Complex molecular simulation methods are typically required to calculate the thermodynamic properties of biochemical systems. One example thereof is the thermodynamic profiling of (de)solvation of proteins, which is an essential driving force for protein-ligand and protein-protein binding. The thermodynamic state of water molecules depends on its enthalpic and entropic components; the latter is governed by dynamic properties of the molecule. Here, we developed, to the best of our knowledge, two novel machine learning methods based on deep neural networks that are able to generate the converged thermodynamic state of dynamic water molecules in the heterogeneous protein environment based solely on the information of the static protein structure. The applicability of our machine learning methods to predict the hydration information is demonstrated in two different studies, the qualitative analysis and quantitative prediction of structure-activity relationships, and the prediction of protein-ligand binding modes. Calculating the thermodynamic properties of biochemical systems typically requires resource intensive, multi-step molecular simulations. Here, two deep neural network machine learning methods generate the thermodynamic state of dynamic water molecules in a protein environment solely from information on the static protein structure. This acceleration is achieved by machine learning concepts that learn collective variables from MD trajectories8 or that generate new atomistic configurations in a statistically independent manner6. The focus of these methods lies in the thermodynamic characterization for structural studies of proteins. Application of these machine learning approaches to investigate the thermodynamic properties of biochemical processes such as protein–ligand or protein–protein binding is still to be explored.The prediction of thermodynamic properties of biochemical systems such as Gibbs free energies is critical in understanding and quantifying essential biological processes, such as protein folding, protein–ligand and protein–protein binding. Resource intensive molecular simulations are routinely used to sample atomistic configurations of the dynamic biochemical system in order to calculate thermodynamic properties. Recently, machine learning methods have been explored to accelerate and improve configurational sampling of protein systems in comparison to molecular dynamics (MD) simulations9 have been devised to identify water molecules adjacent to proteins’ surfaces which includes knowledge-based methods such as WaterScore10 or AcquaAlta11, statistical and molecular mechanics approaches such as 3D-RISM12 or SZMAP13, Monte-Carlo methods such as grand-canonical Monte-Carlo (GCMC) simulations14, and MD methods such as WATCLUST15, WaterMap17, or WATsite20. GCMC- and MD-based hydration-site prediction is accurate and widely accepted as gold-standard to compute the likely water positions in the binding sites of proteins, and the enthalpy and entropy contribution of a replaced water molecule to binding free energies. This statement was confirmed in a recent analysis on the structure-activity relationships for different target systems which demonstrated the superiority of simulation-based water prediction compared to other commercial methods such as SZMAP, WaterFLAP, and 3D-RISM21.(De)Solvation of protein and ligand is typically a driving force for such association processes. The thermodynamic properties of water molecules around protein moieties depend strongly on the formation and dynamics of hydrogen-bond networks in a heterogeneous protein environment. Several methods23. Grid-based adaptations of the inhomogeneous solvation theory (IST)24, for example GIST25, have been developed for direct inclusion of the hydration information in docking algorithms.Hydration information can be used to estimate the desolvation free energy contributions to a ligand’s binding affinity or the potential for water-mediated interactions26. Recently, we demonstrated the influence of conformational changes of the protein on hydration-site positions and thermodynamics28. These studies concluded that hydration-site prediction on flexible proteins needs to be performed on alternative protein states. Furthermore, we recently demonstrated the general importance of water networks around the bound ligand for forming enthalpically favorable complexes29. Thus, it is indispensable to re-calculate hydration information in an efficient manner for each bound ligand or even binding pose during docking.In addition to water replacement and reorganization, ligand binding typically also involves conformational changes of the proteinHydration-site prediction based on GCMC- and MD simulations is accurate but also rather time-consuming. Utilization of these concepts in a real-world compound-design project on flexible proteins and large sets of ligands with alternative binding poses is therefore difficult to attain with current computer hardware and therefore currently impractical. A significantly more efficient method for hydration profiling is necessary, that would allow its incorporation in virtual screening to dynamic and flexible protein entities. In this study, we provide evidence that modern machine learning approaches may present a realistic solution for obtaining thermodynamic hydration information in an efficient manner; we present the first deep learning methods that instantaneously predict the thermodynamics of hydration data Fig. .Fig. 1Ov30. To correctly model and predict hydration data, more complex machine learning methods need to be designed that include potential water interactions. We have designed two different machine learning concepts based on deep neural networks that include those multi-body effects which are critical to determine the positions and thermodynamic properties of water networks , the first approach aims to predict hydration information of all grid points in the binding site in a single calculation. First, interactions are computed between protein and multiple atomistic probes placed on a 3D grid encompassing the binding site. Those interaction grids, called molecular interaction fingerprints (MIF), are then used as input to the CNN to predict hydration occupancy. Due to the use of spatial kernels in CNN, correlations between neighboring grid points are incorporated. This allows to implicitly include water–water interactions in the machine learning model.In contrast, the second model predicts hydration information for each grid point separately using spherical-harmonics local descriptors. Again, interactions between protein and atomistic probes are mapped on a 3D grid. Spherical-harmonics expansions of those interaction maps around each grid point then encode the local environment of a potential water molecule which includes protein–water and water–water interactions.Both models are trained on a large data set of thousands of protein structures. For each protein structure, MD simulation is performed. Subsequent WATsite analysis predicts hydration density and thermodynamic profiles on a 3D grid. This hydration data on the grid functions as ground truth throughout the training and validation of the neural-network (NN) models. After the model has been trained it can be applied to any static protein structure without the need to prepare and run any MD simulations.To incorporate the context of a grid point in the neural network, we utilized CNNs based on the computed MIFs. This approach predicts the water occupancy on a grid point by incorporating spatial context from surrounding grid points during the convolutional feature abstraction process. The CNN network architecture  as those neighboring grid points would lie beyond the boundary of the grid box. This failure to correctly predict the occupancy of boundary grid point, however, does not create a serious issue for the purpose of predicting hydration information in the binding site, as the grid points on the boundary of the box lie outside of the binding pocket volume. A mitigation for this problem is to remove the prediction in the boundary regions of the grid box after model generation. Therefore, we focused our analysis on the relevant region in the vicinity of the bound ligand, i.e., all grid points with a maximum distance of 5 Å around the co-crystalized ligand. Figure 35 to measure the overlap between the reference and the predicted grids. In this metric the confidence of prediction of a label is included. For each metric, both the quality of the full grid and for the area within 5 Å from the ligand is displayed. Table  Tables  Figure 36. This method measures how the absence of a feature decreases the performance of a trained estimator. This method can be directly applied to the validation set without the need of retraining for each feature removal. A feature is replaced with random noise with the same distribution as the original input. One simple way is to shuffle the values of a grid randomly, so that it no longer contains useful information. As expected, the probes which are most influential for the prediction quality were either water probes (OH2) or probes which mediate hydrogen bonding. It should be noted that although water probes from Flap are designed to indicate the water affine areas, they do not linearly correlate with WATsite occupancy, namely, the Pearson correlation coefficient between those MIFs and WATsite occupancy is close to zero. Table We further analyzed which input MIF grids contributed most to the prediction performance. To compute the feature importance, we used the Mean Decrease Accuracy (MDA) or permutation importance methodIn contrast to the segmentation model, in the point-wise model each individual grid point represents a sample that can be used for training and testing of the model. Thus, the size of the data set is significantly increased and allows to design a more aggressive testing protocol compared to the segmentation method. For the point-wise prediction, the same 5-fold splitting procedure of the data set was used. In contrast to the segmentation model, only one-fifth was used for training and four-fifth for testing.For the classification model, i.e., separating grid points between those with and without water occupancy, the normalized confusion matrix over the test set was computed Fig. . Ninety-−5 was used. In practice, it is desirable to identify regions in the binding site with high water densities and occupancy peaks that resemble hydration sites. Therefore, a regression model was designed to identify this high density among low-density regions. Using descriptors encoding only the direct interactions with the protein at the specific grid point location (no inclusion of nearby grid points), a mediocre correlation between predicted and ground truth water occupancy was identified (r = 0.52)  only slightly increases the regression coefficient further to r = 0.85. Further addition of angular functions to represent the environmental grid points (l = 