The assembly of such fragments through duplication and recombination events led to the complex structures and functions we observe today. We previously implemented a pipeline that identified more than 1,000 of these fragments that are shared by different protein folds and developed a web interface to analyze and search for them. This resource named Fuzzle helps structural and evolutionary biologists to identify and analyze conserved parts of a protein but it also provides protein engineers with building blocks for example to design proteins by fragment combination. Here, we describe a new version of this web resource that was extended to include ligand information. This addition is a significant asset to the database since now protein fragments that bind specific ligands can be identified and analyzed. Often the mode of ligand binding is conserved in proteins thereby supporting a common evolutionary origin. The same can now be explored for subdomain-sized fragments within this database. This ligand binding information can also be used in protein engineering to graft binding pockets into other protein scaffolds or to transfer functional sites via recombination of a specific fragment. Fuzzle 2.0 is freely available at https://fuzzle.uni-bayreuth.de/2.0.Modern proteins have been shown to share evolutionary relationships  A main function of proteins is the binding of molecules such as other proteins or smaller compounds. For example, the entire machinery of metabolic pathways consists of proteins that bind various substrates and catalyze diverse reactions . DespiteFor a long time, protein domains have been considered as the evolutionary unit, being structurally discrete and independently folding. However, the analysis of the known sequence and structure space in recent years led to a renewed insight on an old concept: Modern proteins might have arisen from a set of primordial peptides to increasingly larger subdomain-sized fragments . Based ovia a web interface to allow individual analysis . We show how PBPs contain a conserved fragment that is associated with several ligands and we highlight its homologous relationships to several other superfamilies. This conserved protein building block is examined from an evolutionary as well as a protein engineering perspective.The Fuzzle database uses SCOPe  to identThe web interface contains several updates from its predecessor version. It is now possible to search for ligands in two ways: either by its PDB (three-letter) code  or by its SMILES . SMILES https://fuzzle.uni-bayreuth.de/2.0/fragments/network/fragment/1), the number of domains that contain the fragment, the average fragment length, involved folds, and the ligands bound to the fragment. In a detailed view it is possible to visualize protein-ligand interactions in the context of fragments using the NGL Viewer (jquery.com), graph_tool (graph-tool.skewed.de), Datatables (datatables.net), and D3js (d3js.org) to visualize the data.The database has now been extended to include additional information about ligands and fragments. For example, the fragment analysis page now contains a table that includes the statistics of the fragment: A representative domain that contains each fragment .Ligands that are commonly known to be additives to crystallization screens or other experiments were excluded from our ligand-binding analysis to the PBP fragment. These additives are listed in BioLiP  and in tThe original Fuzzle database already contained a large number of conserved fragments that are shared between folds thereby illustrating a remarkable connectivity of the protein universe. The inclusion of the SCOPe 2.07 database increased the size of domains and thereby the number of pairwise hits . As withA major improvement is the addition of ligand information . It is nIn addition, we also enable visualization of networks of proteins bound to certain ligands and provide this information in a downloadable table. The table includes not only the statistics of the fragment and the most connected entry as a representative but also all ligands that are bound to the fragment. It is possible to retrieve additional information for each ligand and to directly visualize the protein-ligand interactions in the context of the selected fragment using the NGL Viewer. One can toggle interactions like π–π stacking, hydrogen bonds, compute distances, and show surface representations.Thermotoga maritima . It belongs to the PBP-like I superfamily c.93.1  . In Fuzzy c.93.1 . We obsey c.93.1 . 1,566 oThis high evolutionary connectivity can be viewed in Fuzzle when looking at the HHsearch hits to sequences of other superfamilies . tmRBP s12)-binding domain), c.23.8 : 2 domains, c.30.1 (PreATP-grasp domain): 11 domains, c.44.3 : 1 domain, c.5.1 : 3 domains, c.78.2 (Aspartate/glutamate racemase): 2 domains, c.92.3 (PrpR receptor domain-like): 1 domain, and c.93.1 (Periplasmic binding protein-like I): 16 domains.For tmRBP, Fuzzle identifies a total of 153 hits to 136 other domains  using thOne of the major goals of Fuzzle 2.0 is not only to update our platform for evolutionary analyses but also to facilitate searches for suitable fragments to design protein chimeras. In the case of d2fn9a_, parts of the protein that correspond to cluster 13 could be replaced with a homologous and structurally well-superimposed fragment of another protein that possesses an interesting function . d2fn9a_To this end, we downloaded the PyMOL session with a superposition of all cluster 13-containing domains, available from the domain-centered network view for all fragments , middle.via a few residues with the respective ligand. Two of these interactions are particularly conserved among the sequences, a Tyr or Phe residue, and a Ser, Tyr, or Ala residue (https://fuzzle.uni-bayreuth.de/2.0/ligands/detailed/d3sg0a_/139/236/), which contains overall 3 interactions with the fragment: the conserved residues Tyr168 and Ala220 , including 5 ligand-binding domains . These d12)-binding superfamily (c.23.6) is represented by two domains in the receptor set . The ligand performs most of its interactions with the fragment (https://fuzzle.uni-bayreuth.de/2.0/ligands/detailed/d1reqa2/598/687).The cobalamin  database which contains a set of evolutionarily related protein fragments that can also be used for protein design. It is known that modern proteins evolved by replicating and recombining smaller sequence fragments. Fuzzle offers the opportunity to identify these fragments and to mimic evolutionary processes in the lab as well as to build new proteins. In this updated version of Fuzzle 2.0, we enhanced the analysis tools and extended them to include detailed information about protein fragment-ligand interactions. This extension now enables the identification and analysis of ligands and their interactions with a conserved fragment. As a note of caution: since Fuzzle is based on a non-redundant dataset of SCOPe, some ligand information might be incomplete. Thus, it will be still necessary to consult the literature or other databases for an in-depth analysis of a specific protein-ligand interaction.via gene duplication from a flavodoxin-like fold (c.23) (Using a periplasmic binding protein (PBP) fragment as an example, we demonstrated the new features of Fuzzle 2.0. SCOP places this fold into a single superfamily, periplasmic binding protein-like I . Its fold consists of two similar intertwined lobes with 3 layers (α/β/α), each composed of a parallel six-stranded β-sheet with the order 213456 . There ad (c.23) . In factd (c.23) , combiniThermotoga maritima (tmRBP), a single domain protein (d2fn9a_). The domain contains many sequence similarities to other superfamilies, especially in its N-terminal region  bind different ligands at similar regions in the protein structure; however, the mode of binding can differ. This region represents a conserved fragment and therefore strengthens the hypothesis that domains contain conserved building blocks even shared by seemingly unrelated folds. This observation gives rise to the possibility to identify potential ligands that could bind to a domain. In the example described the analysis suggests that the identified fragment in d2fn9a_ could be capable of recognizing ligands B12 or INI after performing several rounds of mutations either by protein engineering or directed evolution.Overall, we believe that the new version of Fuzzle will be a valuable tool for various fields of research. On the one hand Fuzzle 2.0 allows evolutionary biologists to strengthen the evidence for common ancestry and on the other hand allows protein designers to use this information in transferring ligand binding sites into other protein scaffolds.


