Identification of the protein targets of hit molecules is essential in the drug discovery process. Target prediction with machine learning algorithms can help accelerate this search, limiting the number of required experiments. However, Drug-Target Interactions databases used for training present high statistical bias, leading to a high number of false positives, thus increasing time and cost of experimental validation campaigns. To minimize the number of false positives among predicted targets, we propose a new scheme for choosing negative examples, so that each protein and each drug appears an equal number of times in positive and negative examples. We artificially reproduce the process of target identification for three specific drugs, and more globally for 200 approved drugs. For the detailed three drug examples, and for the larger set of 200 drugs, training with the proposed scheme for the choice of negative examples improved target prediction results: the average number of false positives among the top ranked predicted targets decreased, and overall, the rank of the true targets was improved.Our method corrects databases’ statistical bias and reduces the number of false positive predictions, and therefore the number of useless experiments potentially undertaken. Drug discovery often relies on the identification of a therapeutic target, usually a protein playing a role in a disease. Then, small molecular drugs that interact with the protein target to alter disease development are designed or searched for among large molecular databases. However, there has been a renewed interest in recent years for phenotypic drug discovery, which does not rely on prior knowledge of the target. In particular, the pharmaceutical industry has invested more efforts in poorly understood rare diseases, and for which therapeutic targets have not been discovered yet. While phenotypic drug discovery has made possible the identification of a few first-in class drugs , once a Our work aims at helping determination of the protein targets for hit molecules discovered in phenotypic screens. Identification of a drug target based solely on experiments is out of reach because it would require to design biological assays for all possible proteins. In that context, in silico approaches can reduce number of experimental tests by focusing on a limited number of high probable protein targets. Among them, Quantitative Structure-Activity Relationship (QSAR) methods were developed for that purpose . They arDocking approaches can address this question , but theIn the present paper, we tackle target identification in the form of Drug-Target Interaction (DTI) prediction based on machine learning (ML) chemogenomic algorithms . These aVarious ML algorithms have been proposed for DTI predictions. They include similarity-based (or kernel-based) methods such as kernel ridge linear regression, Support Vector Machines , or Neigtest set . Deep letest set .However, whatever the algorithm used, training a good ML chemogenomic model is hindered by biases in the DTI databases, such as whether the molecule for which one wishes to make predictions has known interactions or not . An addiong them . In thism and p are in the DB-Database, under the assumption that most of the unlabeled interactions are expected to be negative. This process was repeated 5 times, leading to 5 training datasets called RN-datasets (for Random Negatives-datasets) hereafter, differing only by their negative examples.Random sampling: Negative examples were randomly chosen among the pairs Balanced sampling: To avoid biasing our algorithms towards proteins with many interactions, negative examples were randomly chosen among unlabeled DTIs, although in such a way that each protein and each drug appeared an equal number of times in positive and negative interactions. This process was also repeated 5 times, leading to 5 training datasets again differing only by their negative examples called hereafter BN-datasets . Building this set of negative DTIs is not trivial, and we propose the following algorithm:Each protein and molecule in the DB-Database has a counter corresponding initially to its number of known ligands or targets, respectively;For each protein, starting from those with the highest counter to those with a counter equal to 1, molecules are randomly chosen among those not known to interact with this protein and whose counter is greater or equal to 1;Each time a negative DTI is chosen, the counter of the corresponding protein and of the molecule is decreased by one unit;The process is repeated until all proteins and molecules counters are equal to 0.ML algorithms for DTI predictions need to be trained on datasets of known DTIs in which proteins and molecules are similar to those for which predictions will be performed. Hit molecules in phenotypic screens for drug discovery are mostly drug-like molecules , and proOverall, the RN-datasets and the BN-datasets share the same set of positive DTIs, which are those in the DB-Database, and their total number of negative DTIs are the same and equal to that of positive DTIs. The construction of one RN-dataset (or one BN-dataset) is summarized in Finally, to compare the performance of the algorithm trained on the RN-datasets or the BN-datasets when predicting targets for “difficult” molecules , we considered a small dataset of DTIs involving 200 drugs that have few known targets. We built this dataset as follows: from the 5070 molecules in the DB-Database, we kept approved drugs that do not have more than 4 targets. This leads to 560 drugs involved in 851 interactions, among which we randomly selected 200 of these positive DTIs, involving 200 different drugs, defining the so-called 200-positive-dataset. 200 negative DTIs were also randomly chosen among all unlabeled DTIs involving theses drugs and not belonging to the training RN- or BN-datasets, defining the so-called 200-negative-dataset.All datasets are provided in the github repository mentioned under “Data Availability Statement”.K, without requiring explicit representation of the data.Throughout the paper, the main algorithm we use to address target identification through a chemogenomics approach for DTI prediction is based on the Support Vector Machines (SVM) ML algorithm . Brieflyl trick” , they caA general method to build a kernel on  kernels . Given aLAkernel), which mimics the Smith–Waterman alignment score between two proteins [For proteins, we used a centered and normalized Local Alignment kernel  and extending (e) a gap, and the d of the paths up to which paths on the molecule structure are considered. According to [LAkernel, and C, which controls the trade-off between maximising the margin  and minimizing classification error on the training points. This parameter was set to The rding to , we usedSVM is a kernel-based ML algorithm. In the context of chemogenomics, it relies on similarity (or kernel) matrices between s (ECFP)  as molecs (ECFP) . We consWe used a nested cross-validation (CV), which allows to combine model selection and model evaluation without overfitting the dataset, as classically observed with a simple CV scheme ,24. In tthe Area Under the Receiver Operating Characteristic curve (ROC-AUC) . The ROCthe Area Under the Precision-Recall curve (AUPR) , which ithe Recall, representing the fraction of positive examples that are retrieved;the Precision, representing the fraction of true positives retrieved among predicted positives;the False Positive Rate (FPR), representing the fraction of true negatives among predicted positives.We used the following scores to quantify prediction performance of the classifiers:N = 5 folds. Each fold comprises the same number of positive and negative DTIs. For the BN datasets, all molecules and all proteins appear in the same number of positive and negative DTIs, in each fold, as described in C = 10 consistently leads to the best performance across folds, both in terms of ROC-AUC and AUPR, and both on the RN- and BN-datasets.More precisely, we used a We used the same nested CV scheme to optimize the hyperparameters of the RF algorithm  learning problem. Indeed, in practice, most databases only contain positive examples , while all other possible interactions between molecules and proteins present in the data are unlabeled, whether because they have never been tested, or because they are negative interactions that have not been published or included in the database. Most of the unlabeled interactions are usually considered as true negatives. Therefore, in chemogenomics, the classical approach is to label as negatives a randomly chosen subset of the unlabeled interactions. This allows to convert a PU learning problem a into Positive-Negative (PN) learning problem for which many efficient ML algorithms are available.LAkernel [LAkernel is related to the Smith–Waterman score [LAkernel sums up the contributions of all possible local alignments, which proved to be efficient for detecting remote homology.We considered a ML algorithm based on SVM, with the LAkernel  and the LAkernel  for protLAkernel ,28,29. Tan score , but whiWhile the purpose of this paper is not to discuss the choice of the ML algorithm, but rather to study how best to train it for the particular task of target identification, we also include a comparison of the SVM with a feature-based ML algorithm, i.e., Random Forests (RF) ,31.The two algorithms were trained on the 5 RN-datasets described in We studied the distributions of the probability scores for positive and unlabeled  interactions for the SVM algorithm, according to the nested CV scheme. The DrugBank database  is a widIndeed, the majority of proteins have 4 or fewer known ligands, while around 