nodes . For the calculations, the function “cor.dendlist” from the R “dendextend” package v.1.14.0Supplementary Information 1.Supplementary Information 2.


Protein–protein interactions (PPIs) play a fundamental role in various biological functions; thus, detecting PPI sites is essential for understanding diseases and developing new drugs. PPI prediction is of particular relevance for the development of drugs employing targeted protein degradation, as their efficacy relies on the formation of a stable ternary complex involving two proteins. However, experimental methods to detect PPI sites are both costly and time-intensive. In recent years, machine learning-based methods have been developed as screening tools. While they are computationally more efficient than traditional docking methods and thus allow rapid execution, these tools have so far primarily been based on sequence information, and they are therefore limited in their ability to address spatial requirements. In addition, they have to date not been applied to targeted protein degradation. Here, we present a new deep learning architecture based on the concept of graph representation learning that can predict interaction sites and interactions of proteins based on their surface representations. We demonstrate that our model reaches state-of-the-art performance using AUROC scores on the established MaSIF dataset. We furthermore introduce a new dataset with more diverse protein interactions and show that our model generalizes well to this new data. These generalization capabilities allow our model to predict the PPIs relevant for targeted protein degradation, which we show by demonstrating the high accuracy of our model for PPI prediction on the available ternary complex data. Our results suggest that PPI prediction models can be a valuable tool for screening protein pairs while developing new drugs for targeted protein degradation. Tackling diseases is one of humankind’s most long-lasting challenges. An essential part of this effort concerns targeting pathogenic proteins . Since Fischer’s “Lock and Key” model , drug diOne mode of PIC-based drug design is targeted protein degradation (TPD) with bifunctional degraders, which was first described in the early 2000s . The degThe development of new drugs operating with TPD requires predicting the interactions between the E3 ligase and the POI. Strong protein–protein interactions (PPIs) are required for the formation of a ternary complex  rely onThis work presents a new framework for PPI prediction and shows its usefulness for predicting the interactions within the available ternary complex data. Our model processes proteins via their surface mesh  using deBinding site prediction: One PDB file is used as model input, and the binary output describes whether a particular location on the protein surface constitutes a possible site for protein interactions see A.Interaction prediction: Two PDB files are processed, and the binary output describes whether the two proteins interact at a particular site see B.This section describes the details of our model architectures, which map 3D atomic protein structures from the Protein Data Bank (PDB ) to binaIn both cases, outputs are generated for various locations on the protein surface. Since our models for these two tasks share most of their components, we first provide a high-level overview before describing the components in detail.The overall model architectures for both the prediction of interaction sites A and proOur model uses a deep graph-representation learning (DGRL) pipeline to process the structural information of the proteins using graph representations. The input is based on surface meshes C, which The first processing step maps the initial 3D protein structures from PDB to a suitable surface representation. We use meshes created by EDTSurf ,35, wherOur model learns expressive chemo-geometric features based on 3D atom coordinates, atom types, surface representation, and selected precomputed features  of the protein at each surface location under consideration. These features are fed to different neural network architecture and optimized to learn the most suitable chemical and geometric embeddings D. The rek-NN directed graph; i.e., for each surface point, we aggregate the features of the k nearest atoms. The chemical information associated with these atoms is processed via DGRL methods through an elementary convolution operator as described in [Since we are working with surface representations of proteins, it is necessary to aggregate the near-surface atomic information into abstract feature vectors assigned to the mesh vertices. We, therefore, embed the chemical features using a ribed in . As inpuribed in  defined These are embedded together with the raw atomic information . Although neural networks can learn complex features without feature engineering , we enhaS measures the deviation of the normal vector field An important feature that becomes available when assuming a sufficiently smooth surface is curvature. We assume that the shape of surface patches, quantified by curvature features, plays an essential role in identifying interaction sites . In 1 and k2 , i.e., m1 and k2 (2)S=−∇vS, we employ the strategy introduced in [k nearest neighbors of a mesh node and project the relative vectors and the differences of the normal vectors into its tangent plane. First, a local basis needs to be constructed from the normals. Subsequently, we project the relative coordinates and relative normals to the tangent plane, and the shape operator S can be estimated.To compute duced in . We pickS and provide the eigenvalues, i.e., the principal curvatures In contrast to the two approaches described above, we do not rely on the principal curvatures’ explicit function(s). However, we do not use the raw geometric information imprinted in the principal curvatures, since these quantities are coordinate-dependent. Thus, we diagonalize The main DGRL  pipelineThe input of this processing step consists of the 3D coordinates and surfaces normals of the surface points under consideration, as well as the chemo-geometric features computed at those points. At each location, the features of all nearby points within a fixed radius are weighted and aggregated E. For a  library .Finally, the aggregated features at each point are processed by a final MLP, resulting in an eight-dimensional feature vector per point, which is mapped to a binary output in the case of binding site prediction. For the interaction prediction task B, the ouk-NN ball query.To label the surface meshes into binding and non-binding vertices and aggregate the evaluation metric of the predictions, we followed a similar strategy as described in . For intWe trained our models  using stochastic gradient descend with the AMSGrad version of the Adam optimizer . We usedTo evaluate our models, we followed  and usedThe model described above was implemented using the libraries PyTorch , PyTorchWe present in this work a model capable of predicting possible interaction sites and whether two proteins will interact. As our goal is to perform these prediction tasks in the context of targeted protein degradation, we emphasize achieving good results on existing data of ternary complexes (consisting of two proteins and a degrader molecule). We first present the results of our model on two protein–protein interaction (PPI) datasets before discussing this particular scenario.To train and evaluate our model, we use two datasets. The first one, commonly called the MaSIF