Identify protein contact points between TP53 and minichromosome maintenance (MCM) complex proteins 2, 3, and 5 with high resolution allowing for potential novel Cancer drug design.A next‐generation sequencing‐based protein–protein interaction method developed in our laboratory called AVA‐Seq was applied to a gold‐standard human protein interaction set. Proteins including TP53, MCM2, MCM3, MCM5, HSP90AA1, PCNA, NOD1, and others were sheared and ligated into the AVA‐Seq system. Protein–protein interactions were then identified in both mild and stringent selective conditions.Known interactions among MCM2, MCM3, and MCM5 were identified with the AVA‐Seq system. The interacting regions detected between these three proteins overlap with the structural data of the MCM complex, and novel domains were identified with high resolution determined by multiple overlapping fragments. Fragments of wild type TP53 were shown to interact with MCM2, MCM3, and MCM5, and details on the location of the interactions were provided. Finally, a mini‐network of known and novel cancer protein interactions was provided, which could have implications for fundamental changes in multiple cancers.We provide a high‐resolution mini‐interactome that could direct novel drug targets and implicate possible effects of specific cancer mutations. Using a next‐generation sequencing‐based protein‐protein interaction method, we identified interactions among MCM2, MCM3, and MCM5. While many of the interacting regions we detected overlap with existing structural data of the complex, we also recovered novel domains with higher resolution. Additionally, we were also able to identify, for the first time, that wildtype TP53 interacts with MCM2, MCM3, and MCM5 and provide the location of the interactions. The novelty in this approach affords protein interactions to be mapped with a higher resolution in a high‐throughput manner; millions of protein fragment pairs can be screened simultaneously. The system also can titrate the interaction strength via the competitive inhibitor (3‐AT) concentration in the media. By increasing the 3‐AT concentration, the user can select for stronger protein–protein interactions. Here, the use of 2 and 5 mM 3‐AT concentrations allowed us to detect a range of interaction strengths.The all‐vs‐all sequencing (AVA‐Seq) method has been detailed previously.2.32 fold change of the normalized read counts of greater than or equal to 1 and a False Discovery Rate of <10%.Data were collected from an Illumina NGS platform using standard library and sequencing chemistry methods for paired 150 bp read lengths per the manufacturers' recommended protocol. Fastq files of paired‐end sequences were aligned to a sequence database of the human protein interaction reference set and read counts for each fragment pair were normalized and scaled using methods as described previously.2.4UniProt.org.Using recent structural data (PDB 6xtx), protein contact points between MCM proteins were identified using 5 Å distance33.1The AVA‐Seq method recovered 10 interacting fragment pairs between MCM2 and MCM5 Figure .46 TheThe average fragment length tested in this study is 150 residues3.2The MCM2|MCM3 interaction Figure  shows mo3.3MCM5|MCM3 is a physiological interaction referenced in the literature and detected with our system. The interacting fragments between MCM5 and MCM3 and the enrichment in protein contact points were identified from the structure (PDB 6xtx) Figure . The fra3.4All five MCM2|TP53 interacting fragment pairs were recovered in the mild selective pressure conditions Table .Interacting fragment pairs for TP53|MCM3 are predominantly localized to the C‐terminal disordered region of MCM3 Figure  as seen TP53|MCM5 interacting fragment pairs recovered with this study are shown in Figure Table 3.5Figure 3.6Interactions such as ORC2|MCM2,4This study provided an opportunity to identify physiologically relevant interacting fragments using the AVA‐Seq method. This was done by overlapping interacting fragment pairs between MCM2, MCM3, and MCM5 with cryo‐EM protein contact points Figures , 2, 3. HMCM2 in Figure Interacting fragments near the C‐terminus of MCM3 are enriched in the more robust selective conditions (green fragment pairs) in both the MCM2|MCM3 interaction Figure .Not surprisingly, interacting fragment pairs appear to localize differently when comparing interactions recovered exclusively in the 2 mM 3‐AT condition (gray colored fragments) and fragments recovered in both 2 and 5 mM or exclusively in 5 mM conditions (green colored fragments) in Figures d = ~1–150 μM) but show high specificity.The C‐terminal region of TP53 has at least 40 different binding partners and utilizes the intrinsically “disordered region for mediating and modulating” PPIs.With respect to transient interactions––work by others showed strong interactions between certain MCM proteins and GOF TP53 mutants but failed to detect an interaction with wild type until in vivo methods capable of detecting transient interactions were used. We have recovered many interacting fragment pairs between the MCM2, MCM3, and MCM5 proteins with wild type TP53. Interestingly, there were no interacting fragment pairs between MCM2|TP53 in the 5 mM 3‐AT conditions. This could indicate a more transient interaction between MCM2|TP53 when compared to the TP53 interactions with MCM3 or MCM5. It would be of interest to further investigate the preferences of TP53 binding with regards to the MCM2‐7 proteins especially given their similar domain structures.We observe interactions of the MCM proteins with TP53 localizing outside of the MCM box domains in 5 mM 3‐AT, while the fragment pairs recovered in only 2 mM seem to have a preference to contain the MCM box domains Figure . This isThere are multiple lines of evidence including in vivo and in vitro experiments that both mutant TP53 and wild type TP53 interact with the MCM proteins Table . AdditioA double positive association exists between mutant TP53 R175H and all MCM2‐7 complex proteins in both the cytosol and nucleus as determined via proteomic enrichment analysis.It is worth noting that the TP53 construct from this study contains two mutations (P72R and P278A). The P72R is a common mutation in 40% of many populations and is associated with some diseases but is predicted to be benign.One explanation for the AVA‐Seq method being able to identify these interactions could be the increased sensitivity of the assay. By this, we mean if the binding affinity of wild type TP53 to MCM proteins is higher (weaker or more transient interaction) than the gain‐of‐function mutation R273H with a given substrate, there would be an increased challenge in detecting interactions. The power of AVA‐Seq is it allows us to look beyond the expected interactions to determine other exciting and potentially novel biologically relevant interactions that warrant further evaluation. It would be worth exploring the range of binding affinities detectable by AVA‐Seq by using proteins with known binding affinity and making a “standard curve” by increasing competitive inhibition ranging from 0 to 5 mM 3‐AT, for example.In the future, we plan to screen wild type TP53 and a library TP53 mutants against the human genome, allowing researchers to select potentially subtle differences in protein–protein interactions to examine further. TP53 gain‐of‐function mutations can inherently change the pool of interacting proteins, resulting in different biological effects.In conclusion, we overlayed protein fragment interaction pairs with known protein contact points from a recent cryo‐EM structure of the MCM complex allowing us to obtain a high resolution view of the interacting domains. We show the intrinsically disordered wild type TP53 interacts with MCM2, MCM3, and MCM5. We postulate these interactions may not have been identified previously due to the weaker binding of a wild type TP53 compared to a gain‐of‐function TP53 mutant. Finally, we generated a mini‐cancer interactome highlighting known and novel protein interactions between TP53, MCM2, MCM3, MCM5, NOD1, SMAD1, SMAD4, ORC2, ORC4, and HSP90AA1. We believe these will provide an excellent starting point for potential novel drug design.The authors declare that there are no conflict or competing interests.S.S.‐R. conceived the idea and designed the study; S.S.‐R. and N.M.A. collected the data; S.S.‐R., J.A., and J.A.M. analyzed the data; S.S.‐R. and J.A.M. wrote the manuscript.This research was supported by funding from Qatar Foundation to Weill Cornell Medicine in Qatar in the form of the BMRP2 grant.All analyses were based on previous studies; thus, no ethical approval and patient consent are required.Table S1–S5Click here for additional data file.