140 proteins have more than 21 ligands. We defined categories of proteins, depending on their number of known ligands , and calculated the number of DTIs in the DB-Database in each category. Overall, according to This bias arises from the fact that a few diseases like cancer or inflammatory diseases have attracted most research efforts, and many ligands have been identified against related therapeutic targets, compared to other less studied human proteins. For example, Prostaglandin G/H synthase 2, a well-known protein involved in inflammation, has 109 drugs reported at DrugBank. This statistical bias affects training of the SVM and is expected to perturb identification of targets for hit molecules, potentially by enriching top ranked proteins in false positive targets that have many known ligands.Once trained, a ML algorithm identifies targets for a hit molecule by providing a list of proteins ranked by decreasing order of the estimated probability score of all  pairs. Candidate targets are chosen based on their probability score, their rank, and on potential prior biological knowledge that would highlight their relation to the considered disease. For example, a top ranked protein involved in cell cycle would be considered as a realistic candidate target for a hit identified in a cell proliferation screen in cancer research. The presence of many false positive targets among the top ranked proteins will not only lead to undertake useless experiments, but also potentially to discard true predicted targets pushed further down the list. Let us illustrate this problem in the case of 3 molecules, randomly chosen among marketed drugs with only one known target in DrugBank. Assuming that their targets have been well characterized because they are marketed molecules, most of the other top ranked predicted targets will be false positive predictions. The 3 considered molecules are: alectinib , lasmiditan , and doxapram also known as angiotensin II . We orphanized these 3 molecules , as if they were hits from phenotypic screens, and used the SVM algorithm presented in As shown in the RN-datasets columns of Analysis of the results highlighted that some of the best ranked candidate targets are frequent targets. For example, prothrombin F2 (120 ligands), cyclin dependant kisase CDK2 (137 ligands), and dopamine receptor 2 DRD2 (109 ligands) are top ranked predicted targets respectively for DB11842 , DB11732  and DB11363 . The three ranked lists are provided in full in the github repository mentioned under “Data Availability Statement”.These examples illustrate the impact of false positive predictions for target identification, because they can lead to discard even high-scoring true targets as for DB11363 and DB11842.Our observation that high-scoring false positives tend to have a large number of known ligands led us to make the assumption that the model trained using randomly sampled negative interactions is biased towards proteins with many known ligands, as well as possibly drugs with many known targets. This suggested us to choose negative DTIs in such a way that the training dataset contains, for each molecule and for each protein, as many positive than negative DTIs. The corresponding so-called BN-datasets  are detailed in The SVM algorithm presented in To highlight the impact of this bias correction in terms of target prediction, we show in To better illustrate the interest of the proposed scheme for the choice of negative DTIs on a larger number of drugs we considered the 200-positive-dataset consisting of 200 DTIs involving 200 marketed drugs with 4 of less known targets, as described in Overall, training with the BN-datasets improved the predictions: the number of false positive DTIs decreased for 106 drugs, remained unchanged in 85 drugs, and increased in 9 drugs, as compared to training with the RN-datasets. In particular, this improvement allowed one additional true positive interaction to reach a score above 0.7 and a rank below 27: 104 true targets were retrieved as candidates when training with BN-datasets, compared to 103 when training with RN-datasets. For the corresponding 104 drugs, the number of false positives decreased by 2.9 in average, and the rank of the true interactions decreased by 1.8 in average, bringing them even closer to the top ranked predicted proteins, and more likely to be chosen for experimental validation. Consistent with the results in We also made predictions for the 200 negative DTIs of the corresponding 200-negative-dataset, involving the same molecules as the 200-positive-dataset. Predictions were made by the classifier trained on the RN- or BN- datasets. Overall the distributions of the prediction scores were very similar in both cases, centered around 0.2, and similar to that shown for the RN-dataset in The goal of the present paper was to tackle the question of protein target identification for new drug candidates, using ML-based chemogenomics. Indeed, these approaches can be run at a large scale in the protein space, including in their scope proteins with no known 3D structures, or proteins for which few, or even no ligands are known. Another key asset is that they can be applied to drugs with few, or even no known targets, as illustrated on the 200-positive-dataset. This is of particular importance because new phenotypic drugs are often orphan  when they are identified. No other computational method presents these advantages. However, before making predictions, ML chemogenomic algorithms need to be trained on a database of known DTIs, which raises a few issues.First, these databases are biased in terms of the number of protein targets per molecule, or of ligand molecules per protein, as shown for the DrugBank database used in our study. While we are aware that other and larger DTIs databases could have been used, the purpose of our study was not to discuss the choice of training set, in particular because other databases will also present the same type of bias as the DrugBank, for the same reasons. This point is rarely discussed in ML chemogenomic studies.Second, the performance of ML algorithms in chemogenomics are usually evaluated based on AUPR and ROC-AUC scores in cross-validation procedures. However, the identification of true protein targets for phenotypic hit molecules in real case studies may become a challenge when the algorithm is trained on a biased dataset. Indeed, despite very high AUPR and ROC-AUC scores, false positive targets can be found among top-ranked proteins, and correspond to proteins with many known ligands. In target identification studies, biological experiments are guided by the predicted scores and ranks of candidate proteins. Training on a biased dataset may lead not only to conduct useless experiments, but also to discard true positive targets because their scores are below the considered threshold, or because their rank is too high due to the presence of false positives among the top-ranked proteins. This point is also rarely discussed in ML chemogenomic studies, usually focusing on cross-validation schemes that does not correspond to real case applications.Third, training databases such as the DrugBank only contain positive examples, and therefore, negative examples are usually randomly chosen among unlabeled DTIs in order to train the ML algorithms. It is however unclear that this is an optimal choice for target identification.The key result of the present paper was to show that choosing an equal number of positive and negative DTIs per molecule and per protein helps decrease the FPR in biased datasets, and improves the identification of true targets for a given drug. Three striking examples are given for the case study of three drugs  that were “orphanized”  to illustrate the most difficult situation encountered in the case of new phenotypic drugs: training with the BN-datasets allowed to recover the true target in all cases, while none of them would have been retrieved when training with the RN-datasets. To illustrate the advantage of the proposed scheme for the choice of negative interactions, we used a threshold of 0.7 over the probability scores to identify candidate targets for experimental testing, although proteins with scores above 0.5 are classified as positives. This threshold of 0.7 was guided by the results in We added the stringent threshold of 1% on the ranks of proteins to define which targets would be tested. This threshold could also be adjusted depending on available resources for experimental validation. The issue we identified and addressed in this paper does not depend on the scores and rank thresholds used, and choosing equal numbers of positive and negative DTIs per molecule and per protein for the training set will limit the number of false positives independently of the choice of thresholds, as shown in