dataset, was used to evaluate the MaSIF model , which iWe processed the selected protein’s FASTA sequences to the InterPro software , which cIt was necessary to visually examine all of the PDB structures in the Orthogonal dataset to determine which chains of each PDB structure are represented in a binary protein–protein interaction. We selected two chains for each PDB ID that reflects the PPI based on the proximity of the chains’ 3D structures. Based solely on visual examination, we also speculated that the proteins in the orthogonal dataset had a higher number of tertiary protein structures than MaSIF, which could assist the model in being more generalizable and tolerating to surface variance of the proteins. This property is critical because tertiary protein structures are found in practically all highly complex biological systems, including transcription factors, signaling proteins, and membrane proteins, all of which are critical for drug discovery and development.In total, the Orthogonal dataset contains 2373 and 1111 distinct PDB IDs for training and testing our model of binding site prediction, respectively, and 3201 and 1431 PDB IDs for training and testing of the protein–protein interaction predictions. As the ratio of test data to training data is very high, the evaluation results on the test set of this dataset are likely to be a more accurate and reliable indicator of the generalization capabilities of a model compared to the dMaSIF dataset (where the ratio is lower). Furthermore, the dataset also includes rare complex protein residues that have heavy atoms bound to the side chain of the amino acids and operate as an organometallic bridge, providing more diverse data for further improvement of the evaluation accuracy.We trained and evaluated our model see  using thWe now turn to ternary complex data to evaluate the usefulness of our model for the development of new drugs for targeted protein degradation. In particular, we are interested in recovering the protein–protein interactions involved in ternary complexes using our model. A high recovery rate indicates that our model captures the essential features underlying ternary complex formation and thus can be used to evaluate previously unconsidered protein pairs to find new potential ternary complexes.Following , we evalWe evaluated the interaction prediction AUROC on the ternary complex data for our model, which was trained on different datasets . When trWhen comparing From the high accuracy of this final model, we conclude that the combination of the proposed geometric deep learning architecture paired with the novel dataset can accurately determine whether interactions between the two proteins involved in the ternary complexes will take place. To demonstrate the ability of the algorithm to detect the optimal binding site, Overall, these results suggest that PPI prediction with DGRL is a promising tool for targeted protein degradation, and it can be used, e.g., as a filtering mechanism to select possible protein-of-interest and E3 ligase pairs, which will, given a suitable degrader molecule, form a ternary complex.The machine learning model presented in this work used a novel DGRL pipeline based on surfaces meshes and was shown to be capable of predicting binding sites and interactions between proteins with high accuracy. We have furthermore presented a new, complex dataset that highlights the generalization capabilities of our model, demonstrating an improvement over previous work. Toward our primary goal of using protein–protein interaction (PPI) prediction for targeted protein degradation (TPD), we have demonstrated that our model can accurately predict the PPIs on the presently available ternary complex data.We showed that our model reaches results comparable to the MaSIF  and dMaSA variety of models for PPI prediction have been recently proposed, with some also using the MaSIF dataset as a benchmark. The model presented in  achievedIt is well known in machine learning that adequate datasets are required for models to be useful. In particular, the dataset used for training a model should cover all the relevant diversity of possible inputs. If this is not the case, then the model will not perform well on new, unseen data . This highlights the benefit of our new, more diverse Orthogonal dataset: as it includes protein pairs with significant differences to those within the MaSIF dataset; testing models on this dataset results in a more accurate estimate of the expected performance of a model on real-world data. Thus, the significantly higher performance of our model on this dataset suggests that it better captures the relevant features of diverse proteins than the dMaSIF model.This is also corroborated by the analysis of our model on the ternary complex data, which revealed that adding the data from the Orthogonal dataset to the training data dramatically increases the prediction results. This shows that our new dataset is particularly valuable for PPI prediction in this context. We note that the dataset we employed here only contained positive examples of proteins involved in the ternary complex. For a better estimate of how well models can predict PPIs within ternary complexes, enhanced datasets with decoys  as well as more data (as it becomes available) should be used in future works.As we have shown, training on a dataset that includes real-world diversity is crucial for machine learning models to be used in the context of targeted protein degradation. Our results suggest using a PPI prediction model as part of  a filtering step in a preliminary screening process, where potentially interacting POIs and E3 ligases are identified for subsequent in-depth analysis. Such an approach can utilize the benefits of machine learning-driven methods to limit the number of potential candidates which need to be evaluated in computationally costly docking simulations or in time- and resource-intensive lab experiments.One drawback of the current approach is that the presence of the degrader molecule is not taken into account. As there are three separate entities underlying a ternary complex, the PPIs there do not represent classical binary PPIs. Neglecting the degrader nevertheless constitutes a reasonable approximation when using PPI prediction as a proxy to predict potential ternary complexes, as the PPIs between the POI and the E3 ligase are an essential factor underlying ternary complex stability (Apart from targeted protein degradation with bifunctional degraders, there are also molecular glues , for whiWhile computational methods have proven successful for the prediction of protein interactions, they can be complemented by experimental techniques which provide additional structural information on the exact nature of the aggregation and potential conformers. Examples include protein microarrays , coimmunWe have demonstrated a novel DGRL pipeline based on surfaces meshes that has proven to be capable of predicting binding sites with high accuracy and beating the state-of-the-art performance using AUROC scores on the established MaSIF dataset for interactions between proteins . FurtherTo further evaluate these generalization capabilities, we used our model to predict the PPIs relevant for targeted protein degradation, where we demonstrate high accuracy of our model for PPI prediction on the available ternary complex data .All of the results suggest that our DGRL pipeline can be an informative tool for screening protein pairs while developing new drugs for targeted protein degradation.


