P-value < 0.001), achieving a correlation of up to 0.70 on blind tests. DynaMut also provides a comprehensive suite for protein motion and flexibility analysis and visualization via a freely available, user friendly web server at http://biosig.unimelb.edu.au/dynamut/.Proteins are highly dynamic molecules, whose function is intrinsically linked to their molecular motions. Despite the pivotal role of protein dynamics, their computational simulation cost has led to most structure-based approaches for assessing the impact of mutations on protein structure and function relying upon static structures. Here we present DynaMut, a web server implementing two distinct, well established normal mode approaches, which can be used to analyze and visualize protein dynamics by sampling conformations and assess the impact of mutations on protein dynamics and stability resulting from vibrational entropy changes. DynaMut integrates our graph-based signatures along with normal mode dynamics to generate a consensus prediction of the impact of a mutation on protein stability. We demonstrate our approach outperforms alternative approaches to predict the effects of mutations on protein stability and flexibility ( Proteins are dynamic macromolecules, whose function is intricately linked to their biological motions ,2. We haNormal Mode Analysis (NMA) is a computational approach that approximates the dynamics of a system around a conformation through harmonic motion. This has been used to generate possible movements and therefore provide valuable insights into protein motions, and their accessible conformational repertoires. Previous studies have shown that NMA can be a powerful tool to analyze protein structure–function relationship  and to pTo fill this gap, we introduce DynaMut, a web server that introduces the dynamics component to mutation analysis. This is achieved by implementing and integrating well established normal mode approaches with our graph-based signatures in a consensus predictor for protein stability changes upon mutation, which we show optimizes overall prediction performance.http://biosig.unimelb.edu.au/dynamut/).DynaMut implements NMA through two different approaches, Bio3D  and ENCoIn this work, we used the previously established S2648 dataset , derivedG) using these datasets (GWT→MT) should be equivalent to the negative change in folding free energy of the hypothetical reverse mutation—from the mutant to the wild-type protein (–ΔΔGMT→WT) (Previous studies have reported performance comparisons of difference methods on predicting changes in folding free energy (ΔΔdatasets . Given tdatasets  in orderΔGMT→WT) ,22–24. INMA allows the study of harmonic motions in a system, providing insights into its dynamics and accessible conformations. It has been widely used for studies of protein dynamics as an alternative to more computationally intensive molecular dynamics approaches . While mStructure-based approaches to predict the impact of mutations on stability utilize protein structural information from the 3D space of a natively folded protein. Even though these structure-based methods are essentially based on the same structural data, they are built using broadly different, sophisticated, approaches, such as statistical potential function energy calculations, used in SDM  and strue.g., relative solvent accessibility, residue depth and secondary structure) and graph-based signatures representing the wild-type structure. The graph-based signatures concept, used in the development of mCSM-Stability and to generate the consensus DUET predictions, has been widely applied to the study of protein structure, including protein–ligand interactions . The server front end was built using Bootstrap framework version 3.3.7, while the back-end was built in Python via the Flask framework (Version 0.12.2). It is hosted on a Linux server running Apache.We have implemented DynaMut as a user-friendly, freely available web server , along with the variation in entropy energy between wild-type and mutant structures  in the first tab . For comDynaMut also generates and makes available for download pymol sessions for flexibility analysis and for inter-residue interactions for both wild-type and mutant structures to facilitate easy visualisation and figure preparation.G.The performance of DynaMut was compared to well-established methods that also provide measurements of effects of single-point mutations on protein stability. All mutations from the data set described previously were submitted to each tool and the Pearson's Correlation Coefficient and Root Mean Squared Error were used to assess the comparison among all methods. Moreover, outliers were considered based on the absolute difference between predicted and actual values of ΔΔG ≥ 0 as stabilizing and ΔΔG < 0 as destabilizing. In the case that a method does not follow such definition, its results were adapted.Since this definition can vary across the methods and for comparison purposes we defined ΔΔr = 0.67, and RMSE = 1.31 kcal/mol (r = 0.79 and σ = 0.01 on 90% of the data) under 10-fold cross validation. This correlation was significantly higher than the individual methods used in the consensus prediction . Across the full training set (forward and reverse mutations), DynaMut achieved a Pearson's correlation of r = 0.79  than comparable methods , significantly higher than the performance of either ENCoM, FoldX, SDM or Maestro, but lower than I-Mutant2, DUET and mCSM , significantly higher than all comparative methods (P < 0.01; Previous studies have highlighted that many machine learning based structural approaches are unbalanced, and can less accurately identify stabilizing mutations . We therhttp://biosig.unimelb.edu.au/dynamut/.Here, we present DynaMut, an integrated computational method that provides users with easy access to powerful and insightful analysis of protein motions and their changes upon mutation. By consolidating these insights with our graph-based signatures, DynaMut is able to accurately assess the effects of missense mutations on protein stability. This consensus approach allows for the more accurate and reliable prediction of both stabilizing and destabilizing mutations. DynaMut is a valuable tool for a wide variety of applications, ranging from protein functional analysis, optimization of stability and understanding the role of mutations in diseases. The method is freely available as a user friendly and easy to use web server at Supplementary DataClick here for additional data file.


A sliding window analysis over a protein or genomic sequence is commonly performed, and we present a Python tool, BioStructMap, that extends this concept to three-dimensional (3D) space, allowing the application of a 3D sliding window analysis over a protein structure. BioStructMap is easily extensible, allowing the user to apply custom functions to spatially aggregated data. BioStructMap also allows mapping of underlying genomic sequences to protein structures, allowing the user to perform genetic-based analysis over spatially linked codons—this has applications when selection pressures arise at the level of protein structure.https://github.com/andrewguy/biostructmap and released under the MIT License. An online server implementing standard functionality is available at https://biostructmap.burnet.edu.au.The Python BioStructMap package is available at Bioinformatics online. Consideration of three-dimensional (3D) protein structure is important in many areas of research, including antibody-antigen interactions, protein–protein interactions and drug interactions with proteins. For example, antibody recognition of a dominant epitope can lead to selection pressures on residues associated with that epitope; these residues may be distant in the linear protein sequence despite being spatially connected. In immunology, these non-linear sequence-structure relationships are referred to as discontinuous or conformational epitopes. There are a number of pre-existing online tools that allow for visualization and mapping of pre-defined features onto protein structures  is then passed to a function that returns a numerical value, which is then mapped back to the central residue . A numbeBioStructMap uses the Biopython Bio.PDB module for handling PDB files, and can accept both PDB and mmCIF files as input. Sequence alignments are performed using either the NCBI BLAST+ package or the Biopython Bio.pairwise2 module. Alignment of DNA sequences to protein sequences is performed using Exonerate  which alhttps://github.com/andrewguy/biostructmap) or via the Python Package Index. A simple web-server interface is also available at https://biostructmap.burnet.edu.au, using the JavaScript NGL viewer for visualization of protein structures , a leading malaria vaccine candidate (P. falciparum strains from Kenya and Thailand (In areas of endemic malaria, immune selection pressure on the malaria parasite can lead to balancing selection, in which low-frequency alleles are maintained at a higher proportion than would otherwise be expected under a neutral model of selection. Tajima’s D  is one sandidate . This apThailand  was usedThe BioStructMap package and associated web interface allow for visualization of sequence-aligned data over a 3D protein structure, as well as allowing the incorporation of protein structural information into sequence-based metrics using a 3D sliding window approach. This tool is applicable to a variety of problems, including identification of regions under various forms of genetic, immunological or drug selection pressure and spatial mapping of residue characteristics that may affect immunogenicity, solubility, binding interaction, etc. The tool is easily extensible, allowing users to define their own functions to apply to spatially aggregated data.Supplementary Figure S1Click here for additional data file.Supplementary DataClick here for additional data file.