Altered nuclear receptor signaling is well-established to contribute to a range of hormone-dependent cancers and therefore they are therapeutic targets, for example in breast and prostate cancer. However, nuclear receptor signaling in the cancer, and in response to therapeutic targeting is frequently altered in part by changes to the expression and function of the large numbers of coregulators that modulate sensitivity of the receptor, change their protein-protein interaction and alter the transcriptional consequences. These coregulators perform numerous roles in terms of regulating nuclear receptor responses and the functional diversity of these interactions is an area of emerging insight. Coregulator interactions, functions, and impacts on nuclear receptor signaling across hormone dependent cancers are the topic of this review.Nuclear receptors (NRs) function collectively as a transcriptional signaling network that mediates gene regulatory actions to either maintain cellular homeostasis in response to hormonal, dietary and other environmental factors, or act as orphan receptors with no known ligand. NR complexes are large and interact with multiple protein partners, collectively termed coregulators. Coregulators are essential for regulating NR activity and can dictate whether a target gene is activated or repressed by a variety of mechanisms including the regulation of chromatin accessibility. Altered expression of coregulators contributes to a variety of hormone-dependent cancers including breast and prostate cancers. Therefore, understanding the mechanisms by which coregulators interact with and modulate the activity of NRs provides opportunities to develop better prognostic and diagnostic approaches, as well as novel therapeutic targets. This review aims to gather and summarize recent studies, techniques and bioinformatics methods used to identify distorted NR coregulator interactions that contribute as cancer drivers in hormone-dependent cancers. Transcriptional functioning of TF complexes is regulated further by trans-interacting events such as post-translational RNA and protein modifications and interactions with non-coding RNAs (ncRNAs). In this manner, TF complexes control gene expression by sensing and regulating chromatin architecture, and initiating RNA transcription, splicing, elongation and RNA modifications, as well as initiating feedback control of these very same processes. These regulatory steps result in precise and tunable protein expression in robust signaling networks that are central to homeostasis. Given these diverse functions, TF complexes are large, often being megadaltons in size, and the composition is highly dynamic. In addition, given the importance of TFs and their interacting proteins it is unsurprising that they comprise approximately 15% of the human protein-coding genome; ~1500 TFs, and ~1500 coregulators . AR and REST repressive complexes may also contribute to repressing the transcriptional regulation of the serine peptidase inhibitor, Kazal type 1 (SPINK1) , and ind−/− mice with targeted deletion of the 5′ SIN3 interaction domain exhibit defects in the development of erythrocyte, thymocyte and neural events [−/− mice have an impaired development of the forebrain, and the corepressor is required for maintenance of the neural stem cell state [NCOR1 and its paralog NCOR2/SMRT were discovered separately in 1995 as part of efforts to explain gene repression by NRs. NCOR1 and NCOR2 bind to TR and RARs to inhibit the expression of their target genes in the absence of ligands ,232. Ncol events . Tissue l events . Ncor2−/ll state .Both NCOR1 and NCOR2 have N-terminal repressor domains (RDs) that recruit and associate with additional proteins, forming a large corepressor holocomplex. For example, RDs interact and recruit SANT-like domains  to synergistically promote deacetylation through a deacetylase activation domain and histone interaction domains on histone tails through C-terminal RIDs that tether the corepressor holocomplex to the NR partners on target genes ,238,239.IIB, TAFII32 and TAFII70 [These corepressors undergo significant alternative splicing to generate protein variants that determine function ,243. For TAFII70 . The mos TAFII70 ,245. Fur TAFII70 ,248,249.S. cerevisiae. SIN3A has four paired amphipathic alpha-helix (PAH) motifs which are imperfect repeats of ~100 aa residues [+/− mice have normal expression of SIN3A, whereas SIN3A−/− do not survive post-embryonic day 6.5, indicating the essential role of SIN3A in early development at the peri-implantation stage [The SIN3 gene was first cloned in residues ,251,252.residues . SIN3A+/on stage .SIN3A is capable of multiple protein–protein interactions. SIN3 is devoid of intrinsic DNA-binding abilities and its ability to target chromatin comes through direct interactions with the DNA-binding TFs, such as Mad1 or KLF repressors, and indirect interactions with adaptor proteins including NCOR1/2 . It is tThe CCCTC-binding factor (CTCF) comprises autonomous silencing domains that mediate transcriptional repression when tethered to a promoter sequence ,258. At SIN3A also acts as a regulator of gene expression, survival, and growth in Erα-positive BRCA cells . For exaSIN3A is able to interact with other factors outside its PAH domains. For example COPS2/ALIEN-mediates silencing through the recruitment of SIN3 to its N-terminus and interacting with DNA-bound transcriptional silencers at the C-terminus. This functional interaction has been demonstrated with VDR on the human CYP24 promoter containing VDRE . COPS2 aThe LCOR is a transcriptional corepressor, first cloned from a fetal brain, with high expression in the liver, testis, ovary, kidney and brain. The LCOR is recruited to agonist bound-NRs through a single NR box. The LCOR comprises a NR motif, a nuclear localization signal, tandem PXDLS motifs and a HTH domain . The LCOR binds to the LBDs on the ERα/β, AR, GR, RAR α/β/γ, RXRα, PPARγ, VDR and PGR in a ligand-dependent manner and acts to attenuate signaling by agonist-bound receptors ,267,268.In BRCA, the LCOR has two mRNA variants called LCOR and LCOR2. Both LCOR and LCOR2 inhibit estrogen-mediated signaling and BRCA cell proliferation via the HTH domain. Both the LCORs bind with NRIP1 via the LCOR HTH domain and C-terminal domain (CTD) of NRIP1 and this interaction is crucial for inhibition of gene expression and cell proliferation . The LCOR acts as a corepressor for the AR and inhibits PRAD in vivo. The LCOR interacts with the AR and is recruited to chromatin in the presence of AR. However, the NR box of LCOR is dispensable for interaction with the AR and LCOR and instead interacts with the DBD of the AR on chromatin. In androgen-independent PRAD, the interaction between the LCOR and AR is inhibited by signaling pathways involving SRC . The CTDcis-acting elements [To complement these roles for coactivators and corepressors, it has become clear that these protein complexes link to several co-called cointegrator complexes. For example, the CREB-binding protein (CBP) was initially identified as a coactivator of multiple transcriptional activators, including p53, NF-𝜅B and NRs . The CBPelements . Similarelements .A number of different selective antiestrogens  and aromatase inhibitors  have been developed for hormone-sensitive ER+ BRCA. Drugs with a combined activity of aromatase inhibition and ER degradation activity, such as fulvestrant, have also been approved against ER+ and HER2- advanced BRCA. Resistance to these therapies in BRCA is attributed to the retention of ER signaling, which is mediated by the interactions between activated ERα and coregulator proteins ,280,281.A role for EZH2 has emerged, which mediates epigenetic silencing of the ERα cofactor GREB1 and which causes ERα coregulators to reallocate on chromatin to drive the conversion of antiestrogen into an agonist . FurtherMore widely, other coactivators involved in tamoxifen resistance include TIMELESS, which binds to ERα to enhance its transcription and when overexpressed it increases PARP1 expression and ER-induced gene regulation through its proximal LXXLL motifs .Strong parallels exist in the emergence of ADT-R-PCa in response to drugs, including enzalutamide, fulvestrant, flutamide and darolutamide. In half of the PRAD patients, AR gene amplification has been detected among the most frequent genomic changes for recurrent cancer ,289. NCO2D3 sensitivity [By contrast, 5–10% of recurrent PRADs have loss-of-function mutations in corepressors, such as NCOR1 or NCOR2, that enhance the enzalutamide resistance. Altered expression of NCOR1 and NCOR2 has been reported across several different hormone-dependent cancers including BRCA, PRAD and TRC ,292,293.sitivity . By contsitivity .A reduction in NCOR1 levels causes bicalutamide resistance in LNCaP cells and murine PRAD in vivo . PRAD ceThe central role of coregulators and TF interactions is to fine-tune and integrate endogenous and exogenous signals to impact cell-fate decisions. The regulatory interactions are highly specific and depend on the structural and physico-chemical characteristics of protein domains. There are a myriad of coregulator-dependent mechanisms of altered gene expression patterns in hormone-dependent cancers, and it is challenging to define cancer-driver mechanisms with these alterations. Germline and somatic structural variations alter TFs and coregulators, and the regulatory regions they bind ,298. In One of the most utilized techniques that facilitated the discovery of protein–protein interactions is a two-hybrid assay that resulted in the discovery of NCOR1, NCOR2, NCOA1/SRC1 and many more coregulators. This technique requires using a reporter gene for expression control and fusing both the bait and the prey proteins with the DNA-binding domain  and the transcriptional activation domain , respectively. The two-hybrid assay is one of the basic low-throughput experiments developed in yeast, but now it can be utilized in mammalian cells as well ,310, leaIn the matrix-based, different prey protein-expressing clones will be plated in different wells, and afterwards they will be mated with bait strains in order to form chimeric diploids. Depending on the reporter gene expression, the chimeric colonies will be selected. For example, an RARα interaction with NCOR1 is one of the many interactions resulting from a high-throughput matrix-based approach ,313. In The tandem affinity purification (TAP) method uses the principle of tagging a target protein with a TAP tag, and then this fused protein gets expressed in yeast to form the typical complexes in the cells. After two steps of IgG matrix-protein bond cleaving and calcium calmodulin-coated beads incubation, the fragments can be analyzed using MS. This technique has the advantage of a higher-order interaction direction and is not limited to binary information. Some of the insight into NCORs involved in AR regulation is due to this technique . RecentlGiven that coregulators modulate the NR’s transcriptional activity, scientists have developed a total protein interactome method to understand the role of NRs’ dysregulation in different diseases. RIME allows the study of complexes in their native interactome by formaldehyde fixation and immunoprecipitation of the target protein. In the last step, on-bead digestion reduces the contamination, and the resulting peptides are tested with MS to identify direct protein interactions to the target protein. The advantage of this technique is that there is no need for an extra tag to purify or overexpress the target protein, because RIME utilizes the endogenous proteins and mimics the biologically and clinically relevant interactomes. Complex interactions can be further investigated using the RIME data to model protein networks and the interaction of complexes with each other .Other CRISPR-based approaches, such as CAPTURE  or C-BURThese approaches