With the exponential increase in publicly available protein structures, the comparison of protein binding sites naturally emerged as a scientific topic to explain observations or generate hypotheses for ligand design, notably to predict ligand selectivity for on- and off-targets, explain polypharmacology, and design target-focused libraries. The current review summarizes the state-of-the-art computational methods applied to pocket detection and comparison as well as structural druggability estimates. The major strengths and weaknesses of current pocket descriptors, alignment methods, and similarity search algorithms are presented. Lastly, an exhaustive survey of both retrospective and prospective applications in diverse medicinal chemistry scenarios illustrates the capability of the existing methods and the hurdle that still needs to be overcome for more accurate predictions.  In living organisms, biological processes are regulated through specific molecular recognition at local surfaces. Proteins, one of the major biomolecules composing our cells, interact with different partners: other proteins, peptides, nucleic acids, small molecules, and transition metals. The exploration of the proteome, considering amino acid sequences, makes it possible to rapidly compare proteins but does not necessarily indicate whether potential cavities at their surfaces will be conserved or not. Hence, structure conservation does not always mirror sequence homology . ProgresStructure-based algorithms for protein site comparison emerged in the 1970s, a decade marked by the establishment of the Protein Data Bank (PDB)  were appThe path from the earlier to the current site comparison methods involved several implementations. It was common for the user to define researched features  from prior knowledge to initiate the search . The subThe repertoire of possible comparison algorithms is tailored to the representation made of the pocket . Pocket In practice, the variability of the pocketome  in termsThe identification of potential interaction sites is crucial to structure-based approaches and constitutes the very first step of binding site comparison. Contact surfaces exhibit different geometric and physicochemical characteristics according to the nature of the binding partner . For example, small molecule interaction sites are buried clefts, while protein–protein interaction interfaces are rather flat and hydrophobic . AlthougThe methods can be classified into three levels: (i) the genomic or 3D structure of the input, (ii) the dependency to bound ligands, and (iii) the class of the algorithm . TemplatLigand-centric methods are restricted to protein–ligand complexes and is a site delimitation rather than a prediction. Noticeably, the analysis of crystallization additives binding sites might suggest potential allosteric pockets ,45. TypiLigand-free approaches can operate on a larger range of structures, enabling the discovery of unprecedented sites. According to their search algorithm, they can be classified as geometric, energetic, or data-driven . At firsContrarily, other methods process the protein coordinates directly and are not affected by the grid initialization phenomena. Based on the alpha-shape concept introduced by Edelsbrunner et al. , they ciThe second category of ligand-free methods estimate favorable surfaces for protein–ligand contacts by calculating the potential energy of probes at different positions . GeneralThe final class of methods uses supervised models, trained on the features of well-characterized ligand binding sites. Hence, they differ in the features’ representation, training models, set of parameters, and datasets. P2RANK  is one oAltogether, these methods have been evaluated on their performance to accurately predict binding pockets by comparing predictions on unbound proteins to ground truth ligand locations in their corresponding bound structures. Not only is the accuracy of the location analyzed, but also the delimitation or overlap with respect to the ligand . The det3, a good balance of hydrophobic and polar atoms enabling some binding specificity, and sufficient buriedness. A few methods were developed to predict target druggability [Indeed, all identified clefts do not necessarily correspond to the ability to accommodate a drug-like ligand (druggability). The concept of structural druggability  arose frgability ,105,106.gability  or bind-gability . For morgability . Interesgability ,16,47,76gability . The methods that compare protein cavities operate in three steps: (i) describing the cavity with a suitable representation, (ii) comparing these representations, (iii) scoring the proposed comparison. Hence, successful results reside in a coordinated performance of each of these tasks. Yet, cavity representation, which is the first step of the procedure, is crucial as it influences the later steps. Generally, a poor representation in which relevant characteristics are missing cannot be compensated by the most efficient comparison or scoring algorithm. State-of-the art methods to compare protein cavities are summarized in Once the pockets are delimited, the features are selected by considering different aspects. This step aims at focusing on the relevant characteristics that explain ligand recognition, while discarding unnecessary information. Our brain performs the same exercise on everyday life’s objects, for example, if we are asked to compare two cars, we might decompose the information into major aspects such as the brand, design, color, motor, etc. Interestingly, different people will focus on different combinations of these aspects, resulting in different decision making. For pocket modeling, there is the general knowledge that the attributes  of residues flanking the site and their relative 3D location explain the specific recognition of ligands ,27,132. First, possible representations , from coSecondly, most methods adopt the protein perspective by considering atoms or pseudocenters at the protein surface . A few stand out by projecting these protein patterns into the ligand space, wherein polyhedron, voxels, or points are annotated with the properties of nearest or well-oriented protein features  . Such diFinally, besides the two aspects described above, the methods differ in their definition of chemical and geometric features. For example, Binkowski et al. do not consider the chemical type of atoms but showed that the relative position of the surface atoms describing the shape of the pocket already contains some discriminative information . HoweverIn a nutshell, there are various ways to represent a protein cavity. Challenges reside in finding a good balance between comprehensive representation of features to ensure reliability and loose representation making it possible to detect remote similarities. While the absence of pocket attributes cannot be recovered at the later comparison step, too many attributes may constitute difficulties for the search algorithm in separating the signal from the noise.Following the selection of features characterizing the cavities, similarity is estimated by algorithms that search for common patterns shared between two sites. First, the representations of the protein cavities are converted or organized into comparable and computer-friendly objects that can be processed automatically. There are a variety of search algorithms to this end, which can be categorized according to their inputs, procedure, and visual interpretability .The first category of algorithms searches for geometric  and chemical  constraints to match. It is not safe to expect a perfect match, given the errors in 3D structure resolution, the flexibility nature of proteins, and the aim to find unobvious similarities. Therefore, a certain margin of geometric errors is always tolerated. PocketMatch compares set of distances belonging to 90 combinations of atom types and properties to establish correspondences between two pockets and keeps the solution maximizing the number of correspondences . Global In the second category, selected points form the nodes of a graph. According to the cavity representation, each node is annotated by a property and the edges by their lengths. Comparing two cavities results in comparing two graphs to extract the maximum common subgraphs. To achieve this end, a product graph is built by associating similar nodes (property comparison) and edges of almost equal distances, tolerating a certain deviation. Cliques are identified in this association graph to derive pairs of equivalent points that can be used to superpose the two cavities. CavBase, G-LoSA, ProBiS, etc.  are baseMethods in the third category generally adopt a global vision of the protein cavity. They consider a pocket as a fixed-length fingerprint or histogram, and comparing two pockets is amounts to calculating the similarity or distances between their fingerprints/histograms. BSSF, FuzCav, and KRIPO compute couples or triplets of pharmacophoric features separated by binned distances ,24,25. WFinally, the recent regain of interest for deep neural networks on chemical information favored the emergence of data-driven methods for binding site comparison. Typically, binary classification models are created to discriminate between similar and dissimilar pairs of pockets. Site2Vec transforms the features representing a cavity into a fixed-length vector that can feed a random forest classifier . DeepSitAll the above summarized methods use the protein information only for comparison. Provided that a pocket is delimited, they have a larger scope that may reach target deorphanization . When boLooking for an average match that maximizes the overlap between entire cavities is not necessarily the right solution to similarity estimation. Local comparison is a popular term, often used to differentiate full protein structural comparison from protein site comparison. Here, we refer to the truly local comparison of protein pockets , i.e., sEnabling local similarity detection is relevant for drug design applications since a few similar subpockets between two targets may suffice for a same ligand to bind. This observation was applied to explain the binding of cyclooxygenase type 2 inhibitors to carbonic anhydrase . Local cLogically, methods that can operate locally have implemented detailed site representation and/or adequate algorithms that partition the cavity during the search. In G-LoSA, global matches are decomposed into local subsites to generate other solutions . Local cScoring functions serve two purposes: (i) guiding the alignment by discarding unrealistic solutions and prioritizing the best matches, (ii) quantifying the estimated similarity between the pair of pockets to consider. It is