Proteomics, the large-scale analysis of proteins, is contributing greatly to understanding gene function in the postgenomic era. However, disease protein ranking using shotgun proteomics data has not been fully evaluated. In this study, we prioritized disease-related proteins by integrating the protein-protein interaction (PPI) network and protein differential expression profiles from colon and rectal cancer (CRC) or breast cancer (BC) proteomics. We applied Local Ranking (LR) and Global Ranking (GR) methods in network with three kinds of protein sets as a priori knowledge, which were known disease proteins (KDPs) that were collected from the Online Mendelian Inheritance in Man (OMIM) database, differentially expressed proteins (DEPs), and the collection of KDPs and their direct neighborhood with differential expression (eKDPs). The cross-validations showed that GR method outperformed LR method while using eKDPs as the initial training showed significantly higher accuracy compared to using the other two a priori sets. And then we validated the top ranked proteins using RNAi-based loss-of-function screens in the DepMap database. The results showed that 75% of top 20 proteins in CRC are necessary for tumor survival. In summary, the network-based Global Ranking with protein differential expression can efficiently prioritize cancer-related proteins and discover new candidate cancer genes or proteins. Discovering disease genes is important for understanding the mechanisms in physiological and pathological processes of disease. Genetic studies like linkage analysis  and assoSince proteins are the basic unit of biological function to link genotypes to phenotypes, CPTAC and other cancer proteomics projects have characterized the proteomic features of human cancers –8. HowevIn the previous reports, the gene prioritization approach mainly depends on the similarity of characteristics, including sequence similarity , 10, funIn this study, by integrating PPI network and differentially expressed protein profiles given by shotgun proteomics, we evaluated LR and GR methods with three different initial protein sets as a priori knowledge to prioritize disease proteins. The three initial protein sets contain known disease proteins (KDPs), differentially expressed proteins (DEPs), and the KDPs with their direct neighbor DEPs (eKDPs). The cross-validations and RNAi perturbation screens were used for performance evaluation of different strategies.To test and compare the two ranking methods with three initial protein sets, two cancer proteome datasets were used. The proteomic data of CRC used in our work include 90 TCGA colon tumor samples and 30 normal colon epithelium samples, and the normalized proteomic expression profile is provided in Zhang's work . The othhttp://hint.yulab.org/). HINT [We constructed protein interaction networks based on the HINT database (/). HINT  (High-quhttp://www.ncbi.nlm.nih.gov/omim), 39 of which exist in the HINT, as shown in The 44 known CRC genes were collected from the OMIM  knowledg t-test, and a Bonferroni correction (p < 0.05) was applied, leading to 2455 DEPs in the CRC and 1549 DEPs in BC. We selected the DEPs directly linked with the KDPs in the PPI network to enlarge the initial protein set and made up the third initial protein set eKDPs.The statistical analysis of protein differential expression was Welch'sSimilarly to the previous disease gene ranking work , we emplhttps://depmap.org/rnai/index) to measure the relationship to tumor survival.Genome-scale perturbation screens can now be efficiently performed in many cell lines using RNAi to knock down the target genes. Tsherniak et al. systematically analyzed genome-scale loss-function screens performed in 501 cancer cell lines and the results were publicly available on the Cancer Dependency web . In ordeIn the LR method, the protein rankings are solely based on the directly connected protein neighbors, which can be one of the three kinds of initial protein sets . The GR method can also use the information of the three kinds of initial protein sets by processing a random network walk on the PPI network.The LR method sets the number of proteins in the initial protein set as its only standard for evaluating the probability of a candidate protein being associated with a disease. The more proteins in the initial protein set are associated with the candidate proteins, the higher score the candidate protein will get. In the GR method, the random walk restarts at one of the three kinds of initial protein sets. We adopted the heat kernel rank  method, −1A. A is the adjacency matrix and D is the diagonal matrix. The Laplacian matrix of G is L=I-W. By establishing the diffusion rate α and setting a preference vector p0, the ranking score vector can be got as the following Equation:α is the diffusion rate and N is the number of iterations. As the two parameters are set, a random walk through the network is initiated.Given a graph G, processing a random walk on G, the transition probability matrix W is defined as W=Dion (see ) describα=0.5 and N=3 for the interactions that can make p∂ reach a steady state. We initialized the preference vector p0 with binary values and filled the initial protein set with 1 and all the other candidate proteins with 0. Finally, we can obtain the vector p∂, which contained scores for all of the candidate proteins. It should be noted that the KDP's neighbor DEPs were not initialized when this KDP was selected for the testing set in leave-one-out cross-validation.We set We compared the two ranking methods using local  and globIn CRC, the AUC values of GR with different initial protein sets were 0.834, 0.782, and 0.735, better than the LR method, whose AUC values were 0.814, 0.777, and 0.683, respectively, as shown in The results of BC were the same as the results from CRC. These results showed that both the LR and GR methods can assess the similarity between two proteins in the PPI network for protein ranking. However, the GR method was better than the LR method.After prioritizing the proteins with the TCGA CRC shotgun proteomics, we obtained 6 ranked candidate protein lists for the LR and GR methods with three initial protein sets. We counted the number of KDPs ranked in the top 10, top 20, top 50, top 100, and top 200 proteins in the lists. As the results shown above, the GR method with eKDPs performed better than the other two. We looked at the top 20 and top 50 ranked proteins/genes identified by the GR method with eKDPs in CRC samples. We found 7 KDPs in the top 20 ranked proteins (37%) and 10 KDPs in the top 50 ranked proteins (20%). Except for the KDPs, using the Cancer Dependency Map  we also In this manuscript, we compared the performance of network-based methods using protein expression profiles for cancer protein ranking. We found that the GR method outperformed when the eKDPs were used as the initial protein set. We randomly selected 39 proteins in the PPI network and compared with 39 KDPs for their degrees linking with the differentially expressed nodes. The average link of colon cancer proteins to DEPs was 16.46, which is significantly higher than the average value 5.65 from random controls. This result suggests that the expressions of the close neighbors of cancer proteins are more likely to be changed.β-catenin/TCF pathway through its deubiquitinating activity to contribute to CRC progression [α exhaustion and growth arrest in CRC cells [We further validated the top ranked proteins/genes in CRC samples based on the dependency score given by the Cancer Dependency Map. 75% of top 20 genes showed relationship with tumor survival. For example, UBC has different functions depending on the Lys residue linked by the ubiquitin. UBC is involved in DNA repair, kinase modification, protein degradation, DNA-damage responses, and other pathways . One stugression . Minichrgression  and alsogression . The Jungression . Cullin RC cells .Global network-based ranking is more efficient for proteomics data in cancer protein identification. The network-based proteome analysis is helpful in ranking disease proteins and discovering new candidate proteins.


According to the latest research, lncRNAs (long non-coding RNAs) play a broad and important role in various biological processes by interacting with proteins. However, identifying whether proteins interact with a specific lncRNA through biological experimental methods is difficult, costly, and time-consuming. Thus, many bioinformatics computational methods have been proposed to predict lncRNA-protein interactions. In this paper, we proposed a novel approach called Long non-coding RNA-Protein Interaction Prediction based on Improved Bipartite Network Recommender Algorithm (LPI-IBNRA). In the proposed method, we implemented a two-round resource allocation and eliminated the second-order correlations appropriately on the bipartite network. Experimental results illustrate that LPI-IBNRA outperforms five previous methods, with the AUC values of 0.8932 in leave-one-out cross validation (LOOCV) and 0.8819 ± 0.0052 in 10-fold cross validation, respectively. In addition, case studies on four lncRNAs were carried out to show the predictive power of LPI-IBNRA. LncRNA, a class of ncRNAs (non-coding RNAs) of more than 200 nucleotides, that do not encode proteins, has gained increasing scientific interest in recent years  is the interaction score between proteins pi and pj. AP could then be standardized as follows:Protein-protein interactions (PPI) were obtained from the STRING 9.1 database  is the sum of the elements in i-row of AP.where X(pi) as the ith row vector of matrix I, in which the nonzero values occur at the indices where the corresponding lncRNA have one interaction with a protein pi. Then the similarity between proteins pi and pj based on Gaussian kernel interaction profiles could be calculated as follows:Considering the hypothesis that similar proteins tend to exhibit a similar interaction and non-interaction pattern with lncRNAs  = {ei1, ei2, …, ei24} and E(j) = {ej1, ej2, …, ej24} as the expression profiles of li and lj. The expression similarity AL between lncRNAs li and lj was calculated as follows:LncRNA expression profiles were downloaded from the NONCODE 4.0 database  denotes the absolute value of PCC between li and lj, cov(E(i), E(j)) is the covariance between E(i) and E(j), σE(i) and σE(j) are standard deviations of E(i) and E(j), respectively.where X(pi) as the ith column vector of matrix I, in which the nonzero values occur at the indices where the corresponding protein has one interaction with the lncRNA li. Similar to the aforementioned protein case, the Gaussian interaction profile kernel similarity for lncRNAs could be computed as follows:We denoted whereSimP and integrated the lncRNA similarity matrix SimL as follows:Note that the Gaussian interaction profile kernel similarity is an association information-based measurement, which can be utilized to complement protein-protein interactions and lncRNA expression similarity. Motivated by the study of Chen , we consSP ∈ ℝnp*nl as the resource score matrix based on protein similarity, SL ∈ ℝnp*nl as the one based on lncRNA similarity. These two matrices were computed as follows:The flow chart of LPI-IBNRA is shown in pi and lncRNA lj based on protein similarity, and pi and lncRNA lj based on lncRNA similarity.where SP and SL as follows:Then the integrated resource score matrix was initialized as the weighted sum of Sini. Following the general setting, we set the parameter γ = 0.5 in this paper, making SP and SL equally weighted.where parameter γ ∈  is a scalar controlling the relative contributions of protein similarity and lncRNA similarity in Sini column by column. In other words, the calculation process can be partitioned into nl runs, each of which corresponds to a specific lncRNA. Thus, at the beginning of the kth run, the score for protein pi interacting with the given lncRNA lk can be initialized as follows:The final score matrix can be obtained by