Virtual screening, which can computationally predict the presence or absence of protein–compound interactions, has attracted attention as a large-scale, low-cost, and short-term search method for seed compounds. Existing machine learning methods for predicting protein–compound interactions are largely divided into those based on molecular structure data and those based on network data. The former utilize information on proteins and compounds, such as amino acid sequences and chemical structures; the latter rely on interaction network data, such as protein–protein interactions and compound–compound interactions. However, there have been few attempts to combine both types of data in molecular information and interaction networks.We developed a deep learning-based method that integrates protein features, compound features, and multiple types of interactome data to predict protein–compound interactions. We designed three benchmark datasets with different difficulties and applied them to evaluate the prediction method. The performance evaluations show that our deep learning framework for integrating molecular structure data and interactome data outperforms state-of-the-art machine learning methods for protein–compound interaction prediction tasks. The performance improvement is statistically significant according to the Wilcoxon signed-rank test. This finding reveals that the multi-interactome data captures perspectives other than amino acid sequence homology and chemical structure similarity and that both types of data synergistically improve the prediction accuracy. Furthermore, experiments on the three benchmark datasets show that our method is more robust than existing methods in accurately predicting interactions between proteins and compounds that are unseen in training samples. Most compounds that act as drugs bind to target proteins that can cause disease, and these compounds can control their functions. Therefore, it is necessary when developing new drugs to search for compounds that can interact with the target protein, and this process must be performed efficiently. However, determining the interaction of a large number of protein–compound pairs via experiments is expensive in terms of time and cost. Virtual screening that can computationally classify the presence or absence of protein–compound interactions has attracted attention as a large-scale, low-cost, short-term search method for seed compounds. In particular, machine learning for virtual screening is considered to be applicable to a wide variety of proteins and compounds.Machine learning-based methods for predicting protein–compound interactions are largely divided into those based on molecular structure data and those based on network data. The former use protein and compound data represented in amino acid sequences and chemical structure formulas, and they can be applied to proteins when a docking simulation cannot be performed because the 3D structure is unknown. In our previous studies –3, we peDeep learning, a method developed in the field of machine learning, has been applied in a variety of fields in recent years because it achieves high prediction accuracy in fields such as image recognition, speech recognition, and compound activity prediction . Deep leThe other type of machine learning approach for protein–compound interaction prediction is based on network data. An interaction network is commonly used to comprehensively represent interactions between molecules. For example, the protein–protein interaction network represents the relationships among physically interacting proteins. In the protein–protein interaction network, a node is a protein, and an edge is drawn between a pair of proteins that interact with each other.Some previous studies incorporated data from multiple interaction networks to predict molecular interactions. For instance, multi-modal graphs to handle three types of interactions have been proposed: protein–protein, protein–drug, and polypharmacy side effects. A deep learning method for multi-modal graphs, Decagon , was proIn addition to predicting protein–compound interactions, several studies have predicted other types of molecular interactions. Protein–protein interactions induce many biological processes within a cell, and experiential and computational methods have been developed to identify various protein–protein interactions. High-throughput experimental methods, such as yeast two-hybrid screening, were developed to discover and validate protein–protein interactions on a large scale. Computational methods for protein–protein interaction predictions employ various machine learning methods, such as SVM with feature extraction engineering . The recThe purpose of this study was to improve prediction accuracy by integrating molecular structure data and heterogeneous interactome data into a deep learning method for predicting protein–compound interactions. In addition to the molecular information  itself, protein–protein interaction network data with similar reaction pathways or physical direct binding and compound network data linking compounds with similar molecular activities are incorporated into the deep learning model as multi-interactome data. To the best of our knowledge, there are no deep learning-based solutions for predicting protein–compound interactions that integrate multiple heterogeneous interactome data along with the direct input of amino acid sequences and chemical structures.This study proposes a method for predicting protein–compound (drug-target) interactions by combining protein features, compound features, and network context for both proteins and compounds. The network context is in the form of protein–protein interactions from the STRING database , and theWe designed three benchmark datasets with different difficulties and evaluated the performance of the model using these datasets. In these performance evaluations, we demonstrated that integrating the molecular structure data and multiple heterogeneous interactome data synergistically improves the accuracy of protein–compound interaction prediction. Furthermore, performance comparisons with state-of-the-art deep learning methods based on molecular information  and thosFirst, the protein data were applied to a one-dimensional convolutional neural network (1D-CNN). For the protein input, a one-hot vector was used for the distributed representation of an amino acid sequence of 20 dimensions at a height and width of 5762 dimensions with the maximum length of amino acid sequences.An amino acid sequence is a linear structure (1-D grid). In this study, a filter (kernel) with a 1D convolution operation was applied to the linear structure. Here, a “1D” convolutional operation for linear structures is interpreted as scanning the input structure in only one direction along the linear structure with a filter of the same height (dimension) as that of the distributed representation of the input.l-th hidden layer of the CNN, the input is the set of feature maps in the (l-1)-th hidden layer r is the size of the filter, m is the size of the feature map, and n is the number of feature maps. The output of the k-th filter is a feature map of the l-th layer We denote an input vector sequence that corresponds to the one-hot vector representation of an amino acid sequence by mentclass2pt{minimf is an activation function (Leaky-ReLU), k-th filter in the l-th convolutional layer, and where t represents the last layer of the CNN, and s represents the number of filters in the last layer.where The extended-connectivity fingerprint   is the mA protein–protein interaction network that connects physically interacting proteins and a compound–compound interaction network that connects compounds with similar molecular activities were input as multi-interactome data. First, each network was represented as a graph. A node is a protein in the protein–protein network and a compound in the compound–compound network. An edge is drawn between a pair of proteins (compounds) that interact with each other. By applying this graph to “node2vec” , the feaThe node2vec algorithm was applied to the protein–protein interaction network and the compound–compound interaction network. Using a protein and a compound as vertices, the interaction networks were converted into graphs with edge weights based on the reliability of the experimental data and the similarity in molecular activity. Node2vec (version 0.2.2) from the Python library, which implemented the node2vec algorithm, was applied to the converted graph. The node2vec parameters used the default values .d.Let a protein–protein interaction network be expressed by a weighted graph The feature vectors obtained from the 1D-CNN for the amino acid sequence and node2vec for the protein–protein interaction network were concatenated and fed to the final output layer. Similarly, the feature vectors from the ECFP for the chemical structure and node2vec for the compound–compound interaction network were concatenated and fed to the final output layer.d by applying fully connected layers. The similarity between the vector for proteins and the vector for compounds on the latent space was calculated by the element-wise product calculation method followed by a fully connected layer. When a pair of proteins and compounds was input, it was predicted that there was an interaction between the input pair if the similarity was higher than a predefined threshold (where the default was 0.5); if the similarity was lower, no interaction was predicted. This model is denoted as the “integrated model”.We designed an output layer consisting of an element-wise product calculation followed by a fully connected layer, which is an extension of cosine similarity. The architecture is illustrated in Fig. d by applying the fully connected layers f and g. From this, the similarity between the two vectors for the latent space was calculated.More precisely, let As described above, to handle data from different modalities, such as proteins and compounds, we adopted a method of embedding data of different modalities into a common latent space. Defining the similarity in the obtained latent space enables the measurement of the similarity between the data for different modalities. Visual semantic embedding (VSE) is a typical example of a method that handles data from different modalities and can associate images with text data in acquiring these multi-modal representations . VSE wasTo see the effect of integrating multi-modal features, two baseline models were constructed for the performance comparison. One was based on molecular structure data and used only amino acid sequence and chemical structure information, and the other was based on interaction network data and used only protein–protein interaction and compound–compound interaction information. The single-modality model based on molecular structure data, denoted the “single-modality model (molecular)”, is defined as follows:and the single-modality model based on interaction network data, denoted the “single-modality model (network)”, is defined as follows:For the similarity output The hyperparameters, the number and size of the filters in the convolutional layers in the 1D-CNN, and the number of units in the fully connected output layers were optimized by the Bayesian optimization tool Optuna , which iRegularization is important for avoiding overfitting and improving the prediction accuracy in deep learning for complex model architectures with a large number of parameters. Regularization is especially important in our deep learning model, which integrates multiple datasets of different modalities; hence, we employed several regularization methods.We employed batch normalization , which aw refers to the parameters of the entire model, and the second term of the above equation is the sum of the squared values of all the parameters divided by 2. λ is a parameter that controls the strength of regularization. Adding this term to the objective function has the effect of preventing the absolute value of the network weight from becoming too large, which helps prevent overfitting.where n-grams of amino acid sequences. The second method was NeoDTI [The prediction performance of the proposed models was compared with that of state-of-the-art deep learning methods based on molecular structure data and interaction network data. The first method was based on a graph CNN for protein–compound prediction . It empls NeoDTI , which ds NeoDTI , as the The protein–compound interaction data and compound–compound networks were retrieved from the database STITCH , and theProtein–compound interaction data were