Identification of lncRNA-protein interactions helps to understand lncRNA-related activities. Existing computational methods utilize multiple lncRNA features or multiple protein features to predict lncRNA-protein interactions, but features are not available for all lncRNAs or proteins; most of existing methods are not capable of predicting interacting proteins (or lncRNAs) for new lncRNAs (or proteins), which don’t have known interactions. In this paper, we propose the sequence-based feature projection ensemble learning method, “SFPEL-LPI”, to predict lncRNA-protein interactions. First, SFPEL-LPI extracts lncRNA sequence-based features and protein sequence-based features. Second, SFPEL-LPI calculates multiple lncRNA-lncRNA similarities and protein-protein similarities by using lncRNA sequences, protein sequences and known lncRNA-protein interactions. Then, SFPEL-LPI combines multiple similarities and multiple features with a feature projection ensemble learning frame. In computational experiments, SFPEL-LPI accurately predicts lncRNA-protein associations and outperforms other state-of-the-art methods. More importantly, SFPEL-LPI can be applied to new lncRNAs (or proteins). The case studies demonstrate that our method can find out novel lncRNA-protein interactions, which are confirmed by literature. Finally, we construct a user-friendly web server, available at  http://www.bioinfotech.cn/SFPEL-LPI/.LncRNA-protein interactions play important roles in post-transcriptional gene regulation, poly-adenylation, splicing and translation. Identification of lncRNA-protein interactions helps to understand lncRNA-related activities. In this paper, we propose a novel computational method “SFPEL-LPI” to predict lncRNA-protein interactions. SFPEL-LPI makes use of lncRNA sequences, protein sequences and known lncRNA-protein associations to extract features and calculate similarities for lncRNAs and proteins, and then combines them with a feature projection ensemble learning frame. SFPEL-LPI can predict unobserved interactions between lncRNAs and proteins, and also can make predictions for new lncRNAs (or proteins), which have no interactions with any proteins (or lncRNAs). SFPEL-LPI produces high-accuracy performances on the benchmark dataset when evaluated by five-fold cross validation, and outperforms state-of-the-art methods. The case studies demonstrate that SFPEL-LPI can find out novel associations, which are confirmed by literature. To facilitate the lncRNA-protein interaction prediction, we develop a user-friendly web server, available at  PLOS Computational Biology Methods paper.This is a Long noncoding RNAs (lncRNAs) are a class of transcribed RNA molecules with a length of more than 200 nucleotides that do not encode proteins ,2. SinceIn recent years, many computational methods have been proposed to predict lncRNA-protein interactions, in order to screen lncRNA-protein interactions and guide wet experiments. There are two types of computational methods: binary classification methods and semi-supervised learning methods. The binary classification methods take known interacting lncRNA-protein pairs as positive instances and non-interacting pairs as negative instances, and build binary classification-based models. Muppirala et al.  adopted Existing computational methods utilize diverse lncRNA features and protein features, but features are not available for all lncRNAs or proteins, and these methods cannot work when information is unavailable. In addition, many lncRNAs (or proteins) don’t have known interactions with any protein (or lncRNA), and we name them as new lncRNAs (or proteins). Most existing methods are not capable of predicting interacting proteins (or lncRNAs) for new lncRNAs (or proteins).In this paper, we propose the sequence-based feature projection ensemble learning method, “SFPEL-LPI”, to predict lncRNA-protein interactions. First, SFPEL-LPI extracts lncRNA sequence-based features and protein sequence-based features. Second, SFPEL-LPI calculates multiple lncRNA-lncRNA similarities and protein-protein similarities by using lncRNA sequences, protein sequences and known lncRNA-protein interactions. Then, SFPEL-LPI combines multiple similarities and multiple features with a feature projection ensemble learning frame. Computational experiments demonstrate that SFPEL-LPI predicts lncRNA-protein associations accurately and outperforms other state-of-the-art methods. More importantly, SFPEL-LPI can be applied to new lncRNAs (or proteins). The case studies demonstrate that our method can find out novel lncRNA-protein interactions.Several databases facilitate the lncRNA-protein interaction prediction. NPInter database  includess×t interaction matrix Y, where Yij = 1 if the lncRNA Li interacts with the protein Pj, otherwise Yij = 1.Here, we introduce notations about the dataset. Given a set of lncRNAs In this section, we describe two lncRNA features and two protein features, based on lncRNA sequences, protein sequences and known lncRNA-protein interactions. On one hand, a great number of features –36 can bL is defined as:fk is the normalized occurrence frequency of dinucleotide in the RNA sequence L; the parameter τ is an integer, representing the highest counted rank of the correlation along an RNA sequence; w is the weight factor ranging from 0 to 1; θj is the j-tier correlation factor reflecting the sequence-order correlation between all the j-th most contiguous dinucleotides along an RNA sequence. We obtain PseDNC feature vectors of lncRNAs by using the python package "repDNA”, and more details about PseDNC are described in  is a weight vector which is introduced to control the contribution of different graph Laplacian regularizations, and tr(∙) is the trace of a matrix. η>1 is the exponent of θ, which ensures that all graph Laplacian regularizations contribute effectively for the maintaining of graph local structures.Local structure of data can be maintained effectively through constructing a weighted graph or a similarity graph on a scatter of data points. For example, Xu et al.  introducR, Gi and θi, and obtain the update rules about R, θi and Gi , and the division in (A asBy combining  and 5),,5), we o≥0,∑iθi=1We intro+μ(RTXi)+where e R, Gi and θi based on  and andR, Gibased on  alternatRl could be obtained. Similarly, using protein’s features, similarities and protein-lncRNA interactions, the prediction matrix Rp could be calculated. Then, SFPEL-LPI integrates the predictions based on lncRNAs and proteins as M = (Rl+(Rp)T)/2. Therefore, the unobserved interactions are scored in the corresponding entries of M. Algorithm 1 describes how SFPEL-LPI predicts unobserved associations between known lncRNAs and known proteins.Following the method proposed in the Section “Objective function”, SFPEL-LPI can predict unobserved interactions between known lncRNAs and proteins. First, based on the lncRNA’s features, similarities and lncRNA-protein interactions, the prediction matrix Glu, Glv, θlu and θlv could be obtained. Then, we can use the features of new lncRNAs (or proteins) and the trained parameters to predict their predictions. Algorithm 2 describes how SFPEL-LPI finishes this task.In addition, SFPEL-LPI could also be applied to predict proteins (or lncRNAs) interacting with new lncRNAs (or proteins). After using Algorithm 1 to train the model, the projection matrix and the weighting parameters of lncRNA’s features as well as protein’s features: Algorithm 1: Predicting unobserved associations between known lncRNAs and known proteins by SFPEL-LPI.Input: observed lncRNA-protein interaction matrix, Yl; observed protein-lncRNA interaction matrix, Yp = YlT; lncRNA feature matrices, {Xl1,Xl2,…,Xln}; protein feature matrices, {Xp1,Xp2,…,Xpn}; lncRNA normalized similarity matrices, {Wl1,Wl2,…,Wlm}; protein normalized similarity matrices, {Wp1,Wp2,…,Wpm}; regularization parameter, μ>0,λ>0; exponent parameter, η>1;Output: lncRNA-protein interaction prediction matrix, M; predicted lncRNA-protein interaction matrix, Rl; predicted protein-lncRNA interaction matrix, Rp; projection matrices of lncRNA features {Gl1,Gl2,…,Gln}; projection matrices of protein features {Gp1,Gp2,…,Gpn}; weighting parameters of lncRNA similarity matrices, {θl1,θl2,…,θlm}; weighting parameters of protein similarity matrices, {θp1,θp2,…,θpm};Initialize:for each i(1≤i≤n)Gli, Gpi with random values on interval ;    initialize end forfor each i(1≤i≤m)θli, θpi as 1/m;    initialize end forrepeatRl via (    update Rl via  with fixfor each i(1≤i≤n)    Gli via     θli via     Gpi via     θpi via T)/2ReturnMAlgorithm 2: Predicting interacting proteins (or lncRNAs) for new lncRNAs (or proteins) by SFPEL-LPIInput: feature matrices for new lncRNAs, {Xl1,Xl2,…,Xlu} ; projection matrices of lncRNA features {Gl1,Gl2,…,Glu} ; weighting parameters of lncRNA similarity matrices, {θ11,θl2,…,θlu} ; . In predicting unobserved lncRNA-protein interactions, all known lncRNA-protein interactions are randomly split into five subsets with equal size. Each time, four subsets are combined as training set and the remaining one subset is used as the testing set. In predicting proteins interacting with new lncRNAs, all known lncRNAs are split into five subsets with equal size. The model is constructed based on the lncRNAs in training set and their interactions with all proteins, and then is used to predict proteins interacting with testing lncRNAs. Similarly, we evaluate the performances of models in predicting lncRNAs interacting with new proteins. Hence, we introduce notations for above mentioned cross validation settings. The area under ROC curve (AUC) and the area under precision-recall curve (AUPR) are popular metrics for evaluating prediction models. Since known lncRNA-protein interactions are much less than non-interacting lncRNA-protein pairs, we adopt AUPR as the primary metric, which punishes false positive more in the evaluation process,69. Moreμ, λ and η. μ is a parameter for the error between projected interactions and predicted lncRNA-protein interactions; λ controls the contribution of projection matrix; η describes strength of different similarity measures.SFPEL-LPI has three parameters: μ∈{10−4,10−3,10−2,10−1,100,101,102,103}, μ∈{10−4,10−3,10−2,10−1,100,101,102,103} and η∈{21,22,23,24,25,26,27,28}. We build SFPEL-LPI models by using different parameters, and implement five-fold cross validation CVlp to evaluate SFPEL-LPI models. SFPEL-LPI produces the best AUPR score of 0.473 when μ = 10−3, λ = 10−4 and η = 22. Then, we fix the parameter η = 22, and evaluate the influence of μ and λ. As shown in μ greatly influences the performance of SFPEL-LPI, and a smaller value for μ is likely to produce better result. Further, we fix the parameters μ = 10−3 and λ = 10−4 and test the influence of η. As illustrated in η increases, and then remain unchanged after a threshold.To test influence of parameters, we consider all combinations of parameters η is the index of similarity weights, and could control the relative contributions of different similarities. When fixing μ = 10−3 and λ = 10−4, we analyze the relation between η and lncRNA similarity measures θlncRNA (or protein similarity measures θprotein). As shown in η, different similarities are likely to make equal contributions.The parameter μ = 10−3, λ = 10−4 and η= 22 for SFPEL-LPI in the following