updating the lk from the protein pi, which can be calculated as follows:Then the 1st-round of our allocation model is to allocate the score of the lncRNA pi.where lk can be obtained by summing scores over all proteins connected with lk:The score of lncRNA lk to the protein pi was calculated as follows:In the 2nd-round, resource scores were allocated in a similar way as the first round. The score allocated from the lncRNA lk.where pi was allocated from all lncRNAs that interacted with pi as follows:The score of protein pi from the given lncRNA lk and then updated it by a two-round resource allocation. An example is given in As described from Equation (13) to (17), we first initialized the score of protein S can also be computed in a vectorized form as:Sfin, Sini, and W ∈ ℝnp*np is the weight matrix. Then Equation (17) can also be represented as:where whereIn the lncRNA-protein interaction network, the proteins interacting with the same lncRNA are considered to be directly correlated, i.e., having the low-order correlation, while higher-order correlations between these proteins might also arise from indirect associations. Such high-order correlations might have a negative effect on the lncRNA-protein interaction prediction. Based on the studies of Zhou et al.  and Liu where the parameter α ∈ . The final score matrix for inferring potential lncRNA-protein interactions can then be calculated as follows:lk in descending order by the kth column of After the calculations, we can recommend proteins to the given lncRNA We evaluated the classification performance of the proposed LPI-IBNRA method by applying two types of classification schemes, i.e., LOOCV and 10-fold cross validation. The performance of LPI-IBNRA was evaluated in terms of several widely-used indicators, including precision (PRE), sensitivity (SEN), accuracy (ACC), F1 score, and Matthews correlation coefficient (MCC), expressed as follows:where TP, TN, FP, and FN count the number of true positives, true negatives, false positives, and false negatives, respectively.As a popular method for performance evaluation, the receiver operating characteristic (ROC) curve was also utilized in our experiments. The area under the ROC curve (AUC) = 1 indicates perfect performance, while AUC = 0.5 indicates random performance. The precision-recall curve (PR curve) and the area under the PR curve (AUPR) are also used to reduce the negative influence of false positive data on the method performance. The larger the AUC and AUPR is, the better performance the evaluated method has.We used the aforementioned 4,796 known human lncRNA-protein interactions to carry out the above-mentioned two cross validation schemes. In each LOOCV trial, each known lncRNA-protein interaction was used as a test sample while the rest were used as training samples. To analyze the influence of parameter α on the performance of LPI-IBNRA, we applied LOOCV for the selection of parameter α. As shown in Five previous approaches were used for comparison in the experiments, including collaborative filtering (CF), random walk with restart (RWR), LPBNI, LPIHN, and LPI-BNPRA. LPBNI, LPIHN, and LPI-BNPRA are network-based methods that infer potential lncRNA-protein interactions, while CF and RWR have been used as benchmark methods in Ge et al.  and Wen Here, we reproduced these methods on the same dataset by ourselves. See The aforementioned results indicate that in both LOOCV and 10-fold cross evaluation, LPI-IBNRA outperforms other methods in terms of the AUC values. The outstanding performance of LPI-IBNRA demonstrates its stable and satisfying abilities in inferring potential lncRNA-protein interactions. The superior performance of the proposed method could be attributed to second-order correlation elimination, which is more suitable for our task and can lead to better prediction performance.In addition, four case studies have been carried out to further evaluate the effectiveness of LPI-IBNRA. The interactions in our benchmark dataset were obtained in NPInter v2.0 which was established in 2013. NPInter was then upgraded to NPInter v3.0 in 2016 (Hao et al., In this article, we proposed a novel method LPI-IBNRA for predicting lncRNA-protein interactions, based on the known lncRNA-protein interactions, lncRNA expression similarity and protein-protein interactions. We integrated the known interactions and similarity as the initial resource scores for a two-round resource allocation of a bipartite network recommendation. Furthermore, we optimized the weight matrix by eliminating second-order correlations appropriately, to obtain the final result of lncRNA-protein interaction prediction. We finally acquired gratifying and reliable prediction performance in LOOCV, 10-fold cross evaluation and case studies. Thus, we believe that LPI-IBNRA can make reliable predictions and might guide future experimental studies on lncRNA-protein interactions.LPI-IBNRA has the following improvements over several previous methods in predicting lncRNA-protein interactions. First, with the employment of the bipartite network recommender algorithm, we utilized the known lncRNA-protein interactions to construct a bipartite network between lncRNAs and proteins, and then allocated the resource scores via interaction edges between lncRNA nodes and protein nodes. Therefore, the negative sample set is not required in our methods. Second, we assigned weights to each edge on the bipartite network, which is distinct from most former bipartite network methods. Thus, the resource scores would not be evenly distributed during the resource allocation process. Finally, we eliminated second-order correlations on the bipartite network appropriately, to enhance prediction accuracy.Although impressive results have been achieved, there is still much room for improvement in our method. At first, though known lncRNA-protein interactions have been more than before, it is still very difficult for the proposed method to obtain adequate results based on the prediction. Moreover, as the resource allocation of the bipartite network recommendation algorithm is based on known lncRNA-protein interactions, LPI-IBNRA is not suitable to predict interactions of lncRNAs without any known interacted protein.GX designed the experiments. CW and ZF performed the experiments. GX, CW, YS, ZF, and JL conceived the project and analyzed the data. CW and YS wrote the manuscript and all authors contributed to the writing.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Bacterial surfaces are complex systems, constructed from membranes, peptidoglycan and, importantly, proteins. The proteins play crucial roles as critical regulators of how the bacterium interacts with and survive in its environment. A full catalog of the motifs in protein families and their relative conservation grade is a prerequisite to target the protein-protein interaction that bacterial surface protein makes to host proteins.In this paper, we propose a greedy approach to identify conserved motifs in large sequence families iteratively. Each iteration discovers a motif de novo and masks all occurrences of that motif. Remaining unmasked sequences are subjected to the next round of motif detection until no more significant motifs can be found. We demonstrate the utility of the method through the construction of a proteome-wide motif repository for Group A Streptococcus (GAS), a significant human pathogen. GAS produce numerous surface proteins that interact with over 100 human plasma proteins, helping the bacteria to evade the host immune response. We used the repository to find that proteins part of the bacterial surface has motif architectures that differ from intracellular proteins.We elucidate that the M protein, a coiled-coil homodimer that extends over 500 A from the cell wall, has a motif architecture that differs between various GAS strains. As the M protein is known to bind a variety of different plasma proteins, the results indicate that the different motif architectures are responsible for the quantitative differences of plasma proteins that various strains bind. The speed and applicability of the method enable its application to all major human pathogens.The online version of this article (10.1186/s12859-019-2686-8) contains supplementary material, which is available to authorized users. The rise of antibiotics resistant bacteria poses a major global health issue predicted to cause 10 million deaths per year in 2050, more than heart disease and cancer combined  is the architecture that exists in several serotypes . For the M1 proteins, we identified seven motif architectures  while there are many important motifs also in common (28.85%). Table Conserved protein sequence domains, also referred to as motifs, play an important role in protein function, protein structure and protein-protein interactions. Motifs are the results of several evolutionary processes where, for example, a part of a protein is evolving at a different rate compared to other parts of the same protein. Identifying motifs are fundamental to understanding protein function and to discover putative binding interfaces.Motifs can both be used to shed light on the evolutionary process underpinning the development of a protein family with respect to the protein’s function over time; it can also be used to produce a simplified view on the protein as a series of conserved motifs that together specify a proteins motif architecture. Although several approaches have been developed to address motif discovery on protein sequences, most are either focused on a given motif or finding motifs, such as signal peptides, that can be found in a general population of protein sequences.Here, we developed a de novo motif discovery approach and applied to protein families that share a common ancestral protein; this resulted in a repository of motifs over an entire organism. This approach is focused on understanding the evolutionary processes that have acted on that protein family in a comparatively short evolutionary time. We developed and designed this approach as a software package which is written in Python and distributed via singularity containers  making iGiven the speed and flexibility of our approach, we believe it will be useful in breaking analyzing surface protein of pathogens as these proteins are under high selective pressure and therefore cannot be analyzed using more traditional approaches such as multiple-sequence alignments (MSAs). Our attempts to use various MSA algorithms failed due to high sequence variability in regions between motifs and the varying number of motifs. Also, motif searching approaches failed and only identified a small subset of the motifs that our approach discovered.In this paper, we demonstrate a proof-of-principle approach to parsing large sequence families into motifs using a denovo-based greedy approach. This simple approach can easily handle situations where parts of proteins are repeated or re-arranged, and this can be time-consuming using other approaches. While this general approach can be applied to any bacteria, we used GAS as a model system to make a comprehensive motif repository of its proteins. We further analyzed M1 protein, one of the most important virulence factor of S. pyogenes to show the motif-based architectural analysis. We observe that we over-parse some domains, but also observe that many of these large domains are only partly conserved over the sequence collection. The result indicates that many of the newly discovered motifs are not always present together with adjacent motifs, indicating that they might have different and independent functions. Interestingly, many of our newly discovered motifs are not found in any of the emm1 strains, and some of these might be responsible for binding other ligands.Additional file 1Supplementary Table 1. The logo of all 20 motifs obtained from our approach for M protein family is listed in a multi-page table in the supplementary material. (PDF 1075 kb)