2) does not further improve the regression between ground truth and predicted occupancy values. Consequently, we used the regression model with l = 1 for subsequent analysis (see below).Whereas the classification model allows to identify regions with likely water occupancy with high accuracy, a rather small occupancy threshold of 1052) Fig. . Using or value of 0.83 was achieved.The same trend, although weaker in magnitude, was observed in the regression outcome for the free energy of desolvation at the grid points with occupancy. A maximum r values ranged between 0.85 and 0.86 (standard deviation of 0.004), for free energy it ranged between 0.83 and 0.84 (standard deviation of 0.0044). This highlights the robustness of the model, independent of the specific protein systems used for training.For further evaluation of the neural-network performance, 5-fold cross-validation was used. Again, only a fifth of the data set was used for training in each cross-validation step and four-fifth were used for testing the model. All five models exhibited very similar test set performance. For occupancy the −4, 0.02, 0.045, and 0.07 for two different protein systems. Excellent overlap between predicted water occupancy and ground truth was observed with slight deterioration in accuracy for the highest density maps at 0.07. This visual observation can be quantified by measuring the precision and recall values at different classification threshold values of 0.02, 0.03, 0.045, 0.06, and 0.07 . This failure can be interpreted by the lack of modeling of long-range protein–water interactions and water–water interactions. CNNs based on protein density would allow modeling of local correlation between protein shape/properties and adjacent water occupancy. The stability of water molecules in protein binding sites, however, is strongly influenced by long-range electrostatic interactions and by the formation of hydrogen-bonding water networks40. Both contributions are difficult to model using localized features extracted by the layers of the CNN.Whereas these input data show good performance for binding pose prediction of chemicals binding to proteins41. MIFs were generated as described previously. As described in the subsection Probe selection of “Methods”, 12 probes were selected to generate 12 different channels for the input layer. Neural networks were designed for simple point-to-point correlations, where the different MIF input channels were correlated with WATsite occupancy. In our tests, however, neural networks or other machine learning algorithms were unsuccessful in finding any significant point-to-point correlations. From this observation, we concluded that even the MIFs generated with a water probe differ significantly from the WATsite predictions. This can be explained by the fact that the MIFs only represent direct protein–probe interactions and therefore lack the incorporation of water–water interactions. Thus, the interaction value with a probe at a given point does not provide enough information for a network to infer water occupancy. For example, a grid point in an occluded space buried deep inside a protein may have a similar interaction profile with the protein in context of the MIFs to another grid point in a solvent-exposed area. The former point, however, may have lower occupancy due to the lack of stabilizing water–water interactions.In an another alternative approach, we represented the protein indirectly using molecular interaction fields (MIFs) dataWATsite in contrast includes water–water network interactions explicitly. Furthermore, it explicitly includes entropic contributions, as the water distribution is sampled from a canonical statistical ensemble during the MD simulation. To predict water occupancy at a certain location, the neural network requires not only the interaction information on the corresponding grid point, but also the context of the grid point, i.e., interaction with other water molecules. Those interactions can be represented either by directly including information of neighboring grid points or by the explicit design of input descriptors that include environmental information. The latter approach was described in the section “Neural networks for point-wise prediction using spherical harmonics expansion”, the former was discussed in the section “Neural network for semantic segmentation”.The two NN approaches for the generation of hydration information were applied to three different topics, i.e., the prediction of hydration-site locations in X-ray structures, the qualitative and quantitative analysis of structure-activity relationships (SAR) data, and the improvement of CNN-based pose ranking in docking applications.18 and GAsol (3D-RISM)42. It should be noted that WATsite had been previously tested to reproduce X-ray water molecules27. We show the prediction performance of finding hydration sites within 1.0, 1.5, and 2.0 Å distance to the corresponding X-ray water location. Hydration sites with distances >2 Å to the corresponding X-ray water locations are considered as failed predictions. WATsite is the most accurate of all methods , heat shock protein 90-alpha (1uyl), trypsin I (1s0q), and fatty acid-binding protein adipocyte (3q6l) Fig. . Both ofods Fig. , in partIt should be noted that a comparison between X-ray water molecules and hydration sites has overall its limitations: First, fit of water positions into electron density obtained from X-ray experiments is not free of errors. Second, X-ray structures are typically resolved at low temperatures underestimating entropic effects. Third, crystal effects may have an influence on water networks, in particular if the binding site is partially or fully solvent exposed. Fourth, the identified hydration sites depend on cluster algorithm and settings, thus adding additional inaccuracies to the grid-based prediction of hydration density. In light of those arguments, we believe the hydration-site predictions using both NN are reasonably accurate, considering their significanty higher efficiency compared to running MD simulations.21 for the analysis of the structure-activity relationships of lead series of different target systems. To demonstrate that the instantaneous prediction of thermodynamic hydration information based on our neural networks can be used with similar confidence in lead optimization projects, we performed three retrospective SAR analyses on heat shock protein 90 (HSP90), beta-secretase 1 (BACE-1), and major urinary protein (MUP).Hydration-site prediction using MD-based methods such as WaterMAP or WATsite have been utilized in many recent medicinal chemistry projects to understand ligand binding and structure-activity relationships (SAR), as well as for the guidance of lead optimization. Recently, Bucher et al. demonstrated the superiority of simulation-based water prediction using WaterMAP compared to other commercial methods SZMAP, WaterFLAP, and 3D-RISM43, a series of HSP90 inhibitors were synthesized and tested  is located around the phenyl ring of compound A  is not able to replace the water density, highlighted by a decrease in affinity. Elongated substituents such as O-ethyl (3) and O-isopropyl (4) spatially overlap with the unfavorable water density, replacing those water molecules. This results in significant affinity increase from 21 to 1.3 μM. For BACE-1, two regions with favorable water enthalpy were observed  Fig. 45,46. Th1–5; red diamonds: compounds 6–10; blue triangles: compounds 11 and 12) could be obtained but also among all 12 compounds that contain three different scaffolds  from decoy poses (RMSD >4 Å). Water-occupancy grids predicted by our CNN model were used as an additional input channel to the Gnina CNN.37. However, we increased the number of bad poses for a more realistic scenario. For each target protein, only one native-like pose with RMSD <2 Å was selected. Since we aimed to utilize the Gnina CNN with and without hydration information for pose reranking, systems with no good poses were removed. The final data set consists of 1394 and 593 protein targets for training and test, respectively. The training was performed for 10,000 iterations. We used the default parameters and the reference model for pose prediction which is made available on Gnina’s Github page (https://github.com/gnina/gnina).To provide water-occupancy data for Gnina, we retrained the water predictor network using 2288 and 1133 PDBs for training and test set, respectively. The training and test sets were based on the reduced set from Ragoza et al.37.Here, we evaluated the performance of Gnina+water against Gnina alone and Vina/Smina. The results for Vina were obtained from Ragoza et al.As it can be seen in Fig. Hydration is a key player for biochemical association processes such as protein–ligand and protein–protein binding. The binding partners and the association process itself influence hydration patterns and thermodynamic properties. In order to accurately model hydration in tasks such as flexible protein–ligand or protein–protein docking, the hydration data needs to be computed in an efficient manner without performing time-consuming simulations. In this paper, we demonstrate that instantaneous prediction of thermodynamic properties of biochemical systems is possible due to the development of machine learning algorithms and due to our ability to generate large amount of thermodynamic data. Here, we present the very first deep learning methods to instantaneously predict thermodynamic hydration data, thus providing an efficient alternative to time-consuming MD simulations for the calculation of those properties.We have developed two alternative deep learning approaches. One method predicts the complete binding site hydration information in a single network calculation in form of U-Net neural networks. The second method relies on descriptors that include potential protein–water and water–water interactions calculated on each grid point. The networks were able to generate precise hydration occupancy and, in case of the point-wise model, also thermodynamics data.29 may become possible in structure-based ligand design in the near future.Application of the predicted hydration information to SAR analysis and binding-mode prediction demonstrated the potential of these methods for structure-based ligand design. Future applications include the marriage of protein flexibility and desolvation data in ensemble docking. Due to the efficiency of the methods, precise hydration data could be computed for alternative protein structures, different ligands, and their binding poses in modest computation time, which has been an unfeasible task until now. The routine inclusion of explicit desolvation, water-mediated interactions, and enthalpically stable hydration networks around the protein–ligand complex20. Using 3D-RISM site-distribution function51 and GAsol42 for initial placement of water molecules, WATsite then performs explicit water MD simulations of each protein. Finally, explicit