The recent breakthrough from AlphaFold2 and RoseTTAFold set a profound milestone for solving the protein folding problem, but they were not explicitly trained to predict protein foldability, i.e., if a protein can really fold into the predicted 3D structure. We wondered if the computational models from AlphaFold2 and RoseTTAFold might carry protein foldability information. Therefore, we predicted the structural models of 159 circular permutants and 158 alanine insertion mutants of the 159-residue dihydrofolate reductase. Our data showed that although AlphaFold2 and RoseTTAFold cannot directly identify unfoldable proteins, the RMSD values of computational models are correlated with protein foldability, with higher RMSD values indicating lower protein foldability. Furthermore, this correlation is independent of secondary structures, and the RMSD values of computational models are quantitatively correlated with protein foldability but not protein functions. Additionally, using a dataset of 129 de novo designed proteins, we showed that inter-model RMSD values between AlphaFold2 models and RoseTTAFold models are a good indicator of protein foldability. At last, we showed that inter-model RMSD values are also useful for evaluating protein solubility by modeling 1664 natural proteins. Our work could be of great value to the design of novel proteins and the prediction of protein foldability. The formation of the 3D structure of a peptide sequence, i.e., protein folding, is tremendously challenging because the large dimensionality and its stochasticity. However, the seminal work by Christian Anfinsen and colleagues led to the hypothesis that the peptide sequence of a protein intrinsically determines its specific 3D structure With the accumulated contributions of many scientists, remarkable achievements have been made in solving the protein folding problem in the last fifty years N-terminus and the C-terminus while new termini are generated by the cleavage of a peptide bond other than the original ones  is an intensively studied model in protein circular permutation. By generating circular permutants at all 158 neighbored residue pairs of this 159-residue protein, Masahiro Iwakura and colleagues defined ten folding elements of DHFRcoli based on experimental data In this work, we systematically modelled the 3D structures of the 159 circular permutants of DHFRcoli with both AlphaFold2 and RoseTTAFold. Although not all permutants were experimentally foldable and active, they were all virtually folded and highly resembled the X-ray structure of the wild-type protein, indicating that these computational methods cannot evaluate the foldability of proteins. However, when aligned to the wild-type structure, the theoretical models of unfoldable permutants had larger RMSD values than the foldable permutants. Furthermore, the correlation between RMSD values and foldability was independent of secondary structures. These findings were confirmed by modeling another 158 alanine insertion mutants of DHFRcoli 22.1N-terminal residue was not methionine (M), the extra M was added as required in protein expression. However, as describe in The circular permutated sequences were constructed according to the construction methods described in 2.2th residue is an alanine, (n-1)An and nA(n + 1) have the same sequence. So the actual number of the alanine insertion mutants were 145 since DHFRcoli contains 13 alanine residues.As describe in 2.3https://automeris.io/WebPlotDigitizer).The experimental data of the circularly permutants were obtained from the reference 2.4The sequences of the E. coli proteins reported in Niwa et al. 2.5The 3D structures of the DHFRcoli sequences were predicted with AlphaFold2 using MMseqs2 v1.2 2.6The 3D structures of the DHFRcoli sequences were predicted with the RoseTTAFold using the end-to-end version. The checked out main version was 20,210,803 with UniRef30 HHsuite (2020.06), BFD (id30_c90), and pdb100 (2021Mar03). For each sequence, five models were generated and used for analyses.2.7The RMSD values between the predicted models and the crystal structure (PDB ID 1RX4) were calculated with the “align” function in Pymol 33.1DHFRcoli is a 159-residue protein with well characterized crystal structures. As a control, we modelled the structure of the wild-type protein using AlphaFold2 and RoseTTAFold. The model quality was evaluated by the backbone RMSD value between the model and the X-ray structure (PDB ID 1RX4). The RMSD values of the AlphaFold2 model and the RoseTTAFold model were 0.369 ± 0.022 Å and 0.529 ± 0.003 Å respectively , indicatN-terminal and C-terminal residues N-terminus, it was referred as DHFRcoli-1, leading to 159 circular permutants in total. Accordingly, the circular permutant with the nth residue as the N-terminus was named as DHFRcoli-n in this work  residues form secondary structure units , and 80 (50.3 %) residues belong to random coils. As shown in 3.4th residue is an alanine, (n-1)An and nA(n + 1) have the same sequence. Therefore, the actual number of the mutants was 145 since DHFRcoli contains 13 alanine residues. Again, we computed the 3D structural models of these 145 DHFRcoli mutants with both AlphaFold2 and RoseTTAFold. When the nth residue is an alanine residue, the same computational and experimental model was used for the mutants (n-1)An and nA(n + 1). For simplicity, we refer them as 158 alanine-insertion mutants in this paper.To further investigate the folding of DHFRcoli, Shiba et al. Similar to the circular permutants, the distribution of the backbone RMSD values of the models of these 158 alanine-insertion mutants showed that AlphaFold2 generated near-native 3D models . HoweverDifferent from the circular permutants, RoseTTAFold generated models resembling the wild-type structure for all alanine insertion mutants , likely 3.5cat and KM) and conformational stability data . Interestingly, there were no clear correlations between the RMSD values and these data , is it possible to get any indicators of foldability from the computational models of AlphaFold2 and RoseTTAFold? A possible indicator is the RMSD value between the AlphaFold2 model and the RoseTTAFold model (defined as inter-model RMSD hereafter), since we supposed that foldable proteins might have more evolutionary information for better and more consistent models. To answer this question, we calculated the inter-model RMSD values of the alanine insertion mutants of DHFRcoli . Then weThen we asked if the inter-model RMSD value between AlphaFold2 model and RoseTTAFold model could be useful in de novo protein design. We obtained the sequence of the 129 de novo designs from the recent work of the Baker group, among which 27 were assigned as correctly folded Since unfoldable proteins tend to precipitate, we supposed that the inter-model RMSD value could also be used to evaluate the solubility of proteins. So we tested this idea on a dataset containing 1664 proteins of E. coli reported by Niwa et al. 4The protein folding problem is a “holy grail” problem in biology. To predict the natural 3D structure from a sequence of amino acids has been a tantalizing but extremely challenging task in the last 50 years. With the assembly of the biannual Critical Assessment of Structure (CASP) meetings, global efforts have pushed forward the progress in the computational prediction of protein structure A possible reason of this caveat of the current AI-based protein structure prediction tools is that from the training of PDB data, only the protein folding code could be extracted. That is, neither AlphaFold2 nor RoseTTAFold could directly obtain the kinetics information of protein folding. Meanwhile, the folding of a protein is not only determined by whether it could be theoretically folded, but also affected by its folding kinetics Nonetheless, considering that the structures in the PDB database are intrinsically foldable, it is possible that the network properties of AI-based methods contain some information of protein foldability. For example, the residue-residue contact information from multiple sequence alignment might contain foldability information, since unfoldable mutants have been discarded by evolution. Then a reasonable inference is that there is some foldability information hidden in the computationally folded models. The question is how that information could be extracted.Circular permutation modifies the termini of a protein but does not change its sequence composition. Most circular permutants could fold into native structures, although their folding kinetics might change. The alanine insertion method is more conserved on perturbing protein sequence. Since the protein sequence is minimally changed, the computational tools would be hard to tell the difference on the foldability of different constructs. Our data showed that this is true, since AlphaFold2 and RoseTTAFold predicted near-native 3D models for both foldable and unfoldable DHFRcoli permutants and alanine-insertion mutants. However, it was interesting to notice that the RMSD values of the computational models contains some information on the foldability of a protein in our work. We think our work, along with the contributions from the other colleagues A caveat of our work is that the experimental data only reflected the foldability of a peptide sequence kinetically accessible within a laboratory time scale. For example, Iwakura et al. Our work would be helpful to the design of protein circular permutants and novel proteins. Based on our findings, we hypothesize that including non-foldable protein sequences in the training data of neural networks would be useful for the AI-based prediction methods to predict protein foldability. In this regard, we believe that publishing unsuccessful protein design data is scientifically valuable S.L. conceived the idea and did the computational work. K.W. and C.C. acquired the experimental data of the mutants. S.L. and K.W. analyzed and interpreted the data. S.L. wrote the manuscript. All authors reviewed and approved the submitted manuscript.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


The localization of a protein's submitochondrial structure is important for therapeutic design of associated disorders caused by mitochondrial abnormalities because many human diseases are directly tied to mitochondria. When Lon protease expression changes, glycolysis replaces respiratory metabolism in the cell, which is a common occurrence in cancer cells. The fact that protein formation is a dynamic research object makes it impossible to reproduce the unique living environment of proteins in an experimental setting, which surely makes it more challenging to determine protein function through experiments. This research suggests a model of Lon protease-based mitochondrial protection under myocardial ischemia based on ML (machine learning). To ensure the balance of all submitochondrial proteins, the data set is processed using a random oversampling method, each overlapping fixed-length subsequence that is created from the protein sequence functions as a channel in the convolution layer. The results demonstrate that applying the oversampling strategy increases the ROC value by 17.6%-21.3%. Our prediction method is successful as evidenced by the fact that ML prediction outperforms the predictions of other conventional classifiers. Lon protease is an ATP-dependent serine protease, and it is also a member of ATPase protein family related to various cellular activities. It has a serine-lysine catalytic structure and can play an important role in degrading folding errors and damaging protein, maintaining the stability of intracellular environment and structure. Mitochondrial structure-function relationship is mainly regulated by molecules that regulate mitosis and fusion between mitochondria . In cytoAs the energy factory of mitochondrial cells, it plays an important role in the life process of organism. Studies have shown that mitochondrial