Mycobacterium tuberculosis (Mtb) were retrieved through molecular docking and in-silico structural investigation. The molecular docking datasets comprised 15 natural compounds, seven conventional anti-tuberculosis (anti-TB) drugs and their seven corresponding Mtb target proteins. Mtb protein targets were actively involved in translation mechanism, nucleic acid metabolism and membrane integrity. Standard structural screening and stereochemical optimizations were adopted to generate the 3D protein structures and their corresponding ligands prior to molecular docking. Force-field integration and energy minimization were further employed to obtain the proteins in their ideal geometry. Surflex-dock algorithm using Hammerhead scoring functions were used to finally produce the docking scores between each protein and the corresponding ligand(s). The best-docked complexes selected for simulation studies were subjected to topology adjustments, charge neutralizations, solvation and equilibrations . The protein-ligand complexes and molecular dynamics parameter files have been provided. The trajectories of the simulated parameters such as density, pressure and temperature were generated with integrated tools of the simulation suite. The datasets can be useful to computational and molecular medicine researchers to find therapeutic leads relevant to the chemical behaviours of a specific class of compounds against biological systems. Structural parameters and energy functions provided a set of standard values that can be utilised to design simulation experiments regarding similar macromolecular interactions.Docking scores and simulation parameters to study the potency of natural compounds against protein targets in  Supplementary Files-1–7 (Docking_scores) (https://data.mendeley.com/datasets/94rh86jfpk/3) portrayed the docking scores comprising crash score, G-score, PMF score, d-score, chem scores, polar, total score, consensus (C) score, number of Hydrogen-bonds of the natural compounds against Mtb targets  as compared to respective classical drugs . The optimized protein-ligand complexes used as input files for parameretization and simulation has been provided as “MDS_input_files” (https://data.mendeley.com/datasets/94rh86jfpk/3). The molecular dynamics parameter files along with the set of commands to run MDS are available as “MDS_parameter_files” (https://data.mendeley.com/datasets/94rh86jfpk/3). Fig. 1-Supplementary Files-8–14  (https://data.mendeley.com/datasets/94rh86jfpk/3). The entire simulation dataset has been segmented appropriately based on the PDB IDs of the studied protein-targets. The different input and output files generated are made available under ``MD_simulation_files'' (https://data.mendeley.com/datasets/94rh86jfpk/3).The presented datasets depict the feasibility of certain classes of natural compounds as therapeutic candidates against M2tb proteins were selected, which are already targets of conventional anti-TB drugs https://www.rcsb.org/), while the functional domains/motifs were obtained from InterPro (https://www.ebi.ac.uk/interpro/), Pfam (http://pfam.xfam.org/), and UniProt (https://www.uniprot.org/) databases. The classical drugs and the natural compounds were retrieved from the DrugBank (https://www.drugbank.com/) and PubChem Compound (https://pubchem.ncbi.nlm.nih.gov/) databases. ChemSketch tool http://www.cheminfo.org/Chemistry/Cheminformatics/FormatConverter/index.html). Further, the ligands were optimised with the Avogadro tool https://cgenff.umaryland.edu/) online server with default parameters. The protein structures were placed within the center of the dodecahedron box of uniform edge distance of 1.0 nm, followed by solvation and addition of requisite counter ions (Na+ or Cl−) to the system. Steepest descent algorithm for 50,000 steps and convergence-tolerance of 1000 kJ/mol nm−1 were utilised for energy minimisation following system equilibration under standard NVT  and NPT  ensembles for 100 ps https://plasma-gate.weizmann.ac.il/Grace/).Seven MThe work did not involve any human subjects, animal experiments and data from social media platforms.Sravan Kumar Miryala: Data curation, Formal analysis, Visualization, Writing – original draft. Soumya Basu: Formal analysis, Visualization, Writing – original draft. Aniket Naha: Formal analysis, Visualization, Writing – original draft. Reetika Debroy: Formal analysis, Visualization, Writing – original draft. Sudha Ramaiah: Conceptualization, Methodology, Validation, Writing – review & editing. Anand Anbarasu: Funding acquisition, Conceptualization, Project administration, Supervision. Saravanan Natarajan: Funding acquisition, Conceptualization, Project administration, Supervision.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