obtained from the STITCH database . STITCH The protein–protein interaction information was obtained from the STRING database , which cThe compound–compound interaction data were also obtained from the STITCH database. The compound–compound interaction data in STITCH are based on (1) the chemical reactions obtained from the pathway databases; (2) structural similarity; (3) association with previous literature; and (4) correspondence between the compounds based on molecular activity similarity. For the similarity of the molecular activities in item (4), the activity data obtained by screening the model cell line NCI60 were used. When the compound–compound interaction data were downloaded from STITCH, a score based on the reliability was created for each of the above four items for each compound pair. For the compound–compound interaction data used in this study, the threshold value for the reliability score in item (4) was set to 150. Data that did not satisfy this criterion were removed.From the STITCH and STRING databases, a total of 22,881 protein–compound interactions, 175,452 protein–protein interactions and 69,231 compound–compound interactions were downloaded. Using the downloaded dataset in which the protein–protein interaction, compound–compound interaction and protein–compound interaction data were all available, the three types of datasets below were constructed to perform fivefold cross-validation.k-fold cross-validation, all positive and negative examples are randomly split into k folds. One of them is used as a test sample, and the remaining k − 1 folds are used as training samples; then, the k results obtained are averaged. We call the cross-validation dataset the baseline dataset. As more difficult and more practical tasks, we constructed two more cross-validation datasets, called the unseen compound-test dataset and the hard dataset. In the unseen compound-test dataset, we split the data into k folds such that none of the folds contained the same compounds as the others. In the unseen compound-test dataset, the compounds in the test sample did not appear in the training sample. In other words, the interaction of new (unseen) candidate compounds with the target proteins must be accurately predicted. In the hard dataset, we split the data into k folds such that none of the folds contain the same proteins and compounds as the others. In the hard dataset, neither the proteins nor the compounds in the test sample appear in the training sample. In other words, interactions in which neither the proteins nor the compounds are found in the training sample must be accurately predicted.In typical TP is the number of true positives, TN is the number of true negatives, FP is the number of false positives, and FN is the number of false negatives; the recall is defined by TP/(TP + FN), and the precision is defined by TP/(TP + FP).The following measures were used for the performance evaluation criteria: AUROC, AUPRC, F-measure, and accuracy.The experiment was performed on a computer equipped with an Intel(R) Xeon CPU  with one NVIDIA Tesla V100 GPU. The computational time of our deep learning algorithm was approximately 27 s (CPU time) per epoch in the process of constructing the integrated model. The number of epochs required for the learning process to converge varied greatly from tens to hundreds.The performance of our three models was evaluated to determine the effectiveness of integrating the molecular structure data and the interaction network data. The results derived from the three datasets are shown in Tables Compared with the two single-modality models, the integrated model significantly improved the prediction accuracy in all evaluation measures. For example, in terms of AUPRC, which is a more informative evaluation index in a dataset that is imbalanced between positive and negative samples, the integrated model showed significant improvements of 3.0%, 7.1 and 8.3% over the single-modality model (molecular) and 3.7%, 10.9 and 18.1% over the single-modality model (network) in the baseline dataset, unseen compound-test dataset and hard dataset, respectively. These results demonstrate that integrating multiple heterogeneous interactome data with molecular structure data brought a synergistic effect in improving the accuracy of protein–compound interaction prediction.The prediction performance of our three models was compared with that of state-of-the-art deep learning methods and traditional machine learning methods based on molecular structure data and interaction network data. The results for the three datasets are shown in Tables The integrated model yielded superior prediction performance compared with the other existing methods. For the baseline dataset, the integrated model achieved significant improvements compared with the graph CNN-based method , NeoDTI For the unseen compound-test and hard datasets, a more marked difference in the performance of the integrated model was confirmed. We compared the integrated model with the graph CNN-based method and NeoDTI in terms of AUROC, AUPRC and F-measure. The integrated model greatly outperformed the others, with significant improvements  over the graph CNN-based method. Compared with NeoDTI, significant improvements were also confirmed: 8.1% in terms of AUROC, 8.9% in terms of AUPRC and 17.1% in terms of F-measure for the unseen compound-test dataset, and 11.6% in terms of AUROC, 16.6% in terms of AUPRC and 140.4% in terms of F-measure for the hard dataset. Based on the above results, the integrated model can predict protein–compound interactions with stable accuracy, regardless of the difficulty of the dataset and the types of proteins and compounds that constitute the test data, compared with other existing methods. This is due to the use of features based on sequence information and compound structure information by the integrated model and features obtained from the interaction network, as well as the effect of using the element-wise product of the protein and compound feature vectors in the output layer.n-grams and applying it to the CNN. Therefore, the graph CNN-based method can be defined as having the same molecular structure data-based prediction model as the single-modality model (molecular). For the baseline dataset, the unseen compound-test dataset and the hard dataset, the single-modality model (molecular) outperformed the graph CNN-based method. As an example, the single-modality model (molecular) achieved an improvement of 20.4% in terms of AUROC, 36.8% in terms of AUPRC and 55.0% in terms of F-measure for the hard dataset over the graph CNN-based method . The difference is that the single-modality model (network) first uses unsupervised deep learning (node2vec) to automatically learn feature representations for nodes in the given heterogeneous interaction networks and then applies supervised learning to predict protein–compound interactions based on the learned features, while NeoDTI simultaneously learns the feature representations of nodes and protein–compound interactions in a supervised manner. In the three datasets, the prediction performance of the single-modality model (network) was comparable to that of NeoDTI.To interpret the accuracy improvement obtained by integrating multi-interactome data with molecular structure data, which was shown in the previous section, we analysed whether the protein–protein interaction captured a different perspective than amino acid sequence homology and whether the compound–compound interaction captured a different perspective than chemical structure similarity. More concretely, we investigated the relationship between the amino acid sequence homology and similarity of proteins in the protein–protein interaction network, as well as the relationship between the chemical structure similarity and the similarity in the compound–compound interaction network.For every pair of proteins in the dataset used in the experiments, the amino acid sequence similarity was calculated using DIAMOND, and the cosine similarity between two vectors of the pair output by node2vec using the protein–protein interaction network was calculated. All of the protein pairs were plotted with the amino acid sequence similarity on the x-axis and the cosine similarity in the protein–protein interaction network on the y-axis. The scatter plot is shown in Fig. For example, the protein “5-hydroxytryptamine (serotonin) receptor 6, G protein-coupled (HTR6)” and the compound “Mesulergine” in the test sample in the “hard dataset” have a positive interaction , and ourThis study aimed to improve the performance of protein–compound interaction prediction by integrating molecular structure and interactome data, which was achieved by integrating multiple heterogeneous interactome data into predictions of protein–compound interactions. An end-to-end learning method was developed that combines a 1D-CNN for amino acid sequences, an ECFP representation for compounds, and feature representation learning with node2vec for protein–protein and compound–compound interaction networks. The proposed integrated model exhibited significant performance differences with respect to accuracy measures compared with the current state-of-the-art deep learning methods. This improvement in performance was verified as statistically significant by the Wilcoxon signed-rank test. The results indicated that the proposed model is able to more accurately predict protein–compound interactions even in a hard dataset, whereby neither the proteins nor compounds in the test sample are included in the training sample.An important future task is to integrate the gene regulatory network as additional interactome data to further improve protein–compound interaction prediction. A large number of gene expression profiles for various tissues and cell lines are available in public databases, and gene regulatory networks can be effectively inferred from gene expression profiles. For example, the effectiveness of utilizing gene expression data for drug repositioning was reported in a summary review . Gene ex


Additionally, a further re-extraction confirmed that more agar could be extracted. Protein was also successfully co-extracted in the seaweed residues. Optimized conditions were obtained for both the extractions and the re-extraction of the two methods . Finally, FT-IR characterization demonstrated that the extracts had a similar spectrum to the commercial agar. Compared to commercial samples, the low gel strength of the agar extracts shows that these extracts might have novel and different potential applications.Agar is a hydrocolloid found in red seaweeds, which has been of industrial interest over the last century due to its multiple applications in the food, cosmetic, and medical fields. This polysaccharide, extracted by boiling for several hours, is released from the cell wall of red seaweeds. However, the environmental impact coming from the long processing time and the energy required to reach the targeted processing temperature needs to be reduced. In this study, a response surface methodology was employed to optimize both conventional extraction and ultrasound-assisted extractions. Two different models were successfully obtained (R Green industries have gained importance over the last decades as an alternative to climate-change-contributing industries, specially the industry extracting hydrocolloids from marine resources, which already has been categorized as sustainable production . HydrocoGracilaria spp.) and not for other species , having other effects as lower yields of extraction; (2) an aqueous extraction of agar at temperatures equal or above 85 °C; (3) filtration; and (4) cooling, freezing, and thawing of the agar gel [Agar is a polysaccharide formed by β-1,3-linked galactopyranose and α-1,4-linked 3,6-inner ether-L-galactopyranose . To dateagar gel ,11. Thisagar gel . This tragar gel . On the Gelidium sesquipedale, which is a common source of agar. This technology is based on the cavitation phenomenon that takes place through the formation of cavitation bubbles on the surface of the cell walls. The ultrasound action on the plant materials is reported to degrade the plant matrix into smaller particles with an increased surface contact with the solvent; thus, giving a better interaction between the solvent and compounds of interest. It also increases the size of the plant matrix pores, leading to a more efficient mass transfer compared to conventional methods [As previously mentioned, novel extraction technologies aim to transform traditional and non-sustainable methods into greener methods to extract hydrocolloids. In this study, UAE