dysfunction is the potential mechanism of drug toxicity. Drugs directly or indirectly destroy mitochondrial structure and function, which can lead to mitochondrial toxicity, and may lead to target organ toxicity. As a heat shock protein, one of its main functions is protein quality control. In bacteria, the mutation of Lon protease showed abnormal protein degradation defects, which proved the role of Lon protease in protein quality control. In addition, Lon protease can also bind to DNA and affect DNA replication and gene expression. However, the activation of protease is not enough to degrade all the carbonyl protein accumulated in mitochondria, so Lon protease may be easily inactivated when oxidative stress increases, leading to mitochondrial dysfunction and nerve cell death. It was found that 90% of LONP1 exists in the mitochondrial matrix in soluble form, while 10% exists in the mitochondrial inner membrane. The division process prevents the abnormal elongation of mitochondria by splitting a single mitochondria into two independent daughter mitochondria. The research on crystal structure of Lon protease provides conditions and also lays a foundation for us to screen inhibitors and activators of Lon protease from natural products or synthetic small molecular compounds with high throughput and to develop drugs acting on Lon protease.The function of mitochondria has drawn a lot of interest in the study of cancer mechanisms. In addition to performing oxidative phosphorylation, mitochondrial cells also produce heme protein, lipid, amino acids, and nuclear acids and take role in controlling the stability of the internal environment. Numerous human disorders, including Parkinson's syndrome, diabetes, and Alzheimer's disease, have direct links to mitochondria. Drug design for disorders similar to those caused by mitochondrial abnormalities can benefit from knowing where a protein's submitochondrial structure is located. Each subcellular coordinated the division of labor and worked together under the direction of genetic information, ultimately achieving the unity of the internal system of cells and the orderly progression of various life functions, such as metabolism and heredity. The subcellular localization prediction of Lon protease under myocardial ischemia was thoroughly studied in this paper based on ML (machine learning) method , 4 and sThe method used in this research to extract protein features is based on self-cross covariance transformation and combines position correlation score matrix, pseudoamino acid composition, and dipeptide composition to address the issue of restricted single feature. Utilize the limit gradient hoist to screen out crucial characteristics and eliminate superfluous and pointless featuresIn this paper, ML correlation algorithm is used to analyze the trajectory of molecular dynamics simulation of Lon protease. Taking residues and protein as units, from the details, observe the influence of each residue site on the overall movement of proteinResearch innovation:This paper is divided into five sections, and the specific arrangements are as follows.The first section introduces the background work of the research. The second section mainly introduces the present situation of this research. In Lon protease can degrade oxidized proteins in mitochondrial matrix, so it plays a key role in dealing with oxidative stress such as hypoxia or ischemia. The change of Lon protease expression leads to the change of cell metabolism, from respiratory metabolism to glycolysis, which usually occurs in cancer cells. Pomatto et al. demonstrated that LONP1 performed a crucial “scavenger” role in mitochondria, specifically destroying aberrant protein. In the same way, LONP2 performed a comparable function in the peroxisome . AccordiThe importance of mitochondrial fusion in heart development has been described by intrauterine death in the second trimester of pregnancy after Mfn1 and Mfn2 gene ablation. Andrianova et al. made a new discovery in the experimental results: mitochondrial fusion factor Mfn2 can interact with AMP-activated protein kinase to mediate cell survival under energy stress . Babin eBiology and information industry complement each other, and the combination of them produces bioinformatics. It should be pointed out that at present, almost all these databases are free for academic research departments or personnel and can provide free downloads and other free services. Many of its research results can be industrialized quickly or immediately and become high-value products. This feature of bioinformatics is almost unique in many existing disciplines.The ultimate goal of the study of protein is to understand its function and mechanism. Because protein formation is a dynamic research object, it cannot simulate the specific living environment of protein under experimental conditions, so it undoubtedly increases the difficulty of measuring protein function by experimental methods. Figaj et al. made use of bioinformatics methods to study and discuss it from various angles and achieved certain research results . OsuagwuThe SubMito method was developed by Stocker et al. based on the characteristics of the protein sequence in order to identify and predict the location of the protein's submitochondria. SVM (support vector machine) localization and prediction model was established, and the overall prediction accuracy was 85.2% . The broEscherichia coli. Later, it was found in many organisms, but the full-length crystal structure of protein has not been reported so far. Lon protease contains both ATPase and proteolytic enzyme activities in the same peptide chain. This structure is also found in HflB protease, but these two proteases are located on two different peptide chains in Clp protease family. Most studies think that Lon protease belongs to the family of heat shock proteins , and then, the probability score (binary classification or multiclassification) is output through the fully connected network.After a convolution and pooling operation, the potential feature vector is A computational model based on protein sequence must be built in order to perform exact positioning; hence, it is important to choose unbiased and representative benchmark data sets. The data sets used in this study are M317, M495, and M983, and each data set is broken down into three subregions: the inner membrane, the matrix, and the outer membrane. Few unknown proteins often have more than one known function companion protein. The known interacting protein in these instances, however, does not have the same functional type. When predicting a protein's secondary structure, sequence similarity of 30% indicates that the two sequences have the same folding structure; however, a sequence similarity of 40% indicates that the two sequences are not in the same subcellular location.We use sliding window strategy to extract symmetric peptide segments centered on arginine and lysine from protein sequences to construct positive and negative sample data sets. The methylated skin segments of arginine and lysine labeled as “potential,” “probable,” or “by similarity” are excluded here. Arginine and lysine skin segments from the same protein in the positive sample without any methylation information are defined as negative samples. Finally, in order to ensure fair and objective results, we randomly select 90% of the data sets to build training sets and 10% to build independent test sets. In order to prevent the sampling deviation of the independent test set, the independent test set was sampled 10 times repeatedly, and the result of cross-validation in this paper is the average of 10 times.In this paper, Pytorch backend is used to conduct all experiments on Nvidia Ge Force 2080TiGPU. Adam algorithm is used to optimize. The batch data size is set to 36, the iteration number is 200, and the fixed learning rate is 0.002. To avoid overfitting, we set the dropout ratio to 0.6. Firstly, we explored the performance of Ref , Ref 1414, and tAs can be seen from the table, the features extracted by different methods are integrated by the deep fusion evolutionary information framework in this paper, which can extract more abundant protein sequence information, and the prediction results are improved to different degrees compared with a single feature. Compared with the single special collection Ref  and Ref The practical significance of residue clustering results is the conformational change of residues, that is, the change of spatial structure. The more clusters, the more conformational changes. The result of the conformational change of residues corresponds to the conformational distribution of each residue. According to the conformational distribution, the mutual information between every two residues is calculated to observe the correlation between residues. In order to understand the relationship between mutual information value and distance, we have drawn Figures The signal is divided into a number of wavelet coefficients via the wavelet transform. Different decomposition scales have varying outcomes when used to analyze protein sequences. A lengthy sequence cannot be broken down into its component parts on a tiny scale. We must select an appropriate decomposition scale in order to achieve the best prediction outcomes. The prediction outcomes of mitochondrial outer membrane and chloroplast inner capsule cavity are mostly explored here because this study seeks to increase the prediction quality of those two components.Limited by the characteristics of wavelet decomposition, the analysis results of different decomposition levels are quite different. On the one hand, a large number of redundant data will be introduced when decomposing a short sequence; on the other hand, many details will be ignored when decomposing a small number of layers of a long sequence. We compare the data of 1-8 layers of wavelet decomposition to determine the optimal decomposition level. The results are shown in To forecast the protein-protein interaction, we employ the integrated residual CNN. According on the experimental findings, this approach performs predictions better than SVM and XGBoost classifiers. Through layer-by-layer learning, ML prediction can mine the prospective feature data of protein interaction pairings, which fits the nonlinear relationship between sequence feature data and category labels well. This method's prediction accuracy is 94.28%. Our prediction method is successful as evidenced by the fact that ML prediction outperforms the predictions of other conventional classifiers.As the energy factory of mitochondrial cells, it plays an important role in the life process of organism. The research on crystal structure of Lon protease provides conditions and also lays a foundation for us to screen inhibitors and activators of Lon protease from natural products or synthetic small molecular compounds with high throughput and to develop drugs acting on Lon protease. At present, although some prediction algorithms have high overall prediction accuracy, the prediction results are very unbalanced. Some functions have high prediction accuracy, while others are low. So, based on this, this paper proposes a model of mitochondrial protection by Lon protease based on ML under myocardial ischemia. In order to fully consider the relationship between different features and find out more critical feature information for subcellular localization, attention mechanism is used to weight the fused features. Compared with the single special collection methods in M495 data set, the overall actual accuracy of this method is 0.9553, which is 0.0913 and 0.0763 percentage points higher than other models, respectively. After using the oversampling method, the ROC value increased by 17.6%-21.3%. In this way, it is verified that the effect of adopting oversampling method is better than that of not adopting oversampling method, and the performance of the model can be improved.