studies.Based on above discussion, we adopt CVlp, CVl and CVp.SFPEL-LPI can predict unobserved lncRNA-protein interactions between known lncRNAs and known proteins, and also can make predictions for new lncRNAs (or proteins). For different tasks, we adopt different evaluation schemes to split instances and implement five-fold cross validation under settings: lp, CVl and CVp. According to previous studies [l or CVp. A new matric “recall @ top-ranked k %” is defined as the fraction of true interacting proteins (or lncRNAs) that are retrieved in the list of top-ranked k% predictions for a lncRNA (or protein). In  studies –72, a prTo further test capability of SFPEL-LPI for new proteins, we randomly select ten proteins to conduct experiments. In each experiment, a protein is used as the testing protein, and the model is constructed based on other proteins, all lncRNAs and their associations, and then predict lncRNAs interacting with the testing protein. AUC scores and AUPR scores are calculated based on the results for each protein. As shown in Several state-of-the-art computational methods have been proposed to predict lncRNA-protein interactions. Here, we adopt RWR, LPBNI1, KATZLGOCVlp. As shown in First, we respectively build different prediction models based on the benchmark dataset. The benchmark methods were designed to predict unobserved interaction between know lncRNAs and know proteins. Therefore, we implement these methods and mainly evaluate their performances in predicting unobserved interactions under We conduct 20 runs of five-fold cross validation to evaluate methods, and take the paired t-test to analyze difference between SFPEL-LPI and benchmark methods. The computational complexity is important for a computational method. To test the efficiency of SFPEL-LPI, we repeat 5-fold cross validation 20 times and compare running time of different methods on a PC with an Intel i7 7700k CPU and 16GB RAM. SFPEL-LPI costs the reasonable running time (29.42s) when compared with RWR (25.83s), LPBNI (4.01s), KATZLGO (4.36s), LPI-ETSLP (4.56s) and LPLNP (1337.64s).Further, we randomly perturb all known lncRNA-protein interactions to test the robustness of prediction methods. To be more specific, we randomly remove 5% of known lncRNA-protein interactions and add the same number of inexistent interactions, and then compile the perturbed dataset. We build different prediction models based on the perturbed dataset and evaluate their performances. Clearly, data perturbation brings noise, and decreases the performances of prediction models. As displayed in Here, we conduct independent experiments to evaluate the practical ability of SFPEL-LPI. As described in Section “Dataset”, NPInter v2.0 dataset was compiled from the V2.0 edition of NPInter database. NPInter database has been updated to V3.0 edition, and contains newly discovered lncRNA-protein interactions. Therefore, we train the prediction model based on the NPInter v2.0 dataset and predict new lncRNA-protein interactions, and then check up on predictions in the NPInter database. http://www.bioinfotech.cn/SFPEL-LPI/. Users can input lncRNA sequences (or protein sequences) or upload a text file with FASTA-formatted lncRNA sequences (or protein sequences) for prediction, and freely download the results and visualize the predicted lncRNA-protein interactions. Moreover, gene ontology (GO) terms of proteins are annotated for indicating lncRNAs’ functions.We develop a web server based on SFPEL-LPI to facilitate the lncRNA-protein interaction prediction, available at Moreover, the server can predict interacting lncRNAs for proteins. For example, top 20 interacting lncRNAs of the protein “9606.ENSP00000240185” are shown in the This paper presents a novel lncRNA-protein interaction prediction method, namely sequence-based feature projection ensemble learning (SFPEL-LPI). The novelty of SFPEL-LPI comes from integrating sequence-derived features and similarities with a feature projection ensemble learning frame. Specifically, SFPEL-LPI only utilizes lncRNA sequences, protein sequences and known interactions to extract features, and calculates lncRNA-lncRNA similarities and protein-protein similarities. Since sequences are usually available for lncRNAs or proteins, SFPEL-LPI can make predictions for almost all lncRNA-protein pairs. Moreover, diverse information leads to the good performances of SFPEL-LPI.CVlp, CVl and CVp, compared with state-of-the-art lncRNA-protein interaction prediction methods. The promising results validate efficacy of the proposed algorithm for predicting lncRNA-protein interactions, especially for the new lncRNAs or new proteins, which do not have known interactions. SFPEL-LPI outperforms five methods: RWR, LPBNI, KATZLGO, LPI-ETSLP, LPLNP, and makes 100.4%, 43.3%, 65.4%, 46.9%, 3.1% improvements in terms of AUPR scores. Further, we also analyze the running time of SFPEL-LPI and benchmark methods, and randomly perturb all known lncRNA-protein interactions to test the robustness of prediction methods. A web server is constructed to predict interacting proteins/lncRNAs for given lncRNAs/proteins. We adopt the lncRNA “NONHSAT041930” as an example to predict interacting proteins, and can find evidences to confirm novel lncRNA-protein interactions.To evaluate the performance of SFPEL-LPI, an extensive set of experiments were performed on the benchmark dataset under three CV setting: However, SFPEL-LPI still has several limitations. It has three parameters, and parameter tuning is time-consuming. In addition, known lncRNA-protein interactions are limited, and performances of SFPEL-LPI will be improved if more interactions are known.S1 File(PDF)Click here for additional data file.S2 File(MAT)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.


Candida albicans has not been studied extensively at the level of protein-protein interactions (PPIs) and data on PPIs are not readily available in online databases. In January 2018, the database called “Biological General Repository for Interaction Datasets (BioGRID)” that contains the most PPIs for C. albicans, only documented 188 physical or direct PPIs (release 3.4.156) while several more can be found in the literature. Other databases such as the String database, the Molecular INTeraction Database (MINT), and the Database for Interacting Proteins (DIP) database contain even fewer interactions or do not even include C. albicans as a searchable term. Because of the non-canonical codon usage of C. albicans where CUG is translated as serine rather than leucine, it is often problematic to use the yeast two-hybrid system in Saccharomyces cerevisiae to study C. albicans PPIs. However, studying PPIs is crucial to gain a thorough understanding of the function of proteins, biological processes and pathways. PPIs can also be potential drug targets. To aid in creating PPI networks and updating the BioGRID, we performed an exhaustive literature search in order to provide, in an accessible format, a more extensive list of known PPIs in C. albicans.Despite being one of the most important human fungal pathogens,  Cryptococcus, Aspergillus, Pneumocystis, and Candida .One to five million fungal species are estimated to exist of which only 400–600 (< 0.1%) are documented to be pathogenic to humans and of those only about 100 are commonly found as human pathogens . However Candida . Specififections  and in tfections . Within  species  but CandCandida albicans is a pleiomorphic diploid fungus generally only found in humans. Up to 70% of humans are hosts of this fungus  and has a non-canonical codon usage (CUG translated as serine rather than leucine), in addition, it lacks stable episomal plasmids and has a low transformation efficiency  system was developed to overcome the problem with the non-canonical codon usage and the first small-scale high-throughput screen was performed with this system .One important feature to study in order to understand an organism is its protein-protein interactions (PPIs) . Advanceechnique . A possiechnique  or work echnique  to minimrevisiae . In one revisiae . An adaps system . The TAPC. albicans to study protein-DNA interactions, leading to several networks  database does not even mention C. albicans  also shows that PPI data are not generally sent to databases such as the BioGRID. A remarkable example is that the BioGRID database, release 3.4.156, mentioned only one interaction that was found with crystallography  studies have already been done on networks  and evenalbicans . Howeverresearch  only a salbicans . Severalalbicans  and the albicans . Anotheralbicans . The Intalbicans . The Bioalbicans . Becauselography  while selography  and mentC. albicans PPI data we performed an exhaustive literature search using several keywords    or certaactions) , then itmmunity2 , 2018 do BioGRID . The comterature . This dirganisms  and/or tThe knowledge of PPIs is important for fully unraveling the complexity of organisms . BesidesC. albicans. The majority of them were found using affinity-capture techniques or by using the traditional Y2H technique as described below. The most important techniques used to study PPIs in C. albicans are described below.Several techniques have been used to detect the interactions described for C. albicans  is a type of affinity purification technique that is referred to as “affinity-western” in the BioGRID database  protease site to provide the two-step affinity purification  on medium supplemented with heavy isotope-labeled arginine and lysine (heavy medium). In parallel, a wild-type strain was grown on light medium. After mixing the bait and wild-type strain in a 1:1 ratio, the cells are lysed and the bait was pulled down followed by SDS-page and trypsin digestion. In the subsequent MS analysis, Cdc14-specific interacting proteins showed a heavy-to-light (H:L) ratio greater than 1:1 because specific interacting proteins will originate from the heavy medium, whereas non-specifically bound proteins will have a 1:1 ratio. A total of 126 interacting proteins for Cdc14 could be detected. Remarkably, only a few of the found interactions have also been demonstrated for the orthologous proteins in S. cerevisiae  has several advantages: (1) it can be used in a high-throughput setup, (2) there is no forced cell localization , and (3) post-translational modifications can be preserved. The SILAC technique is also quantitative and not qualitative as is the traditional TAP-tag approach. The samples in SILAC are also analyzed as a whole single sample, thus minimizing the bias in sample handling .Bimolecular Fluorescence Complementation (BiFC) is a technique that can be used to study PPIs in vivo in their native environment and location , another potential explanation for the difficulties with adapting this system is the autofluorescence of C. albicans  or Bioluminescence Resonance Energy Transfer (BRET) system, however, BRET has so far not been reported in C. albicans  assay was developed for use in C. albicans to circumvent the codon usage problem when using the model organism S. cerevisiae. VCI can be used to detect binary interactions. The technique is based on the targeting to endocytic vesicles by the Endosomal Sorting Complex Required for Transport (ESCRT) of which Snf7 (Vps32) is a subunit. The technique uses a vps4Δ mutant strain that promotes vesicular accumulation