It is this absence of protein function that is fundamental to approaches using yeast as an in vivo test system to investigate human proteins. Functionality of the heterologous expressed proteins is connected to a quantitative, selectable phenotype, enabling the systematic analyses of mechanisms and specificity of DNA modification, post-translational protein modifications as well as the impact of annotated cancer mutations and coding variation on protein activity and interaction. Through continuous improvements of yeast screening systems, this is increasingly carried out on a global scale using deep mutational scanning approaches. Here we discuss the applicability of yeast systems to investigate absent human protein function with a specific focus on the impact of protein variation on protein-protein interaction modulation.Yeast is a valuable eukaryotic model organism that has evolved many processes conserved up to humans, yet many protein functions, including certain DNA and protein modifications, are  Saccharomyces cerevisiae and Schizosaccharomyces pombe, are key eukaryotic model organisms in molecular and cell biology research. As basic unicellular eukaryotic organisms they rely on many molecular pathways, cellular functions and biological processes, that are at their core often conserved up to humans. With 4,000 homologous genes between yeast and human, many insights into human biology are drawn from studying yeast and from approaches to humanize yeast functions. As outlined in recent reviews -eIF4G interaction . AnotherA repair . While iA repair . The netA repair , while tA repair . HoweverA repair . The relWhile the large-scale PPI mapping task as such , 167 is Figure 3B). Starr et al. used yeast surface display for characterizing the SARS-CoV-2 spike protein interaction with the human receptor ACE2 [Recently, yeast genetics in combination with deep mutational scanning was the basis of outstanding research approaches related to the SARS-CoV-2 virus pandemic (tor ACE2  and humator ACE2 . The rector ACE2 . The latabsent in yeast nevertheless causing a phenotype. On the other hand, using deep mutational scanning in protein interaction research provides a uniform, more general approach to study the impact of genetic variation on cellular protein function. However, mutations can impact several features of proteins. Therefore, an integrated combination of different yeast assays has the potential to fully characterize coding variation. Various features of proteins have been exploited in deep mutational scanning approaches quantitively assaying protein folding/abundance [de novo [Through continuous improvement of yeast systems and substantial advances in genome scale screening approaches, yeast serves as a prime tool in molecular biology and human genetics. The rapid adaption to urgent research questions like in the current SARS-CoV-2 pandemic highlights its flexibility and broad, almost universal applicability in various fields of research. Here we emphasized deep mutational scanning approaches in yeast to comprehensively characterize coding variation in particular amino acid substitutions of unknown significance in cancer proteins. These protein activities can be conveniently selected because the activity is bundance , 173, inbundance , 163, agbundance , enzymatbundance , degradabundance , transcrbundance  and phasbundance . The com[de novo , 176. De


Three-dimensional (3D) structures of protein complexes provide fundamental information to decipher biological processes at the molecular scale. The vast amount of experimentally and computationally resolved protein-protein interfaces (PPIs) offers the possibility of training deep learning models to aid the predictions of their biological relevance. We present here DeepRank, a general, configurable deep learning framework for data mining PPIs using 3D convolutional neural networks (CNNs). DeepRank maps features of PPIs onto 3D grids and trains a user-specified CNN on these 3D grids. DeepRank allows for efficient training of 3D CNNs with data sets containing millions of PPIs and supports both classification and regression. We demonstrate the performance of DeepRank on two distinct challenges: The classification of biological versus crystallographic PPIs, and the ranking of docking models. For both problems DeepRank is competitive with, or outperforms, state-of-the-art methods, demonstrating the versatility of the framework for research in structural biology. The authors present DeepRank, a deep learning framework for the data mining of large sets of 3D protein-protein interfaces (PPI). They use DeepRank to address two challenges in structural biology: distinguishing biological versus crystallographic PPIs in crystal structures, and secondly the ranking of docking models. Proteins interact with each other and other biomolecules in specific ways. Gaining knowledge on how those biomolecules interact in 3D space is key for understanding their functions and exploiting or engineering these molecules for a wide variety of purposes such as drug designhttps://www.ebi.ac.uk/pdbe/) as of Sep. 2nd 2020). Numerous machine learning methods4, and recently several deep learning techniques7, have been developed to learn complicated interaction patterns from these experimental 3D structures. Unlike other machine learning techniques, deep neural networks hold the promise of learning from millions of data without reaching a performance plateau quickly, which is computationally tractable by harvesting hardware accelerators  and parallel file system technologies. Wang et al5. have trained 3D deep convolutional networks (CNNs) on 3D grids representing protein-protein interfaces to evaluate the quality of docking models (DOVE). Gaiza et al6. have recently applied Geodesic CNNs to extract protein interaction fingerprints by applying 2D ConvNets on spread-out protein surface patches (MaSIF). Graph Neural Networks (GNNs)8, representing protein interfaces as graphs, have also been applied to predict protein interfaces7. Finally, rotation-equivariant neural networks have recently been used by Eisman et al. on point-based representation of the protein atomic structure to classify PPIs9. One outstanding illustration of the potential of deep neural networks in structural biology is the recent breakthrough in single-chain protein structure predictions by AlphaFold212 in the latest CASP14 . Predicting the 3D structure of protein complexes remains however an open challenge: in CASP14 no single assembly was correctly predicted unless a known template was available. This calls for open-source frameworks that can be easily modified and extended by the community for data mining protein complexes and can expedite knowledge discovery on related scientific questions.In the past decades, a variety of experimental methods  have determined and accumulated a large number of atomic-resolution 3D structures of protein-protein complexes (>7000 non-redundant structures in the PDBe databank (13) to condensed matter physics (NetKet14) and have significantly contributed to the rapid adoption of machine learning techniques in these fields. They have stimulated collaborative efforts, generated new insights, and are continuously improved and maintained by their respective user communities.Data mining 3D protein complexes presents several unique challenges. First, protein interfaces are governed by physico-chemical rules. Different types of protein complexes  may have different dominant interaction signatures. For example, some complexes may be driven by hydrophobicity, and others by electrostatic forces. Second, protein interactions can be characterized at different levels: Atom-atom level, residue-residue level, and secondary structure level. Third, protein interfaces are highly diverse in terms of shapes, sizes, and surface curvatures. Finally, efficient processing and featurization of a large number of atomic coordinates files of proteins is daunting in terms of computational cost and file storage requirements. There is therefore an emerging need for generic and extensible deep learning frameworks that scientists can easily re-use for their particular problems, while removing tedious phases of data preprocessing. Such generic frameworks have already been developed in various scientific fields ranging from computational chemistry  format onto 3D grids. DeepRank applies 3D CNN on these grids to learn problem-specific interaction patterns for user-defined tasks. The architecture of DeepRank is highly modularized and optimized for high computational efficiency on very large datasets up to millions of PDB files. It allows users to define their own 3D CNN models, features, target values , and data augmentation strategy. The platform can be used both for classification, e.g., predicting an input PPI as biological or a crystal artifact, and regression, e.g., predicting binding affinities.Here we introduce DeepRank, a generic deep learning platform for data mining protein-protein interfaces (PPIs) based on 3D CNNs. DeepRank maps atomic and residue-level features calculated from 3D atomic coordinates of biomolecular complexes in Protein Data Bank17 and PISA18, which respectively reach an accuracy of 74 and 79%. We then present the performance of DeepRank for the scoring of models of protein-protein complexes generated by computational docking. We show here that DeepRank is competitive and sometimes outperforms three state-of-the-art scoring functions: HADDOCK19, iScore21, and DOVE5.In the following, we first describe the structure of our DeepRank framework. To demonstrate its applicability and potential for structural biology, we apply it to two different research challenges. We first present the performance of DeepRank for the classification of biological vs. crystallographic PPIs. With an accuracy of 86%, DeepRank outperforms state-of-the-art methods, such as PRODIGY-crystalDeepRank is built as a Python 3 package that allows end-to-end training on datasets of 3D protein-protein complexes. The overall architecture of the package can be found in Supplementary Note 22, our fast and versatile PDB file parser using Structured Query Language (SQL), to identify interface residues between the two chains. Interface residues are by default defined as those with any atoms within a 5.5 Å distance cutoff (configurable) from any atom of another chain . The grid size and resolution can be adjusted by users to suit their needs. Fig. 23, are integrated into DeepRank. These include ligand RMSD (Root Mean Square Deviation)24, interface RMSD (iRMSD)24, FNAT (Fraction of Native Contacts)24, CAPRI quality labels24, and DockQ score25. DeepRank leverages pdb2sql22 to perform these calculations efficiently.Flexible target value definitions and calculations. Users may easily define problem-specific target values for their protein structures. For the scenario of computational docking, standard metrics to evaluate the quality of a docking model, i.e., by comparison to a reference structure, as used in the CAPRI Efficient data storage in HDF5 format. Dealing with sometimes tens of millions of small-size PDB files with rich feature representations presents a challenge both for the file system and for efficient training of deep neural networks. DeepRank stores the feature grids in HDF5 format, which is especially suited for storing and streaming very large and heterogeneous datasets. The general structure of the HDF5 generated by DeepRank is represented in