We present the Pharmacorank search tool as an objective means to obtain prioritized protein drug targets and their associated medications according to user-selected diseases. This tool could be used to obtain prioritized protein targets for the creation of novel medications or to predict novel indications for medications that already exist. To prioritize the proteins associated with each disease, a gene similarity profiling method based on protein functions is implemented. The priority scores of the proteins are found to correlate well with the likelihoods that the associated medications are clinically relevant in the disease’s treatment. When the protein priority scores are plotted against the percentage of protein targets that are known to bind medications currently indicated to treat the disease, which we termed the pertinency score, a strong correlation was observed. The correlation coefficient was found to be 0.9978 when using a weighted second-order polynomial fit. As the highly predictive fit was made using a broad range of diseases, we were able to identify a general threshold for the pertinency score as a starting point for considering drug repositioning candidates. Several repositioning candidates are described for proteins that have high predicated pertinency scores, and these provide illustrative examples of the applications of the tool. We also describe focused reviews of repositioning candidates for Alzheimer’s disease. Via the tool’s URL, https://protein.som.geisinger.edu/Pharmacorank/, an open online interface is provided for interactive use; and there is a site for programmatic access. Proteins are currently being mapped to diseases in a comprehensive manner within open, online databases ,2,3,4. TTo aid with the process of drug repurposing, estimates of the likelihoods that drug repurposing candidates could be effective new treatments have been made using various approaches. These approaches include network-based approaches, text-based approaches, and semantics-based approaches, as reviewed by Xue et al. . A centrMethods that prioritize proteins involved in diseases may use the presence of co-occurring words or database terms as part of the prioritization algorithm. Consider, for example, the use of co-occurring database terms in the PolySearch tool . We prevTo further enable drug repurposing efforts, we present the Pharmacorank search tool. To prioritize the candidates, an unsupervised gene prioritization algorithm is implemented, which may be classified as a similarity profiling or a data fusion method . This meTo validate and benchmark the search tool’s accuracy, we calculated the percentages of medication–protein target pairs that were already known to be involved in the disease’s treatment for each priority range, and we termed these values the pertinency scores. An optimal correlation was sought between the priority scores and the pertinency scores. The equation of best fit then served as a predictive model that takes the priority score of each of the protein targets and predicts the pertinency score. We interpret the pertinency scores as being estimates of the likelihoods that the medications associated with the protein targets will be clinically relevant in a selected disease’s treatment.With the goal of helping to spur drug repositioning efforts within the greater scientific community, the predictive model between the priority score and pertinency score was applied to all diseases described in Disease Ontology (DO) , and druThe overall steps of the method to obtain prioritized proteins and medications to inform drug repurposing studies may be summarized as: (1) retrieve the proteins associated with each identified disease; (2) rank the proteins with priority scores calculated using protein functions; (3) obtain the correspondences between medications and proteins along with correspondences between medications and diseases from public databases to determine whether each protein interacts with a medication already known to treat the disease; (4) perform validation studies regarding the contribution that each type of protein function has in generating a priority score that discerns whether a protein interacts with a medication that is already used to treat the disease; (5) derive a predictive, quantitative relationship between the priority score and pertinency score, where the pertinency score is the percentage of protein targets known to interact with medications that are already known to treat the disease; (6) apply the resulting predictive relationship between the priority score and pertinency score to all proteins and medications associated with the disease; (7) identify a recommended pertinency score threshold for the end-user; and (8) review the protein targets and medications that have high predicted pertinency scores for consideration in future drug repositioning studies.A set of proteins associated with each disease was obtained based on information from multiple sources. Known correspondences were retrieved based on the Online Mendelian Inheritance in Man (OMIM) phenotypic descriptions  for a toThe functions used in the prioritization algorithm were retrieved from UniProt files and from coordinating resources that describe protein function. The types of functions include UniProt keywords, Gene Ontology terms, Enzyme Commission (EC) numbers, InterPro assignments, SUPERFAMILY assignments, small molecule interaction assignments from Chemical Entities of Biological Interest (ChEBI) , and UniThere were three types of annotations used from Gene Ontology (GO): molecular function, cellular component, and biological process. GO terms were obtained through UniProt GOA, which had granular GO annotations and excluded those higher up within the GO hierarchy when identified by the same technique ,30. All After the identification of the proteins of each protein–disease dataset, the priority scores of the proteins and their associated medications were calculated. An outline of the prioritization algorithm is shown in To calculate the raw priority score, a Python dictionary was created, where the keys were the unique identifiers of the specific functions in the protein–disease dataset and the values were the corresponding total numbers of proteins that had each specific function. The raw priority score for a protein regarding a type of function was then the sum of the values associated with each of the specific functions that were attributed to that protein. This is numerically equivalent to the dot product but avoids what we found to be a more computationally intense and more error-prone task of creating and multiplying the 1D arrays p and a, respectively, are the number of proteins retrieved in the protein–disease dataset and the number of distinct specific functions represented in the protein–disease dataset with reference to the type of function.The raw priority score of each protein was normalized using the total number of proteins in the protein–disease dataset and the total number of distinct specific functions of the type of function under consideration that were represented in the protein–disease dataset. The normalization factor eta, η was proportional to the combined error associated with the measurements of the number of proteins in the protein–disease dataset and the numbers of unique specific functions represented for proteins in the protein–disease dataset. Based on this inference, the normalization then follows a standard procedure of dividing by the total error [We inferred that al error , where tβ, which considered the average number of functions per protein for each type of function separately. Upon application of both normalization factors, the resulting priority score of a protein was calculated with the following formula:The rate at which functional features were assigned to proteins varied greatly according to the type of function. We therefore introduced the second normalization factor beta, The final normalized priority score of a protein for a given disease was then the average of the normalized priority scores calculated when using each type of function separately. In the formula, n is in the number of different types of functions considered, which was 9. As described above, the types of functions were UniProt keywords, the three types of Gene Ontology terms , Enzyme Commission (EC) numbers, InterPro assignments, SUPERFAMILY assignments, small molecule interactions from ChEBI, and UniProt residue features. To select the diseases from Disease Ontology that were to be used in the validation studies, the following procedure was implemented. The correspondences between medications and indications were retrieved from three sources: Medication-Indication (MEDI-C) resource , DrugCenEach of the diseases analyzed were required to have at least one medication currently on the market for the disease’s treatment, which interacted with a protein in the corresponding protein–disease dataset. To ascertain whether a retrieved protein interacted with a known medication for a selected disease, the list of medications for that disease was compared to the list of medications associated with the protein obtained from ChEMBL . DiseaseTo evaluate the utility of the priority score for discerning medication–protein target pairs that are clinically useful for the treatment of a selected disease, receiver operator characteristic (ROC) curves were generated. The positives were defined as those proteins in the protein–disease dataset that interacted with one or more medications currently known to treat the disease. The rest of the proteins in the protein–disease dataset were the negatives.Fixed values of the priority scores that corresponded to each protein were used as thresholds. The sensitivity and specificity values at each threshold for each of the different protein–disease datasets were obtained. The area under the curve (AUC) for each ROC for each protein–disease dataset was calculated, and an average AUC across the protein–disease datasets was obtained. The AUC calculations were performed using a Python script developed for this purpose. For the analyses, each protein–disease dataset was required to have 30 or more proteins to ensure there was ample data to estimate the sensitivity and specificity measurements at each threshold. Each protein–disease dataset used for validation was also required to have at least one protein that was known to be the target of a medication used to treat the corresponding disease.To identify medications that may be repositioned to treat a selected disease, we first identified those medications already known to treat the disease. A match was sought between the ChEMBL medication identifier and the medication identifier in MEDI-C or DrugCentral. Matching was carried out by one of the following ways: match the identification codes between RxNorm Ingredient ID  of MEDI-The relationship between the priority score and percentage of proteins that interact with medications currently known to treat a selected disease, referred to as the pertinency score, was assessed. To generate a plot of priority scores versus pertinency scores, six equal intervals were considered along the full range of priority scores. Six cross-fold validation sets, each containing 16.6% of the diseases of the full validation set, were generated for each of the intervals. For each fold, the percentage of proteins targeted by medications known to treat the selected disease was calculated. A scatter plot of the average of these percentages, called the pertinency scores, versus the average priority scores was generated. A fit of the scatter plot was constructed using a weighted, second-degree polynomial using the lm package R . The aveTo provide the end-user with a threshold for the pertinency score, tests were conducted using the drug to disease correspondences from MEDI-1, which was created in 2013, and the drug to disease correspondences from MEDI-2. The goal was to empirically identify the pertinency score range(s) of the drug to protein to disease tuples that were identified using MEDI-2 data but were absent when using MEDI-1 data. The study protocol was run using only the MEDI-1 data and only the MEDI-2 data separately.We then examined the number of new entries for different pertinency score ranges to observe empirically where the new entries fell. To normalize the numbers for each range, the number of new drug/protein/disease tuples for each pertinency score range were divided by the total number of protein targets that fell within each corresponding pertinency score range. We then plotted a bar chart of these ratios to empirically observe the cut-off point, where there was a large increase in the estimated likelihood of success.After completing the validation and prediction studies, we expanded the diseases considered and further applied the resultant prediction mode based on the second-degree polynomial fit of the priority score versus pertinency score. For the presentation of the predicted pertinency scores on the Pharmacorank website, analyses of all diseases described in Disease Ontology that mapped to either an ICD-9-CM or ICD-10-CM code were performed. The pertinency score was calculated for each protein in each protein–disease dataset regarding all corresponding proteins within Swiss-Prot, which does not include TrEMBL.We first tested the accuracy of the priority score regarding its ability to discern, among all the proteins of a protein–disease dataset, those that were targets of medications already known to treat the disease. ROC curves were obtained for each disease, where the positives are the known medication targets and the negatives are the rest of the proteins within the protein–disease dataset. All protein–disease datasets with thirty or more proteins that had one or more proteins that were identified as interacting with a currently used medication for the disease were included. There were 513 diseases that met these criteria. The list of these 513 diseases in the validation set is provided in the The effect on the AUC values of using each type of function separately for the calculation of the priority scores is shown in We modeled the relationship between the priority score of a protein in a protein–disease dataset and its pertinency score. As described in the methods, six equal intervals along the priority score range were identified, and the average priority score for each of the intervals was calculated. Then, for each priority score interval, the fraction of proteins in the interval that were targets of medications currently used to treat the disease, referred to as the pertinency score, was calculated. To obtain the average and standard error estimates for the priority