We appreciate the commentary on our article, and we would like to take the opportunity to address several points raised in the reviewers’ commentary.In the comments, it is stated “… it [the study] does not provide information on the abundance of each protein family in the venoms of the snakes”.Every separation step underlies the law of mass action, thereby multiplying the error with the number of fractionation steps. Because of this problem, in analytical chemistry internal standards are used;A single Coomassie stained band does contain much more than one protein. With modern methods of proteomics, we usually detect dozens, very often hundreds of proteins in one single SDS-PAGE band. Even in spots of two-dimensional electrophoresis, we identify on average more than 100 proteins.This is of course not true, because we applied a state-of-the-art analysis of the proteome of the two snakes, which is much more precise than “the most common method”, using liquid chromatography (LC) for fractionation of the venom and subsequent SDS-PAGE of the individual fractions. From the viewpoint of the basics of analytical chemistry, such an approach is only suitable for quantification (which is nothing else than the determination of abundance), if internal standards  are added. In the case of the approach combining LC with SDS-PAGE (sodium dodecyl sulfate polyacrylamide gel electrophoresis), without applying internal standards, only extremely rough conclusions can be drawn regarding the quantities of proteins for the following reasons:With our proteomics approach, we directly digested the proteins in the venom, and injected the resulting peptides thereafter directly into the mass spectrometric system. Thereby, we excluded by a large degree the systematic error induced by the law of mass action.Naja species. The mass spectrometric data was searched against a collection of protein sequences derived from gene sequences of different snake species under the taxonomy “Serpentes”. Identified peptides were grouped into proteins, and similar proteins are then assembled into protein groups, e.g., the same protein from different species. Furthermore, we uploaded all the data from the mass spectrometric analysis in the PRIDE database [Toxins, we can add a table about the relative quantities of the peptides based on spectral counting data, if desired. The number of the PSMs for one protein group can be taken as a measure for the abundance, since more abundant peptides will be measured at a higher frequency in the mass spectrometer than less abundant ones. The protein identification data for the search against “sperpentes” is included in the deposited mass spectrometic data under the identifier PXD018726 at ProteomeXchange.For estimating the abundance of the proteins, the number of identified peptides per protein was used in this study. This indeed gives a rough classification of the abundance  of the proteins in the venom, which in our opinion is appropriate, since our main focus was on the identification of proteins in the venom and not on the accurate quantification. Our study focused on the data generated with a refinement approach of database searching and additional de novo peptide sequencing of unidentified MS/MS spectra. This approach aimed for a deep characterization of the studied venoms, since no sufficient protein sequence database was available for the two database  The absolute pharmacological action of a single venom depends not only on the absolute quantity, but also on its potency (with respect to its enzymatic activity);(2)The potency of a protein in the venom depends on its exact composition of atoms, and of course the three-dimensional fold. Already, small changes in the composition of atoms and the sequence and location of amino acids can drastically change the action and alter the potency of the function of a protein , e.g., references ,3,4,5.We do not agree for the following reasons:In the case of a mixture of many different active components of a venom, synergic effects are also common and need to be considered. An estimation of the overall toxic action thereby is critical.In summary, the data, which we provided in our study and corresponding manuscript , are mucRegarding the criticism about the terms “complexity” and “diversity”, we cannot really follow this comment, because both terms were used only once and in these cases have not been mixed with the meaning of “amounts” and “abundance”.Similarly, we checked all sentences including the terms “amounts” and “abundance”. In no case were these terms mixed with the terms “complexity” and “diversity”.Toxins, and as practiced for other comments sent in before, the person or group sending the comment should identify themselves, and not stay anonymous.Finally, as is good practice for the journal


The studies on lncRNA-protein interactions are of great value to the understanding of lncRNA functional mechanisms. In this paper, we proposed a novel model to predict potential lncRNA-protein interactions using the SKF (similarity kernel fusion) and LapRLS (Laplacian regularized least squares) algorithms. We named this method the LPI-SKF. Various similarities of both lncRNAs and proteins were integrated into the LPI-SKF. LPI-SKF can be applied in predicting potential interactions involving novel proteins or lncRNAs. We obtained an AUROC (area under receiver operating curve) of 0.909 in a 5-fold cross-validation, which outperforms other state-of-the-art methods. A total of 19 out of the top 20 ranked interaction predictions were verified by existing data, which implied that the LPI-SKF had great potential in discovering unknown lncRNA-protein interactions accurately. All data and codes of this work can be downloaded from a GitHub repository ( MALAT1, a functional lncRNA, which is highly expressed in several tumors, can bind the tumor suppressor gene SFPQ  to release proto-oncogene PTBP2  from the SFPQ/PTBP2 complex  and RF (random forest) classifiers  of 0.909 and an AUPR  of 0.685, which indicated that the LPI-SKF method could identify unknown lncRNA-protein interactions accurately. Moreover, LPI-SKF could also be used to identify interacting partners for novel lncRNA/proteins. A total of 19 out of our 20 top-ranked lncRNA-protein interaction predictions were confirmed by existing data.In this paper, we proposed a new model to predict In this work, we proposed an lncRNA-protein interaction prediction model, named LPI-SKF. This model can be summarized in four steps, which are shown in NPInter is an integrated database of ncRNA interactions, which includes vast interactions between ncRNAs and biomolecules uncovered by various high-throughput sequencing approaches  be the i-th lncRNA, and pj  the j-th protein. The adjacency matrix A can be defined as follows:For the convenience of the reader, we first defined the adjacency matrix between lncRNAs and proteins. Let i-th lncRNA is the i-th row of matrix A, which can be noted as the Ai*, while the interaction profile of the j-th protein is the j-th column of matrix A, which can be noted as the Aj.The interaction profile of the lu and lv can be defined as:The interaction similarity between whereand ||.|| is the 2-norm operator.pu and pv can be defined as:Similarly, the interaction similarity between where.i-th lncRNA can be noted as ei. The expression profile similarity is defined as follows:The expression profiles of lncRNAs in 24 different tissues can be downloaded from the NONCODE database. The expression profile of the u,v is the Pearson's correlation coefficient between eu and ev. It can be calculated as follows:where ρwhere cov is the covariance, and σ is the standard deviation operator.pu against pv. The bit score in this alignment can be noted as bu,v. The pairwise sequence alignment similarity can be defined as:Blast+ is a local alignment search tool, which was utilized to calculate the alignment score of proteins in this work  ≠ sp,1.It worth noting that k-nearest neighbors, was adopted to compute the similarity between statistical features , while the similarity between pu and pv can be noted as sp,2.RNA is composed of four types of ribonucleotide . According to the previous work, we calculated the percentage of these four nucleotides and 16 dinucleotides  to represent each lncRNA in a 20-D vector  and three different protein similarities  were calculated in the above sections. Furthermore, the similarity kernel fusion (SKF) algorithm was utilized to integrate these similarities and obtain a more comprehensive similarity.Three different lncRNA similarities  as follows:We take the similarities of lncRNA as an example. Firstly, we can normalize the three lncRNA similarities . The neighborhood constrained normalization of the sl,q can be defined as follows:Secondly, we created a neighbor-constrained normalization for each lncRNA similarity. Given whereThe matrix composed by the neighborhood constrained normalization is noted as:The three similarity matrices were integrated using the following iterative process:T is the transpose operator in matrix algebra, λ is the iterative round parameter, andwhere α is a weight coefficient between 0 and 1, z rounds of the iterative process, we obtained the final integration similarity matrix asAfter k most similar lncRNAs of each lncRNA, we defined an indicator function as follows:Although more information is retained in the similarity fusion, more noise is apparent simultaneously. By considering the The final adjusted lncRNA similarity is defined as follows:l is the element in the u-th row and the v-th column of the matrix Θl.where θSkp,. The value of k in computing protein similarities is not necessarily the same as that of the lncRNAs.By applying protein similarities, and using Eqs. (9)–(18), we obtained the adjusted protein similarity matrix In this work, Laplacian regularized least squares (LapRLS) were utilized to construct the prediction model. Since we obtained the lncRNA similarity matrix and the protein similarity matrix, we could estimate the lncRNA-protein interactions from either the lncRNA similarity matrix or the protein similarity matrix. Without losing generality, we took the lncRNA similarity matrix as an example.Ll be the Laplacian normalized similarity matrix, which can be defined as follows:Let D is the diagonal matrix of the matrix Skl,.where We then found the estimation of the adjacency matrix by minimizing the following objective function:A is the adjacency matrix, Fl is the prediction matrix from lncRNA similarities, βl is a weighting parameter, and ||.||F is the F-norm operator.where We obtained the prediction matrix from lncRNA similarities by calculating the derivative of the objective function as follows:Similarly, we applied Eqs. (19)–(21) on protein similarities to obtain the prediction matrix from protein similarities, as follows:Finally, we integrated the above two prediction matrixes to obtain our final prediction matrix, as follows:where δ ε  is a weighting coefficient.pre), recall (rec), and the F1-score (f), which can be calculated as follows:The prediction performances of the LPI-SKF method was estimated using 5-fold cross-validations. We applied the AUROC and the AUPR as the main performance indicators. We also applied three performance statistics, including precision , the number of neighbors l and βp are the most important regularization terms in the LapRLS, which can influence the performance directly. In this work, we made βl equal to βp for convenience. To obtain the optimal performance, we searched βl and βp both from 2−10 to 2−1 according to a previous work  was applied in our study to integrate different similarities, which could integrate similarity information in different aspects and reduce noise. In this part, we compared SKF with another two similarity fusion methods, similarity network fusion (SNF)  and CF (collaborative filtering), and three other methods, including LPIHN , to predict potential lncRNA-protein interactions. Serval similarities of both lncRNAs and proteins were integrated to obtain a comprehensive similarity matrix by the SKF method. Furthermore, the LapRLS framework was applied to build the prediction model. Finally, LPI-SKF obtained an AUC of 0.909 and an AUPR of 0.685 in the 5-fold CV framework, which demonstrated that LPI-SKF can infer uncovered lncRNA-protein interactions accurately.To evaluate the performance of LPI-SKF, serval state-of-the-art methods were compared to LPI-SKF on the same benchmarking dataset. Finally, LPI-SKF received an AUC of 0.909 and an AUPR of 0.685 in the 5-fold cross-validation framework, both higher than the other models. More importantly, LPI-SKF could also predict potential interacting proteins/lncRNAs for novel lncRNAs/proteins precisely. For a better comparison, we also compared LPI-SKF with another model, SFPEL, on the same database and the same random seed. The result showed that LPI-SKF performed much better both in the prediction for new lncRNAs and new proteins.https://github.com/zyk2118216069/LPI-SKF.Publicly available datasets were analyzed in this study. This data can be found at: Y-KZ curated the dataset, designed, and implemented the algorithm, performed the experiments, and collected the results. JH, Z-AS, and W-YZ helped in collecting the data, and calibrating the parameters of the algorithm. P-FD directed the whole study, conceptualized the algorithm, analyzed the results, and wrote the manuscript. 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.