XCR1, IFNAR2 and TLR8), and to Androgen Receptor (AR). The ERC prescreening approach has elucidated possible functions for relatively uncharacterized proteins and possible new functions for well-characterized ones. Suggestions are made for the validation of ERC-predicted ACE2 protein interactions. We propose that ACE2 has novel protein interactions that are disrupted during SARS-CoV-2 infection, contributing to the spectrum of COVID-19 pathologies.Angiotensin-converting enzyme 2 (ACE2) is the cell receptor that the coronavirus SARS-CoV-2 binds to and uses to enter and infect human cells. COVID-19, the pandemic disease caused by the coronavirus, involves diverse pathologies beyond those of a respiratory disease, including micro-thrombosis (micro-clotting), cytokine storms, and inflammatory responses affecting many organ systems. Longer-term chronic illness can persist for many months, often well after the pathogen is no longer detected. A better understanding of the proteins that ACE2 interacts with can reveal information relevant to these disease manifestations and possible avenues for treatment. We have undertaken an approach to predict candidate ACE2 interacting proteins which uses evolutionary inference to identify a set of mammalian proteins that “coevolve” with ACE2. The approach, called evolutionary rate correlation (ERC), detects proteins that show highly correlated evolutionary rates during mammalian evolution. Such proteins are candidates for biological interactions with the ACE2 receptor. The approach has uncovered a number of key ACE2 protein interactions of potential relevance to COVID-19 pathologies. Some proteins have previously been reported to be associated with severe COVID-19, but are not currently known to interact with ACE2, while additional predicted novel ACE2 interactors are of potential relevance to the disease. Using reciprocal rankings of protein ERCs, we have identified strongly interconnected ACE2 associated protein networks relevant to COVID-19 pathologies. ACE2 has clear connections to coagulation pathway proteins, such as Coagulation Factor V and fibrinogen components FGA, FGB, and FGG, the latter possibly mediated through ACE2 connections to Clusterin (which clears misfolded extracellular proteins) and GPR141 (whose functions are relatively unknown). ACE2 also connects to proteins involved in cytokine signaling and immune response ( The coronavirus SARS-CoV-2 is causing severe pathologies and death among infected individuals across the planet. In addition to the symptoms expected from a respiratory disease, the infection can develop systemic manifestations . As a covia other receptors (Angiotensin-converting enzyme 2 (ACE2) is of obvious interest because it is a primary receptor for SARS-CoV-2 entry into human cells . Howevereceptors .In general, ACE2’s protein-protein interaction network is likely to contribute to COVID-19 pathologies, due to ACE2’s role in systemic processes that are disrupted by the infection. Therefore, a fuller knowledge of ACE2 protein interactions is important to a better understanding of COVID-19 pathologies, including those that go beyond respiratory illness.Common methods to identify protein-protein interactions include protein co-localization and precipitation, genetic manipulation, and proteomic profiling . Evolutie.g. e.g. oxidative phosphorylation proteins or mitochondrial ribosomal RNA) have significantly stronger ERCs than those not directly in contact. This result implicates physical interactions between proteins as one driver of evolutionary rate correlations, at least among nuclear-mitochondrial interactions. Other studies have found evolutionary rate correlations among proteins that do not make direct contact, such as in metabolic pathways (We have developed an evolutionary rate correlation (ERC) method that uses well-established phylogenies based on multiple lines of evidence e.g. . The apppathways .We have developed a reciprocal rank approach to identify ACE2 associated networks and propose that these strongly coevolving proteins reveal ACE2 protein interactions that could be disrupted by COVID-19, thus contributing to its diverse pathologies. Particularly noteworthy are strong connections to coagulation pathway proteins, cytokine signaling, inflammation, immunity, and viral disease response.It is important to note that our approach cannot be used to study coronavirus-ACE2 coevolution. The reason is that coronaviruses move between mammalian species and therefore do not have the same phylogenetic history as mammalian proteins, a prerequisite for the approach. We are also not asserting that coronavirus pressure is causing the evolution of ACE2 observed in the ERCs. Rather, it is our proposition that the ACE2 ERCs are revealing evolved mammalian protein interactions that are not caused by, but could be relevant to COVID-19 pathologies, due to disruption of pathways in which ACE2 is normally involved.We recognize that the predicted protein interactions detected by the ERC approach may not be causal for COVID-19 pathologies. However, the ERC connections to coagulation pathways, cytokine signaling, and immunity are striking and suggest a possible role of these ACE2 protein interactions in COVID-19 syndromes. The ERC results may also have implications for ACE2’s role in the regulation of vasodilation through the renin-angiotensin system (RAS), cardiovascular disease, and protein digestion and absorption in the gut . Furtherhttps://doi.org/10.6084/m9.figshare.14637450) and are listed in Our evolutionary rate correlation (ERC) approach requires orthologous protein sequence data across a large number of taxa with well-defined phylogenetic relationships. Calculation of evolutionary rates requires a resolved phylogeny of the taxa analyzed that is scaled to evolutionary time. Our ERC calculations utilize the TimeTree  to generWell-defined orthologous sequence data is sourced from OrthoDB  at the “To prepare orthologous sequence data for ERC calculation, each set of protein sequences are first aligned using the MAFFT software package  using the.g. in Primates and Rodentia) leading to many ERCs being driven by relatively short branches (Our evolutionary rate correlation (ERC) method is designed to predict protein-protein interactions using evolutionary data , and is branches . We attebranches  and recabranches . The resUsing the adjusted data set, ERCs are calculated for every possible combination of protein pairs for which a tree has been generated. Every protein pair for which an ERC is calculated has each respective tree and the TimeTree topology is pruned to only include the shared taxa between the two, using the “ETE3” Python package . ETE3 isP-values are corrected using the Benjamini–Hochberg FDR multiple-test correction procedure implemented in the Python package “statsmodels”  of each protein(s) of interest (including the protein itself), including only proteins with ERCs that are significant following an FDR correction at a significance level of 0.05. At most, a protein of interest will have 41 proteins included for enrichment analysis . Protein set enrichment analyses are performed using the Enrichr service  via the  package  given thp < 0.05 for all analyses are placed into a single table, organized by the enrichment term database  are produced. Reciprocal ranks refer to the fact that a significant ERC between two proteins can have different ranks in the two respective protein ERC lists because some proteins have more and higher ERCs than others. To focus on networks of proteins with strong reciprocal rank correlations, we have constructed networks based on proteins with reciprocal ranks of 20 or less (RR20), which is the top 1% in each protein’s highest ERCs based on ρ values. Specifically, we have developed an ACE2 centric reciprocal rank network by the following steps (1) for ACE2, select its top 1% (20) proteins, (2) for each of those proteins, select additional proteins in their ERC list with reciprocal rank 20 or less, and then (3) Given the core set of proteins generated in the previous two steps, connect proteins which have a unidirectional rank of 20 or less.The resultant network represents the strongest ERCs centered around a protein of interest (in this case ACE2), along with the immediate neighborhood of the strongest ERCs surrounding the protein of interest. The ACE2 Core Reciprocal Rank (CRR) was initiated with the four proteins to which ACE2 has RR20 ERCs , with GPR141 added due to its RR1 strong connection to CLU and unidirectional connection to ACE2. ACE2 also has highly significant ERCs to proteins that do not rank it in their top one percent. Therefore, a similar approach has been used to generate an ACE2 reciprocal network initiated with the top 10 proteins to which ACE2 has highly significant ERCs, but are not reciprocally RR20 ranked, with a subsequent one cycle RR20 built upon these. This ACE2 Unidirectional Reciprocal Rank Network (URR) contains strong network connections to ACE2 through its high unidirectional ERCs. Steps 2–3 were omitted as the network becomes extremely large following just the first step, and our focus is on examining close connections to ACE2 based on ERC analysis.versus non-interactions, the protein complex database, CORUM (i.e. some complexes contain overlapping protein pairs), the set was further restricted to complexes containing unique protein components—resulting in 139 effective unique complexes considered. To test whether ERCs within complexes are higher than between complexes, all pairwise ERCs within complexes were compared to the median ρ value for each pair to proteins present in non-redundant CORUM set that are not in complex with either of these proteins. A Wilcoxon matched signed-rank test was performed using the “wilcox.test” function in base R  on the in-complex ρ values and the median out-of-complex ρ values, to test if the in-complex ρ values were significantly greater than the median out-of-complex ρ values. In addition, as there were many complexes with a majority of subcomponents not present in our 1,953 datasets, the likelihood of individual pairs directly interacting within the complex decreases with the increasing number of proteins in a complex. Therefore, an additional Wilcoxon matched signed-rank test was performed on members of protein complexes composed of five or fewer proteins.To compare whether calculated ERCs are stronger between known interactions e, CORUM , was usevia ETE3  multiple linear regression, (2) analysis of covariance (ANCOVA), and (3) non-parametric independent contrasts . For thevia ETE3  and treavia ETE3  to