scores and pertinency scores, a six-fold cross-validation was implemented as described in the methods.2 − 0.110x + 0.015. The correlation coefficient for the fit was 0.9978, which indicates a highly predictive relationship, as shown in A weighted least-squares fit with a second-degree polynomial was found to have the following equation: y = 1.399xThe pertinency score is interpreted as an estimate of the probability that the medication would be relevant in the treatment of the corresponding disease. To be clinically useful in the disease’s treatment, the medication would need to oppose the aberrant function of the protein, which contributes to the disease. This determination is left to the user. The tool estimates the strength of the association, but the user must then determine if the drug can theoretically be indicated or contraindicated. For example, if a protein’s function increases in the disease state and the medication inhibits the corresponding protein function, the medication would likely be clinically useful in the disease’s treatment. If the medication further increases the aberrant protein function or the medication exacerbates a loss of function that is associated with the disease, the medication would be relevant as a possible contraindication. The direction of the effect of the medication and the direction of the aberrant protein function in the disease mechanism therefore need to be ascertained to know whether the drug’s effect would be useful in the control the disease .As described in the methods, the analyses were conducted separately using only known drug to disease correspondences from MEDI-1 and then using only the drug to disease correspondence from MEDI-2. The pertinency scores of the drug/protein/disease tuples found from the MEDI-2 analyses but not from the MEDI-1 analyses were used to estimate the pertinency score range(s) where the tuples with a high likelihood of successful development would lie in the future.We reviewed the examples of the top repositioning candidates with the highest priority scores and therefore the highest predicted pertinency scores across all the diseases analyzed. The total number of unique diseases was 4041. The idea is that by considering those candidates with the highest predicted pertinency scores across all the diseases, a focus would be placed on those most likely to be clinically relevant. We selected three medications among the top predicted repositioning candidates to gain insight as to whether these candidates would likely be clinically useful. Additionally, these examples help to illustrate what one would expect to encounter when carrying out such reviews.The main goal of each individual candidate review was to identify cases where the drug has an opposing effect to that of the aberrant function of the protein. This boils down to finding cases where there is a gain of function of the disease protein and the drug inhibits the corresponding function. Alternatively, it corresponds to cases where the protein has a loss of function variation, and the drug increases the corresponding function of the protein. Cases where the drug enhances a gain of function of a disease or where the drug inhibits the function of a protein that had a corresponding loss of function would likely be contraindicated.Three proteins and repositioning candidates selected from across all the diseases are listed in For the review of the second candidate, consider GM2 gangliosidosis, which is progressive lysosomal storage disease marked by the accumulation of GM2 gangliosides in neuronal cells. This condition is caused by loss of function variations in beta-hexosaminidase subunit beta protein, and the phenotype of GM2 gangliosidosis is indistinguishable from that of Tay–Sachs disease. A possible repositioning candidate is pyrimethamine, which is a pharmacological chaperone (PC) that can stabilize the conformation of the mutant protein . This alA third repositioning candidate is tolcapone for the possible treatment of dengue hemorrhagic fever. Tolcapone inhibits the serine protease function of NS3. The dengue virus type 2 NS3 protein is one of the cleavage products of a large 3391-amino-acid glycoprotein from the dengue virus. The combination of several proteins into one large genome glycoprotein that is subsequently cleaved into functional smaller proteins has apparently enabled all the proteins of this genome glycoprotein to have high priority scores since all the functional annotations of all the proteins (cleavage products) that make up the glycoprotein would have been used in the prioritization algorithm. Further, given that there are multiple strains of the dengue virus, which means repeats of similar glycoproteins of the dengue virus are described in UniProt, these glycoproteins have high calculated priority scores. Nonetheless, the NS3 protein is described as being a target of tolcapone, and tolcapone is reported as a hit from a high-throughput screening with a Ki value range of 0.61–1.25 μM ,42. ThesA list of three protein targets and their associated medications to consider for repositioning studies for AD is provided in A second repurposing candidate is riluzole, which has the ability to reduce the alpha-synuclein protein aggregation seeds . The curWe found that angiotensin-converting enzyme 1 (ACE) has a relatively high pertinency score. Some evidence suggests that ACE2 inhibitors are associated with a slower rate of cognitive decline , but thiDrug repositioning can improve treatment outcomes and reduce the cost of drug development . MarketeComputational approaches for drug repositioning prioritize their identified candidates based on their estimated likelihoods of success ,59. The As described through our manual reviews, the results of searches across different diseases with Pharmacorank can be collated, and drug repositioning candidates with the highest predicted pertinency scores can be identified for review. These candidates may constitute the lowest hanging fruits, where a focus on further drug discovery and development may be placed . FurtherWe anticipate that the Pharmacorank search tool will complement other open technologies that are available to aid in the identification of new possible treatments for orphan diseases. There is a need for software applications that identify drug repositioning candidates for orphan diseases ,66. Our Drug repositioning methods span a variety of different experimental and computational approaches ,69,70, wThe application of Pharmacorank for drug repositioning may be classified into the latter group of methods, since the priority scores of medication–protein target pairs are described, and no new physical interactions between protein targets and medications are predicted. Comparable methods include ToppGene , TargetMRegarding the network method reported by Emig et al., the positives were those proteins known to be targeted by a medication in a clinical trial for the treatment of the disease and the negatives were randomly selected protein targets . The EmiFor the Pharmacorank search tool, the following validation approach was undertaken: the priority score was used to discern proteins targeted by medications used to treat the disease from all the other proteins associated with the disease. The Pharmacorank search tool similarly falls into the category of methods that utilize AUC values for validation, and it therefore falls into the category of methods that use both sensitivity- and specificity-based validation (SSV) as described by Brown and Patel . Here, tFor example, in the Emig study, all the proteins of the comparison set have a score that relates each protein to the disease but not all the proteins were deemed to be directly involved in the disease. In our studies, we note that our negatives do not include many random proteins not directly associated with the condition. Specifically, our negatives are proteins directly involved in the disease but are not known to interact with a known drug to treat the disease. Since we are contrasting the functions of proteins directly involved in the disease that interact with medications known to treat the disease versus the functions of protein directly involved in the disease that do not interact with medications known to treat the disease, we infer that the method enabled a more precise identification of those functions most relevant to make the protein a viable target whose corresponding function could be modified as part of the disease’s treatment.We interpret the AUC values obtained here as being more relevant and applicable to drug repositioning. This is because discerning the most clinically useful targets among the proteins known to be directly involved in the disease is likely what will be most useful in practice. Drug discovery and development projects focus on the proteins known to be involved in the disease and then try to figure out which of those will be the most effective drug targets.One of the take home messages from the analyses is that there is a predictive phenomenological relationship between the priority score and pertinency score. Knowledge of the set of proteins involved in a disease along with their normal biological functions can be used to quantitatively predict the likelihood that a given protein and its associated medications would be relevant in the treatment of the selected disease.We note that in the relationship between the priority score and pertinency score, as the priority score reaches relatively high values, the standard deviations of the pertinency scores increase. One reason for this observation was noted earlier, which is that some proteins are part of polyproteins, such as what is typically found in viruses. These proteins will receive high priority scores since all the protein functions would be attributed to a single UniProt entry that represents the entire polyprotein prior to cleavage. Furthermore, since there are multiple viral strains represented in UniProt, protein functions would be repeated multiple times in the prioritization algorithm, thus artificially increasing the corresponding priority scores. Of note is that the predictive relationship between the average priority score versus the average pertinency score holds.The observation that this strong relationship between the priority score and pertinency score exists is significant since the data assembled in UniProt and the data from the clinical trials of medications are of disparate origins. The information in UniProt regarding the characterization of the normal functions of proteins is independent of the testing and validation of medications brought to market via the required clinical trials.When considering the selection of drug repositioning candidates to consider for a selected disease, we recommend starting with those with the highest predicted pertinency scores that are likely to oppose the disease pathway. In many cases, the pertinency score may not reach above 10–20%, but those with the highest predicted pertinency scores are recommended to be the ones to reviewed first. There are many factors that contribute to this low rate such as, simply, not all the drugs have been successfully developed yet so the percentage of proteins that are targeted by clinically useful medications has not achieved its maximum value.As described in the results section, we recommend that the end-user consider a threshold of 0.1 for the pertinency score. We further note that the current success rates for drug development from the downstream points of the beginning of the clinical trial to the point of receiving marketing approval has remained about 10–20% . AlthougNow, the Pharmacorank site reveals that there are hundreds of drug repositioning candidates across a wide range of conditions that meet the 0.1 threshold. These candidates may be particularly ripe for the clinical trials that are required in their development for the selected conditions. As an additional repurposing example candidate that meets the 0.1 threshold, we observe that probucol has a pertinency score of 0.102 through its interaction with vascular cell adhesion protein 1 in the condition multiple sclerosis. We note that probucol has been shown to reduce neural cell apoptosis after cellular injury . This exhttp://protein.som.geisinger.edu/Pharmacorank/. Automated monthly updates of the searchable content and the predictive model are enabled based on the routine updates of the information in UniProt. All analyses are pre-run prior to making them available on the website to make the queries fast since it is then just a matter of looking up the pre-computed results. For interactive queries, an autocomplete tool identifies the corresponding Disease Ontology term as a disease name is being typed. For each protein and drug retrieved, links are provided to the corresponding entries in UniProt and ChEMBL for further information. Programmatic access is enabled to retrieve the results of precomputed searches via the URL http://protein.som.geisinger.edu/Pharmacorank/Downloads/.The web interface for the Pharmacorank search engine is available at the URL The Pharmacorank search tool provides a means to retrieve protein medication targets and their associated medications that are either known or predicted to be relevant in the treatment of a selected disease. The results of searches are prioritized using an objective algorithm that considers each protein target’s complement of functions. The functions are derived from the broad collection of function descriptions in UniProt. Different types of functions are collectively used in the formulation of the priority scores. We find a quantitative, predictive relationship between the resulting priority score of a medication–protein target pair and its probability of being clinically relevant in the treatment of the selected disease.To facilitate drug repositioning efforts across a wide range of diseases, the disease terms and phrases described in Disease Ontology were analyzed. The medications associated with the retrieved proteins were considered as drug repositioning candidates if they were not yet used to treat the queried disease and were likely to oppose the disease mechanism. We anticipate that the drug repositioning candidates described here and those found subsequently through the updated search tool will ultimately be clinically relevant for their predicted indications, which may lead to cost savings and a reduction in disease burden. An emergent feature of the search tool is that the repositioning candidates most likely to be clinically relevant across a wide range of diseases could be readily identified since the priority scores and pertinency scores are normalized across all the diseases analyzed.