water occupancy and free energy profiles of each hydration site  in the binding site are computed. This hydration data is distributed on a 3D grid that encompasses the binding site and is used as output layers for the neural networks to be trained on. Details on WATsite simulations and analysis can be found in the Here, hydration-site data were generated for several thousand protein systems using WATsite Fig. . The recTwo different types of neural networks have been designed to predict hydration information Fig. a. In bot52. Here, we used different forms of U-Nets but extended the segmentation task to multi-class segmentation. The multiple classes represent the occupancy of water molecules above various threshold values in different moieties along the protein surface.In the first approach, to predict hydration data, we adapted deep neural-network concepts commonly used in semantic image segmentation. Semantic image segmentation is the task to identify the pixels in an image that belong to a specific class or category, for example, a specific object in an image. The great advantage of such networks is that they are able to be trained end-to-end by creating a mapping from the input layers to the output images. The resulting output is an image or a grid with the same dimensions as the input layers. Among the various architectures used for this task, U-Net has been demonstrated to often produce superior segmentation performance with smaller training sets compared to other methodsGeneration of descriptors. We used the “refined set v.2016” from the PDBind database54 consisting of 4057 protein–ligand complexes. Hydration-site data was generated using WATsite as described in ref. 29  for assignment of Amber14 force-field parameters. The prepared protein was used as input for WATsite and for the fully connected network .All PDB files were processed by removing ions, water molecules, ligands, and other heteroatoms. No proteins with cofactors in the binding site were used in this study. Preparation scripts available in WATsite’s docker image bundle were used to further process the proteins: PROPKAFor the CNN-based approach, molecular interaction fields (MIF) with different atomistic probes distributed on a 3D grid are used as input. MIFs are generated by first placing a fictitious probe molecule on each point of a 3D grid that encompasses the binding site. The interaction value between probe and protein is calculated at each grid point under the assumption of a rigid protein structure. Instead of providing an image of the protein, this approach rather generates a negative image of it and provides data for the binding site regions of the protein unoccupied by protein atoms but accessible to water molecules.59 and are fed as input descriptors for the CNN. FLAP uses the GRID force field and its own atom types. The internal program GRIN61 is used to preprocess the protein. Additional details can be found in the 63. The process for selecting relevant chemical probes for FLAP is further explained in section “Probe selection”. FLAP occasionally failed to generate output for one or two probes for some proteins due to an internal program issue. As this is a commercial software, it was not possible to correct this error. PDB files for which FLAP failed to generate an output were removed. Finally, 3421 PDBs were used for training and testing of the neural-network models (Supplementary Data Molecular interaction fields (MIF) with different atomistic probes distributed on a 3D grid are computed using FLAPProbe selection. In FLAP, MIFs between protein and 78 different chemical probes are generated. To reduce the number of input layers for the CNN model, we performed k-means clustering of the FLAP grids of three randomly selected protein systems. The distance matrix used during clustering was based on Pearson correlation coefficients between the interaction values on the 3D FLAP grids of a pair of probes. In detail, the distance between two interaction probe types was defined as one minus the Pearson correlation coefficient. The number of clusters was chosen to be 12. One representative probe type from each cluster was used to finally generate a set of 12 representative probes with largest diversity between their interaction grids, i.e., smallest Pearson correlation coefficient. These grids represent 12 input channels to the neural network. Increasing the number of channels (probe types) did not lead to significant improvement of the network and only increased the training time.Processing of hydration occupancy data. Initially, the generated neural-network models were designed to generate regression models to predict continuous occupancy values. These models, however, failed due to significant imbalance between low and high-occupancy values 34 which assigns higher weights to the sparser points:rln and pln are the reference and predicted label (l) values at a grid point n, respectively67. This loss function will strongly penalize sparse grid points, enforcing the learning algorithm to more precisely predict those values in addition to the large number of low-occupancy grid points.As discussed above, regions in the grid with high water occupancy are sparse by nature, resembling a significant imbalance between the number of low-occupancy and high-occupancy grid points. This makes the prediction of higher occupancy grid points difficult, as commonly used loss functions such as mean squared error will not work properly for such imbalanced data. The sparsity of the dense regions causes the network to predict low or zero values for all grid points even for high-occupancy points. This problem also occurs in image segmentation tasks, where the object of interest is small compared to the whole image being analyzed, for example in the detection of small tumors in brain images68 with learning rate of 0.001 and a batch size of 16 was used for training the model. Learning was performed for 100 epochs using Keras69 with Tensorflow70 back-end. Once trained, the six output channels of the network are combined to obtain a grid with a range of values which represent the likeliness of hydration.Adam optimizerIn the second approach, the hydration information of each individual point is predicted based on the input descriptors specifying water–protein interactions at this location and the environment of this water location. The approach consists of two subsequent models, a classifier to separate grid point with water occupancy from those without, and a second regression model only for grid points classified as “with occupancy” in the first model. In this regression model occupancy values and free energies of desolvation are computed. In classification and regression model, parameters for the protein atoms such as van der Waals radius and partial charge are directly taken from the coordinate and topology file prepared for WATsite simulations.Classification model to identify grid points with water occupancy. For each grid point, the spatial environment and flexibility of surrounding atoms are computed. In detail, the distance from grid point k to all atoms i in the neighborhood of the grid point are computed and the van der Waals radius of the protein atom σi is subtracted:i isw = 7 Å/25. All values are finally scaled using Separate vectors are computed in the same manner for hydrogen-bond donor and acceptor atoms. The motivation for these additional descriptors is that shorter distances between water and hydrogen-bonding groups are observed compared to hydrophobic contacts.ti of a side-chain atom i to the corresponding Cα atom is translated using k is then distributed to an additional 25-dimensional vector using a modified Gaussian distributionqk,i from the unmodified vector pk,i generates a vector that measures the flexibility of the environmental atoms around grid point k.Despite the applied harmonic restraints, dynamic fluctuations of the protein atoms are observed throughout the WATsite MD simulations. These fluctuations can have impact on the accessibility of water molecules to different locations in the binding site. To incorporate those atomic fluctuations in the neural-network predictions of occupancy, we designed a simple flexibility descriptor for the side-chain atoms . The shortest topological distance All four vectors are concatenated which generates a 100-dimensional input vector to the neural network for classification.−5 in the input was used to separate occupied from unoccupied grid points.In addition to the input layer, the neural-network architecture consists of a fully connected hidden layer with 1024 nodes with leaky-ReLU activation and dropout layer with dropout probability of 0.5, followed by a second fully connected hidden layer with 512 nodes with leaky-ReLU activation and a final output layer with sigmoid activation to classify each grid point as either occupied (1) or unoccupied (0). A threshold occupancy value of 1068 with learning rate of 0.001 and a batch size of 250 was used to train the model. Learning was performed for 50 epochs using Tensorflow70.Adam optimizerRegression model. For each grid point, first the direct interactions between water probe and protein atoms are computed. In detail, electrostatic fields of the protein atoms i at location Ri with partial charge Qi are computed on each grid point rki at location Ri with van der Waals radius σi and well-depth ϵi is computed using a soft alternative of the van der Waals equationσip = σi + σp (probe σp = 1.6 Å) and well-depth of probe ϵp = 0.012 kcal mol−1. Protein parameters from the Amber14 force field are used.71 usingHydrophobic contacts are computedi are computed usingR0 = 1.94 Å).Hydrogen-bond interactions between water probe and protein acceptor/donor heavy atoms i is determined byw = 2/20 and w = 1/20 for electrostatic interactions and all other interactions, respectively). The five 20-dimensional vectors are concatenated to generate a 100-dimensional input vector to the neural network.Each interaction term is then scaled and transformed by a hyperbolic tangent function to the range ϵ; 1 Å + ϵ], , …,    Fig. . The griion (Eq. ). The veion (Eq. ) are finThe neural-network architecture consists in addition to the input layer a fully connected hidden layer with 2048 nodes with leaky-ReLU activation and dropout layer with dropout probability of 0.5, followed by a second fully connected hidden layer with 1024 nodes with leaky-ReLU activation and a final output layer with occupancy and free energy values.68 with learning rate of 0.001 and a batch size of 250 was used for training the model. Learning was performed for 125 epochs using Tensorflow70.Adam optimizerTo compare hydration occupancy predictions with crystallographic water data and other hydration-site prediction methods, occupancy grids obtained from the two neural-network methods were clustered to predict hydration sites. Two different clustering methods were selected for this purpose. For the Inception+U-Net model, a modified DBSCAN clustering method was utilized  onto the apo form and using the centroid of the aligned ligand as the center of the grids. We compared the performance of our method with two other methods: WATsite18 (MD-based method) and hydration-site prediction generated from GAsol’s clustering method on 3D-RISM grids42 (grid-based method). All crystallographic water molecules and ions were removed as part of the protein preparation process. The proteins were prepared automatically by the scripts available in the WATsite 3.0 package for 3D-RISM and WATsite. Both methods were run using their default parameters. The spatial deviation of predicted hydration sites from crystallographic water locations observed in the PDB files was measured. The distance of each crystallographic water molecule to the closest predicted hydration site was measured. Only X-ray water molecules within 5 Å of any ligand and protein atom were considered.To evaluate and compare the ability of our methods to reproduce water locations in X-ray data, we chose four apo systems from data from Rudling et al.Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3