Protein sequence similarity networks (SSNs) constitute a convenient approach to analyze large polypeptide sequence datasets, and have been successfully applied to study a number of protein families over the past decade. SSN analysis is herein combined with traditional cladistic and phenetic phylogenetic analysis  in order to assist the ancestral reconstruction and integrative revision of the superfamily of metallo-β-lactamases (MBLs). It is shown that only 198 out of 15,292 representative nodes contain at least one experimentally obtained protein structure in the Protein Data Bank or a manually annotated SwissProt entry, that is to say, only 1.3 % of the superfamily has been functionally and/or structurally characterized. Besides, neighborhood connectivity coloring, which measures local network interconnectivity, is introduced for detection of protein families within SSN clusters. This approach provides a clear picture of how many families remain unexplored in the superfamily, while most MBL research is heavily biased towards a few families. Further research is suggested in order to determine the SSN topological properties, which will be instrumental for the improvement of automated sequence annotation methods. Metallo-lactamase; Protein superfamily; Tanglegram; Sequence similarity network; Neighborhood connectivity. While Pseudomonas aeruginosa SdsA1  center under reducing conditions, and structure PDB 2p4z corresponds to an oxidized inactive form. On the other hand, CmlA is a rare β-hydroxylase clustering among phosphoesterases, which hydroxylates L-p-aminophenylalanine, a biosynthetic precursor of chloramphenicol.Some divergent iron-dependent oxidoreductases cluster at the end of the tanglegram, including 3.3E-value, which was set to –log = 20. Unless otherwise stated, nodes are specifically representative nodes, which group several UniProt entries with a 40 % or higher sequence similarity, so that the SSN has fewer edges and is simpler to display graphically. By inspecting the distribution of functionally characterized proteins throughout the SSN it is evident that many MBL families remain to be characterized. In fact, one of the largest clusters in the network comprises proteins involved in DNA internalization and natural competence such as ComEC, for which no structural information is yet available and only one SwissProt entry (Bacillus subtilis P39695) is described. The size of connected components (CC) in the SSN follows a power law distribution, with a few clusters encompassing most nodes, and a long tail of many CCs with one or two nodes  DNA internalization/ComEC proteins; whereas CPSF/β-CASP, tRNAse Z, RNAse J, and FDPs cluster into separate CCs of 673, 350, 333 and 297 nodes, respectively. The remaining 2915 nodes (19 %) include relatively few known MBLs sparsely scattered over 1353 smaller CCs. Analogously, the node degree shows a sharply decaying distribution, skewed towards lowly connected nodes (inset), i.e. it is not a power law distribution. Only 148 nodes have SwissProt descriptions and 91 nodes have at least one PDB experimentally determined structure (41 nodes have both). As depicted in An SSN was here calculated for the MBL superfamily using the EFI-EST webserver , as descwo nodes A. The laed nodes B. This i3.4e.g. truncated or incomplete sequences). For a given set of protein sequences, the SSN topology often matches the corresponding phylogenetic tree topology [i.e. Blast E-value for the SSN as opposed to structural homology for the tanglegram. The NC distribution reaches a maximum of ~ 400 for nodes with ~ 500 neighbors  statistic was introduced in 2002 by Maslov & Sneppen to describe how sets of highly connected regulatory genes control the expression of lowly connected genes  Box 1)..Box 1). topology ; howevertopology . This iseighbors C, decayi4Herein, structural homology and SSN analysis are used to assist the phylogenetic reconstruction of the MBL superfamily, harnessing the protein three-dimensional arrangement of secondary structure elements as a metric for common ancestry inference. The introduced tanglegram graph disclosed structure and sequence similarity relationships between seemingly unrelated enzymes, which is suggestive of a mutual evolutionary history. Tanglegrams comprise a practical framework for protein structure-function analysis, applicable to study other protein superfamilies as well. Analogously, NC network coloring provides an intuitive picture of the distribution of protein families within the superfamily, suggesting that numerous MBL families remain to be characterized. Indeed, manually annotated entries for proteins with available experimental evidence account for only 1.3 % of the superfamily, underscoring an unfortunately frequent bias of research towards relatively few families. Automated annotation algorithms would benefit from further research on protein SSNs; establishing their topological features will give rise to improved metrics for protein function estimation.Javier M González: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.Agency for Science and Technology Promotion (ANPCyT), grant PICT 2017-4590, Argentina.This work was supported by Data included in article/supplementary material/referenced in article.The authors declare no conflict of interest.No additional information is available for this paper.


A rich set of adjustable components allows a user to run the computational steps specific to his task. TREND is freely available at http://trend.zhulinlab.org.Key steps in a computational study of protein function involve analysis of (i) relationships between homologous proteins, (ii) protein domain architecture and (iii) gene neighborhoods the corresponding proteins are encoded in. Each of these steps requires a separate computational task and sets of tools. Currently in order to relate protein features and gene neighborhoods information to phylogeny, researchers need to prepare all the necessary data and combine them by hand, which is time-consuming and error-prone. Here, we present a new platform, TREND ( In prokaryotes, genes that encode functionally linked  proteins are often organized in gene clusters . EfficieThere is a limited number of resources available for protein analysis in evolutionary context. Online version of MAFFT  gives anHere, we describe an intuitive platform, TREND, which has a capability to automatically identify protein features, calculate gene neighborhoods of each gene corresponding to provided prokaryotic proteins, cluster neighboring genes, identify operons, and put all these data in a phylogenetic context. To complete the full analysis, only a set of proteins sequences or protein identifiers is needed as input. The results are presented as an interactive picture on the web-site and can be downloaded for subsequent analysis. TREND is the only resource that can calculate gene neighborhoods for more than 125 000 prokaryotic genomes, identify protein features and correlate this with phylogeny in automated fashion, while providing ample opportunities for adjusting each step of the analysis.TREND has an extensible modular architecture and consists of four main components: (i) a component dedicated to building a phylogenetic tree by running multiple sequence alignment and a tree building software; (ii) a component for identification of protein features and combining the results with the protein tree; (iii) a component for gene neighborhoods identification and mapping this information on the protein tree and (iv) a component for clustering genes and identification of operons. These components are grouped in two pipelines: Domains and Neighborhoods Figure .A typical analysis starts with collecting similar protein sequences, for example, by running BLASTP against a protein sequence database using the protein of interest as a query. The collected set of protein sequences can be used as input for TREND Figure . AnotherThe domain analysis pipeline can be customized in a way that the tree will be built using specified parts of sequences, while protein features will be identified using full-length sequences. This is helpful when the evolutionary history of a specific domain or other fragment of sequences is explored. For this option, a user need to click ‘Add second area’ button and another input area will appear where fragments of sequences should be inserted.If a user already has an alignment in the FASTA format, which was edited and prepared for the analysis, he can skip the alignment step and start the pipeline from tree building. If a user has a tree in the Newick format, it can be used together with the original sequences and  with an alignment, which was used to build the tree, to run the pipeline in a Partial mode. In this case, only the steps not related to tree building will be run. Instead of providing sequences used to build the tree, corresponding protein Ids can be placed at the beginning of the tree leaf names separated from the rest of the names by an underscore, a space, a forward slash or a vertical bar. In this case, the sequences will be fetched from NCBI or MiST. The Partial mode is particularly useful in domain evolution analysis and ancestral domain architecture reconstruction , in whicAs a result, an interactive tree will be produced Figure . ClickinTo start the gene neighborhoods pipeline, four types of input information can be used: (i) a set of protein sequences in FASTA format, (ii) an MSA in FASTA format, (iii) a tree in Newick format or (iv) a list of NCBI and/or MiST compatible protein identifiers separated by a new line character. The main prerequisite for the first tree types of input is the placement of protein identifiers at the beginning of corresponding protein names, separating them by space, vertical bar, forward slash or underscore from the rest of the names. Gene neighborhoods will be identified by fetching corresponding information from MiST database . AlthougA user can specify the number of neighboring genes to be identified and processed for each provided protein. Upon identifying of gene neighborhoods, genes will be clustered based on shared domains of the encoded proteins. A user can control the permitted number of non shared domains for corresponding genes to be grouped in a cluster. In this case, not all domains in proteins should be shared for genes to be grouped. The next step is identification of operons based on the distance between the genes. The default distance between the genes is set to 200 bp . This paTo show how the usual computational workflow can be simplified using TREND we used it for the analysis of oxygen di-iron proteins (ODP) described in . In thisTREND is a highly customizable computational platform that allows researchers to explore protein function identifying protein features, gene neighborhoods and operons, clustering neighboring genes, and integrating all these data into phylogenomic context and cross-referencing with RefSeq, Pfam, CDD and MiST databases. The extensible modular architecture facilitates adding new functionality with not much effort. The platform has a potential to grow into one of the major hubs for computational protein analysis and accelerate the research in computational biology. Among the future developments, we are planning to integrate Markov clustering of homologs and correlating the results with phylogeny, additional software for protein tree building and built-in BLASTP for collecting homologous proteins. We will be updating the web server by integrating new releases of Pfam, CDD and MiST databases and the underlying software when they become available.https://spring.io/projects/spring-boot) and Hibernate ORM (https://hibernate.org/orm/) frameworks, PostgreSQL is used as a database system, Tomcat—as a web server, NGINX—as a reverse proxy. Separate scripts are written in Python and integrated in the application. Thymeleaf (https://www.thymeleaf.org/) is used as a template engine. The frontend is developed using jQuery, d3.js, Bootstrap and custom packages for data manipulation and visualization. TREND is freely available at http://trend.zhulinlab.org. GitHub repository location – https://github.com/ToshkaDev/TREND.The backend is implemented in Java using Spring Boot (gkaa243_Supplemental_FilesClick here for additional data file.