Fig. Data pre-processing and featurization:26. The general network architecture used in this work is illustrated in Fig. To train the neural network, DeepRank relies on the popular deep learning framework PyTorch18 and PRODIGY-crystal17 show the highest prediction performances. PISA is based on six physicochemical properties: Free energy of formation, solvation energy gain, interface area, hydrogen bonds, salt-bridge across the interface, and hydrophobic specificity. PRODIGY-crystal is a random forest classifier based on structural properties of interfacial residues and their contacts16.X-Ray crystallography is one of the most important experimental approaches to determine 3D structures of protein complexes . This experimental technique first requires the proteins to be crystallized and then exposed to X-rays to obtain their structures. When it comes to structures of complexes, the resulting crystals often contain multiple interfaces, some of which are biologically relevant and some are mere artifacts of the crystallization process, the so-called “crystal interfaces” Fig. . Disting27, which consists of 2828 biological interfaces and 2911 crystal ones, only using Position Specific Scoring Matrix (PSSM) features. Each structure was first augmented by random rotation (30 times) before training. Early stopping on the validation loss was used to determine the optimal model  remains a major challenge in docking.Computational docking is a valuable tool for generating possible 3D models of protein complexes and provides a complementary alternative to experimental structure determination. Given the 3D structures of individual proteins, docking aims at modeling their interaction mode by generating typically tens of thousands of candidate conformations (models). Those models are ranked using a scoring function to select the correct (near-native) ones Fig. . Althoug19 to generate a set of docking models of various qualities for the docking benchmark v5 (BM5) set32, including both rigid-body docking, flexible docking, and final refined docking models. In this work, we focused on 142 dimers for which near-native models were available in the generated data sets, excluding all antibody-antigen complexes.We used HADDOCK3 with 1 Å resolution. The network was trained on over 300,000 labeled docking conformations to classify models as near-native or wrong. The DeepRank score, i.e., the predicted likelihood of a model to be a wrong model, was then used to rank the models for each case. To ensure objective evaluations, we conducted 10-fold cross-validation at the level of complexes, i.e., all models for a given complex are used exclusively in either training, validation, or test sets in each fold.We trained the 3D CNN , the trained network (provided in Data Availability) shows satisfying performance leading to a better classification than competing methods, PRODIGY-crystal and PISA. This improvement is due to the use of evolution information through the PSSM and from the use of deep neural network that are capable of learning the subtle differences between the interaction patterns of the two types of interfaces.23. While DeepRank does not significantly outperform HADDOCK iScore nor DOVE on selected cases from previous rounds of CAPRI, it does identify different conformations as being near-native , DeepRank performs especially well on scoring rigid-body HADDOCK models, outperforming the HADDOCK’s rigid-body-specific Score Fig. . Since r35 that naturally incorporate translation- and rotation-invariance and hence avoids the data augmentation that is sometimes needed when using 3D CNN. The use of non-structured geometric data such as graphs7, surfaces6, or point clouds as input, offer additional opportunities for the future development of DeepRank. For example, MaSIF6 exploits geodesic CNN to extract protein interaction fingerprint and therefore only requires data augmentation in 2D instead of 3D. However, the data preprocessing required by MaSIF to determine protein surface patches, calculate polar coordinates and map the features, is about 48 times more computationally demanding and 7 times more memory demanding than computing all the 3D grids required by DeepRank  so that 3D CNN can be applied. These structured 3D grids could also be used with equivariant neural networksD grids sIn summary, we have described an open-source, generic, and extensible deep learning framework for data mining very large datasets of protein-protein interfaces. We demonstrated the effectiveness and readiness of applying DeepRank on two different challenges in structural biology. We expect DeepRank to speed-up scientific research related to protein interfaces by facilitating the tedious steps of data preprocessing and reducing daunting computational costs that may be associated with large-scale data analysis. Its modularized and extendable framework bears great potential for stimulating collaborative developments by the computational structural biology community on other protein structure-related topics and will contribute to the adoption and development of deep learning techniques in structural biology research.wk of atom k (or residue) to a given grid point follows a hence a Gaussian distance dependence:vk is the value of the feature, rk the  coordinate of atom k, and r the position of the grid point. For atomic features the standard deviation σ is taken as the van der Waals radius of the corresponding atom. In case of residue-based features, rk is taken as the position of the alpha-carbon atom of the residue and σ as the van der Waals radius of that atom.The atomic and residue features are mapped on a 3D grid using Gaussian functions. The contribution vk is defined as the sum of the all the interaction terms between atom (residue) k and its contact atoms(residues): vkl is the interaction term between atoms(residues) k and l.For pairwise features, such as interaction energy terms, the value of 27 consists of 2831 biological interfaces and 2912 crystal ones. Entries 2uuy_1, 2p06_1 and 3dt5_1 were excluded due to all zero values of PSSM information content (IC) and 1gc2_5 was excluded due to failed residue density calculation. We thus used 2828 biological interfaces and 2911 crystal ones, which was used to create the training (80%) and validation (20%) sets while maintaining the balance between positive and negative data. The training set was then further augmented by randomly rotating each complex 30 times. The DC dataset28 was used as a test set. It contains 80 biological and 81 crystal interfaces, excluding 3jrz_1 due to failed residue density calculation. All entries of both the MANY and the DC dataset have been previously refined16 using the refinement protocol of our HADDOCK web server36.Data Sets: The original MANY dataset4. We used 10 × 10 × 10 Å3 grids with a resolution of 3 Å. This low-resolution grid is sufficient to capture the variation of the PSSM which are residue-based features and thus less finely defined than atomic-based features.Training and evaluations: The architecture of the CNN used in our experiments is shown in Supplementary Fig. 19. Docking in HADDOCK follows three stages: (1) it0: rigid-body docking, (2) it1: semi-flexible refinement by simulated annealing in torsion angle space, and (3) itw: final refinement by short molecular dynamics in explicit solvent (default: water). We used HADDOCK37 to systematically generate a set of docking models for the docking benchmark v5 set32, BM5. In order to generate a suitable amount of near-native models both guided and ab-initio dockings were performed following five different scenarios: (1) Refinement of the bound complexes , (2) guided docking using true interface defined at 3.9 Å cutoff as ambiguous interaction restraints , (3) guided docking using true interface defined at 5.0 Å cutoff , (4) ab-initio docking with center of mass restraints , and (5) ab-initio docking with random surface patch restraints . Scenarios 2–5 are unbound-unbound docking, providing real-life challenges  to DeepRank. BM5 consists of 232 non-redundant cases . In total, we generated ~5.84 million HADDOCK models  corresponding to 11 TB of data (3D coordinates plus feature grids) in HDF5 format. In this study, we focused on 142 dimers, i.e., cases with two chains (1IRA was excluded due to the lack of PSSM for the short chain A).Data sets: The dataset for scoring was generated using HADDOCK, an integrative modeling softwareAs the models were not clustered, a large degree of redundancy exists in the dataset, with very similar conformations being represented multiple times. After experimentation, we observed that considering only a subset of about 420 K models (~3000 models per complex) was sufficient to accurately represents the available data  of a dimer PPI in the BM5 set, that is about 909 Å2. We evaluate the BSA as:Network architectures and weight optimizations: The architecture of the CNN in our experiment is shown in Supplementary Fig. Because our dataset is highly imbalanced , for training the network we assigned class weights to the loss function, proportional to the class sizes, that is, the class weights are 0.071 and 0.929 for the negatives and positives, respectively so that the loss function penalizes equally errors made on the positive and on the negative dataset.Training, validation, and testing: We performed a 10-fold cross-validation on the BM5 set. In every fold, out of 142 complexes, we used models from 114 complexes for training, 14 complexes for validation, and 14 complexes were reserved for testing. In this way, in total 140 out of the 142 complexes were used in the test sets . The training, validation, and testing sets are disjunctive at complex-level so that complexes in the testing set are never seen by the neural networks during its training and validation. In the end, we trained one network for each fold , and the set-aside independent test set was evaluated on this network.The network is trained to perform positive/negative classification of models, outputting two scores as predicted probability of an input model being near-native or wrong. We then ranked the docking models based on their predicted scores of being wrong models to be consistent with widely-used energy-based scoring functions: i.e., a low prediction score indicates that the function predicts this model is of high quality. For the final analysis  is the number of hits  among top models and M the total number of near-native models for this case. The Hit Rate was calculated for each individual case in our test set and statistics across the different cases  were calculated. As the total number of models varies between cases, these statistical values were only evaluated from K = 1 to K = Nmin where Nmin is the smallest number of models that all cases have.Evaluation metrics: Hit Rate and Success Rate are used to evaluate the performance of scoring functions. The Hit Rate is defined as the percentage of hits (models with iRMSD ≤4 Å) in the top ranked models for a specific complex:nsuccessful_cases(K) is the number of cases with at least one near-native model among top models, and N is the total number of cases.The Success Rate shown in Supplementary Fig. Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Studies on small quantity, highly complex protein samples, such as salivary pellicle, have been enabled by recent major technological and analytical breakthroughs. Advances in mass spectrometry-based computational proteomics such as Multidimensional Protein Identification Technology have allowed precise identification and quantification of complex protein samples on a proteome-wide scale, which has enabled the determination of corresponding genes and cellular functions at the protein level. The latter was achieved via protein-protein interaction mapping with Gene Ontology annotation. In recent years, the application of these technologies has broken various barriers in small-quantity-complex-protein research such as salivary pellicle. This review provides a concise summary of contemporary proteomic techniques contributing to (1) increased complex protein (up to hundreds) identification using minute sample sizes (µg level), (2) precise protein quantification by advanced stable isotope labelling or label-free approaches and (3) the emerging concepts and techniques regarding computational integration, such as the Gene Ontology Consortium and protein-protein interaction mapping. The latter integrates the structural, genomic, and biological context of proteins and genes to predict protein interactions and functional connections in a given biological context. The same technological breakthroughs and computational integration concepts can also be applied to other low-volume oral protein complexes such as gingival crevicular or peri-implant sulcular fluids. The oral cavity serves as a window to the human body where observations made upon analysis of oral fluids could potentially reflect the health, disease or stressful psychological status of the person being investigated. For instance, saliva carries precise developmental and biological information that defines the function of multiple organs including oral and periodontal tissues . SalivarThe salivary pellicle is a proteinaceous film, that forms within seconds after tooth eruption or prophylaxis , and is The term “proteomics” refers to a global technique to study proteins. This review will summarize how state-of-the-art proteomic approaches can be employed to study low abundance protein complexes, such as salivary pellicle. Topics include sample preparation methods, protein separation, identification and quantification, and functional annotation. To complete the review, a brief overview of factors crucial to salivary pellicle sampling are also included.Salivary pellicle proteins derived from saliva, are highly dynamic and variable due to the physiology and bioactivity of host salivary protein biosynthesis and post-secretion modification . Hence, Over the years, both in vitro and in vivo studies have been conducted to investigate salivary pellicle proteins. In situ salivary pellicle collection has recently been on the rise . The majSalivary pellicle proteins exist in small amounts (µg levels) , therefoIntraoral appliances with mounted specimens for in vivo salivary pellicle studies were introduced by Turner . SectionTo date, single-step analytical techniques or instruments, capable of identifying and quantifying complex protein samples such as salivary pellicle are not available . MultiplOne-dimensional electrophoresis, also known as sodium dodecyl-sulphate polyacrylamide gel electrophoresis (SDS-PAGE) separates denatured protein samples by molecular mass, which enables subsequent in-depth protein analysis by mass spectrometry, and simultaneously filtering out the low molecular weight impurities . Two-dimPresently, the applications of gel-based protein separation are greatly reduced in use with the development of mass spectrometry (MS) . Even thLiquid chromatography-based separation techniques involve physical separation of peptide fragments obtained by protease digestion of protein extracts and often coupled with mass spectrometry analysis . In liquAdvanced protein identification techniques with multidimensional protein separation approaches, known as multidimensional protein identification technology (MudPIT), are automated high-throughput protein identification methods and have been widely used in large-scale protein extraction from various organisms . Fang etSubsequent to salivary pellicle sample collection and separation, researchers must select the appropriate procedure for peptide fragmentation and identification. The commonly adopted instrumentation systems for salivary pellicle protein identification are MALDI-TOF-MS  and ESI-The mass spectrum rummaging for protein identification could be achieved using different strategies, such as MS/MS database searching , amino aTooth surface salivary pellicle protein quantification between two or more physiological states would be of great importance but remains a challenge. The electrophoresis gel staining approach helps differentiate the quantities of multiple proteins present in a single 2-DE gel, however, the low level of salivary pellicle proteins largely limits their visualization on 2-DE maps . WhereasMost LC-MS based quantifications are applied in relative quantification of protein or peptide abundance between samples of interest, and the basic strategies fall into two broad categories: those that create stable isotope labelling tags on protein/peptides that could later be analysed by mass spectrometry, and the label-free methods that are based on the comparison of related peak intensities or spectral counts between MS runs  44]..44].The salivary pellicle is only amenable to post-biosynthetic labelling for quantitative LC-MS, which leaves two broader categories of in vitro labelling methods with chemical or enzymatic derivations . Despite0) or eight deuterium atoms (D8) [0 and D8 ICAT reagent, and the relative abundance of peptides and the corresponding proteins in both samples can be determined. Siqueira, et al. [The first robust and universal chemical labelling method developed for LC-MS quantification is an isotope-coded affinity tag (ICAT), which selectively alkylates protein lysis with chemical reagents that contain a linker region with a biotin tag of either none (Doms (D8) . The suc, et al.  detected, et al. .After isobaric tags for relative and absolute quantitation (iTRAQ) labelling method were introduced, it gained popularity as an alternative to ICAT with simplified analysis and increased analytical precision and accuracy . The mosAs an alternative to labelling quantification, label-free quantification has wider application with lower expense, is less time consuming and not constrained to a limited number of group comparisons, which makes it a preferable method, especially in large sample size clinical proteomic evaluations . The labm/z) of peptide fragments are measured at a specific retention time in a sensitivity concentration range [The peak intensity method achieves relative quantification through calculating peak intensity/area under the curve using the chromatographic signal intensity of peptide fragment peaks in MS. Typically, the mass to charge ratio  curation, which is developed for standardized large-scale protein annotations through assigning a structured GO vocabulary set to well-characterized proteins . Three gThe advance of high throughput proteomics has resulted in an enormous amount of data, hence, the traditional methods of investigating individual proteins are no longer adequate for incorporating intricate molecular details of dynamic pathophysiological status. The computational approaches are thus developed to bridge the gap between data acquisition and interpretation.As most proteins exert their functions through interactions with other proteins , one wayAs more protein interactions are identified, two-dimensional protein networks can be constructed with the assistance of software platforms, such as GeneMANIA and STRING, to visualize the dynamic interactions between proteins of interest . As menthttps://david.ncifcrf.gov/, accessed on 28 February 2021), KEGG , and Reactome , are available for users to search their protein data for any statistical enrichment in a given biological phenomenon to gain insights into the biological condition being studied. For instance, Xie et al. [For most of the investigations, the information of differential protein expression in various biological conditions is of great value to gain insights into underlying disease pathogenesis. However, the unprecedented high-resolution data generated by recent advanced high throughput mass spectrometry put great challenges in acquiring correspondingly high-resolution data annotation. Currently, applications of computational strategies in proteomics could address this question in different annotation levels, ranging from a global view of all the available interactions to highly formalized pathways. A variety of online resources, such as DAVID (e et al.  tested te et al. .Mammalian saliva carries a precise developmental and biological program that defines the function of multiple organs. Saliva is at the interface of dynamic interactions between microbial ecosystems and oral immune cells . DysreguFor instance, in patients who suffered from gastroesophageal reflux and erosive tooth wear, in-depth proteomic profiling of the affected salivary pellicles revealed distinct protein profile, especially membrane-binding proteins like lysozyme C, antileukoproteinase, cathepsin G, neutrophil defensins and basic salivary proline-rich proteins while subunits of haemoglobin, albumin and isoforms of cystatin appeared enriched . In headDespite implications of findings from the above two studies by the Brazilian group of Buzalaf are yet to be determined, they showcased the utilities of contemporary salivary pellicle proteomic approaches alongside deciphering the biological relevance of salivary pellicle in health and disease. Interesting, a report by Isola et al.  reportedAs evidenced from research reports investigating effects of irradiation therapy or gastroesophageal reflux etc. on salivary pellicle, the minimally invasive collection, easy access, and the negligible risk to the donor of saliva and/or saliva pellicle proteomic investigation possess major advantages over other biofluids, such as blood, , which mThe complexity of the oral environment, the dynamic protein adsorption process and relatively low protein quantities available for extraction and characterization greatly limit investigations of tooth surface salivary pellicle. Mass spectrometry-based proteomics has become the method of choice for protein complex analyses, yet the technical and inter-individual variations, the comparison of the datasets of the same salivary pellicle proteome between laboratories could result in less agreement . MoreoveThe dynamics of the oral environment and limited amounts of protein that can be collected in vivo pose great challenges for salivary pellicle proteomic endeavours. To date, reproducible and consistent salivary pellicle protein identification and comprehensive characterization are rather scarce and remain protracted. Tremendous progress has been made in the field of proteomics and bioinformatics in the past decade and has greatly enlarged the original scope of protein analysis, which is not only limited to simple cataloguing of protein species but also can characterize proteins in a functional context with a deeper insight into processes in combination of proteomic data with microbiomics, and metabolomics data. Now with the assistance of promising methods and technologies, a revolutionary advance is likely in proteomic investigations of salivary pellicle and other low-volume highly-complex protein samples such as gingival crevicular or peri-implant sulcular fluids.