Systematically discovering protein-ligand interactions across the entire human and pathogen genomes is critical in chemical genomics, protein function prediction, drug discovery, and many other areas. However, more than 90% of gene families remain “dark”—i.e., their small-molecule ligands are undiscovered due to experimental limitations or human/historical biases. Existing computational approaches typically fail when the dark protein differs from those with known ligands. To address this challenge, we have developed a deep learning framework, called PortalCG, which consists of four novel components: (i) a 3-dimensional ligand binding site enhanced sequence pre-training strategy to encode the evolutionary links between ligand-binding sites across gene families; (ii) an end-to-end pretraining-fine-tuning strategy to reduce the impact of inaccuracy of predicted structures on function predictions by recognizing the sequence-structure-function paradigm; (iii) a new out-of-cluster meta-learning algorithm that extracts and accumulates information learned from predicting ligands of distinct gene families (meta-data) and applies the meta-data to a dark gene family; and (iv) a stress model selection step, using different gene families in the test data from those in the training and development data sets to facilitate model deployment in a real-world scenario. In extensive and rigorous benchmark experiments, PortalCG considerably outperformed state-of-the-art techniques of machine learning and protein-ligand docking when applied to dark gene families, and demonstrated its generalization power for target identifications and compound screenings under out-of-distribution (OOD) scenarios. Furthermore, in an external validation for the multi-target compound screening, the performance of PortalCG surpassed the rational design from medicinal chemists. Our results also suggest that a differentiable sequence-structure-function deep learning framework, where protein structural information serves as an intermediate layer, could be superior to conventional methodology where predicted protein structures were used for the compound screening. We applied PortalCG to two case studies to exemplify its potential in drug discovery: designing selective dual-antagonists of dopamine receptors for the treatment of opioid use disorder (OUD), and illuminating the understudied human genome for target diseases that do not yet have effective and safe therapeutics. Our results suggested that PortalCG is a viable solution to the OOD problem in exploring understudied regions of protein functional space. Many complex diseases, such as Alzheimer’s disease, mental disorders, and substance use disorders, do not have safe and effective therapeutics because of the polygenic nature of the diseases and a lack of thoroughly validated drug targets (and their corresponding ligands). Identifying small-molecule ligands for all proteins encoded in the human genome would provide powerful new opportunities for drug discovery of currently untreatable diseases. However, the small-molecule ligand of more than 90% of gene families is completely unknown. Existing protein-ligand docking and machine learning methods often fail when the protein of interest is dissimilar to those with known functions or structures. We have developed a new deep learning framework, PortalCG, for efficiently and accurately predicting ligands of understudied proteins which are out of reach of existing methods. Our method achieves unprecedented accuracy versus state-of-the-art approaches, and it achieves this by incorporating ligand binding site information and the sequence-to-structure-to-function paradigm into a novel deep meta-learning algorithm. In a case study, the performance of PortalCG surpassed the rational design from medicinal chemists. The proposed computational framework can shed new light on how chemicals modulate biological systems, which is indispensable in drug repurposing and rational design of polypharmacology. This approach could offer a new way to develop safe and effective therapeutics for currently incurable diseases. PortalCG can be extended to other types of tasks, such as predicting protein-protein interactions and protein-nucleic acid recognition. Scientific inquiry always aims to deduce new concepts from existing knowledge or to generalize observations, and numerous such challenges and opportunities exist in the biological sciences. The rise of deep learning has sparked a surge of interest in using machine learning to explore previously uncharted molecular and functional spaces in biology and medicine, ranging from “deorphanizing” G-protein coupled receptors  and tranThe training of a deep learning model starts with a domain-specific model architecture. The final model instance that is selected for deployment, and its performance, are determined by a series of data-dependent design choices, including model initialization, how data are split and used for training/validation/testing sets, optimization of loss function, and evaluation metrics. Each of these design choices impacts the generalization power of a trained model. The development of several recent deep learning-based approaches—notably transfer learning , self-suOOD challenges are commonplace in drug discovery and development because of the vastness of chemical genomics space: small molecules act as endogenous or exogenous ligands of numerous proteins, assisting in maintaining homeostasis of a biological system or serving as therapeutics agents to alter pathological processes. Despite tremendous progress in high-throughput screening, the majority of protein space remains unexplored  due to hAccurate and robust prediction of chemical-protein interactions (CPIs) across the genome is a challenging OOD problem . If one In this paper, we propose a new deep learning framework, “Portal Learning”, and its application to chemical genomics , for predicting small-molecule binding to “dark” proteins (whose ligands are unknown) and to dark gene families . Here, we use the word “Portal” to represent multiple training components in an end-to-end deep learning framework, structured so as to be able to systematically address OOD challenges. We show that PortalCG significantly outperforms the leading machine learning and protein-ligand docking methods that are available for predicting ligand binding to dark proteins. Thus, PortalCG may shed new light on unknown functions for dark proteins, and empower drug discovery using Artificial Intelligence (AI). To demonstrate the potential of PortalCG, this work applies it to two case studies: (i) designing selective dual-antagonists of Dopamine receptors for Opioid Use Disorder (OUD) with experimental validations, and (ii) illuminating the understudied druggable genome for targeting diseases that lack effective and safe therapeutics. The novel genes and their lead compounds identified from PortalCG provide new opportunities for drug discovery to treat currently incurable diseases, such as OUDs and AD. We believe that these predictions warrant further experimental validation and exploration.In summary, the contributions of this work are two-fold:We develop and test a new algorithm, PortalCG, to improve the generalization power of machine learning on OOD problems. Comprehensive benchmark studies demonstrate the promise of PortalCG when applied to exploring dark gene families Using PortalCG, we shed new light on unknown protein functions in dark proteins , and open new avenues in polypharmacology and drug repurposing; the latter is demonstrated by our identification of novel drug targets and lead compounds for OUDs and ADPortalCG includes four key, biology-inspired components, as schematized in distilled sequence alignment embedding (DISAE) algorithm  or  [for the ligand compound], and A is the selector matrix [With the protein descriptor pretrained using the sequences from the whole of Pfam, chemical descriptors and a distance learner were plugged in to fine-tune the protein representation. Specifically, the distance learner follows AlphaFold , which fr matrix ,HbindinThe final pair-wise interaction feature tensor,  Pooling  layer foAlgorithm 1 and With a fine-tuned protein descriptor for the protein function space, a binary classifier is then utilized; this step takes the form of a ResNet  built wiAlgorithm 1: Out-of-cluster (OOC) Meta-learning in PortalCGinput: p(D): CPI data distribution over the whole of Pfam, where eachDi ∈ D is a set of CPI pairs for a given family, pfami;    α, β: learning step-size hyperparameters;    L: number of optimization steps in each round of first-level training;    T: number of the second-level training steps;    K: number of points sampled from a local neighborhood    output: θ: set of trained weights for the whole modelθ 1 Initialize whole-model weights, fort in Tdo2 Di ∼ p(D);3  Sample a forl in Ldo4  K pairs in Di;5   Sample a positive-negative balanced mini-batch of forpointjinDido6   K examples;7    Evaluate 8    Compute adapted parameters with gradient descent: end9   10   Update end11  end12 In classic training schemes, a common practice is that there are 3-split data sets, namely “train set”, “development (dev) set” and “test set”. The training set, as the name suggests, is used to train an ML model. The test set, as commonly implemented, is used to set an expectation of performance when applying the trained model to unseen data. Finally, the development set is to select the preferred model instance. In an OOD problem setting, data are split see  such thaR2 = 0.98, p-value = 2.1e-5):x is the raw prediction score of PortalCG.The false positive rate  of predictions can be fitted into an extreme value distribution of the prediction scores  is a widely used approach to predict CPIs. We evaluated the performance of PLD, performed by (i) using AutoDock Vina  with Click here for additional data file.