not uncommon to use distinct scoring functions for the alignment search and its final quantification . ConsequAspects to consider when defining a scoring function for binding site comparison are (i) the discriminative potential, (ii) the minimal and maximal boundaries, (iii) the broadness, (iv) the sensitivity to the size of the cavities, and (v) the interpretability. The very simple and intuitive scoring scheme counts the number of common patterns between two pockets  . HoweverWeights are used to give more or less importance to different variables  but their assignment is at best subjective ,121,139,p-values) of the scoring function by analyzing random distributions or robustness to variations in the cavities . While these studies offer a certain overview on possible scoring thresholds in screening settings, we draw attention to potential bias in setting up calibration datasets.A few studies ,129,141 To demonstrate their applicability, the methods for comparing protein binding sites have been evaluated for their ability to (i) discriminate between similar and dissimilar binding sites (classification), (ii) retrieve similar pairs seeded in decoys (enrichment), and (iii) cluster proteins belonging to the same families according to other classifications . The availability of structural data impacts the design of the evaluation datasets.As for any benchmarking study, the quality of the dataset is instrumental to the reliability of the conclusions. Ligand-based and structure-based virtual screening benefit from well-established standards and datasets ,143. PreNevertheless, many available datasets ,145,146 Chen et al. defines similar pairs as pockets in proteins sharing at least three submicromolar ligands, while dissimilar pairs share at least three ligands large affinity variations going from one target to the other . HoweverGiven the bias in the PDB dataset towards some protein–cofactors complexes and well-studied protein families, methods have been extensively evaluated on nucleotide-binding pockets , althougFragment/ligand promiscuity towards unrelated targets of known 3D structure remains are a rare event ;Direct drug repurposing from in silico  or in viThe experimental validation of putative binding site similarities is not as straightforward as testing many compounds on a single target. For every putative off-target, a suitable assay has to be used if available, or more likely needs to be developed on purpose. In vitro biophysical assays  give a direct answer of shared ligand binding to two different targets ,160 but Known success stories  have notThe explanation of target-mediated side effects and guidelines to optimize the ligand selectivity by suitable structural modifications ,169;The explanation of off-target beneficial effects ;The validation of cross-docking data for repurposing hypotheses ,166;The confirmation of ligand 2D and 3D shape similarities ;The serendipitous discovery of remote similarities across totally unrelated targets during code benchmarking and validation ,167.The best possible validation method of any binding site comparison tool is indeed to experiment. True prospective validations  are stilThe above-cited examples share common characteristics. First, the repurposed ligands usually exhibit (very) weak affinities towards the secondary target, notably when the on- and off-targets are unrelated. In all cases, the studied ligand needs to be optimized for potency and selectivity towards the secondary target, thereby abolishing the benefits of immediate in silico-guided drug repurposing ,167,169.This review presents the current state of ligand-binding site comparison applied to small molecule drug design. As computer-aided drug design strategies, assessing the similarity of protein pockets constitutes a unique way to analyze structural information, as they complement other well-spread approaches. The repertoire of available methods is diverse with respect to the detection and representation of cavities, the search algorithms, and the scoring functions. All of these aspects must somehow be coordinated to achieve the best performance. Still, the limitation of experimental data and biases in datasets represent major obstacles to properly evaluate such methods. In reality, estimating protein site similarity is always context-dependent. The importance of matched features is influenced by the chemical context and physicochemical considerations of the targets, making it hard to predict subtle and specific similarities from generalized principles. One holy grail of computational chemists is to repurpose existing drugs proposed by structure-based experiments. Although this pursuit appears at best hardly probable due to the optimization of drugs for their on-targets ,158, we Binding sites comparisons have demonstrated an effective contribution to medicinal chemistry projects, from the elucidation of previous biological observations to the generation of new hypotheses supported by experimental validation. The majority of the state-of-the-art methods are based on the superposition of the compared structures. The alignment allows for a visual inspection and increases the possibilities of applications. Typically, pocket-bound ligands in the reference frame can be transposed into the target pocket and serve as a starting point for ligand generation. The improvement of the algorithmic efficiency of the methods alongside with technological progress may enable to better follow the current growth of publicly available protein structures, determined experimentally or predicted at near-atomic resolution .


The postsynaptic region is the receiving part of the synapse comprising thousands of proteins forming an elaborate and dynamically changing network indispensable for the molecular mechanisms behind fundamental phenomena such as learning and memory. Despite the growing amount of information about individual protein–protein interactions (PPIs) in this network, these data are mostly scattered in the literature or stored in generic databases that are not designed to display aspects that are fundamental to the understanding of postsynaptic functions. To overcome these limitations, we collected postsynaptic PPIs complemented by a high amount of detailed structural and biological information and launched a freely available resource, the Postsynaptic Interaction Database (PSINDB), to make these data and annotations accessible. PSINDB includes tens of thousands of binding regions together with structural features, mediating and regulating the formation of PPIs, annotated with detailed experimental information about each interaction. PSINDB is expected to be useful for various aspects of molecular neurobiology research, from experimental design to network and systems biology-based modeling and analysis of changes in the protein network upon various stimuli.Database URLhttps://psindb.itk.ppke.hu/ Synapses are communication points between neurons and thus are responsible for transducing information. Chemical synapses can be broadly split into presynaptic sites releasing neurotransmitters and the postsynaptic region, often referred to as postsynapse (PS), receiving and processing these incoming chemical signals. Cellular- and organism-level events, such as neuronal development, memory and long-term potentiation, strengthen synaptic connections . ExcitatProtein–protein interactions (PPIs) play an essential role both in the maintenance of synaptic plasticity and in disease emergence. Postsynaptic proteins exhibit a high degree of multivalency presenting multiple binding sites, and this multivalency aids the formation of an elaborate and dynamic network provided by distinct structural and functional elements . Intrinshttps://psindb.itk.ppke.hu/) aiming to provide a more complete and complex picture of the postsynaptic protein network. PSINDB is a comprehensive database focused on postsynaptic proteins, containing a high number of collected, manually curated or derived interactions with precisely defined binding regions, enriched by various structural and functional features of the constituent proteins. We believe that PSINDB will facilitate model building and experimental design addressing the organization of the PS and thus will ultimately help scientists to get closer to the understanding of the complex nature of synaptic function at the molecular level.Although there is a huge amount of information available on PPIs formed in the PS, they are either scattered in the literature or are collected in databases that were not designed to store complex information that aids the better understanding of synapse formation and function at the molecular level. Here, we present the Postsynaptic Interaction Database . We also included an additional level, namely atomic contacts, that refers to data derived from PDB interactions in atomic resolution. PSINDB contains binding region information at different levels: (i) for each experiment separately, (ii) for each partner (data from different experiments for the same partner are merged) and (iii) for each protein . Each entry in PSINDB also contains structural information about the protein . Nearly To describe regulatory processes for the annotated interactions, PSINDB contains information on posttranslational modifications and alternative splicing . These mPSINDB also includes disease-associated germline mutations : many ilPSINDB has an interactive graphical user interface for the visualization of interaction data. Data can be accessed either by browsing through the entries or via one of the two different types of searches.The protein search will result in a list of proteins, while the interaction search will result in a list of interacting partners. In both cases, the gene name, Entrez and HUGO gene ID, UniProt ID and various aliases can be used as a query.Clicking results from the protein search opens the individual protein entry pages, which includes a graphical overview of the corresponding protein along with 10 sections summarizing structural, interaction and network data . The secThe interaction page can be accessed from the protein page by clicking on the partner or from the results of the interaction search page. The interaction page contains the definition and details about the experiment , includiIn addition to basic search options, we also offer several predefined subsets of postsynaptic proteins/interactions that may be interesting to a broad range of users. These set are defined based on higher-order assemblies: scaffold proteins, cytoskeletal proteins and hub proteins; based on structural features: transmembrane proteins, phase separation proteins and coiled-coil containing proteins; and based on evidence: literature and structural