usually result in low-throughput data collection but can result in more in-depth understanding of two specific protein interactions. Some of these techniques are: fluorescence resonance energy transfer (FRET), X-ray crystallography and NMR spectrometry . FRET inAll aforementioned techniques are examples of protein–protein interaction detection methods and scientists are still developing more advanced techniques. Each method has its advantages and disadvantages, but all of them can open entirely new avenues of research and novel ideas to better understand the protein functions.The first goal of analyses of coregulators from a genome-wide perspective is to establish comprehensive lists of TFs and co-regulator genes. Previously, we applied a text-mining approach to capture gene sets in Gene Ontology (GO) terms that contained phrases including “positive control of transcription”, “negative control of transcription”, “co-activator” and “co-repressor”, as well as other curated collections; for example, Panther, which stands for Protein ANalysis THrough Evolutionary Relationships, and is a classification system that was designed to classify proteins (and their genes) in order to facilitate high-throughput analysis , and FANp-values are then calculated based on the group position relative to the sampling distribution of the genome. Finally, within each group the most altered genes within a class can be focused on for multivariate modeling [Bootstrapping permutation approaches can then be used to test if the proportions of each gene group are altered more than predicted by chance; for example, in TCGA data. For mRNA or protein expression, copy number variation or mutation status, Z-scores of normalized data can be calculated and tested whether the observed alteration frequencies within gene classes  are altered significantly, or a vector of changes for all genes was calculated and the observed values  of a given class tested in comparison to all genes detected in each cancer cohort. Random sampling methods such as bootstrapping can be used to simulate the distribution of changes across the genome for statistical comparison . Empiricmodeling . Similarmodeling .Such lists of coregulators can be used to annotate other high-dimensional datasets to investigate enrichment. For example, recently we undertook RIME analyses of NCOR2  to identSimilarly, the annotation of ChIP-Seq data for either a given NR or a coregulator with a ChromHMM algorithm  that defFinally, to test how coregulators impact correlations between, for example, AR and AR-target genes, partial correlation analyses can be used  to test


To ensure efficiency in discovery and development, the application of computational technology is essential. Although virtual screening techniques are widely applied in the early stages of drug discovery research, the computational methods used in lead optimization to improve activity and reduce the toxicity of compounds are still evolving. In this study, we propose a method to construct the residue interaction profile of the chemical structure used in the lead optimization by performing “inverse” mixed-solvent molecular dynamics (MSMD) simulation. Contrary to constructing a protein-based, atom interaction profile, we constructed a probe-based, protein residue interaction profile using MSMD trajectories. It provides us the profile of the preferred protein environments of probes without co-crystallized structures. We assessed the method using three probes: benzamidine, catechol, and benzene. As a result, the residue interaction profile of each probe obtained by MSMD was a reasonable physicochemical description of the general non-covalent interaction. Moreover, comparison with the X-ray structure containing each probe as a ligand shows that the map of the interaction profile matches the arrangement of amino acid residues in the X-ray structure. Drug development is a cost-intensive and time-consuming process. The cost is estimated to be more than USD two billion, and it may take 10–15 years for a new drug to reach the market ,7,8,9,10To improve activity and selectivity, structural optimization that is strongly and specifically directed at the target protein is necessary. Therefore, the mechanism by which proteins recognize compounds and the prediction of protein–ligand binding modes are important in lead optimization. Several studies have been reported that comprehensively analyze interaction patterns from protein–ligand complex structures registered in the Protein Data Bank (PDB) . Imai etMixed-solvent molecular dynamics (MSMD) simulations involve MD in the presence of explicit water molecules mixed with probe molecules or functional group fragments such as for hotspot detection ,24 and bIn this article, we propose an “inverse” MSMD for analyzing a probe’s preference of interaction patterns. First, MSMD simulations with 15 diverse proteins were performed to sample various protein residue environments preferred by the probe. The residue environments were then integrated to a residue interaction profile, followed by the visualization of it. We assessed the proposed analysis using three probes with an aromatic ring: benzamidine, catechol, and benzene. Their residue interaction profiles provided a physicochemical account of general non-covalent interactions, such as electrostatic interactions, hydrogen bonding interactions, and amide-The selection of proteins used in MSMD sampling is crucial to obtain the various residue environments utilized to construct a residue interaction profile. We chose 15 proteins that were previously selected by Soga et al.  because The probe of interest was then pre-processed. The restrained electrostatic potential (RESP) procedure in the Antechamber module of AmberTools18  was emplWe conducted MSMD using the protocol of EXPRORER . It is w−6 kcal/mol; Rmin = 20 Å) was introduced only between the center of the probes to prevent their aggregation.The probes were randomly placed around the protein at a concentration of 0.25 M using PACKMOL 18.169 . The hig2), and then the systems were minimized a further 200 steps using the steepest descent algorithm without any position restraints. After minimization, the system was heated gradually to 300 K during 200 ps constant-NVT MD simulations with harmonic position restraints on the solute heavy atoms . During the subsequent 800 ps constant-NPT MD simulations at 300 K and 105 Pa, the force constants of the position restraints were gradually reduced to 0 kcal/mol/Å2. The P-LINCS algorithm [After the construction of the initial structures, the systems were minimized to include 200 steps using the steepest descent algorithm with harmonic position restraints on the heavy solute atoms  under the MIT license.The scripts used to generate a residue interaction profile of a probe were implemented using Python with Biopython . The impWe tested the proposed method with benzamidine, catechol, and benzene. Ring systems are key scaffold components in medicinal chemistry ; therefostacking . TherefoCatechol exists as a substructure in several ligands, such as dopamine. It has two hydroxy groups and a phenyl group. Although it does not have any net charge, which is different from benzamidine, hydroxy groups can form hydrogen bonds, resulting in stable binding to proteins.The residue interaction profile of catechol is shown in Benzamidine and catechol both have phenyl groups, and their groups show clear residue interaction profiles. On the other hand, benzene did not display a profile in the vertical direction . InsteadWe compared the constructed residue interaction profiles and crystal structures for further verification of the appropriateness of the method.To demonstrate the validity of the residue interaction profile obtained by MSMD, the crystal structures of kinase CK2 (1LPU) and trypsin (2ZPS) with the residue interaction profile are shown in To validate the residue interaction profile of catechol obtained by MSMD, we compared it with X-ray structures that included catechol molecules. Despite several agreements between the X-ray structures and residue interaction profiles obtained from MSMD simulation, the profiles of aromatic residues were not detected sufficiently in spite of the threshold being the same among all residue types. One possible reason is the size of the side chains. Aromatic side chains have a relatively large structure compared to other types of residues, leading to a wider placement of the Cβ atoms. In this study, we aimed to show the residue-based interaction profile; thus, we focused on the Cβ atoms, which are common among almost all residues. However, it is also important to know the interaction profiles of specific atoms of side chains. The implementation already has the functionality to generate the profiles of any specific atoms, as well as Cβ. An exemplary interaction profile of aromatic rings is shown in Strong binding affinity between a probe molecule and the protein surface, which allows a single probe molecule to bind stably to the surface.Frequent access of probe molecules to the protein surface, which makes multiple probe molecules bind to the protein surface alternatively.In this study, we sampled residue environments at preferable protein surfaces to omit artifacts of MSMD simulation. The above results indicate that the sampled residue environments were informative; however, preferable protein surfaces can be classified into two types:The aim of this sampling was to obtain a residue interaction profile in which a probe molecule binds stably. The protein surfaces of the first type were suitable for this purpose. The surfaces of the second type might be important in providing access to the binding site, but the binding affinity with the probe may not necessarily be strong.To obtain more appropriate residue interaction profiles, we tried to filter out residue environments whose probes were not stably situated. Here, we defined a stable probe molecule as a probe molecule that moved less than 3 Å from a place where the molecule was 500 ps before. The selection of stable samples omitted 77.4% of the residue environments of benzamidine, 88.0% of those of catechol, and 94.5% of those of benzene. However, As mentioned in the Introduction, structural optimization that is strongly and specifically directed at a target protein is necessary in lead optimization. Residue interaction profiles can help the optimization step by suggesting whether a substituent is feasible for a protein binding pocket space. For instance, superimposing the interaction profile on a co-crystalized structure indicates how well the existing substituent matches the protein surface environment . FurtherIn this article, we proposed inverse MSMD for analyzing a probe’s preference of interaction patterns. Unlike the analysis from known data, such as X-ray, Cryo-EM, and NMR structures, the method can process arbitrary probes, and the results are free from any chemical context of molecules. We assessed the method using benzamidine, catechol, and benzene, resulting in good agreement with the experimental structures. This method indicates where protein surfaces provide suitable binding sites for a probe and this, in turn, can be applied to lead optimization by suggesting substituents based on the vacant spaces of a binding pocket. More precisely, for a target protein surface where the next substituents on hit compounds are placed, the method provides information on which substituents are better in accordance with the residue interaction profiles of multiple probes or possible substituents. The next step in our method will involve proposing a quantitative metric of how well the residue environment and the residue interaction profile match. Furthermore, the construction of the residue interaction profile database is effective for the computational lead optimization process because the profiles can be applied to arbitrary proteins.