Baby Boom (BBM) is a key transcription factor that triggers embryogenesis, enhances transformation and regeneration efficiencies, and regulates developmental pathways in plants. Triggering or activating BBM in non-model crops could overcome the bottlenecks in plant breeding. Understanding BBM’s structure is critical for functional characterization and determination of interacting partners and/or ligands. The current in silico study aimed to study BBM’s sequence and conservation across all plant proteomes, predict protein-protein and protein-ligand interactions, and perform molecular docking and molecular dynamics (MD) simulation to specifically determine the binding site amino acid residues. In addition, peptide sequences that interact with BBM have also been predicted, which provide avenues for altered functional interactions and the design of peptide mimetics that can be experimentally validated for their role in tissue culture or transformation media. This novel data could pave the way for the exploitation of BBM’s potential as the master regulator of specialized plant processes such as apomixes, haploid embryogenesis, and CRISPR/Cas9 transgenic development. Transformation, embryogenesis, and plant regeneration are the key developmental stages in any plant improvement or breeding program. The advent of gene editing (GE) tools such as zinc finger nucleases (ZFNs), transcription-activator-like effector nucleases (TALENs), and clustered regulatory interspaced short palindromic repeats associated with protein 9 (CRISPR/Cas9) has enabled precise gene editing both in vitro and in planta leading to significant developments in crop improvement programs ,2. AlthoDRs have been extensively researched to enhance transformation efficiency and induce meristematic or embryogenic division ,16,17,18BBM, a member of the AP2/ERF family and a key regulator of plant cell totipotency, was identified during the in vitro microspore embryogenesis of Brassica napus ,25,26. Thttps://phytozome-next.jgi.doe.gov/ (accessed on 3 February 2022)), which consists of 278 genomes [−5 [https://www.jalview.org/ (accessed on 3 February 2022)) [https://itol.embl.de/ (accessed on 6 February 2022)) to generate a rooted circular Newick tree [Complete plant proteome sequences were downloaded from Phytozome )  was usedick tree .https://string-db.org/) (accessed on 23 January 2022), and the interacting proteins with scores ≥ 0.90 were selected as primary interacting proteins [https://thebiogrid.org/) (accessed on 23 January 2022) was also used to predict interactions [https://alphafold.ebi.ac.uk/) (accessed on 15 March 2022) [Protein-protein interactions (PPI) are key to deciphering protein functions and providing insights into biochemical and/or metabolic processes. For this study, two approaches were employed to predict BBM PPIs: first via sequence-based prediction tools and the second based on the 3-dimensional structure of BBM. AtBBM was searched in the STRING v11.5 Database (proteins . In addiractions . For thech 2022) . The conhttps://zhanggroup.org/COACH/) (accessed on 27 January 2022), a meta-server online prediction tool, which recognizes ligand-binding templates from the BioLiP protein function database by binding-specific substructure and sequence profile comparisons [https://zhanggroup.org/ (accessed on 27 January 2022). C-Sore is the confidence score of the prediction, which ranges between (0–1), where a higher score indicates a more reliable prediction.Predicting ligands that have an affinity for BBM can further elucidate its functional mechanisms. Protein-ligand binding sites and potential ligands were predicted using COACH , Nuclear transcription factor Y subunit B-9 (LEC1), Protein WUSCHEL (WUS), and B3 domain-containing transcription factor (LEC2) were the interacting proteins with an interaction score ≥ 0.9. Fragment 1 of BBM contained residues numbered 208–217, 221–229, 243–268, 271–285, 295–296, and fragment 2 contained residues 316–317, 324–328, 339–358. Similar structure selection criteria were applied to BBM’s interacting proteins. Herein, AGLI5 also contained two fragments with a confidence score > 90, and thus they were separately prepared. The residues in AGLI5 fragment 1 were (4–69), and fragment 2 were . However, LEC1, WUS, and LEC2 had a single fragment with a prediction confidence score > 90. All these fragments were extracted from the complete 3D structure of the protein. http://hex.loria.fr/) (accessed on 18 May 2022). A standard docking protocol was followed where the correlation type was selected as “Shape + Electro + DARS”. The grid dimension that refers to sampling grid size was set at 0.6, solutions as 2000, receptor range (angle of scanning at protein surface)180°, ligand range (angle of scanning at ligand surface) 180°, twist range (intermolecular twist angle) 360°, distance range  at 40, translation step as 0.8, and score threshold at 0.0. It was observed that BBM had two continuous stretches of amino acids responsible for DNA binding, and both fragments were docked sequentially with their primary interacting proteins. BBM was treated as the receptor and the interacting proteins as ligands. The docked score was then reported, and the first 100 conformations were collected and merged into a single file for each docked protein-ligand complex. The Hex tool removed all water molecules and other “hetero” atoms from these input files. Hex then rotates each protein about its coordinate origin and measures the separation between the two origins during the main docking calculation. The docked score was calculated for each orientation, and the highest-scoring orientations were returned as output. PDB files were converted to GRO format (GROMACS format) to perform clustering using GROMACS packages (https://www.gromacs.org/) (accessed on 20 May 2022). The GROMOS cluster method was used with a cutoff of 0.3 nm. The central structure from the most populated cluster was considered representative of the docked complexes.BBM was docked with its interacting partners using a standalone application of Hex  was used to perform the molecular dynamic simulation of protein-peptide complexes. Topology parameters were created using CHARMM force fields and allocated to peptides and proteins. The Ewald Particle Mesh method was used to calculate the distant electrostatic force. The neutralization of the system was achieved by adding Na+ and Cl− ions, and the solvation was performed using the TIP3P water cube model. The simulation involved 50,000 steps of minimization, deploying the steepest descent algorithm. The entire system was heated for 500 ps to 310 K under the constant temperature and volume (NVT) ensemble. Post NVT equilibration, the system was processed under the constant temperature and pressure (NPT) ensemble for 1 ns at 1 bar of constant pressure. The SHAKE method was used to limit all hydrogen bonds, a 50 ns  production simulation run was performed, and the coordinates were stored every 2 ps during the simulation trajectory. During the production run, temperature coupling was performed using the V-rescale method, which is the modified Berendsen thermostat with a time constant of 0.1 ns both for the protein and peptide. Pressure coupling was deployed using the Parrinello-Rahman method. Short-range van der Waal force and electrostatic cut-off were fixed at 1.2 nm. The root means square deviation (RMSD) and root mean square fluctuation (RMSF) were used to examine the conformational variation and structural stability.The Gromacs2021 package (version 2021.5 https://bianca.science.uu.nl/prodigy/) (accessed on 28 May 2022), and the results were collected for binding affinity (ΔG) and dissociation constant (Kd) of the representative structure. PRODIGY counts the number of interfacial contacts (ICs) made at the inner protein complex within a 5.5 Å distance threshold. The server also classified these residues based on polar/nonpolar/charged characteristics.PDB files were uploaded to the PRODIGY server (http://curie.utmb.edu/usercomplex.html) (accessed on 1 June 2022) to detect the residues at the interface of the protein-protein surface. The longest continuous stretch of amino acids within the interacting proteins was collected with an allocation for a single missing residue during the selection, which was then replaced with Glycine (G). The peptides ≥ 8 aa in length were selected and modelled into a 3D structure using the APPTEST server (https://research.timmons.eu/apptest) (accessed on 2 June 2022). The primary sequence of the peptide chain was uploaded to the APPTEST server, and the result was stored as PDB files for each structure. PRODIGY server presented a list of residues interacting at the interface of the protein and peptide complex. In BBM, the two continuous stretches of amino acids responsible for DNA-binding (a) residues 210 to 276 and (b) residues 312 to 370 and those that interacted