Protein complex structure prediction is important for many applications in bioengineering. A widely used method for predicting the structure of protein complexes is computational docking. Although many tools for scoring protein–protein docking models have been developed, it is still a challenge to accurately identify near-native models for unknown protein complexes. A recently proposed model called the geometric vector perceptron–graph neural network (GVP-GNN), a subtype of equivariant graph neural networks, has demonstrated success in various 3D molecular structure modeling tasks.Herein, we present G-RANK, a GVP-GNN-based method for the scoring of protein-protein docking models. When evaluated on two different test datasets, G-RANK achieved a performance competitive with or better than the state-of-the-art scoring functions. We expect G-RANK to be a useful tool for various applications in biological engineering.https://github.com/ha01994/grank.Source code is available at kds@kaist.ac.krBioinformatics Advances online. Protein–protein interactions play key roles in many biological processes. Knowledge on the 3D structures of protein complexes is key for understanding protein–protein interactions, which can be useful in various applications such as protein design or drug discovery. As experimental approaches pose limitations in terms of time and cost, computational docking is commonly used to predict the structures of protein complexes. Computational docking involves the generation of a large number of candidate structural models, from which final models are selected according to a certain scoring function. The scoring function is highly important for the successful identification of near-native docking models among all generated candidates.Various scoring functions have been developed based on physics-based, statistical potential-based, or machine learning-based methods  and ITScThe models introduced above have achieved considerable success in scoring protein-protein docking models, but there is still room for improvement .Herein, we propose G-RANK, a new GVP-GNN-based method, for ranking protein–protein docking models. We compare our model with DeepRank-GNN . When teWe used the GVP-GNN model architecture from The architecture of the model is shown in The graph propagation layer first computes messages from node and edge embeddings and then uses the message to update node embeddings. This layer can be formulated as:The feed-forward layer then updates node embeddings in a point-wise manner. This layer can be formulated as:After five repetitions of the GVPConvLayer block, the final node embeddings are passed through a GVP layer, after which all node embeddings are reduced to scalars. This is followed by a mean pooling layer that averages the embeddings across all nodes. The embeddings finally pass through two dense layers, thus resulting in a single scalar value.https://data.sbgrid.org/dataset/843/), and the details on the dataset generation can be found in The authors of DeepRank (https://data.sbgrid.org/dataset/843/). This dataset consists of more than 16 000 docking models for 13 protein–protein complexes, which were generated using a variety of docking methods by more than 40 research groups. We left out one complex containing no positive samples, resulting in 12 complexes. Details on the size and composition of this dataset can be found in We assessed our model on an independent test dataset called the CAPRI score set , which wAs in DeepRank-GNN  at a cutf-nat (fraction of native contacts)  and the precision-recall area under the curve (PR-AUC) to assess model performance. We also used a per-complex assessment metric called the hit rate , lRMSD (ligand RMSD) and f-nat values, and each docking model is classified as ‘high’, ‘medium’, ‘acceptable’ or ‘incorrect’ (k is small, it shows a slightly lower performance compared to other models, but it generally shows a good predictive performance. In terms of the success rates, its performance is similar to other methods in general.Additionally, we performed a model assessment by labeling the correct and incorrect models using the CAPRI scoring criteria (correct’ . In thisIn this study, we developed G-RANK, a new method based on GVP-GNN for assessing the quality of protein–protein docking models. When tested on two different test sets, G-RANK’s performance was competitive with or outperformed the existing state-of-the-art methods. Analysis on hit rates and success rates demonstrated that G-RANK performs well in identifying near-native models among a large pool of candidate models. Our results confirm the previous finding (As shown by the generally low PR-AUC of the predictors on the CAPRI score set, the identification of several near-native models among a large pool of candidate models remains a difficult problem. Development of more advanced equivariant neural network architectures in the future may help overcome this problem. In addition, the continual accumulation of experimentally determined protein complex structures may enable better prediction in the future. Also, the recent development of AlphaFold2-Multimer (The GVP-GNN model used in this study could be further modified and applied to other research objectives related to protein structure and function, such as ranking of protein–ligand docking models. We expect that G-RANK may contribute to various applications in biological engineering, such as protein design and drug discovery.vbad011_Supplementary_DataClick here for additional data file.


The HRAS gene has been reported to cause cancer, and identifying alleles that could potentially predispose one to cancer could lead to early diagnosis and better prognosis. Here for the first time, we conducted a machine-learning approach to identify high-risk predictive alleles of the HRAS gene. Our study reported alleles that may serve as potential targets for different proteomic studies, diagnoses, and therapeutic interventions focusing on cancer.The Harvey rat sarcoma (HRAS) proto-oncogene belongs to the RAS family and is one of the pathogenic genes that cause cancer. Deleterious nsSNPs might have adverse consequences at the protein level. This study aimed to investigate deleterious nsSNPs in the HRAS gene in predicting structural alterations associated with mutants that disrupt normal protein–protein interactions. Functional and structural analysis was employed in analyzing the HRAS nsSNPs. Putative post-translational modification sites and the changes in protein–protein interactions, which included a variety of signal cascades, were also investigated. Five different bioinformatics tools predicted 33 nsSNPs as “pathogenic” or “harmful”. Stability analysis predicted rs1554885139, rs770492627, rs1589792804, rs730880460, rs104894227, rs104894227, and rs121917759 as unstable. Protein–protein interaction analysis revealed that HRAS has a hub connecting three clusters consisting of 11 proteins, and changes in HRAS might cause signal cascades to dissociate. Furthermore, Kaplan–Meier bioinformatics analyses indicated that the HRAS gene deregulation affected the overall survival rate of patients with breast cancer, leading to prognostic significance. Thus, based on these analyses, our study suggests that the reported nsSNPs of HRAS may serve as potential targets for different proteomic studies, diagnoses, and therapeutic interventions focusing on cancer. The HRas proto-oncogene GTPase (HRAS) is one of the pathogenic genes that cause cancer. The human HRAS gene is located on chromosome 11p15.5 and is responsible for cell division by regulating the cellular signaling pathway using a molecular switch mechanism . RAS is Single nucleotide polymorphisms (SNPs) account for more than 90% of all nucleic acid sequence variations in humans. Non-synonymous single nucleotide polymorphisms (nsSNPs) cause amino acid substitution, which may affect protein function and lead to pathogenic phenotypes . nsSNPs Mutations in the HRAS gene have been reported to be common among different types of cancer; however, the structural and functional effect of the reported mutations remains vague. Understanding how the HRAS nsSNPs lead toward cancer could provide better insights into understanding the influencing pathways, which hold implications for cancer diagnosis and prognosis. Therefore, this study aims to investigate the nsSNPs of HRAS in understanding its pathogenesis for elucidating the diagnosis and prognosis of cancer using in silico analysis. The nsSNPs of the HRAS gene were extracted from the NCBI database and screened for high-risk pathogenicity using multiple bioinformatics software tools based on their high accuracy and frequency of use ,17. The http://www.ncbi.nlm.nih.gov/ (accessed on 12 October 2020)). The amino acid and DNA sequences, SNP IDs, wild-type amino acids, amino acid positions, missense amino acids, and minor allele frequency (MAF) were also retrieved. A total of 180 nsSNPs were extracted from this database. The human HRAS protein structure was accessed from the Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (PDB) (ID: 4Q21).The HRAS nsSNPs were obtained from the NCBI dbSNP 2.0 (gene ID: 3265) database  (http://provean.jcvi.org/genome_submit_2.php?species=human (accessed on 18 October 2020)], PolyPhen-2 (Polymorphism Phenotyping v2) . The nsSNPs predicted deleterious by all five in silico tools were considered high-risk nsSNPs and investigated further.Five different bioinformatics tools were used to predict the functional effects of nsSNPs. These tools were: PROVEAN  embedded with SIFT (Sorting Intolerant from Tolerant) [https://mmb.irbbarcelona.org/PMut/ (accessed on 20 August 2021)] was used to verify the nsSNPs identified by the previous five tools. The prediction has a range of 0–1, where the range of 0–0.5 is considered neutral while that of 0.5–1 is considered disease [PMut [ disease .http://gpcr2.biocomp.unibo.it/cgi/predictors/I-Mutant3.0/I-Mutant3.0.cgi (accessed on 28 October 2020)] was used to determine protein stability. Its predictions are based on two support vector machines (SVM), and it uses ProTherm, which is now the most comprehensive database of thermodynamic experimental data on protein stability when mutated  [I-Mutant3.0 [lue DDG) . It alsohttps://consurf.tau.ac.il/ (accessed on 28 October 2020)] was used to estimate the evolutionary conservation of amino acid residues in a protein based on phylogenetic relations between homologous sequences [ConSurf [http://www.sbg.bio.ic.ac.uk/~phyre2/html/page.cgi?id=index (accessed on 28 October 2020)] and I-TASSER (Iterative Threading ASSEmbly Refinement) [https://zhanglab.ccmb.med.umich.edu/I-TASSER/ (accessed