of Snf7. A bait protein is fused to the ESCRT subunit Snf7 while the prey protein is fused to a GFP protein. When bait and prey interact, a punctate GFP signal can be detected compared with a diffuse signal if no interaction occurs. The technique uses the native promoters so overexpression is avoided and real-time imaging facilitates the detection of transient interactions. The system was, so far as we know, only used in two studies from the lab that developed it , in a “bait” protein to covalently capture the binding partner (prey) after UV-activation. To incorporate AzF into the protein of interest, an amber stop codon needs to be introduced into this protein in a C. albicans strain that expresses the optimized orthogonal tRNA and tRNA synthetase for AzF. It is at the amber stop codon where AzF, provided in the medium, is incorporated (rather than terminating the translation). So far, only two interactions, TUP1 and TSA1 have been studied using this technique  system has become, since the first publication, one of the most used systems to detect PPIs in vivo  system is a special adaptation of the Y2H system  it is not always a good idea to extrapolate data from S. cerevisiae to C. albicans. While many pathways perform similar cellular functions, differences are also present, making it harder to simply extrapolate data from S. cerevisiae to construct pathways in C. albicans . Snf6 was however, identified as a member of the SWI/SNF complex and it was shown that the N-terminal domain, which interacts with Snf2, is conserved in S. cerevisiae  or glucose. The HOG pathway is important to react upon osmotic and oxidative stress while the cell wall integrity (PKC) and Cek1-mediated (or SVG) pathways play a role in the response toward cell wall damage and stress. All four pathways play a crucial role in morphogenesis and the survival of C. albicans under stress conditions  and PPIs deposited at the rcsb protein structure database, but not yet published.Of the plethora of available techniques to study PPIs, only a select few have been used intensively for large-scale high-throughput screenings. Two of the most important techniques are the TAP-MS and Y2H system . Both terevisiae . Lookingrevisiae . Supplemchniques . Despitechniques , the difprogress . This isplicated . Until nsee also , S2. A cS. cerevisiae, has an estimated total of 30,000 to 40,000 interactions    and the se (CGD)  are crucS. cerevisiae to aid in constructing C. albicans PPI networks, caution is advised considering the low overlap between the PPI data . The latter was noticeable by the absence of a huge number of interactions in the BioGRID at the start of this review (compare releases 3.4.156 and 3.5.174), yet it is of the utmost importance for researchers to send in their data as the whole research community depends on such databases to perform their experiments with a high-quality dataset  so that a more complete overview regarding PPIs can be achieved for C. albicans.With an exhaustive literature search, we tried to include most of the PPIs known in FS initiated this review, compiled all the data, provided all the input to BioGRID, and wrote the draft version of the manuscript. FS and PV wrote the final 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.


The degree and mechanisms by which gene copy-number changes are buffered at the protein level are not understood. We have identified up to 40% of genes with protein-level buffering of gene dosage changes in cancer. Using structural data, we show that interaction-dependent control of abundance is correlated with interface size. These findings in cancer were reflected in variation in protein levels in normal tissues with buffering of natural genetic variation for highly attenuated proteins. HighlightsUp to ∼40% of proteins have copy-number changes attenuated, likely via degradation.Interaction-dependent protein attenuations correlates with interface size.Protein attenuation is reflected in variation in protein levels in normal tissues.Protein attenuation buffers expression differences due to natural genetic variation. Proteogenomic studies of cancer samples have shown that copy-number variation can be attenuated at the protein level for a large fraction of the proteome, likely due to the degradation of unassembled protein complex subunits. Such interaction-mediated control of protein abundance remains poorly characterized. To study this, we compiled genomic, (phospho)proteomic and structural data for hundreds of cancer samples and find that up to 42% of 8,124 analyzed proteins show signs of post-transcriptional control. We find evidence of interaction-dependent control of protein abundance, correlated with interface size, for 516 protein pairs, with some interactions further controlled by phosphorylation. Finally, these findings in cancer were reflected in variation in protein levels in normal tissues. Importantly, expression differences due to natural genetic variation were increasingly buffered from phenotype differences for highly attenuated proteins. Altogether, this study further highlights the importance of posttranscriptional control of protein abundance in cancer and healthy cells. Cancer cells can harbor a large number of somatic DNA alterations ranging from point mutations to gene copy changes that can occur from deletion or amplification of small regions or whole chromosomes. While these events are the source of the genetic variation that can confer a selective advantage and lead to cancer, large changes in gene numbers can be detrimental and cause imbalances in the corresponding protein levels. Several studies have shown that the majority of changes in gene copy number will propagate to changes in the corresponding protein levels –3. HowevThese results support a long-standing view that protein complex formation can set the total amount of protein levels . The degWhile this phenomenon of gene dosage attenuation in protein complexes has been well documented, we still do not understand (i) what protein properties are associated with the propensity for a protein to be attenuated or (ii) if the characteristics of the attenuation process are seen in noncancerous cells. Here we have extended on a previous analysis , performproteomics.cancer.gov/data-portal), for breast cancer (BRCA) (firebrowse.org) data portal  for TCGA samples and from the CCLE data portal for cancer cell lines .Proteomics and phosphoproteomics quantifications at the protein/phosphosite level from TCGA cancer patients were obtained from the CPTAC data portal (r (BRCA) , colorecr (BRCA) , and ovar (BRCA) . The samr (BRCA)  and for r (BRCA) , 18. Genr (BRCA)  for TCGAr (BRCA) –21 for cr (BRCA) , 23. BrinormalizeQuantiles function from Limma R package (z-score transformation.The label-free protein quantifications (precursor areas) for COREAD CPTAC samples  were fir package . In totavoom  were regressed out using a multiple linear regression model. This model was implemented with the protein or mRNA abundance of a given gene as a dependent variable and the potential confounding factors, i.e. cancer type, experimental batch, proteomics technology, age, and gender, as independent variables. The residuals from the linear model were the protein and mRNA variation not driven by the confounding effects, as the second principal component analysis demonstrated .Potential confounding factors revealed by principal component analysis  for the mRNA of Y, β2, β3, β4, β5, and β6 the regression coefficients for the covariates cancer type, experimental batch, proteomics technology, age, and gender, respectively. ε is the noise term.β7 is the regression coefficient for the CNV (Gx) of protein X. An LRT was used to assess the significance of the association:eLik corresponds to the log likelihood of the alternative and null models. p values were then calculated using the LRT statistic over a chi-squared distribution and adjusted for false discovery rate (FDR) using the Benjamini–Hochberg method. This model was applied for a given protein association pair X and Y if: X ϵ CNV Λ Y ϵ Protein Λ Y ϵ mRNA, where CNV, Protein, and mRNA are the sets of genes detected with the respective assays.For a given protein physical interaction pair X and Y, it was tested whether protein X can control the protein levels of Y through protein–protein interaction, potentially constraining the degradation rate of Y. For each interacting pair, two nested linear models were fitted. The first model (null) was used to predict the protein levels of Y (Py) using its mRNA (Ty) and a set of other covariates, d gender . In a sed gender . A likeld gender  was thenA total of 411,591 protein pairs followed these criteria and were tested across 368 tumor samples. The same analysis was performed with the mRNA, instead of the CNV, of protein X for 392,128 protein pairs. To avoid spurious protein associations that might occur due to the genomic co-localization of the controlling proteins, the top-ranked association was selected using the Borda ranking method. This was done systematically for every case where multiple controlling proteins in the same chromosome were associated with the same controlled protein. More than one controlling protein in the same chromosome for the same controlled protein were allowed if their CNV profile Pearson correlation was lower than 0.5.lm R function and the LRT test with associated statistics were calculated using the lrtest function from the lmtest R package. The Borda ranking method was implemented using the Borda function from the TopKLists R package  using its mRNA (Ty), the CNV and protein levels of protein X (Gx and Px), and the covariates experimental batch, patient age, and gender . In a segithub.com/evocellnet/int3dInterfaces) that extracts protein interfaces from Interactome3D structures (bioinf.manchester.ac.uk/naccess) to calculate the solvent accessibility of the bound and unbound monomers. Every residue changing its relative solvent accessibility is considered to form part of the interface. From the 11,530 human protein interaction structures analyzed with this pipeline, structures of protein homodimers or structures with less than 100 amino acids were removed. Also, structures with chain lengths bigger than the respective UniProt protein lengths and with the same chain length for each partner were removed. After applying these filters, 3,082 structures with 6,147 protein interactions were used in the subsequent analyses.Protein interface sizes were calculated using an in-house pipeline  were obtained from UniProt for 20,349 proteins .2).The percentage of area inside complex for the protein subunits from the COP9 signalosome was calculated using FreeSASA . For eacportals.broadinstitute.org/Achilles) . These data contain gene-dependence levels adjusted for copy-number-specific effects for 17,670 genes across 341 cancer cell lines. Genes with an essentiality score lower than −1×S.D. (the standard deviation for the entire dataset corresponds to 0.3) in more than 5% of the cell lines were considered essential and used in the remaining analysis . The median gene essentiality was calculated for 3,548 genes with attenuation and essentiality data across the 341 cancer cell lines.Gene essentiality data obtained with CRISPR-Cas9 screenings  were dowz-scores and quantile normalized.Gene and protein expression data for