categories.Every interaction stored in PSINDB can be downloaded in a structured format. PSINDB uses the MITAB 2.5 format, which is standard in representing molecular interactions . We alsoThe following examples highlight how PSINDB can serve as a central resource for PS-related protein research, far surpassing previous similar repositories in terms of both data volume and granularity. This enables both the in-depth study of individual proteins and protein sub-networks. In addition, PSINDB can also contribute to generating new, testable hypotheses concerning the interactions and functional roles of PS proteins.PSINDB can highlight local PS interaction networks in detail. Both shisa-6 and shisa-7 (SHSA6_HUMAN and SHSA7_HUMAN) are single pass transmembrane receptors located in the synapse membrane and are known to be involved in regulating transmission in CA3-CA1 synapses, possibly via regulating AMPA-type glutamate receptors. The existing interactions mapped for shisa-6 and shisa-7 were low in numbers, limiting the assessment of the full functional repertoire of these receptors based on easily accessible interaction data. Our manual curation efforts significantly extended the available interaction data for both receptors, adding 8 and 11 interactions for shisa-6 and shisa-7 on top of the already known 6 and 2 interactions. This means a total of 3.3-fold increase in the number of interactions for the two proteins. et al.  protein family are among the most important postsynaptic scaffold proteins . These met al. .et al., lrrk2 interacts with eif4ebp1 (4EBP1_HUMAN) at the PS in the neuromuscular junction  . Sometimjunction . In sevejunction , which ijunction . Moreovejunction  and GABAjunction . Ntrk1 , all this information is not collected together in any other database. By integrating all this information into PSINDB, users do not have to use multiple prediction tools and databases when analyzing their protein/interaction of interest.PSINDB also provides a rich annotation for proteins to help users in better understanding the nature of interactions: (i) binding regions driving the interactions, together with (un)structural segments mediating the binding, (ii) functional processes playing a role in regulating the binding  and (iii) functions and associated diseases of proteins and highlighting those that have importance considering the interaction network. Although some of these features are available in other databases  from UniProt  and genehttps://www.ebi.ac.uk/ols/ontologies/mi). We considered every described interaction between rat, mouse and human proteins and then mapped them back to the human reference proteome.For the first step in the curation, we extensively searched PubMed and Google scholar for relevant publications. Since many of these publications were already included in other PPI databases, we only used articles that were not processed elsewhere. The curation work was done adhering to the HUPO-PSI Molecular Interaction (MI) workgroup standards, and at least MIMIx curation level was used , while tWe integrated binary PPI information from the IntAct , BioGridWe used the following protein structure-related information: transmembrane segments (Human Transmembrane Proteome database ), liquidFor each experiment, we encoded the properties of the interacting region using terms from the HUPO-PSI MI Ontology. In the case of PDB-derived interactions, the constructs were defined as sufficient for binding (in line with the curation policies), and we introduced an additional term for atomic contacts that could not be fitted onto the ontology.When collecting these regions from different experiments for the same interaction, all binding regions were merged, and the ones with the most specific information were used (i.e. atomic contact > necessary for binding > sufficient for binding > binding-associated regions). Similarly, when binding regions of a protein with different partners were merged, all regions were included, and the most specific one was displayed.We used GeneOntology and DiseaseOntology to define fingerprints: first we mapped back the ancestor terms of each annotation to the corresponding protein and then counted how many times different terms appear in the network of the protein. This procedure was done separately for GO:Biological Process, GO:Molecular Function and Disease Ontology trees.


Elucidating protein–ligand interaction is crucial for studying the function of proteins and compounds in an organism and critical for drug discovery and design. The problem of protein–ligand interaction is traditionally tackled by molecular docking and simulation, which is based on physical forces and statistical potentials and cannot effectively leverage cryo-EM data and existing protein structural information in the protein–ligand modeling process. In this work, we developed a deep learning bioinformatics pipeline (DeepProLigand) to predict protein–ligand interactions from cryo-EM density maps of proteins and ligands. DeepProLigand first uses a deep learning method to predict the structure of proteins from cryo-EM maps, which is averaged with a reference (template) structure of the proteins to produce a combined structure to add ligands. The ligands are then identified and added into the structure to generate a protein–ligand complex structure, which is further refined. The method based on the deep learning prediction and template-based modeling was blindly tested in the 2021 EMDataResource Ligand Challenge and was ranked first in fitting ligands to cryo-EM density maps. These results demonstrate that the deep learning bioinformatics approach is a promising direction for modeling protein–ligand interactions on cryo-EM data using prior structural information. Proteins are a building block of life and carry out many vital biological functions. Whether acting as an enzyme to accelerate the chemical reactions, or as regulatory molecules binding to other molecules to activate their functions, the detailed characterization of proteins and their interaction with their binding partners  is of great importance. Protein–ligand interactions are necessary requirements for signal transduction, immune responses, and gene regulation in living organisms. The study of protein–ligand interactions is important in understanding the mechanisms of biological regulation and provides a theoretical basis for the design and discovery of new drugs. A fundamental objective of computational structural biology is to understand and model such molecular interactions of living systems in sufficient detail so that the behavior of the system can be predicted or modified as desired. In order to characterize the thermodynamic and kinetic behavior of components and their interactions of living organisms, an image of interacting molecules, such as protein–ligand complexes, at near atomic resolution is required to analyze and understand the physical and geometrical constrains of the molecules.cryo-EM, an acronym for the cryogenic electron microscopy technique , is a reOne of the most popular approaches to modeling the protein–ligand complexes is the molecular docking ,6,7,8,9,Inspired by the success of AlphaFold , which uWe attempted to solve the problem of protein–ligand interaction by using a set of bioinformatics methods, incorporating cryo-EM data and known structural information such as reference protein structures. In particular, we leveraged the recent advance of applying deep learning to directly predict the structure of proteins from high-resolution cryo-EM density maps; a succinct review of the methods can be found in Ref. . To predUsing DeepTracer , we firsAlgorithm 1 depicts the pseudo code of averaging the backbone atoms’ coordinates of reference and predicted structures. We started by initializing an empty PDB file named “average structure” that followed the guidelines of wwPDB . For eacAlgorithm 1 Average predicted structure and reference structure.Require: threshold ▹ threshold can be modified per chain  1: compute   2: initialize: if then  3:   4:       5:       6:     else  7:   8:       9:      10:     end if 11: Here, let Equation  for TargAfter the backbone atoms were computed using Algorithm 1, we utilized SCWRL4  to add tres be each residue in the reference structure that we checked for whether it was a ligand. PyRosetta’s is_ligand function works by comparing the ligand to a chemical component dictionary and returns a bool value  for each residue.Algorithm 2 Identify ligands and include them into average structure.Require: pyrosetta 1: initialize  2: if then 3: with  4:      5:           else 6:  7:     end if 8: After the protein structure that can accommodate ligands was generated using Algorithm 1, we utilized PyRosetta  to identref2015_cst.wts, which is a default score function, repeated five times. Finally, after the refinement of the protein complex, we used UCSF Chimera’s Fit in Map function to perform a rigid body optimization of the refined model. The 3D structure was rotated and aligned so that it fit to the density map. This refinement step was optional. During the blind experiment of the 2021 EMDataResource Ligand Challenge, we submitted both an unrefined model and a refined model for each target.After the prediction of the protein–ligand complex structure using the approach outlined above, we further refined the predicted complex structure using Rosetta FastRelax. Relax does not perform extensive refinement and only searches the local low-energy backbone and side-chain conformations near the starting conformations by implementing rounds of packing and minimizing, with repulsive weight in the scoring function gradually increasing from a low value to a normal value. The scoring function we used was We blindly tested the protein–ligand modeling pipeline DeepProLigand on three targets that were released as 2021 EMDataResource Ligand Challenge targets from February to April 2021. The next section elaborates the three targets and the experimental setting used for each target.The tosidase  target wThe nsp12-nsp7-nsp8 complex bound to the template-primer RNA and triphosphate form of Remdesivir(RTP)  target whttps://github.com/jianlin-cheng/DeepProLigand, accessed on 8 January 2023.The SARS-CoV-2 3a ion channel in lipid nanodiscs  target wThe analysis of the models in this section is based on the official results provided by the organizers of the 2021 Ligand Model Challenge. The fit to a map for a ligand was assessed by the Q-score  and the We used the Q-score to compare the map-to-model fit for all the models that were submitted to the challenge. We also noticed the limitation of our approach in terms of the geometric quality of the atoms in the predicted protein structure, however. Particularly, there