was used to extract agar from  methods . Followi methods .Gelidium sesquipedale. In order to optimize these processes, a statistical design approach based on Response Surface Methodology (RSM) was used [The main objective of this study is to optimize the traditional agar extraction method, and to compare the results to an alternative, optimized novel extraction method: UAE from was used . OptimizG. sesquipedale was kindly donated by Hispanagar  from a unique batch to ensure the reproducibility of this study. The raw material was washed with tap water until no sand leftovers were found in the washing water. The raw material was then placed in aluminium trays in a 60 °C oven overnight to dry until reaching to constant weight.Dried G. sesquipedale was performed by either the traditional method or following the UAE methodology.The agar extraction from G. sesquipedale was mixed with 500 mL of distilled water inside a round bottom flask inside an oil bath (LAUDA E100 ecoline 011) and attached to a reflux condenser to prevent water evaporation. Different extraction times and temperatures were performed, as indicated by the RSM design  was placed inside the mixture. Different extraction times and ultrasound probe powers were performed as indicated by the RSM design   and Equation (2). (WRAE: weight residue after extraction):A two level, three-variable Composite Central Design (CCD) was employed in this study, requiring a total of 13 experiments for the optimization of traditional extraction parameters . The parameters and levels employed were the extraction time (2–6 h) and temperature (80–110 °C). The coded and original values of the independent variables used in this experiment are listed in A two level, three-variable Composite Central Design (CCD) was employed in this study, requiring a total of 13 experiments for the optimization of the UAE parameters . The parameters and levels employed were extraction time (30–60 min), and power (50–100%). The coded and original values of the independent variables used in this experiment are listed in i and xj, are the independent variables, β0 stands for the constant term, βj stands for the linear coefficient, βij stands for the interaction coefficient, βjj stands for the quadratic factor, and k is the number of independent parameters.The extraction yield of the agar from the CCD, together with three other responses (yield of re-extraction (%), protein extraction (%), and protein re-extraction (%)), were analysed using the response surface regression  and fitt2HPO4 in 10 mM Na2B4O7 · H2O (pH 8.2), and the B mobile phase consisted of a mixture of acetonitrile, methanol, and water .The protein was hydrolysed to determine the total amino acid content. These analyses were conducted on the residues generated after the first extraction and the following re-extraction. The protein was calculated based on the total amino acid content.The analysis was performed as explained by Hildebrand et al.  using thw/w) solutions of agar in distilled water were prepared at 95 °C until all the soluble compounds were dissolved, and then transferred to a bloom jar and kept in a fridge at 4 °C overnight. The next day, the samples were left to reach room temperature before the analysis. A texture analyser  equipped with a radiused cylinder probe  was operated at a penetration rate of 1 mm/s to a depth of 5 mm on the formed gel as performed by [Gel strength measurements of the agar extracts obtained from the optimized conditions for conventional agar extraction and the ultrasound-assisted extraction were based on penetration tests on the formed agar gels using gel fracture force to determine the gel strength. For this purpose, 1.5%  were collected over the wavenumber range 600–4000 cmp < 0.001) and a coefficient of regression of 0.8773. In the case of the other responses studied, it can be seen on the 3D figure that none of the parameters studied affects the yield of protein re-extraction. In the case of the protein extraction response, it was found that only temperature was significant, while a longer time shows a trend in where it may have a positive effect. This may be explained based on the fact that even the mildest time and temperature conditions here tested were sufficient to reach the highest protein yield extraction. Regression analysis was performed on the experimental data and the coefficients of the model were evaluated for statistical significance using ANOVA analysis.The four response dependent variables (yield of extraction (%), yield of re-extraction (%), protein extraction (%), and protein re-extraction (%)) for each run in the experimental design are listed in Equation (4) describes the relationship between the extraction time and temperature for the yield of the extraction, the yield of the re-extraction, the protein extraction, and the protein re-extraction. 3D surface plots illustrate the relationship between the yield of the extraction A, the yi2 = 0.801) with Equation (4). In the case of protein extraction, only temperature was found to be significant and the model regression coefficient was R2 = 0.618. The other responses had no significant linear terms. In As the quadratic terms were not significant, the model for the “yield of extraction (%)” response was changed into a linear model (R1: 6 h and X2: 110 °C) (H. cornea in 1.8 L) compared to this study  and the differences in the yields could be observed due to this [The optimized conditions for the conventional agar extraction and re-extraction were obtained based on the highest agar yield achievable. For that purpose, Equation (4) was used to calculate the estimated agar yield of the extraction that could be obtained, also to compare this yield to the one obtained after performing the extraction with the optimal conditions (X 110 °C) . The est to this .p < 0.01) and a coefficient of regression of 0.7436. It can be seen in The four response dependent variables (yield of extraction (%), yield of re-extraction (%), protein extraction (%), and protein re-extraction (%)) for each run in the experimental design are listed in 2 = 0.5338) with the following Equation (5). The other responses had no significant linear terms.As the quadratic terms were not significant, the model for the “yield of extraction (%)” response was changed into a linear model (R1: 60 min and X2: 100% ultrasound power) , reporting a yield of extraction of 18.5 ± 2.4% using a 1:25 ratio, higher than the yield obtained for this experiment, where the only difference is the frequency of the ultrasound probe (20 kHz). This finding shows that a higher frequency could have a potential impact on the yields of extraction.The optimized conditions for the Ultrasound–Assisted Extraction and re-extraction were obtained based on the highest agar yield achieved. For that purpose, Equation (5) was used to calculate the estimated agar yield of the extraction that could be obtained, also to compare this yield to the one obtained after performing the extraction with the optimal conditions (Xd power) . From Eqp < 0.01), whereas extraction temperature significantly affected the yield of extraction (p < 0.001) and the protein extraction (p < 0.05). As a result, linear effects of the independent variables were dependent for the protein of extraction, and only temperature in the case of the yield of re-extraction. However, in the case of yield of re-extraction and protein re-extraction, analysis regression of the model shows that the linear, quadratic, and interaction regression did not fit the experimental data well. Nevertheless, it is seen that the re-extraction achieved extracting more agar from that already used for extraction of the G. sesquipedale sample, and that the protein was also co-extracted in the residues from both the extraction and re-extraction. No relation was found among the samples with the lowest extraction yields, and the samples with the highest re-extraction yields. The highest yield of extraction (15.85%) was obtained at 5 h and 105 °C; the highest yield of re-extraction (21.85%) was obtained for the re-extraction of the 4 h and 95 °C samples; the highest protein content for extraction (11.80%) was obtained for the sample treated at 3 h and 105 °C; and the highest protein content for re-extraction (26.14%) was obtained for the sample treated at 3 h and 85 °C. At higher temperatures and times of extraction, higher yields of extraction were achieved; the same trend can be seen for the protein content of extraction whereas the yield of re-extraction was not significantly affected by the increase in temperature, and the protein content of re-extraction was not affected by any of the experiment variables  affected significantly only the yield of extraction (%), whereas extraction power did not affect significantly the yield of extraction (%) (p > 0.05). These independent variables did not significantly affect any of the other responses, which are yield of re-extraction (%), protein extraction (%), and protein re-extraction (%). In the case of yield of extraction (%), protein extraction (%), and protein re-extraction (%), analysis of the regression model show that the linear, quadratic, and cross product (interaction) did not fit the experimental data well. The same findings as for the conventional extraction for the yields of re-extraction and for the protein yields were found for the UAE. The highest yield of extraction (6.82%) was obtained at 60 min and 75% ultrasound power; the highest yield of re-extraction (16.14%) was obtained for the re-extraction of the 60 min and 50% ultrasound power samples; the highest protein content for extraction (12.02%) was obtained for the sample treated at 30 min and 100% ultrasound power; and the highest protein content for re-extraction (14.56%) was obtained for the sample treated at 45 min and 50%. For all the responses, a higher time of extraction contributed to a higher yield, except for the yield of protein extraction (%) as at short times all the extractable protein was already solubilized. In turn, higher ultrasound power did not seem to have a significant effect on any of the responses (−1) did not have an effect on the yield of the polysaccharide extracted from Silvetia compressa [Sargassum muticum, increasing from 5.7 to 15% from 5 min to 30 min of extraction [Extraction time , Sigma-Aldrich, Wicklow, Ireland were collected and ranged from 600 to 4000 cm002-18-0,ose ring . A stronproteins . Finallyl sample .The spectral shape of all the agar extracts shown in 2) while the lowest gel strength was reported for Ultrasound-Assisted Extraction (26.84 ± 15.6 g/cm2). This finding is opposite to the initially expected results of a higher gel strength for the Ultrasound-Assisted Extraction samples compared to the conventional extraction samples. No negative impact from the six hours of extraction of the conventional method was found. A 750 g/cm2 value for gel strength is considered a high-quality agar; therefore, none of the agar extracts can be considered a high-quality agar [p < 0.05) gel strength compared to the extraction extracts, being in the case of the Ultrasound-Assisted re-extraction higher than the Ultrasound-Assisted Extraction, which could suppose that additional agar was extracted. A study where agar was also extracted by conventional extraction and by Ultrasound-Assisted Extraction reported the following values, 377 ± 136 g/cm2 and 438 ± 47 g/cm2, respectively; compared to this study, a similar gel strength for the conventional extraction was obtained (394.93 ± 81.93 g/cm2), whereas a much lower gel strength was obtained for the Ultrasound-Assisted Extraction (26.84 ± 15.6 g/cm2) [The different agar extracts obtained following the optimized conditions for conventional extraction  and ultrasound assisted extraction  were tested for gel strength following a penetration test. ity agar . Furtherity agar . This hiity agar . The re-6 g/cm2) . p > 0.05) between the extraction time or temperature applied, and the total concentration of amino acids was found, as observed also for total protein yields, which supports these findings. If the re-extraction samples are compared, it can be seen that the highest total amino acid concentration was obtained for the  sample. In the case of UAE, it was found that not the power nor the time had any significant effect (p > 0.05) on the total concentration of amino acids. In all cases, the highest amino acid concentration was found for aspartic acid, which agrees with what was found by Trigueros et al. [G. sesquipedale.The residues obtained after performing conventional agar extraction and Ultrasound-Assisted Extraction were characterised to obtain their