Identifying lncRNA–protein interactions (LPIs) is vital to understanding various key biological processes. Wet experiments found a few LPIs, but experimental methods are costly and time-consuming. Therefore, computational methods are increasingly exploited to capture LPI candidates. We introduced relevant data repositories, focused on two types of LPI prediction models: network-based methods and machine learning-based methods. Machine learning-based methods contain matrix factorization-based techniques and ensemble learning-based techniques. To detect the performance of computational methods, we compared parts of LPI prediction models on Leave-One-Out cross-validation (LOOCV) and fivefold cross-validation. The results show that SFPEL-LPI obtained the best performance of AUC. Although computational models have efficiently unraveled some LPI candidates, there are many limitations involved. We discussed future directions to further boost LPI predictive performance. Long non-coding RNAs (lncRNAs) are transcripts with greater than 200 nucleotides but lack protein coding capacity . lncRNAsRecently, one broad theme is that lncRNAs can drive the assembly of RNA–protein complexes by facilitating the regulation of gene expression . lncRNAsIn this study, we introduced relevant repositories, summarized computational models and algorithms for LPI prediction, discussed their advantages and weaknesses by comparison, and presented further directions for boosting LPI prediction performance. We focused on two categories of computational models: network-based methods and machine learning-based methods. The machine learning-based methods contain matrix factorization-based methods and ensemble learning-based methods.There are abundant repositories related to LPI prediction. These repositories provide diverse information for efficiently uncovering potential LPIs.http://www.noncode.org/) is an interactive database aiming to collect the most complete annotation for ncRNAs, especially lncRNAs. The latest NONCODE database (current version v5.0) contains lncRNA information from 17 species including human, mouse, cow, rat, chimp, gorilla, orangutan, rhesus, opossum, platypus, chicken, zebrafish, fruit fly, Caenorhabditis elegans, yeast, Arabidopsis, and pig. There are 548,640 lncRNAs in the latest version. There are 172,216 and 131,697 lncRNAs from human and mouse, respectively. More importantly, NONCODE has introduced some important features including conservation annotation, lncRNA–disease associations, and an interface to select credible datasets.The NONCODE database  (http://http://www.bioinfo.org.cn/NPInter/contact.htm) provides abundant association data that are experimentally verified. For example, the database contains information on interactions between noncoding RNAs (ncRNAs) and biomolecules including proteins, mRNAs, miRNAs, and genomic DNAs. The database contains 491,416 interactions in 188 tissues/cell lines from 68 types of experimental technology.The NPInter database  (http://http://www.rna-society.org/raid/) includes more than 40,668 lncRNA-associated RNA–protein interactions and more than 34,790 lncRNA-associated RNA–RNA interactions.The RAID database  (http://http://starbase.sysu.edu.cn/) contains more than 1,100,000 miRNA–ncRNA (CLIP) interactions, 117,000 RNA-binding protein (RBP)–ncRNA interactions, and 32,000 miRNA–ncRNA interactions. In addition, it provides more than 10,800 RNA-seq data and 10,500 miRNA-seq data from 32 cancer types and 3,236,000 mutations from 366 disease types.The starBase database  (http://http://www.rna-society.org/virbase) integrates experimental and predictive association information from manual literature curation and other resources based on one common framework from 119 species, especially ncRNA-associated virus–virus, host–host, host–virus, and virus–host interactions.The ViRBase database  (http://http://lulab.life.tsinghua.edu.cn/postar2/index.php) provides various post-transcriptional regulation data based on CLIP-seq, Ribo-seq, RNA-seq, and other high-throughput sequencing information from six species: yeast, Arabidopsis, fly, worm, mouse, and human. It hosts about 40 million RBP binding sites validated by CLIP-seq experiments. It provides three modules: the “RBP” module, “RNA” module, and “Translatome” module. The “RBP” module contains RBP binding sites and their annotations and functions. The “RNA” module is composed of a few sub-modules, including “disease,” “variation,” “crosstalk,” and “binding sites,” and is applied to annotate the RBP binding sites.The POSTAR2 database  (http://http://rna.sysu.edu.cn/chipbase/) is used to identify transcription factor binding sites and motifs, and decode transcriptional regulatory networks of miRNA, lncRNAs, and other ncRNAs from ChIP-seq data. It provides about 10,200 curated peak datasets from 10 species: human, mouse, fruit fly, worm, Arabidopsis thaliana, yeast, rat, zebrafish, Xenopus tropicalis, and chicken.The ChIPBase database  (http://https://lncipedia.org) is a comprehensive database. Its central work is to merge redundant transcripts from different data sources and group the transcripts into genes, thus producing a highly consistent database. The latest update of lncRNA (LNCipedia 5) contains information about annotation and sequence for 1,555 human lncRNAs from 2,482 lncRNA publications. This information originates from Ensembl  contains a comprehensive repository of lncRNA target genes to provide information about target genes regulated by lncRNAs. The latest version provides a special web interface in which users can search the targets for a particular lncRNA or the lncRNAs for a particular gene.The lncRNA2Target database  (http://http://lncrnadb.org) is a comprehensive database in compliance with the International Nucleotide Sequence Database Collaboration. It provides 287 eukaryotic lncRNAs and an interface enabling users to access sequence data, expression information, and the literature. The latest update of lncRNAdb integrated nucleotide sequence information, Illumina Body Atlas expression profiles, and a BLAST search tool.The lncRNAdb database  (http://http://bioinfo.life.hust.edu.cn/lncRNASNP2) provides 7,260,238 single nucleotide polymorphisms (SNPs) on 141,353 human lncRNA transcripts, and 3,921,448 SNPs on 117,405 mouse lncRNA transcripts. More importantly, it contains abundant information about mutations in lncRNAs and their impacts on lncRNA structure and function. It also provides online tools for analyzing new variants in lncRNA.The lncRNASNP2 database  (http://http://lncrna.big.ac.cn) integrated various human lncRNAs from different resources. It makes existing lncRNAs able to be updated, edited, and curated by diverse users. More importantly, any user can add newly uncovered lncRNAs.The lncRNAWiki database  (http://http://www.bio-bigdata.net/lnc2cancer) provides lncRNA–cancer associations supported by experiments. It contains 4,989 associations between 165 human cancer subtypes and 1,614 human lncRNAs, 366 experimentally validated circulating-related lncRNA-cancer associations, 593 drug-resistance-related lncRNA-cancer associations, and 1,928 prognosis-related lncRNA–cancer associations, and abundant lncRNA regulatory mechanisms in cancers including 211, 1139, 225, and 319 lncRNAs regulated by variant, miRNA, transcription factor, and methylation, respectively.The Lnc2Cancer database  (http://http://www.rnanut.net/lncrnadisease/) integrated experimentally validated circular RNA–disease associations, and regulatory mechanisms among mRNA, miRNA, and ncRNA. Particularly, it contains more than 200, 000 lncRNA–disease associations. In addition, it gives confidence scores for all ncRNA–disease associations and maps each disease to disease ontology and medical subject headings.The lncRNADisease database  (http://http://www.rna-society.org/mndr/) integrates more than 260,000 ncRNA–disease associations. These associations are supported by 10 experiments and 4 predictive algorithms. The experimental repositories include Lnc2Cancer . In addition, it provides 153,508 predicted lncRNA–disease associations and 28,144 predicted miRNA–disease associations for H. sapiens. MNDR contains 19,575, 110, 4,150, and 23 non-redundant lncRNA–disease, piRNA–disease, miRNA–disease, and snoRNA–disease interactions, respectively, associated with 1,416 disease.The MNDR database   is an important database providing protein sequences and annotations. It provides 80 million sequences and is a useful tool. Users can calculate a new proteome identifier to find a particular assembly for a species or subspecies. It also provides an effective measurement for computing an annotation score for all entries.The UniProt database   is the covariance of E1 and E2, and E1 and E2, respectively.where SP. SP is normalized as follows:Step 3 Extracting protein–protein interactions (PPIs) from STRING 9.1  and obtaM is a diagonal matrix, and M is the sum of row i in SP.where Step 4 Propagating the random walk to score for unknown lncRNA–protein pairs based on the following iterative equation:The details are shown as Step 1 Extracting 4,158 LPIs between 27 proteins and 990 lncRNAs from NPInter  and NONCStep 2 Obtaining three types of features for lncRNAs  and two types of features for proteins [interaction profile and CTD ].Step 3 Computing linear neighborhood similarity and regularized linear neighborhood similarity between lncRNA/proteins by Eqs. (4) and (5), respectively:iX denoted the feature vector of the ith lncRNA, and N(iX) is K nearest neighbors of iX.where where Step 4 Computing the interaction probabilities for unobserved lncRNA–protein pairs:The details are shown in Step 1 Extracting 4,158 high-confidence LPIs between 990 lncRNAs and 27 proteins from NPInter  and NONCStep 2 Calculating lncRNA–lncRNA similarity based on the Smith–Waterman technique:sw denotes the Smith–Waterman score between two lncRNAs il and jl.where Step 3 Calculating the protein–protein similarity matrix based on the Smith–Waterman technique:sw denotes the Smith–Waterman score between two proteins ip and jp.where jl, computing its bias ratings of lncRNAs for a protein ip with the agglomerative hierarchical clustering and associated measurement of minimum variance method:Step 4 For a given lncRNA crn is the number of lncRNAs in the cluster cr including jl, and iTp is the number of all lncRNAs interacting with ip.where Step 5 Finding LPI candidates based on the recommended