were some atom–atom clashes in the models, which may be caused by the violations of some geometric constraints of atom–atom distances in the protein structure predicted by the deep learning, as well as in the averaging of the coordinates of the predicted structure and the reference structure. The violations of geometric and stereochemical restraints were not fixed by the current refinement protocol in the prediction pipeline. The refinement protocol even introduced some new clashes into the model. AlphaFold demonstrated that the well-trained sophisticated deep learning architecture can accurately capture the geometric restraints of atoms and bonds in protein structures by predicting high-quality protein structures of atomic resolution that are highly similar to natural protein structures; this means more advanced deep learning architectures can be developed to predict high-quality protein structures compatible with the geometric and stereochemical restraints of proteins from cryo-EM density maps and reference structures.In this work, we demonstrate that the deep learning prediction of protein structures from cryo-EM maps can generate good protein structures for constructing protein–ligand complexes and the template-based protein–ligand interaction prediction can fit ligands well into the predicted protein structures according to the outstanding performance of our protein–ligand modeling pipeline. It is also worth noting that our method was fully automatic and did not involve any manual tweaking of the models to improve the scores. As discussed before, the current protein–ligand prediction pipeline cannot resolve some violations of some geometric and stereochemical restraints of atoms in protein structures. We plan to soon develop advanced end-to-end deep learning architectures, similar to some components in AlphaFold, to better predict better protein structures from cryo-EM maps and reference structures. Moreover, we plan to design 3D-equivariant deep learning architectures like the SE(3)-equivariant Transformer network ,48,49,50


Here we present ProDFace, a web tool that characterizes the binding region of a protein-DNA complex based on amino acid propensity, hydrogen bond (HB) donor capacity (number of solvent accessible HB donor groups), sequence conservation at the interface core and rim region, and geometry. The program takes as input the structure of a protein-DNA complex in PDB (Protein Data Bank) format, and outputs various physicochemical and geometric parameters of the interface, as well as conservation of the interface residues in the protein component. Values are provided for the whole interface, and after dissecting it into core and rim regions. Details of water mediated HBs between protein and DNA, potential HB donor groups present at the binding surface of protein, and conserved interface residues are also provided as downloadable text files. These parameters can be useful in evaluating and validating protein-DNA docking solutions, structures derived from simulation as well as solutions from the available prediction tools, and facilitate the development of more efficient prediction methods. The web-tool is freely available at structbioinfo.iitj.ac.in/resources/bioinfo/pd_interface.Protein-DNA interactions play a crucial role in gene expression and regulation. Identifying the DNA binding surface of proteins has long been a challenge–in comparison to protein-protein interactions, limited progress has been made in the development of efficient DNA binding site prediction and protein Protein-nucleic acid recognition plays an essential role in all mechanisms of gene expression and control. Over the last two decades several groups have attempted to study and characterize the DNA-binding region that is crucial for recognition . SeveralWhile there are a number of web servers dealing with the structural features of protein-protein interactions, such programs are almost non-existent for protein-DNA interactions, those few available earlier like WebPDA , are nowNcons), the number of potential hydrogen bond donors (Dp) and residue propensity score (Rp), for community use. We have also integrated other important physicochemical and geometric features from two of our earlier developed web-tools ProFace  and binding free energy (ΔG)  , our proHence, we anticipate that ProDFace would be useful for analysing the increasing number of DNA-binding proteins and that the features it provides can be a useful implementation in the development of protein-DNA docking algorithms.Given the atomic coordinates of a protein-DNA complex, ProDFace does data mining using a collection of in house programs running at the backend, and extracts various features that helps one to study the nature of the binding region. In addition, some free softwares used are: NACCESS  for the The user provides two main pieces of information to the program. First, a protein-DNA complex structure file in PDB  format iBriefly we define here the different characteristics that ProDFace investigates and we refer the reader to the original paper  where th2 of surface area upon complexation constitute the protein interface. Those residues that have at least one atom fully buried at the interface are referred as core of the interface; the remainder are referred as the rim, has a composition similar to the surface  were considered as conserved and their total number in each interface is denoted by Ncons. s(i) is the Shannon entropy of the aligned sequences at position i calculated from the MSA of the homologous proteins (Interface residues with sequence entropy lower than the average (proteins .2 are assumed to be capable of getting involved in hydrogen bonding with DNA and their number (Dp ) in each interface/patch is calculated.Side-chain groups of positively charged amino acids such as arginine , histidine  and lysine (NZ), as well as of asparagine (ND2), glutamine (NE2), tryptophan (NE1), serine (OG), threonine (OG1) and tyrosine (OH) with accessibility ≥10 Åni is the number of residues of type i and pi is its propensity to be in the interface.Amino acid composition was used to calculate residue propensity score  given byHaving identified the interface water molecules (those at a distance of ≼4.5Å from both protein and DNA molecule) we find out if they are hydrogen bonded to either of the components, or both (bridging water molecules), or only to other interface water.The interface residues are spatially clustered using 20 Å as the default value  of the dRp, Dp and Ncons) described above were used to identify the DNA-binding region on the protein component and they performed very well ; .hbd file contains information about all possible hydrogen bond donor groups at the interface along with their accessible surfaces areas; .water file contains the information about all water-mediated hydrogen bonds between protein and DNA; and .cont files contain information about the nucleotides which are in contact with each of the protein residue, and vice versa , which has been by far the best discriminator (Dp) performed notably well, discriminating the actual interface from a decoy having 94% overlapping residues. The reason behind this may be Dp is not merely a frequency of possible donor groups at the binding region, but an area criterion is also incorporated in its definition (only those with accessibility ≥10 Å2 are counted) (Dp) may be missing. As a result, for the real binding region there are more number of solvent exposed donor atoms capable of forming hydrogen bonds with DNA.All the features mentioned above were calculated for all the docking decoys and these, along with the values for the actual interface, were ranked. The interface was ranked #1 if it occurred within the top 10% of all the decoys . There wiminator , did notiminator . Interescounted) . In thesRp improves on removing the overlaps, we gradually removed the overlaps and re-ranked all at various percentages of overlaps, such as 50, 20, 10 and 0%. We found that at 10% overlap the performance of Rp improved considerably and it could identify the actual interface among the decoys, ranking it as 1 in 66% cases  for which Dp could not rank the actual interface in top 3, this however was ranked 1 by Rp. So, it seems that Dp and Rp complement each other and in all the entries the interface patch is ranked 1 by more than one parameter; as such there is no single entry whose interface cannot be discriminated from the decoys by any of the features defined by us. Performance of Ncons is also comparable to that of Rp but when overall top 3 rankings are considered Rp outperforms Ncons (To see if the performance of 6% cases . At 0% o 3 ranks . The lonNcons . All theWe describe a web-tool, ProDFace that enables researchers to upload structures obtained from experimental methods, docking programs, or derived from simulation, for analysis. The program derives the overall characteristics of the binding region of a protein-DNA complex structure, in particular of the protein component.Targeting protein-DNA complexes with small molecule inhibitors is difficult compared to protein-protein complexes. Recent developments in computer-aided drug discovery approaches are using key oncogenic transcription factors and have developed candidate inhibitors targeting the DNA binding region, are presently under clinical trials . Now, wiFurther, understanding the various features that characterize protein-DNA interfaces would help us develop empirical algorithms that can identify the DNA-binding patch in protein structures . The top