As more data of experimentally determined protein structures are becoming available, data-driven models to describe protein sequence–structure relationships become more feasible. Within this space, the amino acid sequence design of protein–protein interactions is still a rather challenging subproblem with very low success rates—yet, it is central to most biological processes.in silico. Furthermore, our model does not need a precise backbone location, making it an attractive tool for working with de novo design of protein–protein interactions.We developed an attention-based deep learning model inspired by algorithms used for image-caption assignments to design peptides or protein fragment sequences. Our trained model can be applied for the redesign of natural protein interfaces or the designed protein interaction fragments. Here, we validate the potential by recapitulating naturally occurring protein–protein interactions including antibody–antigen complexes. The designed interfaces accurately capture essential native interactions and have comparable native-like binding affinities https://github.com/strauchlab/iNNterfaceDesignThe source code of the method is available at Bioinformatics online. The success rates for the de novo generation of protein–protein interactions achieved by existing methods are very low, with only a few examples demonstrating that it is possible , MERS-CoV, SARS-CoV and SARS-CoV-2 proteins co-crystalized with antibodies , resulti. Set T is divided into two subsets: T-ho and T-he, based on whether they are correspondingly derived from homo- or hetero-oligomeric PPIFs. Additionally, a subset T-ho-asymm containing complexes from subset T-ho is introduced; complexes in this subset are not extracted from symmetric patches of homo-oligomeric PPIFs. Set B was used for the evaluation of sequence recovery of interfaces of antibody-antigen protein complexes. This set is divided into subsets as well  peptide variations of all-glycine peptide ligands as part of our case studies. Backbones were generated by the iNNterfaceDesign method . Due to Input data for the neural network is based on topological features of the complex and amino-acid sequence of the binding sites . We use Other input data are amino acid types of residues in the binding sites, and input 5 defines the homo- or hetero-oligomeric nature of PPIFs. The ablation study of the impact of different types of inputs on the method’s performance is presented in https://www.tensorflow.org/tutorials/text/image_captioning), especially the decoder part .PepSeP6 model was trained ten times using the pretrained PepSeP1 using the following three stages: 4 epochs with a learning rate of 0.001, 4 epochs with a learning rate of 0.0001 and 2 epochs with a learning rate of 0.00002. The Adam optimizer was used to minimize the mean squared error during optimization. A custom loss function was implemented for the training of PepSeP6; the details can be found in The target binding sites were relaxed without peptide ligands. Perturbed peptides were then added back either with their native or redesigned AAS . These factors likely drove the lower peptide recovery rates of 27.71% for hetero-oligomeric PPIFs  of the Severe Acute Respiratory coronavirus 2 (SARS-CoV-2) . To evalThe 5J8 antibody is a broadly neutralizing antibody that binds to residues within the receptor binding site of influenzas hemagglutinin (HA). Its main contact CDR-loop exceeds six residues, so we performed the PepSeP1 design by aligning two structures predicted for 6-residue fragments . This ilOne of the most important contacts of the antibody loop with the receptor binding site is the aspartate at position 100B, mimicking the carboxy group of HA’s receptor sialic acid (To illustrate performance on highly perturbed (HP) peptide fragments (c5), we applied PepSeP6 to design the main contact CDR-loop of the broadly neutralizing stem antibody FI6. We generated 9-residue HP peptide ligands (c5) with an RMSD of about 4.0 Å relative to the CDR-loop of its original location. The FI6 antibody fragment has four hot-spots residues: Arg99  outputs. The native sequence recovery rate of PepSeP1 is 40.83% on the independent test set; the average accuracy of PepSeP6 designs is 38.67%, with recovery rates of 48.63% across the output sequences resembling the native structures the most. The models are characterized by training on non-perfect backbones of structures which make them more applicable either to work with homology models which are likely not at atomic accuracy or for the engineering of novel interaction in which the motifs are derived btac733_Supplementary_DataClick here for additional data file.