Increasing attention has been focused on the study of protein–metabolite interactions (PMI), which play a key role in regulating protein functions and directing an orchestra of cellular processes. The investigation of PMIs is complicated by the fact that many such interactions are extremely short-lived, which requires very high resolution in order to detect them. As in the case of protein–protein interactions, protein–metabolite interactions are still not clearly defined. Existing assays for detecting protein–metabolite interactions have an additional limitation in the form of a limited capacity to identify interacting metabolites. Thus, although recent advances in mass spectrometry allow the routine identification and quantification of thousands of proteins and metabolites today, they still need to be improved to provide a complete inventory of biological molecules, as well as all interactions between them. Multiomic studies aimed at deciphering the implementation of genetic information often end with the analysis of changes in metabolic pathways, as they constitute one of the most informative phenotypic layers. In this approach, the quantity and quality of knowledge about PMIs become vital to establishing the full scope of crosstalk between the proteome and the metabolome in a biological object of interest. In this review, we analyze the current state of investigation into the detection and annotation of protein–metabolite interactions, describe the recent progress in developing associated research methods, and attempt to deconstruct the very term “interaction” to advance the field of interactomics further. A combination of intermolecular interactions determines the phenotype and functionality of a cell. The total interactome is represented mainly by protein–protein interactions (PPIs) and protein–metabolite interactions (PMIs). The current understanding of the interactions between proteins and small molecules significantly lags behind even the fragmentary knowledge of protein–protein interactions. The current understanding of the interactions between proteins and small molecules significantly lags behind even the fragmentary knowledge of protein–protein interactomes. For example, it is not clear which interactions between proteins to consider relevant . The strCompared with protein–protein interactions, studying protein–metabolite interaction is more complicated since it depends not only on proteomics techniques but also on methods of metabolomics, which are currently less mature compared to the former . PMIs plKnowledge accumulated today is focused mainly on exogenous ligands with promising pharmacological properties . Beyond Recent advances in mass spectrometry presently allow the routine identification and quantification of thousands of proteins and metabolites, but they still need to be improved to enable a complete inventorization of all biomolecules present in biological objects, e.g., cells. In order to explore a large part of the proteomic iceberg, consisting of low-copy proteins and aberrant proteoforms , the proThe complexity of studying the interactome is exacerbated by the fact that existing methods work either in vitro or in vivo but with substantial violation of the natural environment of the cell. The data obtained are contradictory and often do not coincide when analyzed by different methods, which can be caused by both technical reasons and biological ones, for example, in the case of studying different types of cells .The knowledge accumulated today is focused mainly on exogenous ligands that demonstrate potentially valuable properties from a pharmacological point of view. However, the development of high-throughput methods for the exploration of endogenous protein–metabolite interactomes gains speed, offering new opportunities and challenges for the field. The practical demand for information about PMIs contributes to the development of analytical and bioinformatic approaches and advances the omics community in terms of a fundamental understanding of the mechanisms of the interconnection of molecules. In this review, we systematize the current knowledge about PMIs, experimental methods for their detection and annotation, and outline further prospects for protein–metabolite interactomics.Molecular interaction is the cornerstone of all cellular processes, many of which are governed by the branched net of contact between proteins and small molecules. During the last decade, the depth of understanding of the molecular machinery of life has advanced dramatically. This progress is fueled by continuously improving technological (mostly mass spectrometric) solutions for exploring the molecular content of biological samples in “omics” mode. Understanding the interactions between the proteomic and metabolomic layers is the second, much more challenging derivative of the task of proteome and metabolome profiling. This complexity is explained primarily by the lack of clear criteria determining what should be considered an “interaction” and what should not.A pool of covalently bound complexes represents the most investigated area of PMIs, resulting from post-translational modifications or non-enzymatic modifications induced directly by reactive metabolites. However, in interactomics, the focus is generally on non-covalent contacts between proteins and small molecules. These highly dynamic events are particularly exciting to researchers. Such non-covalent binding is characterized by two parameters: specificity and affinity. The degree of specificity distinguishes highly specific  from less specific bindings  . AffinitThe “driving force” of PMI is the synergy of energy exchanges between the protein and the small molecule, surrounded by water and buffer ions. Like any spontaneous interaction, binding between a protein and a metabolite occurs if and only if the change in the Gibbs free energy of the system is negative when the system reaches an equilibrium state. The Gibbs energy change is formed from two fundamental components: the enthalpy change and the entropy change, and these components might compensate for each other. Thus, tight binding resulting from multiple noncovalent contacts between metabolites and proteins decreases enthalpy. Such negative changes are often accompanied by a decrease in entropy resulting from the restriction of the mobility of interacting molecules and subsequent changes in binding free energy . In retuA complete description of a protein–metabolite complex includes data regarding its composition, formation dynamics, stoichiometry of the interacting partners, strength of the interaction, and the environment that allows the interaction to happen. Thus, a comprehensive description of even a single protein–metabolite interaction is nontrivial and usually requires the utilization of several complementary techniques. Furthermore, there is yet no consensus regarding what to consider an interaction between a protein and a metabolite, since in addition to direct covalent or non-covalent interactions, there exist much weaker and less well-defined interactions, e.g., allosteric interactions, which are significantly harder to identify .Even having sorted out all the components of the Gibbs energy and taking into account all the factors that determine the possibility of interaction between a pair of proteins and metabolites in a truly “panoramic” mode, we can only formulate a very flexible, context-dependent definition of protein–metabolic interaction. By PMI, we mean physical contact between protein and metabolite, implying the formation (and subsequent disturbance) of non-covalent bonds as a result of which the specific physicochemical parameters of each interacting partner change. We detected only the change in these parameters, and from this change inferred the interaction event. However, in the absence of any standardized, physically rigorous definition of PMI, this term has come to mean any positive results from methods that capture a physical association between the protein and the metabolite, meaning that the current use of the term heavily reflects the specifics and biases of the methods utilized. Consequently, in order to advance the field of protein–metabolite interactomics, we need to provide an overview of current state-of-the-art methods for the detection of interactions between proteins and metabolites, highlighting their principles of work, as well as their strengths and weaknesses. Even though the interactomics community has yet to develop a standard definition of PMI, we have many more questions than answers. The entire professional community has nothing left but to eat an elephant of PMIs one bite at a time. Next, we consider the main strategies for eating this elephant and outline prospects for further research in this area.The interest in elucidating PMIs has accelerated the development of new methods that rely on the integration of analytical chemistry, synthetic chemistry and systems biology. Traditionally, these methods are divided into three categories: small molecule-to-protein, protein-to-small molecule and panoramic screening. This division separates approaches that aim to identify protein or small molecule targets in panoramic or targeted modes. The major advantage of panoramic approaches over targeted techniques is that discovery methods fuel data-driven research rather than hypothesis-driven research, making them superior for the early stages of biomarker discovery when dealing with complex biological objects. In turn, targeted methods make it possible to focus on the interacting partners of a particular protein or metabolite and to strictly determine the parameters of such an interaction. These and other features of analytical methods and their areas of applicability will be discussed in the following sections.However, the chosen classification system of methods does not take into account such essential characteristics as the need for chemical modification of molecules before their interaction or the possibility of obtaining quantitative information about PMI. In our review, we included methods that deviate from the mainstream classification in the “Modification of small molecules” and “Biophysical approaches” subsections .The variety of PMI methods that start with a particular metabolite is expansive. However, in a broad sense, all of these approaches detect changes in the physicochemical parameters of protein molecules interacting with the metabolite of interest .The methods based on the chemical modification of small molecules  involve several stages of confirming a non-covalent interaction using individual functional groups ,22,22. FThe principal disadvantage of such methods is the presence of additional groups on the metabolite since they can affect the affinity and selectivity of binding to target proteins. Like affinity-based approaches (see below), chemoproteomic target identification is limited by its ability to synthesize suitable derivatives of the compound of interest. In addition, even when using well-functioning photo-reactive modifications to attach the protein to the bait, there is always the possibility of binding to background proteins, which affects the number of false positive findings .Despite the shortcomings, the method of chemoproteomic identification, with proper planning of the experiment and carefully selected functional groups, minimizes false positive results. This method is a reliable option for orthogonally validating results obtained with other methods and new combinations of metabolites and reactive groups, as well as methods for their activation, are being actively developed ,27.The group of the most common approaches to panoramic exploration of PMIs uses limited proteolysis in combination with mass spectrometry (LiP-MS). The main idea behind this method is to compare the results of the proteolysis of free proteins with those of proteins interacting with metabolites. When interacting with a metabolite, a part of the protein becomes sterically inaccessible to proteases, which ultimately affects the final list of detectable peptides. The experiment includes two stages of proteolysis: first, a non-specific protease is used to digest proteins for a short amount of time in conditions close to native ones for the proteins of interest; then, a more specific protease is used for complete digestion in denaturing conditions. Tryptic peptides quantified by MS allow for the comparison of the structural “fingerprints” of all detected proteins, both free and bound to small molecules . To reduE. coli led to the identification of 1678 protein–metabolite interactions (86% of which were detected for the first time), with 2564 proteins identified in total . The mePlasmodium falciparum, which causes malaria in humans [Cellular thermal shift assay (CETSA), combined with mass spectrometry  ), allowsn humans .Thermal shift-based methods are being actively improved. For instance, Shaw et al. used microplates to develop an approach with significantly improved throughput termed HT-CETSA . This meAnother method that leverages thermal profiling is Proteome Integral Solubility Alteration (PISA), which does not use temperature-stability graphs. Similar to CETSA/TPP, aliquots of both experimental (with added metabolite of interest) and control samples are incubated at different temperatures. In the next step, aliquots are pooled back together and the protein abundance change is measured between such combined experimental and control samples, rather than between each aliquot pair . The proAnother MS-based method used for PMI discovery is solvent-induced protein precipitation (SIP) . SIP is The advantages of the SIP method include ease of implementation and the ability to determine the affinity of a metabolite (or a drug) for a protein using dose-dependent analysis. As with thermal shift-based methods, the main disadvantage of SIP is that it is not universal .A. thaliana seedlings treated with a combination of light and