Target drugs play an important role in the clinical treatment of virus diseases. Virus-encoded proteins are widely used as targets for target drugs. However, they cannot cope with the drug resistance caused by a mutated virus and ignore the importance of host proteins for virus replication. Some methods use interactions between viruses and their host proteins to predict potential virus–target host proteins, which are less susceptible to mutated viruses. However, these methods only consider the network topology between the virus and the host proteins, ignoring the influences of protein complexes. Therefore, we introduce protein complexes that are less susceptible to drug resistance of mutated viruses, which helps recognize the unknown virus–target host proteins and reduce the cost of disease treatment.Since protein complexes contain virus–target host proteins, it is reasonable to predict virus–target human proteins from the perspective of the protein complexes. We propose a coverage clustering-core-subsidiary protein complex recognition method named CCA-SE that integrates the known virus–target host proteins, the human protein–protein interaction network, and the known human protein complexes. The proposed method aims to obtain the potential unknown virus–target human host proteins. We list part of the targets after proving our results effectively in enrichment experiments.Our proposed CCA-SE method consists of two parts: one is CCA, which is to recognize protein complexes, and the other is SE, which is to select seed nodes as the core of protein complexes by using seed expansion. The experimental results validate that CCA-SE achieves efficient recognition of the virus–target host proteins.The online version contains supplementary material available at 10.1186/s12859-022-04792-x. The novel coronavirus (2019-nCov for short) outbroke at the end of 2019, and has been causing varying degrees of disease symptoms in people of different ages, such as gastrointestinal symptoms in children while other ages experienced pneumonia, stroke, and blood clots [s groups , such ass groups . For patProteins with similar functions tend to form protein complexes and work together, as do virus proteins. Therefore, it is very important to recognize protein complexes in predicting virus–target host proteins. Detecting complexes from Protein–Protein Interaction (PPI) has become an important research field in proteomics. Many graph clustering algorithms use it to find community structures in networks, and recognize protein complexes. Nepusz et al.  proposedAccording to the World Health Organization, over 17 million people die of infectious diseases every year. It is necessary to identify interactions between viruses and human proteins in virus disease treatment. Ho-Joon Lee  presenteStudying PPI between the new virus and its known host proteins can accurately predict the virus. Fatma-Elzahraa Eid  proposedHowever, most existing methods often ignore the inherent biological structure of the complexes, and many only consider the structure as dense subgraphs. Few of them consider the modularity of PPI, let alone search for potential virus–target proteins from protein complexes, such as Babak Khorsand . Here weWe developed the CCA-SE method based on the principle of coverage clustering-core-subsidiary structure, which contained two parts, CCA was used to recognizing protein complexes, and SE was applied to select seed nodes. Especially, We demonstrated a new selection strategy for seed nodes. We clustered seed nodes to obtain the nuclear structure of protein complexes. Then, based on the density and modularity, we expanded the core structure and formed protein complexes. Next, integrate downloaded complexes data with our results. At last, we calculated the similarity between unknown human proteins and known virus–target proteins in the same protein complexes. We defined a scoring function, which was helpful in getting potential virus–target human proteins. Figure We constructed a virus–target human PPI network based on the human protein interaction database HIPPIE and the human protein dataset of 2019-nCov infection. Researchers often regard direct neighbor proteins as potential host proteins in some typical virus–host proteins research [Node2Vec  not onlyWe extracted the hidden layer weight of PPI and demonstrated each protein node with a 128-dimensional vector . In Fig.u is defined in Eq. (u) indicates summation of ECC and degree(u) indicates degree of node u. Equation  database to predict and analyze gene function. Usually, a gene or gene product is annotated by one or more GO terms. We can calculate the similarity between genes to analyze and predict gene function. The traditional coverage clustering algorithms (CCA)  are lesst to balance the weight of subgraph modularity and subgraph density, as shown in Eq.  The first-order neighbor nodes of each protein complexes core are obtained from the network diagram as candidate proteins of the complexes. (ii) Calculating the sum of functional similarity between the core and their neighbor nodes based on edge weight. (iii) ranking nodes according to the functional similarity score, completing protein complexes by adding affiliated nodes. The local score of the protein complexes calculates when a new node adds. (iv) Repeating (iii) until no more nodes can improve the local score, then terminating the expansion of the protein complexes core.Protein complexes are molecular polymers that participate in the same functional region at the same time and space and have direct or indirect effects . StudiesWe obtained 255 protein complexes on the human PPI network dataset. Considering incomplete data, we downloaded all human complexes data from the CORUM database, including 1948 protein complexes. Then we integrated them with our results, choosing protein complexes data with nodes than three and known host proteins while deleting the redundant data. Therefore we got 455 human complexes, with complexes that contain more than two known proteins having sixty. The more known host proteins contained in the complexes, the more likely it participate in viral survival and reproduction. Since the proteins phenotypes in the same protein complexes are similar, the remaining proteins may also become the proteins required for virus replication.Table Based on Table C belongs. nN indicates the total number of proteins in the complexes. wC and the known proteins in its complexes. The formula is as follows.PC, in which the complexes in the Equation select the complexes corresponding to the maximum value in Eq. , and remove the redundant data. The function annotation of protein a obtains as follows.GO is an internationally standardized gene function classification system. It consists of a predefined set of GO terms, which can limit and describe the function of gene products. GO terms provide the logical structure and correlation of biological processes and classify biological process (BP), molecular function (MF), and cellular component (CC). GO annotations  are respWe adopt Cross-Validation to adjust the parameters reasonably under the condition of moderate source datasets and apply them to practical problems. Furthermore, we use the K-fold Cross-Validation method to evaluate the experimental results.k, we conduct ten groups of control experiments to verify the results and use the ratio as the final target proteins classification standard. We consider deleting the candidate protein with zero scores in the final ranking process to reduce the data redundancy. Moreover, k ranges from 0 to 100 with a step of 10, The Cross-Validation shows that the prediction results can be divided into four categories, as shown in Table Firstly, we divide the identified host protein complexes into a training set and validation set according to the ratio of 8:2. To know how many validation data are in top N represents candidate proteins that are not in the validation data. P represents candidate proteins that are in the validation data. TP means the recognized correctly by CCA-SE. FP means the recognized quantity wrongly.In this section, the true-positive rate (TPR) and false-positive rate (FPR) values are obtained according to the four results in Table Considering the unbalanced data distribution between known and unknown proteins, we use the values of receiver operating characteristic (ROC) and area under the receiver operating characteristic curve (AUC) as evaluation indexes, then plot the ROC curves under different thresholds of TPR and FPR. The abscissa represented the FPR while the ordinate represented the TPR.k, then select the best k value. Let the value of k vary from 0 to 100. The ROC curve shows in Fig. k% data considered as the predicted potential target proteins, and the second (100-k)% data not considered as the predicted potential target proteins. It can be seen from Fig. k is 40, CCA-SE successfully predicts 22 known host proteins. According to the above host PPI network, 80% of the proteins are non-structural protein interactions encoded by the 2019-nCov. In addition, this experimental result also shows that the predicted protein scores are high when the value of k increases, indicating that the use of Eq.  is a database with functional information about each gene. The core of the KEGG database is KEGG PATHWAY and KEGG ORTHOLOGY. In KEGG PATHWAY, biological metabolic pathways are divided into six categories, namely Cellular Processes, Environmental Information Processing, Genetic Information Processing, Human Diseases, Metabolism, and Organismal Systems. Once we get the differential gene information, in order to learn their functions more clearly, gene enrichment analysis may be used to discover biological pathways that play a key role in biological processes, so that we can better understand the molecular mechanisms of biological processes. KEGG pathway analysis  selects In Fig. The existing studies have shown that the MAPK pathway is related to cell growth and mutations. TNF is mainly produced by T cells and NK cells, and both are closely related to inflammation. They can release signals to interact with specific receptors on the cell surface, making them conservative, so that MAPK-JNK, 5-lipoxygenase, and other signaling pathways are activated, making cytokines related to inflammation disorders, such as abnormal expression of gp130 and IL-1, and promoting human inflammatory response .The PI3K-Akt signaling pathway is closely related to the renal cell carcinoma pathway, as shown in Fig. https://david.ncifcrf.gov/). We conducted GO function enrichment analysis and KEGG signal pathway enrichment analysis. The analysis explained the correlation between the predicted results obtained by the CCA-SE and the life process of virus infection and replication and proved the accuracy from the biological perspective. The experimental results showed that CCA-SE can effectively recognize human proteins targeted by the 2019-nCov and play a fundamental role in detecting virus drug targets.In this paper, we proposed a protein complexes recognition method CCA-SE. The protein complexes obtained by CCA-SE were integrated with the human protein complexes to obtain a more reliable protein complexes dataset, then we defined a score function to get potential target proteins. The scoring function takes into account not only the relationship between the protein complexes and the virus-encoded proteins but also the protein itself to predict the virus–target human proteins. Moreover, we verified the effectiveness of CCA-SE on the biological network under different parameter settings. At the same time, the selected target proteins were imported into the DAVID v6.7 database (Additional file 1. The Additional file 1 contains “new targets”. “new targets” lists our predicted results and shows our method of predicting virus–target human proteins does achieve good results.