bipartite network projection technique and bias ratings of every lncRNA for proteins:whereThe details are shown in Step 1 Obtaining 4,467 LPIs between 1,050 unique lncRNAs and 84 unique proteins from NPInter  and NONCStep 2 Constructing a protein–protein similarity network. LPISNFHS fused the sequence similarity, functional annotation semantic similarity (Go), domain similarity, and STRING similarity into a unified protein–protein similarity network based on the SNF technique.Step 3 Inferring novel LPIs by combining the HeteSim algorithm and heterogeneous LPI network.Step 1 Obtaining 4,796 LPIs between 1,105 lncRNAs and 26 proteins from NPInter  and NONCLsim based on lncRNA expression similarity and Gaussian interaction profile (GIP) kernel similarity, and protein similarity matrix Psim based on protein interaction similarity and GIP kernel similarity.Step 2 Computing lncRNA similarity matrix ip and lncRNA jl based on protein similarity and lncRNA similarity by Eqs. (18) and (19), respectively.Step 3 Computing the score between protein Step 4 Obtaining the initialized association score matrix as follows:kl over all proteins:Step 5 Computing the first-round scores of the lncRNA ip over all lncRNAs:Step 6 Computing the second-round scores of the protein Step 7 Computing the final association score matrix:W′ = W + αW2 and α ∈ .where The details are shown in Homo sapiens.” LPBNI then selects 2,380 lncRNAs based on a human lncRNA dataset provided by the NONCODE database .Step 2 Utilizing the LPI network to construct a bipartite graph G. For a lncRNA jl, LS (jl) denotes the score on jl after the first step of propagation:Step 3 Propagating known biological information in S0 (i) = ijs, i ∈ {i, 2,…, m} denotes the original information of P for a given lncRNA jl. ij=s 1 if ip associates with jl; otherwise, ijs = 0. ip.where L back to P. FS(ip) represents the final information on protein ip to denote the associated score between ip and jl:Step 4 Propagating all information in jl.where FS after the above two-step information propagation yieldsStep 5 Computing the final associated score S0, where The details are shown in Step 1 Describing lncRNA interaction profiles and protein interaction profiles as row vectors and column vectors based on the LPI network, respectively.ix and jx belong to the same cluster based on the ant colony clustering method:Step 2 Calculating the probability that two entities wherer is the cluster radius, jc is the cluster center of the jth cluster, and α ∈ , β ∈ , ρ ∈ , and Q ∈ .where kp, its association scores with all lncRNAs at the tth iteration Step 3 Applying lncRNA–protein bipartite network to identify LPI candidates. Given a protein W is a similarity matrix.where p1, p2,…, pm} can be represented as follows:The association scores for all proteins {Machine learning-based LPI prediction methods utilize machine learning-based models and algorithms to uncover potential LPIs. This type of method can be roughly classified into two categories: matrix factorization-based methods and ensemble learning-based methods.Y and two nonnegative matrices W ∈ ℜkxn and H ∈ ℜkxm the problem of predicting LPIs can be formulated as the following objective function:Matrix factorization is exploited in recommendation systems and has been widely applied to bioinformatics . Matrix A few LPI identification methods have been designed based on matrix factorization method.Step 1 Extracting LPI information based on data provided by NONCODE , NPInterStep 2 Computing lncRNA similarity and protein similarity.lS :LPGNMF computes the lncRNA expression profile similarity E1 and E2, LPIHN calculates lncRNA expression similarity based on the Pearson correlation coefficient:Given the expression profiles of two lncRNAs cov is the covariance of E1 and E2, and E1 and E2, respectively.where LPGNMF computes the weight matrix based on lncRNA similarity:N(il) and N(jl) denote the p nearest neighbors of il and jl.Here, l*S:LPGNMF then calculates the sparse similarity matrix of lncRNAs pS*.Similarly, LPGNMF calculates the sparse similarity matrix of proteins Step 3 Building the following optimization model based on the graph regularized nonnegative matrix factorization method:The details are shown in Step 1 Extracting the lncRNA sequence, protein sequence, and LPIs based on data provided by NONCODE , NPInterLSM and protein sequence similarity matrix PSM based on the Smith–Waterman algorithm:Step 2 Computing lncRNA sequence similarity matrix A of lncRNAs:Step 3 Defining neighborhood information for lncRNAs and obtaining the adjacency matrix B of proteins.Similarly, LPI-NRLMF computes the adjacency matrix NS for unknown lncRNA–protein pairs based on the neighborhood regularized logistic matrix factorization model:Step 4 Computing associated scores iu ∈ ℜ1xr and jv ∈ ℜ1xr can be computed by the following neighborhood regularized logistic matrix factorization model:Here, PL can be computed. U ∈ ℜmxr and V ∈ ℜ1xr can be calculated by dividing L.where The details are shown in Step 1 Extracting the lncRNA sequence, protein sequence, and LPIs from NONCODE , NPInterLS and protein sequence similarity matrix PS based on the Smith–Waterman algorithm:Step 2 Computing the lncRNA sequence similarity matrix RS for unknown lncRNA–protein pairs:Step 3 Building a random walk model to compute associated scores ijr represents the extent of association between a neighbor jv and a protein p for a given node iv. L(ijl)MxM is computed by L into two arrays of UL and QL.where NS for unknown lncRNA–protein pairs based on the neighborhood regularized logistic matrix factorization model:Step 4 Computing associated scores iu ∈ ℜ1xr and jv ∈ ℜ1xr can be computed by the following neighborhood regularized logistic matrix factorization model:U ∈ ℜmxr and V ∈ ℜnxr.where Step 5 Computing the final associated scores for unknown lncRNA–protein pairs:The details are shown in Step 1 Computing lncRNA kernels and protein kernels from four levels.Level 1 GIP kernel:The GIP kernels between two lncRNAs and two proteins are defined as follows, respectively:Level 2 Sequence kernel:The sequence kernels of two lncRNAs and two proteins are defined as follows, respectively:SW is the Smith–Waterman score, and S represents the sequence information of a lncRNA/protein.where Level 3 Sequence feature kernel:Constructing radial basis function kernels Level 4 lncRNA expression kernel:Calculating the expression kernel of lncRNA database .Step 2 Fusing the above kernels to generate the optimal kernel based on kernel target alignment:Step 3 Constructing the following model to compute interaction probabilities for unobserved lncRNA–protein pairs based on matrix factorization, low-rank approximation, and eigen decomposition:The details are shown in Ensemble learning methods are widely applied to LPI prediction. HLPI-Ensemble  and SFPEIn the first process, HLPI-Ensemble downloads lncRNA sequences, protein sequences, and LPIs from NONCODE , UniProtIn the second process, HLPI-Ensemble utilizes the ensemble technique and generates three ensemble learning frameworks, HLPI-SVM, HLPI-XGB, and HLPI-RF. These three frameworks are based on support vector machines (SVMs), extreme gradient boosting (XGB), and random forests (RFs), respectively. The details are shown in ℓ1,2-norm regularization, ensemble graph Laplacian regularization, and various biological information into a unified framework. It can be roughly broken down into five steps.Step 1 Downloading LPIs, lncRNA sequences, and protein sequences from NPInter , NONCODEStep 2 Describing lncRNA and protein features based on sequence information and known LPIs.L can be represented asSFPEL-LPI describes lncRNA features based on parallel correlation pseudo dinucleotide composition (PSEDNC). Given the occurrence frequency of different dinucleotides and the physicochemical properties of every dinucleotide, the PseDNC feature vector for an RNA sequence whereY: In addition, SFPEL-LPI represents the interaction profile of an lncRNA as a row vector of the LPI matrix SFPEL-LPI describes protein features based on the parallel correlation pseudo amino acid composition (PseAAC):whereY: Similarly, the interaction profile of a protein can be defined as a column vector of the LPI matrix a features for lncRNAs/proteins can be represented as feature matrix: Therefore, Step 4 Computing lncRNA similarity and protein similarity.SFPEL-LPI first computes the linear neighborhood similarity of lncRNAs based on PseDNC and IP.SFPEL-LPI then computes the Smith–Waterman subgraph similarity (SWSS) of lncRNAs:Similarly, the PseAAC similarity, IP similarity, and SWSS similarity of proteins can be computed.b types of similarities of lncRNAs/proteins can be represented as b similarity matrices Therefore, Step 5 Computing the association scores for novel lncRNAs/proteins based on Eqs. (63) and (64).iG, R, and θ can be obtained by solving the following optimization model:The details are shown in There are several methods used to predict possible LPIs except for matrix factorization-based methods and ensemble learning-based methods, for example, Fisher's linear discriminant-based LPI prediction method (IncPro) , eigenvaStep 1 Downloading complexes data from the PDB database.Step 2 Encoding sequence information into numerical feature vectors for lncRNAs and proteins based on the secondary structure, the Van der Waals' propensities, and the hydrogen-bonding propensities.Step 3 Transforming the feature vectors to unify the dimension based on the Fourier series:L is the length of feature vector of lncRNAs/proteins.where p|M|r> for the RNA feature vector r and a protein feature vector p based on Fisher's linear discriminant method:Step 4 Calculating the final score matrix < Step 1 Downloading lncRNA sequences, protein sequences, and LPIs from NONCODE , UniProtLSM and protein sequence similarity matrix PSM based on the Smith–Waterman algorithm:Step 2 Computing the lncRNA sequence similarity matrix Step 3 Calculating the score matrix based on the following objective function:wherelL = I – LSM and pL = I – PSM denote the Laplacian matrices of lncRNAs and proteins, respectively.and LPI-ETSLP can obtain the final scores between unobserved lncRNA–protein pairs by integrating eigenvalue transformation into Eq. 70:lŪ is a diagonal matrix with lL = I – lD–0.5l DlK–0.5 and the eigen decomposition of lK can be expressed as where The details are shown in Step 1 Computing lncRNA GIP, sequence