with proteins were used to score the BBM-peptide complexes. Solvent accessible surface area (SASA) was estimated for the protein structure in both the bound (containing peptide) and unbound states using the InterProSurf server. Later, the ∆SASA (Solvent Accessible Surface Area) for each residue was calculated for the central structure of the docked complex. The ΔSASA > 0 implies the presence of residues at the interface.The representative structure was uploaded to the InterProSurf server (A. helleri and A. lyrate), followed by many wild species from the Brassicaceae family. The most distant relatives were wild and domesticated soybean (Fabaceae), followed by tree species. These 506 protein sequences were aligned using ClustalW  belongs to the Brassicaceae family, it appears to be distantly related to AtBBM along with two ferns, Ceratopteris richardii and Pharus latifolius, and Thuja placata, the western red cedar.AtBBM has two important DNA-binding domains, which belong to AP2/ERF family and one disordered region. The AP2/ERF domains have significant conservation across all the 506 orthologous sequences. The midpoint rooted tree showed  that AtBA. thaliana [AtBBM is known to localize in the nucleus in thaliana , and theThe COACH server identified biomolecules such as Chlorophyll-a (CLA), Zn, Fe, c2F, OHX, SO4, streptolydigan, methionine sulfoxide, etc., and unknown proteins that are potential ligands interacting with AtBBM. Further, their active sites were also predicted. The ligands that scored 0.04 or higher on the C-Sore indicate a more reliable prediction, and they are Zn, Fe, CLA, and the unknown protein, all of which are depicted in Protein interactions, as predicted by STRING, indicate major transcriptional factors such as WUS, LEC1, AGL15, and MYB, among others, to be potential partners of AtBBM, as indicated in Using a standalone Hex protein docking tool, the BBM protein was docked with the primary interacting proteins—AGLI5, LEC1, WUS, and LEC2. Both fragments of the BBM protein, fragments 1 and 2, were docked sequentially with the primary interacting proteins of BBM. Here, AGLI5 also shows two fragments. Therefore, these two fragments of AGLI5 were separately docked with each fragment of the BBM protein. The docked scores are listed in −10 M and 7.5 × 10−8 M, respectively.The binding affinity indicates the strength of the interaction between the receptor and the ligand, and the dissociation constant indicates the affinity of the receptor for the ligand. The higher the dissociation constant lower is the strength of the bound complex. The binding affinity ΔG and dissociation constant Kd calculated from the PRODIGY server are shown in Interatomic contacts (ICs) at a protein-protein complex interface correlate with the experimental binding affinity, largely influencing the interaction . The numThe results of InterProSurf were used in the construction of a continuous stretch of solvent-exposed residues. These amino acid residues of the interacting proteins with the ∆ SASA score were plotted in the bar graph shown in Henceforth, the continuous stretch of the amino acid residues of the interacting proteins at the interface of the BBM proteins was analyzed. These stretches of amino acid residues with their respective lengths were listed in The best-docked scores for each protein-peptide pair are listed in −6, which is ten times lower than P6 and P7.The central structure of these docked complexes of BBM-peptide complexes was submitted to PRODIGY for binding energy calculation. The binding affinity ΔG and dissociation constant Kd calculated from the PRODIGY server are presented in Eventually, the residues at the interface of the protein-peptide complexes were also detected using the PRODIGY server. These BBM residues were matched with the known DNA-binding site residues (210–276) and (312–370) and belonged to fragments 1 and 2, respectively. The interacting residues of BBM that overlapped with these binding sites were counted and shown in Molecular docking applied in this study was a rigid method where protein and peptide were not in motion. Rigid docking brings a remarkable limitation to the interaction profile. This can be addressed by applying molecular dynamic (MD) simulation to the docked pose. Here, the P2 (dock10) and P4 protein-peptide complexes are in an explicit MD simulation for 50 ns. P2 had two equally populated clusters (14 members), while other peptides had only one largest cluster. This made P2-protein complexes more structurally uniform than other peptides, enabling their selection for MD simulation. However, P2 (dock10) had a higher number of binding site residues at the interface than P2 (dock89). So, the dock10 pose was preferred for MD simulation, while P4 was also selected based on the binding energy. The protein-peptide system (P2-dock10 and P4) was first brought to 310 K and 1 bar pressure using NVT and NPT ensembles during the equilibrium phase. However, there was fluctuation from 1 bar in the pressure, but it falls under the acceptable range. The RMSD and RMSF analysis provides critical information about the stability and flexibility of the protein-peptide complexes.Peptide: Root mean square deviation (RMSD) measures the change in structure when two structures are superimposed. Here, the equilibrated structure was considered the reference structure, and all the structures during the 50 ns simulation were compared with the reference structure. After every 20 ps, the simulation trajectory structure coordinates were stored. However, RMSD calculation used the coordinates after every 10 ps. RMSF values were calculated for peptides in protein-peptide complexes to estimate the individual fluctuation of each residue. Here, the RMSF was calculated for the complete 50 ns of the simulation, and the average fluctuation for each residue for all its atoms was recorded. These are small peptides with 11 and 8 residues. P2 (dock10) had three residues  with RMSF > 0.3 nm, where the highest RMSF was reported by terminal residue . In the P4 complex, there were five residues  with RMSF > 0.3 nm, but the maximum RMSF was 0.39 nm for the terminal residue (Residue 8). Similar to peptide RMSF calculation, protein RMSF was also calculated for all atoms for each residue averaged over 50 ns simulation. RMSF for the peptide is shown in Six clusters formed in the P2 (dock10) complex, while in P4, there were 62 clusters. The most populated cluster of P2 (dock10) consisted of 1410 structures. However, the P4 complex had 948 structures in the most populated cluster. The central structure of the two most populated clusters was submitted to PRODIGY for binding energy calculation. The PRODIGY server was used to find the cluster structures’ binding energy ΔG and dissociation constant Kd. The binding affinity ΔG and dissociation constant Kd calculated from the PRODIGY server is shown in Interaction Residues: The central structures for the best cluster for P2 (dock10) and P4 were used for detecting the binding site residues using the LigPlot server, as shown in P2 (dock10) formed three hydrogen bonds with LEU246, GLU279, and GLU285. As the ligand molecule P2 is larger than P4, many hydrophobic contacts  formed in the complex. The interaction map indicates the bigger binding cavity that a peptide can accommodate. Atoms of BBM residues under 4 Å were analyzed for potential hydrophobic contacts. Heavy atoms observed under this range were: TYR295 , VAL296 , TYR245 , GLY247 , SER278 , PRO276 , LEU 261 , TYR249 , and VAL284 .However, Viridiplantae and Rhodophyta, which includes all green algae and plants and red algae. The 506 highly similar proteins ranged from 191 to 746, with an average length of 372 amino acids. AtBBM was reported to be conserved with 98 to 99% sequence identity with certain Brassica species, while the average conservation in these regions was at least 60% across all the orthologous plant sequences [B. stricta—apomixes has been widely studied, B. rapa—model plant for anther culture and embryogenesis, Schrenkiella parvula—extremophyte model that thrives under salt, drought, flooding, chilling, high light, and heat stresses, and Eutrema salsugineum  a salt-tolerant relative of both the genetic model Arabidopsis thaliana (Arabidopsis) and agriculturally important members of the genus Brassica. Understanding the structure and interactions of BBM in these plants can enable exploiting these plants to breed superior crop varieties.The Baby Boom protein, containing an AP2/ERF domain, is a major transcription factor in plants involved in diverse growth and metabolic pathways . The 584equences . This stBBM is known to localize in the nucleus, although recent reports suggest a translocation between the nucleus and cytoplasm . Since iOne of the prominent features of BBM is its interaction with major transcriptional factors such as WUS, LEC1, AGL15, and MYB, among others. All these proteins regulate embryogenesis and development via complex gene regulatory networks where various crosstalk and feedback loops play a major role. The immediate next step would be to run a simple pull-down assay to establish the physical interaction of BBM with these other key regulators. From the PPI predictions in this study, BBM was found to interact with BRAHMA (BRM), a large chromatin-remodeling protein with key roles in all stages of plant development . AlthougThe determining factor in PPI is the amino acid residues in the binding sites. A number of distinct groups identified the interacting surfaces of a protein with a precision of up to 80% based on the unbound structure ,46. DockThese data are valuable for experimental studies and in developing synthetic peptides or peptide mimetics that mimic these interactions, a novel avenue for understanding and manipulating BBM’s mode of action. The addition of these peptide mimetics in the media could have a significant impact on embryogenesis and transformation efficiency. Zhang and co-workers demonstrated that using the basic structure of a protein, information regarding its neighboring structures, co-expression data, and functional and evolutionary similarity generates a scorable predictive power on a genome-wide scale . Along s