Electron microscopy (EM) continues to provide near‐atomic resolution structures for well‐behaved proteins and protein complexes. Unfortunately, structures of some complexes are limited to low‐ to medium‐resolution due to biochemical or conformational heterogeneity. Thus, the application of unbiased systematic methods for fitting individual structures into EM maps is important. A method that employs co‐evolutionary information obtained solely from sequence data could prove invaluable for quick, confident localization of subunits within these structures. Here, we incorporate the co‐evolution of intermolecular amino acids as a new type of distance restraint in the integrative modeling platform in order to build three‐dimensional models of atomic structures into EM maps ranging from 10–14 Å in resolution. We validate this method using four complexes of known structure, where we highlight the conservation of intermolecular couplings despite dynamic conformational changes using the BAM complex. Finally, we use this method to assemble the subunits of the bacterial holo‐translocon into a model that agrees with previous biochemical data. The use of evolutionary couplings in integrative modeling improves systematic, unbiased fitting of atomic models into medium‐ to low‐resolution EM maps, providing additional information to integrative models lacking in spatial data. The generation of this data has led to an abundance of information in databases. UniProt, for example, is flooded with over 180 million sequences, most of which still lack complete structures or annotations.ab initio protein structure prediction.Residue co‐evolution as it applies to structural biology is based on the idea that amino acids close in three‐dimensional (3D) space within a protein structure exhibit statistical coupling through the course of evolution, such that mutations in one may occasionally be accompanied by mutations to the other to compensate for the change without altering the 3D structure.Beyond intramolecular patterns, evolutionary coupling has been used to detect intermolecular interactions. This has been demonstrated between amino acids and nucleotides as well as nucleotide/nucleotide interactions in RNA‐protein complexes.via statistical testing and validated against biological data such as genetic or biochemical experiments. Additional distance restraints derived from evolutionary coupling data have the potential to provide useful information for systems with few crosslinks and could potentially be used as the only distance restraints when no other data are available.When combined with other experimental techniques, evolutionary couplings have the ability to improve structural predictions. The Integrative Modeling PlatformDuring interpretation of low‐resolution EM maps, the reconstruction is often treated as an envelope where crystal structures or homology models can be docked into regions following either segmentationWe demonstrate that information from evolutionary couplings can be used as a distance restraint in the integrative modeling of atomic structures into low‐resolution electron microscopy maps. We present a method for selecting intermolecular couplings to be used for integrative modeling based on internal structural controls as well as a weighting of couplings that is tolerant of false‐positive pairs. We show that a lack of distance restraints from evolutionary couplings results in a poor structural prediction with the quinol‐fumarate reductase complex. We investigate evolutionary couplings in dynamic protein complexes that experience conformational changes within the macromolecular machine and within individual subunits using the BAM complex. Finally, we build an integrative model of the bacterial holo‐translocon using evolutionary couplings between the six subunits, a 14 Å cryo‐EM map, and a combination of X‐ray crystal structures and high‐confidence homology models. We validate our model against another independently derived model as well as extensive data regarding the mechanism, chemistry, and physics of the complex.22.1E. coli complexes with stoichiometries ABC, ABCD, and ABCDE. Complexes for modeling were selected based on Neff rating and shape of the coupling plot. For example, complexes without high scoring intramolecular couples cannot identify intermolecular couples. Information on the complexes modeled can be found in Table We used the browse stoichiometry search in the Protein Data Bank to select 2.2https://github.com/marcottelab/CoEVxIMP. We do an all‐by‐all coupling analysis for all subunits in the protein complex. For example, if the complex has subunits ABC, we calculate couplings between three groups: AB, AC, and BC. Likewise, ABCDE couplings are calculated between 11 groups. A complex of size N has couplings between N! groups. For couplings between each protein pair, we take the top 50,000 coupling scores (cn scores) of all residue pairings; beyond this, the couplings would not pass any selected threshold. We selected a large number of couplings because our method requires us to sample both positive and negative examples as part of our internal structure‐based calibration. Table We computed the evolutionary coupling analysis using the EV coupling Python framework.2.3x¯±2s and cn score greater than the 10‐Å cutoff, c0, was determined to be an intermolecular coupling between identical subunits. These couplings were then removed from the intramolecular structural calibration and a new 10‐Å cutoff, c1, was calculated. The process was then repeated to identify more intermolecular contacts using the new cutoff until cn remains constant.We handle more complex stoichiometries, such as A2B in PDB: 4I98, by separating intramolecular contacts from intermolecular contacts of the same protein. We calculate a rolling mean and standard deviation of intramolecular distance across 100 coupling increments. We use the moving average of 100 couplings so that there is a larger sample size for the standard deviation calculation, which gives a better representation of the data. Any distance that was greater than 2.4We selected complexes for integrative modeling with IMP (Integrative modeling platform) based on the signal from evolutionary couplings. Complexes that showed no correlation within intramolecular contacts that is, low‐scoring intramolecular pairs that are close in distance are not reliable for intermolecular interpretation. The low signal can be a result of not having enough sequences or even simply too much variation in sequences. We selected five protein complexes that fit these criteria, PDB IDs: 1FFT, 1L0V, 5MRW, 5D0O, and 5D0Q.We used the Integrative Modeling Platform Python modeling interfaceEach protein complex was subjected to three different modeling runs. The first used intermolecular interactions predicted from the 10‐Å score cutoff couplings, the second used 15‐Å score cutoff couplings, and the third used the 20‐Å score cutoff couplings. The minimum distance for the restraint was set to 0 Å and the maximum distance was the cutoff score distance used plus one standard deviation. This maximum distance was also used in defining the true and false positive predicted couplings ). Each residue pairing distance restraint was weighted as 10 x cn score. All runs were optimized using Monte Carlo sampling and score based on excluded volume, sequence connectivity, evolutionary coupling distance, and EM map. Table 2.5We validated our models using the sampling exhaustiveness approach.2.6E. coli YidC crystal structure (PDB ID: 6AL2)The bacterial holo translocon (HTL) is made up of seven subunits: SecY, SecE, SecG, YidC, SecD, SecF, and YajC. Our modeling includes all subunits with the exception of YajC due to the small size of YajC and its suspected instability within the HTL complex. Full‐length YidC (P25714), SecD (P0AG90), SecE (P0AG96), and SecG (P0AG99) were modeled using I‐TASSER.We used the 10‐Å score cutoff for the selection of intermolecular couplings. In total 15 couplings were used between six subunit pairs, in which each subunit had at least one coupling to another subunit. Each coupling was weighted according to the method described above. We used the 14‐Å resolution cryo‐EM map (EMDB‐3506) for our modeling of the HTL. The map was approximated by a Gaussian mixture model (GMM) of 50 components. Each atomic model was approximated by a GMM for every 20 residues. 200,000 models were computed from 10 initial positions. Clustering based on scores gave a high‐scoring cluster of 10,000 models that satisfied the restraints used in the sampling. Clustering the models selected gave a sampling precision of 37 Å with 90.27% of the high‐scoring models included in the final cluster.3We were interested in utilizing co‐evolutionary information that is available in the form of sequence data to guide the modeling of molecular assemblies. Specifically, we wished to develop a systematic method for fitting atomic structures into medium‐ to low‐ resolution electron microscopy maps using information from evolutionary coupling data to illuminate subunit orientation and protein–protein interaction sites. To do this, we focused on building a distance‐based restraint for evolutionary couplings into the Integrative modeling platform (IMP) that can use several lower‐scoring couplings but is still tolerant of some false‐positive pairs.3.1The determination of a score cutoff is essential when making intermolecular contact predictions based on evolutionary coupling data. This is due, in part, to the nature of the evolutionary coupling analyses that look at every pair of amino acids, both intra‐ and intermolecular, and assign a score based on the assumption that most pairs are not coupled, and those that are, are outliers that lie in the tail of the distribution.E. coli ubiquinol oxidase complex . We hypothesized that this method could be used to identify both the obvious outliers and more subtle contacts.We tested this approach by applying it to the symmetric condensin Smc homodimer in the context of their interaction with the protein kleisin, forming a 2:1 Smc:kleisin assembly3.3Incorporating the evolutionary coupling data into IMP involves expressing the pairs as a weighted distance restraint. We use our internal control method to select the score cutoff for each protein pair. Each of the couplings is represented as a basic distance restraint in IMP, where it is weighted based on the cn score, weighting high scoring couples more heavily in the model scoring. It is worth mentioning that while the lower score cutoffs may increase false‐positive contacts, this is balanced by the weighting of each coupling, and the distribution outliers will still be given a larger weight in the scoring function Figure . To evalWe show the importance of including the evolutionary coupling distance restraint in our model for quinol‐fumarate reductase (PDB: 1L0V) Figure . We were3.4E. coli BAM complex, again, using only X‐ray crystal structures, evolutionary couplings, and 10‐Å low‐pass filtered PDB models to simulate EM maps. The BAM complex exists in a lateral‐open state (PDB: 5D0Q) and an inward‐open state (PDB: 5D0O) where the inward‐open state has conformational shifts of each protein and includes an additional subunit.To assess the ability of the evolutionary coupling restraints to capture protein complex dynamics, we built integrative models for the highly flexible 3.5After evaluating the accuracy of combining evolutionary distance restraints and EM maps, we were interested in using this method to systematically model the structure of the bacterial holo‐translocon (HTL). The bacterial HTL is an assembly of three subcomplexes ‐SecYEG, SecDF‐YajC, and YidC. It is responsible for protein secretion across the membrane and membrane protein insertion. While partial structures are available for each of the subcomplexes, capturing the structure of the full complex is difficult due to the transient association of the subcomplexes.de novo model building, it still provides spatial information regarding the shape of the complex.Recently, a 14‐Å resolution cryo‐EM map for the bacterial HTL was published. While this map cannot be used for In addition to validating our model against the cryo‐EM map used, there is agreement between a high‐scoring model from the final ensemble and the intermolecular evolutionary couplings used in the modeling Figure . We usedSecYEG is believed to form the membrane pore that works with YidC to insert hydrophobic protein segments into the lipid bilayer, which is accelerated by SecDF‐yajC. When the probability densities for each of the subunits are combined into their prospective subcomplexes, we see a distinct localization of the subcomplexes, a feature that was not an input to the model Figure . A hydro4Here, we introduce an integration of evolutionary coupling data into an integrative modeling framework. Many previous reviews on integrative structural modeling methods have suggested the use of evolutionary coupling data would greatly benefit the models as a source of additional input information.In building the evolutionary coupling distance restraint, it was imperative that we had a universal method for selecting the residue pairs from the coupling data, and that we were somewhat judicious in doing so, as to allow for the appropriate selection and weighting of lower‐scoring pairs. Previous use of evolutionary coupling data has shown to be effective in supplementing lower resolution data such as SAXS and EM maps; however, the selection criterion varies across studies.As a result of our selection method, we will likely have more false‐positive couplings as compared to other more conservative score cutoffs. In the early days of residue co‐evolution for protein–protein interactions, the goal was to maximize the precision of the prediction, which may be done by using a stricter cutoff. The authors; however, urge users to explore lower cutoffs.While the co‐evolutionary information stored in protein sequences is rich for predicting these pairwise contacts, it is important to note that the contact predictions are dependent on the depth of the sequence data. A limitation exists for those proteins that do not have sufficient coverage, as the recommended number of sequences is about one‐third of the length of the sequence of interest. Nonetheless, there are now metagenomics databasesIn our implementation of the evolutionary coupling distance restraint in IMP, we explore the combination of the restraint with medium‐ to low‐resolution EM maps as a systematic method of stitching together protein subunits. We show that despite a lack of topological features in the maps, the information provided is sufficient to produce a convergent, accurate model of the structure of a protein complex Figure . This isUsing our method, we built an integrative model for the bacterial holo‐translocon. While this model was built exclusively using the evolutionary coupling data at the 10‐Å score cutoff and 14‐Å resolution cryo‐EM map, there are extensive experimental studies that support the model. The study that generated the cryo‐EM map we used built a model for the complex using SANS data, the cryo‐EM map, and biochemical information.via chemical crosslinks and deletion analysis. Deletion studies in YidC have shown that YidC interacts directly with SecDF, in particular with SecF.Our model is also supported by previous protein–protein interactions studies In this work, we have developed a distance restraint based on evolutionary coupling data to be used in IMP. We have rigorously benchmarked appropriate thresholds that account for noise in the data through an internal, structural score calibration. We have tested this approach on complexes of varying complexity including complexes with conformational changes and additional subunits. Finally, we applied this method to building an integrative model of the bacterial holo‐translocon that agrees with previous experimental data. We believe that because this restraint is built using co‐evolutionary information and does not require any additional experiments, it will be useful for assembling atomic structures into medium‐ to low‐resolution EM maps, providing additional restraints to integrative models when required.Caitlyn L. McCafferty: Conceptualization; data curation; formal analysis; investigation; methodology; software; visualization; writing‐original draft. David W. Taylor: Conceptualization; formal analysis; funding acquisition; supervision; writing‐review & editing. Edward M. Marcotte: Conceptualization; formal analysis; funding acquisition; supervision; writing‐review & editing.AppendixS1: Supporting informationClick here for additional data file.