feature, sequence similarity, and lncRNA expression kernels Step 2 Computing protein GIP, sequence features, protein sequence similarity, and protein GO kernel Step 3 Generating the optimal lncRNA and protein kernels with fast kernel learning:wlnc and wpro, respectively; where lncw or prow:Step 4 Constructing the optimization model to compute the optimal solution for w denotes the optimal solution lncw or prow, uK and vK denote two different kernel matrices, and tr(·) denotes the trace function.where Step 5 Computing lncRNA–protein association score matrix:F* by adjusting the parameters λℓ and λp.Step 6 Producing the optimal The details are shown in lncRNAs play important regulatory roles in diverse biological processes, such as protein modification, DNA methylation, and chromosome . HoweverHowever, experimental methods are expensive and time-consuming. For limited existing knowledge, computational methods become vital as a silver-bullet solution to capture LPIs on a large scale, which contributes to prioritize LPI candidates and deploys further experimental validation .In this study, databases involved in LPI identification are summarized. More importantly, the components of state-of-the-art computational models for LPI prediction, such as network-based methods and machine learning-based methods, are introduced. Particularly, machine learning-based models can be broken into matrix factorization-based methods and ensemble learning-based methods. To consider the performance of LPI prediction methods, we compared nine models  on leave-one-out cross-validation (LOOCV). These nine models are conducted on the datasets provided by the corresponding papers. Parameters are set as the values recommended by the corresponding studies. To further detect the performance of SFPEL-LPI, we compared it with four representative LPI prediction methods, LPBNI, LPI-ETSLP, LPIHN, and LPLNP, on fivefold cross-validation. The experiments were conducted on the same dataset, i.e., LPIs, lncRNA sequences, and protein sequences are from NPInter , NONCODEIn general, network-based methods have become one type of effective tool in possible LPI identification by utilizing LPI network, lncRNA similarity network, and protein similarity matrix. Although network-based methods efficiently discovered unknown LPIs and obtained promising results from the perspective of propagation , this tyParts of computational methods tested their performances only on one database, which may result in biased predictions because of the sparse nature of LPI data . More imIt is important to unravel potential LPIs for lncRNAs/proteins without any associated information (we represent these lncRNAs/proteins as new lncRNAs/proteins); however, most network-based models fail to capture LPI candidates .Current network-based methods tend to be biased to the lncRNAs/proteins with more known associated proteins. Some lncRNAs/proteins interact with multiple proteins/lncRNAs and others interact with a few or even only one protein/lncRNA in an LPI network. The unbalanced nature of degree distributions in the LPI network may affect prediction performance. Increasing resistance based on the random walk may improve predictive accuracy for LPI prediction models .Parts of methods compute lncRNA similarities based on the expression profile and may produce incomplete coverage of the lncRNA similarity network when adding LPI datasets. This problem may be solved by increasing appropriate data including LPIs .Network-based methods can be applied to an LPI network in which there exists at least one link between two nodes. Especially for a bipartite network, network-based methods require that each node in the network has at least two linkages. However, the LPI network is usually composed of a few isolated subnetworks, and most of the existing network-based models fail to identify the LPIs between the lncRNAs in one subnetwork and the proteins in another .Most current network-based methods utilized local network information and showed better performance; however, many previous computational biology studies showed that global network information contributes to capturing the associations between two entities, such as LPIs .Biology finally aims at providing personalized medicine for cancer patients, and it is a key issue to predict relevant drugs/targets for a certain disease by integrating multiple heterogeneous networks and constructing multiple-partite biological networks, such as protein–lncRNA–disease association networks and drug–protein–lncRNA–disease networks. However, current network-based methods are still not applied to this type of prediction .In summary, machine learning-based LPI prediction methods have some limitations.There are no non-LPIs (negative samples) with experimental validation; therefore, most supervised learning-based LPI prediction models can only randomly select unknown lncRNA–protein pairs as negative LPIs. However, this part of randomly selected negative LPIs may contain true LPIs (positive samples) as well, which significantly influences the predictive performance . AlthougSome machine learning-based methods constructed two different classifiers, based on lncRNAs and proteins, respectively. The final results are an average of the performances of two predictive models. This type of model will produce biased results .Many lncRNAs/proteins do not have known association information with any proteins/lncRNAs, and we represent them as new lncRNAs/proteins. Most current predictive models are unable to capture possible proteins/lncRNAs for new lncRNAs/proteins .The proposed methods rely heavily on known LPI data; however, the current number of known LPIs is still very low. Therefore, most machine learning-based models are trained using RNA–protein interaction information instead of LPI data. This results in limited predictive performances . With thThe better performances of existing machine learning methods rely severely on data called features . CurrentMost experimental data are provided by the NPInter database. NPInter is a relatively abundant database for lncRNA and protein data, but it only provides gene–protein interaction data corresponding to relevant lncRNAs instead of direct LPIs. Gene–protein interactions were directly applied to machine learning-based methods to find possible ncRNA–protein associations and did not discover true LPIs .Most current computational models for LPI interaction prediction are measured based on cross-validation. There are a few LPIs and numerous unknown lncRNA–protein pairs not validated by experimental methods in the existing databases. In addition, similar lncRNAs tend to interact with similar proteins, and vice versa . TherefoParts of computational methods tested their performances only on one database, which may result in biased predictions because of the sparse nature of LPI data . More imFocusing on the drawbacks of current network-based LPI identification methods, future research can begin with integrating more heterogeneous networks, such as protein–protein interaction network , lncRNA–Although there are not currently data conflict solutions for LPI prediction, we can find some clues by other problems in the area of bioinformatics. For example, There are some known LPIs (positive samples) and abundant unknown lncRNA–protein pairs in existing LPI data resources. More importantly, there are no experimentally validated non-LPIs, and thus most supervised learning-based models have no other choice but to randomly screen negative LPIs from unlabeled lncRNA–protein pairs or even regarded all unlabeled lncRNA–protein pairs as negative samples . Howeveri and a protein j, for an RNA k, the more differences between i and k, the less possibility that k interacts with j, and vice versa. FIRE screened negative RNA–protein interactions through the following steps: computing the protein similarity matrix, building a positive sample set based on known interaction information, scoring an unknown RNA–protein pair not included in positive sample set based on protein similarities, generating m negative samples by sorting these RNA–protein pairs via their scores in increasing order, and selecting the top-m RNA–protein pairs. Similarly, we may generate negative LPIs based on lncRNA–lncRNA similarities, protein–protein similarities, and the above assumption.Positive-unlabeled (PU) learning  is appliExisting computational methods have utilized different lncRNA features and protein features. For example, Deep learning-based computational models composed of multiple processing layers require very little engineering knowledge and can efficiently extract features from raw data and construct high-level representations . These tParticularly, deep learning can be combined with PU learning and improve the performance of computational models . For exaNetwork-based methods can be applied to an LPI network that has least one link between two nodes. For a bipartite network especially, network-based methods require that each node in the network has at least two linkages. That is to say, network-based methods cannot discover possible proteins for any lncRNA–protein pair without any known reachable paths in the LPI network . These lGiven a known LPI dataset, we aim to predict (S1) LPIs between known lncRNAs and known proteins; (S2) LPIs between new lncRNAs and known proteins; (S3) LPIs between known lncRNAs and new proteins; and (S4) LPIs between new lncRNAs and new proteins. S1 has the most abundant association information, S2 and S3 have less data, and S4 has the least data. Computational models appropriate for S2 can still be applied to S3, and vice versa.To the best of our knowledge, SFPEL-LPI provided by Inspired by the evaluation methods proposed by LP and FL contributed equally to this work. LP, FL, XD, CP, and LZ introduced LPI data repositories and computational models. LP and FL wrote the paper. XL and YM revised original draft. LP, JY, GT, and LZ discussed the computational models and gave conclusion and further research. All authors read and approved the final manuscript.This research was funded by the Natural Science Foundation of China (Grant 61803151), the Natural Science Foundation of Hunan province , and the Project of Scientific Research Fund of Hunan Provincial Education Department (Grant 17A052). Authors GT and JY were employed by the company Geneis (Beijing) Co. Ltd.The remaining 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.