Understanding gene functions and their associated abnormal phenotypes is crucial in the prevention, diagnosis and treatment against diseases. The Human Phenotype Ontology (HPO) is a standardized vocabulary for describing the phenotype abnormalities associated with human diseases. However, the current HPO annotations are far from completion, and only a small fraction of human protein-coding genes has HPO annotations. Thus, it is necessary to predict protein-phenotype associations using computational methods. Protein sequences can indicate the structure and function of the proteins, and interacting proteins are more likely to have same function. It is promising to integrate these features for predicting HPO annotations of human protein. We developed GraphPheno, a semi-supervised method based on graph autoencoders, which does not require feature engineering to capture deep features from protein sequences, while also taking into account the topological properties in the protein–protein interaction network to predict the relationships between human genes/proteins and abnormal phenotypes. Cross validation and independent dataset tests show that GraphPheno has satisfactory prediction performance. The algorithm is further confirmed on automatic HPO annotation for no-knowledge proteins under the benchmark of the second Critical Assessment of Functional Annotation, 2013–2014 (CAFA2), where GraphPheno surpasses most existing methods. Further bioinformatics analysis shows that predicted certain phenotype-associated genes using GraphPheno share similar biological properties with known ones. In a case study on the phenotype of abnormality of mitochondrial respiratory chain, top prioritized genes are validated by recent papers. We believe that GraphPheno will help to reveal more associations between genes and phenotypes, and contribute to the discovery of drug targets. Phenotypes refer to observable physical or biological traits of an organism. Revealing the relationships between genes/proteins and their related phenotypes is critical for designing diagnosis, therapy and prevention strategies against diseases . Human PHPO annotation prediction is essentially a large-scale multi-label classification problem, which is also well-known as automated function prediction (AFP). To advance the performance of AFP, several Critical Assessment of Functional Annotation (CAFA) challenges ,4,5 wereFor sequence feature, the GoFDR  method uses BLAST to find homologous sequences and transfers their functional annotations to the query protein . Funfam Integrating sequence and PPI network information would be promising to improve the prediction performance of AFP. In fact, several approaches , and the results show that our algorithm is able to identify novel relationships between human genes/proteins and abnormal phenotypes, and these newly identified DAMRC-associated genes might be potential biomarkers of multiple mitochondrial related diseases.However, all above integration methods are rather simple feature concatenation between sequence and PPI network. Ignorance of the interaction between these features might underestimate the performance of prediction. To address this problem, we developed GraphPheno, a variational graph autoencoders based architecture, which can extend the feature extracted from protein sequences using topological information of PPI network to predict the relationships between human genes/proteins and abnormal phenotypes. GraphPheno does not rely on any manually crafted features and is entirely data driven. Cross validation and independent dataset test were performed to test the prediction ability of GraphPheno. Then, the performance of our model was compared with existing classifiers, and results prove that our model has equivalent performance with other state-of-the-art classifiers. Further bioinformatics analysis shows that predicted and known phenotype-associated genes share similar biological properties. Finally, we performed a case study on HP:0008972 . We expanded every gene-phenotype association in the original dataset to all the ancestor terms of the phenotype so that we could fully utilize existing data for gene-phenotype association prediction. This expansion was done using true path rule  is the Kullback–Leibler divergence [q (∙) and p (∙). Here, we assume p (Z)~N . The cost function reflects how accurately our model can reconstruct the input network and how closely the latent variables can match p (Z). Stochastic gradient descent was used to train the model in order to optimize the cost function with respect to the parameters of the encoder.Finally, to make the approximated adjacency matrix vergence  between Z as final protein feature vectors and train a two-layer NN as the final supervised classifier. We define a function mlpf:NN has become one of the most popular and powerful techniques for supervised learning . We takeEach layer of our NN can be defined as follows:Pl)( is the output after each NN layer. We have P0 = Z in our work. Wl) for each threshold and obtained its maximum value over all thresholds. Precision (pr), recall (rc), and the resulting F-max are defined as:Protein-centric evaluation measures how accurately methods can assign functional terms to a protein. We calculated the Term-centric evaluation measures how accurately methods can assign proteins to a functional term. Here, we use AUC (the area under ROC curve) for term-centric evaluation. The ROC curve can show the efficacy of one test by presenting both sensitivity and specificity for different cutoff points. For cutoff SpWe then computed X to the latent variable Z with the network information A. As our latent embedding already contains both node attributes and sequence information, we define a simple inner product decoder that aims to reconstruct adjacency matrix A using learned latent variables Z. In the third stage of GraphPheno, as shown in Z as the final protein feature vectors and train a two-layer NN as the final supervised classifier. Each dense layer consists of three parts: “Fully connected layer”, “Batch Normalization” and “Dropout”. The inputs to the NN are feature vectors for each protein, whereas the output layer represents HPO terms that we aim to predict (We have developed a protein-phenotype associations prediction pipeline. The first stage of GraphPheno is protein embedding. The model takes STRING PPI network and proteins amino acid sequences as input. PPI network was converted to the format of adjacency matrix. Protein amino acid sequences were embedded using the conjoint triad (CT) method and served as the proteins’ initial features A. The se predict C.To verify the effectiveness of model integrating protein sequences and PPI network, we constructed six baseline methods.Baseline 1, ‘Sequence’: Variational autoencoder was used to generate feature vectors based on protein sequences, and then NN  was used to give prediction scores for each protein-phenotype associations. Baseline 2, ‘BLAST’: Blast was used to retrieve the query protein against the gold standard dataset, and make predictions about a protein-phenotype association based on the number of blast hits with proteins annotated with the HPO term divided by the total number of blast hits.Baseline 3, ‘PPI network’: The prediction scores for each protein-phenotype association were calculated as the number of interaction partners with the query protein which are also annotated to the query HPO term, divided by total number of query protein’s interaction partners in the STRING PPI network.Baseline 4, ‘PPI network (VGAE)’: VGAE was used to