Complexome profiling combines native gel electrophoresis with mass spectrometry to obtain the inventory, composition and abundance of multiprotein assemblies in an organelle. Applying complexome profiling to determine the effect of a mutation on protein complexes requires separating technical and biological variations from the variations caused by that mutation.We have developed the COmplexome Profiling ALignment (COPAL) tool that aligns multiple complexome profiles with each other. It includes the abundance profiles of all proteins on two gels, using a multi-dimensional implementation of the dynamic time warping algorithm to align the gels. Subsequent progressive alignment allows us to align multiple profiles with each other. We tested COPAL on complexome profiles from control mitochondria and from Barth syndrome (BTHS) mitochondria, which have a mutation in tafazzin gene that is involved in remodeling the inner mitochondrial membrane phospholipid cardiolipin. By comparing the variation between BTHS mitochondria and controls with the variation among either, we assessed the effects of BTHS on the abundance profiles of individual proteins. Combining those profiles with gene set enrichment analysis allows detecting significantly affected protein complexes. Most of the significantly affected protein complexes are located in the inner mitochondrial membrane , or are attached to it .http://github.com/cmbi/copal.COPAL is written in python and is available from Bioinformatics online. Complexome profiling combines native gel electrophoresis with protein mass spectrometry to obtain a comprehensive inventory of the proteins and multiprotein complexes (the complexome) in a biological sample. First, blue-native gel electrophoresis separates intact protein complexes from each other     iBAQ values are many orders of magnitude higher than the length of the x-axis that is maximally the number of slices in which the gel is cut plus the number of inserted slices (gaps). To increase the contribution of shifts in migration patterns to the Hausdorff distance, we normalize the data per protein so that the maximum intensity value per protein over all the gels compared is equal to the length of the final alignment, creating a square plane in which the Hausdorff distance is calculated. The scaling parameter that is in this normalization can in principle also be used to weigh shifts in mass higher than shifts in intensity, and can be adapted in COPAL. In the analysis presented below we also normalized the abundance data to have a maximum ¼ as high as the final alignment length, effectively weighing changes in protein complex mass 4-fold higher than changes in protein abundance. While the results per protein differed slightly, the set of significantly affected protein complexes remained the same.As the Hausdorff distance is a Euclidean distance, it considers differences on both axes equally: i.e. it gives equal weight to shifts in the migration pattern of a protein corresponding to the protein complex mass, measured along the B and W are the number of protein sample pairs between and within groups, respectively]. Proteins were subsequently sorted based on this score.To select for proteins with the largest difference in migration between TAZ mutation fibroblast mitochondria, hereafter names BTHS mitochondria, and controls, while showing consistent migration patterns within either group, we calculated the effect of the mutation by taking the average Hausdorff distance between the two groups and dividing them by the average Hausdorff distance within the groups [http://github.com/cmbi/copal.COPAL is written in python (version 2.7 or higher) and uses the pandas data analysis toolkit. It has a graphical user interface that is built using TkInter, the native python GUI package. The standalone executable tool is packaged with pyinstaller (version 3.2.1). COPAL requires complexome profiles in excel or comma-separated values text document format as input. It outputs aligned complexome profiles as formatted excel files, as well as text files. Optionally, the tool also computes Hausdorff effect sizes between pre-defined controls and test complexomes. Extensive documentation of the program is included with the program itself at To prioritize and integrate differences between complexome profiles at the level of protein complexes, we used GSEA , to examWe compared four complexome profiles from BTHS mitochondria carrying different mutations in the TAZ gene  with fivTo make the relative protein abundance values comparable across the complexome profiles, we normalized the individual iBAQ values using a correction factor deduced from the sum of iBAQ values for all detected mitochondrial proteins in each profile. Subsequently we performed pairwise alignments of all profiles using COPAL. As expected, clustering the complexomes based on their alignment score showed that the largest differences occurred between the different BNE gels . Progres2–S4) appeared reduced in mitochondria from patient fibroblasts . The 845 mitochondrial proteins that were detected show a wide range of effects of the mutation . InteresP-val = nominal P-value, FDR q-val = false discovery rate: i.e. after correcting for multiple testing.Proteins were ranked on differences in abundance profiles between BTHS mitochondria and controls, followed by GSEA (software.boardinstitute.org/gsea/index.jsp). A total of 1000 permutations were performed to obtain normalized enrichment scores and statistical significance values. ES = enrichment score, NES = normalized enrichment score, NOM P-value <0.001 and a false discovery rate (FDR) <0.05  <0.05 . Of thesP = 0.031) but not after correcting for multiple testing (FDR = 0.138). This is because even though MTX2 and SAMM50 appear affected and rank high on the list of affected mitochondrial proteins , MTX3 did not (rank 183). Migration patterns of all seven ‘classic’ components of the human MICOS complex . Note that under the experimental conditions used, the complete 55S ribosome separates into the large 39S and the small 28S subunit. The abundance of the 39S subunit migrating at ∼2.5 MDa was clearly increased and the maximum of the profile averaged over all detected subunits was shifted by about 200 kDa to a higher and more narrowly centered mass (We observed significant changes for the large subunit (39S) of the mitochondrial ribosome , but notred mass  suggestired mass . They fored mass .The comparison of complexome profiles holds the promise to systematically map how metabolic or genetic challenges affect the composition, abundance and stability of protein complexes and whether there are tissue specific variations in protein complex compositions. To this end, we implemented a combination of bioinformatic approaches in COPAL, allowing large-scale comparative analyses of complexome profiles. It is instructive to examine the differences between the results obtained with COPAL and the subsequent GSEA with those obtained using expert, manual curation. In a previous study, the complexome profiles obtained from BTHS mitochondria were manually compared with that of a control that was run on the same gel (C1_G1) (Aside from the change in the MICOS complex abundance also the 2-oxoglutarate dehydrogenase complex  and prohOur study provides proof-of-principle for the usefulness of COPAL to analyze multiple complexome profiling datasets. It shows that COPAL can be a useful tool to combine and compare biological or technical repeats of all kinds of complexome profile experiments. Thereby, it will allow the large-scale comparative analysis of complexome profiles, facilitating robust assessment of complexome inventories and allowing detailed and reliable conclusions.btz025_Supplementary_MaterialsClick here for additional data file.


Simple protein elastic networks which neglect amino-acid information often yield reasonable predictions of conformational dynamics and are broadly used. Recently, model variants which incorporate sequence-specific and distance-dependent interactions of residue pairs have been constructed and demonstrated to improve agreement with experimental data. We have applied the new variants in a systematic study of protein fluctuation properties and compared their predictions with those of conventional anisotropic network models. We find that the quality of predictions is frequently linked to poor estimations in highly flexible protein regions. An analysis of a large set of protein structures shows that fluctuations of very weakly connected network residues are intrinsically prone to be significantly overestimated by all models. This problem persists in the new models and is not resolved by taking into account sequence information. The effect becomes even enhanced in the model variant which takes into account very soft long-ranged residue interactions. Beyond these shortcomings, we find that model predictions are largely insensitive to the integration of chemical information, at least regarding the fluctuation properties of individual residues. One can furthermore conclude that the inherent drawbacks may present a serious hindrance when improvement of elastic network models are attempted. Proteins are involved in most cellular processes. Their functions are often accompanied by conformational motions which can have timescales ranging from picoseconds (atomic group fluctuations), nanoseconds (collective movement of residue groups), to micro- and even milliseconds (relative domain motions). Slow dynamics cannot typically be followed in atomistic molecular dynamics (MD) simulations, despite the use of supercomputers. Instead, coarse-grained models are often employed ,2. In paIn a protein elastic network, beads represent amino-acid residues and are connected by elastic springs, effectively taking into account potential interactions between them. The success of ENMs is related to their ability to reproduce well the pattern of residue displacement in protein structures due to thermal fluctuations (B-factors). In the original formulation, and in most current applications too, a uniform interaction strength is considered for all residue pairs, i.e., all elastic springs have the same stiffness which is used to scale B-factors. In a plethora of studies, this assumption was lifted aiming to improve ENM predictions, e.g., by iteratively changing stiffness constants to optimize correlation between experiment and model , or by iRecently, based on the analysis of a large set of protein NMR structures, elastic network force constants which are specific to the amino acids of residue pairs, and also include a dependence on the distance, have been extracted . This apWhile in the original publication  an improWe aimed to fill this gap by applying the novel model variants to a specific set of protein structures which have been considered also in several previous publications of protein ENMs ,17,18. TN beads isi and j at positions For our analysis, we employ the anisotropic network model of proteins ,20. The k , the corresponding PDB file was rejected. The numbers of eventually accepted structures were 2009 for ANMWe have analyzed how residue fluctuations within a protein elastic network are related to the network connectivity. Since the network architecture is determined by the cutoff distance for residue interactions, which is different for the considered models, we had to specify a relative connectivity for each residue bead within a given protein elastic network. It was defined as the ratio of the degree of each bead, i.e., The criteria which were applied to classify the chemical specificity of a residue bead and its location within secondary structural motifs are provided in the We have compared traditional anisotropic network models (ANM) and recently proposed model variants  which inIn particular, we have compared the sequence-specific models sANMThe results obtained for the studied set of protein structures can be separated into two groups. In one group of proteins, the correlation coefficient for agreement between model and experiment