Forests present one of the most challenging environments for computer vision due to traits, such as complex texture, rapidly changing lighting, and high dynamicity. Loop closure by place recognition is a crucial part of successfully deploying robotic systems to map forests for the purpose of automating conservation. Modern CNN-based place recognition systems like NetVLAD have reported promising results, but the datasets used to train and test them are primarily of urban scenes. In this paper, we investigate how well NetVLAD generalizes to forest environments and find that it out performs state of the art loop closure approaches. Finally, integrating NetVLAD with ORBSLAM2 and evaluating on a novel forest data set, we find that, although suitable locations for loop closure can be identified, the SLAM system is unable to resolve matched places with feature correspondences. We discuss additional considerations to be addressed in future to deal with this challenging problem. Mobile robotic systems have the potential to aid in forest management by improving the efficiency, scale and accuracy of tree health data gathering. In order to do this, robots must be able to navigate autonomously around large GPS-denied forest environments where modern visual Simultaneous Localization and Mapping (SLAM) techniques often struggle  and can update its beliefs about intervening states, reducing the effect of drifting sensor readings. The place recognition algorithms used for loop closure in many classic SLAM techniques work by converting images to local descriptors and performing a search over previous descriptors for a likely match.Deep learning approaches, particularly Convolutional Neural Networks (CNNs), such as NetVLAD  and do not usually overlap with each other. A proposed match here is clearly correct or incorrect. In a robotic loop closure context, input images are a continuous, if down-sampled, video stream with no discrete places and plenty of overlap. The challenge here is not to always find all of the closest matches for all images, but rather to recognize the general similarity of the current location to a previously visited region, and thus initiate a process of view alignment.Three state of the art loop closure algorithms are compared in Lowry and Andreasson : a binarImage matching is a popular domain for Deep Neural Networks, and when the images being matched against represent locations the task is known as place recognition  and passed through the network, which provides an ordered list of possible scene category labels. The top prediction is used as the classification label for the frame. We record the percentage of frames classified as one of the “forest” labels, as well as noting other commonly assigned labels. This process is repeated for each dataset.We set up NetVLAD using the Tensorflow code and pre-trained weights provided by Cieslewski et al. . The onlWe duplicate the methodology described in Lowry and Andreasson  for evalTo make this task more difficult, database and query images are also cropped from the full image frame such that they are offset by 40% from one another.We use the same two SFU video pairs as Lowry and Andreasson : with thWe wish to evaluate performance on more forest data than just the SFU videos, but what little data is available does not have ground truth, primarily due to the poor performance of GPS under canopy cover. Instead we use a methodology of splitting a single video into two by alternately sampling frames. This way, our database and query videos do not contain any of the same frames but two frames sampled from next to each other should usually still represent the same place.There are two parameters to consider when using this method. First, the step size between places in a video, which we set to 3 s  in order to generate an equivalent number of places as in the SFU dataset (~150). Given the speed of the UAV recording the Hillwood data, this offset should represent a maximum of 5 m between places. The second parameter is the offset between the database and query frames. At an offset of 0, the sets would be identical, whereas if the offset equals the step size the “ground truth” identity of the frames would be misaligned. We test a range of values between these two unhelpful extremes.Our final evaluation is of NetVLAD's potential as a loop closure mechanism in visual SLAM, which we perform in two ways. Firstly, we compare the descriptors generated for each dataset route against themselves and plot the distances as a confusion matrix to look for regions of low distance at the sites of known loop closures. Settings for distance thresholds or other mechanisms of identifying a loop closure will vary between SLAM systems, but usually rely on repeated recognition of similarity within a short period of time. This method allows us to evaluate whether regions of similarity emerge from the system without artificially defining the boundaries of “places” within the data.Secondly. we perform an integration with ORBSLAM2 . The artificial lighting conditions seem the most likely explanation for this, which is not unsurprising given the results of Garforth and Webb  and furtAside from in simulation, initial results show that the network underlying NetVLAD is able to learn features specific to forests, which reassures us that full NetVLAD has the potential for describing places within forests.The rates of correct matches in Somewhat surprisingly, NetVLAD also performs better on the canopy-covered sections of forest at the end of the SFU datasets than it does on the track as a whole. The track at the start of the SFU videos must be difficult to distinguish, likely due to most frames consisting solely of a road and surrounding treeline. To the eye these images are very similar, as noted in Garforth and Webb . Under tUnder canopy cover NetVLAD also shows resistance to changing environmental conditions, achieving performance on the difficult dry-night pairing that matches VLAD's performance on the two easier pairings. The skyline is not visible in the night time data, so the performance here implies that this isn't the only useful feature being learned.We generated confusion plots of distances between pairs of NetVLAD descriptions for videos from robots traversing forest terrain. We can make out columns of blue (low distance) in The only repeated traversals of the same path in the SFU video occur while facing in opposite directions. These are challenging conditions for any place recognition, so this video is test of how far NetVLAD can be pushed. If we look at sample frames from the regions NetVLAD indicates are similar in The results so far demonstrate this network's potential for use as a loop closure mechanism for SLAM in forests. Given this, our final experiment was the integration of NetVLAD descriptors in ORBSLAM2 as a loop closure proposal mechanism. Unfortunately, even though the system proposed loop closures at appropriate frames (such as those shown in The likely cause of these difficulties is the very highly textured scenes providing an abundance of potential features, or the notable change in lighting that occurs even in the few minutes between the images in As we are reporting work with a new forest dataset, we present here the scene statistics (as described in Garforth and Webb, We can see that “Forest_Loop” is less variable in its lighting , 10 thanIn this paper we presented an investigation into the use of place recognition network NetVLAD for loop closure in challenging forest mapping scenarios. Even though NetVLAD itself has not been trained to recognize places in forests, we showed that its underlying VGG-16 network does categorize a variety forest datasets correctly. We then demonstrated that NetVLAD performs better than state of the art loop closure approaches in this difficult environment, robust to the presence of changing lighting, time of day, weather conditions, rotation, and translation. Our results marked NetVLAD as a candidate for proposing loop closures for SLAM, and we put together a test system as well as gathering new data to confirm. We found that NetVLAD reliably proposed loop closures to ORBSLAM, but that the frame to frame ORB feature matching of the SLAM system was not able to integrate these into the map. This is a useful warning for anyone attempting SLAM in forests, and supports feature matching as one avenue for further research.A number of other improvements can be made to our test integration with SLAM to produce a system ready for deployment. Given ORBSLAM's difficulty aligning feature points at proposed loop closures, we would propose investigating a feature-less “direct” SLAM method like DSO instead. Once a pairing with good performance is found, we would replace our offline NetVLAD descriptor calculation with one that runs in real-time. Finally, to improve scalability as the map gets larger we would consider a hierarchical graph and sequence based search method, like that described in Vysotska and Stachniss .http://autonomy.cs.sfu.ca/sfu-mountain-dataset/. The rest of the data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.The SFU dataset can be found at The work for this paper was undertaken by JG. Supervision and proof reading were provided by BW. All authors contributed to the article and approved the submitted version.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.