essential amino acid profiles. In s et al.  after usTwo Response Surface Methodology studies were conducted to optimise the agar extraction processes by means of conventional or Ultrasound-Assisted Extraction; also, we investigated the residues generated as a source of proteins to be re-extracted, aiming to obtain other valuable compounds, such as amino acids and proteins. In this study, models were successfully obtained for both conventional extraction and UAE, with a regression coefficient of 0.8773 and 0.7436, respectively. Optimized conditions were also successfully obtained . It has been found that increasing time and temperature for conventional extraction, or increasing time for Ultrasound-Assisted Extraction, can result in higher yields in agar extraction; nevertheless, ultrasound power did not affect the agar yields of extraction. Especially for the Ultrasound-Assisted Extraction method, the seaweed cell walls can be effectively damaged, and therefore lead to a more efficient mass transfer. On the other hand, protein extraction was not affected by the conditions here studied. Additional re-extraction methodologies for agar and protein were analysed, too, although the models developed were not significant. The reason behind a better fit of the conventional extraction model could be that the parameters tested were synergetic, whereas the effect of ultrasound power is not so clear, as it does not show a clear trend on the yields. This could be explained as the parameters were not chosen based on enhancing these responses (re-extractions and protein extractions). The characterization of the optimized extracts confirmed that agar was extracted as the spectra obtained for the FT-IR matched the spectrum of the commercial agar. The gel strength of the conventional extracts was significantly higher than US extracted ones, although not as high as the commercial sample. Nevertheless, this low gel strength could be used in novel applications such as dressings, or fat replacers. Further studies focusing on testing ultrasound action on the cell walls are required to figure out the conditions to minimize agar depolymerisation. Re-extraction protein protocols led to higher concentrations of total essential amino acids compared to extraction protein protocols, for conventional and ultrasound-assisted methods. Based on our study, it can be concluded that UAE would allow to extract a higher yield compared to conventional extraction when the same extraction time is used; therefore, it is more convenient from an economical point of view, as less energy is required (


Identifying protein complexes from protein–protein interaction (PPI) networks is a crucial task, and many related algorithms have been developed. Most algorithms usually employ direct neighbors of nodes and ignore resource allocation and second-order neighbors. The effective use of such information is crucial to protein complex detection.Based on this observation, we propose a new way by combining node resource allocation and gene expression information to weight protein network (NRAGE-WPN), in which protein complexes are detected based on core-attachment and second-order neighbors.Through comparison with eleven methods in Yeast and Human PPI network, the experimental results demonstrate that this algorithm not only performs better than other methods on 75% in terms of f-measure+, but also can achieve an ideal overall performance in terms of a composite score consisting of five performance measures. This identification method is simple and can accurately identify more complexes.The online version contains supplementary material available at 10.1186/s12859-021-04543-4. Proteins are the basis of biological activities, and their functions are generally expressed by the interactions between proteins . In orgaComplexes take part in many diverse biochemical activities that are fundamental to all kinds of functions, such as cell homeostasis, cell cycle control, growth, and proliferation. Moreover, specific functional modules usually are related to certain diseases.Although great progress has been made in identifying protein complexes, laboratory-based methods are expensive, ineffective and sometimes even infeasible, and only parts of protein complexes are located. In addition, experiments in the laboratory are often incomplete because of the constraints of experimental conditions. As it is necessary to overcome the lacking of laboratory-based methods, a large number of computational algorithms have been designed as alternative methods to identify protein clusters, such as density-based clustering –8, hieraAiming at the solution for the above-mentioned problems, we introduce a novel method based on resource allocation and gene expression in weighted PPI networks  with based on core-attachment structure and second-order neighbors searching. First, based on the resource allocation and gene expression of the PPI network, a new weight metric is designed to accurately describe the interaction between proteins. Then our method detects a series of dense complex cores based on density and network diameter constraints and the final complexes are recognized by expanding the second-order neighbors of nodes in core complexes. This identification method is simple and can accurately identify more complexes.Protein complex detection with a computational approach from PPI data is useful as the useful supplement to the limited experimental methods. Besides the enhancement in graph clustering techniques, successful and accurate methods for protein complex prediction depends more on the construction of weighted graphs. Therefore, constructing weighted graph for protein interactions is essential. In this section, we introduce a novel method based on resource allocation and gene expression in weighted PPI networks with two main steps. First, a method is proposed to evaluate the reliability of the protein interaction data considering both the common neighbor information and gene expression profiles through the weighted graph construction. Second, protein complexes are detected based on core-attachment and second-order neighbors in this new weighted graph. The workflow of our method is shown in Fig. To represent a PPI network, a 3-element tuple eference , resourcTaking Fig. mentclass2pt{minimWp is formed by the PCC calculation formula, which is defined in Eq.  based on PCC and RA information between two interaction proteins. The final weighted construction formula is proposed in Eq. .5\documeMatrix  the Eq.  can measWN with the given PPI datasets. Line 2 is for constructing matrix.Wp with the gene expression data. Line 3 is for constructing the new matrix W based on WN and Wp, and the protein interaction confidence is the sum of the weights of WN and Wp. Lines 4–8 are for identifying core clusters. Lines 9–11 are for enlarging core clusters based on second-order neighbors of nodes in each core.The proposed algorithm, NRAGE-WPN, consists of two phases: weighted graph construction and core-attachment protein complex detection based on second-order neighbors searching. In the weighted graph construction phase, gene expression information and common neighbor information are integrated. A detailed description of the algorithm is outlined in Algorithm 1. Line 1 is for constructing matrix In this algorithm, density and diameter are employed as the condition for complex detection.If a node meets the two constraints in condition , it is amentclasspt{minimaferences .(1) Density: The degree of a node V is the sum of the weights for each edge connecting to this node. Density in the weighted subgraph G =  is defined in . \docume(2) Network Diameter: Diameter is the shortest path in a cluster.The effectiveness of our method is evaluated using PPI networks and gold standards of protein complexes from yeast and human and the detail information is shown in Table ith benchmark complex jth predicted complex ith benchmark module. Here, n is the number of benchmark complexes and m is the number of predicted complexes.To evaluate our method on benchmark datasets and compare NRAGE-WPN with other methods, evaluation measures are given in this section, such as sensitivity (SN), positive predictive value (PPV), accuracy (ACC), separation (SEP), fraction match (FRM), maximum matching ratio(MMR), precision (Prec), recall (Rec) and f-measure, precision+, recall+, f-measure+, the sum (F_MMR) of MMR and f-measure+, the composite score(CS) of MMR, FRM, SEP, ACC and f-measure . Given aTo evaluate protein complex prediction in terms of precision and recall, the Jaccard index is employed. The located complex In terms of precision+, recall+ and f-measure+, neighborhood affinity score d in Eq.  can be uTo inspect the performance of our proposed algorithm, we compare our algorithm with MCODE , CfinderComparative analysis is performed with the sum score of MMR, FMR, SEP, ACC and f-measure. Performances among different methods are compared for yeast and human with the corresponding complex datasets and PPI networks. First, as is illustrated in Fig. By evaluating the importance of parameter In order to illustrate the comprehensive performance of NRAGE-WPN, we demonstrate f-measure+ performances with nine thresholds mentclass2pt{minimP value are shown in Table P value of each complex can be divided into one of four intervals from small to large: < E-15, , , . When P value is greater than 0.001, it is generally considered that the function of the complex is very likely to be randomly assigned and has no biological significance. The percentages in brackets in Table For the protein complexes identified by the NRAGE-WPN algorithm, we measure the effectiveness of the algorithm quantitatively and qualitatively. We analyze the biological significance of the identified protein complexes. Real protein complexes often present high functional homogeneity, so the function enrichment test is employed to demonstrate the biological significances of detected protein complexes . The funDue to the noise data in the PPI network, NRAGE-WPN uses gene expression and RA information to score a weight to each interaction of the PPI network. To assess the effect of using RA in the f-measure+ for complexes detection, we conduct NRAGE-WPN without considering RA information and compare its results with normal the NRAGE-WPN which employs both gene expression and RA information. Without using RA situation, a weighted PPI network is constructed by gene expression only. Figure The second phase of the NRAGE-WPN method is to enlarge the core complexes by second-order neighbors. After detecting core protein complexes from weighted PPI network, due to the nature of complexes of core-attachment, there may be many attachment parts to be added to the cores. In this situation, the cores and attachment parts are combined to form final complexes. In order to assess the effect of introducing second-order neighbors searching(SNS), we conduct NRAGE-WPN without its second phase. Figure Although PCC cannot identify whether gene variables are directly regulated or indirectly regulated –35, in tThe identification of protein complexes is important for discovering and understanding the cellular organizations and biological processes in PPI networks. In this paper a new approach named NRAGE-WPN is proposed for identifying protein complexes in protein–protein interaction networks. Based on the resource allocation and gene expression of the PPI network, we first design a new weight metric to accurately describe the interaction between proteins. Our method then constructs a series of dense complex cores based on density and network diameter constraints, and the final complexes are recognized by expanding the second-order neighbors of nodes in core complexes. Through comparison with eleven methods in Yeast and Human PPI network, the experimental results demonstrate that this algorithm not only performs better than other methods on 75% in terms of f-measure+, but also can achieve an ideal overall performance in terms of a composite score consisting of five performance measures. In the future work, we will focus on locating sparse and density protein complexes by integrating multiple information.Additional fle 1. The Additional fle 1 contains Figures 1, Tables 1 and 2. Figure 1 shows comparative analysis of approaches for prediction of protein complexes in Yeast on different threshold t. Table 1 shows Comparative analysis of eleven algorithms with respect to different measures. Table 2 shows the average of enrichment score of predicted complexes with at least one enrichedannotation over all clusters compared among eleven methods across six datasets.