heat stress [One of the first methods developed for detecting intermolecular interactions in close-to-native conditions is affinity chromatography (AC), also known as affinity purification (AP). This method features cross-linking of small molecules of interest with the matrix, such as agarose, which are then packed in a chromatography column. Samples, e.g., cell lysates, are then passed through the column, and proteins that interact with the metabolite of interest stay in the column, while the other proteins are eluted. Affinity-based methods have various modifications and are often used together with protein labeling approaches for increased protein target detection rate ,57. In ot stress .The immobilization of small molecules can affect both the affinity and specificity of the interaction, so AC/AP is limited to studying only a handful of stable interactions. Most of the weak noncovalent interactions are destroyed during affinity purification . AnotherIn many cases, the task of identifying a certain PMI originates from the interest in a particular protein being a potential drug target or therapeutic agent. Along with methods for identifying the protein receptors of a preselected small molecule, several approaches have been developed to study small molecules interacting with a target protein.Unfortunately, most of these methods are limited by the availability of purified proteins and the use of incomplete small molecule libraries. Recent examples of protein-to-small molecule strategies include nuclear magnetic resonance (NMR) with ligand detection , differeLigand-detected nuclear magnetic resonance (NMR) spectroscopy is a method utilized to analyze changes in metabolite properties when they interact with the protein of interest. This method was used to study PMIs between several proteins and a couple dozen metabolites, and several new interactions were detected in addition to the ones already known . HoweverE. coli K-12, which demonstrates the analytical capabilities of this method [Another approach to studying protein–metabolite interactions from the protein point of view is the differential radial capillary action of ligand assay (DRaCALA). A mixture of proteins with an isotopically labeled metabolite standard is applied on a nitrocellulose membrane. Nitrocellulose membranes are able to retain proteins due to hydrophobic interactions, while free metabolites can move through nitrocellulose under the action of capillary forces. This method is based on the difference between the mobility of free and protein-bound metabolites . Knowings method . DespiteOne more not so widely used “protein to metabolite” approach is the integration of mass spectrometry with equilibrium dialysis . The fiArabidopsis thaliana, which led to the identification of a new mode of regulation of these enzymes [The tandem affinity purification (TAP) allows the metabolomic identification of interacting small molecules under close to native conditions to be carried out. It is an adaptation of the AP protocol, which is traditionally used to analyze protein–protein interactions. A protein of interest is epitopically labeled and produced in cells. The protein and metabolite complexes are then immunoprecipitated from the native cell lysate with the help of immobilized antibodies designed to recognize the epitope. Finally, partner proteins and metabolites are analyzed using a mass spectrometric platform. The main disadvantage of TAP is its tendency to produce false positive results due to the presence of an epitope tag and non-specific binding to the matrix. Non-specific interactions are usually excluded using several negative controls, such as a vector control with an empty epitope tag, other proteins, several purification steps, and/or subcellular localization filters. Due to the similarity of this method to approaches for studying PPIs, TAP provides an opportunity to study PMI and PPI in parallel under close to physiological conditions, which distinguishes this method from the other approaches described above . In one  enzymes .The above-mentioned approaches use very different concepts, but they all share a common signature: they start from a selected protein or metabolite as bait. These methods allow the exploration of interactions of a target protein or small molecule, but they cannot provide a panoramic picture of the whole interactome.The non-targeted PROMIS (PROtein-Metabolite Interactions using Size separation) strategy allows PMI analysis under native conditions. Protein–metabolite complexes are separated by size exclusion chromatography, followed by quantitative metabolomic and proteomic analyses of the resulting fractions. The definition of putative intermolecular interactions is based on coelution .Saccharomyces cerevisiae cells allowed for the detection of interaction events between 3982 proteins and 74 small molecules, and most of these interactions were detected for the first time [The main advantages of PROMIS are its ability to work at in vivo concentrations and the absence of the need to modify small molecules or proteins. However, by its very nature, coelution is a sign but not a proof of interaction. PROMIS should be considered an exploratory approach for constructing an interactome map, which should be combined with orthogonal methods to refine the composition of complexes. Like gene expression studies, integrating multiple PROMIS datasets will likely be sufficient to double-check putative interaction . Recentlrst time .The ideologically similar TICC (target identification by chromatographic coelution) method is based on identifying a characteristic shift in the chromatographic elution profile of a compound bound to a protein target. TICC was used to test known drug–protein interactions (micromolar to nanomolar Kd) in a native cell lysate or lysate doped with the molecules of interest. The idea is that the recombinant protein is incubated with a mixture of compounds; unbound small molecules are separated via single-stage size exclusion chromatography, and interacting compounds are determined using metabolomics methods. In addition to size exclusion, ion exchange chromatography has also shown the ability to separate free compounds from those bound with proteins.The methods mentioned earlier allow the detection of PMIs in panoramic mode but do not provide information about these interactions’ kinetics, degree of affinity, and specificity. For these purposes, various biophysical methods (such as surface plasmon resonance (SPR), isothermal titration calorimetry (ITC), and nuclear magnetic resonance (NMR) spectroscopy) are used that specialize in the rigorous determination of interaction properties .In the Surface Plasmon Resonance (SPR) method, a protein of interest is immobilized on a sensor chip, and upon binding of a metabolite, the chip detects the change in the refractive index of the complex . SPR reqN-acetyl glucosamine trimer (NAG3) and monomer NAG [Isothermal Titration Calorimetry (ITC) is a solution-based and label-free direct quantitative method measuring the change in heat that is released or absorbed upon interaction between two molecules. This allows us to directly measure such fundamental thermodynamic parameters of binding as the binding affinity or the dissociation constant. As an example of its application, it was utilized in a study of interaction parameters between lysozyme and its inhibitors, omer NAG . This meomer NAG  or the eomer NAG . In someomer NAG . AlthougNuclear Magnetic Resonance (NMR) spectroscopy is a technique in which radio frequency waves are used to induce changes in the energy levels of certain atomic nuclei subjected to a strong magnetic field. In the context of PMI studies, NMR is the method of choice for obtaining structural information about the protein–metabolite complex ,83. The Cryogenic electron microscopy (Cryo-EM) is one of the key methods for resolving the macromolecular structures of proteins, including their complexes with substrates or even intermediate states . SamplesThe microscale thermophoresis (MST) technique utilizes the directed movements of macromolecules in the temperature gradient to detect interactions, including those between proteins and metabolites. In brief, the concentration of macromolecules, identified via fluorescence, decreases in the heated area since macromolecules move to colder zones. Binding of a ligand to a protein can induce changes in the size, charge, hydration shell or conformation of the resulting complex. This can affect the movement of a protein in the temperature gradient, which permits quantification of the interaction constant between the protein and the metabolite . This apEach biophysical method has its limitations and application areas; thus, a comprehensive understanding of thermodynamic, kinetic, structural and dynamic properties of a particular interaction is achieved by a combination of the methods. For instance, FTIR and Raman spectroscopy complement each other quite well: the former is not recommended for water-containing samples, while the latter is the method of choice for such samples. On the other hand, the sensitivity of Raman spectroscopy is drastically reduced in colored or fluorescent samples, but this is not a problem for FTIR spectroscopy. Since the vast majority of biophysical methods are carried out in vitro , the results obtained in such studies cannot be unequivocally transferred to a “cellular” environment. For a correct interpretation of such data, additional experiments should be carried out with methods that work in conditions close to native ones. A brief description of the principles of the methods reviewed in this manuscript, as well as their strengths and weaknesses, are listed in The data itself is often not enough, even in the field of the omics sciences, in which it is common to initiate research projects based on not hypotheses but rather experimental data. New knowledge emerges from the intersection of diverse data in a new way that facilitates data exploration for powerful and systematic analysis. Genomics has passed this path, and now proteomics and metabolomics are passing it, too . Public Although the field of protein–metabolite interactomics is still relatively young, there are already a number of databases that contain quantitative and qualitative information regarding the results of experiments studying protein–metabolite interactions. Experimental confirmation of PMIs is a complicated task, so the amount of knowledge in this area lags behind PPIs. To date, there are 17 databases that aggregate information about PMIs, but fewer resources are regularly updated and curated. Several key databases are listed in From this list of the most influential PMI-related databases, it is apparent that most of the databases are devoted to clinically relevant compounds and some of the databases are protein-centric, rather than metabolite-centric. Thus far, only one of the listed databases, PMI-DB, significantly leverages the aforementioned new high-throughput techniques for capturing PMIs. This reflects the very young state of research into PMIs from a panoramic point of view. However, most of the presented databases are still indispensable for such investigations, as they provide high-quality, ground truth information that is used to annotate PMIs captured during the experiment and separate well-known interactions from new ones. In the near future, we expect that the quantity and quality of PMI-centric databases will grow significantly, reflecting the fast rate of development and application of new technologies for the detection of PMIs.Interactomics, including protein–metabolite interactomics, sits squarely at the intersection of chemistry, biology and bioinformatics, providing an opportunity for systematic studies of the transfer of biological information from one layer to another. A close relationship between the proteome and the metabolome has been demonstrated by the recent development of several models for predicting changes in the metabolome from the proteome ,112. SucAccordingly, in a larger context, knowledge of protein–metabolite interactions is vital for insightful interpretation of multi-omics experiments. In such experiments, several layers of omics data from the same samples are combined to provide the most information possible about the experimental system, for instance, a model of disease . TypicalWhile the importance of detecting and describing protein–metabolite interactions is undoubtedly significant, this area of research is still quite early in its development and has several opportunities for much-needed improvements, especially in the context of high-throughput experiments. The first of these is the need for a systematic and standardized procedure of reporting and integrating experiments. As demonstrated in the present review, there are numerous methods for detecting protein–metabolite interactions, which have widely different requirements and capabilities. Efforts to systematically compare and integrate data from multiple methods, such as the one attempted by the PMI-DB database, are going to be critical for the confident use of data from interactomics experiments, as has been demonstrated for a very similar but more mature field of protein–protein interactomics . AnotherIn spite of all the challenges, the field of protein–metabolite interactomics enters an exciting phase, where freshly developed high-throughput technologies can be applied to many objects of interest, revealing an underappreciated layer of biological complexity. Such information about unannotated interactions between proteins and metabolites can be further annotated by ever-improving targeted structural and functional assays. Overall, considering the importance of the proteome and the metabolome layers in biological systems, as well as the intensive cross-talk between them, the field of protein–metabolite interactomics has the potential to solve many riveting questions in health and disease.