Automatic functional annotation of proteins is an open research problem in bioinformatics. The growing number of protein entries in public databases, for example in UniProtKB, poses challenges in manual functional annotation. Manual annotation requires expert human curators to search and read related research articles, interpret the results, and assign the annotations to the proteins. Thus, it is a time-consuming and expensive process. Therefore, designing computational tools to perform automatic annotation leveraging the high quality manual annotations that already exist in UniProtKB/SwissProt is an important research problemIn this paper, we extend and adapt the GrAPFI (graph-based automatic protein function inference)  method for automatic annotation of proteins with gene ontology (GO) terms renaming it as GrAPFI-GO. The original GrAPFI method uses label propagation in a similarity graph where proteins are linked through the domains, families, and superfamilies that they share. Here, we also explore various types of similarity measures based on common neighbors in the graph. Moreover, GO terms are arranged in a hierarchical manner according to semantic parent–child relations. Therefore, we propose an efficient pruning and post-processing technique that integrates both semantic similarity and hierarchical relations between the GO terms. We produce experimental results comparing the GrAPFI-GO method with and without considering common neighbors similarity. We also test the performance of GrAPFI-GO and other annotation tools for GO annotation on a benchmark of proteins with and without the proposed pruning and post-processing procedure.Our results show that the proposed semantic hierarchical post-processing potentially improves the performance of GrAPFI-GO and of other annotation tools as well. Thus, GrAPFI-GO exposes an original efficient and reusable procedure, to exploit the semantic relations among the GO terms in order to improve the automatic annotation of protein functions More and more protein sequences are accumulating in public databases thanks to advanced high-throughput sequencing technology . To get Gene ontology (GO)  lists coAnnotation by association rules based on sequence and structural similarities is still the dominating approach for automatic protein function annotation problem. The central idea is to find the statistically significant pair-wise similarities between un-annotated query protein and the annotated proteins stored in the database and then transferring the known annotations from the highly ranked similar proteins. That is, when a new protein arrives in the database, it is matched against known proteins in the database and the known annotations are transferred from the best matched proteins.UniProtKB managed by European Bioinformatics Institute (EBI) deploys automatic annotations tools for accelerating the annotation of incoming un-annotated proteins. Primarily, a rule based system called UniRule  is used To alleviate difficulties with curating manual rules in UniRule, data-driven machine learning techniques have been put into the annotation pipeline. The system is called Statistical Automatic Annotation System (SAAS)  and it aIn August 2020, SAAS was replaced by another updated rule-based system called ARBA (Association Rule-Based Annotator) –12, whicAlong with functions, ARBA rules can annotate other protein properties such as catalytic activity, pathway membership, sub-cellular location and protein names. It generates around 23 thousands models/rules that associates InterPro signatures and taxonomic identities to corresponding functions and the rules are learned completely from the annotated entries of UniProtKB/SwissProt.Various attributes were also used for protein function annotation. In –16, struNetwork science  is perceIn pursuit of developing automatic protein function annotation tools, a number of pipelines are proposed.These tools exploit various attributes for example, sequence encoding, functional domain similarity, structural similarity, phylogenetic tree, protein–protein interaction network etc. Followings are brief description of a few state-of-the-art GO prediction tools.Blast2GO or B2G [O or B2G  is a cloO or B2G  is a popGoFDR [GoFDR  is a seqGoFDR  or PSI-BGoFDR  to compuDeepGO [DeepGO  is a deePANNZER [PANNZER , 49 applCOFACTOR as described in [COFACTOR [ribed in , 16 is mCOFACTOR  integratGrAPFI-GO. We propose a pruning technique based on semantic similarity to eliminate the outlier annotations and a hierarchical post-processing step to enrich the remaining annotations with term ancestors. More specifically, our contributions are the followings:We extend GrAPFI to perform GO Prediction. GrAPFI is a neighborhood-based label propagation approach that works on a network of proteins connected using domains and family information. GrAPFI was originally proposed for Enzymatic protein function prediction using Enzyme Commission (EC) Number.We incorporate various approaches for computing node similarity based on common neighbors to take into account the local graph sub-structures around each node of a pair.We integrate semantic similarity to take into account the hierarchical nature of the GO data and prune outlier annotations based on their distance in the GO semantic space. To find functional similarity, we used GOGO  which isWe experimentally evaluate the performance of the proposed approach by annotating a benchmark of protein sequences with GO terms and report a comparative analysis of the efficacy of the proposed pruning and post-processing technique for GO term prediction.In this paper, we extend the GrAPFI method , 2, a GrIn order to validate the performance of the proposed method, we have used a dataset of 1000 proteins . For GrATo prepare the test set, we have used MetaGO benchmark sequences and run InterProScan  to identIn Table In the rest of Table In Table Among the top performing methods, only a few have their source code available to run experiments. Therefore, we focus on two easily available tools namely PANNZER and DeepGOPlus , an imprWe run the three selected annotation tools on the MetaGO benchmark data and obtain sets of predicted annotation terms. These predicted sets are further pruned using semantic similarity and enriched with hierarchical post-processing. Results are shown in Table From Table Automatic protein function annotation is an important topic in the field of bioinformatics because of the lack of annotation of proteins due to high costs and time-consuming nature of manual procedures for function identification. There exist a number of tools to perform automatic protein function annotation using GO terms, EC numbers, ligand binding sites etc. These tools use various attributes and different methods to accomplish the task. Although they show higher performance based on F1 score, the high F1 score is coming from a higher recall as they predict a large number of candidate annotations. This, in turn, increases the number of false positive annotations. In this paper, (1) we present GrAPFI-GO, a graph-based protein function inference method for GO term prediction, (2) We incorporate node similarity scores with link weight to use sub-structures among the neighbors, and (3) we propose an efficient pruning and hierarchical post-processing technique by integrating semantic similarity of candidate annotations. We experimentally validate that the proposed method can significantly improve the annotation outcome. In fact, for the two tools tested in addition to GrAPFI-GO, recall is significantly lower as the number of annotations decreases compared to the initial number of annotations predicted by these tools. Nevertheless, the precision is improved by many folds as the post-processing method helps to select highly coherent semantically close annotations. Any of the available annotation tools can benefit from the proposed post-processing approach. Therefore, any improvement in the automatic annotation pipeline would be magnified with the proposed hierarchical post-processing.Functional annotation of protein is a challenging problem equally in the case of manual and automatic pipeline. In the manual annotation process, curating rules is a difficult step. In case of automatic annotation pipeline, the models are trained with insufficient ground truth knowledge. And, as the number of manually reviewed proteins increases in every release, more facts are being available to be used in the automatic prediction methods. This eventually provides enhanced training data. On the other hand, due to fast progress in computing systems, this is now possible to perform large scale complex annotation pipeline.The work presented in the paper, rather, presents promises of the proposed approaches. A practically relatable large scale experiment would require powerful computing systems, distributed processing, and large biologically significant training data along with advanced AI techniques merged with expert rules. It would be also interesting to explore other semantic similarity approaches to establish the efficacy of the pruning and hierarchical post-processing techniques. There is also scope of using advanced representation learning for computing low rank vector representations or embeddings of the GO terms. These embeddings eventually can be utilized to compute semantic similarity and thus, develop new membership scores for the GO terms.The automatic protein function annotation is a big support to the protein annotation process. However, this is not the complete alternative of the manual process.GrAPFI-GO—the proposed the automatic