Knowledge about protein-protein interactions is beneficial in understanding cellular mechanisms. Protein-protein interactions are usually determined according to their protein-protein interaction sites. Due to the limitations of current techniques, it is still a challenging task to detect protein-protein interaction sites. In this article, we presented a method based on deep learning and XGBoost  for predicting protein-protein interaction sites. The deep learning model served as a feature extractor to remove redundant information from protein sequences. The Extreme Gradient Boosting algorithm was used to construct a classifier for predicting protein-protein interaction sites. The DeepPPISP-XGB achieved the following results: area under the receiver operating characteristic curve of 0.681, a recall of 0.624, and area under the precision-recall curve of 0.339, being competitive with the state-of-the-art methods. We also validated the positive role of global features in predicting protein-protein interaction sites. Proteins are one of the most important components of the cell, and also are the principal undertaker of the activities of life. The functions of proteins are manifested mainly by interacting with various molecules such as DNA/RNA, proteins, or other ligands . The proIt is not only costly but also time-consuming and labor-intensive to identify PPIS by experimental methods such as alanine scanning mutagenesis and crystallographic complex determination . Since JFor a fair comparison with other state-of-the-art methods, we used the same three datasets as in the literature . These d2, the amino acid was considered to be a binding site, and otherwise it was a non-interaction site. There were 5,517, 6,096, and 1,923 binding sites, as well as 30,702, 27,585, and 16,217 non-interaction sites in the Dset_186, Dset_164, and Dset_72 datasets respectively. 83.3% of the protein sequences were randomly selected as the training set and 16.7% of the protein sequences as the testing set. The training set was further divided into two parts: 90% of the training set was used for training and 10% was used for verification. Finally, 300 protein sequences were used for training , 50 protein sequences for verification , and 70 protein sequences for independent testing  (Protein-protein interaction binding sites are determined by the absolute solvent accessibility of amino acids. If the absolute solvent accessibility was less than 1 Åesidues) .The proposed method called DeepPPISP-XGB consisted of three main steps: extracting features, training a classifier, and predicting PPIS . The DeeAs shown in α-helix, 310- helix, π-helix, β-bridge, β-strand, β-turn, bend, loop or irregular, and no secondary structure. Therefore, each amino acid residue corresponded to a 9-dimensional vector. The primary protein sequence is valuable information and thus is essential to predict protein properties. One-hot encoding was used to encode the protein sequences. There are 20 kinds of common amino acids in the protein sequences, so each amino acid residue corresponds to a 20-dimensional 0/1 vector. The protein-protein interaction is closely associated with neighboring residues of interaction sites. The local feature of interaction sites contributes to the identification of PPIS. The sliding window method was used to collect the neighboring residues of the interaction sites. The size of the sliding window was seven. For example, if the interaction site was at position i, residues at position i-3, i-2, i-1, i, i+1, i+2, and i+3 were separated. Because each residue corresponds to a 20-dimensional PSSM feature, a 9-dimensional secondary structure feature, and a 20-dimensional one-hot feature vector, a window of seven amino acid residues was encoded into a 343-dimensional vector which was called the local feature.Many protein-protein interfaces are related to secondary structures . InformaProtein-protein interaction is not only linked to the local information of interacting sites, but also to global information. The local and the global features were fed into the DeepPPISP . The DeeBoth the local features or global features would contain a certain degree of noise. The dimension is large, especially for global features. The DeepPPISP was used to extract a more informative representation. The DeepPPISP was trained on the training data in a supervised manner. The local and global features were fed into the trained DeepPPISP, and the input to the first fully connected layer was the abstract representation of the raw features. Compared with the raw features, the abstract representation was of low dimension and had low noise.The XGBoost proposed by The XGBoost is an addition model. At each iteration, the XGBoost learns a new tree that fits the residual between the predicted result of the previous trees and the true values of the training samples.Assume that The set of instances of the leaf node j was defined byThe objective function was further represented asGiven a fixed tree It was expensive and impossible to exhaust all the possible trees for the training data. In practice, the greedy algorithm was used, which started from one node and iteratively split the node. Assume that before the node was split, the objective function of the tree wasAfter the node k was split into the left treeThe gain of node splitting was calculated byThe gain was used to assess the split candidates.In the area of machine learning, the frequently used evaluation metrics include accuracy (ACC), Recall, Precision, F1-score (F1), and Matthews correlation coefficient (MCC) which are respectively calculated by the following formulas:To investigate the ability of the features to discriminate protein-protein interaction sites from non-interaction sites, we used the Uniform Manifold Approximation and Projection (UMAP)  to depicDue to its versatile roles in the cellular process, the identification of protein-protein interaction sites is increasingly becoming a hot topic and is also a challenging task. Over the past decades, more than 10 methods have been proposed to predict protein-protein interaction sites . We compThe DeepPPISP-XGB method achieved the highest value in terms of Recall, F1-score, AUROC, AUPRC, and MCC, and it reached the second-highest performance in terms of Precision. Although ISIS got the best ACC, its performance in other respects was lower than those of DeepPPISP-XGB. The DeepPPISP-XGB method improved the Recall by 4.7%, 5.5%, 11.6%, 11.2%, 2.6%, 15.6%, 26.2%, and 16.5%, in comparison with DeepPPISP, SCRIBER, IntPred, RF.PPI, SPRINGS, PSIVER, ISIS, and SPPIDER, respectively. The DeepPPISP-XGB method increased F1-score and MCC by 0.5% and 0.3%, and the AUROC by 1%, in comparison with DeepPPISP.K-fold cross-validation is a common method in regression or classification questions. In the k-fold cross-validation, the training set was split into k parts. One part was tested and other k−1 parts were trained. The procedure was performed k times. We carried out 10-fold cross-validations, and the principle was shown . Figure To further evaluate the predictive performance of the DeepPPISP-XGB method, four machine learning algorithms were used for PPIS prediction. Decision tree  is a widAfter removing global features, we trained DeepPPISP-XGB. The user-defined parameters of the DeepPPISP-XGB were the same as the previous. We presented a PPIS prediction algorithm based on the DeepPPISP and the XGBoost. The DeepPPISP served as a feature extractor to remove redundant information of the protein sequences. The XGBoost was used to construct a classifier for predicting PPIS. The DeepPPISP-XGB achieved competitive performances with other state-of-the-art methods.https://github.com/fatancy2580/DeepPPISPXGB-master.Source code is available at:


Identifying the molecular systems and proteins that modify the progression of Alzheimer’s disease and related dementias (ADRD) is central to drug target selection. However, discordance between mRNA and protein abundance, and the scarcity of proteomic data, has limited our ability to advance candidate targets that are mainly based on gene expression. Therefore, by using a deep neural network that predicts protein abundance from mRNA expression, here we attempt to track the early protein drivers of ADRD. Specifically, by applying the clei2block deep learning model to 1192 brain RNA-seq samples, we identify protein modules and disease-associated expression changes that were not directly observed at the mRNA level. Moreover, pseudo-temporal trajectory inference based on the predicted proteome became more closely correlated with cognitive decline and hippocampal atrophy compared to RNA-based trajectories. This suggests that the predicted changes in protein expression could provide a better molecular representation of ADRD progression. Furthermore, overlaying clinical traits on protein pseudotime trajectory identifies protein modules altered before cognitive impairment. These results demonstrate how our method can be used to identify potential early protein drivers and possible drug targets for treating and/or preventing ADRD. Protein changes in a brain with Alzheimer’s are not often accompanied by transcriptional differences. Here, using a deep neural network that predicts protein abundance from mRNA expression, the authors track the early protein changes in Alzheimer’s dementia. To find molecular drug targets for ADRD, community-based aging cohort studies and patient-centered research consortia have generated thousands of brain transcriptomes from individuals at various stages of disease2. However, the large discrepancies between mRNA and protein make it challenging to confidently advance mRNA findings in drug discovery because drugs typically target protein. The severity of this disconnect is illustrated by the fact that the most classic protein signs of ADRD—accumulations of β-amyloid and helical filament tau (PHFtau) pathology—are not accompanied by transcriptional changes of APP and MAPT genes2, and hence would not be detected by the standard transcriptome analysis. However, proteomic characterization of ADRD brains still lags behind transcriptomics. Therefore, developing a method to more accurately relate mRNA levels to proteins would dramatically increase the utility of existing transcriptome data. The predicted proteomes would inherit the size and regional diversity of RNA-seq, and enable systems biology analysis of ADRD at the protein level.Without transformative drug discovery, Alzheimer’s disease and related dementias (ADRD) are projected to affect 100 million individuals in 20503. Specifically, we utilize RNA-seq data from the dorsolateral prefrontal cortex (DLPFC) (n = 1192) generated through the Accelerating Medicines Partnership-AD (AMP-AD). For a subset of participants (n = 384), global proteomics using tandem mass tags (TMT) from the DLPFC was conducted previously4. This multi-omics data enables us to build a deep learning model to predict differences in protein abundance