Soil is a complex matrix where biotic and abiotic components establish a still unclear network involving bacteria, fungi, archaea, protists, protozoa, and roots that are in constant communication with each other. Understanding these interactions has recently focused on metagenomics, metatranscriptomics and less on metaproteomics studies. Metaproteomic allows total extraction of intracellular and extracellular proteins from soil samples, providing a complete picture of the physiological and functional state of the “soil community”. The advancement of high-performance mass spectrometry technologies was more rapid than the development of ad hoc extraction techniques for soil proteins. The protein extraction from environmental samples is biased due to interfering substances and the lower amount of proteins in comparison to cell cultures. Soil sample preparation and extraction methodology are crucial steps to obtain high-quality resolution and yields of proteins. This review focuses on the several soil protein extraction protocols to date to highlight the methodological challenges and critical issues for the application of proteomics to soil samples. This review concludes that improvements in soil protein extraction, together with the employment of ad hoc metagenome database, may enhance the identification of proteins with low abundance or from non-dominant populations and increase our capacity to predict functional changes in soil. The term “rhizosphere” was coined in 1904 by the agronomist and plant physiologist Hiltner to identify the portion of soil near the roots that, as he first postulated, welcomed an abundant microbial community influenced by the root-released substances. Over the years, the definition of the rhizosphere has expanded and, to date, it is described as composed of the endorizosphere, the rhizoplane, and the ectorhizosphere . The subThe rhizosphere of a polluted soil is generally deeply altered, as it has undergone particular and specific adaptations that are a consequence of the degree and type of pollution. The ecto–endophytic properties are altered and conditioned by polluting substances and by the soil stationary conditions . Further complexity is added to the system by the engineering of the rhizosphere  in bioremediation processes.To understand the complex communication between plant and soil microorganisms, metagenomics and metatranscriptomics are not enough, and a metaproteomic approach must be used in order to assess the real functionality of soil microbes in the rhizosphere. In fact, metagenomics is subject to various limitations, such us the molecular extraction techniques, bioinformatics of annotation, and data processing, but above all for the lack of correlation between the presence of a functional gene and the actual activity of that gene . Moreovehttp://www.webofknowledge.com/), using “soil” and “metaproteomics” as topics it was possible to find 126 records: 95 articles, 28 reviews, two proceedings papers, one book chapter  published from 1990 to date (June 2020). According to their pertinence, 71 were selected. The same research on Scopus (https://www.scopus.com/) yielded 109 documents: 73 articles, 20 reviews, eight book chapters, three short surveys, two conference papers, one data paper, one letter, and one undefined (June 2020). Of the 73 articles, only those that were truly relevant and in English were taken into consideration. A cross-comparison between these 54 elected papers and the 71 obtained from the search on Web Of Science showed that 49 papers were shared between the searches, four were present in Scopus but not in Web Of Science and vice versa; 22 articles were present in Web Of Science but not in Scopus, with a final count of 75 articles taken into consideration. The same search on PubMed (https://pubmed.ncbi.nlm.nih.gov/) provided 83 results, 31 of which were rejected because they were reviews, non-related or non-English articles. Of the remaining articles, only five were not already present in the search results from the Web of Science and Scopus and were added to the total papers taken into consideration.The study of protein expression in environmental samples is a hot topic. On the Web of Science ) at room temperature, followed by a classic phenol extraction and a proteic precipitation with five volumes of cold methanol plus 0.1 M ammonium acetate overnight at −20 °C. DTT, not used in previous protocols, was useful for reducing intra-molecular and inter-molecular disulphide bonds between cysteine residues by helping protein solubilization and aggregate disruption. The protein pellet obtained after centrifugation was washed with cold methanol and twice with cold acetone, air-dried, and solubilized for SDS-PAGE and 2DE. Although the results showed that the SDS buffer was more powerful than other extractants tested and phenol extraction was effective to remove interfering substances, this approach was not considered applicable for deep proteome studies, due to the reduced number of proteins extracted and to the low resolution of 2-DE separation [Different approaches and protocols have been used over the years and are present in literature in order to obtain feasible protein extraction in quantitative and qualitative terms . The gen authors  and withprotocol . Despiteprotocol . The SDSprotocol . Chen etprotocol  devised paration . A new pparation . This prparation . This apparation . The samparation  has beenparation ,48,49. Tparation  was refiparation , increasparation . Keiblinparation  comparedparation . Lin et paration  adopted paration , testingparation .Escherichia coli cells, although the need for a better extraction buffer was still underlined [2. The subsequent analysis of the protein pellet obtained allowed the detection of 640 proteins by nano-LC-MS/MS [2, NaOH and K2SO4 , methanol, and ethanol  were used. The results obtained by Greenfield and colleagues have shown that protein extraction is strongly influenced by solvent and soil characteristics. Although no type of extraction permitted complete incubated protein extraction, the maximum recovery was observed with NaOH  and Na-pyrophosphate  [v/v) in a 5:1 ratio over sample volume in MilliQ water, with vigorous mixing. After sonication, incubation at −80 °C and centrifugation, the protein-containing interphase was taken and precipitated in ice-cold 100% methanol. The pellet obtained after centrifuging was frozen and lyophilized before being resolubilized in an SDS-Tris buffer containing DTT, then incubated for 30 min at 300 rpm, 50 °C. The samples were centrifuged and, in order to remove humic substances, the protein extracts were again precipitated in 20% trichloroacetic acid (TCA); the obtained pellet after the centrifugation underwent three washes in ice-cold acetone [In all the protocols seen so far, the main obstacle to performing good soil protein extraction seems to be the co-extraction of humic acids, which are arduous to separate from proteins and make it difficult, if not impossible, to quantify proteins with spectrophotometric approaches and to obtain a good resolution on polyacrylamide gels, for both the SDS-PAGE and 2DE. In fact, soils with high microbial biomass are usually associated with a higher organic matter content (including humics). In contrast, soils with low organic matter usually also have lower microbial biomass, which also limits protein extraction. Electrostatic attraction drives encapsulation of positively charged proteins, at pH 5 to 8, by soil humic and fulvic acids derived from terrestrial and mixed terrestrial–aquatic sources . To obtaderlined . Given tderlined ,22,38, wderlined . Althougderlined . Howeverderlined  tested tderlined . The comderlined , it is rderlined  applied LC-MS/MS . In imprLC-MS/MS  took intLC-MS/MS . An expeLC-MS/MS . The maiLC-MS/MS . The firLC-MS/MS  and the LC-MS/MS ,59 basedLC-MS/MS . To eval 45–75%) . HoweverH 0.1 M; –80% and H 0.1 M; –80% and  acetone . Althoug acetone ,64.3+ was proposed and the pH adjustment of the extraction solution to 5.5-6 to effectively remove humic substances [While a limited number of protocols are currently available for the extraction and purification of soil proteins, several protocols for the isolation of soil DNA are available. In these works, the addition of Albstances ,66,67,68bstances , evaluatbstances , that prbstances . In one bstances ,23, withbstances . The usebstances .® by MO BIO Laboratories, Carlsbad, CA, USA); although this kit does not involve toxic reagents and its use is practical and fast, it is rather expensive and does not allow adapting the extractive protocol to the specific characteristics of the soil under consideration. Despite the limitations, protein extraction is entrusted to the NoviPure Soil Protein Kit only, while in some papers the kit performances are compared to classic extraction techniques  database, these were mainly involved in primary metabolism, in secondary metabolism, and in the response to stress [Vitis vinifera cv. Pinot Noir. The extracted protein analysis allowed the identification of 150 bacterial genera and the understanding of the functional state of the rhizosphere [Soil rhizospheric metaproteomics is a promising tool to understand the complex plant–microorganism interactions and the rhizosphere in situ biological/functional properties and how they can adapt to environmental factors. In a metaproteomics study on rice crop phyllosphere and rhizosphere, Knief et al.  used theo stress . The meto stress  to evaluo stress . Bao et o stress  combinedo stress . These ao stress . Microbio stress  comparedo stress . Starke o stress , with a o stress . Moreoveo stress  focused zosphere . In 2019zosphere .Several works in literature aimed to elucidate how the natural environmental conditions or their alterations influence and alter the rhizosphere. The long-term effects of deforestation on soil microbial communities have been explored through protein extraction from soil, carried out according to , allowinThe possibility of using soil proteomic analysis as an indicator of cadmium contamination was proposed by Singleton and colleagues in a 2003 paper that highlighted the reduction in the number of total proteins extracted from soil samples containing cadmium. However, no subsequent analysis was performed on the protein extracts obtained . BenndorSoil metaproteomics is a research field of great interest given the information obtained from it, not only to understand the physiological and functional states of the interaction between microorganisms and plants present in the rhizosphere, but also to be able to “engineer” the rhizosphere, modifying the microbial pool and/or resident plants, in response to environmental stress, climate change, and in order to promote the bioremediation of contaminated soils. Indeed, more than any other approach, the analysis of the rhizosphere metaproteome allows us to evaluate the responses of the rhizosphere microbiome to environmental changes and also to determine how the rhizosphere can respond to targeted engineering interventions to enhance its capabilities. To evaluate the phylogenetic and functional correlations of a contaminated soil, the proteomics approach represents a valid means to test the effective potential of different bioremediation and soil restoration approaches. In spite of the limitations of metaproteomics, which are certainly shared by many other soil approaches based on the extraction of biomarkers in soil , the evolution of this field has been enormous in recent years and is providing important insights into the relationships between the phylogeny and functionality of microbial communities. One of the major limitations to the progress of the metaproteomics approach for soil is the lack of a universal extraction protocol. More effort should be addressed to improve protein extraction methods while it is acknowledged that extensive soil heterogeneity in chemical, physical, and biological properties makes this task difficult. The first major distinction between the extraction methods proposed over the years is that between direct and indirect extractions. In the direct one, cell lysis occurs directly in the native soil sample. This guarantees a greater understanding of the interactions present in the rhizosphere but is more influenced by the matrix characteristics and is subject to the interfering substances co-extraction. The indirect method involves the physical separation of microbial cells from the soil and only after cell lysis and protein extraction. Although in a way the problem of interfering substances is solved, there is the risk of isolating only some microbial species, losing those strictly adhering to soil particles or that are non-culturable, and the whole pool of proteins released by the roots and microorganisms in the rhizosphere. Therefore, in order to fully exploit the enormous potential of the metaproteomics approach for soil, it is necessary to develop and use a type of direct and in-situ extraction. As seen in the literature, the various protocols used to date for cell lysis and direct and indirect extractions from the soil significantly affect the quality and quantity of extracted proteins. In the absence of an optimal protocol, suitable for all soil types, a strategy to solve the problem could be to combine several extractions, including the pool of extracted proteins, before subsequent analysis. Another important aspect to improve is the quantification of the extracted proteins. Several studies concluded that colorimetric methods are not valid, and thus alternative approaches  must be taken into consideration to quantify and normalize the amount of proteins to be used in downstream electrophoresis and mass-spectrometry analyses. Finally, improvement of the overall metaproteomic workflow, not only the protein extraction methods, but also protein/peptide separation, mass spectrometry approaches, and bioinformatics coupled to metagenomics, will provide a more complete picture of the microbially mediated processes taking place in soil, also including the identification of low abundant proteins which are of paramount importance for soil sustainability and bioremediation.