protein annotation technique—performs GO annotation using a neighborhood-based label propagation proposed in GrAPFI combined with hierarchical post-processing to take advantage of the hierarchical organization of Gene Ontology. Essentially, there are two parts in the pipeline: (1) predicting the set of GO terms using GrAPFI, and (2) pruning the annotation set using semantic similarity and hierarchical post-processing. In its original form, GrAPFI build graph of proteins exploiting their similarity in domain composition and applies neighborhood-based label propagation to automatically annotate proteins with EC number. In the current form, GrAPFI is extended as GrAPFI-GO for protein function annotation using Gene Ontology (GO) terms.More specifically, the objective is to develop a pipeline that will leverage the functional annotations of manually reviewed proteins in Swiss-Prot to predict those of non-reviewed or un-annotated proteins in TrEMBL by building a graph of proteins and then applying function inference technique on the graph. The GrAPFI algorithm first constructs an undirected weighted graph of the proteins using the domain composition of the annotated proteins. Each protein is a node and a link exists if there is similarity in the domain composition between two proteins.Then, given an non-annotated protein, a label propagation algorithm is applied to the domain similarity graph in order to infer appropriate annotations.In this section, we first present some definitions and notations used in the paper.Graph Graph is a data structure to represent relations and connections among objects and entities. Symbolically, a graph is defined as a collection of vertices and edges denoted by V contains the set of vertices/nodes and Weighted graph The links or edges in a graph may have weights for quantifying the bonding. Such graph is called weighted graph. In a weighted graph, in addition to V and E, there is another component W to hold the weights of the edges. Symbolically,the graph is denoted as V is a set of nodes,W is a weight matrix where each cell Labeled graph In some settings, graphs contain labels for vertices or edges exposing the class of the nodes and type of relations that the edges are holding. THis type of graphs are termed as labelled graph. The notation to represent a labeled graph V is a set of nodes,L is a set of labels,L to nodes V and edges E.Directed graph In a directed graph, the direction of the edges are explicitly expressed by specifying the source and destination vertices of the edges. u, v) such that Undirected graph In an undirected graph, the direction of the edges is not explicitly mentioned. u, v) such that if Neighbors The vertices of an edge are neighbors of each other. Therefore, for a node, there can be more than one neighbor depending on how many edges it belong to. The neighbors of a node u are defined as Degree The degree of a node in a graph is the number of its connected edges. The degree of a node u in a graph G is denoted N(u) is the directly connected neighbors.Average degree The average degree of a graph E compared to the number of vertices in the set V. The average degree of a graph GrAPFIGO is a neighborhood-based GO annotation approach that works on a network of proteins linked through domain and family information. The domain is an evolutionarily conserved region of protein sequences. A long enough protein sequence can have multiple conserved regions or domains that make up the domain composition. GrAPFI-GO follows the following steps to perform functional annotation .u in the graph represents a protein. An edge  between two nodes/proteins u and v means that the linked proteins share some attributes like domains and functional sites. A node u may have a set of labels L(u) (one or more GO annotations to propagate), has a set of neighbors N(u), and for every neighbor i.e. annotations) from nodes having labels to similar nodes that lack labels. Jaccard similarity index is used to compute the link weight as D1 and D2, respectively.Firstly, it builds a graph by using data from protein database. Each node P1, P2, P3, P4, and P5. The domain compositions of these proteins are For instance, let us consider we have five annotated proteins: Domain composition is the set of domains arranged without order of appearance in the amino acid sequence. For example, the domain information may come in ordered form like d1 and d3. However, in the former case, there are only 5 different domains of which 2 are shared, whereas in the later case there are 11 different domains in total. Although two domains are shared in each case, P1 is intuitively more aligned with P2 than P5. Therefore, instead of using the raw similarity score, we used the Jaccard similarity index that better reflects the proximity of two proteins in terms of domain composition.Using the Jaccard similarity index, the link weight between P1 and P2 is calculated as,MS is the weighted score of the candidate function i for the query protein u. And j of the protein v is the same as the candidate function i, otherwise, 0.Secondly, a label propagation approach is applied to the protein graph in order to infer functional properties of the unlabeled nodes. Given a query protein, based on the domains and family information it contains all the neighboring proteins and their annotations are retrieved from the weighted graph. After getting the neighbors, all labels of the neighbor proteins are weighted with the edge weight that these neighbors exhibit with the query protein. When retrieving neighbors, it is possible to select only those neighbors which meet a certain similarity threshold. This means that the links can be filtered based on a predefined cut-off weight. For each candidate annotation, GrAPFI provides a confidence score, named model score (MS), that is computed as:P with a set of domains P is connected with P2 and P4 in the domain similarity graph. Therefore, the protein P will have P2 and P4 as neighbors i.e.N(P) is computed, the GO annotations from neighbor proteins are propagated along with the corresponding weights computed using Eq. N(p). All of the functional annotations are ranked based on their cumulative weights. The top-ranked annotations are selected as the best functional annotations for protein P.The protein similarity graph contains both annotated and unannotated proteins. The objective of the label propagation algorithm is to transfer the annotation from annotated proteins to the unannotated proteins. To illustrate the algorithm, let us take an unannotated protein mentclass2pt{minimGrAPFI  propagatWe explore following eight node similarity approaches based on common neighbors. These approaches uses node neighborhood-related structural information to compute the similarity of each node with other nodes in the network. A detail description of the approaches can be found in .Common neighbors (CN) computes the similarity as,p1 and p2 in the domain similarity graph.The Jaccard index (JA) computes the node similarity as,The preferential attachment index (PA) computes the node similarity as,The Salton index (SA) computes the node similarity as,The Sorensen index (SO) computes the node similarity as,The hub promoted index (HPI) computes the node similarity as,The hub depressed index (HDI) computes the node similarity as,The local Leicht–Holme–Newman index (LLHN)  computes the node similarity as,S in Eqs. u and v  from a large gene annotation corpus. It rather considers the number of children nodes in the GO DAG when calculating the semantic contribution of an ancestor node toward its descendent nodes. GOGO is based on GO DAG topology instead of IC which means that it is comparatively stable.g, its ancestors set nc(t) is the total number of children of term g is computed asg1 and g, having g1 and g is as follows:A. Then, we can compute the membership of each annotation in set A as follows:Given In all state-of-the-art GO annotation models, used for experiments in this study, there is a prediction score associated with each predicted annotation for every protein. We refer to this as model score (MS).u with a set of predicted annotations A, each annotation MS, which shows the credibility with which the annotation was predicted by a particular annotation tool and (2) second, the Semantic Similarity (SS) score, defined as g to the predicted set A. Now, we need to combine these scores to find a combined prediction (CP) score, defined as u. Joining the scores into a single score provides an overall assessment. A score should be able to distinguish between annotations that score average in both MS score and SS score, from those that score high in one scoring scheme and low in the other scheme. Therefore, instead of averaging the scores, we follow the following scheme:For a protein, The final step of the process is hierarchical post-processing of predictions in the new predicted set. In the Gene Ontology DAGs, the GO terms hold various parent–child relations putting biologically closer GO terms hierarchically nearer in the graph. We implemented a methodology to include more complete predictions by including the ancestors of all the GO terms in the new set of predictions. The ancestors of a GO term in the DAG it belongs to, have a very high semantic similarity with the term. Therefore, we first topologically sorted the DAG for each GO aspect and determined all possible paths from each GO term to the root of the corresponding aspect. Finally, we follow these paths from terms to the root, one by one and add corresponding ancestors to the set of predictions to obtain the final prediction set.