normal human tissues were obtained from the Genotype-Tissue Expression (GTEx)  and HumaHaving assembled the gene and protein expression datasets for normal tissues, pairwise Pearson correlation coefficients were calculated between the protein of the controlling and controlled genes, mRNA of the controlling and controlled genes, and mRNA and protein of the controlled gene. The Pearson correlations were calculated for 91 highly significant associations (FDR < 0.01), 210 significant associations (0.01 ≤ FDR < 0.05), and 161,945 nonsignificant associations at the CNV and mRNA level (FDR ≥ 0.05). In order to assure that the increase in protein–protein correlations were not simply due to an increase in mRNA–mRNA correlations, we selected the protein pairs with mRNA Pearson's correlation coefficient between 0 and 0.4, corresponding to 57,145 pairs .i.e. direction of effect size, and discarded those with consistent slope in less than three tissues.Following the approach in HipSci proteomics , we conscis-eQTL as variants in high LD  within the same cis window. Next we grouped eQTLs in high LD blocks (r2 > 0.8), excluded from this analysis 247 genes having each more than 100 eQTL blocks) and obtain a final set of 66,197 eQTL blocks corresponding to 2,953 genes and 441,194 eQTL–gene associations. We then define these blocks as GWAS tagging if for at least one eQTL in the block at least one LD proxy variant was annotated in the NHGRI-EBI GWAS catalogue. Finally, we report the fraction of GWAS-tagging eQTL stratified by the attenuation level of the corresponding cis genes. To assess the robustness of this analysis and to study the effects on GWAS-tagging probability of eQTL recurrence across tissues, we compute the number of tissues in which an eQTL was called with the same slope and report results by stratifying the eQTLs by increasing number of tissues.We defined proxy variants of each We rely on core protein complexes from CORUM to identify the gene complex membership status and segregate those that are annotated in at least one large complex (>5 subunits). Out of the genes with eQTL evidence and with annotation scores, 961 are annotated in CORUM, and 576 are members of large complexes.k et al. (BRCA)   (A). Principal component analysis revealed the presence of confounding effects in the RNA and protein expression data . These effects are related to cancer type, experimental batch, type of proteomics experiment, and also patient gender and age. Therefore, these potential confounding effects were regressed out from the RNA and protein expression data (“Methods”). After correction, the association between the principal components and the potential confounding effects was removed . In the combined dataset, the average mRNA–protein correlation is 0.44, which is in agreement with previous studies.In order to study protein posttranscriptional control, we collected matched gene copy number, mRNA, and protein expression cancer datasets made available by the TCGA and CPTAC consortia, for BRCA , 44, ova. (BRCA) , Roumeli(COREAD) , and Law. (BRCA) . In tota. (BRCA) A. Princiet al. . A higher attenuation potential suggests genes that have CNVs buffered at the protein level. As previously mentioned, we then clustered the genes by attenuation potential using an unsupervised Gaussian mixture model. Using this strategy, we identified 3,435 (42%) genes as attenuated at the protein level  and 4,689 as nonattenuated . These results indicate that up to 42% of genes show signs of gene dosage buffering at the protein level, probably due to a posttranscriptional control of protein degradation, and robustly recapitulates previous findings on a smaller set of 6,418 genes . As can be seen by the receiver operating curves, protein interaction pairs are better predicted by protein than mRNA correlations in all classes, with the difference increasing with the attenuation level. If the attenuation was mostly explained by noise in the protein-level measurements, then the opposite trend would be expected, where correlation of protein measurements would be noisier for highly attenuated proteins and a worse predictor of protein–protein interactions. These results indicate that attenuated genes are not defined as such because they have noisier protein measurements.We then investigated the impact of CNV in cancer proteomes, using the strategy reported in Gonçalves et al. . Due to Fig. S4). More, the attenuation potential is correlated with the number of subunits in a protein complex, indicating that members of large complexes have higher attenuation than those of small complexes . Attenuated genes are also expected to show increased ubiquitination after proteasome inhibition, which was confirmed here using previously published data with three different proteasome inhibitors—MG-132, epoxomicin, and bortezomib . Having defined a comprehensive list of genes/proteins with different degrees of attenuation, we then set out to characterize their physical and genetic properties.In line with previous findings, the list of attenuated genes is strongly enriched in well-characterized protein complex members and, notably, in members of large complexes  . This result is likely to be driven by the enrichment of protein complex members of essential complexes, such as the ribosome and spliceosome. We then studied the physical characteristics of these proteins, such as length and structural properties. We found that the highly attenuated proteins tend to have a smaller size (D) , suggesting a size-dependent buffering mechanism. For the structural analysis, we considered a total of 2,392 proteins having structurally defined interface models (E) where we noticed a trend in which the subunits with a larger surface buried in interfaces had the strongest attenuation (F). While the trend on a single complex is not significant (F), this trend was supported across all proteins, with the average fraction of residues at interfaces increasing from the nonattenuated to the highly attenuated proteins in a statistically significant manner (G).We first asked if the level of attenuation relates to distinct essentiality features, based on gene essentiality defined by CRISPR-Cas9 screens . Highly ler size D . In this set of associations, we classified the proteins as controlling (353)—those capable of controlling the protein levels of their interactions partners; controlled (423)—whose abundance levels depends on their interactions; and both (60)—proteins with the two characteristics . Out of 423 controlling proteins, 62 had at least two interactions. The top controlling protein was TCP1, which was predicted to control the protein abundance of seven complex partners, including CCT3, CCT5, CCT7, and CCT8 . As expected, the controlled proteins had higher attenuation potential, a consequence of the posttranscriptional regulation of their protein levels  . The controlled proteins also show a smaller size , which corroborates the hypothesis that protein size is important for the buffering mechanism . These results increased the evidence of interactions and regulators that may act as drivers of protein complex assembly.Out of 572,856 physical interaction,s we had data to test associations for 411,591 with this model, finding 516 protein–protein associations as significant using CNV and mRNA (FDR < 5%) . We found that both statistics are positively and significantly correlated with interface size . We selected two examples to illustrate the observed differences (G). Posttranscriptional regulation of TRMT61A by TRMT6, that form the tRNA (adenine-N1-)-methyltransferase enzyme, is the second-strongest association found in our analysis, and the interface formed between these two proteins covers a total of 72 residues. In contrast, a weaker association between CSNK2A1 and CSNK2B may be explainable by a much smaller interface of 13 residues.We hypothesized that protein-interaction-dependent control of degradation could depend on the protein interfaces size. To test this, we identified 60 significant associations with available structural models (“Methods”) and correlated the protein interface size with the effect size  and significance (FDR) of the respective protein association pairs F. We fouferences G. PosttrThese results show that interface sizes are an important determinant of the protein-interaction-mediated control of protein degradation. This may be due to an effect of binding affinity or differences in the recognition of exposed interfaces of different sizes by the degradation machinery.A). For this analysis, we used proteins and phosphosites measured in at least 50% of the 170 samples, corresponding to 8,546 proteins and 5,733 phosphosites.The role of phosphorylation in modulating protein-binding affinities has been well described –49. We rFig. 3B, listed in Supplemental Table 4). Our interpretation of these associations is that these phosphosites can regulate the protein interaction and thereby modulate the degradation of the complex subunits.Using the compendium of physical interactions , we tested whether the changes of a phosphosite Xp from protein X is associated with the protein levels of the interacting protein Y. As before, we used a linear regression model where the protein abundance of protein Y is predicted using the phosphosite levels of protein X (Xp), while taking into account the protein and CNV levels of protein X, the RNA of protein Y, and other covariates (“Methods”). Out of 315,772 phosphosite–protein pairs tested with this model, 11,672 associations were significant (FDR < 5%). To ensure the associations are directional, we overlapped these associations with the 516 protein–protein associations found with the CNV and mRNA models, identifying 32 overlapping associations . EIF3A is predicted here to be a “rate-limiting” subunit of the eukaryotic initiation factor 3 complex and has been previously experimentally implicated in the control of protein levels of several of the other subunits (i.e. after regressing out EIF3D mRNA levels) and that the phosphosite levels of EIF3A S492 correlates better with EIF3D protein residual than the EIF3A total protein levels (D). We further confirmed that the residual phosphosite levels of EIF3A S492 phosphosite (once accounting for the protein abundance of EIF3A) is significantly correlated with the protein levels of EIF3D in both datasets analyzed . However, we found dataset-specific differences in the correlation of the protein levels of EIF3A and EIF3D . Overall, these results suggest that EIF3A S492 may have an impact on protein complex assembly.The 32 associations involve 28 phosphosites, and of these, two phosphosites are already known to regulate interactions (POLD3 S458 and MYH9 S1943) and an additional case (EIF3A S492) is not yet known to regulate protein interactions but is at the interface with other complex members C. EIF3A subunits . One phon levels D. We furThe study of the impact of CNVs in cancer proteomes indicates that up to ∼40% of genes have copy number changes that are buffered at the protein level. Such posttranscriptional regulatory processes should not be specific to cancer; however, the extent that these effects are observed in normal cellular states is still largely unknown. To address this question, we analyzed gene and protein expression datasets for normal tissues, made available by the