Oryza sativa (O. sativa) due to Magnaporthe grisea (M. grisea) fungus. Protein–protein interaction (PPI) between rice and fungus plays a key role in causing rice blast disease.Plant–microbe interactions play a vital role in the development of strategies to manage pathogen-induced destructive diseases that cause enormous crop losses every year. Rice blast is one of the severe diseases to rice O. sativa and M. grisea in a whole-genome scale.In this paper, four genomic information-based models such as (i) the interolog, (ii) the domain, (iii) the gene ontology, and (iv) the phylogenetic-based model are developed for predicting the interaction between O. sativa and M. grisea. Understanding the molecular processes behind rice resistance to blast fungus begins with the identification of PPIs, and these predicted PPIs will be useful for drug design in the plant science community.A total of 59,430 interacting pairs between 1,801 rice proteins and 135 blast fungus proteins are obtained from the four models. Furthermore, a machine learning model is developed to assess the predicted interactions. Using composition-based amino acid composition (AAC) and conjoint triad (CT) features, an accuracy of 88% and 89% is achieved, respectively. When tested on the experimental dataset, the CT feature provides the highest accuracy of 95%. Furthermore, the specificity of the model is verified with other pathogen–host datasets where less accuracy is obtained, which confirmed that the model is specific to  Among biotic stresses, the Magnaporthe grisea (M. grisea) fungus is the most harmful and causes a loss of 30%–40% in yield that is enough to feed millions of people (O. sativa and M. grisea that are inadequate to explore the pathogenic molecular mechanism (Homo sapiens (H. sapiens) and Plasmodium falciparum  are predicted previously  genome were collected from the Broad Institute website (http://www.broadinstitute.org/annotation/genome/magnaporthe_grisea/MultiHome.html). Similarly, 66,153 protein sequences of rice genome were collected from the MSU database .A total of 11,054 protein sequences of 2.2http://planttfdb.cbi.pku.edu.cn/). These keywords are related to the transcription factor of rice and utilized to filter positive-like candidates from the whole-genome rice sequence. The keywords present in rice protein annotation were filtered out as positive candidates. These filtered protein sequences are likely to participate in the interaction. From the above filtering process, only 3,665 rice proteins are extracted. To get homologs of 3,665 proteins, these are subjected to blast analysis against the remaining 62,488 rice proteins having an E-value of 10-5. From this analysis, a total of only 8,426 homolog proteins were also obtained. Thus, a cumulative total of 12,091  positive-like rice proteins were obtained that might participate in the interactions. On the other hand, the remaining 54,062 proteins that do not participate in the interaction were considered as probable negative samples.In this study, a new approach based on keyword filtering was used to obtain the probable interacting rice proteins. From different kinds of literature surveys [6-15], a set of keywords  and considered as positive samples. Thus, a cumulative total of 6,151  proteins of M. grisea were obtained and taken as positive-like samples that might be participating in the interactions. On the other hand, the remaining 5,256 proteins were considered as negative samples that do not participate in the interactions.The positive-like candidate proteins of ng TMHMM . BUSCA  to recognize homologous proteins as present or absent in reference organisms. Each protein of rice and M. grisea was compared with the 4,045 reference organisms from UniProt using BLASTP. If any homologs are found in any reference organism, we put 1 in that place (and 0 otherwise), indicating the presence or absence of the target protein in that organism. Thus, a binary phylogenetic profile of dimension 4,145 was constructed for each protein. Subsequently, hamming distance is used to compute the similarity of the profiles. The threshold value for prediction is calculated from the positive PPIs and negative PPIs as shown in The phylogenetic profile of proteins is used for predicting the PPIs . It is bM. grisea proteins.If the similarity score is less than a threshold (0.15), the protein pairs are interacting. In the phylogenetic model, 6,706 PPIs are predicted between 160 rice and 477 2.4.5From the computational approach, it is observed that all the individual unsupervised models  predict the interactions efficiently. To obtain the potential PPIs, the consensus of any of the two models was selected and then all the obtained PPIs were merged as shown in All the consensus interactions between possible combinations of four developed models were searched and finally a total of 59,430 unique PPI pairs are obtained  were extracted from protein sequences.The widely used features, amino acid composition (AAC), and conjoint triad  denote the frequencies of occurrence of its 20-amino acid constituent. Hence, the amino acid composition (xP) in the query protein has been represented byAAC provides a 20-dimensional feature vector for each protein. For each query protein x in the composition space was defined as: P(x) = . By combining their distinct AAC, each pair of host–pathogen PPI is represented by a 40-length feature vector.and the protein 2.5.3Shen et al.  first in3PPI has a very important role in predicting the target protein function . The com3.1M. grisea proteins has been listed in p-value of less than 0.05 were identified to be enriched in predicted proteins.The Database for Annotation Visualization and Integrated Discovery (DAVID) v6.8 is a widely used tool to verify the functional significance of the predicted host and pathogen proteins implicated in PPIs . The GO M. grisea, the biological process such as ATP binding, protein kinase activity, protein serine/threonine kinase activity, and peptidyl-serine phosphorylation was enriched in the predicted genes. From the literature, it has been inferred that the identified biological process and function are closely related to the host–pathogen interaction , hepatitis C