We investigate aspects of topology in protein folding. For this we numerically simulate the temperature driven folding and unfolding of the slipknotted archaeal virus protein AFV3-109. Due to knottiness the (un)folding is a topological process, it engages the entire backbone in a collective fashion. Accordingly we introduce a topological approach to model the process. Our simulations reveal that the (un)folding of AFV3-109 slipknot proceeds through a folding intermediate that has the topology of a trefoil knot. We observe that the final slipknot causes a slight swelling of the folded AFV3-109 structure. We disclose the relative stability of the strands and helices during both the folding and unfolding processes. We confirm results from previous studies that pointed out that it can be very demanding to simulate the formation of knotty self-entanglement, and we explain how the problems are circumvented: The slipknotted AFV3-109 protein is a very slow folder with a topologically demanding pathway, which needs to be properly accounted for in a simulation description. When we either increase the relative stiffness of bending, or when we decrease the speed of ambient cooling, the rate of slipknot formation rapidly increases. Topological techniques are often very powerful. They are used with great success to analyze numerous problems in Physics, from theories of fundamental interactions to models of condensed matter. Here we propose to introduce topological techniques to study protein folding and dynamics, where thus far these techniques have been used only sparsely. As an example we analyze the formation of knots and self-entanglement in protein folding. A knot along a protein backbone is a delocalized structure, with a definite topological character. A knot can not be removed by any small amplitude local motion such as twisting, bending or crumpling of the protein backbone. A knotty self-entanglement persists as long as there is no backbone chain crossing, and provided the C and N terminals of the protein are not circumnavigated.For a long time it was thought that there are no, or only very few knotted proteins –3. But sTopology is a global, collective property of a system. Thus, in the analysis of topological structures, such as knotty entanglements in a protein, global techniques that are based on topological concepts should be preferable. However, the prevailing ambition in the protein modeling community is to simulate the entire folding process as atomistically as possible, within the limitations of available computer resources. The commonly available techniques aim to describe protein folding primarily as a local process, with little if any regard to global aspects such as the backbone topology. Instead, global aspects of protein structure and dynamics are presumed to only reflect the local character of native contacts, and other local details. For example, in all-atom simulations the interaction range between individual atoms is commonly cut off beyond 10-12 Å, and in Gō-type models the range of interactions can be even shorter. Since knottiness is not a local, but a global characteristic of a protein backbone, we suspect that such a preference to localize all interactions is a reason why it appears to be very hard to simulate the folding of a knotty protein with a high success rate. At least unless one introduces some kind of bias such as funneling, steering or other method of  augmentation , 12, 19,Here we introduce and develop global techniques, as a complement to the already existing local ones, to account for topological aspects of protein folding dynamics. For this we scrutinize the folding and unfolding processes of a protein with a knotty self-entanglement. Our approach builds on an effective theory description. We employ a mechanical free energy that describes the entire protein tertiary structure as a single topological object, in lieu of the individual atoms or other highly localized interaction centers. The protein is modeled as a topological multi-soliton solution of a nonlinear difference equation that determines the critical points of a mechanical free energy; the equation that identifies the critical points resembles the discrete nonlinear Schrödinger (DNLS) equation , 21.α backbone as a single entity. From the point of view of an energy landscape description [Soliton solutions of non-linear difference and differential equations are commonly encountered when searching for principles of structural self-organization in physical scenarios. The nonlinear Schrödinger equation is the paradigm equation for describing topological solitons. In our approach an individual topological soliton models a single super-secondary protein structure such as a helix-loop-helix or a strand-loop-strand motif. We use the DNLS equation to combine together several mutually interacting individual soliton profiles into a multi-soliton that models the entire Ccription , the mulOur multi-soliton description is computationally highly effective: When we start from a random chain, the folding simulation proceeds several orders of magnitude faster than in any other computational approach to protein folding that we are aware of. Thus we can perform numerical simulations to analyze the mechanism of protein self-entanglement and the dynamics of knot formation, with a very high level of computational efficiency using a simple laptop computer.By its design, our mechanical free energy with its multi-soliton critical points models the protein backbone at very low temperature, with no consideration to thermal fluctuations. In order to study the folding and unfolding patterns, we subject the multi-soliton to a series of repeated heating and cooling simulations. For this we use Monte Carlo methodology and the Glauber algorithm that we adapt to proteins , to descα backbone of the folded AFV3-109 protein as a solution of the generalized DNLS equation. We use the crystallographic Protein Data Bank (PDB) structure with PDB code 2J6B [α/β protein that, when folded, supports a self-entangled structure akin the shoelace slipknot. Even though the slipknot is technically not a proper knot, it has nevertheless a topological character, with a large degree of structural stability under local backbone deformations; the topological character of the slipknotted AFV3-109 is similar to that encountered in other, more elaborately self-entangled knotty proteins [We start and construct a multi-soliton that models the three-dimensional Code 2J6B  as a decproteins –19. Moreproteins , but theproteins –19.topological solitons [α protein backbone geometry. For this we use the formalism of discrete Frenet framing [ith Cα atom, a discrete Frenet frame comprises the mutually orthonormal backbone tangent (ti), binormal (bi) and normal (ni) vectors, as followsri  are the Cα coordinates. These vectors are subject to the discrete Frenet equation [θ are the virtual backbone bond angles, they are computed as follows,ϕ are computed as follows,T2 and T3 are 3 × 3 matrices that generate three dimensional rotations with (Ti)jk = ϵijk, the permutation symbol. By substituting  in terms of the Cα coordinates ri. Conversely, when we know  and the distances between neighboring Cα atoms, we can reconstruct the Cα coordinates ri by solving  coordinates [We model protein structures in terms of solitons , 21. The framing : At the equation (ni+1bi+(bi+1·bi)The T2 ardinates –31, evenrdinates . Thus, iθ, ϕi) values governs the entire conformational space of the Cα backbone. Various effective theory conformational free energy functions have been previously constructed in terms of these coordinates. Examples include the fully flexible chain model and its extensions [α backbone angles  of each and every Cα atom, as the dynamical variables to introduce a refined extension of these earlier models. Ours is the following mechanical free energy [The set of all possible  coincides with the energy function of the discretized non-linear Schrödinger (DNLS) equation in the standard Hasimoto representation [The first sum over index entation .i extends the DNLS energy function in a manner appropriate for modeling a folded protein: The first term in this sum, with parameter ck, is a Proca mass term. In combination with the second term of the first sum, it constitutes the Kirchhoff energy of an elastic rod [bk, is the conserved momentum in the integrable DNLS hierarchy. The third term, with parameter ak, is the conserved helicity in the DNLS hierarchy. These two terms break the parity symmetry, and make the backbone right-handed chiral .The second sum over the index stic rod . The secV(ri − rj), models long distance interactions along the chain. However, a topological soliton is also highly non-local, it is an extended topological object. Thus the topological multi-soliton that we construct to model the Cα chain also describes non-local effects due to long range interactions. Thus, to avoid double counting we include in this last term only a hard-core Pauli repulsion that prevents the chain from self-crossing. For this we use a step-wise profile that keeps any two Cα atoms at least 3.8 Å apart. We refer to [V(ri − rj).The last term in the free energy, a sum over two-body interactions refer to  for detak∈solitons extends over all the individual super-secondary loop segments, such as a helix-loop-helix or a strand-loop-strand motif, that characterize the local Cα geometry. The parameters  have constant values in any individual motif, but these parameter values are in general different for different motifs, with different amino acid structures.Finally, the sum with index The mechanical free energy  is desigti: For the  dependent terms this follows from  and andti: Fows from  and for e energy  can be iα geometry, with a prescribed precision. The critical points of  are variable while the parameters λk, mk, ak, … have constant values, over the entire single soliton. The multi-soliton solution of θi=0δFδϕi=folding experiments where the protein folding and unfolding processes are controlled by temperature variations, rather than by e.g. ō models , 6 that n we independently change either the torsion angle ϕi or the bond angle θi at a randomly chosen site i and compute the ensuing change ΔF in the free energy  where Fθ is computed from all the θ-dependent terms in  of a knotted configuration.α/β protein consists of a β-sheet with five β-strands that are connected by helices and loops. The residues 78-106 make up a looped segment that is threaded into a slipknot through a knotting loop that consists of residues 8-77: The In the case of the archaeal virus protein AFV3-109 studied here, the Protein Data Bank structure 2J6B that we use as a decoy for constructing the multi-soliton solution has 109 amino acids. The core of this GaugeIT and for determining the parameter values we use the software Propro. Both are accessible through https://protoin.ru/propro/index.php.For identifying the soliton profiles we use the software α backbone of 2J6B as a minimum energy critical point of the mechanical free energy  between the two is 1,23 Å, comparable with the reported resolution 1.3 Å of the experimental structure. α trace bond angles  between the 2J6B crystallographic structure and the multi-soliton, during the heating a) and cooling b) phases of the simulation. The final average RMSD value shown in the Rg.The Rg at low temperatures, during the very early stages of the heating simulation. We observe a very slight but systematic initial decrease in the Rg value. This means that when the chain starts unknotting, its effective volume initially shrinks, before the chain then expands and unfolds: The initial slipknotted multi-soliton structure appears to be (slightly) swelled in comparison to an unknotted but collapsed structure, even though the free energy of the slipknot has a lower value. As shown in Rg i.e. swelling at the end of the cooling period, before the energy minimum is reached.In the Rg, at the visual level of the structure the effect is much more clear. The Rg together with the fully folded conformation; the configuration with minimal Rg value appears more compact.Even though the swelling is very small in terms of the radius of gyration value A swelling that is caused by knottiness has been previously reported in an analysis of knotted crystallographic PDB structures .