genere.g. ACE2) and then for the full protein set. Results are described in the To test whether increasing branch length results in increasing protein evolutionary rate, we selected separate phylogenetic groups (clades) from the full phylogeny  that conThe basic methods are outlined here to provide context for the results which follow. To identify candidate protein interactions using evolutionary rate correlation, we utilized the consensus TimeTree phylogenetic reconstruction for mammalian species . A totalversus a taxonomic effect. This was accomplished by examining changes in protein rates in independent clades as terminal branches were effectively extended by selective removal of flanking taxa. The analysis shows that evolutionary rates for many proteins increase as branch length is increased , all of which are highly significant after FDR correction . Some ofX-C Motif Chemokine Receptor 1 (XCR1) provides an illustrative example. XCR1 is a cytokine signaling receptor and ACE2’s 2nd highest ranked ERC, with a highly significant evolutionary rate correlation. XCR1 is in a small genomic region that is implicated in severe COVID-19 by genome-wide association studies . AnotherDifferences in ERC rank between protein pairs for the same correlation can occur because some proteins have higher and more extensive ERC connections than others. As a result, while two proteins can have a significant ERC with each other, each one’s rank may differ in their respective ERC lists, as illustrated for ACE2 and GEN1 . GEN1 F. This pri.e. both partners evolving reciprocally due to selective pressures acting on interacting domains between them). In contrast, protein pairs with a significant evolutionary rate correlation only one ranks highly (e.g. within the top two percent) in the ERC set of the other, are more likely to be due to “unidirectional” evolution. The rationale is that proteins with many significant ERC partners are under selective pressures primarily from their top evolutionary partners, whereas other interactors evolve primarily in response to the forces shaped by their stronger partner(s). We emphasize that this interpretation is speculative, and requires further exploration to determine what factors shape reciprocal ERC ranks between proteins.Because our focus is on identifying strong candidate interactions involving ACE2 and its predicted partners, we utilize the rank information to identify proteins with high reciprocal ranks. Specifically, we focus on the strongest reciprocal ranks (RR) defined by ranks of less than or equal to 20 (RR20), which is the highest one percent of each protein’s ERCs, and use these to develop reciprocal rank networks . Although speculative, we posit that protein pairs with high reciprocal ranks are likely to be strongly coevolving . Noteworthy in this regard are additional proteins in the coagulation pathway, such as Coagulation Factor V (F5), Fibrinogen Alpha Chain (FGA), Fibrinogen Beta Chain (FGB), and Fibrinogen Gamma Chain (FGG). Thrombosis (blood clotting) is a major pathology of COVID-19 . ConnectIn yet other cases, we have found proteins with significant ACE2 ERCs or ACE2 network connections, but for which there is little functional information, such as GPR141. We can use their ERCs to suggest possible functions for future investigation. Finally, ERCs for proteins of known function (such as F5 and GEN1) indicate likely additional roles, suggesting these proteins have unrecognized “moonlighting” functions .Below, we first describe proteins of interest to which ACE2 has significant ERCs, summarize aspects of their known biological functions, and examine significantly enriched functional categories for these ERCs. We then build and evaluate two different networks for ACE2 interacting proteins , one of which reveals connections to coagulation pathways and the other to cytokine-mediated signaling, viral response, and immunity. Finally, we discuss the potential implications of these predicted ACE2 interactions to COVID-19 pathologies and propose some specific hypotheses that emerge from this analysis.ρ) using the gene set enrichment package Enrichr  is ACE2’s 2nd top-ranked ERC . It is a chemokine XCL1 receptor involved in immune response to infection and inflammation . StrikinAnother striking connection of ACE2 ERC to immunity is through IFNAR2 , which has a highly significant ACE2 ERC correlation . IFNAR2 forms part of an important receptor complex with IFNAR1  involvedCoagulation pathway proteins figure prominently in ACE2 ERC-predicted protein interactions , Fig. 2.Also relevant to coagulopathy are Clusterin (CLU) and the orphan G protein-coupled receptor 141 (GPR141). The chaperone protein CLU has a soluble form that circulates in the blood and is part of the “cleaning squad” that clears misfolded extracellular proteins for delivery to lysosomes and degradation . It is te.g. “Complement and coagulation cascades”, FDR = 6.3E−12), as well as significant terms that are relevant to immunity, such as “Immune system” (FDR = 4.8E−03) and “activated immune cell type” (FDR = 3.4E−05). Among its top ERC proteins relevant to coagulation process are Coagulation Factor V , Fibrinogen Gamma chain , Coagulation Factor XIII B chain , and Fibrinogen Alpha chain  (CLU’s top 2% strongest ERCs show highly significant enrichment for terms relating to coagulation cascades and clot formation (rank 27) , File 1.rank 27) . Little rank 27) , File 1.GPR141 has a highly significant ERC to CLU, with these two proteins being each other’s first ranking ERCs . The pattern suggests a strong biological interaction, although none is described in the literature. The result supports investigating functional interactions between CLU and GPR141, based upon their high ERC and reciprocal ranks. Our network analysis below  further supports extensive interconnections among ACE2, Clusterin, GPR141, and coagulation pathway proteins, implicating the protein interaction pathway as a possibly significant contributor to disruption of coagulation in COVID-19 disease. Coagulation cascade proteins found in the ACE2’s top 2% ERCs, ACE2 reciprocal rank network, and Clusterin-GPR141 associated proteins are highlighted in Androgen Receptor  is the receptor for the male hormone androgen. It plays a major role in reproductive system development, somatic differentiation, and behavior . AndrogeOther notable significant ACE2 ERCs  include As mentioned previously, two proteins with a significant evolutionary rate correlation (ERC) may often “rank” each other differently in their respective top ERC connections. This occurs because some proteins have more extensive ERC connections than others. High reciprocal ERC ranks between protein pairs may be more indicative that they are under strong coevolutionary pressure in their sequence and function. We have thus found it useful to evaluate these reciprocal rank connections as a network. The rationale is that such proteins are likely to be reciprocally evolving (“coevolving”). To build reciprocal rank networks, we use protein pairs that reciprocally share ranks less than or equal to 20 (RR20), which are the top one percent for each protein’s respective ERC set.A core ACE2 reciprocal rank network was generated by building reciprocal rank connections (RR20) outward of ACE2, to provide a backbone set of RR20 protein connections. The backbone was expanded on by adding the RR20 connections of the non-ACE2 backbone proteins. Unidirectional ERCs (≤ rank 20) were then added between proteins within the RR set to produce an ACE2 Core Reciprocal Rank (CRR) Network . The netACE2 also has highly significant ERCs to proteins that do not rank ACE2 within their top 1% of ERCs, due to those proteins having more protein interactions with higher ERCs. A second network was therefore generated using ACE2’s top ten unidirectional ERCs, followed by calculating the RR20 associations for those proteins. This second network is referred to as the ACE2 Unidirectional Reciprocal Rank (URR) Network .These are presented below. In general, the reciprocal ranks analysis lends credence to our proposition that ERCs reveal real biological interactions, as well as providing predictions for novel protein interactions possibly of importance to COVID-19 pathologies and protein-interaction networks.The CRR network  is desigThe most striking aspects of the ACE2 CRR Network are extensive connections to the coagulation pathway and blood-associated proteins , Table 3e.g. CD34, CPB2, C14or129, and ZBTB43). ZBTB43 is noteworthy, as it is associated with the blood diseases Diamond-Blackfan Anemia 4 and Hemochromatosis Type 2 , it is FAM3D’s top-ranking ERC. F13B functions to stabilize clotting through cross-linking of fibrin . Thus, tvia the MAS1 receptor of angiotensin-(1–7). via higher levels of angiotensin 2, promoting vasoconstriction, fibrosis, coagulation, vascular and cardio injury, and ROS production. Similar arguments are made by Blood pressure and vasoconstriction regulation also show functional enrichment in the CRR network. Naturally, ACE2 is a crucial component of the Renin-Angiotensin System (RAS), which converts angiotensin II to angiotensin (1–7). This, in turn, binds to the MAS1 receptor, promoting vasodilation and reduced blood pressure. As seen in FBXL3 has a RR20 connection to FGB and ranks GPR141 in its top 2%. This protein is a component of circadian rhythm regulation . Many asCD34 (Hematopoietic Progenitor Cell Antigen CD34) is believed to be an adhesion protein for hematopoietic stem cells in bone marrow and for endothelial cells . Our ERCAdditional proteins of interest are discussed further in the Consistent with the descriptions above, the CRR network shows enrichment , XCR1 (X-C Motif