was very similar when comparing sequence-dependent variants to ANMs. Representative examples from this group are shown in the table in In the second group of protein structures, we found notable differences between predictions by the new model variants and traditional ANMs. The results for a selected set of structures are shown in the table in In the case of adenylate kinase, both sANM models yield higher correlations than the ANMs and the highest value tors see b shows tices see d. In theilar see e.Proceeding with the observations made for proteins in the second group, we wanted to check whether effects related to flexible regions were already present for examples from the first group, where the variation in predictions by the different models was minor (except for the discussed cutoff-related cases). In the case of the kinesin KIF1A motor domain, the correlation coefficients between model predictions and experiments were the same for sdANM and ANMshown in a shows ttion see a. For thions see b. We nottern see b.The results obtained by us for a relatively small set of proteins indicate that the quality of how well residue fluctuations are predicted by elastic network models appears to be intrinsically linked to poor estimations in highly flexible protein regions. While we originally attempted to better understand how sequence-specific elastic networks can improve model predictions, we found this drawback to be present in the traditional ANMs as well as in the considered proposed model variants with sequence specificity and distance dependence. Those results point towards a generic problem of elastic network models which we will refer to in the Discussion section. In the context of how newly proposed models compare to traditional models, we wanted to go beyond a small set of proteins and further explore the observed effects in a systematic way by applying all different models to a large set of proteins (>2000 structures) to compare their predictions with corresponding experimental data. The focus of our analysis was to relate the agreement between model and experiment to the connectivity within an elastic network. For details on the chosen set of PDB entries and the analysis of data, we refer to the Methods section. Our results show that fluctuations of weakly connected network residues are intrinsically prone to be wrongly predicted by all models, and poorly connected parts are most vulnerable to over-estimation. In Even in elastic networks with a large cutoff distance  False prediction of motions in flexible protein regions is enhanced in the sequence and distance-specific sdANM model. The reason is that the softening of long-ranged interactions, reinforced by the stiffening of short-ranged interactions in this model, effectively increases the occurrence of poorly connected residue beads. (ii) On the average, the performance of sdANM resembles that of a traditional ANM with a small cutoff distance. (iii) Related to the chemical nature of amino acids, fluctuation predictions of hydrophobic residues by the sequence-specific models are prone to disagree with X-ray experimental data of protein structures. The sequence-dependent stiffness constants have been determined for elastic networks corresponding to solution NMR conformational ensembles, where the presence of hydrophobic core residues is taken into account. In crystal structures, however, this aspect is often not correctly reproduced, e.g., in a situation when only parts of a larger protein complex became determined in isolation.Inherent shortcomings in the prediction of residue fluctuations by anisotropic elastic network models have been previously discussed. In particular, problems related to structural components which protrude out of a globular protein structure  have been identified and referred to as the “tip effect” . It descA separate study has also shown that mean-square fluctuations of residues at the surface of a protein are systematically overestimated . False pOn the other side, generic limitations in the accuracy of B-factor patterns determined from X-ray crystallography are a well discussed issue. In particular, perturbations generated by the crystal packing should affect the mobility of more flexible residues, located e.g., at the surface of a protein. Including molecular contacts in the crystal into the model has been demonstrated to improve agreement of GNM predictions with experiments ,17. SimiAlthough not as systematic as the case of crystallographic structures above, to assess the performance of these models in reproducing structural fluctuations observed in solution NMR, we additionally analyzed 132 protein NMR structures. We indeed found that sdANM reproduces fluctuations in stable secondary structures such as alpha-helices and beta-sheets better than ANMs, supposed to be benefitted more from the distance-dependence rather than the sequence-dependence of the model’s parameters. The problem related to predictions of very flexible regions however persisted, as shown by the large prediction errors for the unstructured regions. In ENMs, the solvent environment is usually not taken into account, and hence, possible damping effects are ignored. Since the new sequence- and distance-dependent ENM stiffness constants have been extracted from the analysis of solution NMR conformational ensembles, they should already include some effective contribution by the solvent. Nonetheless, our results suggest that, to counter the “tip effect”, additional energy barriers should be introduced to restrict otherwise too soft motions of protruding protein parts.While previous works mentioned the liability of elastic networks to correctly reproduce fluctuations of very flexible protein regions, our study provides a systematic exploration of such inherent drawbacks. We have also demonstrated that recently proposed models which include sequence- and distance-dependent interactions do not resolve such problems; they may even be enhanced. Beyond these effects, our results are another demonstration that predictions of elastic network models are rather insensitive to the integration of chemical information, strengthening the picture that fluctuation properties of a protein are largely determined by the architecture of its corresponding elastic network (representing its stable folded conformation).A further conclusion from our study is that the inherent drawbacks generally present a serious hindrance when improvement of elastic network models is attempted. In this study, the comparison of models was based on the evaluation of fluctuation magnitudes of individual residues. A more complete picture can in principle be obtained by considering the direction of fluctuations and their correlations between different sites . It can be furthermore interesting to discuss the effects of sequence- and distance-dependent stiffness in dynamical simulations of elastic networks, where conformational changes in a protein are directly resolved as over-damped relaxation motions of network residues e.g., ,38). The. The38])


F-value in protein complexes detection than the-state-of-the-art methods. The interactions in our reconstructed PPI network have more significant biological relevance than the exiting PPI datasets, assessed by gene ontology. In addition, the performance of existing popular protein complexes detection methods are significantly improved on our reconstructed network.Identifying protein complexes from protein-protein interaction networks (PPINs) is important to understand the science of cellular organization and function. However, PPINs produced by high-throughput studies have high false discovery rate and only represent snapshot interaction information. Reconstructing higher quality PPINs is essential for protein complex identification. Here we present a Multi-Level PPINs reconstruction (MLPR) method for protein complexes detection. From existing PPINs, we generated full combinations of every two proteins. These protein pairs are represented as a vector which includes six different sources. Then the protein pairs with same vector are mapped to the same fingerprint ID. A fingerprint similarity network is constructed next, in which a vertex represents a protein pair fingerprint ID and each vertex is connected to its top 10 similar fingerprints by edges. After random walking on the fingerprints similarity network, each vertex got a score at the steady state. According to the score of protein pairs, we considered the top ranked ones as reliable PPI and the score as the weight of edge between two distinct proteins. Finally, we expanded clusters starting from seeded vertexes based on the new weighted reliable PPINs. Applying our method on the yeast PPINs, our algorithm achieved higher  Protein complexes play important roles in biological systems and perform numerous biological functions, such as DNA transcription, mRNA translation, and signal transduction. Hence, identifying protein complexes in an organism is critical in molecular biology. With the advances of high-throughput technologies, many large-scale PPI networks have been constructed  method to remove spurious protein interactions and recover missing ones for protein complexes identification. First, we generated all combinations of each two proteins and represented each protein pair as a vector which included 17 features gathered from six sources . Second, protein pairs with same vector are mapped to an ID which is called protein pair fingerprint ID. Each fingerprint ID represents a set of protein pairs which have same vector. Third, a fingerprint-similarity network is constructed, in which a vertex represented a fingerprint and an edge represented the similarity between two distinct fingerprints. Forth, we performed a random walk with restart algorithm on this fingerprints similarity network. Some fingerprints of reliable protein interactions are given prior probabilities 1. At the end of the iterations, every fingerprint reached a steady state and got a probability. The protein pairs are selected as reliable PPI whereby the corresponding fingerprints probability from random walk algorithm. Finally, we expanded clusters starting from seeded vertexes based on the new weighted reliable PPINs for identifying protein complexes. Figure For a given organism, the proposed protein complex identification approach contains two steps. The first step is to reconstruct a high quality PPI network by removing spurious interactions and recover missing ones. The second step is to expand clusters starting from seeded vertexes based on the new weighted reliable PPINs for identifying protein complexes. Here, we first describe Multi-Level PPINs reconstruction approach for getting reliable PPI and then present the detailed protein complexes identification method on the new reliable PPINs.n features gathered from m sources (Level 2). Consequently, protein pairs represented by same vector were mapped to same fingerprint ID. A fingerprint similarity network is constructed, in which a vertex represents a protein pair fingerprint ID and each vertex is connected to its top t similar fingerprints by edges (Level 3). Then we performed a random walk with restart algorithm on this fingerprints similar network. Some fingerprints of reliable protein interactions are given prior probabilities 1. At the end of the iterations, every fingerprint reached a steady state and got a probability. The steady state probability of each fingerprint is the probability of corresponding protein pairs to be a reliable PPI. The top ranked protein pairs are selected as reliable PPI. The details are described below.Existing PPI datasets are transferred to a protein pair fingerprint similarity network for getting reliable PPI Figure . We firsFollowing our previous method .GO  for each protein pair.The corresponding genes of the proteins in a protein complex are expected to be coexpressed   for a protein pair was set as the sum of the confidence scores of all possible DDI pairs between them.A protein domain