ϕ in the values of the torsion angle  slowly increases. This increase coincides with the decrease in the radius of gyration Rg shown in 10β ≈ 13.0 we observe a sharp order-disorder transition in the fluctuation amplitude Δϕ(61). At this β-value the slipknot opens, and becomes converted into a trefoil knot shown in Rg as shown in We have analyzed the folding and unfolding transitions by following the temperature dependent fluctuations Δon angle . In termThe folding transition proceeds similarly but in the opposite direction, the slipknot forms through an intermediate trefoil knot.ϕ along the entire chain. The first entire regular structure that melts is the β-strand that is located between PDB residues 74-80. This strand forms the edge of the looped segment that is threaded into a slipknot. In general, the β-strands appear to unfold at lower temperatures than the α-helices, and the third α-helix between residues 93-99 appears to be the last to unfold as temperature increases.The α structure based Gō models. These authors do not report on trefoil knot folding intermediate, but they report significant difficulties in reaching the natively folded knotty conformation. Apparently, a knotted fold either does not appear at all [Previous authors have investigated the folding of knotted proteins, mostly using different variants of Cr at all  without r at all  . We havr at all , 46, pror at all .α backbone bond angles are known to be very stiff and the torsion angles are known to be quite flexible. Our simulation results show that when one properly accounts for the relative stiffness/flexibility of the bond and torsion angles, the Cα backbone of AFV3-109 practically always folds into a slipknot; misfolded conformations are indeed very rare.Finally, when we increase the relative stiffness of the bond angles, the rate of the slipknotted final conformations increases: We always observe both the trefoil folding intermediate and the slipknot final fold, at the end of our long and slow simulations in the limit where the bond angles have constant values during the entire heating and cooling process, when only torsion angles are truly mobile. In actual proteins the virtual CTopology and in particular self-entanglement play an important role in protein folding and dynamics. But topological effects are difficult to investigate. Moreover, conventional simulation approaches aim to describe a protein and its folding as a local process, at atomic level precision. Due to limitations in available computational resources it then becomes very difficult to detect large scale collective motions and global, topological phenomena in the conventional simulation approaches. Nevertheless, knotty and other kind of self-entanglements are often important to protein stability, and presumably also important for the correct biological function. Thus there is value to develop global, topological approaches to protein folding and dynamics, as a complement to local, atomic level scrutiny based approaches.α backbone of a protein in terms of a single topological multi-soliton entity; the multi-soliton describes the minimum of a mechanical free energy. As a case study, we have investigated the folding and unfolding of the slipknotted AFV3-109 protein, instigated by variable ambient temperature, using powerful state-of-art Monte Carlo techniques of non-equilibrium thermodynamics. We have found that the multi-soliton describes the formation of the slipknot very accurately, and we are able to describe the folding pathways and make predictions on the physical origin of knot formation. In paricular, we have been able to observe a trefoil knot as a folding intermediate. Our results demonstrate the value of developing global approaches to protein folding and dynamics; global approaches are highly accurate, and even though they may lack in atomic level details they appear to correctly capture the global, topological aspects of self-entanglement during protein folding and dynamics.We have developed a global technique that is rooted on topological concepts, to analyze and describe the formation of topological structures in proteins, in particular aspects of knottiness and self-entanglement. For this, we have modeled the entire CS1 File(PDF)Click here for additional data file.S1 Movie(MP4)Click here for additional data file.S2 Movie(MP4)Click here for additional data file.S3 Movie(MP4)Click here for additional data file. 27 Oct 2020Attachmentreply.pdfSubmitted filename: Click here for additional data file. 14 Dec 2020On topology and knotty entanglement in protein foldingPONE-D-20-33773Dear Dr. Niemi,We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at onepress@plos.org.If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact Kind regards,Oscar MilletAcademic EditorPLOS ONEAdditional Editor Comments :Reviewers' comments:Reviewer's Responses to QuestionsComments to the Author1. Is the manuscript technically sound, and do the data support the conclusions?The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes**********2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: N/A**********3. Have the authors made all data underlying the findings in their manuscript fully available?PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.The Reviewer #1: Yes**********4. Is the manuscript presented in an intelligible fashion and written in standard English?PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.Reviewer #1: Yes**********5. Review Comments to the AuthorPlease use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. Reviewer #1: The revised manuscript "On topology and knotty entanglement in protein folding" from authors Alexander Begun, Sergei Liubimov, Alexander Molochkov, Antti J Niemi can be accepted for publication in PLOS One.**********what does this mean?). If published, this will include your full peer review and any attached files.6. PLOS authors have the option to publish the peer review history of their article (If you choose “no”, your identity will remain anonymous but your review may still be made public.Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.Reviewer #1: No 4 Jan 2021PONE-D-20-33773 On topology and knotty entanglement in protein folding Dear Dr. Niemi:I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. onepress@plos.org.If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact plosone@plos.org. If we can help with anything else, please email us at Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staffon behalf ofDr. Oscar Millet Academic EditorPLOS ONE


Noncovalent interactions are the keys to the structural organization of biomolecule e.g., proteins, glycans, lipids in the process of molecular recognition processes e.g., enzyme-substrate, antigen-antibody. Protein interactions lead to conformational changes, which dictate the functionality of that protein-protein complex. Besides biophysics techniques, noncovalent interaction and conformational dynamics, can be studied via mass spectrometry (MS), which represents a powerful tool, due to its low sample consumption, high sensitivity, and label-free sample. In this review, the focus will be placed on Matrix-Assisted Laser Desorption Ionization Mass Spectrometry (MALDI-MS) and its role in the analysis of protein-protein noncovalent assemblies exploring the relationship within noncovalent interaction, conformation, and biological function. Brieflytrometer . This tetrometer ,4. On thtrometer . Understtrometer , modulattrometer , and immtrometer . In thistrometer . The inftrometer . The inttrometer ,12. Thertrometer . To analIn this scenario, mass spectrometry with its high sensitivity, resolution, and accuracy gives a strong contribution to understanding the chemical reasons for a noncovalent interaction within proteins .In this review, the principle of noncovalent interactions, protein folding/conformation, and their biological function is explained, with the structural proteomics. Therefore, MALDI mass spectrometry will be introduced as an analytically relevant contribution to understanding functional conformation and specific noncovalent interactions.The amino acid sequence of a protein dictates the conformational folding to which it undergoes determining secondary, tertiary, and ultimately quaternary structure. Protein folding is a process in which a highly dynamic equilibrium between folded and unfolded states, dictated by its covalent backbone and by noncovalent interactions between amino acid side chains, determines its native three-dimensional structure, also called conformation . In the Homomers can form intertwined assemblies, whereby small fragments of the protein called domains are swapped between two interacting subunits . This reIt has been shown that homodimers are involved in allosteric regulation, whereby a ligand binding event at one site of the protein induces conformational changes affecting the binding activity elsewhere in the protein ,20.Allosteric regulation is an important feature that can regulate the function of a protein in a positive (effector) or negative (inhibitor) manner through conformational changes. As an example, the binding of the effector, that can be a small molecule or a protein, at a distal site from the active site can change its ligand affinity at the active site due to conformational changes post-binding of the effector ,22. ConsAllosteric regulation has been primarily observed in enzymes involved in branching points of a metabolic pathway, in receptors sensible to mild signal perturbation  as well The process of generally homo-oligomer formation can contribute to enhancing protein stability, protecting against aggregation in some systems . The proAn example is the equilibrium between high active octamers and low inactive hexamers of the enzyme porphobilinogen synthase (PBGS) involved in cellular respiration . PBGS isA similar mechanism is observed in tumor factor p53, which explicates its function and activity as a tetramer. The process of tetramerization of the p53 tumor factor is essential for its tight binding to DNA elements and ultimately to induce transcription of p53 target genes. Its equilibrium is regulated by interactions with other proteins, such as proteins from the 14-3-3 and S100 families and numerous kinases .2 to hemoglobin as well as in allosteric enzymes [de novo DNA biosynthesis; the aspartate transcarbamoylases involved in pyrimidine biosynthesis, in which cytidine triphosphate (CTP) acts as an allosteric inhibitor, and adenosine triphosphate (ATP) functions as an allosteric activator [In the case of protein interacting with other proteins or molecules, allosteric cooperativity can play a major role not only in the protein folding but as well in the binding receptor-ligand, whereby their function is ligand concentration-dependent ,30. This enzymes  e.g., rictivator .At last, the recognition process between a membrane receptor and a pharmacological agent, resulting ultimately in allosteric activation of ligand-gated ion channels and G-protein-coupled receptors .At the basis of such allosteric cooperativity, there are noncovalent interactions of proteins that are essential in different biological processes, e.g., signaling cascades, enzyme-substrate, or enzyme-drug interactions, and multi-protein types of machinery.Therefore, proteins are dynamic structures with a defined temporal and spatial conformational flexibility, empowering them to perform specific functions. The interconversion of the protein from one conformation to another is associated with specific free energy . It alloProtein residues are strictly involved in its dynamic structure and strongly related to its function , e.g., tAt the basis of such tertiary and quaternary protein structure, there are noncovalent interactions, which are involved as well in other biomolecules complexes, e.g., proteins, DNA, RNA, carbohydrates. The main noncovalent interactions are hydrogen bonds, ionic bonds, hydrophobic interactions, Van der Waals force, and π-π interactions ; they arElectrostatic forces are strong determinants of disordered protein ensembles. The relationship between electrostatics, conformational ensembles, and quaternary interactions is unclear. Therefore, the impact of electrostatic interactions on the association of calmodulin and calcineurin phosphatase was explored and showed how charged amino acids could affect the conformation ensembles and