Protein structures, usually visualized in various highly idealized forms focusing on the three-dimensional arrangements of secondary structure elements, can also be described as lists of interacting residues or atoms and visualized as two-dimensional distance or contact maps. We show that contact maps provide an ideal tool to describe and analyze differences between structures of proteins in different conformations. Expanding functionality of the PDBFlex server and database developed previously in our group, we describe how analysis of difference contact maps (DCMs) can be used to identify critical interactions stabilizing alternative protein conformations, recognize residues and positions controlling protein functions and build hypotheses as to molecular mechanisms of disease mutations. Protein structures have complex three-dimensional shapes and are most often visualized as cartoons depicting their overall arrangement of secondary structure elements and neglecting interaction details. Such cartoons were popularized by Jane Richardson  and gainWidespread use of such images to depict protein structures, often combined with wording that talks about “the structure” when referring to entities illustrated by such images, may incorrectly suggest that protein structures are unique and static. In fact, protein structures are far from static and, as any physical system in constant temperature, can assume any of the conformations from the canonical ensemble describing the system . This poThe most often used measure of structural difference between protein structures is the root mean square deviation (RMSD) between Cα atoms . While uProtein structure visualizations that directly focus on interactions stabilizing it may be better suited for this purpose and were indeed quite popular in the early days of structural biology, but mostly fell out of favor with the growing popularity of ribbon diagrams. For instance, difference distance  or contaAn increasing percentage of protein coordinate sets deposited to the PDB consists of multiple depositions of independently solved structures of the same protein, either in different functional states or simply in different conditions, resolution etc., resulting in apparently redundant deposition. For instance, in the latest PDB release  there were 478,448 independent protein chains with 70,567 unique (at 100% identity level) sequences–for an over 6-fold redundancy . Many global analyses of structures in the Protein Data Bank try to remove this redundancy, selecting a single chain to represent each group of alternative conformations and both specialized servers  and the Differentially Stabilizing Residues or DSRs). We hypothesize that DSR-based description of differences between alternative protein conformations, in contrast to more popular RMSD-based descriptions, is a natural way to identify factors that may control the natural balance between functional/structural states of a protein and which, if modified by disease mutations or interactions with drugs, may disrupt this balance.We decided to characterize the differences between two conformations of a protein in a language of residue interactions and analyze differences between contact maps of proteins. Such an approach was used in the past by us and other groups to optimize protein structural alignment , to clasTo identify the possible DSRs and evaluate this approach on a large set of proteins, we compared residue contact maps for conformations representing the most divergent structure models of all proteins with “redundant” structures deposited in the Protein Data Bank . The staThe PDBFlex database is based on clusters of PDB entries with >95% sequence identity, representing independently solved structural models of the same protein, with the 95% sequence identity threshold adapted to allow for variations in construct boundaries and/or single mutations. From each cluster, the two sets of coordinates with largest RMSD were selected to represent the two most divergent conformations of the protein in the cluster, although this approach can be applied to study the differences between any two conformations. This picture is qualitatively similar to one presented in a previous analysis from our group , showinginduces a conformational change, then a large RMSD would be observed only when comparing the ligand bound and unbound structures of a protein. In Several studies have shown that ligand binding or complex formation does not “induce” structural changes, but results in the stabilization of one of many possible conformations and a shift in the conformational equilibrium towards that conformation , 27. In To assess the contribution of different crystallization conditions/experiments to the structural differences, we classified the clusters into two groups based on whether the two most divergent structural models in the cluster were different chains/coordinate sets in the same PDB entry  or were from different PDB entries . We found that while overall the two distributions looked similar, the maxima were shifted by 0.2 Å and the ratio between both groups reversed between the small and larger RMSD values See . For insWe hypothesized that differential contacts could be modulating the equilibrium between alternative conformations seen in the PDB. To verify this, we looked at the fraction of the total number of contacts that have changed between two conformations  for three classes of clusters: those represented by one bound and one free (or holo- and apo-)) structure . In ordebind domain, the LID domain and the CORE domain [bind and LID domains move from an “open” to a “closed” conformation (Adenylate kinase (ADK) is an enzyme that catalyzes the transfer of one phosphate group from ATP to AMP, to form two ADP molecules. ADK consists of three domains: the NMPE domain . Substraormation . Howeverbind and LID domains, nine between the NMPbind and CORE domains and eight contacts were between the LID and CORE domains. The remaining contacts were intra-domain contacts. We hypothesized that the inter-domain contacts would be responsible for modulating the conformational change seen in this protein. One of the six contacts between the NMPbind and CORE domains was formed by Glu170 and Leu58 and seen only in the closed conformation. It has been previously shown that a hydrogen bond exists between these residues in the closed conformation and that the disruption of this bond (by mutation of Glu170 to Alanine) resulted in a shift of the conformational equilibrium towards the open form in the absence of the substrate [We created contact maps for the closed and open conformations based on the PDB models with codes 4jzkB and 4akeA , respectubstrate . In ordeubstrate  to buildubstrate .BRAF is a protein kinase and one of the components of the Ras-Raf-MEK-ERK signaling pathway that plays a central role in cell proliferation and differentiation. Activation of BRAF allows it to phosphorylate MEK via its kinase domain . The BRAWe created contact maps for BRAF based on the models with PDB codes 4mneB (in which it is complexed to MEK1)  and 4h58Given its role in cell growth and division, BRAF is frequently mutated in many different cancers. One of the most common mutations seen in this protein is the V600E mutation, that accounts for 80% of all BRAF mutations . This reIn this manuscript we have explored a contact map based description of protein structures with a goal of identifying residues that preferentially stabilize alternative conformations, potentially controlling the balance between different functional isoforms. Exploring the redundancy in the Protein Data Bank we realized that, for most proteins, multiple coordinate sets deposited in PDB provide some level of sampling of these proteins’ conformational ensemble. We have shown that while for most proteins the differences between various coordinate sets available in the PDB is small, for a significant number of them the differences are not trivial and, most likely, sample different functional states. Somewhat surprisingly, these differences are not necessarily related to ligand binding or complex formation and even models of unliganded proteins solved in different crystal forms provide a significant amount of information about the shape of the conformational ensemble.We developed an automated protocol to identify residues involved in stabilizing alternative conformations of proteins and integrated this information with the PDBFlex database, previously developed in our group. As PDBFlex is integrated directly with the PDB database and website, this provides information about such differentially stabilizing residues (DSRs) for hundreds of proteins to a broad group of users. We explored the DSRs identified by our protocol on few examples of clinically important proteins and confirmed that they indeed overlap with residues previously identified to stabilize alternative conformations and/or mutated in disease states.Differential contact map visualization and identification of DSRs is now implemented on the PDBFlex server, with thousands of examples open for user analysis. We believe that this resource can be used as a hypothesis-generation tool to identify the residues that could be modulating the conformational equilibrium of any given protein.We have developed a method based on contact maps to identify residues that could be modulating the conformational flexibility of a protein by forming contacts that differentially stabilize two different conformations of the protein. This method is described below and illustrated by We used structural models deposited in the Protein Data Bank clustered at 95% sequence identity, as done in PDBFlex . Each clFor each structural model, PDB files were downloaded and the coordinates of the residues in the alignment, corrected as described above were extracted. Only residues with an empty insertion code and hetero-flag were used. . Based on the coordinates for these residues, contact maps were created for the two structural models . A pair Many different definitions of a residue-residue contact have been used in the literature. The differences are either in the choice of the atom used to measure inter-residue distances  or the distance threshold used to determine a contact. We use side-chain heavy atoms to measure inter-residue distances in order to prioritize side-chain interactions, a definition that has been previously shown to be optimal for threading and protein structure comparisons . IndepenA differential contact map was then created by subtracting one contact map from the other. This map therefore, represents the set of contacts seen in only one conformation, but not the other . We furtMODELLER v 9.21 , 38 was For adenylate kinase, the SEQRES sequence for chain A from the PDB file for 4ake was used to create a sequence file in the MODELLER .ali format. This sequence was manually edited to create a sequence corresponding to the E170A mutation. The WT and mutated sequence were modeled using 4akeA and 4jzkB as templates.For BRAF, the SEQRES sequence for chain C from the PDB file for 4h58C was used. 4mneB and 4h58C were used as template structures. Since the PDB ATOM records for both 4mneB and 4h58C start from residue D449 (D448 in 4h58C), the sequence file was manually edited to start the sequence from the same residue and reduce the number of gaps in the resulting alignment. The same procedure was followed for the V600E mutated sequence.S1 File(TXT)Click here for additional data file.S2 File(PY)Click here for additional data file.S1 Table(CSV)Click here for additional data file.S2 Table(CSV)Click here for additional data file.