between individuals from RNA-seq data.To accelerate drug discovery via predicted protein levels, we perform a deep-learning-based, multi-omic analysis of postmortem brain samples from participants of two large, longitudinal cohort studies of aging and dementia, which have extensive antemortem cognitive assessments, postmortem neuropathologic data, and structural brain imaging5. The basis for this analysis is to identify covaried proteins across individuals in a cohort. The groups of covaried proteins termed “modules” represent the activity of major molecular systems.We conduct a series of systems biology analyses to evaluate whether the predicted proteome can be utilized to identify novel drug targets and molecular mechanisms of ADRD. In the first application, we perform a molecular coexpression approach to defining molecular systems and disease-associated changes7. Since the massive AD GWAS (genome-wide association study) efforts ostensibly direct drug development efforts towards particular mechanisms, the lack of mRNA effects for these GWAS genes has been limiting.In the second application, we test the accuracy of predicted differential protein abundance in ADRD. In particular, we examine the predicted protein levels for genes harboring noncoding variants associated with AD8 - are responsive to major trends in unlabeled samples, which may be related to disease progression10. Here, we employ a pseudo-temporal reconstruction approach based on estimated proteomes, as well as RNA-seq data.The third major application of this model addressed a fundamental challenge to cross-sectional brain omic studies: differentiating upstream molecular changes from downstream or correlative changes in ADRD. Pseudo-temporal trajectory analyses - which are often utilized in single-cell transcriptomicsDue to the integrative nature of deep neural networks, we find that it is possible to robustly predict protein levels, despite their divergence from corresponding mRNA. Further, we find comparable accuracy of this model in predicting protein levels for an entirely separate cohort. The systems biology applications of deep neural networks to predictive protein abundance indicate that it can blend the advantages of the wide availability of transcriptomics with the strong phenotypic relationships of protein, to enable a more accessible and disease-relevant target selection for drug discovery.3. Collectively, ROSMAP has enrolled >3500 older persons, all of whom have agreed to brain donation and annual clinical and detailed cognitive evaluation. Brain transcriptomes of 1192 individuals and unbiased global proteomes of 384 individuals in the DLPFC brain region  and the Rush Memory and Aging Project (MAP), referred to as ROSMAPon Table  were pron = 384), and the average correlation for all proteins was 0.09. This low correlation is indicative of the high degree of discordance between RNA and protein, which was also observed in an independent ADRD brain cohort from the Mount Sinai brain bank (MSBB) . Thus, even with large (n = 100+) sample sizes, standard transcriptome analysis will likely miss many proteins truly related to ADRD, challenging a premise of transcriptome analysis, wherein RNA expression levels are a surrogate for protein abundance. This is indeed the case with the ROSMAP cohort: considering the 384 ROSMAP brain samples wherein both mRNA and protein are available, differentially expressed genes  and differentially abundant proteins (201) for AD diagnosis only overlap in two instances . To overcome the discrepancy of protein levels and corresponding mRNA levels, we developed a predictive model of the proteome based on multiple variables extracted from transcriptome data. The application of this model to large-scale brain transcriptomes allowed us to predict estimated protein abundance and conduct subsequent systems biology analyses to identify early influential changes in specific protein levels  at 5%, 11 that is designed to find the latent state representation of the transcriptome profile. Specifically, each transcriptome profile is encoded into low dimensional probabilistic distributions and then the decoder network takes an encoded vector sampled from the distributions to create the corresponding proteome abundance. Then, this decoded proteome was merged with the transcript features of each protein via a linear regression (LR) layer to generate a predicted proteome profile , and the average correlation for all proteins was 0.18 (Supplementary Data n = 384) is sufficient for building a model predicting protein abundance, we trained the clei2block model with sub-sampled training data. The result indicates the predictive accuracy is saturated and the performance gain from an increased sample size will be minimal  clei2block that is a combination of the scGen and LR modules, (ii) the NN-linear that is a combination of a fully connected layer and the LR module, (iii) the scGen model. The clei2block model (i) performed the best, followed by the scGen and then the NN-linear model Fig. . This inn = 196). This is a challenging assessment, as tissue sampling, brain region, sample preparation, measurements, and data processing were conducted independently from the ROSMAP cohort. However, the predicted proteomes based on the model trained with ROSMAP data showed better concordance with actual proteins than did the raw RNA expression from that very cohort  for 637 samples independent of the training sample. The model showed superior performance for targeted proteomics data over the raw RNA expression Fig. . We alsoort Fig. . This reR > 0.1) and 808 samples from the DLPFC (RNA cohort) that were never used in the training process, ensuring unbiased and rigorous assessments.Coexpression network analysis is a common approach to discovering gene or protein systems associated with the disease. Therefore, we compared the coexpression network structure between proteomes, transcriptomes, estimated proteomes. For these analyses, we used high-quality predictions for the 5998 proteins  score for each input of our predictive model. For this analysis, we used the model using principal components (PCs) of mRNAs as an encoder input and mRNA, pre-mRNA, and the 3′ UTR length as inputs for the LR module. Overall, mRNA levels of the corresponding protein are the most informative predictor, reiterating the central dogma of molecular biology. Several PCs showed greater or comparable contributions than those of the protein-specific inputs. Interestingly, genes projecting to influential PCs were enriched for cell-type-related genes such as immune response and neurogenesis, and protein translational and degradation, such as ribosomal proteins and a proteasome accessory complex Fig. . The resIt is important to understand which kinds of proteins can or cannot be predicted by the model. We conducted an extensive investigation in protein characteristics defining its predictability by RNA-seq Fig. . We founR = 0.38) , in contrast to the baseline transcriptome vs. estimated-proteome relationship (R = 0.39). Moreover, the association of predicted protein modules with AD diagnosis is highly correlated (R = 0.91) with that computed from actual proteome data  the transcriptomes, (2) the actual proteomes, and (3) the estimated proteomes, and then contrasted test statistics of the differential expression. To ensure no bias from the training samples, we used the RNA cohort for #1 and #3 and the TMT cohort for #2. The correlation of differential expression statistics of actual proteomes and transcriptomes are moderate (38) Fig. , which aata Fig. . We straata Fig. . To veriata Fig. .Fig. 4Dip-value < 0.05. In contrast, 80 out of the top 100 genes in the predicted protein data matched the direction of the association with the actual protein data and 36 showed p-value < 0.05. For reference, we also selected genes randomly, resulting in an 11% success rate. Our prediction model improved the success probability of disease protein discovery 1.6-fold over the RNA-based gene selection and 3.4-fold over random selection. This demonstrates that the clei2block deep learning approach significantly improves disease protein discovery over traditional processes.To further provide more evidence that our model indeed facilitates disease protein discovery, we conducted an additional analysis with the MSBB data. We followed the typical scenario for discovering disease-related proteins with RNA-seq data, where (i) run association analysis with the RNA-seq data, (ii) select candidates from differentially expressed genes, (ii) measure protein levels for those genes to validate that they are differentially expressed at the protein level. We used the CERAD (Consortium to Establish a Registry for Alzheimer’s Disease) score, neuropathologic diagnosis of AD, as a phenotype for this experiment. We picked the top N-genes associated with the phenotype in RNA-seq data and the estimated protein data, respectively. Then we examined how many of them are differentially expressed with the actual protein data from the same individuals Fig. . For ins12. In this assessment, we used the 808 samples of high-quality predictions for 5731 genes measured in both RNA-seq and TMT proteomics. We found 3277 proteins associated with cognitive decline (Bonferroni-corrected p < 0.05)  Fig. , and cog05) Fig. . There a05) Fig. .Fig. 5Esn = 645) and the anterior caudate (n = 705) regions from the ROSMAP cohort to examine whether the model trained by DLPFC samples can also augment the connection of RNA-seq data with cognitive decline in different brain regions. Indeed, we detected a notable increase in the number of genes associated with cognitive decline based on the estimated proteome compared to the mRNA  but not at the mRNA level . There is strong biological enrichment in elevated immune response, proteolysis, and a part of mitochondrial pathways for faster cognitive decline, whereas higher ribosomal proteins and endoplasmic reticulum proteins, and some mitochondrial protein levels were associated with slower cognitive decline  analysis for genes associated with cognitive decline at the protein level  stronger than the RNA pseudotime   Fig. . The ass−5) Fig. . Variancn = 459), as well as hippocampus volume, a key brain region for memory and learning. We found that individuals with advanced protein pseudotime tended to have smaller hemisphere volume      Fig. , and hip12) Fig. . The ass−8) Fig. . Convers03) Fig. . This in15 and gene expression16 differently in older adults, we also examined the impact of sex on pseudotime and detected no or subtle sex differences in pseudotime. It is interesting because we have detected a strong sex effect on estimated protein abundance, indicating that our trajectory learning approach has removed the sex bias in the molecular signature and effectively aligned pseudotime with the rate of cognitive decline.Because sex has been shown to affect AD pathology17. Then, we applied the same approach to the protein modules  across genes within a