Three-dimensional structure and dynamics are essential for protein function. Advancements in hydrogen-deuterium exchange (HDX) techniques enable probing protein dynamic information in physiologically relevant conditions. HDX-coupled mass spectrometry (HDX-MS) has been broadly applied in pharmaceutical industries. However, it is challenging to obtain dynamics information at the single amino acid resolution and time consuming to perform the experiments and process the data. Here, we demonstrate the first deep learning model, artificial intelligence-based HDX (AI-HDX), that predicts intrinsic protein dynamics based on the protein sequence. It uncovers the protein structural dynamics by combining deep learning, experimental HDX, sequence alignment, and protein structure prediction. AI-HDX can be broadly applied to drug discovery, protein engineering, and biomedical studies. As a demonstration, we elucidated receptor-binding domain structural dynamics as a potential mechanism of anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody efficacy and immune escape. AI-HDX fundamentally differs from the current AI tools for protein analysis and may transform protein design for various applications.  •A deep learning method to predict protein intrinsic dynamics from sequence•Reveals protein dynamics information different from other machine learning tools•High-throughput prediction for protein dynamics analysis•Broad applications including drug discovery such as SARS-CoV-2 spike protein dynamics Immunology; Virology From a structural perspective, the solution-phase HDX experiments present the most physiologically relevant conditions to study protein function and protein/ligand interactions,In this study, we develop the first artificial intelligence (AI)-based approach to predict the protein dynamics information through HDX rate modeling. The AI-based HDX (AI-HDX) is fundamentally different from the MD approach and other current HDX prediction models that rely heavily on interaction algorithms and molecule physical movement prediction. We integrated experimental HDX data with the evolutionary approach, the protein 3D structure, and deep learning algorithms through modular sequence alignment, 3D folding prediction, and a deep neural network for HDX exchange rate prediction. AI-HDX successfully estimates the HDX rate in the most commonly measured range (0.2–0.7) for two testing proteins and assigns a confidence index to guide end users in interpreting the predicted HDX rates. It is worth mentioning that the HDX prediction is complimentary to MD simulation in terms of timescales, as it could probe protein structure dynamics at minute-to-hour timescales. MD is superior in providing dynamic information at sub-second timescales for fast conformational changes. Thus, the accurate and sophisticated AI-HDX holds significant potential in deciphering protein dynamics and functions, empowering protein design.,The training datasets for AI-HDX were collected from two HDX-MS databases, PRIDE and MassIVE.,,The convolutional neural network showed outstanding performance on various tasks including protein structure predictions and membrane protein family predictions.,Trichoderma longibrachiatum EC 3.2.1.8) is one of the enzymes required for complete hydrolysis of xylem,T. longibrachiatum) were obtained experimentally in previous studies.,Estrogen receptors (ERs) are nuclear receptors that transduce estrogen signals essential to the growth and development of a wide range of tissues.To prove the broad applicability of the AI-HDX, the experimental HDX rates of two proteins  are compared to the AI-HDX predicted values. Our results showed that the HDX prediction in ERβ and xylanase had a similar accuracy, with RMSE of 0.25 and 0.26, respectively. We noticed that the performance of AI-HDX was reasonably accurate when the HDX rate is smaller than 0.7 A and 2C.de novo model to predict the HDX rate, we defined a parameter as a confidence index (CI) to evaluate the prediction confidence for each peptide’s HDX rate. We assessed the HDX predictions of proteins in the model trained by five randomly split training and validation sets. The CI value largely decreased when the predicted HDX was more than 0.7 and dropped to zero in the range of HDX prediction between 0.9 and 1 in all five training models, suggesting a low prediction accuracy of AI-HDX for the peptides with high HDX rates (The predictions on the peptides with low (<0.2) or high (>0.7) HDX rates were less accurate than those on peptides with HDX rates within 0.2–0.7 A and 2C.DX rates .The five training models showed RMSE ranging from 0.09 to 0.42 in the proteins from validation sets. We found that the second training model showed a significantly low RMSE compared to others, indicating unbalanced training data A. It is ,,,7B3O),7TLY),6WPS),To access the broad application of AI-HDX, we applied the AI-HDX analysis on the receptor-binding domain (RBD) of the RNA virus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein. Recent studies have used HDX-MS to investigate the protein dynamics of the SARS-CoV-2 spike protein when it binds to the human receptor angiotensin converting enzyme 2 (ACE2) or neutralizing antibodies, providing insights into neutralization effects by spike protein-targeted antibodies.6WPS).42, where HDX rates did not show a significant difference between Omicron and Wuhan variants. However, we predicted HDX rate reduction in the amino acid 337-344 region when S309 binds with the Omicron RBD  to estimate HDX rates for apo-proteins. The model is able to encode structural information from protein-protein complexes in order to predict HDX rates for protein-protein interaction events. However, 3D structures are required. Different from the AI-HDX, previous studies (HDXmodeller and PyHDX) have focused on using machine learning to optimize the available experimental HDX data and improve the HDX resolution, where residue-level protection factor information can be obtained.,The AI-HDX model presents as the first ,This work provides a proof of concept that the deep learning technique can predict the HDX rate of peptides in HDX-MS experiments. Our previous work established the experimental method that HDX-MS measurements of ERs can be used to classify ER modulators like tamoxifen and raloxifene.First, the performance of the current model depends on the available HDX datasets. A limited amount of training samples  restricted the learning depth of the DNN model. Currently, we are only able to retrieve 63 good-quality HDX datasets in the literature. The curated training dataset contains a small number of peptides with HDX rates <0.2 and from 0.7 to 1, which leads to a less-optimal prediction in those two ranges. For example, we performed AI-HDX analysis on apo-RBD and ACE2-bound RBD to compare whether the predicted HDX rate for protected regions is correlated with experimental HDX-MS.The prediction accuracy of the AI-HDX model is largely dependent on the structural information. In the xylanase dataset, we found a higher prediction error for short peptides with residues less than 10 amino acids . For larger peptides with more than 10 amino acids, the RMSE values were smaller than 0.2 . We expede novo deep machine learning model to predict apo-protein structure dynamics directly from amino acid sequences. We illustrated the model utility using SARS-CoV-2 RBD and its variants interacting with antibody recognition. Our AI-HDX analysis of SARS-CoV-2 RBD and its variants suggested that the variant structure dynamics change may play an essential role in antibody recognition, thus potentially providing strategies for COVID antibody treatment. We believe that in the future, the AI-HDX could accelerate data-driven protein dynamics characterization to study protein-protein interaction, which can guide future drug discovery, protein design and engineering, enzyme dynamics, and essential mechanism studies. Our study provides a strategy to leverage experimental data, deep learning modeling, and data science to address fundamental questions in protein biochemistry and biophysics.Overall, we have established the first The current AI-HDX model has several limitations. Firstly, the performance of AI-HDX is dependent on the quality and availability of HDX datasets. With only 63 public HDX datasets curated and less than 25% of peptides with HDX rates greater than 0.7 or smaller than 0.2, the learning depth and prediction accuracy on the high or low dynamic regions are compromised. Secondly, AI-HDX is not a standalone program and relies on protein 3D prediction from other tools such as AlphaFold2 or RoseTTAFold, which may affect its accuracy. Finally, there is a need for standardization of the HDX experiment practices and data-reporting format, which may help improve the prediction accuracy of AI-HDX. The standardization of the experimental practices will help reduce the HDX data variation across different laboratories. The characterization of interlaboratory HDX measurement precision and a consistent data-reporting format can facilitate curation of the HDX database and improve the reliability of AI-HDX predictions.sydai@tamu.edu).Further information and request for resources should be directed to and will be fulfilled by the lead contact, Susie Y. Dai  approach. Protein primary sequences were obtained from Uniprot as indicated IDs in ,https://alphafold.ebi.ac.uk/) provides predicted 3D structures for proteins from 21 model organisms. Our training data obtained protein 3D structures from model organisms from the AlphaFold database. Proteins from non-model organisms were predicted using RosettaFold, which has similar accuracy as AlphaFold but is less computationally intensive than AlphaFold.Protein 3D structure is usually missing two termini due to the difficulty of crystallization. To calculate the SASA of each amino acid for full length, protein 3D structures were predicted by the latest machine learning prediction algorithms, AlphaFold2 and RosettaFold.Two models were constructed to perform the HDX rate prediction: 1) a k-nearest neighbor (KNN) algorithm assuming that peptides with similar sequence information showed a close rate of H/D exchange; 2) a deep neural network (DNN) algorithm with the assumption that residue composition and their relationships are the key components of determining H/D exchange . We randWe tried different machine learning algorithms to learn the peptide patterns and predict the HDX rates. To start with a simple model, we used the KNN algorithm with k equal to 10 to predict the HDX rates from input peptides. The KNN model predicted the HDX rate of a peptide based on the 10 known peptides showing the closest features to it.We constructed a 2D convolutional neural network (CNN) coupled with a bidirectional recurrent neural network long-short term memory (biLSTM) model to learn the HDX rates of digested peptides. The 2D convolutional block consists of two 2D CNN layers with ReLu activation, kernel regularization of 0.0001, max pooling, and dropout. The recurrent neural network block has one LSTM layer with TanH activation and dropout. The dense block consists of one dense layer with linear activation, one dense layer with ‘Softplus’ activation, and the output layer with sigmoid activation. The binary cross-entropy loss function was employed in the training model, and we used the ‘Adam’ optimizer with a 0.01 reduced learning rate for each epoch to update the model weights. A total of 55,567 trainable parameters were generated. The 63 HDX datasets were randomly split into training data which contains 70% of the entire dataset, and validation data which contains the rest of the 30% dataset. The model was trained for 100 epochs to reach a validation mean square error smaller than 0.3.Due to the limited number of publicly available HDX data, we calculated a confidence index for each of the predicted HDX rates, indicating a probability that the predicted HDX rate truly represents the actual rate. Hudgens et al. (2019) reported that the reproducibility of HDX-MS from different laboratories is (0.9 ± 9) %.CI is given to the prediction when the predicted HDX ratio sits within a specific interval. For example, suppose the predicted HDX rate for a peptide is 0.22. In that case, the prediction CI is calculated based on the number of correctly predicted peptides divided by the total number of peptides in the experimentally measured HDX range of 0.2–0.3 in the training dataset.P0DTC2. Omicron spike protein mutations were obtained from the public domain: https://outbreak.info/. Both original Wuhan RBD and Omicron RBD were embedded following the same protein sequence embedding method described above. The SASA for each amino acid in apo-RBD was obtained from the 3D structure predicted by RoseTTAFold. The SASA in antibody-bound Wuhan RBD was obtained from the X-ray crystal structure of RBD and STE90-C11 Fab complex (PDB: 7B3O).7TLY).6WPS).in silico protease digestion using the online tool PeptideMass with pepsin (pH 1.3).http://enm.pitt.edu/) with two antibody-bound RBD structures.The sequence of SARS-CoV-2 RBD spike protein was amino acid 319 -541 from Uniprot ID T. longibrachiatum purchased from Megazyme . The purified proteins used in the HDX experiments were conducted as previously described.The wild-type (WT) XYN I used in this study was from The mass spectra were analyzed as previously describedAs a regression model, the model performance was evaluated by the root mean standard error. Spearman’s correlation coefficient determined the coefficient of predictions and experimental HDX rates. Statistical dependence was determined by hypothesis testing with a p value less than 0.05, using the R function `cor.test`.