GTEx and Human Protein Map projects. In total, we collected expression for 5,239 proteins and genes, across 14 tissue types (“Methods”).p value = 8.96e-4 between nonsignificant and significant pairs; p value = 8.25e-06 between nonsignificant and highly significant pairs) (A). Also, as predicted the protein to mRNA correlation values across tissues, of the controlled subunits, decreases with the association strength  (A). We provide two examples for protein-interacting pairs ARCN1 and COPA and TRAPPC8 and TRAPPC11 where the mRNA levels of the controlling subunits (ARCN1 and TRAPPC8) appear to dictate the protein abundance of both proteins (B). These results suggest that the protein associations identified in the cancer datasets can also be observed in normal tissues, at least in aggregate. Importantly, they demonstrate that cancer data can be a useful resource to study protein homeostasis in normal conditions.We tested if the posttranscriptional control dependent on protein interactions observed in cancer is present in normal tissues. For this, we asked if the protein abundance of controlling–controlled protein pairs will tend to correlate more strongly than other protein interaction pairs. Similarly, we expected that the correlation between the mRNA and protein levels of controlled subunits would tend to be weaker than for non-posttranscriptionally controlled proteins. We tested this using protein–protein interaction pairs measured in the tissue data with significant controlling–controlled relationships from cancer data (301 pairs) and all other 161,945 protein–protein interactions pairs (“Methods”). Reassuringly, we observed that the correlation of protein abundance across tissues increased for protein pairs with stronger association strength, for similar levels of mRNA-mRNA correlation values (Wilcoxon rank-sum test t pairs) A. Also, t pairs) A. We proproteins B. These i.e. in high LD (r2 > 0.8) with GWAS variants . To this end, we relied on cis-eQTLs reported in GTEx and compared the fraction of GWAS tagging eQTLs for different classes of protein attenuation . We found that eQTLs corresponding to genes classified as highly attenuated have a lower fraction of GWAS tagging eQTLs, and that the difference between the degree of attenuation increases for eQTLs mapped in multiple tissues .If mechanisms controlling the protein levels are consistent across cell types, then the attenuation models studied here could help elucidate how natural variation may sometimes result in changes in mRNA but not protein and consequently phenotypic traits. Single-nucleotide polymorphisms associated with gene expression via quantitative trait loci (QTL) analysis—known as expression QTLs (eQTLs)—should also tend to be attenuated at protein level potentially for the same genes as those found in cancer. To study this, we analyzed if protein-level CNV buffering could explain the probability of eQTLs to have phenotypic impact, Fig. S7).Highly attenuated genes tend to be enriched in protein complexes and are likely essential to the cell, and therefore could have specific biases as to how eQTLs are linked to GWAS associated traits. To account for this potential bias we replicated the analysis on members of protein complexes. Interestingly, this shows that the attenuation score has a higher impact on GWAS tagging probability for members of protein complexes, and more specifically for members of large protein complexes (>5 subunits)  is under posttranscriptional control. The set of genes with protein-level buffering of CNVs is enriched in gene products belonging to large protein complexes. In addition, we found that the fraction of interface residues of a protein is a strong determinant of attenuation. Together with experiments on pulse-chase degradation , aneuplo1We observed that the fraction of residues at the interface correlates with the probability that a protein shows gene dosage attenuation. Similarly, the size of the interface correlates with the strength of association between pairs of physical interactions in which one subunit appears to control the abundance level of the interaction partner. The size of the interface typically correlates with increasing binding affinity between proteins as well as larger amounts of hydrophobic residues that are exposed in the absence of interactions. We speculate that either of these consequences could play a role in the attenuation. In particular, larger fraction of hydrophobic regions could increase the propensity to form aggregates, and in some cases hydrophobic regions are known to be recognized for degradation . This coWe have used data from cancer samples to identify the attenuated proteins and physical interactions with rate-limiting subunits. We find that most of the controlling–controlled protein–protein associations we predict have a positive relationship. Given the working model that these are explained by protein complex formation, the negative associations could be explained by cases of mutually exclusive complex membership. The fact that few associations predicted are negative are consistent with the idea that most complex members are not mutually exclusive.It is still unclear if the same proteins and interactions will have the same posttranscriptional control in other systems and/or species. When studying expression variation in normal tissues and the association of eQTLs with phenotypes, we observed that, in aggregate, the same proteins and interactions show signals consistent with posttranscriptional buffering of mRNA expression variation. Of note, we find that eQTLs are less likely to be linked to phenotypes in highly attenuated proteins. This is in line with studies of mRNA and protein QTLs in human induced pluripotent stem cell lines, showing that genetic variation driving mRNA changes are more likely to be associated to genotype differences when they are observed at the protein level . These f


Protein oligomers are more common in nature than monomers, with dimers being the most prevalent final structural state observed in known structures. From a biological perspective, this makes sense as it conserves vital molecular resources that may be wasted simply by generating larger single polypeptide units, and allows new features such as cooperativity to emerge. Taking inspiration from nature, protein designers and engineers are now building artificial oligomeric complexes using a variety of approaches to generate new and useful supramolecular protein structures. Oligomerisation is thus offering a new approach to sample structure and function space not accessible through simply tinkering with monomeric proteins. The vast expanse of protein sequence space has been the driving force behind molecular evolution, with the quaternary structure adding another level of diversity so further expanding the variation observed. Individual polypeptide chains amalgamate to produce a macromolecular complex through a process of oligomerisation . The molThe fundamental basis behind protein oligomerisation is the network of intricate interactions holding the individual units together. Protein oligomers are normally formed and stabilised by a variety of mechanisms, the most common of which is the formation of a mutually compatible non-covalent interface . The intGiven the abundance of oligomeric proteins, a key question is what makes oligomeric proteins so evolutionarily successful and why are the majority symmetrical? ,9 Numero1/7 of proteins are released from the ribosome before the full-length protein is created . Th. Th62].  defined –66 with  defined ,65. Resi defined . Mutatio defined ,54. The The archetypal technique for monitoring protein–protein interactions (PPIs) is fluorescent biosensors, which transduce real-time ligand-binding events into a measurable fluorescence signal . These pFluorescence resonance energy transfer (FRET) ,71, utilIn an attempt to expand this repertoire of proximity-based biosensors, dimerisation-dependent FP (ddFP) biosensors have become a new focus for the scientific community –84. Thisin silico design to the use of new chemistry types. Such potential is already being shown with the simple peptide-based system (reviewed by Beesley and Woolfson [Focusing on converting perceived ‘hard-to-use' oligomeric proteins to monomers limits the available function space open to the protein designer and engineer. Taking inspiration from nature and its concept of building up complex supramolecular structures from simple components (protein monomers), we can now envisage assembling the complex protein–based structure. Approaches available to expedite such ‘bottom-up' assembly are expanding all the time taking in classical protein engineering to Woolfson ). We shoWoolfson . So someOligomeric proteins complexes are the most common structural form of proteins found in nature.Taking inspiration from nature new artificial protein oligomeric systems are being constructed to sample new functional and structural features not accessible in monomeric proteinsMajor steps have been taken in constructing artificial protein oligomers but more is to come in terms of function, complexity, applications and even inclusion of non-biological components


A large body of work has gone into understanding the effect of mutations on protein structure and function. Conventional treatments have involved quantifying the change in stability, activity and relaxation rates of the mutants with respect to the wild-type protein. However, it is now becoming increasingly apparent that mutational perturbations consistently modulate the packing and dynamics of a significant fraction of protein residues, even those that are located >10–15 Å from the mutated site. Such long-range modulation of protein features can distinctly tune protein stability and the native conformational ensemble contributing to allosteric modulation of function. In this review, I summarize a series of experimental and computational observations that highlight the incredibly pliable nature of proteins and their response to mutational perturbations manifested via the intra-protein interaction network. I highlight how an intimate understanding of mutational effects could pave the way for integrating stability, folding, cooperativity and even allostery within a single physical framework. Mutations in proteins occur via multiple well-understood molecular mechanisms primarily at the level of DNA contributing to variability in the population. Such variability is the cornerstone of evolution as functionally advantageous mutations get fixed in the presence of a selection pressure. Decades of work on mutations have revealed rich information on protein conformational behavior, binding site identities and thermodynamics, folding mechanisms and allostery. In parallel, understanding and modeling mutational effects has tremendous implications in not just designing proteins with enhanced solubility, stability, and catalytic efficiency, but also to understand evolutionary trajectories of proteins, enzyme evolvability and the contribution of mutant phenotypes to organismal fitness –5. NumerA ‘neutral’ mutation is conventionally defined as a perturbation that has little effect on the organismal fitness . However, the same mutation in conjunction with mutations at other sites can have a positive or a negative effect on the fitness landscape. How