Hereditary spastic paraplegia (HSP) and primary lateral sclerosis (PLS) are rare motor neuron diseases, which affect mostly the upper motor neurons (UMNs) in patients. The UMNs display early vulnerability and progressive degeneration, while other cortical neurons mostly remain functional. Identification of numerous mutations either directly linked or associated with HSP and PLS begins to reveal the genetic component of UMN diseases. Since each of these mutations are identified on genes that code for a protein, and because cellular functions mostly depend on protein-protein interactions, we hypothesized that the mutations detected in patients and the alterations in protein interaction domains would hold the key to unravel the underlying causes of their vulnerability. In an effort to bring a mechanistic insight, we utilized computational analyses to identify interaction partners of proteins and developed the protein-protein interaction landscape with respect to HSP and PLS. Protein-protein interaction domains, upstream regulators and canonical pathways begin to highlight key cellular events. Here we report that proteins involved in maintaining lipid homeostasis and cytoarchitectural dynamics and their interactions are of great importance for UMN health and stability. Their perturbation may result in neuronal vulnerability, and thus maintaining their balance could offer therapeutic interventions. Upper motor neurons (UMNs) are an important component of the motor neuron circuitry ,3,4,5,6.Almost all cellular events that take place inside the neurons require protein-protein interactions. Each protein binds to a distinct set of proteins and require interaction partners to perform its function. When genes that code for proteins are mutated, their protein products also become mutated. Depending on where the mutation is, the protein may lose some or all of its function. The mutation may not allow them to interact with their usual interaction partners, and thus fail to contribute to their normal cellular events. Not all proteins are equally essential for a given cellular event. Some protein’s activity may be compensated by others. Therefore, mutations in the genes that code for those proteins may not have an imminent impact on neurons. However, when a protein is so critical for the proper function of a given cellular event, even a slight reduction in its function, or a change in its ability to interact with other proteins would lead to debilitating and severe consequences for the neuron, leading to their vulnerability and progressive degeneration. Interestingly, most of the mutated genes identified in patients code for these significant players, with irreplaceable functions. In an effort to reveal the key cellular events that are essential for the health and function of UMNs, we must reveal the identity of key players and the cellular events they belong.Neurons become vulnerable to degeneration when they fail to perform cellular events that are required for their proper function . For exaHere, we investigated the protein products of mutated genes detected in HSP and PLS patients and their interactions. The proteins are determined by a stringent inclusion criteria and IPA , a large-data management tool box is utilized to study the presence of canonical pathways they are involved in, as well as upstream regulators and key cellular events that are highlighted by the presence of these proteins. Our unbiased computational protein-protein interaction studies revealed the presence of a signature of cellular events that are particularly important for the health and function of UMNs, suggesting that when these are perturbed UMNs may become vulnerable to degeneration. We also uncovered the list of growth factors UMNs most respond to and potentially are important for their improved health.http://circos.ca/ accessed on 27 April 2021)Previously published results and open public resources and databases, such as OMIM and Pubmed were used to compile the mutations detected in HSP and PLS patients (end date March 2021). For determining the list of proteins that bind to the protein product of the mutated genes, large data management tool box ingenuity pathway analyses  was used as previously reported . IPA enahttps://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis accessed on 27 April 2021). Statistical analyses use Fisher’s exact test. In summary, the significance value associated with functional analysis of a dataset is a measure of the likelihood that the association between the experimental group  and the given pathway is due to random chance or not. For the IPA analysis, the ratio is calculated by taking the number of genes from the HSP-PLS protein list that participate in a Canonical Pathway and dividing it by the total number of genes in that Canonical Pathway. The ratio is therefore useful for determining which pathways overlap the most with the HSP-PLS protein list. The p-value measures how likely the observed association between a specific pathway and the dataset would be if it was only due to random chance. p < 0.05 or  is considered significant and that the ratio of proteins in that canonical pathway cannot be explained by randomness. The p-value is calculated by considering: (1) the number of functions/pathways that participate in the cellular event; (2) the total number of molecules in the HSP-PLS protein list known to be associated with that pathway; (3) the total number of molecules in the selected reference set. The p-value calculation depends on the statistical null model, such as the “random” model. Fisher’s exact test is used to determine the likelihood of randomness. The activation z-score predicts the activation state of the upstream regulator, using the expression patterns of the genes/proteins that are downstream of an upstream regulator. The z-score calculation needs a minimum of 4 targets with an expected expression pattern. An absolute z-score of ≥ 2 is considered significant. An upstream regulator is “Activated” if the z-score is ≥ 2 and “Inhibited” if the z-score < 2.Ingenuity Pathway Analysis (IPA) uses an array of statistical analyses to determine whether the analyzed data set has significant coverage with any of the previously determined canonical pathways, cellular events, protein-protein interaction domains, and pathways. ), some were present in the plasma membrane (n = 45), in the cytoplasm (n = 167), or in the nucleus (n = 91). For example, FN1, fibronectin 1 is an extracellular protein, which had direct interactions with 8 other proteins  emerged as one that has the highest number of direct interactions. This subunit is important for controlling neuronal excitability and becomes active due to increased levels of cytosolic Caytoplasm ,23,24,25ytoplasm ,26. TRIMytoplasm . FBXO6 aytoplasm ,31,32,33About half of 332 proteins were in the cytoplasm (51%), 28.3% in the nucleus, 13.9% on the plasma membrane, and 4.3% were secreted proteins. A large population of proteins had multiple functions , 20.19% were enzymes, and 16.7% were transcription regulators B,C. The p value, suggesting their potential importance for the health and function of UMNs. These transcription factors include HSF1, NFE2L2, TCF7L2, CTNNB1, and ESR1  was the adipogenesis pathway . In addition, leptin signaling, and white adipose browning pathway are highlighted for the high ratio levels, and significance. Visualization of the adipogenesis pathway also confirmed the presence of numerous proteins at key sites and with important functions , one of the master regulator of heat shock response, was able to reduce TDP-43 protein aggregation in neural stem cells . HSF1 waGrowth factors are important for neurons to survive and thrive. However, not all neurons respond to the same set of growth factors. Our analyses suggest that UMNs indeed have a very distinct selection of growth factors that they respond to. For example, many of the proteins identified by interaction partners are involved in NGF, IGF1, VEGF, HGF, and CNTF mediated signaling and therefore suggest that UMNs respond to these growth factors. It is possible that a combination or a cocktail of these factors will need to be developed and tested for their ability to improve the health and the function of UMNs. Interestingly, the signaling cascade of events converge on key kinases, and especially the MAPK3, MAPK2, AKT1, and PIK3R2 emerge to be key enzymes with very important function especially for the UMNs. It would be important to investigate how modulation of their function would impact the health of diseased UMNs.UMNs degenerate in HSP and PLS patients, even though these are two distinct and potentially unrelated neurodegenerative diseases. However, the gene mutations hold the clue for the cellular events that are primarily important for the health and stability of UMNs. Investigation of protein-protein interactions, begin to reveal the canonical pathways and the cellular events that are particularly crucial for UMNs. This information not only helps us understand how UMNs retain their cellular homeostasis, but also reveals key potential targets for future therapeutic interventions.