Dear Editor,Across all living organisms, ribosomes are large macromolecular complexes that synthesize proteins by translating messenger RNA codes into amino acid sequences. Structurally, ribosomes are composed of ∼50–80 ribosomal proteins (r-proteins) and 3 or 4 ribosomal RNAs (rRNAs). Over the past 4 billion years, ribosomes have evolved some differences in rRNA and r-protein composition, with certain subunits specific to bacteria, archaea and eukaryotes, plastids, or mitochondria, although many subunits are universally conserved with clear homology across all of life. Historically, the nomenclature of r-proteins was different in each species investigated, based on certain biochemical properties; that is, they were numbered in the order that they were separated by electrophoresis and/or chromatography , Maize Genetics and Genomics Database (MaizeGDB), and colleagues in the plant ribosome biology field, we propose new names and symbols for all of the r-proteins encoded by the Arabidopsis, tomato, maize, and rice genomes, which we intend will serve as a template to guide future plant genome annotations . We expeThe r-protein nomenclature established by apiculata, embryo defective, evershed, hapless, oligocellula, piggyback, pointed first leaves, short valve, and suppressor of acaulis. Bifunctional r-proteins, such as eL40, which is proteolytically cleaved during ribosome assembly to separate the mature eL40 protein and its fused ubiquitin domain, are occasionally named not for the r-protein subunit, but for ubiquitin . These examples clearly illustrate the need for the new, unifying nomenclature for r-proteins in plant genomes so that our community can engage with other biologists.Where feasible, the new r-protein symbols retain their traditional numbers—for example, archaeal/eukaryotic RPS6 is now eS6. Bacterial RPS6 is not homologous to eukaryotic RPS6, however, which previously caused some confusion; now, bacterial RPS6 is bS6, to indicate that it is not related to any archaeal/eukaryotic r-protein. Conversely, uS8 is now the universal symbol for bacterial r-protein S8, yeast r-protein S22, and human r-protein S15A, which all had different names despite their homology. Plant r-proteins occasionally have their own names, as well; for example, uL3, which was previously called L3 in bacteria, humans, and yeast, is called RIBOSOMAL PROTEIN1 (RP1) in Arabidopsis. Many Arabidopsis cytosolic r-proteins were first characterized from genetic screens for developmental defects, and the genes encoding these proteins were first named according to their mutant phenotypes, such as Nonetheless, for continuity, past r-protein names and symbols should be maintained in databases as aliases. Moreover, we recommend that aliases should also be mentioned parenthetically as alternative gene names and symbols in future publications to ensure clarity for readers, e.g., “We detected that phosphorylation of r-protein eS6z (RPS6a) was reduced by rapamycin…”. This way, readers more familiar with the acronym “RP” to indicate “ribosomal protein” will not be confused by the new names, but the updated nomenclature will reconcile with the established nomenclature in other fields.Animal r-proteins are encoded exclusively by the nuclear genome, so biomedical researchers have not emphasized the genomic location of r-protein genes in recent nomenclatures. Plant r-proteins, however, can be encoded by the nuclear, mitochondrial, or plastid genomes, with some variation in the location of these genes across species. There is even a special case, mitochondrial uL2, which has split into two genes in plants: the nucleus encodes a polypeptide homologous to the C-terminus of uL2 and the plastid encodes a polypeptide homologous to the N-terminal portion of uL2. To indicate cases when an r-protein is encoded by the organellar genome, we recommend using uppercase letters for the suffix  in publications.The greatest challenge in adopting this new nomenclature for plant biology is how to best indicate paralogy of r-proteins . In the tor1 in fission yeast) or to designate protein phosphorylation . Third, hyphens are typically used in plant nomenclatures to indicate alleles, so naming genes eS6-a and eS6-b could give the false impression that these are two alleles of a single gene, rather than paralogs. Fourth, relying on uppercase versus lowercase letters or on non-standard alphabets would require that database curators, computational biologists annotating new genomes, journal editors, and ribosome biologists working outside plant biology all pay strict attention to a slight typographical difference or expand the standard alphabet to accommodate this one set of genes, whereas starting from the end of the alphabet avoids any potential confusion.After soliciting community feedback through a preprint version of this letter, social media, e-mails to additional community members, and the Plant Biology 2022 conference, we came to prefer the last option for several reasons. First, there is already literature on chloroplast ribosomes using the “c” to indicate plastid-targeted r-proteins, and there is considerable literature placing “m” or “c” at the end of the r-protein symbol to indicate organelle-targeting, so changing these would not serve the larger purpose of reaching a consensus nomenclature with r-protein biologists in other fields. Second, “p” is used as a suffix in many nomenclatures to distinguish proteins from nucleic acids (e.g., Tor1p is the protein encoded by the gene We have provided a provisional table of r-protein names and symbols for Arabidopsis, tomato, maize, and rice for the plant biology community to consider, alongside their historical symbols in Arabidopsis and their symbols as recently proposed by The following materials are available in the online version of this article.koac333_Supplementary_DataClick here for additional data file.


Bacteriophages are viruses infecting bacteria. Being key players in microbial communities, they can regulate the composition/function of microbiome by infecting their bacterial hosts and mediating gene transfer. Recently, metagenomic sequencing, which can sequence all genetic materials from various microbiome, has become a popular means for new phage discovery. However, accurate and comprehensive detection of phages from the metagenomic data remains difficult. High diversity/abundance, and limited reference genomes pose major challenges for recruiting phage fragments from metagenomic data. Existing alignment-based or learning-based models have either low recall or precision on metagenomic data.In this work, we adopt the state-of-the-art language model, Transformer, to conduct contextual embedding for phage contigs. By constructing a protein-cluster vocabulary, we can feed both the protein composition and the proteins’ positions from each contig into the Transformer. The Transformer can learn the protein organization and associations using the self-attention mechanism and predicts the label for test contigs. We rigorously tested our developed tool named PhaMer on multiple datasets with increasing difficulty, including quality RefSeq genomes, short contigs, simulated metagenomic data, mock metagenomic data and the public IMG/VR dataset. All the experimental results show that PhaMer outperforms the state-of-the-art tools. In the real metagenomic data experiment, PhaMer improves the F1-score of phage detection by 27%. Bacteriophages (phages for short) are viruses infecting bacteria. They are highly ubiquitous and are widely regarded as the most abundant organisms on Earth . There iHowever, despite the importance of phages to both environmental and host-associated ecosystems, our knowledge about this vast, dynamic and diverse population is very limited. Previously, the limitation is partially caused by the need of the host cell cultivation in labs. Recently, metagenomic sequencing, which allows us to obtain all genetic materials directly from a wide range of samples regardless of cultivation , has larCaudovirales, the largest order of phages containing about 93% of sequenced phages, has about 187 006 hypothetical proteins. It is not trivial to identify hallmark genes without functional annotation.There are two main challenges for phage identification in metagenomic data. First, both lytic and temperate phages can integrate the host genetic materials into their genomes, leading to local sequence similarities between the genomes of phages and bacteria . For exa genomes . These cMany attempts have been made for phage identification . AccordiTo overcome the limitations of alignment-based methods, several learning-based tools have been proposed for phage identification. These learning models are mainly binary classification models with their training data containing phages and bacteria. Some learning models use extracted sequence features such as irFinder  utilizedirtifier  uses theirtifier  and Deepirtifier  encode tirtifier  is a thrrSorter2  employs Despite the promising results, a third-party review  shows thIn this work, we present a method, named PhaMer, to identify phage contigs from metagenomic data. Because previous works have shown the importance of protein composition for phage classification , 25, we d motifs , we are d motifs . Our expInspired by semantic analysis problems in NLP, we employ the state-of-the-art contextualized embedding model, Transformer, to automatically learn abstract patterns from the ‘language’ of phages. In this language, the contigs are regarded as sentences defined on a phage-aware vocabulary. There are three major advantages behind this formulation. First, some proteins play critical roles in phages’ life cycle. For example, coat proteins and receptor-binding proteins can help us distinguish phages from bacteria. These proteins can act as strong signals similar to the words describing obvious emotions in human language. Second, proteins often interact with other proteins to carry out biological functions . SimilarIn order to automatically learn the importance of proteins and their associations, we adapt the Transformer model to phage identification task. Transformer has achieved the state-of-the-art performance on a variety of NLP problems . In partIn the following sections, we will first introduce how we construct the protein-cluster tokens and encode the sequences into sentences. Then, we will describe the Transformer model optimized for phage identification.Each token in our model is derived from a protein cluster, which contains homologous protein sequences from sequenced phages.Generating protein clusters Our protein clusters are constructed on the training data. Specifically, they are extracted from 2126 phage genomes released before December 2018 from the RefSeq database, which constitute our training phages. More recently sequenced phages are used as test samples. Constructing the protein vocabulary using only the training sequences allows us to rigorously test our method in scenarios where newly sequenced phages harbor novel proteins outside the vocabulary. Although there are available gene annotations and their corresponding proteins for the reference genomes, we did not use the annotation. Instead, in order to be consistent with the gene prediction process of the test