Computational prediction has become an indispensable aid in the processes of engineering and designing proteins for various biotechnological applications. With the tremendous progress in more powerful computer hardware and more efficient algorithms, some of in silico tools and methods have started to apply the more realistic description of proteins as their conformational ensembles, making protein dynamics an integral part of their prediction workflows. To help protein engineers to harness benefits of considering dynamics in their designs, we surveyed new tools developed for analyses of conformational ensembles in order to select engineering hotspots and design mutations. Next, we discussed the collective evolution towards more flexible protein design methods, including ensemble-based approaches, knowledge-assisted methods, and provable algorithms. Finally, we highlighted apparent challenges that current approaches are facing and provided our perspectives on their further development. Due to their unique structural and functional properties, proteins constitute an essential element of life as well as various branches of the emerging sustainable economy ,2,3,4,5.However, proteins are known to be dynamical entities, performing their function as an ensemble of diverse conformations rather than a single static structure. Protein dynamics is a highly complex phenomenon comprising numerous contributions from motions with different mechanisms of action and happening with diverse timescales and amplitudes  that higWhen we consider the reliable treatment of protein dynamics as an essential component of a successful protein design, it is natural to resort to the molecular dynamics (MD) simulation technique as a golden standard to investigate the conformational behavior of a protein. Nowadays, various MD simulation protocols can be utilized to deliver insights into protein dynamics on millisecond timescales with the growing utilization of graphics processing unit (GPU)-enabled parallelism and the development of more efficient software, gradually making such simulations even more affordable ,29,30,31Regarding their utilization for protein engineering, MD simulations are commonly incorporated into different stages of the design process in order to modulate protein stability, alter interactions of proteins with cognate ligands or perturb dynamics of functional sites . Next, tIn this review, we focus on the recent developments in computational methods and tools, which aim to overcome significant challenges brought by integrating protein dynamics into predictions. First, we discuss tools developed for analyzing the fluid nature of interactions in protein ensembles and the elusive transport of ligands in a user-friendly way. In the second part, we critically review the efforts towards the efficient integration of protein flexibility on the backbone level into protein designs and engineering algorithms that are available in established software packages.Accessing information embedded in trajectories produced by MD simulations is a nontrivial task, especially when we focus on phenomena as complex as the networks of interacting residues and their correlated motions or as rare as the events connected with small molecules permeating through protein structures. To alleviate these challenges, we provide an overview of four recently developed tools aiming at understanding and controlling protein allostery and two tools that provide insights into the transport of small molecules .Protein stability and function are dependent on their three-dimensional structures and are frequently conditioned by elaborate networks of noncovalent interactions between numerous residues . Those nTo provide a comprehensive view of interactions, the residue interaction network in protein molecular dynamics (RIP-MD) tool was developed . RIP-MD A new software package, Java-based Essential Dynamics (JED), was developed to facilitate comparative PCAs of MD simulations of different proteins , includiRomero-Rivera and coworkers proposed a promising protocol combining information on residues proximity and their correlated movements into the so-called shortest path map (SPM), which can be applied to infer allosteric communication within a protein structure . The firSimilarly, by combining network analyses with PCA, the computation of allosteric mechanism by evaluating residue–residue associations (CAMERRA) tool aims to capture allosteric motions based on the residue–residue contact analysis of protein dynamics ,61. The Detailed tracking and analysis of ligand behavior across MD trajectories of biomolecular systems represent another strategy to enrich the protein design process by highlighting regions crucial for the transport of ligands, i.e., molecular tunnels, channels, and gates ,77, whicSolanum tuberosum, and based on their experience, they designed a relatively straightforward protocol for the detailed analysis of cavities networks and tunnels capable of pinpointing hotspots for engineering experiments [Pyrococcus furiosus [d-amino acid oxidase (DAAO) [AQUA-DUCT ,63 aims eriments . Such anfuriosus  and d-ame (DAAO) . In these (DAAO) ,85.As an alternative to very costly explicit MD simulations, the passage of ligands through biomolecules can be explored by docking these ligands to an ensemble of precomputed molecular tunnels with CaverDock software ,65 FiguB. BenefiDuring the past few years, we have witnessed a surge in the efforts to develop novel design methods capable of robust treatments of protein dynamics . These mThe generation of molecular ensembles by using MD and Monte Carlo (MC) simulations has become more affordable for a wider group of users, creating a means to face novel protein design challenges. By utilizing conformational ensembles, protein design algorithms can take the dynamic nature of the protein structures into account, providing a biologically sound strategy and frequently improving the performance of the employed methods ,99.We start this section by reviewing insights from two studies aiming at benchmarking generic procedures for ensemble generation on the success of protein design or redesign tasks. In the first comparative research by Ludwiczak and colleagues, 10 protocols combining methods from Rosetta software  with MD The predictive performance of the Flex ddG method in estimating the change in binding free energy after mutation (ΔΔG) at protein–protein interfaces has also been boosted when using a structural ensemble instead of a single static structure . In thisNotably, the Flex ddG method was evaluated in three comprehensive benchmarking studies focusing on different engineering scenarios. Aldeghi and coworkers evaluated alchemical free-energy calculations and three Rosetta protocols including Flex ddG in combination with different force fields for the prediction of changes in binding the affinity of ligands upon mutation . In totaThe advantages of incorporating conformational ensembles during design have also been noted during the development of a multistate framework that enables the adoption of reliable methods implemented in the Rosetta package for single-state design (SSD) and also for multistate design (MSD) . BrieflyA similar idea of combining an ensemble-based design and a multistate approach was behind the development of a meta-multistate design procedure (meta-MSD) to rationally design proteins that spontaneously switch between conformational states . In thisFollowing the expansion of protein structure databases, which contain a considerable amount of data related to structure–dynamics–function relationships in proteins, new methods to assess backbone flexibility have been designed, benefiting from this wealth of knowledge. The methods introduced here are implemented in the Rosetta software and represent an exciting direction for improving protein design processes by more efficiently exploring alternative backbone conformations.The first among the reviewed data-driven approaches is the flexible backbone learning by Gaussian processes (FlexiBaL-GP) method  that useA different approach to harnessing knowledge from structural databases and to navigating sequence space sampling with a flexible backbone has been explored by the structural homology algorithm for protein design (SHADES) . This apDue to the high complexity of protein design tasks, especially when employing ensemble-based approaches , the majThe development of provable algorithms started with the adaptation of the dead-end elimination (DEE) method  that wasOwing to persistent optimization efforts ,127,128,In contrast with proteins evolved through directed evolution, constructs predicted by computational protein engineering methods have so far been focusing mainly on hotspot residues close to functional sites. By considering the proximity of relevant regions, mutations have the highest chance of altering the target function, and at the same time, the number of variants to evaluate is kept tractable. Unfortunately, this restriction often hampers the performance of rationally designed proteins. It is clear that we need more efficient workflows and tools that can pinpoint hotspots at crucial distal sites as well. One class of such hotspots involves residues forming allosteric networks capable of inducing a shift in populations of protein conformations to support their altered function upon mutation. Here, we would like to highlight the availability of tools for rapid analyses of protein allostery focusing on residue–residue interactions in a single static structure or employing normal mode analysis (NMA) to approximate protein dynamics . HoweverThroughout this review, we have witnessed a consistent success of methods incorporating different degrees of protein dynamics in increasing the accuracy of their predictions owing to the innate ensemble nature of the proteins. These methods frequently require user expertise in complicated computational methods and protocols. Considering that some of fully automated and easy-to-use methods available nowadays originate from very sophisticated and computationally extensive approaches ,140,141 First, the successful utilization of molecular ensembles in protein design and redesign is dependent on the quality of input ensembles emphasizing the importance of sufficient and representative sampling. Since this is not a trivial task, but rather an art itself, the ensemble-based approaches reviewed here employ limited sampling. Despite sampling somewhat restricts conformational changes in protein backbones, these approaches achieve substantial advantages over the predictions relying on a single structure. The systematic utilization of a more extensive sampling via much longer, enhanced, or adaptive simulations will be required to thoroughly describe more global conformational transitions ,29,30,31Second, with the increasing amplitude of introduced perturbations, the protein structures will more frequently be drawn from the conformational space further away from the structures produced by protein crystallography. Following the precedent of unsatisfactory performance observed for simulations of intrinsically disordered proteins using standard force fields, which were developed for folded and stable protein structures ,144, to