is a conserved part of a given protein sequence and structure that can evolve, function and exist independently of the rest of the protein chain. Many proteins consist of several structural domains. Domains often suggest the propensity for the proteins to interact or form a functional unit, such as protein complex. So we used one feature to capture Domain-Domain interaction (DDI) information for a protein pair. The domains (Pfam) of yeast proteins were downloaded from UniProtKB  to capture STRING-predicted evidence of PPI information.STRING  and extended (TAP2) data    .Not every interaction pair is presented in accurated PPI networks. We used two types of evidence to capture existing PPI network information. Type I is the direct information from existing PPI data. If one pair is recorded in one exiting PPI data, its EPPI value is equal to 1, otherwise, the value was 0. We downloaded yeast protein interaction data from DIP . MFpi, BPpi, CCpi are boolean vectors and the others are numeric vectors.As described above, each protein pair Fsim =  is constructed, in which a vertex v in vertex set Vsim represents a fingerprint fi and an edge  in edge set Esim represents a connection between two distinct fingerprints fi and fj. To construct Fsim, we define the fingerprints pairwise similarity matrix Mij between any two fingerprints fi and fj as follows:dist is the Euclidean distance. A high value in Mij indicates that the two fingerprints fi and fj share the similar PPI evidences and thus likely belong to same category (PPI or non-PPI). For each fingerprint fi ∈ Vsim, we connect it with another fingerprint if their similarities are among top T similar ones to fingerprint fi.A PPI network is constructed from existing PPI knowledge by considering individual proteins as nodes and the existence of a physical interaction between a pair of proteins as a link. Based on the nodes in these existing PPI networks, full combinations of every two nodes are generated. These generated protein pairs are represented by the vectors as described above. For reducing computational complexity, the protein pairs with same vector are mapped to the same fingerprint ID. So each fingerprint represents a set of protein pairs and it is also represented by the corresponding vector. Then a fingerprint similarity network Fsim = , we can then perform a random walk with restart algorithm to detect the likely reliable PPI fingerprints and unreliable PPI fingerprints as below.With the above resulting protein pair fingerprints similarity network R0 and U0 denote the prior probability vector of the reliable and unknown fingerprints, respectively. In R0, the prior probabilities of reliable fingerprints are assigned an equal probability +1. This is equivalent to letting the random walk begin from each of reliable PPI fingerprints with equal probability. In U0, the prior probabilities of unknown fingerprints are assigned 0 and their posterior probabilities will be decided in step 2. We represent the overall prior probability vector for the fingerprints similarity network as We first initialize the prior probabilities of fingerprints. The fingerprint is considered as reliable PPI fingerprint if it is from at least two accurated PPI database and above half PPI evidence components are non-zero. The other fingerprints are considered as unknown fingerprints. Let Fsim. The prior influence flows of reliable fingerprints are distributed to their neighbors, which continue to spread the influence flows to other nodes iteratively. Here, we used a variant of the random walk in which we additionally allow the restart of the walk in every step at one node with probability. Formally, the random walk with restart is defined as:F0 is the initial probability vector, Fr is the probability vector at step r, F1 = F0, Mij is row-normalized adjacency matrix of the graph. In this work we set parameter to 0.8, as recommend in Li and Patra . When Dif fell below 10−6, a steady stage has been reached and the iterative process is terminated.After initialing the prior probabilities for reliable and unknown examples above, we score all the remaining unknown fingerprints in the network by transmission. We propose to do flow propagation for this and adopt the Random Walk algorithm . Here, we also pick the highest weighted vertexes as the seeds. Our procedure proceeds as follows. We pick the first vertex in the queue Sq and use it as a seed to grow a new cluster. Once the cluster is completed, all vertexes in the cluster are removed from the queue Sq and we pick the first vertex remaining in the queue Sq as the seed for the next cluster.Motivated by previous methods :Evk ≥ Tin; andThe(SP(K + v) ≤ d)Let v is satisfied the conditions, can it be added to the cluster. Once the new vertex v is added to the cluster, the cluster is updated.Only when the candidate vertex http://wodaklab.org/cyc2008/) with a total of 408 manually accurated heteromeric protein complexes. After filtering out complexes composed of a single or a pair of proteins, the final benchmark set contains a total of 231 protein complexes.We downloaded 7,018 yeast proteins from the Saccharomyces Genome Database  between a predicted cluster p ∈ P and a real complex b ∈ B, where P is the set of predicted complexes by a computational method and B is the set of positive ones in the benchmark.We applied three approaches  ≥ ω, a real complex and a predicted complex are considered to be matching  , |Ncp is the number of predicted complexes that match at least one real complex, and Ncb is the number of real complexes that match at least one predicted complex  with respect to given functional annotation can be computed by the following hypergeometric distribution in Equation (9)  and more than half PPI evidence components are non-zero. After random walking on the fingerprints similarity network, each fingerprint has a posterior probability.From the Saccharomyces Genome Database  for protein complex detection. Our method does not use the negative data, but only utilize the noisy existed database and incorporate more PPI evidences to reconstruct higher quality network. We mapped existing noisy data to multi-level networks and used the new level fingerprints similarity network to get high quality PPIs. Then we expanded the clusters from seed vertexes based on the reconstructed PPINs. The evaluation of our method indicates that our method achieved a higher BX conceived the study, participated in its design, carried out all experiments, and drafted the manuscript. YL drafted the manuscript. CL, JD, XL, and ZH reviewed the manuscript. CL conceived the study, participated in its design and coordination, and helped draft the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Background: Auditory brainstem response (ABR) testing is an invasive electrophysiological auditory function test. Its waveforms and threshold can reflect auditory functional changes in the auditory centers in the brainstem and are widely used in the clinic to diagnose dysfunction in hearing. However, identifying its waveforms and threshold is mainly dependent on manual recognition by experimental persons, which could be primarily influenced by individual experiences. This is also a heavy job in clinical practice.Methods: In this work, human ABR was recorded. First, binarization is created to mark 1,024 sampling points accordingly. The selected characteristic area of ABR data is 0–8 ms. The marking area is enlarged to expand feature information and reduce marking error. Second, a bidirectional long short-term memory (BiLSTM) network structure is established to improve relevance of sampling points, and an ABR sampling point classifier is obtained by training. Finally, mark points are obtained through thresholding.Results: The specific structure, related parameters, recognition effect, and noise resistance of the network were explored in 614 sets of ABR clinical data. The results show that the average detection time for each data was 0.05 s, and recognition accuracy reached 92.91%.Discussion: The study proposed an automatic recognition of ABR waveforms by using the BiLSTM-based machine learning technique. The results demonstrated that the proposed methods could reduce recording time and help doctors in making diagnosis, suggesting that the proposed method has the potential to be used in the clinic in the future. Auditory brainstem response (ABR) is a global neural activity in the auditory brainstem centers evoked by acoustic stimulations. It can observe the functional status of the auditory nerve and lower auditory center and reflect the conduction ability of the brainstem auditory pathway , 2. GiveIn clinical diagnosis, the minimum stimulation intensity of wave V is usually used as ABR threshold. Sometimes, when wave III is greater than wave V, the ABR threshold is judged by stimulation intensity of wave III . In deteThe application of computer technology in assisting medical diagnosis can effectively reduce errors caused by repetitive work and complex waveform characteristics. This research direction has been important for ABR consultation for a long time . For exaThus, automatic recognition of ABR waveforms through computer-assisted methods can assist clinicians and audiologists in ABR interpretation effectively. It also reduces the errors caused by subjective factors, the interference of complex waveforms, and the burden of a large number of repetitive tasks for the medical staff. This study proposes a method of using the long short-term memory (LSTM) network to identify waves I, III, and V in the ABR waveform and proposes a new idea for the recognition of ABR characteristic waveforms by neural networks. The structure of the study is organized as follows: The experimental data and the detailed description of the proposed method are presented in the Materials and Methods section. The Results section presents the experimental design and the corresponding results. Finally, the Discussion section provides an elaboration of the findings of this work.The data are provided by the Department of Otolaryngology Head and Neck Surgery, Chinese PLA General Hospital. The SmartEP evoked potential test system developed by the American Smart Listening Company is used for measurement and acquisition. A and B were generated as the classification train and label, respectively. A represents the potential of the input ABR data. The position of the serial number corresponds to the position of the ABR data sampling point. B represents nonfeature (0) and feature points (1), respectively. Thus, according to the position of the label value of the label data, the data that corresponded to the position of the label matrix was changed to 1 to meet the binary classification requirements of all sampling points. However, noise created by myogenic potential is observed in some experimental data  before and after the original marking point were marked as the characteristic area, which expands the marking range of the characteristic waveform.Ct−1 value passed from the previous time step, add new information into the memory cell V, and predict and output the information transmitted by the memory cell and continue to pass it to the next time step.LSTM is a recurrent neural network and mainly improved on the basis of the time step unit by adding the output of memory cells to carry information that needs to be transmitted for a long time. Three gate structures are also added. These gate structures are used to select the retention of the memory cell ht−1 is the hidden state value passed at the previous time step; and Wf, …, and bf are the corresponding weights and biases. The activation function usually uses the sigmoid function to map the activation value between . To control the proportion of information updated into the memory cell, the sigmoid activation function was first applied to obtain the output ii. Then, the tanh activation function is applied to obtain, and the product of the two is used as the information to update the memory cell. it and at