sequences, we apply gene finding and protein translation for the downloaded DNA genomes. According to a recent review of gene finding in viruses [ viruses , Prodigahm (MCL)  is emplohm (MCL) .Encoding a contig into a protein token sentence With the generated protein clusters as the tokens in our vocabulary, we will use them to convert contigs into sentences. As shown in Figure Because the lengths of the contigs can vary a lot, the converted sentences also contain different number of protein-cluster tokens. We follow the original paper of Transformer  and set The model’s inputs are the converted sentences, represented by 300-dimensional vectors, and the output is a score representing how likely the input contig is a phage. The main purpose of Transformer is to automatically learn whether these sentences contain essential features for phage identification: the marker tokens (important proteins) and phrases (protein–protein associations). Two main components in Transformer contribute to these aims: (1) the embedding layers and (2) the self-attention mechanism.The embedding layer As shown in Figure Because Transformer contains no recurrence or convolution, it uses the positional embedding to encode the position information. The input to the positional embedding is the position index vector. The implementation is the same as the protein-cluster embedding layer with an individual learnable look-up table. The output of the embedding layer has the same dimension as the protein-cluster embedding so that the two embedded vectors can be summed. Mathematically, the embedding layers can be presented by Eqn. entclass1pt{minimaeline in . The padng space . BecauseSelf-attention After embedding the sentences, each token is converted into a vector of size 512 and the embedded sentences will be a Figure stion of , we set Because the attention matrix only contains pairwise protein cluster information, to model different combinations of pairwise relationships, we use The parameters in the FC layers are projections matrices: While convolution and recurrence in CNN and RNN can record the relative positions directly, the attention mechanism is more suitable for biological data, especially for protein-cluster tokens, because the attention score can learn the remote interactions between proteins from the embedded feature. CNN and RNN can be limited by their architectures that only give them access to local context with a limited window size or receptive field. However, the self-attention mechanism of the Transformer grants access to all positions in the embedded sentence. In addition, the positional embedding enables the model to leverage the position of each protein for prediction. Then all the information can be used in the attention mechanism simultaneously.Feed-forward networks The output of the multi-head attention block is fed to a two-layer neural network, which is called feed-forward network, as shown in Figure Model training During training, we first generate protein clusters and vocabulary using the phage genomes released before December 2018 (i.e. our training phages). Then, we convert phage sequences into sentences using the sentence construction method. We also apply data augmentation by randomly generating short segments, ranging from 3 to 15 kbp, to enlarge the training set. These segments are used to improve the robustness to the short contigs, which might not contain many protein clusters. We use both the segments and the complete genomes to train PhaMer to prevent the model from overfitting to the complete genomes. The training data also include the host bacterial genomes of the training phage sequences. Because phages usually share local similarities with their hosts, we use the host genomes as the negative set to create harder negative samples for model training. Compared with using randomly selected bacterial sequences, using the hosts as the negative samples can help the model learn a more accurate classification surface and thus improves the model’s generality. We download the host genomes from RefSeq database and both chromosomes and plasmids are included. The bacterial genomes go through the same sentence encoding process as the positive samples. Because some phages do not have known hosts, we sample segments from bacterial genomes to balance the training the positive and negative training data. We employ binary cross-entropy (BCE) loss and Adam optimizer with a learning rate of 0.001 to update the parameters. The model is trained on HPC with the GTX 3080 GPU unit to reduce the running time. Finally, the pre-trained model will be used to identify phages in input sequences.Metrics We use the same metrics as the previous works to ensure consistency and a fair comparison: precision, recall and F1-score. Their formulas are listed below (Eqn.elow Eqn.: (5)\docTP, FN and FP represent the number of corrected identified phages, missed phages and falsely identified phages by PhaMer, respectively. If the input contigs do not contain any protein-based token, we will directly assign ‘non-phage’ label to them, which become part of the FN.Dataset We rigorously tested PhaMer on multiple datasets with increasing complexity. The detailed information is listed in Table In this section, we will show our experimental results on different datasets and compare PhaMer against the state-of-the-art tools. Because we want to include both alignment-based and learning-based methods in the experiments, we choose the tools that have top performance in each category based on a recent review . Thus, wTen-fold cross-validation We trained our model using 10-fold cross-validation. First, we split our training set into 10 subsets. Second, we use nine subsets for training and the remaining one for validation. Finally, we repeated step two by iteratively choosing one subset for testing and recording the performance. The final results are shown in Figure Without positional embedding means that we only use the sentence as input  to control the quality of the data and removed overrepresented reads with Cutadapt [After validating PhaMer on the RefSeq database and the simulated datasets, we compare all the methods on real shotgun-sequenced metagenomic datasets that are used for testing phage identification by the review . The seqCutadapt . The cleCutadapt  and the Cutadapt . Only thIn general, the F1-scores of other tools were considerably lower on this dataset than on the RefSeq benchmark dataset, with an average F1-scores drop by Recently, IMG/VR published the largest public virus genome database IMG/VR v3 . The virhuman, plant, marine, freshwater and other. The results reveal that PhaMer outperforms all other methods on these five domains. In particular, PhaMer significantly improved the recall of phage identification in plant-associated samples. Figure Figure The most resource-demanding components in PhaMer are the translation  and sequence alignment (DIAMOND BLASTP). We used these two steps to convert input sequences into protein-based sentences. Both the protein cluster vocabulary and position information are utilized as input features of the Transformer model. Table As shown in the experiments, existing approaches, such as Seeker, VirFinder, DeepVirFinder, PPR-meta and VirSorter, failed to achieve high precision or recall in phage identification, especially on metagenomic data. In this work, we demonstrate that PhaMer can render better performance for novel phage identification. The major improvement of our method stems from the adoption of the language model Transformer for bidirectional contextual protein embedding and our careful construction of negative samples. By constructing protein-based vocabulary, we can incorporate the similarity between phages. Using the positional embedding and the self-attention mechanism in Transformer, we can learn the importance of proteins and also their associations. In addition, we carefully construct our negative training samples using phages’ host bacterial genomes. Because of the local similarities between phages and their hosts, this negative training set is more difficult to classify and tend to have direct bearing on learning the optimal decision surface. The benchmark experimental results on complete genomes, short contigs, simple and complicated metagenomic data show that our model outperforms others in different scenarios. Importantly, its performance is more robust than others on short contigs. And it increases the F1-score by 27% on the mock metagenomic data.Considering that we first constructed protein clusters from phage proteins, it is a fair question to ask whether we can achieve an optimal classification by controlling the protein matching criteria . When constructing our negative set , we found that over 80% of bacterial genomes have multiple alignments with the protein clusters in our vocabulary. Thus, there is a trade-off between the recall and precision in terms of the E-value cutoff. Because phages are highly diverse and some protein clusters can have remote homologs in new phages, using a very stringent E-value cutoff can miss new pages. We found that if we use a very strict E-value cutoff , most of these bacterial genomes can be rejected. However, the recall of identifying phages will drop to 0.3. Thus, it is hard to strike an optimal balance between sensitivity and precision by adjusting E-value cutoffs.proportion to record the ratio of matched tokens in our vocabulary to the predicted proteins by Prodigal for each input contig. Intuitively, for each input that is predicted as ‘phage’ by PhaMer, we can further check the value of proportion. If this value is very small, the input contig is likely to be a prophage rather than a phage. Users can filter the contigs according to their needs conveniently. In the future, we will incorporate prophage detection tools in our pipeline.Although PhaMer has greatly improved phage contig identification, we have several aims to optimize PhaMer in our future work. One possible extension is to incorporate prophage detection in PhaMer. There are a number of prophage annotation tools, such as Prophage Hunter  and PhagLike a majority of phage identification tools, PhaMer is a binary classification model with the goal of distinguishing phages from bacterial contigs, which can originate from either chromosomes or plasmids. In our current design and test, we have plasmid sequences in both the training and test sequences. For example, some contigs assembled from the mock metagenomic data are from plasmids. Because the plasmid-originated sequences are treated as ‘negative’ samples as the bacterial chromosomes, PhaMer does not distinguish them from chromosomes. There are other tools available to distinguish plasmids from the host genomes . Users cPhage identification is a key step for novel phage discovery in metagenomic data.We developed a phage identification tool named PhaMer that employs the Transformer and protein-based tokens to learn both the protein composition and their associations in phage sequences.Our rigorous test of PhaMer on both simulated and real sequencing data and the benchmark experiments against five recently published tools show that PhaMer is the most accurate phage identification tool.Supplementary_file_1_bbac258Click here for additional data file.