sample (ii) across samples for each gene. For example, the RNA‐to‐protein ratio was proposed to estimate the former relationship from RNA expression19. Our method modeled the latter relationship, which is usually difficult to investigate as it requires a large number of samples. Therefore, our model was developed using the largest human brain data, with paired mRNA and protein measurements. Although most proteins showed a positive correlation with their mRNA levels, the overall correlation was quite low. Notably, this is not specific to a particular proteomics technology themselves, or limited to a specific cohort, as we observed consistent results with three proteomic technologies and three independent cohorts. Therefore, the discrepancy is likely due to the various technical noises, post-transcriptional regulation, and translational activity for individual genes.Due to the complexity of gene regulation, the discrepancy between mRNA and protein levels has been a standing question since paired genome-wide measurements became availableN = 384) measured by TMT and SRM. Even though the majority of proteins showed a positive correlation, an average correlation between protein abundance quantified by SRM and TMT methods is as low as 0.20  Xeon(R) CPU @ 2.30 GHz, and thus 20 h for all 8391 proteins. In addition to this, the 10-fold-cross validation experiment requires 10 times more of this, which is 200 h of computing time in total. For the CatBoost model, it took around 435 s per protein  and thus will require more than 10,000 h of computation in total. On the other hand, the clei2block model only took 9 min to train and thus 1.5 h in total using the same CPU with an NVIDIA Tesla T4.There are likely multiple reasons why we found more proteins associated with AD phenotypes than mRNAs. First, protein levels likely reflect cognitive decline more than transcript levels, and the estimated proteome successfully gained this phenotypical corollary. The actual data supported this hypothesis as we detected more differentially expressed genes at protein levels than mRNA levels in the training cohort. Second, the scGen component in the clei2block model potentially reduces the measurement noise in data. The reason for that is when the model is properly trained, it will not be able to learn noise that is independent between protein and mRNA measurements and between different genes. Thus, the predicted protein should have less noise. If this is the case, it may be one source of the stronger statistical significance we observe with AD phenotypes.The increased relevance of the predicted proteome to cognitive decline facilitated pseudotime trajectory analysis that translates all of the adjusted mRNA effects into a global estimate of brain state. While a pseudotime trajectory is estimated without a priori reference to any phenotypes, it explained 11.2% of the rate of cognitive decline independently from demographics and RNA pseudotime, indicating that protein pseudotime likely captures a significant component of cognitive decline Fig. . As the In summary, deep neural network-based prediction of protein abundance helps address a long-standing biological conundrum and also practically provides the basis for improved systems biology analysis of ADRD data. Specifically, we show how it facilitates common coexpression and pseudo-temporal approaches, thereby producing more accurate estimates of the protein systems with early causal effects on ADRD progression.The ROS and MAP studies were each approved by an Institutional Review Board (IRB) of Rush University Medical Center. Both studies enroll older persons without known dementia. All participants agree to an annual detailed clinical evaluation and organ donation at the time of death. Prior to enrollment, each participant signed informed consent and an Anatomical Gift Act for (AGA) donation of the brain, spinal cord, nerve, and muscle to the investigators for research purposes. The AGA is recognized in all 50 states and the District of Columbia. It is an advanced directive that foregoes the need to obtain consent for autopsy from a next of kin at the time of death. Participants also sign a repository consent to allow their data and biospecimens to be shared in accordance with procedures established by the relevant IRB.All human subjects are participants in one of two prospective studies of aging (ROS and MAP). ROS/MAP is a community-based cohort study. As community-based cohorts, ROS/MAP has much less referral bias, which can introduce significant sociodemographic, clinical, and genetic differences into studies of patients. At enrollment, mean education was 16.2 years, 67.8% were female, 98.4% were non-Latino white, and 23.2% had one or more APOE 4 alleles. While all were without known dementia at study enrollment, 7.8% met a research diagnosis for dementia at their baseline evaluation. These are community-based observational studies. They are not our patients. Information on other diagnoses and treatments is limited. The mean age at death was 89.6 years and 35.9% were diagnosed with Alzheimer’s disease at the time of death.The ROS study is comprised of older catholic priests, nuns, and monks throughout the USA. The MAP study recruits older laypersons from the greater Chicago area. Participants are not compensated for their participation. All visits and data collection, other than optional biennial MRI, are performed as home visits to ensure convenience for the participant and data close to death.20. Uniform structured cognitive and clinical evaluations are administered each year by examiners blinded to data from prior years. Briefly, the cognitive battery contains a total of 21 cognitive performance tests, of which 19 are used to construct a global composite measure of cognitive function21. The longitudinal rate of decline was computed for each participant using linear mixed models with adjustment for the effects of age, sex, and education, which estimate person-specific residual slopes21.AD diagnosis was based on criteria of the joint working group of the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association (NINCDS/ADRDA) as previously reported22.Tissue was dissected from eight brain regions to quantify the burden of parenchymal deposition of beta-amyloid and the density of abnormally phosphorylated paired helical filament tau (PHFtau)-positive neurofibrillary tangles. Tissue sections (20 µm) were stained with antibodies against beta-amyloid protein and PHFtau protein, and quantified using image analysis and stereology, as previously described4. To increase the number of samples that have both TMT proteomics and RNA-seq data, we sequenced additional 63 TMT cases without RNA-seq data at the Rush Alzheimer’s Disease Center. RNA was extracted using Chemagic RNA tissue kit . RNA was concentrated  and RQN (RIN score) was calculated using Fragment Analyzer . RNA concentration was determined using Qubit broad range RNA assay  according to the manufacturer’s instructions. Totally, 500 ng total RNA was used for RNA-Seq library generation and rRNA was depleted with RiboGold . A Zephyr G3 NGS workstation (Perkin Elmer) was utilized to generate TruSeq stranded sequencing libraries  with custom unique dual indexes according to the manufacturer’s instructions with the following modifications. RNA was fragmented for 4 minutes at 85 °C. First-strand synthesis was extended to 50 min. Size selection post adapter ligation was modified to select for larger fragments. Library size and concentrations were determined using an NGS fragment assay  and Qubit ds DNA assay (Invitrogen) respectively, according to the manufacturer’s instructions. The modified protocol yielded libraries with an average insert size of around 330–370 bp. Libraries were normalized for molarity and sequenced on a NovaSeq 6000 (Illumina) at 40–50 million reads, 2× 150 bp paired-end. RNA-Seq data processing was implemented using three parallel pipelines, an RNA-seq QC pipeline, a gene/transcripts quantification pipeline, a 3′ UTR quantification pipeline. In the QC pipeline, paired-end RNA-Seq data were first aligned by STAR v2.623 to a human reference genome. The primary assembly of reference genome fasta file and transcriptome annotation came from Gencode (Release 27 GRCh38). Picard tools were applied to the aligned bam files to assess the quality of RNA-Seq data. In the quantification pipeline, transcript raw counts were calculated by Kallisto (v0.46)24. To quantify pre-mRNA abundance, the transcriptome reference was customized. Transcript counts were aggregated at the gene level to obtain gene counts separately in mRNAs and pre-mRNAs. Samples were excluded if the total reads mapped were less than 5 million. A total of 17,294 mRNAs and 17,804 pre-mRNAs were expressed in >50% of the sample with at least 10 counts in each sample. These genes were included for the downstream normalization process. First, the CQN  was applied to adjust a sequence bias from GC content and gene length25. Next, the adjusted gene counts matrix was converted to log2-CPM (counts per million) followed by quantile normalization using the voom function implemented in the limma R package26. Finally, an LR model was applied to remove major technical confounding factors, including post-mortem interval, sequencing batch, RQN , total spliced reads reported by STAR aligner, and metrics reported by Picard and Kallisto. To quantitate the relative proportion of 3′ UTR isoforms in each gene, we used QAPA27. A normalized 3′ UTR length was computed as the percentage of isoform-weighted 3′ UTR length28. We filtered out genes with missing UTR length in more than 100 samples or with identical UTR length in more than 95% of samples, resulting in 3204 genes. UTR length was log2-transformed, and then technical confounding factors were removed as described above.ROSMAP RNA-seq data of DLPFC, PCG, and AC regions were downloaded from the AMP-AD platform (syn3388564)29. Briefly, digested protein samples were labeled with isobaric TMT and fractionated by high pH liquid chromatography (LC). Fractions were then analyzed by LC–MS. The resulting MS spectra were searched against the Uniprot human protein database and quantified. The effect of the experimental batch and PMI on quantified protein abundance were regressed out via LR. Proteins with missing values in more than 50% of the 384 subjects that overlapped with RNA-seq samples were excluded. A total of 8,391 proteins in 384 persons passed the final QC.We utilized ROSMAP TMT proteomics data from frozen tissue of the DLPFC generated by other researchers and their methods are already published in multiple papers in detail30. The SRM proteomics was performed using frozen tissue from the DLPFC region for proteins suggested by the consortium members of AMP-AD. We used 121 genes corresponding to 171 peptides that were measured both in the TMT and RNA-seq measurements. The samples were prepared for LC-SRM analysis using the standard protocol30. The abundance of endogenous