In a previous study, we have shown that PEPscan can provide a cheap and rapid means to identify candidate interfering peptides (IPs), i.e., peptides able to disrupt a target protein-protein interaction. PEPscan was shown to be effective in identifying a limited number of candidate IPs specific to the target interaction. Here, we investigate the results of 14 new PEPscan experiments for protein complexes of known 3D structures. We show that for almost all complexes, PEPscan is able to identify candidate IPs that are located at the protein-protein interface. The information it provides about the binding site seems, however, too ambiguous to be exploited in a simple manner to assist the modeling of protein complexes. Moreover, these candidates are associated with false positives. For these, we suggest they could correspond to non-specific binders, which leaves room for further optimization of the PEPscan protocol. Another unexpected advance comes from the observation of the applicability of PEPscan for polysaccharides and labeled peptides, suggesting that PEPscan could become a large spectrum approach to investigate protein-binder interactions, the binder not necessarily being a protein. Peptide arrays have a wide range of applications in basic and applied research. The arrays consist of hundreds of different peptide sequences immobilized on a solid support, which are analyzed in terms of spots associated with a signal. Their usage differs depending on the choice of the peptide synthesis method, the solid support, the immobilization method, the size of the peptides, the density of peptides on the solid support, and the detection method. The most widely used technique to generate peptide arrays is the SPOT synthesis. This technique is based on solid-phase Fmoc chemistry to synthesize peptides on a membrane support, which is usually nitrocellulose ,2,3. ThePEPscan is a particular class of peptide arrays that has been developed to identify, within the sequence of a protein of interest , the regIn this manuscript, we describe the versatility of the PEPscan approach for identification of protein-protein binding sites, we demonstrate that it can effectively be used for the characterization of protein-polysaccharide binding sites, and suggest it can be extended to peptides as well, making it a versatile approach to the characterization of protein-binder interactions.The seven protein complexes of known structure that have been studied are detailed Overlapping dodecapeptides with two amino acid shift, spanning the complete sequence of a protein, were prepared by automatic spot synthesis  on an amino-derived cellulose membrane, as described. The membrane was saturated using 3% non-fat dry milk/3% BSA (2 h at room temperature), incubated with the purified protein partner , and, after several washing steps, incubated with an antibody against the protein used for the hybridization (2 h at room temperature), followed by an HRP-conjugated secondary antibody for 1 h at room temperature. Positive spots were visualized using the ECL system.Overlapping dodecapeptides with two amino acid shift, spanning a fragment of the VAR2CSA sequence, were prepared by automatic spot synthesis  on an amino-derived cellulose membrane, as described ,5. The mPeptides were synthesized in an automated multiple peptide synthesizer, with solid phase procedure and standard Fmoc chemistry by GL Biochem . The purity and composition of the peptides were confirmed by reverse phase high performance liquid chromatography (HPLC) and by mass spectrometry (MS). For some experiments, the peptides were synthesized with the fluorochrome FITC at the N-terminus.The interaction VAR2CSA/CSA  was competed in vitro using 1 mM of chondroitin sulfate A (CSA), incubated for 30 min at room temperature with the peptides identified on the PEPscan approach . After several washing steps, the CSA/peptide mix was incubated with the membrane containing the VAR2CSA protein, followed by incubation with an anti-CSA antibody, and an HRP-conjugated secondary antibody. The interaction was detected using the ECL system.Overlapping dodecapeptides with two amino acid shift, spanning the complete PP1a sequence, were prepared by automatic spot synthesis  on an amino-derived cellulose membrane. The membrane was saturated using 3% non-fat dry milk/3% BSA  (2 h at room temperature), incubated with FITC-labeled LRRK2 peptide  and after several washing steps, the membrane was developed using a fluorescence scanner (FITC fluorescence filter).We assess here, for seven cases for which the structure of the complex is known, how well PEPscan is able to identify peptides that are located at the protein-protein interface. A first observation is a visual heterogeneity in the aspects of the arrays. For some, such as that of TGFβ3 or Ras, there are a large number of positive spots, whereas for others, such as EPOR or NGAL, there are very few. The habit is usually to consider that only a series of at least three consecutive spots can correspond to true positives; we now question this. Looking at We now turn to membranes with a large number of positive spots, such as Ras/Ral or TGFβ3/TGFR2. For TGFβ3, most of the positive spots are located in a region not resolved in the structure; the 3D structure starting at position 6:2 in the membrane. Three series of consecutive darker spots are observed , one (6:6–9) corresponding to the interface. For Ras, only very few spots show no signal. The interaction site corresponds to spots 1:20–23 and does not correspond to the largest series of consecutive spots that are observed on line 2. This suggests that probably, some non-specific binding occurs, and the experimental signal should be filtered using some processing to remove such noise. This is, however, beyond the scope of this study. Although the direct interpretation of the membranes seems to be challenging, we recall that the use of PEPscan for the identification of interfering peptides for instance, requires a confirmation by experiments, such as in vitro competition. We also recall that the reproducibility of PEPscan experiments is fairly good (see ).Overall, in all the cases in this study, fragments associated with MOFs  are in limited number among the domains in interaction (30 for 14 proteins), i.e., 2.1 average, ranging from none to eight for NGAL. For this case, four out of the eight fragments identified interact with the partner. Moreover, it seems their number could be reduced to only four, looking at only the most positive (darkest) series .How well do the fragments overlap the binding site? In terms of the identification of residues at the protein-protein interface, apart from the EPO and NCF2 cases discussed above, it is interesting to note that in all cases, PEPscan could identify fragments involving residues at the interface. Considering the experiments on both sides per complex, we observe that for all cases (100%), PEPscan was able to return information about the binding site for at least one experiment. Considering that only one experiment over the two could be conducted, this rate of success falls to 86% (12/14), which remains high. In total, 46% (18 out of 39) of candidate fragments with MOFs identified in the 3D structures have residues located at the interface. Moreover, as in our previous study, we observe that MOFs that correspond to a subset of these fragments usually contain residues, located at the interface, allowing the number of candidate residues to be narrowed. In total, the ratio of the number of candidate residues of the MOFs at the interface, over that of the MOFs identified in the 3D structures, is 28% (80/282). We recall, however, that PEPscan experiments must be supplemented by in vitro competition experiments to fully identify which fragments effectively interfere with the complex formation.Finally, we consider the information of the MOFs in terms of the 3D modeling of the complexes, i.e., exploring if the information of the candidate MOFs could help modeling the structures. As can be observed in We now investigate if PEPscan could be extended to the identification of the binding site of small polysaccharides, in conditions similar to that of the protein-protein interface, i.e., an antibody targeting the polysaccharide exists. In order to determine the amino acid residues mediating the binding of the small molecule, CSA, overlapping dodecapeptides covering a fragment of VAR2CSA protein were immobilized in a nitrocellulose membrane and hybridized with CSA A. A set An in vitro competition assay was performed to confirm that peptides 1, 2, and 3 specifically target the VAR2CSA/CSA interaction. CSA was pre-incubated with 1 mM of each peptide and then the mixture was hybridized with the membrane containing the protein. We turn, here, to considering if PEPscan could also be applied to the identification of the binding site of shorter entities, such as peptides, for which no antibody exists, but some means to reveal the positive spots exists, such as labeling. For this, we consider the identification of PP1a sequences involved in binding to one FITC-labeled LRRK2 peptide. We have previously identified and published the sequence of the interfering peptide, blocking the interaction between the kinase LRRK2 and the phosphatase PP1 . As LRRKPEPscan has recently raised new interest in the identification of interfering peptides blocking protein-protein interactions. For partners known to interact, PEPscan is of particular interest when the structure of the partners in interaction is not known, and in silico analysis to design interfering peptides cannot be conducted. The PEPscan approach combines the advantage of being successful, with also being cheap and rapid. We have previously demonstrated PEPscan’s ability to identify peptides at the protein-protein interface . We haveInterestingly, it seems PEPscan can probably be extended to other kinds of interactions involving a protein. For the LRRK2 peptide, we show that a protocol based on fluorescence, directly coupling a small peptide to a marker such as FITC, is likely to be effective—this remains however, to be confirmed. Particularly, the impact of the FITC on peptide conformation and binding is largely unknown. For the CSA, we demonstrate that the protocol can be extended to other categories of binders for which antibodies exist, such as polysaccharides. Indeed, it was possible to confirm the binding of two of the three candidates by in vitro competition. Even if the effectiveness of PEPscan for protein-polysaccharides should be confirmed with more examples, this clearly opens new perspectives for PEPscan.Overall, the present study confirms that, without any structural knowledge, PEPscan is an approach of interest to characterize the regions of proteins involved in the binding with a partner, and the fragments that could interfere with this binding. This opens the door to the characterization of new interactions involved in pathologies, that fall out of the scope of routine structural biology approaches or structural modeling.