is this epistatic characteristic enabled? It is important to realize that the interior (surface) of a protein is a highly unique environment determined by the unique protein sequence with specific packing (electrostatic/polar) interactions. Therefore, any perturbation from a mutation is expected to be complex, as it would involve an abrupt reorganization of the evolutionarily tuned interaction network. In fact, it is well recognized in the field of protein NMR that mutations manifest as non-trivial effects on chemical shifts and order parameters of a majority of residues in the protein. However, there is an apparent disconnect between an NMR view of mutational effects (complex changes in multiple terms) and studies that merely quantify the change in stability or folding rate of the mutant compared to the wild-type. In this review, I highlight and summarize some of the recent developments towards resolving this apparent conflict and how a mere consideration of the intra-protein interaction network provides a convincing rationale with several testable predictions.1-adrenergic receptor upon mutations and ligand binding, Grzesiek and coworkers identified that both the structural perturbations propagate to long distances determining the functionally relevant motions of the transmembrane helices [A reductionist approach in viewing proteins is to treat protein residues as nodes and the intra-protein interactions as edges . When th helices . A study helices . While t helices . Such exThe fact that distal mutations can alter activity (to variable extents) raises questions on whether this is an intrinsic response of proteins to structural perturbations due to the fluid-like nature of the protein interior . True toWhat are the molecular origins of destabilization induced by truncation mutations? An analysis of microsecond-long molecular dynamics trajectories of Ubiquitin WT and seven aliphatic truncation mutations revealed a distinct weakening of packing interactions across nearly the entire protein. The relative residue-level van der Waals interaction energy approaches zero (i.e. no perturbation) only at longer distances from the mutated site  11•]. T. T11•]. –1 destabilization at 310 K from merely single-site backbone entropy considerations [While the discussion above is primarily on truncation mutations, mutations that enhance the molecular volume also decrease protein stability though the mechanism is still unclear. Since the interior of a protein displays the packing density of a solid , anyerations ) and couProtein native states are accurately defined as ensembles of multiple conformations or substates that are critical for function. Thermodynamic fluctuations are a feature intrinsic to polymeric protein chains arising from the finite size of protein molecules and the weak nature of the non-covalent interactions . From a Redistribution of populations in the native ensemble tune functions and downstream signaling responses revealing avenues by which molecular responses to environmental variables could be acquired, the molecular mechanisms of drug-resistance and onset of disease conditions. Work on p97 ATPase and NAD(P)H: quinone oxidoreductase 1 (NQO1) highlight that disease causing mutations shift the conformational substates in a graded manner thus compromising innate activity ,43. SimiNote that such population redistributions upon mutations need not be restricted to folded proteins but even intrinsically disordered proteins. The outcome of such mutations in disordered proteins is expected to be non-trivial due to the heterogeneous nature of IDP ensembles manifesting as large signal changes in equilibrium ,47 Figu, bindingIt is well established that protein residues are coupled to each other through both the hydrogen-bond network and packing interactions leading to correlated motions or fluctuations in equilibrium that are critical for function. Such intra-molecular interaction-networks serve as channels for signal transmission playing a prominent role in dynamic allostery –55  and the total perturbation (ΣΔQ) — that provide information on the extent to which the perturbed residue is coupled to its neighbors  might be a way forward to test mechanistic models without preconceived notions or biases in the analysis, as also argued for in a recent work . An in sch  whereinch folding rates upon point mutations . One of Cooperativity is feature intrinsic to systems held together by weak non-covalent interactions, and in proteins it is quantified in terms of the slope of the unfolding curve, folding barrier heights or other extent of similarity of atomic-level unfolding curves –79. CoopRecent experiments and computational works highlight that mutations in the protein interior manifest as altered chemical-shifts, order parameters (dynamics), HX protection factors, and modulation of packing interactions involving a significant fraction of protein residues. Accordingly, mutational perturbations are better understood in terms of their impact on the underlying interaction network or correlated motions and likely serve as the evolutionary first-step towards altered protein activity, diseased states, functional promiscuity and fold-switching. Given the robustness of the interaction network to mutations, successful engineering of enzymatic activity might require multiple perturbations –82 whileSurface mutations also contribute to complex alteration of folded and unfolded ensembles apart from folding mechanisms –86. It i


Amyloids are unbranched protein fibrils with a characteristic spatial structure. Although the amyloids were first described as protein deposits that are associated with the diseases, today it is becoming clear that these protein fibrils play multiple biological roles that are essential for different organisms, from archaea and bacteria to humans. The appearance of amyloid, first of all, causes changes in the intracellular quantity of the corresponding soluble protein(s), and at the same time the aggregate can include other proteins due to different molecular mechanisms. The co-aggregation may have different consequences even though usually this process leads to the depletion of a functional protein that may be associated with different diseases. The protein co-aggregation that is related to functional amyloids may mediate important biological processes and change of protein functions. In this review, we survey the known examples of the amyloid-related co-aggregation of proteins, discuss their pathogenic and functional roles, and analyze methods of their studies from bacteria and yeast to mammals. Such analysis allow for us to propose the following co-aggregation classes: (i) titration: deposition of soluble proteins on the amyloids formed by their functional partners, with such interactions mediated by a specific binding site; (ii) sequestration: interaction of amyloids with certain proteins lacking a specific binding site; (iii) axial co-aggregation of different proteins within the same amyloid fibril; and, (iv) lateral co-aggregation of amyloid fibrils, each formed by different proteins. Amyloids are unbranched protein fibrils, in which monomers form intermolecular β-structures stabilized by numerous hydrogen bonds and consisting of β-strands oriented perpendicular to the axis of the fibril . ThInitially, amyloids were discovered as macroscopic tissue abnormalities  linked with different diseases (reviewed in ). To datSc  [PSI+], [PIN+] and [URE3], which are associated with the amyloid aggregation of Sup35, Rnq1, or Ure2, respectively  . Most ofed PrPC) . At the iewed in ). Increaiewed in ). The paiewed in ,20, and iewed in ). While iewed in ). OveralRecent studies have demonstrated that formation of amyloid fibrils not only involves aggregation of the particular protein, but also has a significant influence on the quantity of some other proteins and the depletion of their functions caused by co-aggregation. For example, it was demonstrated that α-Syn might serve as a promiscuous binder, leading to its co-aggregation with other proteins or modulation of their activities (reviewed in ). The anTraditional methods for investigation of protein-protein interactions can also be used to characterize interactions either between different amyloids or between monomeric proteins and amyloids. These methods include co-immunoprecipitation, colocalization, affinity chromatography, gel filtration, and other techniques. Also, several amyloid specific techniques have been developed. Among them, are cross-seeding, co-incubation, and other methods, including proteomics and bioinformatics approaches. Typically, several methods are combined to characterize the interaction of specific proteins with amyloid aggregates. Some of these methods are discussed below. Similar approaches are grouped together.+ [Co-immunoprecipitation (co-IP) is a widely used assay to study protein interaction in vivo. This approach includes the extraction of a certain protein from the lysate or in vitro prepared mixture of proteins with a specific antibody and subsequent identification of co-eluted proteins A. For ex+ . The sam+ . The majPSI+] [Affinity chromatography is one of the widely used methods allowing for one to purify specific molecules interacting with protein bound to the affinity matrix A. In genPSI+] ,30) aggrPSI+] . The samPSI+] .Gel filtration allows for one to separate aggregated proteins from different tissues or cells. In this technique, the identification of proteins in the same fraction, their co-elution, is considered to be a result of their interaction. In the case of co-aggregation of the proteins, they are expected to be found together in fractions of high-molecular-weight complexes B. For exSaccharomyces cerevisiae, in which manipulation with plasmids containing fusion genes is a routine procedure. The colocalization experiments were employed to visualize the interaction of Sup35NM and Rnq1 aggregates during the de novo induction of [+PSI] prion  prion ,38. The +] prion . Another+] prion ,40, Tau +] prion ,41, Htt +] prion , and AAp+] prion .Electron microscopy (EM) analysis is a more precise method of colocalization and could provide information about the orientation of separate protein aggregates relative to each other. For example, using immunogold EM, it was shown that the co-incubation of Tau and α-Syn leads to the formation of bundled fibrils labeled with both Tau and α-Syn antibodies. The analysis of these bundled fibrils reveals that they are composed of Tau or α-Syn aggregates (composed only by one protein) annealed “end-to-end” . EM has The evidence of the colocalization between different amyloids that were obtained by this method cannot prove the direct interaction between their structural proteins. For this purpose, other techniques must be used, such as fluorescence cross-correlation spectroscopy (FCCS) or Förster resonance energy transfer (FRET). The FCCS is based on the monitoring of migration of differently labeled protein molecules, and their coordinated movement allows for researchers to suggest that molecules interact with each other. Another method, which is based on the analysis of fluorescence fluctuation, is FIDA . The FRET technique requires the application of two fluorescent tags with specific properties: emission spectrum of the first tag has to overlap with the excitation spectra