are calculated as follows:Wi, Ui, bi, Wa, Ua, and ba are the weights and biases. Finally, the memory cell Ct is calculated to the next time step by using Equation (5):where where ⊙ is the Hadamard product, which indicates that the corresponding positions of the matrix are multiplied. The right side refers to the output gate, and the output of the output gate is calculated by using Equation (6):Wo, Uo, and bo are the weights and offsets. Finally, the output value ht at the time step is obtained through using Equation (7):where The predicted output weight and bias are applied to activate the output value to obtain the predicted value, as shown in Equation (8):Finally, the loss values In this work, BiLSTM is established as the network structure to enable the input sequence to have a bidirectional connection with one another . Figure In the traditional mode, wavelet transform is a commonly used method in ABR extraction and recognition research . In ABR f(t) is the signal in the time domain, and the part of a,τ(t). Two variables, namely, scale a and translation τ, are available. Scale a is applied to control the expansion and contraction of the wavelet function, and the translation amount τ controls the translation of the wavelet function. Scale a is inversely proportional to its equivalent frequency, which is defined as φ(t). The complete wavelet expansion is as follows:where c and d are the coefficients of the corresponding function, j is the frequency domain parameter that determines the frequency characteristics of the wavelet, and k is the time domain parameter that controls the position of the wavelet base in the time domain. Although the scale and wavelet functions are complex and have different characteristics, the process of wavelet decomposition can be regarded as using a low-pass filter and a high-pass filter to decompose the signal by frequency. The low-frequency components decomposed in each layer are called approximate components, and the high-frequency components are called detailed components. Thus, approximate components and detailed components were applied to the reconstructed waveform.where In this study, three sets of experiments, namely, (1) comparison between various network structures, (2) comparison experiment of wavelet transform, and (3) comparison experiment of different hidden layer nodes, were designed. In the comparison experiment of multiple network structures, seven network structures, namely, (1) single-layer LSTM, (2) double-layer LSTM, (3) single-layer BiLSTM, (4) double-layer BiLSTM, (5) three-layer BiLSTM, (6) four-layer BiLSTM, and (7) five-layer BiLSTM network layers, were selected. In the comparative experiment of different hidden layer nodes, a three-layer bidirectional LSTM network was used for training, and different numbers of hidden neurons were applied. The experiment applied four groups of different numbers of hidden neurons, namely, 64, 128, 256, and 512.K = 9), and the test was performed to obtain the average value.In the comparative experiment of the wavelet transform, all data added noise as interference. Seven different network structures were used for testing. For instance, the training data preprocessed by wavelet transform were used as the experimental group, and the training data trained using the original data were used as the control group. In this experiment, ABR data were decomposed in six layers, and the approximate and detailed components of the sixth layer and the fourth, fifth, and sixth layers were retained to reconstruct the waveform, respectively. The parameter configuration is consistent. The network was trained with five K-fold cross-validation  rate is calculated using rp/pn, as shown in Equation (13):Generally, an error scale of 0.2 ms is applied as a scale range of clinically marked points. Three criterion values for the maximum allowable error value (ME) were tested: −0.1, 0.15, and 0.2 ms. The prediction result was deemed acceptable if the prediction point and the manually identified point were within the ME criteria range. According to the number of correct prediction points In this study, three error scales (ME) of 0.1, 0.15, and 0.2 ms were calculated, respectively, to further explore the recognition accuracy and other related laws. Loss value of training results with different network structures and the ACC under different error scales are revealed in When testing the ACC of the wavelet transform, ABR data was decomposed in six layers. Also, approximate components of the sixth layer and detailed components of the fourth, fifth, and sixth layers were retained to reconstruct the waveform. Recognition ACC values of preprocessing in the LSTM network using wavelet transform are slightly higher than those of the control group. However, they are not as good as those in the control group in the BiLSTM network. Especially, the highest ACC difference reaches 6.46% when calculated with a 0.1-ms error scale. Also, the difference reduces to <3% when calculated with a 0.2-ms error scale. Results indicate that wavelet transform preprocessing does not obtain a higher ACC by smoothing curves. Due to wavelet decomposition and reconstruction, a slight deviation was created in the position of wave crest. Some information was destroyed in the ABR waveform; therefore, the results of training and recognition were affected. This means that the BiLSTM network has noise immunity and can handle low-quality ABR data.Based on the above results, the three-layer BiLSTM network is a better choice. The ACC results with different hidden node numbers were discussed in this work . Figure Furthermore, this work mainly discusses the characteristic wave recognition process of a click ABR with a 96-dB nHL stimulus. Also, only parameters such as latency and wave interval can be obtained. In clinical applications, many indicators can still be used as a diagnostic basis, such as the relationship between potential values of different stimulus sizes, response and disappearance of wave V, and change of interwave latency of each characteristic wave. This also provides a new idea for the subsequent computer-assisted ABR diagnosis and treatment.This work proposes an automatic recognition method for ABR characteristic waveforms using the BiLSTM network. The main purpose is to identify positions of characteristic waves I, III, and V, which assist the medical staff in obtaining relevant clinical test parameters, such as interwave latency and wave interval. A data quantification process is designed to analyze the characteristic waveform of ABR, including selection area of potential signal and expansion of label position. An optimal network model structure is obtained through multiple sets of comparative experiments. In 614 sets of clinically collected ABR waveform experiments, the network's overall recognition of characteristic waves showed an ACC of 92.91%.Experimental results express that the method proposes a new idea for the identification of ABR characteristic waveforms, and helps professionals to obtain interwave latency parameters in ABR waveforms. Therefore, a computer automatic identification method can obtain deeper information, avoid subjective judgment error by the medical staff in the manual identification process effectively, reduce the number of repeated stimulations during a test, and also avoid vision fatigue of the tested person. Because of noise immunity of the proposed network model, it can effectively reduce repetitive detection of patients. In the process of large-scale identification, the average time of each data by using the method only takes approximately 0.05 s, which is much faster than the speed of manual identification. Thus, it has great advantages in repeatable work.Some efforts have been proposed to analyze ABR waveforms using deep learning methods. For example, Fallata and Dajani  proposedThe original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.The studies involving human participants were reviewed and approved by The ethic committee of the PLA General Hospital. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.CC and LZ: conceptualization and writing—original draft preparation. CC: methodology. XP: software and data curation. HQ, FX, and WS: validation. MS: formal analysis. FJ: investigation. QW: resources. RX and NY: writing—review and editing. LZ: visualization. NY: supervision. ZW and XG: project administration. RX: funding acquisition. All authors have read and agreed to the published version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Microorganisms rely on protein interactions to transmit signals, react to stimuli, and grow. One of the best ways to understand these protein interactions is through structural characterization. However, in the past, structural knowledge was limited to stable, high-affinity complexes that could be crystallized. Recent developments in structural biology have revolutionized how protein interactions are characterized. The combination of multiple techniques, known as integrative structural biology, has provided insight into how large protein complexes interact in their native environment. In this mini-review, we describe the past, present, and potential future of integrative structural biology as a tool for characterizing protein interactions in their cellular context. Most of these processes are the result of low-affinity interactions between cellular components, e.g. transient protein interactions, termed quinary interactions in vitro. Finally, we will discuss how a combination of techniques and recent advances in cryogenic electron microscopy can enable the characterization of quinary interactions in situ. ISB is a catch-all term that generally indicates the combination of a classical structural biology technique and any other technique that gives structural information to form a more complete, higher resolution picture than can be created with classical structural biology alone 2One of the most common ways to define protein interactions is to first determine the structures of individual components of the complex. Using these pieces, interactions can then be modeled and built into larger structures. In this mini-review, we refer to this approach as bottom-up ISB. In this section, we describe five common approaches to individual protein structure determination, starting with more traditional methods such as X-ray crystallography, SAXS, and NMR and finishing with methods that have become feasible in the past decade such as cryo-EM and computational modeling.2.1https://www.rcsb.org/stats/summary). Advances in every aspect of the workflow required for X-ray crystallography have had a transformative effect on the time required for and complexity of solving a crystal structure. The limiting step of biological macromolecular crystallography (MX) is crystallization. While robotics and a wealth of know-how have drastically sped up the process, it is still considered a ‘dark art’ – the ability to predict crystallization conditions remains elusive, with the crystallization step itself being the most time and labor intensive in solving a structure. Failure in this single step of macromolecular crystallography is often the main reason to look for other techniques to acquire reliable protein structures.Since the first protein structure – of sperm whale myoglobin – was solved by John Kendrew in the late 1950s using X-ray crystallography https://www.rcsb.org/stats). MR uses known homologous structures to compute predicted phases of the unknown structure by placing them in the same position and orientation in the unit cell of the crystal. For this reason, as the number of available structures grows, so does the use of MR to solve the phase problem. SAD is a form of experimental phasing, that is, the phases of the diffraction pattern are measured, rather than predicted. It relies on a breakdown of Friedel’s law  when the energy of a diffracting X-ray is at the absorption edge of a heavy atom (e.g. 0.9795 Å for Se) within the crystal. For an in-depth review