Molecular dynamics (MD) simulation is a rigorous theoretical tool that when used efficiently could provide reliable answers to questions pertaining to the structure-function relationship of proteins. Data collated from protein dynamics can be translated into useful statistics that can be exploited to sieve thermodynamics and kinetics crucial for the elucidation of mechanisms responsible for the modulation of biological processes such as protein-ligand binding and protein-protein association. Continuous modernization of simulation tools enables accurate prediction and characterization of the aforementioned mechanisms and these qualities are highly beneficial for the expedition of drug development when effectively applied to structure-based drug design (SBDD). In this review, current all-atom MD simulation methods, with focus on enhanced sampling techniques, utilized to examine protein structure, dynamics, and functions are discussed. This review will pivot around computer calculations of protein-ligand and protein-protein systems with applications to SBDD. In addition, we will also be highlighting limitations faced by current simulation tools as well as the improvements that have been made to ameliorate their efficiency. Proteins are vital constituents of living organisms, responsible for myriads of life-sustaining cellular processes such as molecular recognition, signal transduction, protein localization, and enzyme catalysis. These biological events are navigated by protein motions as well as physical interactions established between proteins and partnering molecules such as ligands, peptides, proteins and nucleic acids . ProgresThe introduction of MD simulation to molecular biology has enabled researchers to access a microscopic visualization of biological processes. While the history of MD simulation of macromolecules began with a simple structural investigation of a small protein, namely bovine pancreatic trypsin inhibitor, the field has matured to a stage that calls for the development of new technology to facilitate the study of larger proteins and protein complexes with more intricate dynamics ,3,4. MD The advancement in computing architecture and algorithms has spurred the extensive growth of MD simulation tools over recent decades. Architectural developments of computers resulted in the invention of graphics processing units (GPUs) and parallel computing while the refinement of algorithms leads to a more accurate rendering of the potential energy surface (PES) of the system and better conformational sampling. The combination of both facets of computing has enabled a more reliable prediction of the physical properties of proteins as well as the observation of biologically relevant protein structures at the atomic level, a feat that requires timescales beyond milliseconds, expanded length scales, and enhanced sampling techniques that facilitate high free energy barrier crossing . The burMD simulation based on classical mechanics has been widely used for sampling conformational space of biomolecules, ranging from small molecules , peptideReplica-exchange molecular dynamics (REMD) method has been suggested by Sugita and Okamoto . MultiplHydrogen mass repartitioning (HMR) allows increment of intrinsic time-step from 2 fs to 4 fs for solvated biomolecular systems. The time-step of normal MD is determined by the fastest motions in the system, which is normally a vibrational motion of hydrogen bond, limited to 2 fs for a stable simulation. The 4 fs time-step, a factor of 2 speed up, is granted by increasing the mass of hydrogen to about 3 amu and decreasing the equivalent mass from connected heavy atom. Hopkins et al. tested HMR on various benchmark systems, ranging from 3-residue peptide to 129-residue protein, hen egg white lysozyme (HEWL), and concluded there is no significant difference in kinetics and thermodynamics when HMR is used . BalusekOne can enhance the sampling by running simulations on modified potential energy functions, boosted and smoothened ones. Accelerated Molecular Dynamics (aMD) suggested by McCammon  directlyAnother type of enhanced sampling method embraces continuous modifications of the energy profile during the simulation. Metadynamics (MTD) suggested by Parinello’s group modifies potential energy function in a regular basis during the simulation. Addition of potential energy to visited states discourages repeated sampling of these states, hence promoting the inspection of unexplored conformational space c. AdaptiThe Markov state model (MSM) is a different type of approach, generating a kinetic model from a long unbiased MD trajectory ,41. MSM PeptiDock+GaMD method, Wang et al. successfully predicted the binding poses of three distinct peptides to their partnering proteins, namely, SH3 domain, XLP protein and PIM1 kinase [Protein-protein association plays a pivotal role in a myriad of cellular signaling pathways. Misguided protein-protein interactions (PPIs) have been linked to neurodegenerative diseases such as Alzheimer’s diseases and Parkinson’s disease, and the inhibition of protein dimerization/oligomerization has been proposed as a potential therapeutic treatment for cancer ,49,50. T1 kinase . The resBesides modelling protein-peptide binding, mechanistic studies delineating protein-protein association and dissociation at the molecular level are also necessary to obtain a comprehensive picture of how native protein-protein contacts are established. Pan et al. introduced a protocol involving the synergistic use of long-timescale MD simulations with their newly developed enhanced sampling method, termed “tempered binding”, to investigate reversible protein-protein association . The “teEven though enhanced sampling methods advocate better sampling efficiency than conventional MD simulations in most cases, selecting the best enhanced sampling technique for your system of interest is also vital to ensure that a reliable free energy profile can be obtained. In a recently published study by Wingbermühle and Schäfer, a systematic comparison of the performance of four enhanced sampling techniques, namely, US, REUS, well-tempered MTD, replica exchange with solute tempering 2 (REST2), and biased exchange umbrella sampling (BEUS), was performed to examine the partial dissociation of the N-terminus of an antigenic peptide from Major Histocompatibility Complex class I (MHC I) protein . ThroughThe prediction of protein-ligand binding affinity has been a main goal in drug discovery field since the magnitudes of the affinity and the selectivity of designed ligands/drugs are crucial in SBDD. Free energy perturbation (FEP) and thermodynamic integration (TI) methods have been suitable for this aspect as both provide a relatively reliable prediction of protein-ligand binding affinity. Both require MD simulations at different values of lambdas ranging from 0 to 1 which represent their perturbation state, 0 being the original state and 1 being the fully perturbed state. Then, applying either Zwanzig’s equation for FEP or thermodynamic integration formalism would disclose the associated free energy of perturbation ,58. AbsoThis alchemical route can also be utilized when mutating a key residue in silico, obtaining the relative free energy difference upon mutation, ∆∆G . Two typTo calculate protein-protein binding free energy, however, the alchemical FEP is not suitable because a simulation of ‘annihilating a protein’ from a solvated system is essentially impractical. If one can observe multiple associations/dissociations of protein complex with unbiased MD, constructing MSM upon the trajectory would be advantageous . On the 3P2P1L1) . String 3P2P1L1) . The metWhile many replicas are required in between the perturbation states for FEP methods and along the separation path for PMF-based and string methods, the computationally cheaper way to estimate binding free energy would be the molecular mechanics combined with Poisson-Boltzmann or generalized Born and surface area solvation (MM/PBSA or MM/GBSA). With the relatively short amount of sampling, one can utilize snapshots from the trajectory to obtain the binding free energy that is estimated by getting the difference between the free energy of the complex and that of each body. The free energies are calculated as creening . Though creening .The prediction of residues crucial for the formation of favorable protein-ligand or protein-protein interactions is an asset in the field of rational drug design. These decisive sets of amino acids are called ‘hotspots’ and numerous studies with the objective of quantifying the contribution of residues towards binding affinity have been conducted to facilitate precise identification of ‘hotspot’ residues ,75,76,77The cosolvent or mixed-solvent MD simulation is one of the most widely used computational tool in SBDD for its accuracy in identifying ligand binding sites ,81. ThisAs the name suggests, cosolvent MD simulation uses solvent mixtures that usually consist of organic molecules or fragments that possessed key pharmacophore features crucial for ligand binding such as hydrophobic centers, aromatic rings, hydrogen bond donors and hydrogen bond acceptors . There awww.playmolecule.org), formulated a similar protocol, which engages benzene probes to facilitate the identification of cryptic pockets [CrypticScout, accessible through PlayMolecule platform  [G [Alanine scanning mutagenesis is another experimental technique that has been computerized to accelerate ‘hotspot’ prediction, mostly at protein-protein interface. This method involved the site-directed mutagenesis of specific residues to alanine to assess the contribution of the amino acid towards protein stability and function ,104,105.G) . RosettaG) [G .G [G prediction from mutation Cutoff Scanning Matrix (mCSM), a machine learning approach that was trained based on several curated databases documenting changes in protein stability and protein-protein affinity upon mutation, such as SKEMPI and ProTherm [Another recently established CAS tool is the BUDE Alanine Scanning (BudeAlaScan) method, which was built upon the empirical free energy approach of a molecular docking algorithm, BUDE (Bristol University Docking Engine) ,110. SimG . This coProTherm ,113,114.ProTherm . All of G especially when a sizable mutation scan is required. The introduction of the IE approach offers a more rigorous and less expensive computation of binding entropy as it explicitly calculates the entropic contribution by directly observing the fluctuation in interaction energy between individual residues and ligand from a single MD simulation, hence eliminating the additional computational cost required for entropy calculation [Besides the utilization of CAS in determining ‘hotspots’ at protein-protein interface, CAS has also been adapted for protein-ligand systems ,77,115. N log N), where N is the number of particles in the system, for particle mesh Ewald (PME) long range interaction computation, however, is unavoidable. Using the state-of-the-art machines and algorithms of unbiased MD, about 2 milliseconds of sampling was needed to generate a reasonable kinetic model to observe associations and dissociations of two proteins showing femtomolar binding affinities [Current computational approaches utilizing all-atom MD simulations, despite its fruitful aspects, are still limited due to high computational cost. The technological advances and usage of GPU significantly aid in speeding up the sampling . The benfinities . ConsideAnother concern for MD simulation would be the validity of the result. An ultimate goal of MD would be to yield the same thermodynamic and kinetic observables, which one would find in experiments, but with more atomistic details. To draw this outcome, one would need sufficient amount of sampling from the correct force field. While more precise force fields are being developed, MD users might want to focus on if the right answer, a converged result, has been drawn out from the