of the second one. If such molecules are located in proximity to each other, the excitation of the first fluorophore will lead to the excitation of the second one. The detection of this energy transfer is the evidence of interaction between the analyzed molecules.PSI+] cells; interaction of Sup35 with other prion proteins, such as Ure2, Rnq1, or New1 was also shown by the same combination of the techniques . T. T37]. TDifferent methods may be used for monitoring the aggregation kinetics. A number of such approaches are based on the aggregate staining with amyloid-specific dyes, like ThT or CR . Also thThe critical step in the preparation of a monomeric protein for cross-seeding experiments is the removal of preformed aggregates . Without+PIN] and [+PSI] prions in yeast. It was shown that aggregates of Rnq1 stimulated conversion of Sup35NM into the amyloid, although much less efficiently than Sup35NM stimulated its own conversion [+PIN] fibers cross-seeded [+PSI] variants  induction in the prion-free yeast cells , a prion of Rnq1 protein with an unknown function [PIN+] remains mysterious, now it is clear that this prion acts as the heterologous spatial template initiating the induction of other prions in yeast, including QN-rich [MOD+] [PIN+] interacts with other prions at the initiation phase, it was shown that its aggregates do not physically interact with the aggregates of co-existing prions [PSI+], inhibits [URE3] [PSI+] appearance [SWI+] [PIN+] and [PSI+] appearance de novo, but it is destabilized when simultaneously present with [PIN+] and [PSI+] in a cell [PIN+] increases translational read-through caused by [SWI+], but the aggregates of these prions do not colocalize  . Though g prions ,176,177.s [URE3]  formatios [URE3] ,178, whie [SWI+]  enhancesn a cell . Moreoven a cell . Also, tlocalize . Thus, alocalize .NUP100+] strains causes the co-aggregation of several QN-rich nucleoporins in yeast [SWI+], but not in [swi−] strains when overexpressed [SWI+] strain were analyzed, did not reveal these proteins  model and revealed about 40 proteins, most of which were chaperones, stress-response and metabolic proteins [Yeast prions also interact with different proteins that are considered to be non-amyloidogenic. The most detailed proteomic study of such interactions was performed in a [proteins . Interacproteins ,183), thproteins ,185,186,proteins . Hsp104 proteins . Severalproteins , while tproteins . Thus, uPreviously, we discussed examples of proteins, interactions of which with amyloids have pathological or neutral consequences. In this section, we demonstrate that amyloid interactions may be beneficial. Recently, it was shown that human proteins Rip1 and Rip3 can form co-aggregates with amyloid properties, and this is a part of the signaling pathway triggering necrosis . FurtherPodospora anserina proteins HET-s and HET-S [The analysis of the RHIM motifs diversity across different species allowed for us to propose their evolutionary link with the prion-forming domain of nd HET-S . These pnd HET-S  and implnd HET-S . HET-s ind HET-S ,196, whind HET-S . Furthernd HET-S . These dnd HET-S ).The similarity of RHIM motifs assumes that they may adopt similar structures , though D. melanogaster proteins, peptidoglycan recognition proteins (PGRP-LC and PGRP-LE), and Imd. Peptides with these motifs can form amyloids, and, as in the case with Rip1-Rip3, their aggregation is an essential step for signal transduction. Notably, the presence of preformed fibrils of PGRP-LE stimulates the aggregation of Imd [Finally, cryptic RHIMs should be mentioned. They are found in n of Imd . RHIMs in of Imd ,205,206,n of Imd ,208,209.n of Imd , it was n of Imd .−csgB strains secrete soluble CsgA, which can be converted into curly by +csgB cells [Another well-known example of a functional amyloid cascade is the curli formation in bacteria. Proteins named CsgA-G (from curli-specific genes) are implicated in this process . The majB+ cells . Taken tB+ cells ,216.A number of different membrane-less organelles, including stress granules (SG), P-bodies, centrosome, Balbiani body, and Nucleolus were found in eukaryotic cells (reviewed in ). Among Recently discovered antimicrobial properties of several amyloids also may be considered as an example of the functional co-aggregation. In particular, it was shown that Aβ peptides behave as antimicrobial agents and inhibit the replication of several viruses. However, particular molecular mechanisms are still unknown (reviewed in ). AnotheIn previous sections, we demonstrated that protein co-aggregation with amyloids is very versatile. It is apparent that the recent accumulation of data about different types of protein aggregation related to the amyloids requires their classification. Our analysis of these data resulted in the following classification in four classes: titration, sequestration, axial, and lateral co-aggregation. The first and second classes describe the interactions of monomeric proteins with amyloids. We propose the term titration for the interaction of certain proteins with amyloid aggregates via a specific binding site. It is the case when two proteins specifically interact with each other in the monomeric state and co-aggregate when one of them forms the amyloid A. One ofIn the second class of co-aggregation, which we propose to call sequestration, a particular protein can bind different types of amyloids. In this case, two proteins do not physically interact with each other in their monomeric states B. The weThe next two classes describe interactions between amyloids that are formed by different proteins. In the third class, called axial co-aggregation, two (or more) proteins form a common fibril structure C. The thThe fourth class, called lateral co-aggregation, comprises amyloids that interact with each other but do not form the same amyloid fibril. Such fibrils may stick to each other by their sides D. The inIn this paper, we illustrated different aspects of protein interactions with amyloid aggregates and the impact of this process on pathogenesis or functional mechanisms. The review of the methods that are used in the field, its limitations and benefits demonstrated that an investigation of protein-amyloid interactions requires the combination of different cutting-edge approaches. The diversity of existing methods reflects the complexity of the amyloid interactomes and determines the boundaries that limit our ability to study this phenomenon. Nevertheless, recently developed methods allow for us to discriminate between different types of protein-amyloid interactions and propose their classification that includes four classes of interactions: titration, sequestration, axial, and lateral co-aggregation. We believe this first systematic classification of protein-amyloid interactions to encourage the investigators to add new examples of co-aggregating proteins to four proposed classes or to improve this classification if necessary.


We also explored the detailed reasons for the performance of DeepBindRG, especially for several failed cases by vina. Furthermore, DeepBindRG performed better for four challenging datasets from DUD.E database with no experimental protein–ligand complexes. The better performance of DeepBindRG than autodock vina in predicting protein–ligand binding affinity indicates that deep learning approach can greatly help with the drug discovery process. We also compare the performance of DeepBindRG with a 4D based deep learning method “pafnucy”, the advantage and limitation of both methods have provided clues for improving the deep learning based protein–ligand prediction model in the future.Proteins interact with small molecules to modulate several important cellular functions. Many acute diseases were cured by small molecule binding in the active site of protein either by inhibition or activation. Currently, there are several docking programs to estimate the binding position and the binding orientation of protein–ligand complex. Many scoring functions were developed to estimate the binding strength and predict the effective protein–ligand binding. While the accuracy of current scoring function is limited by several aspects, the solvent effect, entropy effect, and multibody effect are largely ignored in traditional machine learning methods. In this paper, we proposed a new deep neural network-based model named DeepBindRG to predict the binding affinity of protein–ligand complex, which learns all the effects, binding mode, and specificity implicitly by learning protein–ligand interface contact information from a large protein–ligand dataset. During the initial data processing step, the critical interface information was preserved to make sure the input is suitable for the proposed deep learning model. While validating our model on three independent datasets, DeepBindRG achieves root mean squared error (RMSE) value of pKa (−logK Many complex diseases still prevailed due to lack of effective therapeutic drugs; for instance, many type of cancers, dengue viral disease, Human Immunodeficiency Virus, hypertension, diabetes, and Alzheimer’s disease . As the Some molecular drugs exert their therapeutic effect usually by blocking or activating protein targets. Computational virtual screening by molecular docking of ligands against protein target is a widely used procedure to identify active drug like molecules . The docIn recent years, there is a trend of using machine learning to predict the binding affinity from structural data  and it iDeep neural network contains many more layers and enables the model stronger in identifying more complicated patterns . ConvoluBesides of the model architecture and various parameter settings, how to represent the protein–ligand interface data is a critical problem . In a veConsidering the above facts, we proposed a native-like protein–ligand identification method by applying the ResNet CNN model with a two-dimensional (2D) binding interface related matrix as input. To estimate the native protein–ligand effectively, the interface information, such as atom pairs, atom type, and spatial information were kept appropriately for balancing the computational efficiency and accuracy. Instead of using 4D to include all the spatial information and atomic type, we use 2D map to simplify the information as a picture like format. ResNet allows much deeper layers to identify more complex feature that may contribute to the protein–ligand binding affinity. Based on the data processing and ResNet model, we built a regression model that can accurately predict the protein–ligand binding affinity. By comparing our method performance in diversified datasets with other methods including traditional docking scores and 4D based deep learning scoring method, we show the generalized advantage and limitation of the current protein–ligand affinity prediction method, and provide helpful clues to overcome those limitations for protein science community.The protein–ligand binding complex coordinates and binding strength data are retrieved from PDBbind database version 2018 . The PDBSeveral familiar protein–ligand complex datasets were chosen as independent test sets, including the CASF-2013 set, CSAR_HiQ_NRC_set, and Astex Diverse Set. Each of the datasets contains 195, 343, and 74 protein–ligand complexes, respectively. These datasets are used