on 5 November 2020)]. PDB formats for wild-type HRAS and its mutants were obtained through Phyre2 by substituting each nsSNP into the HRAS protein sequence [https://zhanglab.ccmb.med.umich.edu/TM-align/ (accessed on 28 October 2020)] was employed to compare wild-type with mutant protein structures. This algorithm computes a template modeling score (TM-score) and the root mean square deviation (RMSD) along with the superposition of the structures. The TM-score gives values between 0 and 1, where 0.00 < TM score < 0.30 means random structural similarity, 0.50 < TM score < 1.00 means in about the same fold. While higher RMSD indicates greater variation between wild-type and mutant structures [https://servicesn.mbi.ucla.edu/ERRAT/ (accessed on 5 November 2020)], and the resulting structures were viewed using Chimera 1.15.The 3D models for wild-type HRAS protein and its mutants were generated using Phyre2  and GPS-MSP 1.0 [http://msp.biocuckoo.org/online.php (accessed on 20 August 2021)]. A local database from UnitProtKB/Swiss-Prot was always provided and updated in MusiteDeep. The known PTM sites were shown on the sequence according to the confidence threshold [https://services.healthtech.dtu.dk/service.php?NetPhos-3.1 (accessed on 20 August 2021)] and GPS 5.0 [http://gps.biocuckoo.org/online.php (accessed on 20 August 2021)]. NetPhos 3.1 uses ensembles of neural networks to complete this task, and residues with scores >0.5 the residue was considered phosphorylated [http://bdmpub.biocuckoo.org/prediction.php (accessed on 20 August 2021)] and iUbiq-Lys [http://www.jci-bioinfo.cn/iUbiq-Lys (accessed on 20 August 2021)]. In BDM-PUB, the Bayesian Discrimination Method (BDM) was used to determine the probability score [The putative methylation sites at lysine and arginine residues in the HRAS protein sequence were predicted using MusiteDeep [hreshold . GPS-MSPhreshold . Phosphoorylated . Likewisorylated . Putativty score . In iUbity score .https://string-db.org/ (accessed on 3 December 2020)]) is a database that calculates protein–protein interactions [STRING (ractions . The dathttps://compbio.uthsc.edu/miRSNP/ (accessed on 3 December 2020)]) analyses SNPs and INDELs in microRNA, which may affect the miRNA-mRNA interactions resulting in altered expression of protein [PolymiRTS  simulation for wild-type and each mutant structure using GROMACS version 2020 ,33. The For analysis, the first and last frame of the protein throughout the production of the MD simulation, as well as the computed stability metrics such as root mean square deviation (RMSD), root mean square fluctuation (RMSF) per residue, and radius of gyration (Rg) were extracted.nsSNPs were retrieved from the dbSNP database as it is the more extensive SNP database . A totalPROVEAN, SIFT, PolyPhen-2, SNPs&GO, and PhD-SNP were used to predict and identify the most deleterious nsSNPs. A total of 33 nsSNPs were predicted as “pathogenic” or “harmful” by all five tools, hence they were high-risk nsSNPs .The 33 high-risk nsSNPs were further verified using PMut. ConSurf was used to calculate the evolutionary conservation of amino acid residues of the HRAS protein. V14, D38, T58, G60, R73, K117, E143, and A146 were predicted as functional, highly conserved, and exposed residues. G15 was predicted as structural, highly conserved, and buried residue. G12, G13, R102, R123, R161, and R164 were predicted as exposed but not functional residues. Y4, I36, M72, G75, G77, F90, L113, G115, A130, L133, A134, and I163 were predicted as buried residues .A total of eight: 38H, T58P, G60S, G60V, R73C, K117R, E143Q, and A146V nsSNPs were predicted to be highly conserved and exposed with decreased protein stability. These were confirmed as the most damaging and selected for comparative modeling.To determine whether the eight high-risk nsSNPs alter the wild-type structure of HRAS protein, Phyre2 was used to generate 3D structures of the wild-type protein and its eight mutants. The c5c2kA template was used for predicting the 3D models. I-TASSER was used to generate the 3D models for the wild-type HRAS protein and mutants. The common templates used were 6cuoA, 5wdrA, 1c1y, 6zioA, 5tarA, and 1ctqA. For each input, five models were generated by I-TASSER, which was verified by ERRAT, and the model with the highest possible C-score and highest possible ERRAT value was selected . The modPost-translational modifications (PTMs) are biochemical modifications of amino acids that extend the structures and change the properties and functions of the protein, regulating the structural confirmation of proteins, protein–protein interactions, and cellular signaling processes . ListingProtein–protein interactions analysis was carried out to determine if the nsSNPs interact with other proteins, thus altering phenotypic effects. The interaction analysis showed that HRAS is related to RAS1, PIK3CA, RASA1, BRAF, NF1, RALGDS, SOS1, ARAF, RIN1, and PIK3CG gene . PolymiRTS database was used for the prediction of nsSNPs within 3′ UTR of the HRAS gene. nsSNP within the 3′ UTR region may disrupt and/or create miRNA target sites. A total of three: rs142218590, rs151229168, and rs140060409 functional SNPs were predicted to affect the miRNA target sites, further creating new miRNA binding sites .p-value were calculated. HRAS gene showed a hazard ratio (HR) = 9.53 (95% CI: 1.3–69.88) and log-rank p-value = 0.006 for breast cancer; indicating that the result was statistically significant . However, no significant differences were observed for bladder carcinoma (p = 0.6591), cervical squamous cell carcinoma (p = 1), colon adenocarcinoma (p = 0.4552), cutaneous melanoma (p = 0.3256), head-neck squamous cell carcinoma (p = 0.5036), kidney renal clear cell carcinoma (p = 1), kidney renal papillary cell carcinoma (p = 1), lung adenocarcinoma (p = 9474), and ovarian cancer (p = 1). This indicates that HRAS deregulation can serve as a prognostic marker for patients’ breast cancer.Kaplan–Meier plotter was used to determine the prognostic value of HRAS gene expression for breast, ovarian, lung, and gastric cancers by combining gene expression and cancer patient survival. Hazard ratio (HR) with 95% confidence intervals (CI) and log-rank RMSD, Rg, and RMSF per residue distributions using a Python library and Matplotlib were visualized and plotted . The RMSHRAS gene has been annotated in the cell proliferation of several carcinomas; however, the in silico analysis understanding of the structural and functional effect of deleterious nsSNPs has remained uncharacterized. The HRAS gene is reported to contribute to germline mutations that activate RAS/MAPK signaling to lead toward “RASopathies” [HRAS nsSNPs at molecular, functional, and structural levels using computational analysis to identify the most harmful nsSNPs and their impact on RASSF5 protein.The pathies” . HRAS ispathies” . TherefoHRAS.Damaging nsSNPs were predicted using five different tools , resulting in 33 nsSNPs predicted as “highly damaging”. In order to further narrow down the number of possible pathogenic nsSNPs, PMut, I-Mutant, and ConSurf tools were used to predict protein stability, the evolutionary conservation of amino acids, the physical and chemical properties, and the changes in protein structure after mutations. This resulted in eight “high-risk” nsSNPs: rs750680771, rs770492627, rs1589792804, rs730880460, rs749674880, rs104894227, rs909222512, and rs121917759, which were predicted (i) pathogenic by all five predicting tools; (ii) reduced protein stability; and (iii) evolutionary conservation showed these nsSNPs as highly conserved. This indicated that these nsSNPs with altered protein stability could cause misfolding, degradation, or aberrant conglomeration of proteins . MoreoveHRAS gene. The phosphorylation of HRAS at Y4C, T58I, and T58P was predicted to cause functional impairment leading to destabilization of the protein, eventually enhancing the harms of PTM impairment (https://www.activedriverdb.org/ (accessed on 3 December 2020)), the phosphorylation sites in PTMs are essential in regulating the structures and functions of proteins and are involved in protein–protein interactions and cell signaling ,42. The pairment . As PTMsThe structural consequences of these deleterious nsSNPs were predicted using the Phyre2 homology modeling tool. Six templates: 6cuoA, 5wdrA, 1c1y, 6zioA, 5tarA, and 1ctqA, were utilized to generate the wild-type and mutant protein models of the HRAS protein. Moreover, I-TASSER was used to calculate the C-score, which was verified by ERRAT. The model with the highest possible C-score and highest possible ERRAT value was selected. Based on these criteria, the eight nsSNPs were selected.HRAS determined using the STRING tool showed a strong network , and a negative regulation of the fibroblast apoptotic process. HRAS, an upstream activated protein, binds to RAF and MEK kinases and transduces intra and extracellular signals tyrosine receptor kinases (Trk) in the MAPK/ERK pathway. Therefore, nsSNPs in HRAS may inhibit the MAPK/ERK pathway causing the restoration of tumor cells to a non-transformed state and increasing the activation of the ERK/MAPK signaling pathway leading to the occurrence and development of tumors [The protein interaction network of f tumors .microRNAs have been associated with cancer-associated biological processes, such as proliferation, differentiation, apoptosis, metabolism, invasion, metastasis, and drug resistance. Furthermore, the pathological origin of cancer has also been proven to be directly related to the dysregulation of miRNAs . The polHRAS nsSNPs against different types of cancer using the Kaplan–Meier bioinformatics analyses. The results indicated that the HRAS gene deregulation might affect the overall survival rate of patients with breast cancer and thus affect prognosis significance. This finding is in agreement with a study by Bieche and colleagues (2021) that reported that HRAS-mutated AMEs could potentially be treated with MEK inhibitors [HRAS as a prognostic factor or to serve as a therapeutic target for breast cancer treatment [This study also evaluated the hibitors . Anotherreatment .Lastly, we performed 10 ns MD simulations for the wild-type HRAS and each mutant structure to simulate the structural stability of the mutant over time. All models were relatively stabilized without big structural fluctuations, as illustrated by the RMSD and RMSF scores of less than 1 nanometer (nm) . As expeHRAS protein plays a vital role as a tumor suppressor. This study reports three major nsSNPs in HRAS: Tyrosine to Cysteine at position 4 (rs764622691), Threonine to Isoleucine at position 58 (rs121917758), and Threonine to Proline at position 58 (rs770492627) as high-risk. Furthermore, nsSNPs deregulation was also reported to affect the prognosis of breast cancer. These nsSNPs can be strongly considered as key molecular biomarkers for the diagnosis and prognosis of cancer. Nonetheless, in vitro studies are needed to explore the effects of these polymorphisms on the structure and function of the protein.The