virus (set 2), measles virus (set 3), Yersinia (set 4), and herpes virus (set 5) were used for analysis. While the dataset of the Arabidopsis thaliana plant was used for analysis with Pseudomonas syringae (set 6). The animal pathogen database was extracted from HPIDB 2.0 version while the Arabidopsis thaliana–Pseudomonas syringae database was extracted from Arabidopsis thaliana with Pseudomonas syringae (set) was 41%. On the other hand, CT feature analysis revealed that the percentage of false-positive values for sets 1 to 6 was 12%, 14%, 11%, 9%, 6%, and 5%, respectively. The percentage of false-positive value between Arabidopsis thaliana with Pseudomonas syringae was 15%. From M. grisea.The predicted model was further assessed with various host–pathogen systems to evaluate its reliability. Datasets of animal with M. grisea protein sequence. A machine learning model using support vector machine is developed using 532 positive and 532 negative PPIs. The obtained machine learning model was tested with 22 experimental datasets (The machine learning model performance was compared with a similar study reported previously by The main difference between the work of M. grisea. Furthermore, it is accurate and computationally inexpensive because of its filtering process prior to computational model development. Furthermore, a validation study on predicted PPI subcellular localization may also be carried out in the future.PPI has a key role in predicting the functions of uncharacterized protein as well as in determining its role in the phenotypic responses. PPIs are involved in controlling the various biological processes like cell-to-cell interactions as well as metabolic and developmental processes . Reports4M. grisea in a genome. A total of 59,430 highly confident PPIs are predicted between 1,801 rice proteins and 135 M. grisea proteins. The GO enrichment analysis shows that the predicted proteins are involved in interactions related to functionalities. Furthermore, to assess the effectiveness of predicted PPIs, a machine learning model based on support vector machine is developed. Based on the fivefold cross-validation test, better accuracy is obtained using AAC and CT features of protein sequence. Furthermore, the proposed model was tested on 22 experimentally identified PPIs between rice and M. grisea in an independent test that resulted in the prediction of 21 PPIs as positive using CT features. The reliability of the proposed model is also checked for PPIs on various host–pathogen systems. The proposed model predicted a lower number of PPIs as positive, inferring that the method is specific to rice and M. grisea. The predicted PPIs could be a useful resource for further studies on the rice–M. grisea interaction mechanism.In this study, several computational models are developed using the interolog, domain, GO, and phylogenetic information to predict the PPI between rice and The original contributions presented in the study are included in the article/BK: Methodology, Software, Writing – original draft; SM: Validation, Investigation, Writing - review & editing. SS: Methodology, review & editing; DP: Validation, Investigation; SC: review & editing. All authors contributed to the article and approved the submitted version.


The allosteric modulation of peripheral membrane proteins (PMPs) by targeting protein-membrane interactions with drug-like molecules represents a new promising therapeutic strategy for proteins currently considered undruggable. However, the accessibility of protein-membrane interfaces by small molecules has been so far unexplored, possibly due to the complexity of the interface, the limited protein-membrane structural information and the lack of computational workflows to study it. Herein, we present a pipeline for drugging protein-membrane interfaces using the DREAMM (Drugging pRotein mEmbrAne Machine learning Method) web server. DREAMM works in the back end with a fast and robust ensemble machine learning algorithm for identifying protein-membrane interfaces of PMPs. Additionally, DREAMM also identifies binding pockets in the vicinity of the predicted membrane-penetrating amino acids in protein conformational ensembles provided by the user or generated within DREAMM.https://dreamm.ni4os.eu.DREAMM web server is accessible via Bioinformatics online. Nevertheless, studies targeting the protein-membrane interfaces support the fact that these interfaces are druggable, and targeted drug design can be implemented for PMPs involved in deregulated cellular pathways and disease  have emerged as promising therapeutic targets for several diseases, such as tuberculosis and cancer . Drugginhttps://dreamm.ni4os.eu.Several computational tools and web servers for predicting protein-membrane interfaces, membrane domains and lipid-binding sites have been designed in the past . These bB = 0.4. With this tolerance weighting factor value, we expect to generate structures with a small root-mean-square deviation compared to the input structure (<3 Å), albeit still observing changes in the binding sites. It should be noted that ExProSE does not perform MD simulations to generate a conformational ensemble of protein configurations. A more accurate description of the conformational ensemble can be achieved using MD simulations coupled with enhanced sampling techniques such as metadynamics, replica exchange and umbrella sampling  amino acids are predicted to insert into the membrane, and are visualized in the web server with JSmol  [MIS-5000432 to A.C.]; and the Hellenic Foundation for Research and Innovation (H.F.R.I.) [1780 to Z.C.]. We thank the European Union's Horizon 2020 European research infastructures, “National Initiatives for Open Science in Europe – NI4OS Europe” project (grant agrrement no. 857645) for providing us with a virtual machine, and a domain for our web server and onboarding this service to the European Open Science Cloud. We acknowledge computational time granted from the Greek Research & Technology Network (GRNET) in the National HPC facility – ARIS under project IDs pr008033_gpu/Mem-Surf and pr008033_thin/Mem-Surf.Conflict of Interest: none declared.btac680_Supplementary_DataClick here for additional data file.