for testing the model performance as independent sets, and can help in detecting generalization problems related to database specific artifacts. The structures in three external datasets were prepared and stored in the format as in the PDBbind database. The range of experimental binding affinity for each class has been provided in P value  for analyzing the performance of our model which is presented in In order to standardize the atom name and type in the PDB coordinate file, the Amber tool was used to convert the ligand into mol2 format, and the protein into PDB format . Except The workflow of the methodology is shown in We compute the interactions between protein atom and ligand atom in order to keep the critical contact information. Several cutoff values were tried, and we choose 0.4 nm as final setting. In order to keep the spatial information between the pairs, we cluster the protein atoms into five groups using kmeans from the sklearn package . During The keras  package The training process automatically tunes the weights for minimizing the loss function. The independent test set can guide the choice of hyperparameter. We have used different input parameters. For instance, the influence of hydrogen, the influence of atom type, and the influence of atom pair distance, respectively. The hyper-parameters such as epoch, percentage of dropouts, were evaluated. The model complexity influence was evaluated by comparing the performance of normal CNN model and ResNet model. We check the convergences by observing the change in mean squared error (MSE) value both in training and test sets over the increasing epoch number. The limited data with complex network can lead to overfitting. We used different dropout values to check the performance discrepancy among the training and validation sets. R) value were used to estimate the performance of the deep learning network model. Both MAE and RMSE are most common metrics used to measure the average magnitude of the error. The RMSE gives a relatively high weight to large errors, which is useful when large errors are undesirable. The correlation coefficient R was used to measure the strength and direction of a linear relationship between predicted and experimental measured binding affinity. Since the overfitting problem makes the validation extremely important, we choose several independent validations sets to further test the performance, and also compare the performance with the traditional docking score, machine learning score, as well as some recently developed deep learning methods.The metrics such as mean absolute error (MAE), MSE and root mean squared error (RMSE), mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (sMAPE), and correlation coefficient  and standard deviation (RMSE). R = 0.6779 is achieved for the training dataset whereas R = 0.5829 and 0.5993 for validation and testing datasets. The errors on training and validation sets monitored during deep learning are presented in The DeepBindRG model’s performance on the training and test set were shown in P reveals the binding affinity of hydrophobic ligands (Group B and C) can be predicted better than the hydrophilic ligands (Group A) which is presented in R = ∼0.6) over each run. The performance of the normal four-layer CNN model on the training and testing set are also shown in R = ∼0.5). We note that the normal CNN have serious overfitting, while adding large dropout would decrease both the testing and training set performance. The performance of using element as atom type is shown in R = ∼0.5) is not as good as DeepBindRG, but only using element is more flexible for the application.The performance of our method after grouping the ligands based on LogR value, MAE, MSE, MAPE, sMAPE, and RMSE as performance indicators. The R value for CASF-2013 and CSAR_HiQ_NRC_set is about ∼0.6, whereas it is low for Astex_diverse_set (R = ∼0.46). It is also observed from R value of 0.5855 for CASF-2013, 0.7167 for CSAR_HiQ_NRC_set and 0.5146 for Astex_diverse_set, respectively. The relatively better performance of pafnucy indicates the incorporation of detailed atomic spatial information helps to improve prediction of protein–ligand binding affinity in high resolution crystal structures.We have chosen CASF-2013, CSAR_HiQ_NRC_set, and ASTEX_diverse_set as extra testing data set, which contains 195, 343, 74 protein–ligand complexes, respectively. The performance of DeepBindRG on these three extra testing datasets are presented in R value of 0.6394, which is better than other methods such as X-Score, ChemScore, ChemPLP, PLP1, and G-Score, with R values of 0.61, 0.59, 0.58, 0.57, and 0.56, respectively. The standard deviation in regression (SD) of DeepBindRG on the CASF-2013 is 1.7306, which is also better than X-Score, ChemScore ChemPLP, PLP1, and G-Score which have SD value of 1.78, 1.82, 1.84, 1.86, and 1.87, respectively. The RMSE value of DeepBindRG on the CASF-2013 is 1.8170, which is higher comparing to the RMSE values of the validation and testing datasets (around 1.5). The possible reason is that many complexes in the CASF-2013 contain relatively small ligands , whereas training dataset has relatively lower percentage of such small ligands .R value of 0.6585, and a RMSE value of 1.7239, which indicates the relative strong correlation and small deviation between predicted and experimental measured values. From R value for the ASTEX_diverse_set is not as good as other datasets (0.422), while it still performs better prediction than the autodock vina.The performance of DeepBindRG on the CSAR_HiQ_NRC_set achieves an Ten failed predictions by autodock vina on the CASF-2013 were shown in In order to further test the effectiveness of our method, we selected the data samples from DUD.E dataset. Since the DUD.E dataset does not contain the experimental protein–ligand binding complex, it is a much more challenging test for our DeepBindRG model. The performance of DeepBindRG, Autodock vina, and pafnucy on four randomly selected subsets are shown in R = 0.66), But it is the one with the largest RMSE. All other models are completely unpredictive and the RMSE is just quantifying the amplitude of the noise or the spread of experimental values themselves. We notice that for some prediction cases of DeepBindRG, the R value is close to zero, while the RMSE value is relatively small. However, it should be noted that the accuracy of DeepBindRG has a lot of room for further improvement. The major challenges are: (1) how to generate conformations as close as possible to native structure, and (2) how to select a conformation that is native-like. From Except kith dataset, the DeepBindRG_Z shown better performance than DeepBindRG_X, DeepBindRG_Y in terms of RMSE. The correlation coefficient between predicted and experimental binding affinities are presented in The increasing availability of experimental protein–ligand complexes have allowed us to learn the underlying rules of protein–ligand interactions from the data by deep learning method. However, our work shows several challenges need to overcome before deep learning-based protein–ligand affinity estimators when applied to real applications. Both the DeepBindRG and pafnucy have poor performance on the four datasets from DUD.E while comparing with other extra test sets which have experimental conformation, this indicates the current method needs improvement in close-to-real application. The structure-based method requires high accurate protein–ligand binding conformation before prediction, while the accurate protein–ligand binding conformation is hard to obtain and such process is relatively time-consuming.There are several situations that can seriously affect the prediction accuracy: (1) the native-like conformation is not in the conformation pool ; (2) the criterion to select native-like conformation is not accurate enough; (3) the model trained by the accurate experimental structures cannot identify near to native conformation. The distribution discrepancy between training data and real application data is another challenge. In the training dataset, the strong binders are dominant, while in the real application, the non-binders are dominant, and weak binders are usually more than the strong binders. This can lead to the poor performance of the model. Our work shows above generalized problem of current deep learning methods, and indicates protein–ligand binding estimator models should focus to solve such problem instead of pursuing high accuracy on data which have experimental structure. A possible solution is to add many near native conformations to the training data set, for instance, the near-native as positive, and docked non-binder complexes as negative. Another possible solution is to increase the accuracy and efficiency of the docking method in sampling native-like conformation.https://github.com/haiping1010/DeepBindRG).In the present work, we developed a deep learning model “DeepBindRG” for identifying native-like protein–ligand complex. The accuracy of our method for evaluating protein–ligand binding affinity is comparable with pafnucy which uses much complicated 4D input representation. In normal cases, the simple deep learning model is susceptible to the artificial enrichment of the dataset, resulting in overly optimistic predictions of training dataset and test data set; however, DeepBindRG has performed well for several external independent data sets from different source. Since the datasets from DUD.E do not contain a native protein–ligand, this test is very challenging and close to real application. In this paper, we demonstrated the potential of ResNet and CNNs in identifying native-like protein–ligand complexes than other publicly available popular methods. Our result shows the more complicated CNN model ResNet can improve the prediction result comparing to the normal CNN. We also show that our model using more elaborate atom types from force field as input performs better than the simple element-based input. Using more spatial information of the interface between protein and ligand will aid to predict the affinity strength by implicit learning critical factors that determine protein–ligand interactions. By comparing with the 4D based CNN model pafnucy, our research shows the generalized problem (extreme dependent on native protein–ligand conformation) of the current deep learning model in protein–ligand affinity prediction, and indicates several critical point for developing high accurate protein–ligand affinity model: keeping spatial information; using deeper neural network to learn more abstract information; making the training and testing data set have the same feature distribution as the real application; keeping the elaborately atom type information. We believe DeepBindRG is a promising model to facilitate the drug development process, especially in discovering novel biologically active lead compounds for specific therapeutic protein targets. Our software is freely available for download in the GitHub public repository  rarely occurred in the training data of DeepBindRG model.Click here for additional data file.10.7717/peerj.7362/supp-12Supplemental Information 12The results of DeepBindRG_Z and Pafnucy on 4 datasets from DUD.E database. The left panel is the DeepBindRG_Z, the right panel is the Pafnucy.Click here for additional data file.