the binding rate between the two proteins .This interaction usually involves the hydrophobic side groups of the amino acid contributing the carboxyl group to the bond .Egas) of a noncovalent interaction can be estimated by subtracting the calculated energies of the interacting monomers (Emonomer1 and Emonomer2) from the total energy of the complex (Ecomplex) according to Equation (1).The interaction energies  has a unique ability to preserve weaker noncovalent bonds when transferring species from solution into the gas phase. In fact, MS has been proven to be extremely useful to study and characterize protein conformations in noncovalent protein complexes, including their dynamic behavior. This review will address protein-protein complexes, given their ubiquity in biology processes .Nature provides good examples of self-assemblies, e.g., the phospholipid bilayer or microtubules involved in the cytoskeleton of the mitotic spindle in prokaryotes and eukaryotes cell division . TypicalGenerally, amphiphilic peptides are self-assembly characterized by two functional groups: A hydrophobic tail group, usually an alkyl chain, involved in hydrophobic interactions; a functional groups able to establish intermolecular hydrogen bonds responsible for the curvature of the self-assembly . TherefoPeptide self-assemblies include lipopeptides e.g., the antimicrobial agent daptomycin ; surfactWhether there is a relationship between bioactivity and peptide self-assembly is still an open question. Nevertheless, they continue to gain attention due to their biocompatibility, biodegradability, and bio functionality.Protein-protein self-assemblies are involved in numerous amounts of biological processes, where they present a homo-oligomeric structure in which identical protein subunits associate. The process of oligomerization can regulate the function of ion channels, receptors, transcription factors, and enzymes, but can lead to the pathogenic condition when nonnative oligomers are associated .In the case of enzymes, oligomerization plays an essential role in enzyme regulation, e.g., enzyme activation or deactivation based on enzyme concentration. Alternatively, oligomerization can regulate an enzyme allosterically by promoting cofactors binding to a non-substrate site, facilitating substate-induced cooperativity .2, water, chloride ions, iron, and folded protein respectively. Additionally, oligomerization can participate in the transferring of signals across the cell membrane, as a response to the recognition of an agonist by the receptor, enzyme activation. An example of receptor activation is given by G-protein coupled receptors [Protein-protein self-assemblies include proteins involved in molecules transport e.g., hemoglobin ; aquaporeceptors , proteineceptors , TNF, aneceptors . Oligomeeceptors .A biologically and medical-relevant example of protein assembly is the one established between antigen-antibody, which results in an immune response e.g., the assembly of MHC, major histocompatibility complex with epitope regions of the antigen ,60,61,62c‒cytochrome c peroxidase and amicyanin‒methylamine dehydrogenase‒cytochrome c551i [The process of oligomerization can be observed intracellularly e.g., the noncovalent complex given by post-translational modifications of ubiquitin-like proteins, which require the aid of three enzymes  ; and theme c551i .Those examples show the strong interdependency between conformation and function; therefore, the understanding of protein-protein noncovalent complexes might help to elucidate the functional mechanism and help in engineering noncovalent assemblies as potential drugs or drug carriers.The determination of a protein is studied at different levels by implementing a wild set of experimental techniques that provide a set of information regarding the three-dimensional structure, the conformation of a protein along a specific environment.Briefly, the X-ray crystallography reveals static three-dimensional structure at an atomic resolution ; NMR speThe most important challenge of these techniques is the conformational analysis of intrinsically disordered protein (IDP) and its protein-protein interactions due to its rapid interconversion within several conformations or so-called conformational heterogeneity ,73,74.Therefore, single-molecule fluorescence resonance, sensitive to transient conformational populations, can provide the time resolution to snap different conformations and the dynamic of protein interactions . Other tThe conformational data elaborate by those techniques provides a conclusive picture of the relationship between structure and protein function. For this purpose, several bioinformatics tools such as molecular dynamics (MD) were proposed to unravel the information hidden in conformational ensemble data and to guide toward the rationalization for the data .m/z of several ionized compounds that are analyzed through the use of a mass analyzer and highly sensitive detectors giving several advantages in analytical procedures such as high specificity, sensitivity, speed, and stoichiometry [Mass spectrometry is a technique able to measure the hiometry ,81. In gThe ion source is the main component and the heart of the mass spectrometer in which the analytes are ionized through specific and diverse ionization processes, and therefore detectable by the mass spectrometer. The parameters of the ion source and the sample preparation strongly affect the energy stability of noncovalent interactions, that can be preserved during the ionization process in the gas phase . TherefoSoft ionization processes, such as electrospray ionization (ESI) and MALDI, are employed for structural analysis of the protein elucidating not only noncovalent interactions but also conformational changes of the proteins. For such purpose, several techniques such as hydrogen/deuterium exchange (HDX), native MS, limited proteolysis, cross-linking, and fast photochemical derivatizations are applied to investigate the relationship function and conformation ,83.Although Electrospray Ionization (ESI) is the preferred method to study conformations and noncovalent interactions between proteins , MALDI-MLimited proteolysis mass spectrometry is a trend method to study conformational effects upon interaction with small molecules . The limA combination of limited proteolysis and MALDI-MS was applied for the first time to study a multi-component biomolecular assembly DNA-protein  and to pHDX is a powerful labeling tool that investigates protein conformational alteration by monitoring differential deuterium incorporation in altered regions . ProteinAn example of the bottom-up approach was reported by Mandel et al., where the combination of HDX-MALDI-MS was employed to identify peptic fragments from protein complexes at the interacting interface between kinase inhibitors and cyclic-AMP-dependent protein kinase. This complex retains deuterium under hydrogen exchange conditions due to decreased solvent accessibility at the interface of the complex ,109. Theα bonds allowing for a detailed analysis of the deuterium content of the backbone amides with a minimal hydrogen scrambling. In this way, the dynamic behavior of cytochrome c in solution is accurately reflected in the deuterium content of the fragment ions [On the other hand, a HDX-MALDI-MS using a top-down approach trough in-source decay analysis of deuterated cytochrome c yielded an extensive series of c-fragment ions that originate from the cleavage of nearly all N-Cent ions .Moreover differential and native HDX-MALDI-MS semiautomated conformational screening was performed by changing the polarity of the solvent and different conformations of the protein were evaluated . In detaThe coupling of MALDI-MS to HDX is limited by undesired back-exchange reactions from the air humidity, but optimized protocols were developed to minimize this issue in sample preparation . For exaSince chemical modification has a conformational impact on protein, protein derivatization is another approach to study the functional conformation of a protein ,118. TheN-hydroxysuccinimide esters) [Hundreds of chemicals described in the literature or commercially available are based on a limited number of reactions ,125. Exa esters) , sulfhyd esters) , glutara esters) ,128 and  esters) ,130,131.N-succinimidyl ester (BipS), a photocleavable, isotopically coded, fluorescent cross-linker, the first direct evidence was provided for the docking site of a phosphorylated G-protein-coupled receptor C terminus on the multifunctional adaptor protein beta-arrestin, clearly demonstrating the broad potential and application in structural and cellular biology [Promising for future applications in structural proteomics and imaging is the development of a derivatization method based on nanosecond photoreactions of neuropeptides using nitrobenzaldehyde giving the reactive 2-nitrosobenzoic anion able to react with the primary group of a protein . By comb biology First studies reported that the observation of entire noncovalent complexes, without using any derivatization procedures, in high mass MALDI-MS is possible only by acquiring the mass spectra by the first few laser shots at a new sample spot (“first shot phenomenon”) ,135,136.Recently, Beaufour et al. developed a method for native mass spectrometry of proteinsbased on liquid deposits. The molecular arrangement between matrix and complex is involved in the stability of noncovalent interaction during the MALDI events. In contrast to the solid deposit that has an irreversible frozen arrangement, in liquid deposit, regeneration of an ideal rearrangement following a single-shot accumulation is hypothesized. Moreover, the gas phase dissociation was minimized by parametric optimization . TherefoIn conclusion, the interdependency between the conformation and the noncovalent interaction is an essential factor to understand and predict the protein behavior in a biological context. This investigation is relevant in the pharmaceutical, clinical, and structural biology fields, which finds application in engineering new drug candidates, new drug delivery systems, and tissue engineering. Therefore, MALDI-MS can infer the structural features of a protein and/or protein complex from a small volume of matrix co-crystallized sample. This allows us to perform high-throughput analysis of several proteins at different conditions of substrate, ligands, and co-factors. Especially, in pandemic time, the need for a fast-response tool to validate specific vaccines can be achieved with MALDI-MS, based on its previous applicability in studies of epitope-antibody interaction analysis. Furthermore, MALDI-mass spectrometry imaging can provide functional and spatial information of protein complex as a part of tissue In this regard, the intracellular spatial resolution in mass spectrometry imaging is developing ,141,142