Chemokine Receptor 1), and ICOS (Inducible T Cell Costimulator). There are also Toll-Like Receptors TLR8 and TLR9, which stimulate innate immune activity , and TumHomo sapiens (FDR = 3.7E−03).Enrichment analysis of the URR network generates 72 significant terms . The netXCR1 is the 2nd highest ACE2 ERC. It is the receptor for chemokine XCL1, which is produced in response to infection and inflammation, and during development of regulatory T cells . FurtherIFNAR2 is another protein that is highly correlated with ACE2  and is also implicated in severe COVID-19 by GWAS and expression data . It has GEN1 is the highest-ranking ACE2 ERC protein , and it functions as a resolvase of Holliday junctions and a DNA damage checkpoint signaling . FranklyThe Collagen Type IV A4 subnetwork , 5 lendsCOL4A5 also has significant ERCs to COL4A3  and COL4A4 , but these do not qualify as RR20 due to the large number of high ERCs for COL4A5. Interestingly, COL4A5-MUC15 are top-ranking partners  with a very high ERC . MUC15 is a cell surface protein that is believed to promote cell-extracellular matrix adhesion and it is implicated in affecting influenza infection , which mCoagulation Factor V (F5) is known for its role in the coagulation cascade. However, F5 is a highly ERC-connected protein, with 43 proteins ranking it in their respective top 5 highest ERCs. This connectedness is also reflected in the RR20 network shown below . F5 has We postulate that ERCs detect proteins that are coevolving due to functional interactions. Furthermore, we propose that physical binding is an important mechanism contributing to significant ERCs between proteins. This is consistent with anecdotal observations from this study of high reciprocal rank ERCs among the fibrinogen components FGA, FGB, & FGG, the Collagen Type IVA proteins COL4A4, COL4A3, and COL4A6 proteins, and Interferon alpha/beta proteins IFNAR2 and IFNAR1.p = 5.2E−04), with a median increase of 6.3% (p = 6.2E−03). The results support the view that proteins within known complexes show higher ERCs than between complexes, and further implicate physical contact as a contributor to ERCs. However, other studies have found ERCs between proteins that do not bind to each other, but are involved in shared function, such as metabolic pathways  which constitutes the clotting molecule fibrin. Abnormal clotting and coagulation such as “hypercoagulability” has been observed as a major symptom of COVID-19 infection . AdditioAnother mechanism for ACE2’s influence on the coagulation effects of COVID-19, based on ERCs, is through F5. F5 canonically is activated by the same enzyme (Thrombin) that converts fibrinogen into fibrin for clotting . OmarovaA second major finding is ACE2 protein-protein interactions that connect to cytokine signaling and immunity. “Cytokine storms”, an overreaction of the immune system which can lead to inflammation and organ failure, is a second major hallmark of severe COVID-19, and its management is a major target of medical treatment research . ChemokiType 1 interferons are among the first types of cytokines produced after viral infection . A compohttps://csb.pitt.edu/erc_analysis/) to screen for ERC interactions for Drosophila, yeast, and mammals. Their mammalian data set is based on 33 mammalian species (p < 0.05) and our top 2% ACE2 ERCs. There are many methodological differences between our approaches, including the number and specific mammalian taxa used, the method for calculating protein rates, and the phylogeny used for calculating branch lengths. In addition, their dataset includes 17,487 proteins, whereas our analysis is currently restricted to 1,953 proteins for which we were confident about 1:1 orthology and therefore for which there are minimal paralogy complications. Furthermore, we are uncertain how their database dealt with potential short branch artifacts on ERC calculations. In our case, we found that short branches in the phylogeny resulted in significant correlations between branch time and protein rate, thus both inflating estimated ERCs and introducing branch time as a confounding factor which can lead to spurious correlations, and we removed these by branch trimming.Overall, the underlying concept behind the evolutionary rate correlation approach  is that coevolving proteins will show correlated rates of change across evolution and that this reflects functional interactions . Clark a species . We haveIn another study, in vitro and in vivo with proteins showing high evolutionary correlation to it, in particular CLU, XCR1, GEN1, and IFNAR2. Similar binding affinity is predicted between CLU and GPR141 based on their high reciprocal rank ERCs. CLU-FGG and GPR141-FGB provide connections to fibrinogen based on their evolutionary correlations, suggesting binding affinities. Applicable methods could include protein complex immunoprecipitation, tagged protein analysis, and yeast-two-hybrid analysis  amino acid sequences to identify predicted sites of interaction among protein partners. These data may be able to inform docking simulations for protein pairs using software that allows for the incorporation of terfaces . For exaIn this paper, we take an exploratory approach to ACE2 protein interactions using evolutionary rate correlations. Our key findings are that the ERC analysis predicts ACE2 to have previously unidentified protein partners, and to be part of interaction networks relevant to COVID-19 pathologies. Most notably, ACE2 forms strong ERC networks relevant to coagulation and immunity. A potential mechanism is that reduced abundance of membrane-bound ACE2 disrupts these signaling networks. Additionally, the presence of the soluble ACE2 ectodomain may explain the systemic pathologies of COVID-19 infection as its circulation in the blood can affect pathways throughout the body. We recognize that the new ACE2 protein connections predicted by ERCs may not be causal in severe COVID-19 pathologies. However, our novel findings that the ACE2 ERC network connects to coagulation and immunity pathways is noteworthy, with clear potential implications to some of the unusual features of COVID-19. In addition, results may have relevance to other functions of ACE2, such as circulatory homeostasis and digestion. The ERC analysis predicts additional protein connections that can be relevant to biological processes and disease. For instance, ERCs predict novel interactions for cytokine and immunity related proteins, such as for XCR1, IFNLR1, IFNAR2, and TLR8. Future investigations of the ERC networks of these and related proteins could be worthwhile. ERCs also suggest strong but previously undescribed connections for proteins such as CLU, GPR141, F5, and GEN1. Validation studies are necessary to determine to what extent strong ERCs predict biological interactions among proteins, such as the ones detected here.Further computational analyses of ERCs are needed to better understand their relationship to protein function and evolution. For instance, machine learning and simulation approaches can be used to determine which aspects of protein structure, amino acid properties, and rates of protein evolution, improve ERC predictive power. We are currently expanding the mammalian protein set for such analyses. Finally, if evidence mounts that ERCs can be informative in predicting protein interactions, the approach can be applied more broadly as an additional tool for detecting protein interaction networks involved in many biological processes and disease.10.7717/peerj.12159/supp-1Supplemental Information 1Click here for additional data file.


Although researchers have efficiently used distance matrices to classify proteins and find homologous proteins, much less work has been done on quantitative analysis of distance matrix features. Therefore, the distance matrix was analyzed as gray scale image using KAZE feature extractor algorithm with Bag of Visual Words model. In this study, each protein was represented as a histogram of visual codewords. The analysis showed that a very small number of codewords (~1%) have a high relative frequency (> 0.25) and that the majority of codewords have a relative frequency around 0.05. We have also shown that there is a relationship between the frequency of codewords and the position of the features in a distance matrix. The codewords that are more frequent are located closer to the main diagonal. Less frequent codewords, on the other hand, are located in the corners of the distance matrix, far from the main diagonal. Moreover, the analysis showed a correlation between the number of unique codewords and the 3D repeats in the protein structure. The solenoid and tandem repeats proteins have a significantly lower number of unique codewords than the globular proteins. Finally, the codeword histograms and Support Vector Machine (SVM) classifier were used to classify solenoid and globular proteins. The result showed that the SVM classifier fed with codeword histograms correctly classified 352 out of 354 proteins. The analysis of protein structures using the distance matrix of Cα atoms has a long history in structural biology. To date, a protein distance matrix has been used for structural alignment, protein classification, and finding homologous proteins , 2. Receet al Reviewers' comments:Reviewer's Responses to Questions  Comments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?  The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: NoReviewer #2: Yes**********  2. Has the statistical analysis been performed appropriately and rigorously?   Reviewer #1: N/AReviewer #2: Yes**********  3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.  The Reviewer #1: YesReviewer #2: Yes**********  4. Is the manuscript presented in an intelligible fashion and written in standard English?  PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: YesReviewer #2: Yes**********  5. Review Comments to the Author  Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: The manuscript submitted by J. Praznikar and A. N. Tharanga describes some features of the protein distant matrix.Although it is potentially interesting, it is rather confused and a drastic revision is necessary. Actually, this manuscript contains two research projects. The first deals with some features of protein distance matrices and the second is a prediction method of solenoid proteins.The first part is described superficially and it would be difficult to repeat the calculations for a third part. Methods should be described in more detail, describing the algorithms and not just mentioning them.The second part is superficial, too. It is unclear if the problem is interesting and useful. I note that the Authors use a database compiled by Tosatto more than a decade ago. This is not acceptable given the PDB dynamics. As a consequence, the results shown in Table 1 might be biased, too.I strongly suggest the Authors to communicate their results, hopefully in two separate manuscripts, with more details about their methods.Other issuesLine 112: it is unclear to me what “mediodis” means here.Line 134: it is unclear why the distance matrices contain the inverse square distances.Line 214: did the Authors use other similarity measures beside the cosine similarity measure?Line 247: after “computation time”, it would be interesting to insert some information about the computational requirements (cpu time etc.).Line 258: “other studies”. The Authors should mention them.Line 317: perhaps “and solenoid protein domains” might be removed.Line 336: the “ratio” might be defined by writing an equation.Line 342: “most” might be associated with a numerical percentage.Line 343: “few” might be associated with a numerical percentage.Line 356: “in many biological processes [26-31]” is really very very vague. Might it be possible to expand this discussion.Line 384: “a database”; which one?Line 415: “the gold standard database”; which one?Line 421: The methods “RAPHAEL” and “ReUpred” should be described.Line 422: “When the vocabulary was reduced from 1000 to 500 words, the accuracy decreased only slightly”; it might be necessary to provide a number.arxiv.org?Reference 14: is there any other publication beside Figure 4: titles should be provided to the horizontal and vertical axes.Figure 5. The expression “(aa)” should follow “Domain size”. Moreover, it might be interesting to insert the slopes of the regression lines.Minor issuesLine 87: the reference to DALI Z-score is probably wrong. This score has been designed by Chris Sander and Liisa Holm.Line 96: The reference “Sivic J. et al.” is misspelled .Line 130: “[16-18]” should be inserted after “database”.Reviewer #2: Authors take a refreshingly different approach to protein structure classification by using a (relatively) novel technique of image analysis by "bag of "visual" words" to classify distance maps, a particular visualization approach of protein structures. The paper is well written, results are presented clearly. I have two problems with the paper- training. No details are given on creation of the training and testing. If, as I assume, the division was done randomly, it is very likely that closely homologous proteins were split between the training the testing set. Such proteins have practically identical structures and hence distance maps. With thousands of features, many of them rare, it is very likely that the system is simply memorizing classifications of such homologs, this is probably easy to test. The training and testing sets must be constructed taking into account the relations between proteins. This is critical to the overall validity of the papers.- its conclusions are conceptually disappointing. Why introduce a new formalism and do all this work to build a lousy protein structure classifier? The paper provides zero useful information to a structural biologist. What does it tell me that tens of thousands of features are needed to classify a protein. This is a computer science paper written for other computer scientists. If you could appeal to the domain researchers, the paper would be much more valuable**********  6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.If you choose “no”, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.  Reviewer #1: NoReviewer #2: Nohttps://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool,  28 Dec 2021Dear Editor,I hereby resubmit a revised and re-edited article. A detailed document containing the authors' response to the reviewers' comments was attached. The changes have been incorporated into the manuscript and are highlighted in yellow. We thank all reviewers for their positive evaluation, reading our paper, and valuable comments to improve the manuscript. Following the reviewers' comments, we have addressed the comments and questions in detail below. We have better formulated the implications presented in a new section entitled: “Quantitative measurement of repeats in protein domain: inter and intra comparison". We also stated that the dataset does not contain homologous proteins (sequence identity is less than 35%). We believe that the first part of the paper, which is more related to the development of a new method and the presentation of a new approach to distance matrix analysis, and the second part, which is related to practical examples, are closely related. Moreover, the paper is not too long in our opinion with about 6000 words.*************************************************************************************************************************Reviewer #1: The manuscript submitted by J. Praznikar and A. N. Tharanga describes some features of the protein distant matrix.Although it is potentially interesting, it is rather confused and a drastic revision is necessary. Actually, this manuscript contains two research projects. The first deals with some features of protein distance matrices and the second is a prediction method of solenoid proteins.Methods should be described in more detail, describing the algorithms and not just mentioning them.ANSWER: We have improved the description of the methods, see updated manuscript. We have added a description of the K-means clustering and Support Vector Machines.The first part is described superficially and it would be difficult to repeat the calculations for a third part. ANSWER: For third parties who have Matlab skills, it would be quite easy to repeat the calculations. The supplemental data includes all 7308 distance matrix images  and a Matlab script that calculates the bag of features, histograms (feature vector), and classification accuracy. To repeat the calculations for a vocabulary of 1000 words, third parties can invoke the script (BOW_accuarcy.m) by mouse click in Matlab (or run it from the command window). If the user wants to repeat the calculation with a vocabulary of 5000 words, only one parameter needs to be changed . We also provided a dataset of globular and solenoid proteins  and a Matlab script (SVM_2fold.m) to classify proteins using SVM. In summary, the supplementary material contains all the inputs needed to run Matlab scripts.The second part is superficial, too. It is unclear if the problem is interesting and useful. I note that the Authors use a database compiled by Tosatto more than a decade ago. This is not acceptable given the PDB dynamics. As a consequence, the results shown in Table 1 might be biased, too.ANSWER: Indeed, we used data first compiled by Marsella et al. (2009) and available on the website of Tossato's research group. We used this dataset intentionally because it is a gold standard for distinguishing between solenoid and globular proteins and has been used by many others: RAPHEL, ReUPred, ConSole, Repetita, Wavelet, ...Another possibility would be to use a new dataset. In this case, we would have to apply all the other methods and algorithms to the new dataset in order to make a comparison between the different methods/algorithms. Anyway, classification was not the only focus of this work. Rather, we introduced an alternative approach to analyse the distance matrix and introduce a qualitative measure of protein repeats.I strongly suggest the Authors to communicate their results, hopefully in two separate manuscripts, with more details about their methods.ANSWER: We have improved the description of the methods. The first part of the paper, which is more related to the development of a new method and the presentation of a new approach to distance matrix analysis, and the second part, which is related to practical examples, are closely related. Moreover, the paper is not too long in our opinion with about 6000 words.Line 112: it is unclear to me what “mediodis” means here.ANSWER: Medoids are representative objects of a dataset or a cluster within a dataset. The idea of K-medoids clustering is to consider the final centres as actual data points rather than mean data points (K-means clustering). In the mentioned study, one hundred medoids (submatrix of size 10x10) were extracted from the training set sampled proteins). Thus, 100 medoids represent a local feature of the protein distance matrix. In calculating the distance matrix, the authors used a threshold of 20 Å. Larger distances were set to "null", so that a submatrix of size 10x10 contained only "null" elements. To make it clearer for the readers, we rephrase the text as follows:For example, Soug-Hou Kim et al. pointed out that the "null" pattern (15th mediodis) is the most common. In the mentioned study, one hundred