Given that most catalytic and regulatory functions of the cell are carried out by protein complexes, their composition and characterization is of greatest importance in basic and disease biology. The new CORUM 4.0 release encompasses 5204 protein complexes offering the largest and most comprehensive publicly available dataset of manually curated mammalian protein complexes. The CORUM dataset is built from 5299 different genes, representing 26% of the protein coding genes in humans. Complex information from 3354 scientific articles is mainly obtained from human (70%), mouse (16%) and rat (9%) cells and tissues. Recent curation work includes sets of protein complexes, Functional Complex Groups, that offer comprehensive collections of published data in specific biological processes and molecular functions. In addition, a new graphical analysis tool was implemented that displays co-expression data from the subunits of protein complexes. CORUM is freely accessible at  Cellular systems in mammals can be viewed as the product of ∼20 000 individual protein-coding genes acting in concert to carry out catalytic, structural and regulatory functions. Coordination is orchestrated through protein-protein interaction networks that assemble functionally related gene products into structures such as protein complexes and organelles. Therefore, it is of central interest towards a better understanding genotype-phenotype relationships to obtain knowledge about all protein complexes in living cells.A first high-throughput approach in an eukaryotic organism was performed in Saccharomyces cerevisiae and defined 232 distinct multiprotein complexes . Recent In previous decades, thousands of protein complexes from mammalian organisms have been characterized in individual experiments with respect to subunit composition and cellular function. The CORUM database offers the largest publicly available compendium of manually curated mammalian protein complexes based on experimental results from the literature . It servhttp://mips.helmholtz-muenchen.de/corum/.With the new release CORUM 4.0 we further extended the dataset to 5204 protein complexes. In the first releases our aim was to cover a broad spectrum of protein complexes from different areas. A representative set of complexes is a requirement to serve as reference dataset for large-scale approaches for investigating the mammalian complexome, for the development of data analysis tools . With thCompared with the CORUM 3.0 release  the numbAnother option of cells providing protein complexes with different functions is the utilisation of isoforms (splice variants). Driven by progress in mass spectrometry technology there is an increasing number of publications where particular isoforms of complex subunits were identified. Hence, we are now also annotating isoform information in CORUM if the respective information is available (e.g. complex 7590: CASP2(S isoform)-SPTAN1 complex). Similar to isoforms, we also provide information about post translational processing (see complex ID 7529).In order to streamline to content of the dataset we removed protein complexes with identical subunit composition from the same organism which have been characterized in more than one publication or by different methods. As a consequence, we do not offer a core set any more. Compared with the previous release, the distribution of organisms as source of protein complex analysis has not changed considerably. The vast majority of complexes was characterized in human cells with 70% (3637) followed by 16% mouse (847) 9% rat (461).An important application of the CORUM dataset is disease research. About one third of citations from the latest CORUM article is dedicated to disease research. These include various applications such as databases , tools tDiseases are often caused not by dysfunction of individual proteins but by deregulated protein complex function. Examples are the spliceosome in cancer  and the In addition to various kinds of data analyses, CORUM became a broadly used information resource which is applied in more than 50 biomedical databases and research tools . Beside As shown above, there are multiple experimental and bioinformatics approaches trying to achieve a complete representation of the cellular machinery. To obtain an estimate about the completeness of the results requires reference datasets which are comprehensive, at least based on current knowledge. Also, for the analysis of high-throughput data with a medical context it is valuable information to know if a biologically meaningful group of complexes is overrepresented in a disease condition. Hence, we started to annotate groups of protein complexes belonging to a biological process or the same molecular function Figure . The comThe FCG ‘Potassium ion transmembrane transport’ for example covers a group of protein complexes playing important roles in vital cellular signalling processes in both excitable and non-excitable cells. Naturally occurring mutations in various K(+) channels cause cardiovascular, neurological or metabolic diseases . In the Most of the mitochondrial proteins are encoded by nuclear DNA and their precursors have to be transported into the mitochondria. The FCG ‘Mitochondrial protein import’ describes the set of complexes that perform this process. Pathological effects of e.g. TOM  and TIM  complex dysfunctions affect the protein import into mitochondria and are associated with Alzheimer's disease where it results in an inhibition of respiratory complexes and finally in an increased level of reactive oxygen species .Non-motile (primary) cilia are sensory organelles, that transduce signals from the environment or from other cells, motile cilia move extracellular fluids like cerebrospinal fluid or propel sperm cells. The FCG ‘Cilium assembly’ comprises complexes like the CPLANE complex, the intraflagellar transport complexes IFT-A and IFT-B, or the BBSome  which inFor the investigation of various biological systems, the scientific community established the use of particular mammalian model organisms, for example rat for potassium ion transporters. However, as no organism covers all known protein complexes in a field and we aim to be as complete as possible, FCGs are a collection of complexes from different organisms. If a protein complex has been characterized in different organisms, we preferably use representatives from human cells or tissues. FCGs and the number of representatives are shown on the homepage Figure  and can Analyses of eukaryotic protein complexes revealed that complexes are comprised of a core in which subunits are highly co-expressed and represent functional units ,29. ThisIn order to provide an insight into the organization of protein complexes, and to provide a perspective on the role of the various protein subunits, the new CORUM release presents a graphical tool that integrates known protein co-expression information. A large publicly available collection of co-expression data is provided by the STRING database . STRING The application of the tools is shown for the proteasome Figure , a molecIn recent years, CORUM became an important resource in the fields of biology and biomedicine, by collecting experimentally characterized protein complexes to create a reference dataset for data analysis and bioinformatics applications. Expert curation is a key focus of our activities, to inform on composition, functional role and other features of protein complexes. The CORUM dataset continues to grow and to incorporate more complexes in particular from human tissues and cells. Recent improvements were driven by our efforts not only to increase the amount of protein complexes (5204) but also the coverage of the mammalian proteome (26%).The growing importance and application of protein complex information in disease research is demonstrated by the high number of publications that made use of the CORUM 3.0 dataset for the analysis of disease-associated data or the creation of biomedical analysis tools. In particular worth mentioning is pioneering research for the understanding of molecular processes in COVID-19. To provide the CORUM dataset with additional disease-related information, the complex subunits are now linked to respective OMIM diseases.With CORUM 4.0 we have begun to offer Functional Complex Groups. These represent comprehensive sets of protein complexes with common biological or biomedical relevance (molecular function or bioprocess). For projects aiming to represent a complete mammalian complexome, FCGs will give hints to what extent this goal has been accomplished. Also, for the analysis of biomedical data, it may offer valuable insight if representatives of FGCs are overrepresented.Future plans include (i) the addition of further FCGs with a focus on biomedical relevance, (ii) to serve the community with an expanding the set of tools for exploring protein complex information, (iii) to enable the prediction of impacts of deregulated protein complex function in the context of disease symptoms and (iv) to integrate structural information such as data from cryo-electron microscopy.andreas.ruepp@helmholtz-muenchen.de.We invite all researchers to send us feedback and suggestions for incorporation of additional protein complexes from the user community. Please contact us at http://mips.helmholtz-muenchen.de/corum/#download) under the Creative Commons Attribution License (CC BY 4.0).The provided data can be freely downloaded in various formats from our downloads page (gkac1015_Supplemental_FileClick here for additional data file.


The domain of protein biology has historically been on the lookout for a reliable method to discern the 3D-shape (structure) of a protein given its amino acid sequence. Precise knowledge of a protein’s structure enables us to first, explain how it works as a tiny molecular machine, and then devise rules to modify existing proteins or design new ones for therapeutic and engineering applications spanning–healthcare, green chemistry, energy, and novel functional materials.Accessibility to ever improving computing infrastructure has led to a paradigm shift towards data-driven modeling in all areas of science and arts. Eponymously, data-driven modeling relies on 1) well curated, domain-knowledge-driven datasets, and 2) appropriate utilization of said data  to catalog the 3D-Cartesian coordinates of each atom that are present in the protein. Such per-atom information is stored in a PDB (Protein Data Bank) format. Set up in 1976, the PDB (One way to accurately determine the 3D-shape of a protein is  the PDB  is a pub the PDB ). This hde novo proteins. While neither FSA nor SS models are explicitly trained to be performative on structural changes arising from point mutations, at the limit of performance, SS models are better poised to capture such effects. Moreover, the biological event of protein folding upon synthesis is a molecular process which depends on the sequence of amino acids that make up the protein, not how similar proteins are folded in other species.Two emerging, yet different schools of thought that fuel these deep-learning pipelines for structure prediction are: 1) family sequence alignment-based (FSA), and 2) single sequence-based (SS). FSA methods such as AlphaFold2  and RoseVillalobos-Alva et al. which provides an extensive summary of ML/AI-based recipes and neural architectures that have been developed and deployed till date to learn, predict, and design proteins for various use cases  can be utilized to predict secondary structure of proteins as a function of the inputs—1) amino acid sequence, and 2) similarity with known proteins with experimentally verified structure . Next, Wang et al. first construe a protein structure as a network of amino acids either connected by covalent or non-covalent bonds and show how the network topology appended to node properties  can be mapped to protein function. The study reveals that DNA binding proteins tend to have buried hydrophobic pockets which are thermodynamically amenable to drug-binding. On the engineering front, Zhou et al. use information about active site of a particular enzyme (glutamate dehydrogenase) and tailors it to catalyze a stereoselective reaction with high efficiency. Pan et al. zoom out of a single protein and focuses on the entire protein-protein interaction (PPI) network in a single plant cell. In such a network, each node is a protein . A PPI dictates cellular phenotype i.e., how an organism behaves as a consequence of proteins interacting with each other. They describe DWPPI–a deep walk algorithm that traverses a plant PPI to discern how information cascades through such a network.In this Research Topic, one of the key contributions is by ases see . Along t