simulation or not. Evaluating ergodicity from the trajectory and determining whether convergence has been achieved are difficult problems. Sampling complete 3N-dimensional configuration of complex biomolecular system with N particles is simply impossible . When thOne can argue the convergence of the result using the statistical tools, but it can never be a definitive statement.The predictive capacity of MD simulations lies in the reliability of classical potential energy functions, i.e., force fields, to describe the inter- and intramolecular interactions between particles in a system of interest. Force fields encompassed empirically derived energy terms that approximate torsional potentials and interatomic interactions, namely bonded and non-bonded interactions, in a condensed phase. The establishment of a force field that realistically represents the PES of a system is one of the main concerns in MD simulation that developers try to ameliorate through several alterations such as the inclusion of the polarization effect of proteins and solvent, refinement of torsional parameters, and improvement of water models ,124. Of viz. induced dipole model, the Drude oscillator model, and the fluctuating charge model for straightforward inclusion of electronic polarizability in MD simulations [Developers of well-established force fields such as AMBER, AMOEBA. CHARMM, and OPLS customarily utilized three popular methods, ulations ,131,132.ulations ,136,137.ulations . The fluulations . Despite+ and Na+ ions through the Gramicidin A channel showed good agreement with experimental observations, in comparison to classical MD simulations [Numerous tinkering has been done to further justify the superiority of polarizable force fields over classical force fields in precisely modelling the physical processes of proteins. Recent improvements in the aforementioned polarizable force fields include the incorporation of screening effects and charge penetration corrections to precisely model the electrostatic energy of short-range interactions, and the consideration of anisotropic polarization of water/solvent molecules to further justify the thesis of polarizability being a many-body effect ,139,140.ulations . The utiulations . Anotherulations . Using ADES-Amber force field [Besides refinement related to polarizability, there are also other improvements, which specifically target certain issues related to current interest in protein research. For instance, the growing focus on PPIs in computational SBDD have unraveled a shortcoming of existing force fields in accurately representing intrinsically disordered proteins (IDPs) or regions (IDRs). IDRs is one of the major contributors towards protein-protein association and current torsion angle potential in existing force fields are not sufficient to precisely depict the flexibility of IDRs. Yu et al. recently developed a non-polarizable, residue-specific force field, called AMBER ff99SBnmr2, to balance the torsional potential to cater to both folded and disordered protein regions. This force field assimilated per-residue dihedral angle modifications, based on experimental information curated from PDB structures of protein fragments (coil library), into the extant ff99SBnmr1—force field that was formulated based on the backbone dihedral potential of ff99SB and optimized using NMR chemical shifts of fully solved proteins . Anotherce field . This foRecent improvements in MD simulation accompanying the state-of-the-art sampling methods and their applications reveal the robust potential of MD in the field, and the remaining limitations points out where MD needs to be further developed. The unique insights upon the dynamics given by all-atomistic MD simulations are crucial for drug discovery and elucidation of mechanism for many biological processes. MD is an interdisciplinary output from numerous theoretician and experimentalists standing on the fields of physics, chemistry, mathematics, computer science, and many more. As a recent illustration, the burgeoning field of artificial intelligence has now started aiding in boosting the efficiency and accuracy of MD simulation . Machine


Long non-coding RNAs (lncRNAs) regulate diverse biological processes via interactions with proteins. Since the experimental methods to identify these interactions are expensive and time-consuming, many computational methods have been proposed. Although these computational methods have achieved promising prediction performance, they neglect the fact that a gene may encode multiple protein isoforms and different isoforms of the same gene may interact differently with the same lncRNA.In this study, we propose a novel method, DeepLPI, for predicting the interactions between lncRNAs and protein isoforms. Our method uses sequence and structure data to extract intrinsic features and expression data to extract topological features. To combine these different data, we adopt a hybrid framework by integrating a multimodal deep learning neural network and a conditional random field. To overcome the lack of known interactions between lncRNAs and protein isoforms, we apply a multiple instance learning (MIL) approach. In our experiment concerning the human lncRNA-protein interactions in the NPInter v3.0 database, DeepLPI improved the prediction performance by 4.7% in term of AUC and 5.9% in term of AUPRC over the state-of-the-art methods. Our further correlation analyses between interactive lncRNAs and protein isoforms also illustrated that their co-expression information helped predict the interactions. Finally, we give some examples where DeepLPI was able to outperform the other methods in predicting mouse lncRNA-protein interactions and novel human lncRNA-protein interactions.Our results demonstrated that the use of isoforms and MIL contributed significantly to the improvement of performance in predicting lncRNA and protein interactions. We believe that such an approach would find more applications in predicting other functional roles of RNAs and proteins. Long non-coding RNAs (lncRNAs) are RNA transcripts of more than 200 nucleotides that are not translated to proteins. Previous research , 2 has dCurrent methods to identify lncRNA-protein interactions are based on biological experiments and computational models. With the rapid development of molecular biology techniques, large-scale experimental approaches such as PAR-CLIP , RNAcompNote that, there are several recently developed methods for predicting general ncRNA-protein interactions based on machine learning –24, but The network-based methods integrate heterozygous information associated with lncRNAs and proteins into a network   and utilDeep learning method for predicting LncRNA-Protein Isoform interactions). DeepLPI uses sequence, structure and expression data of lncRNAs and protein isoforms. Instead of using the canonical proteins of each gene, DeepLPI considers all protein isoforms, which could help to detect lncRNA-protein interactions more accurately. DeepLPI extracts intrinsic features such as functional motifs from the sequence and structure data, and obtains network topological features from the expression data. Note that, DeepLPI uses mRNA expression data to extract network topological features instead of PPI data as done in the existing methods because most of the available PPI data do not provide the details about isoforms. Moreover, it is possible to build an isoform-isoform interaction network based on mRNA expression data . At last, Pearson’s correlation coefficient (PCC) was used to measure the pairwise correlation between lncRNA sequence similarity and protein isoform sequence similarity , the performance of all models dropped. The AUC of DeepLPI decreased from 0.866 (human) to 0.753 (mouse), but it was still the best since the highest AUC of the other models on the mouse test data was 0.68. An obvious reason for the performance drops might be because lncRNAs do not show the same pattern of evolutionary conservation as protein-coding genes .To further investigate the prediction performance of DeepLPI on interactions between proteins and lncRNAs conserved between human and mouse such as Gas5, Rmst, Neat1 and Meg3 , 51, we We further validate our model using some new lncRNA-protein interactions from the recent literature that were not included in the NPinter v3.0 database. After a careful literature search, we found 12 new lncRNA-protein interactions –56. The The knowledge of interactions between lncRNAs and protein isoforms could help understand the functions of lncRNAs. In this paper, we proposed a machine-learning based method, DeepLPI, to predict interactions between lncRNAs and protein isoforms. DeepLPI uses a multimodal deep learning neural network to extract intrinsic features from the sequence and structure data of lncRNAs and protein isoforms and a conditional random field to extract network topological features from their expression data. We designed a multiple instance learning iterative algorithm to train the prediction model using an availablelncRNA-protein interaction dataset, and performed extensive experiments to show that DeepLPI achieves a significantly better accuracy in predicting lncRNA-protein interactions compared with the state-of-the-art methods. The multimodal learning feature of DeepLPI allows it to integrate more types of data besides sequences, structures and expression profiles. With minor modifications, DeepLPI could be adapted to predict miRNA-protein interactions, as well as more complex interactions such as lncRNA-miRNA-protein interactions.Our divergence analysis shows that many isoforms of the same gene interact with different lncRNAs. Hence, it would be of practical importance to study the interactions between lncRNAs and protein isoforms (as opposed to proteins or genes). However, as far as we know, DeepLPI is the first attempt to predict lncRNA-protein isoform interactions, and its performance is still far from being desirable. It might be possible to improve the performance of DeepLPI by using better  expression data, more detailed protein secondary structure representations and high quality isoform-isoform interaction network data. We plan to investigate these directions in the near future.The ground truth interactions between lncRNAs and proteins were downloaded from the NPInter v3.0 database . This ishttp://github.com/fabriziocosta/EDeN) as in [The lncRNA sequences and the protein isoform sequences of human genome were downloaded from GENCODE  and ENSEN) as in . An lncRN) as in . SPIDER2The third type of data that we collected are mRNA and lncRNA expression data. The mRNA expression data are obtained from the literature , and then is represented as n is represented as An lncRNA is a character sequence composed of 4 unique ribose nucleotides: cytosine (C), adenine (A), guanine (G), and uracil (U). A protein isoform is a sequence consisting of 20 unique amino acid codes. We generate hexamers and trimers from an lncRNA sequence and a protein isoform sequence, respectively. An lncRNA of length 1. As in , to help1. As in . A densen, a ith row and jth column is the sum of probabilities of the predicted structures with the jth nucleotide of the lncRNA being of the ith structural component type. Similar to the lncRNA structure representation, a n amino acids.Different from the sequence data, the structure of an lncRNA or a protein is often not unique, since multiple structures could be predicted for a single sequence by RNAShapes and SPIDER2. To keep more predicted structural information of an lncRNA of length DeepLPI predicts the interactions between lncRNAs and protein