medoids (submatrix of size 10x10) were extracted from the training set (sampled proteins). Thus, 100 medoids represent a local feature of the protein distance matrix. However, no analysis was performed on the frequency of all 100 mediodis, the most frequent position in the distance matrix (map to image), or the correlation between the number of unique features and the solenoid proteins.Line 134: it is unclear why the distance matrices contain the inverse square distances.ANSWER: This was a (typo) error. We have corrected it as follows:The images of the distance matrix contained the distances between all Cα-atoms. Therefore, each distance matrix was represented as a grayscale image in the range , where 0 (black) represents long-range contacts and 1 (white) represents short-range contacts.Line 214: did the Authors use other similarity measures beside the cosine similarity measure?ANSWER: No, we used default similarity metric (cosine) of Matlab function “retrieveImages”.Line 247: after “computation time”, it would be interesting to insert some information about the computational requirements (cpu time etc.).ANSWER: We have added the following sentence in the manuscript. The creation of bag-of-visual-words with a vocabulary of 1000, 5000 and 10000 codewords required a CPU time (3.4 GHz processor) of about 20, 27 and 39 minutes, respectively.Line 258: “other studies”. The Authors should mention them.Fixed, we mention them.ANSWER: Fixed, we mention them.We have shown here that the extraction of descriptors in an image of the distance matrix using the KAZE algorithm, followed by the construction of the codebook of 5000 words, gives a good distribution of domains in the low-dimensional space and a good prediction of nearest neighbours, and is consistent with the studies of Hou et al, Choi et al, and Shi et al, who studied the folding space and structural similarity in proteins.Line 317: perhaps “and solenoid protein domains” might be removed.ANSWER: Fixed.Line 336: the “ratio” might be defined by writing an equation.ANSWER: We have added an explanation in the manuscript.For example, if the distance matrix image contains 500 features extracted by the KAZE algorithm, and these features belong to 400 unique codewords (taken from the precomputed codebook), then to calculate the proportion of unique words you need to divide 400 by 500 and get 0.8. The value 0.8 means that 80% of the codewords are unique.Line 342: “most” might be associated with a numerical percentage.ANSWER: We add a numerical percentage (~96%), see updated manuscript. Line 343: “few” might be associated with a numerical percentage.ANSWER: We add a numerical percentage (~0.6%), see updated manuscript. Line 356: “in many biological processes [26-31]” is really very very vague. Might it be possible to expand this discussion.ANSWER: We improved text as follows:Solenoid proteins and proteins containing tandem repeats [25] have been and still are the subject of extensive research, as they have been shown to play fundamental roles in many biological processes such as signal transduction and molecular recognition . Due to their elongated structures and flexibility, Ankyrin, Leucine-rich, and HEAT -repeat proteins are involved in protein-protein interactions (Kobe). Fibromodulin, for example, is a leucine-rich repeat protein of the extracellular matrix that binds to fibrillar collagens and affects fibril velocity (Svensson). In addition, solenoid proteins also play a crucial role in the interaction with nucleic acids. Exportin-5, for example, is a transporter for microRNA (Fournier).Line 384: “a database”; which one?ANSWER: In Methods, we described the dataset used in previous studies, which serves as the gold standard for distinguishing globular from solenoid proteins. We have expanded the description in the Methods section. We have also added a new section in Methods entitled: "Solenoid and non-solenoid protein dataset - benchmark".Line 415: “the gold standard database”; which one?ANSWER: In Methods, we described the dataset used in previous studies, which serves as the gold standard for distinguishing globular from solenoid proteins. We have expanded the description in the Methods section. We have also added a new section in Methods entitled: "Solenoid and non-solenoid protein dataset - benchmark". We also improved the sentence, as follows:The results of the solenoid and non-solenoid protein dataset, which contains 105 solenoid proteins and 247 globular proteins, are shown in Table 2. Line 421: The methods “RAPHAEL” and “ReUpred” should be described.ANSWER: Done, see section in Methods entitled: "Solenoid and non-solenoid protein dataset - benchmark"Line 422: “When the vocabulary was reduced from 1000 to 500 words, the accuracy decreased only slightly”; it might be necessary to provide a number.ANSWER: Done.When the vocabulary was reduced from 1000 to 500 words, the accuracy decreased only slightly, from 99.4 to 99.1.arxiv.org?Reference 14: is there any other publication beside ANSWER: To our knowledge, there is no other publication. Figure 4: titles should be provided to the horizontal and vertical axes.ANSWER: Done.Figure 5. The expression “(aa)” should follow “Domain size”. Moreover, it might be interesting to insert the slopes of the regression lines.ANSWER: We added the term "amino acids" to the axis, and also added the regression line.Line 87: the reference to DALI Z-score is probably wrong. This score has been designed by Chris Sander and Liisa Holm.ANSWER: The reference of DALI and Z-score  is at the very beginning of the introduction. Hou et al. used the DALI Z-score to construct a similarity matrix of 498 protein folds and create a global representation of the protein folding space. We have included the reference of Sander and Holm, 1993 in line 87.Line 96: The reference “Sivic J. et al.” is misspelled .ANSWER: Fixed.Line 130: “[16-18]” should be inserted after “database”.ANSWER: Fixed. **************************************************************************************************************************Reviewer #2: Authors take a refreshingly different approach to protein structure classification by using a (relatively) novel technique of image analysis by "bag of "visual" words" to classify distance maps, a particular visualization approach of protein structures. The paper is well written, results are presented clearly. I have two problems with the paper- training. No details are given on creation of the training and testing. If, as I assume, the division was done randomly, it is very likely that closely homologous proteins were split between the training the testing set. Such proteins have practically identical structures and hence distance maps. With thousands of features, many of them rare, it is very likely that the system is simply memorizing classifications of such homologs, this is probably easy to test. The training and testing sets must be constructed taking into account the relations between proteins. This is critical to the overall validity of the papers.ANSWER: The dataset we used is a benchmark dataset that has been used by many other research groups. The dataset contains globular (247) and solenoid (105) proteins where the maximum sequence identity is 35%. Thus, the dataset does not contain homologous proteins. We have expanded the description in the Methods section. We have also added a new section in Methods entitled: "Solenoid and non-solenoid protein dataset - benchmark".- its conclusions are conceptually disappointing. Why introduce a new formalism and do all this work to build a lousy protein structure classifier? The paper provides zero useful information to a structural biologist. What does it tell me that tens of thousands of features are needed to classify a protein. This is a computer science paper written for other computer scientists. If you could appeal to the domain researchers, the paper would be much more valuableANSWER: The first part of the paper focused on the general representation and showed that a general classification can also be done using the histogram of codewords . Then we introduced a new qualitative measure - the ratio of unique words. This parameter clearly shows that it tends to be low for solenoid proteins and higher for globular proteins, thus quantitatively describing the repeats in the protein domain. Moreover, the histogram of codewords combined with SVM showed an improvement in discriminating between globular solenoid proteins. And we believe this is valuable for domain researchers. However, we feel that the use of the ratio of unique words - a quantitative measure of repeats in the protein domain - is not adequately represented in the study. Therefore, we have added a section titled "Quantitative measure of repeats in protein domain: inter and intra comparison" and included two tables and a figure. Please read and view the improved manuscript.AttachmentResponsetoReviewers.docxSubmitted filename: Click here for additional data file. 24 Jan 2022Quantitative analysis of visual codewords of a protein distance matrixPONE-D-21-31856R1Dear Dr. Pražnikar,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact Kind regards,Bostjan Kobe, Ph.D.Academic EditorPLOS ONEAdditional Editor Comments :Reviewers' comments: 27 Jan 2022PONE-D-21-31856R1 Quantitative analysis of visual codewords of a protein distance matrix Dear Dr. Pražnikar:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. If we can help with anything else, please email us at Thank you for submitting your work to PLOS ONE and supporting open access. 