generate feature vectors based on PPI network, and then NN was used to generate prediction scores for predicted protein-phenotype associations.Baseline 5, ‘Concatenation’: To verify the effectiveness of using graph autoencoder for data integration, we constructed a baseline integration method called ‘Concatenation’. This method integrates protein sequences and PPI network by using a simple vector concatenation protocol.Baseline 6, ‘Naive’: This method was developed by Wyatt et al.  for protp < 0.001) by Student’s t test.The results in Interestingly, ‘PPI network’ shows the performance second only to GraphPheno in term-centric evaluation, yet its performance decreases dramatically in protein-centric evaluation. This method makes predictions by counting the number of interaction partners with the query protein, so it is only effective when HPO term has a considerable amount of annotation genes. However, we found that HPO terms with high appearances, i.e., Very common, extremely common, occupy only small parts  of all HPO terms , and thiIn particular, ‘Naive’ shows the performance second only to GraphPheno in protein-centric evaluation. ‘Naive’ baseline uses the number of appearances of HPO term as the prediction score, but it is not possible to evaluate it for term-centric evaluation because it reports the same score for an HPO term for any gene.In addition to the five-fold cross-validation, we assessed our model on a novel independent test set of 128,115 gene-phenotype associations that were newly included in the database between June 2019 and April 2021 . The distributions of AUC for five-fold cross-validation and independent test prediction using GraphPheno of each phenotype are shown in F-max . It should be noted that HPOLabeler can make use of wide variety of biological evidences from Gene Ontology [Moreover, we conducted performance comparison on automatic HPO annotation for no-knowledge proteins under the benchmark of the CAFA2 challenge  as descrF-max . GraphPhF-max . In partOntology , InterPrOntology , and HPONext, we trained GraphPheno by all the available gene-phenotype relationships in HPO database before April 2021, and implemented a proteome-wide gene-phenotype association scanning to generate a predicted gene-phenotype association data set. We use Youden Index  to find We also found that predicted genes have higher gene expression correlation with known annotated genes than random genes C, which Known annotated genes and predicted genes associated with certain phenotype are supposed to be of the same biological functions. Here, we introduce the smallest shared biological process (SSBP)  to measuMitochondrial oxidative phosphorylation is the major ATP-producing pathway, which supplies more than 95% of the total energy requirement in the cells. The mitochondrial respiratory chain (MRC) comprises ~92 nuclear and mitochondrial DNA-encoded protein subunits that are organized into five different multi-subunit respiratory complexes . These cWe predicted 995 candidate DAMRC (HP:0008972)-associated genes using GraphPheno. In a quantitative functional analysis of these genes, we considered the enrichment of GO terms and KEGG pathways through a hyper-geometric test. We found that a relatively larger shared enriched GO terms and KEGG pathways appeared in the known annotated and predicted genes A. In parSeveral studies demonstrate that there are reduced expression and impaired activity of respiratory chain Complexes in NDs . We explFurther on, we manually examined the top 150 prioritized DAMRC-associated genes. Among these 150 genes, 67 genes were associated with the assembly of mitochondrial respiratory chain complex I–V , 5 predicted genes were subunits of translocase of the outer mitochondrial membrane (TOM) 40 and translocase of the inner mitochondrial membrane (TIM) 23 . These rNDUFA8 (Rank:1) and COA6 (Rank:15) were annotated to HP:0011923  and HP:0008347 , respectively, which are sub classes of HP:0008972 in the newest version of HPO database (December 2021).In more detail, UQCRH (Rank:8)-deficient mouse model shows impaired CIII activity [ATP5MC3 (Rank:24) encodes a structural complex V (CV) subunit, and heterozygous ATP5MC3 variants were reported to reduce mitochondrial complex V activity in a paper published in October 2021 [Our predictions about 2 DAMRC-associated genes were validated by recent papers. An article published in November 2021 reported that activity . ATP5MC3ber 2021 .UQCRQ, Rank: 6 [NDUFA5, Rank: 10 [NDUFB8, Rank: 13 [COX5B, Rank: 16 [Moreover, GraphPheno has successfully identified 4 DAMRC-associated genes  which wPMPCB  is a member of the family of mitoproteases, which can modulate several biological activities necessary for proper mitochondrial functioning, such as apoptosis. PMPCB has been reported to be associated with the chemoresistance of HCC to sorafenib. Silencing of PMPCB can increase HCC tumor cell susceptibility to sorafenib therapy and is a potential drug target [GraphPheno also has the ability to find potential drug targets. For example, the predicted g target .Determining the HPO annotations of human genes can promote disease gene identification and prioritization and hence assist in clinical diagnostics. However, the current HPO annotations are still incomplete. To address this challenge, we developed a novel computational strategy called GraphPheno to formalize gene features via protein homology and protein interactions for automatic HPO annotation prediction. GraphPheno exhibits superior efficacy over routine strategies.SOAT1  (Rank:65) was reported to alter the distribution of cellular cholesterol, and effectively suppresses the proliferation and migration of hepatocellular carcinoma [SOAT1, was reported to markedly reduce the size of tumours that had high levels of SOAT1 expression. Besides SOAT1, several proteins related to cholesterol homeostasis including CYP7A1 , HMGCR  and SREBF2  are reported to be significantly up-regulated in the HCC tumour tissue, suggesting these genes might be potential targets for HCC drugs [Case studies on mitochondrial related diseases show that GraphPheno has the ability to find novel gene-phenotype associations and potential drug targets. In fact, it is easy to extend GraphPheno to other diseases. For example, among the top 80 prioritized Hypercholesterolemia (HP:0003124)-associated genes (only 18 of them are membrane proteins) predicted by GraphPheno, the knockdown of arcinoma . FurtherCC drugs  (SupplemCYP3A4 and CYP3A5 (which are members of cytochrome P450 enzyme system (CYP) family 3 subfamily A), there are 4 overlap phenotypes: HP:0001939 , HP:0011849 , HP:0004349  and HP:0000818 . Meanwhile, if we randomly selected two genes and count the number of overlap phenotypes, we found that the average number of the overlap phenotypes is significantly lower than that of CYP3A4 and CYP3A5 , which means GraphPheno can describe the commonalities and differences between protein sequences and lead to the grouping of paralogous genes with similar sequences in the same functional category in the prediction output. It should be pointed out that the baseline ‘Sequence’ method without integration of PPI network information has an average AUC of 0.497, which is worse than random, meaning it is difficult to learn vectors that can distinguish functional differences between proteins based on primary structures only. However, by integration of protein sequences and PPI network information, GraphPheno can obtain good predicting performance.The sequence homology of proteins is very important for prediction. In GraphPheno, genes with similar sequences tend to group in the same functional category in the prediction output. For example, among the predicted phenotype terms for F-max than GraphPheno . It should be pointed out that GraphPheno only uses protein primary sequence and protein interaction data as input without any other prior knowledge. As announced by authors of HPOLabeler, temporal validation has low prediction performance, which might be caused by incomplete annotation of new proteins [F-max of two methods (HPOLabeler and GraphPheno) against one independent test dataset of newly annotated 70 genes (HPO annotations added between 9 March 2018 and 21 December 2018) which have less than 10 GO annotations. The results show that GraphPheno was of significantly higher F-max than HPOLabeler .HPOLabeler can make use of wide variety of evidences from Gene Ontology, protein domains, trigram frequency and HPO term frequency to predict protein-HPO associations. It is the first method which has outperformed a simple Naive approach in the second Critical Assessment of Functional Annotation, 2013–2014 (CAFA2). In performance comparison under the benchmark of the CAFA2 [In general, we believe GraphPheno will contribute to novel gene-phenotype association discovery and be a valuable resource for disease/phenotypes research.