Identifying localization of proteins and their specific subpopulations associated with certain cellular compartments is crucial for understanding protein function and interactions with other macromolecules. Fluorescence microscopy is a powerful method to assess protein localizations, with increasing demand of automated high throughput analysis methods to supplement the technical advancements in high throughput imaging. Here, we study the applicability of deep neural network-based artificial intelligence in classification of protein localization in 13 cellular subcompartments. We use deep learning-based on convolutional neural network and fully convolutional network with similar architectures for the classification task, aiming at achieving accurate classification, but importantly, also comparison of the networks. Our results show that both types of convolutional neural networks perform well in protein localization classification tasks for major cellular organelles. Yet, in this study, the fully convolutional network outperforms the convolutional neural network in classification of images with multiple simultaneous protein localizations. We find that the fully convolutional network, using output visualizing the identified localizations, is a very useful tool for systematic protein localization assessment. A major determinant of protein functions is their localization in and traffic between intracellular compartments specialized in certain cellular tasks. Thus, identifying protein localizations and specific subpopulations associated with certain cellular compartments is often a key task in protein research.Fluorescence-based imaging techniques are the norm in protein localization studies due to their superior ability to visualize intracellular proteins, either through the expression of fluorescent fusion proteins or recognition of target proteins by fluorophore-detected, antibody-based techniques. Considering that the human genome contains approximately 20,000 protein-coding genes, that many of these proteins can be expressed as several protein isoforms, and that each protein can, in principle, be recognized by several antibodies identifying specific and possibly different functional forms , the number of protein populations to study in localization assays is enormous.Recently, systematic and high throughput approaches to visualize all human proteins in cells have surfaced . The visPhenotype analysis of high content screening images poses a challenge for image-based classification systems. The traditional approach of quantifying the images on a single cell level by representing the cells as a set of features and using the feature representation for phenotype analysis has been successfully applied in several studies, e.g., in ,3,4. TooConvolutional neural networks (CNN) have emerged as most common and successful tool for image classification tasks in recent years, especially in data-rich applications ,23. Usuawww.proteinatlas.org accessed on 10 February 2021) [Here, we studied the prediction of protein signal localization for the Cell Atlas data from Human Protein Atlas ry 2021)  using tw  using tData consist of 20,000 samples of human proteins immunostained in cell culture samples to acquire the protein localization patterns for Cell Atlas of Human Protein Atlas. The dataset was provided for Image Analysis Challenge of Cyto conference in 2017. Images were acquired with confocal fluorescence microscopy. Each sample is a four-channel confocal image. Image with green color shows the localization pattern of the protein of interest, labeled with immunofluorescence. Other channels are provided for reference: channel represented in blue color showing nucleus counterstained with DAPI, channel in red with microtubules marked with antibody against tubulin, and channel in yellow showing endoplasmic reticulum (ER) [In this dataset, protein distributions were classified into 13 major organelles of the cell: actin filaments (AF), centrosome (CE), cytosol (CY), endoplasmic reticulum, golgi apparatus (GA), intermediate filaments (IF), microtubules (MT), mitochondria (MC), nuclear membrane (NM), nucleoli (NI), nucleus (NU), plasma membrane (PM), and vesicles (VE). The protein localization classes with examples of each class , and distribution of classes are presented in In total, 22 different cell lines were used to create the dataset. Majority of the samples belong to either U2-OS, A-431, or U-251 MG cell lines. The distribution of samples with respect to the cell lines is presented in Two different deep learning architectures were used. The base architecture for both networks is a straightforward convolutional neural network with sets of two convolutional layers followed by batch normalization and max pooling layers. The amount of kernels doubles after each max pooling layer. In total, 10 convolutional layers were used in the base network. The depth of the network was chosen experimentally by testing various depths between 4 to 16 convolutional layers. Since one aim of this study was to compare CNN and FCN, a simple architecture that can easily be transformed to closely matching CNN and FCN implementations was chosen. CNN performance could improve with a deeper model, but the FCN output would then be too small to find locations of the cells where a class is present. On the other hand, U-Net , for exaFrom the base architecture, CNN was created by adding three fully connected layers to the end of the network, last being the predicting layer with 13 output values. FCN was created by removing the last max pooling layer to increase the output size, and adding an additional convolutional layer with 13 kernels to the end of the network, one for each class. Both architectures are illustrated in All four channels were used as input for the networks, and were resized to https://gitlab.com/BioimageInformaticsGroup/cytoclassifier/ (accessed on 10 February 2021).The codes implementing the models used in this study are available at In addition to the network architecture, also the details of training process are key factors in understanding the implementation and in determining the performance. Here, we present the parameter choices used when training the deep neural networks.From the 20,000 sample data set we used 16,000 samples for training the models. From these samples, 10 percent were used for validation during training. The remaining 4000 samples were used for testing only. The data was divided into train and test sets simply by indices of the images, using first 16,000 images for training. The same division to training and test set was used for both models. The class distribution in train and test sets is very similar to overall class distribution shown in The networks were trained from scratch, without using transfer learning from pre-trained models. However, we also experimented with transfer learning approach using Inception V3 network  that wasThe hyperparameters for training were tuned experimentally. The training process was run for 30 epochs and the hyperparameters providing the lowest validation loss were used for final training. First, an initial hyperparameter combination was chosen, with the principle of limiting the network complexity. Then, a suitable number of layers was searched using Adam optimizer with default parameters. Next, a trade-off between the number of kernels (doubling after each max pooling for memory reasons) and the batch size was searched. After that, batch normalization layers were added. Then, 5 × 5 filter size was tested against the initial 3 × 3 filters. Activation function was chosen by comparing leaky ReLU and exponential linear unit (ELU) with the default ReLU, and the optimizer was chosen by comparing SGD and Adam optimizer. The dropout parameter value was optimized in a grid search . Finally, different learning rate decay factors, as well as the number of epochs without improvements to wait before decaying the learning rate were experimentally defined. The final model was trained for 100 epochs with the chosen hyperparameter combination. The combination of the structural parameters and the batch size is a compromise dictated by the GPU memory available, since the whole batch needs to be handled by the GPU in a single go for performance reasons.The final choices used for model training were the following. The model weights were initialized with Glorot initialization . The modnitially . The leanitially ,35, and nitially ,37 for tWe studied the efficiency of using convolutional neural networks for classification of protein localization patterns in a large scale. For this, we utilized a dataset from Cell Atlas composed of 20,000 confocal microscopy images containing information on four channels. There are 13 cellular localizations, or subcompartments, to which each target protein is annotated to reside in. However, the target proteins may have several localizations in a given image.To understand what type of convolutional network suits the protein intracellular localization task, we compared CNN and FCN networks. Performance of the CNN and FCN networks in detecting the annotated localizations is shown in In Another biologically interesting, and computationally challenging, property in the data is the multilocalization of proteins. Proteins can localize in multiple subcellular compartments, creating a multi-label classification problem. Multilocalization of proteins is presented in We also studied the effect of different cell lines on the prediction accuracy. As illustrated in Finally, we compared the properties of the two network architectures by analyzing their training performance. The decrease of training and validation loss for both network models is presented in Here, we studied the applicability and compared the performance of two standard DL-approaches in classification of protein localization from fluorescence microscopy images. We find that both CNN and FCN-type deep neural networks perform well in such a task, and enable AI-based automated phenotyping. The mean of the percentage of correctly classified samples was 0.676 for CNN and 0.696 for FCN. For perspective, the highest  contest  the highrall F1) ,31. We wFrom our results it is clear that the localization prediction accuracy is much dependent on in which cellular compartment the protein resides and, especially, whether the localization is heterogenous in relation to cellular structures. The applied dataset is heterogenous in terms of cell lines , which hNot unexpectedly, the classification success was clearly best with images containing only one annotated localization, an observation shared also in . For theBased on our results, we find that the class imbalance in the dataset had a clear effect on the results. The low number of samples in of some of the classes seems to make them more prone to misclassifications—this could be expected as it is well-known that DL-based methods perform particularly well in data-rich settings. For example, actin filaments are often misclassified as nucleus even though they reside on different locations. Many classes are often misclassified as cytosol or nucleus, which are the most common classes in the training data. Class imbalance is a common problem in the field of machine learning, and could be solved with e.g. over- and undersampling or by generating new data of the rare classes. However, with a multi-label problem this is not straightforward, since rare classes often reside with common classes, and oversampling rare classes would also oversample the common classes.The dataset used here is composed of confocal section images, representing one z-plane each. This may have an effect in the distribution of the signals in the images as it is likely that each image represents the strongest signal plane for the cell/staining in question, creating a possible bias for at least some of the protein localizations. Especially those that are related to protein relative spatial density in z and whose distribution patterns in xy are correlating with variable signal intensities might benefit from data with the full spectrum of protein localization along all z-planes. In the future, a comprehensive set of images representing all z-planes, signal intensities, and localization distributions would allow addressing whether addition of the third dimension to the data would aid classification of proteins of rare classes and with multiple locations.The task of creating a large-scale annotated dataset is extremely challenging. Based on visual inspection, the annotations in the dataset are likely to contain variation and inaccuracies as missing and/or misclassifed phenotypes. In , a gamifIn , the resEven though the performances of the models studied here are similar when comparing total scores, they differ when looking at individual samples. In addition, we observed that the thresholds for CNN were overall much smaller than thresholds for FCN. The predictions of CNN often had lower confidence value, which translates directly to threshold values. Thus, it is essential to select thresholds for each class in a data-driven manner based on the predictions. While the accuracies of CNN and FCN are very similar, a major benefit from using FCN is that it not only predicts which class is present, but also where it locates. Even though the output of FCN is considerably smaller than the original image, we can roughly estimate which cells the protein locates to. When considering the variation between cells in one sample, the knowledge of which cells contain the specific protein in specific compartment can be biologically meaningful. While CNN and FCN were found to perform well, it would be beneficial to create an ensemble of the networks. By combining the class predictions of CNN and predictions with class locations of FCN, the classification accuracy could possibly be further improved.