peptides was quantified as a ratio to spiked-in synthetic peptides containing stable heavy isotopes. The “light/heavy” ratios were log2 transformed and shifted such that the median log2-ratio was zero. Finally, an LR model was applied to remove technical confounding factors including PMI and experimental batch.SRM data were downloaded from the AMP-AD knowledge portal (10.7303/syn10468856)11 that is designed to find the latent state representation of the transcriptome profile. Specifically, each transcriptome profile is encoded into low dimensional probabilistic distributions and then the decoder network takes an encoded vector sampled from the distributions to create the corresponding proteome abundance. Then, this decoded proteome was merged with the transcript features of each protein via an LR layer to generate a predicted proteome profile. The overall model architecture will be found in Fig. To estimate the tissue state that can inform the global proteome profile, we modified the scGen frameworkFor training the clei2block model, we split the samples into ten folds by balancing cognitive status and brain pathologies. Then, for each fold, we removed the fold from the data and used the remaining data to train the clei2block model. Within the training samples, 10% of them were used as validation data to monitor learning progress and select the best model during the iteration. The trained model was applied to the holdout testing data so that we obtained predicted protein data for the holdout samples. We repeated this process for each of ten holds, resulting in obtaining ten models trained with different training samples and holdout testing data. To calculate overall prediction accuracy, predictions for the holdout samples were concatenated after scaling within each hold and then contrasted with actual proteome abundance with Pearson’s correlation Fig. . This prModel parameters were optimized using Adam optimizer with a learning rate of 0.001 and 0.01 for the scGen module and the LR module, respectively. The number of maximum training epochs was set to 10,000 with an early-stopping of 30 based on validation loss. The clei2block model is implemented in Pytorch and trained using an NVIDIA Tesla T4 GPU with the public docker image for Pytorch (v1.2) on Google Cloud Platform.The elastic-net-based protein prediction model was built based on the same data used for training the clei2block model for each protein separately. First, two hyperparameters, alpha, and lambda were tuned via fivefold cross-validation. Then with the hyperparameters showing the best RMSE (root-mean-squared error), we trained the elastic-net model using the whole training data. Lastly, the trained elastic-net model was applied to the hold-out test samples to generate predicted protein abundance. The elastic-net model was generated using the glmnet R library (v4.1-2).The CatBoost model was built using the catboost R library (v0.21) with the default setting. We used 10% of the training samples as validation data. We tuned the number of trees based on the RMSE of validation data with the maximum number of trees of 10,000 and early stopping of 50 iterations. Because the computation time for the CatBoost was significantly longer than elastic net or neural networks, we did not optimize other hyperparameters. Due to this limitation, the performance of CatBoost might be underestimated.31. The demographic of this cohort is similar to that of ROSMAP. For instance, the majority of participants are white, and their average age of death is at least 83, whereas their age over 90 has been censored to 90. About 63% of those are female and 51% are diagnosed as AD based on the CERAD criterion. Fastq files were processed using the same pipeline for the ROSMAP cohort to obtain normalized mRNA, pre-mRNA, and 3′ UTR length values. To correct batch and technical effects, we removed the sequencing batch, RIN, and PMI from these transcriptional matrices. Label-free quantification of 3415 proteins for the same individuals was normalized so as to remove the effect from the experimental batch and PMI using the script in the Synapse repository (syn8495241). We applied each submodel trained with ROSMAP data to MSBB data and then averaged model prediction. Then we compared the predicted protein levels with the actual ones for 3222 proteins measured both in ROSMAP and MSBB data.To validate model prediction with data from an external cohort, we downloaded RNA-seq data (syn8612191) paired with label-free proteomics data (syn8495241) measured in the frontal pole (Brodmann area 10) from 196 participants of MSBB cohort32. We computed the normalized protein level by subtracting the mean level for that variable across all individuals and dividing it by the standard deviation. Then, we summarized the composite metric of each module in each individual by computing the mean of the normalized levels across all variables in that module. To annotate protein modules, we performed enrichment analysis for GO terms and brain cell-specific gene sets using Fisher’s exact test. The cell-specific genes were obtained from the single-nucleus RNA-seq of the DLPFC region from the ROSMAP cohort16. Specifically, reads from each cell type were summed to create pseudo-bulk RNA-seq data, and then for each cell type genes expressed more than twofold compared to other cell types were defined as cell-specific. To examine whether protein modules are preserved in transcriptome or estimated proteome, we ran modulePreservation function implemented in WGCNA R package33. The network type was set to signed and other parameters were set to default.To statistically identify protein modules, we used a consensus clustering approach SpeakEasy34. The GradientExplainer method estimates the contribution of each input based on the difference in the gradient of the input from the background input distribution35. To compute the SHAP score, we used the model that takes PCs of mRNA features and mRNA, pre-mRNA, and UTR length because the independence of input features is desirable for an accurate estimate of variable contribution. The SHAP score was computed for testing data of 100 random proteins. To obtain robust estimates of SHAP score, we averaged SHAP scores from models built from different subsets of data for each submodel. We ranked PCs based on the average absolute SHAP score. To understand the key biological system for the prediction, we conducted GO enrichment analysis for genes whose loading of the PCs exceeded 95 percentile of all genes. The enrichment analysis was conducted using Fisher’s exact test with GO terms in MSigDB v6.136.To evaluate the importance of input features to the prediction, we calculated the SHAP score using GradientExplainer37 as of October 2018 and 833 TFs from GTRD38 as of October 2018. We then counted the number of RBPs, and TFs bound to promoters and exons. A promoter region of each gene was defined as the region from 2000 bp upstream of the transcriptional start site (TSS) to 1000 bp downstream of the TSS. Protein-protein interaction networks were downloaded from eXpression2Kinases Web39, which consists of 209,459 interactions across 15,452 proteins. The degrees of proteins in the networks were computed using the igraph R package (v1.2.6).We downloaded the genomic locations of the binding sites of 171 RBPs from POSTAR2An LR model was used to test the associations of transcripts or proteins with a continuous or categorical outcome. Age at death, sex, and years of education were included in the model as covariates. Bonferroni correction was employed to reduce the false positive due to the multiple testing.To estimate the SHAP score, which is the feature contribution to predicted protein abundance, we applied GradientExplainer to the model with mRNA-PCs and all protein-specific inputs. The SHAP score satisfies the properties of local accuracy where the sum of feature contribution matches the original model output. With this property, we can evaluate how each feature affects the result of differential expression by subtracting feature contribution from the original prediction. We calculated SHAP scores for AD GWAS genes for the 808 samples that were not used for the model training. For each gene, we computed the variance of protein abundance explained by AD diagnosis, cognitive decline, and global AD pathology using the original estimated abundance and ones with the removal of the feature contribution for each mRNA-PC. We determined influential mRNA-PCs based on the difference in variance explained between them. To understand the key biological systems for the gene-disease association, GO enrichment analysis for each influential PC was conducted as described adobe.k = 10) among individuals was constructed based on the Euclidean distance with the top 40 PCs of their omics profiles. Then, the structure of the graph representing individual-to-individual relationships was embedded into low-dimensional vectors by computing eigenvectors of its graph Laplacian. With the two-dimensional representation of the graph, we inferred the trajectory using SCORPIUS14 which is one of the best performers in the recent method comparison8. Briefly, SCORPIUS partitioned samples into three clusters and optimized the shortest as well as the smooth path that goes through the center of clusters. Samples were projected onto the given path and pseudotime was assigned to each sample. The pseudotime estimates were highly robust with a range of k (5 to 50) and the number of PCs (30–60) as the average Spearman’s correlation between estimates was 0.99. Based on the pseudotime, smoothed pseudo-temporal changes of clinical phenotypes and protein module expression were estimated using LOESS  curves, fitting with a degree of two. The proportion of variance in cognitive decline explained by pseudotimes and demographics was computed using relaimpo R package.To infer the brain omics trajectory, we first mapped each individual into two-dimensional space based on the similarity of omics profile. To do this, we used the spectral embedding method implemented in the scikit-learn python library. Briefly, the k-nearest neighbor graph (41. Images were warped to a previously developed postmortem cerebral hemisphere template, on which we had manually drawn a mask encompassing the hippocampus. We back-transformed this mask onto images in their original space by applying the inverse of the individual-to-template transform to the mask image, as previously described43. After eliminating non-tissue-containing voxels, we extracted the resultant volume of each back-transformed mask, yielding a measure of hippocampal volume. The total hemisphere volume was calculated based on the number of tissue-containing voxels.At approximately 30 days postmortem, cerebral hemispheres were imaged in a 3-Tesla MRI scanner using previously described techniquesFurther information on research design is available in the Supplementary InformationReporting SummaryPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9