Protein–protein interactions play a ubiquitous role in biological function. Knowledge of the three-dimensional (3D) structures of the complexes they form is essential for understanding the structural basis of those interactions and how they orchestrate key cellular processes. Computational docking has become an indispensable alternative to the expensive and time-consuming experimental approaches for determining the 3D structures of protein complexes. Despite recent progress, identifying near-native models from a large set of conformations sampled by docking—the so-called scoring problem—still has considerable room for improvement. We present MetaScore, a new machine-learning-based approach to improve the scoring of docked conformations. MetaScore utilizes a random forest (RF) classifier trained to distinguish near-native from non-native conformations using their protein–protein interfacial features. The features include physicochemical properties, energy terms, interaction-propensity-based features, geometric properties, interface topology features, evolutionary conservation, and also scores produced by traditional scoring functions (SFs). MetaScore scores docked conformations by simply averaging the score produced by the RF classifier with that produced by any traditional SF. We demonstrate that (i) MetaScore consistently outperforms each of the nine traditional SFs included in this work in terms of success rate and hit rate evaluated over conformations ranked among the top 10; (ii) an ensemble method, MetaScore-Ensemble, that combines 10 variants of MetaScore obtained by combining the RF score with each of the traditional SFs outperforms each of the MetaScore variants. We conclude that the performance of traditional SFs can be improved upon by using machine learning to judiciously leverage protein–protein interfacial features and by using ensemble methods to combine multiple scoring functions. Proteins are among the most abundant, structurally diverse, and functionally versatile biological macromolecules. They come in many sizes and shapes and perform a wide range of structural, enzymatic, transport, and signaling functions in cells . HoweverAgainst this background, computational docking has emerged as a powerful tool for modeling 3D structures of protein–protein complexes . Given 3The scoring functions that have been developed for protein–protein docking can be broadly grouped into several categories : (1) phyAgainst this background, we introduce MetaScore, an approach to scoring docking conformations that combines any existing scoring function with a random forest (RF)  classifii-RMSD) of the decoy is less than or equal to 4 Å). ; (2) The number of interface residues in a conformation is greater than or equal to 10. Interfacial residues are determined using an alpha carbon–alpha carbon (CA-CA) distance of 8 Å between two residues belonging to two different proteins in the conformation (a decoy or a bound form). Among the 176 cases in BM4, 63 cases with decoys HADDOCK generated and 45 cases with only bound structures remained. We labeled a decoy near-native if its i-RMSD relative to the bound form is less than or equal to 4 Å. Otherwise, the decoy was labeled as non-native. This process yielded 1221 near-native and 35,957 non-native conformations. We refer to this set as the BM4 decoy set. However, the proportion of near-native and non-native conformations is highly unbalanced. There are various techniques to handle the class imbalance in data sets such as under-sampling, over-sampling, and cost-sensitive training [non-native conformations for each case so that the near-native to non-native ratio is 1:1 . We chose non-native decoys whose i-RMSDs are greater than 14 Å for training a model . Our final training set consists of 1221 near-native models (i-RMSD ≤ 4 Å) and 1221 non-native models (i-RMSD > 14 Å) for 108 cases.We used the protein–protein docking benchmark Version 4.0 (BM4) , which htraining ,62. We cnear-native and 3384 non-native conformations and 1115 near-native and 3485 non-native conformations for the BM5 decoy set and CAPRI score set, respectively.For independent testing, we used sets of decoys generated by HADDOCK from the 55 newly added docking cases to the BM5   and the hit rate . Both were calculated for an increasing number of predictions N varying between 1 and 400. For easier comparisons, area under the curve of success rate (ASR) and area under the curve of hit rate (AHR) were computed from the plots of corresponding success rate and hit rate, respectively, for N between 1 and 400 predictions. We focus on curves of ASRs and AHRs for the top 10 and top 400 predictions because the top 10 decoys are considered for further analysis in the biologists’ perspective [The performance of a scoring method to correctly rank decoys based on spective  and CAPRspective  competitMetaScore is an approach that combines the random forest (RF)-based score produced from our RF classifier trained using several features with the score from a traditional scoring function.We trained an RF classifier using a diverse set of features of the interfaces between the interacting partners in decoys of our training data set to discriminate between near-native and non-native conformations. Random forest (RF) is an ensemble tree-structured classifier, which is used for a data set with a large number of training data points and input features . A randoBefore combining the scores from different scoring functions including the RF score, we normalized the scores of decoys for each case from each scoring function using the Min-Max normalization method. Min-Max normalization scales a list of data from 0 to 1. The minimum value in the data is mapped to 0 and the maximum one in the data is mapped to 1. The strength of this method is that all relationships among the data values can be preserved exactly and that any potential bias is not introduced into the data . Howeverz) for x in a set of decoy scores for a case, X, using this method is calculated as follows:min(X) and max(X) are the minimum and maximum values in the X given its range excluding outliers.A normalized value  (see We used the Position-Specific Scoring Matrix Information Contents (PSSM-ICs) of interfacial residues as conservation scores. PSSM-IC is a measure of the information content for a residue in a PSSM based on Shannon’s uncertainty using prior residue probability and the relative frequency of the residue at a specific protein sequence position . The hig018) see . We collx and y, IP, was defined as the ratio of the observed frequency in the protein–protein complexes and the expected frequency derived as the random probability to pick the interaction pair of x and y.Previous studies ,73,74,75PSSMIP) and both conservation scores and distances between interfacial residue pairs . For each interfacial residue pair  in the ith decoy (iD), which consists of protein A and B, PSSMIP and PSSM,DistIP are defined as:Dist represents the CA-CA distance between residue x in protein A and residue y in protein B, IP represents the interaction propensity value for a pair of residue x and y that InterEvScore provides, and APSSM is the position-specific score corresponding to the value of the m-th amino acid in the 20-element vector for interfacial residue x in the PSSM profile from the sequence of protein A. All PSSM values were normalized by the sigmoid function.Moreover, we assumed that interaction propensities weighted by conservation scores and/or distances between interfacial residue pairs can be promising features by reflecting evolutionary closeness and geometrical tightness into the interaction propensity. We generated two additional interaction-propensity-based features weighted by only conservation scores  from interfacial residue pairs in a decoy by summation and averaging, which results in 6 features.Because the sizes of interfaces of different decoys are various, we summarized a list of values for each type of interaction-propensity-based value , modifying the equation to calculate the ratio of CX values of alpha-carbon atoms of each interfacial residue pair . The CX value measures the ratio of the volume that atoms occupy within a sphere with a radius of 10 Å to the volume of empty space in the sphere . It has 1A, I2A, …, InA denote a set of interfacial residues in a protein A of a decoy. Here, IiA where n denotes the number of interfacial residues in the protein A. For each interfacial residue pair  of a decoy, which consists of protein A and B, CXratio is defined as:iA and CXjB represent CX values calculated by centering the 10 Å sphere on alpha-carbon atoms of IiA and IjB, respectively.Let Iratio is larger than or equal to 1. The higher value of CXratio can be regarded as evidence that the alpha-carbon atom of IiA or IjB protrudes and the alpha-carbon atom of the one is dented. The lower values of CXratio can be considered that as evidence that both the alpha-carbon atoms of IiA and IjB protrude or are dented. Those CX-related values obtained are as many as the number of interfacial residue pairs in the decoy. We summarize them as forms of average and standard deviation, which ends up making a couple of features.CXThe buried surface area  is one oThe relative accessible surface area (rASA) of each interfacial residue was calculated using both its solvent accessible area obtained using STRIDE  and the It is well known that particular secondary structures are preferred at protein interfaces ,91. To cTo capture the connectivity of interfacial residues and the size of the interface, we added three features: the number of interfacial residue pairs, the total number of interfacial residues, and the link density. The link density feature was implemented as defined in Basu et al. , which iTo test our hypothesis that combining a machine learning model trained using potent interaction features with an existing scoring function can improve the performance of the original scoring function, we chose HADDOCK firstly as a representative of traditional scoring methods. We compared three scoring methods, HADDOCK, our RF classifier, and our MetaScore approach combining scores from HADDOCK and the RF classifier (MetaScore-HADDOCK) using 10-fold case-wise cross-validation with the