FTO) gene was highly altered and identified as one of the key biomarkers for obesity. This study has been undertaken to investigate the mutational profile of the FTO gene and elucidates its effect on the protein structure and function. Harmful effects of various missense mutations were predicted using different independent tools and it was observed that all mutations were highly pathogenic. Molecular dynamics (MD) simulations were performed to study the structure and function of FTO protein upon different mutations and it was found that mutations decreased the structure stability and affected protein conformation. Furthermore, a protein residue network analysis suggested that the mutations affected the overall residues bonding and topology. Finally, molecular docking coupled with MD simulation suggested that mutations affected FTO substrate binding by changing the protein-ligand affinity. Hence, the results of this finding would help in an in-depth understanding of the molecular biology of the FTO gene and its variants and lead to the development of effective therapeutics against associated diseases and disorders.An excessive amount of fat deposition in the body leads to obesity which is a complex disease and poses a generic threat to human health. It increases the risk of various other diseases like diabetes, cardiovascular disease, and multiple types of cancer. Genomic studies have shown that the expression of the fat mass obesity ( FTO) gene was reported to be highly dysregulated and largely contributed to obesity   and 3-methyl uracil (m3U) in vitro and in vivo, respectively (The Genome-wide association studies (GWAS) have shown that various single nucleotide polymorphisms (SNPs) of the ex (BMI) , 10. In e cancer –15. The pathways –19. FTO ectively , 21.FTO gene and to monitor the effect of non-synonymous (missense) mutations on the structure, dynamics, conformation, and substrate binding of the FTO protein. Different mutations were obtained through online databases and their pathogenicity was predicted through multiple online tools. Hence after, the impact of highly deleterious mutations on protein structure and functions was elucidated through molecular dynamics simulation, protein residues network, and molecular docking approaches.Single nucleotide polymorphisms are the most common type of genetic alterations that occur both in coding and non-coding regions of DNA sequences, out of which, the mutations that occur in the coding part of genes directly affect the encoded protein . HoweverFTO SNPs and clinically important mutations were collected from dbSNP and ClinVar databases, respectively  and different mutant (MT) models were constructed through the PyMOL graphical software . Both WT and MT models were initially energy minimized by GROMACS  suite  and late+) and chloride (Cl–) ions. Once the systems were neutralized, the energy minimizations were followed through the steepest descent (SD) method. Two common ensembles, NVT and NPT were employed using positional restraints for 100 and 500ps under a constant temperature of 300K and pressure of 1bar, respectively. The linear constraint solver (LINCS) algorithm was used to constrain all the bonds. The temperature and isotropic pressure of the system were maintained using modified Berendsen thermostat  and Parrinello-Rahman methods, respectively. Further, the well-equilibrated complex molecules were subjected to a production run of 100ns time for each complex system in duplicate, by applying 2fs time steps. The analyses of trajectories were done through various utilities of GROMACS such as gmx energy, gmx rms, gmx rmsf, and gmx gyrate.Molecular dynamics (MD) simulations were performed using the GROMACS program . The AmbTo obtain a precise view of the prominent motions in a trajectory generated during MD simulation, we employed essential dynamics (ED) also known as principal component analysis (PCA). ED is a computational approach to understanding the correlated motions of a given protein that relate to its biological functions . The covB) for both WT and MTs which helped us to identify the essential residues imparted during signaling.The functions of protein rely on its residues and interactions among them or with their surrounding environment. Each tertiary structure of protein exhibits its territory of network. To elucidate how non-synonymous mutations influenced the interatomic synergy, we studied residue interaction network (RIN) using the RINalyzer tool . InitialMolecular docking is a key approach to studying the interaction between protein and small molecules such as ligand and the dynamics of that molecule within the binding pocket of the protein. Here, we used ensemble docking with 5 docking tools such as AutoDock Vina , QuickViFTO gene investigated in this study were retrieved from the dbSNP database. The database exhibited approximately 94,000 SNPs out of which about 91,312, 2,133, 383, 169, 2, and 1 SNPs were intronic, non-coding transcript variants, non-synonymous (missense), synonymous, inframe deletion, and initiator codon variants, respectively (FTO gene were inspected and it was found that the G > A (20.6%) transition showed the highest prevalence followed by A > G (17.41%), C > T (15.83%), and T > C (8.17%) (The variants or mutations of ectively . For fur (8.17%)  while tr (8.17%) .After removing the redundancy, about 245 missense variants were further used for the PredictSNP server to examine the deleterious nature of mutations . Out of FTO is ∼559 amino acid long protein (Uniprot: Q9C0B1) and its crystal structure is available at PDB which was resolved at ∼2.5Å resolution. However, the above reported structure has missing residues at flanking ends of N- (∼31) and C-terminals (∼62). Thus, we computationally modeled the full-length tertiary structure of FTO using the I-TASSER server service. The predicted tertiary model has −0.59 and 0.64 C- TM-scores, respectively, which indicate that the model is reliable and has a better global topology. Further reliability of the generated model was verified by superposing the template and model . We omitThe sequence level prediction of protein stability was examined through various tools as discussed above. The protein stabilities at the structure level of WT and all MTs were assessed through MD simulations which were performed in duplicate for 100 ns each . During Next, we wanted to check the stabilities and flexibilities of WT and MT proteins at residues and secondary structure levels. To accomplish this, we measured root mean square fluctuation (RMSF) and secondary structures  of WT and MT proteins for individual