of phasing methods see in situ beamline in which diffraction experiments take place in crystallization plates mounted directly onto the beamline in vacuo (to reduce background diffraction from air) SAD experiments using native sulfur and phosphorous atoms and has been touted in particular for solving membrane protein structures c oxidase Advances over the last couple of decades have resulted in a current state of technology once a protein has been crystallized and demonstrated to diffract X-rays, a dataset can be collected in seconds due to high brilliance synchrotron beamlines 2.2https://www.rcsb.org/stats). Atomic nuclei with an odd mass number have a quantum mechanical property known as spin that can interact with external magnetic fields and is the basis for NMR 13C or 15N isotopes as one or more of the extra dimensions in addition to 1H 1H-15N heteronuclear single quantum coherence (HSQC) is typically the first NMR experiment performed on a protein and is used to assign peaks to all N atoms with a bonded hydrogen and used for fingerprinting and evaluating whether the expected number of peaks are present, which can indicate the homogeneity and monodispersity of the sample 15N labelling has long been used to study backbone dynamics, and has been supplemented by development of pulse sequences for 1H and 13C for studying the movements of amino acid side chains. Such dynamics can be studied on the timescale of picoseconds to milliseconds depending on the methods used NMR is the second most commonly used technique for determining protein structures after MX, with 7.7% of all structures deposited in the PDB (2.3g), maximum dimension (Dmax), and molecular weight. A model can be built using dummy atoms confined by this experimentally determined Rg value, and this model is refined until the calculated scattering pattern of the model matches the observed diffraction pattern, giving an ‘envelope’ of the molecule in solution  measures the scattering of radiation by biological molecules. Unlike MX, it derives low resolution (about 10 Å) structural information from disordered molecules in an aqueous environment. This means that SAS can be employed to interrogate the innate dynamic nature of biological macromolecules that is not captured by MX (and for SAXS without lengthy sample preparation). In the case of SAXS, this is achieved by measuring X-rays scattered at low angles (0.1–10°). Because the signal from the scattered X-rays is so weak and the angle so small, SAXS requires high brilliance, collimated radiation, and long sample-to-detector distances. Hence, beamlines are almost always situated at synchrotrons. Since the scattering pattern obtained is from a molecule that is constantly tumbling in solution, it is continuous and radially symmetric, and gives information on the radius of gyration (Rsolution C. As thi2O and D2O in varying ratios) and a specific component of the sample (termed contrast matching) allows the removal of that component from the scattering pattern Small angle neutron scattering (SANS) operates in a similar manner to SAXS, with the main difference arising from the way in which the radiation used to analyze the sample interacts with nuclei. While X-ray scattering intensity by electrons is in correlation with the number of electrons (and by extension the atomic number of the elements within the sample), the interaction of neutrons with atomic nuclei is dependent on the nucleus type and is not correlated with atomic number of the element. This has two consequences relevant to biological SANS. First, common biological elements such as carbon, nitrogen, and oxygen are similarly visible to neutron scattering as heavy atoms. Second, and more importantly, scattering  varies depending on the isotope present. For isotopes of the above-mentioned biological elements the difference is too small to exploit, but the SLDs for hydrogen (H) and deuterium (D) are drastically different 2.4https://www.emdataresource.org/statistics.html). Cryo-EM has been especially promising for membrane proteins, due to the small amounts of sample needed for a high-resolution structure. Each year, the high-resolution limit of cryo-EM has been pushed closer towards atomic detail, as the equipment and data processing algorithms continue to improve. Currently, the highest resolution structure is the 1.22 Å reconstruction of mouse apoferritin in silico by a variety of programs. For example, cryoDRGN is a neural network used to model reconstructions along a continuous spectrum of protein movement The recent resolution revolution in the field of cryo-EM single particle analysis (cryo-EM SPA) has provided an alternative to X-ray crystallography for large (>100 kDa), flexible molecules While the field of cryo-EM SPA has grown exponentially, it is not a panacea for all structural biology problems. Currently, the rate-limiting step for most SPA cryo-EM is grid preparation. Samples that are too heterogeneous require extra purification or optimization of sample conditions to be analyzed at high-resolution. The blotting in plunge freezing is also a problem due to its high variability, even when working on the same day – resulting in variable ice thickness that limits sample acquisition to only portions of a grid 2.5Despite the ever-increasing throughput of experimental methods for structure determination, structure prediction for new or engineered proteins from primary sequence remains an important and challenging problem. Computational structure prediction methods are most accurate when templates are available for proteins with a high sequence similarity Machine learning (ML) methods have rapidly advanced the prediction quality of template-free methods, as evidenced by the most recent critical assessment of protein structure prediction (CASP 13) Future machine learning methods may address some of these limitations. Models based on neural network architectures from natural language processing have seen increased application in protein structure prediction. Similar to techniques in language translation, these models process sequences of input data to make sequences of predictions. These models implicitly use information from an MSA through unsupervised pretraining, with the benefit of being able to make individualized predictions for each member of a protein family. Recurrent neural networks have shown best-in-class performance for predicting remote homology, an important step in determining a protein’s 3D structure sparse data in protein structure prediction is still not widely considered in machine-learning based approaches Finally, the use of 3Structural determination of protein complexes is often more difficult than that of individual proteins due to the inherent flexibility and low affinity interactions of the subunits. In the previous section, we described techniques to structurally characterize individual proteins and stable complexes. The next step of bottom-up ISB aims to address the problems of subunit flexibility and weak interactions by providing additional biophysical information about the interactions, which can be used to stabilize the complexes for further structural studies. This section will give a brief overview of three main types of protein interaction characterization: 1) identification of interacting partners, 2) modifications to capture complexes, and 3) combinatorial techniques to characterize interactions more accurately. The first two categories are the more traditional approach to protein complex characterization, while the third category is a more modern solution to the determination of transient interactions.3.1Arguably the most important step to characterizing complexes is identifying the components of interest which can be achieved using computational and experimental approaches. There exist many databases of known protein interfaces that can be used to identify protein complex partners 3.2Complexes can be chemically modified to stabilize interactions that would otherwise disrupt stable structures. If a protein interaction has a predicted interface, observed either from molecular docking or experimental confirmation, the complex could be locked into place by engineering a disulfide bridge. There are online servers such as Disulfide by Design 3.3et al. used ISB to fully characterize the S. cerevisiae nuclear pore. They combined XL-MS with fluorescent protein labeling to determine relative localization and stoichiometry of the nucleoporins (NUPs). Then, using prior structural data from crystal structures, SAXS, and integrative modeling, the structures of the NUPs were docked into a low-resolution cryo-electron sub-tomogram averaged map (described in the following section) of the nuclear pore, resulting in a detailed map of the 87 MDa complex Structural characterization of protein complexes is often of lower resolution than that of individual globular proteins, due to flexible subunits and incomplete complex formation. ISB is a means of supplementing traditional structural data with other sources to provide a more complete picture of the protein–protein interactions 4In the previous sections, we described methods used in bottom-up ISB, where protein interaction networks are built from individual structures and biophysical data. We now change our focus to look at more recent advances that can be used for a top-down approach to ISB. In top-down ISB, data from the whole cell are collected, followed by characterizing captured complexes with molecular details. Here, we describe the advantages and disadvantages to three cryo-EM techniques that could be used for top-down ISB.4.1Logically, the most reliable way to characterize transient interactions in the cell would be to avoid disrupting the cell entirely. The best technique to examine these interactions at the nanometer, and potentially sub-nanometer scale, is whole-cell cryo-electron tomography (cryo-ET) A. Perfor4.2To overcome some of the technical limitations of cryo-ET, the Braun group has developed the cryoWriter system in conjunction with the lysis of individual cells B. The ly4.3et al. used this combination of experiments to determine that fatty acid synthetase interacted with another protein in approximately 10% of the particles they observed, which their XL-MS data showed to be a carboxylase Another promising technique that has been utilized to examine transient protein interactions is the combination of EM and XL-MS C whereinOne disadvantage of all the above techniques is that quinary interactions are, by definition, less stable due to their transience. Thus, the structures determined may still be at a lower resolution due to the highly flexible nature of the complexes and lower frequency of observation during computational sorting. Further, cryo-EM of heterogeneous samples and whole-cell cryo-tomography are currently limited to lower resolution due to the complexity of the samples being studied. Strong modelling is necessary to interpret the data gained from all of the above structural techniques. Therefore, it is still necessary to employ structural techniques such as those described in the first and second section to determine high-resolution structures that can be docked into lower resolution maps generated by heterogeneous EM samples. Failing at determining the structure, structural models created computationally, such as those described in the first section, can be fit into low-resolution maps, as described in the second section.5The field of ISB is rapidly approaching the realization of whole-cell structural biology. This is in large part due to recent developments in structural biology, such as the cryo-EM resolution revolution and the increasing accuracy of computational protein structure prediction. When structures from individual proteins are combined with rigorous biochemistry and biophysical experiments, one can begin to build up protein complex structures in a bottom-up ISB approach, whether they are stable or transient . These cThe 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.