training set derived from BM4  RF(Group), which is the RF classifier trained using only the raw scores and the normalized scores of members in a group of scoring methods (group);(2)RF(Group + Features), which is the RF classifier trained using the protein–protein interface features including the raw scores and the normalized scores of members in a group;(3)Avg(Group), which is a method averaging the normalized scores of members in a group;(4)Semi-MetaScore-Group, which is a method combining the score from the RF classifier trained using only the raw scores and the normalized scores of members in a group with the averaged score of the normalized scores of members in the group;(5)MetaScore-Group, which is to combine the score from the RF classifier trained using the protein–protein interface features including the raw scores and the normalized scores of members in a group with the averaged score of the normalized scores of members in the group.Ensembles of multiple predictive models are known to often outperform individual models ,94,95. WWe tested fifteen MetaScore-Ensemble methods in total using combinations of three groups and five approaches . For exaThe comparison results using ASR and AHR on our independent test set, BM5 decoy sets, are shown in Avg(Group), applied to three “Groups” , outperforms each members in each group. Regardless of which “Group” is used, the Avg(Group) is outperformed by RF(Group + Features), Semi-MetaScore-Group, and MetaScore-Group. Moreover, MetaScore-Group outperforms not only Semi-MetaScore-Group in every “Group” but also each of the MetaScore variants using each member in the corresponding “Group.” In addition, RF(Group + Features), which incorporates the features of the interfaces for training the RF classifier outperforms RF(Group), which does not. Taken together, we can conclude that combining methods using any “Approaches” we tested except RF(Group) outperform individual methods, and that a machine learning model trained with the additional features of interfacial regions outperforms a simple averaging method and a machine learning model not using features for interfacial regions in decoys.Moreover, Additionally, regardless of which one of the five “Approaches” is used, “Approaches” using the ExpertsHigh Group outperform ones using the ExpertsLow Group. In Avg(Group), Semi-MetaScore-Group, and MetaScore-Group approaches, the use of the Experts Group outperforms the use of either the ExpertsHigh or ExpertsLow Group. Except for the RF(Group) and Avg(Group), the “Approaches” using all members in the Experts Group were ranked in the top five methods in MetaScore offers a new approach to scoring docking models. MetaScore combines an RF classifier trained to distinguish near-native conformations from non-native decoys using features of their interfaces with a traditional scoring method (by simply averaging their respective scores). We have shown that MetaScore improves upon on each of the published scoring functions including HADDOCK, iScore, DFIRE, DFIRE2, MJ3H, PISA, pyDOCK, SIPPER, SWARMDOCK, and TOBI’s method. This suggests that the interface features carry information beyond that captured by the traditional scoring functions for distinguishing native conformations from non-native decoys.Not surprisingly, the results of our experiments show that ensembles of better-performing scoring functions outperform individual scoring functions. The superior performance of the ensembles that combine more better-performing scoring functions over those that combine fewer scoring functions suggests that the different individual scoring functions carry complementary information for distinguishing native conformations from non-native decoys.The results of our experiment point to several possible directions for further improving the performance of MetaScore: (1) using better-performing machine learning methods and/or utilizing more informative features to improve the performance of classifiers trained to distinguish between near-native conformations from non-native decoys; (2) increasing the quantity and quality of the data used to train the classifiers; (3) the use of more effective ensemble methods ,95 for cWe have proposed a new approach, MetaScore, to score docking models. MetaScore combines an RF classifier trained to distinguish near-native conformations from non-native decoys using features of their interfaces with a traditional scoring function. The results of our experiments on standard docking benchmarks show that, in the case of each of the scoring functions we tested from the literature, the combination of the RF classifier and the individual scoring function consistently outperforms the individual scoring function. Furthermore, MetaScore-Ensemble, which combines MetaScore with a set of traditional scoring methods, improves upon not only each of original scoring methods but also MetaScore in combination with each individual scoring method in terms of success rate and hit rate . We conclude that ensemble methods that combine multiple scoring functions offer promising ways to leverage the complementary strengths of the individual scoring functions.


Protein–protein interaction datasets, which can be modeled as networks, constitute an essential layer in multi-omics approach to biomedical knowledge. This representation gives insight into molecular pathways, help to uncover novel potential drug targets or predict a therapy outcome. Nevertheless, the data that constitute such systems are frequently incomplete, error-prone and biased by scientific trends. Implementation of methods for detection of such shortcomings could improve protein–protein interaction data analysis.We performed topological analysis of three protein–protein interaction networks (PPINs) from IntAct Molecular Database, regarding cancer, Parkinson’s disease  and Human Reference Interactome. The data collections were shown to be often biased by scientific interests, which highly impact the networks structure. This may obscure correct systematic biological interpretation of the protein–protein interactions and limit their application potential. As a solution to this problem, we propose a set of topological methods for the bias detection, which performed in the first step provides more objective biological conclusions regarding protein–protein interactions and their multi-omics consequences.https://github.com/AlicjaNowakowska/ETNA. The software includes a graphical Colab notebook: https://githubtocolab.com/AlicjaNowakowska/ETNA/blob/main/ETNAColab.ipynb.A user-friendly tool Extensive Tool for Network Analysis (ETNA) is available on alicja.nowakowska@pwr.edu.pl or malgorzata.kotulska@pwr.edu.plBioinformatics online. In the era of the constant growth of biological information, data analysis techniques are a topic hotter than ever in life sciences. The question of how to analyze such data is the key for retrieval of nature’s secrets and development of the society. The mathematically driven studies e.g. applied to discovery of novel disease genes, prediction of therapy outcome or improvement of diagnosis show a promising potential of this approach.One of the disciplines that generate a lot of data of such great importance is protein–protein interaction (PPI) studies that generate protein–protein interaction networks (PPIN). The core characteristic of proteins is their ability to interact with one another and also with other types of molecules, in order to carry out their function. PPINs, which hold this property, can be understood as mathematical objects consisting of nodes—proteins and links between them—interactions. Understanding such graphs is crucial for further investigation of complicated molecular mechanisms that stand behind them.The workflow necessary for the investigation of PPINs is provided by theoretical studies of graphs and networks. Modern Network Science concentrates on topological analysis of systems and their dynamics  with a cGiven high complexity of biological systems, their analysis is hard to be performed with conventional tools. Therefore, research in disease comprehension and treatment development may hugely benefit from a network-based modeling. Identification of the most important proteins in a system, obtained with topological measures, could give a list of potentially best drug targets. Such a study was, e.g. performed by Although the above mentioned studies already demonstrate the power of the method, it should be mentioned that network studies of PPI systems have several limitations. It is often not taken into account that proteins create a dynamic system in which the interactions happen within time and have a temporal character. In reality, a single protein only interacts with a subset of its possible partners at a specific moment. Despite that, in a common static network model each protein has permanent links with all its possible partners. Even more importantly, the studies of PPINs are varied in terms of data gathering procedures. Different detection methods of the protein interactions are known, each of them having its pros and cons, and all of them being error-prone and leading to some false positive results  included in the IntAct resources. Finally, for those who may need to perform such analysis, we provide a user-friendly tool to exhaustively evaluate their networks quality based on multiple measures and simulations.IntAct datasets were analyzed: two sets as manually curated datasets: Parkinson and Cancer, and HuRI . This dataset was downloaded in two timestamps: November 4, 2020 and November 8, 2021. The original set from November 4, 2020 contained 59 912 links between 5955 proteins. The year after it was enriched by 22 new proteins and 70 interactions and it lost 21 proteins and 65 interactions, which gave, in total 55 930 links between 5956 proteins. Due to such minor changes, this network can be assumed to be structurally stable within time and only the newer version of the dataset is considered. The second dataset, Cancer, regards interactions of proteins that are involved in cancer. As previously, the data were also derived from a literature survey. This dataset was also downloaded in two timestamps: February 1, 2021 and November 8, 2021. In the first route (Cancer I) it contained 20 826 links regarding 5380 proteins and in the second (Cancer II) 23 263 links regarding 6027 proteins. Cancer II contains 2295 new interactions  and Wroclaw Centre for Networking and Supercomputing at Wroclaw University of Science and Technology is acknowledged for the free access to computational resources.Conflict of Interest: none declared.btac440_Supplementary_DataClick here for additional data file.