isoforms by integrating the information of sequence, structure and expression data into a unified predictive model. It consists of two learning submodels. The first is a multimodal deep learning neural network (MDLNN) model and the second a conditional random field (CRF) model. The MDLNN model extracts and fuses the (intrinsic) features from the sequence and structure data of lncRNAs and protein isoforms, and calculates the initial scores of the interactions between lncRNAs and protein isoforms. The CRF model makes a final prediction based on both the initial interaction scores and the expression data of mRNAs (corresponding to protein isoforms) and lncRNAs. To overcome the lack of ground truth interactions between lncRNAs and proteins, we develop a semi-supervised algorithm following –41 to trTo learn intrinsic features related to lncRNA-protein isoform interactions from the sequence and structure data, we construct a multimodal deep learning neural network (MDLNN). We use convolutional layers to extract local features and long short-term memory layer (LSTM) layers to extract short-range and long-range dependencies. At first, MDLNN uses a standard dense embedding technique  to map ti and j has weight Based on the experimental evidence that we have found in the literature, co-expressed isoforms and co-expressed lncRNAs often exhibit similar interactions . To incoested by  for unsiested by  for infeith lncRNA-protein isoform pair, denote the lncRNA sequence as y for each lncRNA-protein isoform pair by minimizing the Gibbs energy function below:ith and jth lncRNA-protein isoform pairs, their pairwise potential is defined as follows:i and j in the q-th isoform co-expression network and i and j in the r-th lncRNA co-expression network. For the By searching for an assignment on as in  to obtaiD(Q||P) to approximate P, and adopt the following iterative update equation to obtain a Q with the minimum KL-divergence:It is easy to see that minimizing the Gibbs energy is equal to maximizing the following probability:Because the ground truth lncRNA-protein isoform interactions are generally unavailable, conventional supervised training algorithms cannot be directly applied to our model. Similar to  again, hw and in terms of the CRF parameters w so Given a batch of training instances hm as in  to updatWe perform inference for every instance in the positive bags after each update of the parameters of the model, using the model with the updated parameters. Here, the label of an instance is updated according to the inference: Additional file 1. Supplementary Materials


DeepMind presented remarkably accurate predictions at the recent CASP14 protein structure prediction assessment conference. We explored network architectures incorporating related ideas and obtained the best performance with a 3-track network in which information at the 1D sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The 3-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables the rapid solution of challenging X-ray crystallography and cryo-EM structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate protein-protein complex models from sequence information alone, short circuiting traditional approaches which require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research. As described at the CASP14 conference, the AlphaFold2 methodological advances included 1) starting from multiple sequence alignments (MSAs) rather than from more processed features such as inverse covariance matrices derived from MSAs, 2) replacement of 2D convolution with an attention mechanism that better represents interactions between residues distant along the sequence, 3) use of a two-track network architecture in which information at the 1D sequence level and the 2D distance map level is iteratively transformed and passed back and forth, 4) use of an SE(3)-equivariant Transformer network to directly refine atomic coordinates (rather than 2D distance maps as in previous approaches) generated from the two-track network, and 5) end-to-end learning in which all network parameters are optimized by backpropagation from the final generated 3D coordinates through all network layers back to the input sequence.The prediction of protein structure from amino acid sequence information alone has been a longstanding challenge. The bi-annual Critical Assessment of Structure (CASP) meetings have demonstrated that deep learning methods such as AlphaFold , 2 and thttps://predictioncenter.org/casp14/zscores_final.cgi).Intrigued by the DeepMind results, and with the goal of increasing protein structure prediction accuracy for structural biology research and advancing protein design , we explWe reasoned that better performance could be achieved by extending to a third track operating in 3D coordinate space to provide a tighter connection between sequence, residue-residue distances and orientations, and atomic coordinates. We constructed architectures with the two levels of the two-track model augmented with a third parallel structure track operating on 3D backbone coordinates as depicted in The 3-track models with attention operating at the 1D, 2D, and 3D levels and information flowing between the three levels were the best models we tested , clearlyThe improved performance of the 3-track models over the 2-track model with identical training sets, similar attention-based architectures for the 1D and 2D tracks, and similar operations in inference (prediction) mode suggests that simultaneously reasoning at the multiple sequence alignment, distance map, and three-dimensional coordinate representations can more effectively extract sequence-structure relationships than reasoning over only MSA and distance map information. The relatively low compute cost makes it straightforward to incorporate the methods in a public server and predict structures for large sets of proteins, for example, all human GPCRs, as described below.Blind structure prediction tests are needed to assess any new protein structure prediction method, but CASP is held only once every two years. Fortunately, the Continuous Automated Model Evaluation (CAMEO) experiment  tests stWe experimented with approaches for further improving accuracy by more intensive use of the network during sampling. Since the network can take as input templates of known structures, we experimented with a further coupling of 3D structural information and 1D sequence information by iteratively feeding the predicted structures back into the network as templates and random subsampling from the multiple sequence alignments to sample a broader range of models. These approaches generated ensembles containing higher accuracy models, but the accuracy predictor was not able to consistently identify models better than those generated by the rapid single pass method . NeverthIn developing RoseTTAFold, we found that combining predictions from multiple discontinuous crops generated more accurate structures than predicting the entire structure at once . We hypoWith the recent considerable progress in protein structure prediction, a key question is what accurate protein structure models can be used for. We investigated the utility of the RoseTTAFold to facilitate experimental structure determination by X-ray crystallography and cryo-electron microscopy and to build models providing biological insights for key proteins of currently unknown structures.Bos taurus (Phanerochaete chrysosporium (Solution of X-ray structures by molecular replacement (MR) often requires quite accurate models. The much higher accuracy of the RoseTTAFold method than currently available methods prompted us to test whether it could help solve previously unsolved challenging MR problems and improve the solution of borderline cases. Four recent crystallographic datasets  structure in a heterodimeric PI3Kγ complex. The top HHsearch hit has a statistically insignificant E-value of 40 and only covers 14 residues out of 167 residues. The predicted structure could readily fit into the electron density map despite the low local resolution . The Cαsolution , bottom.Experimental structure determination can provide considerable insight into biological function and mechanism. We investigated whether structures generated by RoseTTAFold could similarly provide new insights into function. We focused on two sets of proteins: first, G protein-coupled receptors of currently unknown structure, and second, a set of human proteins implicated in disease. Benchmark tests on GPCR sequences with determined structures showed that RoseTTAFold models for both active and inactive states can be quite accurate even in the absence of close homologs with known structures  lead to metabolic disorders, and the protein plays an unknown role in Golgi membrane redistribution into the ER , 17. TheThe ADAM  and ADAMTS families of metalloproteases are encoded by over 40 human genes, mediate cell-cell and cell-matrix interactions , 21 and Transmembrane spanning Ceramide synthase (CERS1) is a key enzyme in sphingolipid metabolism which uses acyl-CoA to generate ceramides with various acyl chain lengths that regulate differentiation, proliferation, and apoptosis . StructuThe final layer of the end-to-end version of our 3-track network generates 3D structure models by combining features from discontinuous crops of the protein sequence (two segments of the protein with a chain break between them). We reasoned that because the network can seamlessly handle chain breaks, it might be able to predict the structure of protein-protein complexes directly from sequence information. Rather than providing the network the sequence of a single protein, with or without possible template structures, two or more sequences (and possible templates for these) can be input, with the output the backbone coordinates of two or more protein chains. Thus, the network enables the direct building of structure models for protein-protein complexes from sequence information, short circuiting the standard procedure of building models for individual subunits and then carrying out rigid-body docking. In addition to the great reduction in compute time required (complex models are generated from sequence information in ~30 min on a 24G TITAN RTX GPU), this approach implements “flexible backbone” docking almost by construction as the structures of the chains are predicted in the context of each other. We tested the end-to-end 3-track network on paired sequence alignments for complexes of known structures  see Met or threeTo illustrate the application of RoseTTAFold to complexes of unknown structure with more than three chains, we used it to generate models of the complete four-chain human IL-12R/IL-12 complex . A previRoseTTAFold enables solutions of challenging X-ray crystallography and cryo-EM modeling problems, provides insight into protein function in the absence of experimentally determined structures, and rapidly generates accurate models of protein-protein complexes. Further training on protein-protein complex datasets will likely further improve the modeling of the structures of multiprotein assemblies. The approach can be readily coupled with existing small molecule and protein binder design methodology to improve computational discovery of new protein and small molecule ligands for targets of interest. The simultaneous processing of sequence, distance, and coordinate information by the three-track architecture opens the door to new approaches incorporating constraints and experimental information at all three levels for problems ranging from cryo-EM structure determination to protein design.Supplementary Material