residue and domain throughout the simulation period. The fluctuation intensities varied among different regions of WT and all MTs proteins . The reg2, respectively , protein–protein (intra), and protein–water (inter) hydrogen bonding. Overall, SASA was further divided into hydrophobic and hydrophilic SASA which showed steady behaviors throughout the simulation period in WT as well as in all MTs . The totectively  All MTs  all MTs .Wild Type and MTs showed consistent behaviors of intra- and inter-H-bonds formation . The aveThe function of protein depends upon its motion which is elucidated by conformational sampling in phase space from MD simulated trajectories. To study the appropriate motions of the protein resulting from atomic trajectories, we performed essential dynamics and reduce the dimension of data using principal component analysis (PCA). R322Q MT exhibited relatively higher trace values of covariance matrix while E234P, R316Q, and C392 MTs consisted of lower matrix values as compared to WT suggesting that the R322Q MT accompanied higher motions as compared to WT . During B) analysis was performed to understand the key residues involved in the function of the protein. During this analysis, the residues in the protein were represented as nodes while the connections among these residues were denoted as edges. Here, we calculated 3 main centralities: betweenness centrality (CB), closeness (CC), and degree (CD) for WT and all MTs out of which CB has the significance of calculating the importance of each residue in the signaling of proteins upon different mutations. We mapped the residues having CB ≥ 0.05 in WT and all MTs and found a similar number of residues involved in signaling , hydrogen bonds (H-bond), pi-pi, and ionic interactions were represented as edges . FurtherFTO and its substrate binding were monitored by calculating the protein-ligand binding affinity using the docking approach which was measured in kcal/mol. N6-Methyl-deoxy-adenosine-5′-monophosphate (6mA) is one of the major substrates of FTO and we used this as a ligand for measuring the binding affinity upon different mutations. A combination of 5 docking tools was employed for WT- and MT-6mA complexes and it was found that all docking tools showed a similar pattern of docking scores even when all docking tools have different scoring functions . The aveStabilities and rescoring of binding energies of docking complexes (WT and MTs) were assessed through 100ns MD simulation . BindingFTO gene which is highly dysregulated and known to be involved in various types of cancer and obesity has been recently identified -induced AML (acute myeloid leukemia) cell differentiation and induced malignant characteristics in breast cancer cells by targeting BNIP3 (BCL2 Interacting Protein 3) (FTO increases the level of 6mA which promotes cell proliferation and invasion by activating PI3K (Phosphatidylinositol-3-kinase) signaling in gastric cancer and increases lung squamous cell growth and invasiveness by inhibiting the cell apoptotic genes  , 65. Knoic genes , 67. FTOarcinoma .FTO have been previously explored. Independent GWAS studies reported two different SNPs  in the first intron of the FTO gene significantly associated with BMI  as the most significant marker of BMI  were mostly associated with obesity  and transversion (Tv) analyses from all non-synonymous missense mutations showed that the G > A and A > C were highly prevalent Ti and Tv, respectively. After that sequence and structure levels prediction of pathogenic mutations were analyzed through a pile of tools and it was observed that most of the mutations were pathogenic and deleterious. Around 245 missense mutations were analyzed out of which 7 mutations  were shown to be highly pathogenic. Previous experimental studies have suggested that these mutations led to various disease disorders. Structural, conformational, and functional consequences of the above mutations were further inspected through 3D structural modeling and molecular dynamics simulation approaches. The tertiary structure of FTO protein was constructed through the I-TASSER server. The I-TASSER has been proved to be the best protein structure prediction tool by CASP  analysis , which is a parameter to identify crucial residues involved in the protein structure and functions, was measured for WT and all MT proteins. The results suggested that substitution of ARG316 and ARG322 to GLN316 and GLN322 largely affected the residues network of the FTO protein. In addition to that, residue-residue bindings were also analyzed and it was found that H-bonding and all non-covalent interactions were largely affected and impaired in MTs as compared to WT.The structure and functional impacts of various MTs on the protein were elucidated by examining the SASA, intra- and inter-H-bonds. SASA helps to determine the conformation of protein through its accessibility in the surrounding environment which mediates its interaction with the solvent molecule. The SASA results suggested that the lower surface area available for interactions was accompanied by almost all the MTs as compared to WT. Hydrogen bond plays a crucial role in protein stability, protein folding, and molecular interactions . Intra-HConsidering the contributing role of 6mA modification on gene expression, it has been enormously involved in various human diseases like psychiatric, metabolic syndrome, cardiovascular diseases, and various forms of cancer . It has The FTO protein consists of 2 domains located at the N-and C-terminals, and hence, known as N and C terminal domains (NTD and CTD) . The curFTO gene has shown that the missense mutations are highly pathogenic and largely affect the structure and conformation of the protein which ultimately affects the function of FTO. Furthermore, mutations also lead to amending the FTO substrate binding. To our knowledge, this is a prime study where the entire pool of FTO variants was conducted which will assist in understanding the mechanism of the associated diseases and disorders and would set a path for precise therapeutics.The comprehensive mutational study of the The original contributions presented in this study are included in the article/RakK, SN, and PT conceptualized and designed the study. SN and RakK performed SNP mining and annotation. RakK and RahK performed MD simulation and docking experiments. HG, AG, SK, SD, and SM helped in the literature study. RakK, SN, RahK, and HG analyzed the data and wrote the manuscript. RakK, SN, RahK, HG, and PT reviewed and approved the final version of the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.