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Knowledge of protein motions is significant to understand its functions. While currently available databases for protein motions are mostly focused on overall domain motions, little attention is paid on local residue motions. Albeit with relatively small scale, the local residue motions, especially those residues in binding pockets, may play crucial roles in protein functioning and ligands binding.A comprehensive protein motion database, namely D3PM, was constructed in this study to facilitate the analysis of protein motions. The protein motions in the D3PM range from overall structural changes of macromolecule to local flip motions of binding pocket residues. Currently, the D3PM has collected 7679 proteins with overall motions and 3513 proteins with pocket residue motions. The motion patterns are classified into 4 types of overall structural changes and 5 types of pocket residue motions. Impressively, we found that less than 15% of protein pairs have obvious overall conformational adaptations induced by ligand binding, while more than 50% of protein pairs have significant structural changes in ligand binding sites, indicating that ligand-induced conformational changes are drastic and mainly confined around ligand binding sites. Based on the residue preference in binding pocket, we classified amino acids into “pocketphilic” and “pocketphobic” residues, which should be helpful for pocket prediction and drug design.www.d3pharma.com/D3PM/index.php.D3PM is a comprehensive database about protein motions ranging from residue to domain, which should be useful for exploring diverse protein motions and for understanding protein function and drug design. The D3PM is available on The online version contains supplementary material available at 10.1186/s12859-022-04595-0. The conformational diversity of protein is rooted from its structure and is often a key feature of its function , 2. A fu1 [As summarized in a number of reviews, most studies about the protein motion have focused on the hinge and shear motions of protein domain –11. Seve1 . Further1 . TherefoThe protein data bank (PDB)  containsIn this study, we constructed a database that covers all kinds of protein motions ranging from overall structure to local residue, namely D3PM. The motion patterns in the database are classified into 4 types of overall structural changes and 5 types of pocket residue motions. Considering that the form of structural pairs is more convenient to analyze motion features than that of structural clusters, all the protein motions were provided with structural pairs in the D3PM. We hope that the D3PM will be helpful to explore diverse protein motions and promote the drug discovery and development.α root mean square deviation (RMSD), which was often used as a threshold for drug discovery [All the X-ray structures with resolution better than 3.0 Å were downloaded from the PDB , and were divided into pairs of identical proteins that have the same UniProt ID. The oligomerization state was limited to either monomer or homo-multimer to exclude the influence of protein–protein interactions on structural changes. Many of small molecules bound into proteins are crystallographic additives  structures. As observed by Rebecca et al., the protein dynamics could lead to the opening, closing and adaptation of binding pocket, resulting in the appearance/disappearance of a sub-pocket or an allosteric pocket and the pocket breathing motion [www.d3pharma.com/D3Pocket/index.php). It is well-known that one type of residue motion can dramatically regulate the “on” and “off” states of binding pocket, which is also called ‘gatekeeper’ [apo FOX-4 cephamycinase moves 2.5 Å inwards upon ligand binding, forming a putative T-shaped π-stacking interaction with the substrate [g motion . To furtekeeper’ . For exaekeeper’ . Noticeaubstrate . The resSimilarly, we calculated the RMSD matrix of pocket residues for protein pairs of holo structures, including the pairs bound with different ligands and the pairs bound with the same ligand. For the pairs bound with different ligands, there are 2,176,460 pairs with at least one residue’s RMSD that is greater than 2.0 Å. We then selected a typical pair with the largest RMSD, and obtained a final set of 1183 cases, viz., 793 pairs with the same ligand binding pocket and 390 pairs with different ligand binding pockets. The 390 cases could be regarded as protein pairs of apo and holo structures, which could also be classified into the five classes . For those pairs bound with the same ligand, a final set of 1465 cases was selected from pairs with at least one residue’s RMSD that is greater than 2.0 Å.For protein pairs with overall RMSD that is greater than 2.0 Å In this set, protein conformational change may result from both the inherent flexibility and external perturbations like ligand binding. Consequently, to explore how those motions occur, we classified the protein pairs with overall RMSD that is greater than 2.0 Å into four parts: (a) pairs of apo structures, (b) pairs of apo and holo structures, (c) pairs of holo structures with different ligands, (d) pairs of holo structures with the same ligand. For the pairs of apo structures, the inherent flexibility of protein contributes mostly to their conformational changes, since we have excluded the influence of protein–protein and protein–ligand interactions. These datasets containing apo-holo pairs and pairs of holo structures bound with different ligands should be a useful resource to evaluate the protein motions induced by ligands. There are 1111 protein pairs bound the same ligand with RMSD that is greater than 2.0 Å, which should result mainly from the inherently flexibility of protein–ligand complex. For the pairs of holo structures, we calculated the RMSD matrix of the pocket residues, and found that 125 apo-holo pairs have obvious pocket residue motions, which could also be classified into the five classes .Finally, the D3PM collects 7649 proteins with overall motions and 3513 proteins with pocket residue motions, as shown in Table The DrugBank is a free web resource containing comprehensive drugs information with their targets, which greatly facilitates the drug discovery and development . To makewww.d3pharma.com/D3PM/index.php  pairs of apo structures, (b) apo-holo pairs, (c) pairs bound with different ligands, (d) pairs bound with the same ligand. By searching the database, we found 1970 proteins forming 3,990,497 protein pairs, among which each protein possesses all the 4 different motion types.If a protein pair has overall RMSD that is smaller than 2.0 Å, it was regarded as motionless. In Fig. To explore pocket residue motions, the RMSD of pocket residues that around ligand by 5.0 Å was calculated Fig. B. SimilaInteractions with pocket residues are indispensable to the binding process of ligands, e.g., hydrogen bond, hydrophobic interaction and so on. In order to evaluate how amino acids that bind to ligands (pocket residues) differ from that of overall structure, the residues around ligands and the whole protein were analyzed. Usually, the definition of pocket residues is the ones with a minimum distance to ligand shorter than 5.0 Å . With 17To further explore the frequency of pocket residues that are easy to motion in responding to ligand binding, we analyzed the pocket residue motions in the D3PM. As shown in Fig. The current strategy of virtual screening using a selected inhibitor bound conformation as receptor structure may miss putative ligands, due to protein conformational adaptations in ligand binding site. To evaluate how significant conformational adaptations are for molecular docking, the set of holo proteins bound with different ligands was used for cross-docking of ligand to a bound receptor structure crystallized in the presence of another ligand. The results (Additional file We developed the D3PM database to analyze all kinds of protein motions involving overall structures and binding pocket residues. In addition, we classified pocket residue motions into 5 types for studying different function mechanism of ligand binding. Currently, the information provided in the D3PM is in list form. The D3PM will be regularly updated to reflect new entries in the PDB database.Using the D3PM, we firstly compared the ability of different factors that are related to protein conformational changes. The results showed that protein motions induced by ligands are significant in binding pocket according to 53.4% of protein pairs have pocket RMSD greater than 2.0 Å, but only less than 15% of protein pairs have obvious overall conformational adaptation. However, there are nearly 5% of protein pairs bound with the same ligand have overall RMSD greater than 2.0 Å. Although factors of both external perturbations like ligand binding and intrinsic flexibility of macromolecule have been studied here, there are still other factors like pH, temperatures and mutation that can impact protein motions, which is valuable for further study.In addition, we analyzed the preferences of 20 amino acids in binding pocket. The results revealed some residues likely to interact with ligands by forming short-range or long-range interactions, which could be called “pocketphilic”. However, “pocketphobic” residues like Leu, Ala, Val, Glu, Ile, Pro and Gln have smaller frequencies in binding pocket compared with that in overall structure. The results could provide important information for pocket prediction.Additional file 1. The Additional file 1 contains Figures S1 and S2, Tables S1 and S2. Figure S1 shows the mean unsigned error of the frequencies of the 20 amino acids between the pocket and overall structure. Figure S2 shows the ROC curve for the cross docking case. Table S1 shows the list of crystallographic additives. Table S2 shows the detailed docking scores of the cross docking case.