In the process of drug discovery, identifying the interaction between the protein and the novel compound plays an important role. With the development of technology, deep learning methods have shown excellent performance in various situations. However, the compound–protein interaction is complicated and the features extracted by most deep models are not comprehensive, which limits the performance to a certain extent. In this paper, we proposed a multiscale convolutional network that extracted the local and global features of the protein and the topological feature of the compound using different types of convolutional networks. The results showed that our model obtained the best performance compared with the existing deep learning methods. Drug discovery contains multiple steps that take a long time and cost a lot of money. Predicting and identifying the compound–protein interaction (CPI) play an essential role in the discovery and development of safe and effective new drugs. In the initial stage of drug discovery, screening out the compounds that interact with the target protein can greatly increase the success rate of drug discovery . A largeThe predictive models based on a 1D structure.These methods usually integrated the information of proteins and molecules into one framework for binary classification. The existing deep learning methods for compound–protein interaction can be summarized into three categories:The protein is composed of multiple amino acids, and each amino acid is represented by one character or multiple characters. A protein could be expressed as a string of multiple characters. The 1D sequence of the protein is similar, with the text in the field of natural language processing. Therefore, some researchers tried to apply the end-to-end representation learning method in order to learn the feature of protein sequences.2.The predictive models based on a 2D structure.DeepDTA  built a 3.The predictive models based on a 3D structure.Recently, some studies constructed a 2D structure descriptor through a protein sequence and extracted features based on it or based on an original secondary structure in various tasks. Fout et al.  introducThe structure of an active protein is not a simple combination of amino acids, but a 3D structure formed by protein folding. The stability of the 3D structure benefits from the interaction between amino acids, which also affects the compound–protein interaction. Researchers tried to learn the interaction from the 3D structure of the compound–protein complex. AtomNet  was the The spatial structure feature of the binding site provides precise information for the binding between proteins and compounds, but the global feature of the protein may also affect the binding process. It is mainly embodied in two aspects. First, in consideration of the shape and volume of the protein, the binding site only takes up a small spatial proportion of the protein volume. The choice of the compound is influenced by the global feature of the protein. Second, in the process of binding, the folding mode of protein conformation is more complicated. The formation of the binding site is the result of the combined effect of many factors. Atoms that do not belong to the binding site may also affect binding.Based on the consideration of the above factors, this work not only considers the local feature of the binding site that closely relates to the docking process, but also considers the global feature of the protein. Due to the large volume of the protein and the complex spatial structure that could result in high computational complexity, the 3D spatial structure of the whole protein is discarded. The 1D amino acid sequence is used to represent the global feature. In this work, the local feature from the binding site and the global feature from the amino acid sequence are processed by two convolutional neural networks to extract the information of the protein. Besides, the graph convolutional network is used to extract the feature of the compound. All these features are combined to predict the compound–protein interaction.In this work, we propose a predictive model for compound–protein interaction using three convolutional neural networks. The architecture is shown in The effective binding of the compound and the protein is the key to the function of the drug. The binding site plays an important role in the process of binding. The essence of the combination of the protein and molecule is that the atoms of the molecule interact with the atoms of the binding site. The focus of this work is finding the molecules that interact with the binding site of the protein. Therefore, in the description of the protein, the main information is the characteristics of the binding site. Since the formation of the binding site is based on its 3D structure, we extract features from the 3D structure of the binding site.For the spatial structure, the first problem that needs to be solved is how to construct the spatial descriptor of the binding site in order to extract effective information. In the 3D spatial structure, angstrom (Å) is used as the basic unit of the measurement. Inspired by image pixels in the field of image processing, the protein is represented by the basic units of each voxel with a size of 1 Å × 1 Å × 1 Å. The method of using voxels to represent proteins has been used in the docking scoring AutoDock Vina  in the vIn the compound–protein complex composed of the protein and the molecule, non-covalent bond interactions play an important role in the formation of the protein conformation, the stability of the protein conformation and the stability and activity of the binding of the protein and the molecule. Similar to DeepSite , the feaThe occupancy of a single atom is calculated as follows:The calculation method of the above descriptors of the protein has been integrated into HTMDs . In DeepThe binding site occupies a small space in the whole protein, shown in P, Q, R), the output at the position  of the feature cube is calculated as the following equation.x, y, z) in the Through the descriptor of the binding site, the atoms belonging to the binding site were divided into eight different channels and many voxels were constructed with DeepSite. Thus, the descriptor of the binding site is represented by the cube box composed of voxels of eight channels. Furthermore, the model uses 3D convolutional neural networks to process these cube boxes to obtain the feature of the binding site, which is shown in The protein sequence is a representation of the primary structure of a protein, which consists of multiple amino acids. There are 20 kinds of known amino acids, and each amino acid is usually represented by a three-letter string or one character. Since proteins are a biomacromolecule, there are many amino acids in a single protein. The amino acid sequence expressed in characters is similar to the text. In our work, each protein is represented by 1000 characters in length. If the number of amino is less than 1000, the sequence will be filled with 0, and if the number is more than 1000, the sequence will be cut. The amino acid sequence is vectorized in a manner similar to text processing. The word embedding is used to convert each amino acid into a 128-dimensional vector. Thus, the amino acid sequence is transformed into a feature matrix with 128 × 1000 size.The 1D convolutional neural networks, which are composed of three 1D convolutional blocks, are utilized to extract the global feature. Each convolutional block includes one convolutional layer, one LeakyRelu function and one maximum pooling layer. The convolutional process is shown in The convolutional process for the amino acid sequences is as follows:The molecule is described as a graph with topological connection. Each node in the graph corresponds to an atom, and the edge corresponds to a chemical bond between atoms in the molecule. The atom has many attributes, such as atom type, atomic degree, number of connected hydrogen atoms, etc. These attributes, which are regarded as the feature of atoms, are described as one-hot vectors. The details are shown in One molecule is described as A graph convolutional network  is perfoIn the predictive task of the compound–protein interaction, the local feature of the protein from the binding site is obtained through the multi-channel 3D convolutional neural network. The global feature of the protein from the amino acid sequence is obtained through the 1D convolutional neural network, and the molecular feature is obtained through the graph convolutional network. These three features are combined as Equation (5) to predict the compound–protein interaction in order to determine whether the molecule can effectively dock with the protein. The overall process is shown in Molecules that can bind to the protein are labeled as positive samples, and others are labeled as negative samples. There are hundreds of millions of molecules in nature or generated with the aid of computer-aided technology, but there are a small number of molecules that can effectively bind to specific proteins. A huge imbalance between positive samples and negative samples results in the inefficiency of the training process. In order to minimize the interference caused by the imbalance, the focal loss  is introAssume that the number of positive samples is Directory of Useful Decoys, Enhanced (DUD-E) is a dataset that provides 102 unique proteins that correspond to 124 docking molecules on average. As for each docking molecule, 50 decoys are prepared. These decoys own similar properties to the actives. In our experiment, 91 target proteins and their corresponding compounds constitute a dataset, since it is hard to extract the precise channel information from the rest targets. For each target protein, there are multiple docking molecules that are recorded as positive samples, and others that are labeled as negative samples. In the experiment, one sample pair contains one protein, one compound and one label. To compare our model with other models more objectively, the splitting of the dataset followed the same experimental setting of Lim et al.  and TsubIn the DUD-E dataset, each sample pair contains a protein, a molecule and a label. The label shows whether the protein and molecule in the sample pair could be docked. If they are docking, the corresponding label is 1; otherwise it is 0. For the test set, given a protein and a molecule, the model needs to predict the docking possibility, which is a classification task. The general ROC AUC is used as the measurement indicator. A higher AUC value indicates a better performance.In order to evaluate the performance of the proposed model objectively, we compared it with seven other models. These models contain open source molecular docking programs that are widely used in virtual screening tasks  and the global feature (the feature of the composition of the amino acid sequence) from the protein and extracts the topological feature from the molecular structure.In this section, a comparative experiment was set up to evaluate the impact of different feature combinations, especially different features of the protein. Due to the fact that the molecular feature is indispensable, we combined different features of the protein with the molecular feature. The different feature combinations are: the local feature and molecular feature, the global feature and molecular feature and the combination of all three. The models with different combinations own the same network architecture and hyperparameter configuration. A 5-fold cross validation was performed on all three combinations. From After the training is completed, the three models are also tested on the test set, and the ROC curve is shown in Besides, in order to explore the difference between models with three feature combinations, we calculated four indicators: the AUC value, precision value, recall value and This section shows the model performance on the balanced and imbalanced dataset. For each ligand, we randomly selected one, three and five decoys consisting of the training dataset, and the ratio of positive and negative is 1:1, 1:3 and 1:5, respectively. Four indicators are set for these three ratios, which includes the AUC value, precision value, recall value and In the proposed compound–protein interaction predictive model, there are three convolutional networks, including the 3D convolutional network performed on the 3D structure of the binding site, the 1D convolutional network performed on the 1D amino acid sequence of the protein, and the graph convolutional network performed on the molecular structure. Each convolutional network could consist of multiple layers. The purpose of the experiments in this section is to evaluate the influence of different numbers of convolutional layers on the model. In this section, the number of convolutional layers in each model is set from one to three. The number of convolutional layers here corresponds to three convolutional networks . For example, in a model where the number of convolutional layers is set to one, all three convolutional networks consist of one layer. The results are shown in As for the feature extraction from the spatial structure of the binding site, a multichannel 3D convolutional neural network is applied. The channel setting detail of the proposed model is listed in the first line of In this work, we proposed a compound–protein interaction predictive model based on multiscale convolutional neural networks, which combined the global and local feature of the protein and the feature of molecular topology. Aimed at the binding characteristics of proteins and molecules, three convolutional networks were designed to extract the spatial feature of the binding sites, the feature of amino acid sequences and the feature of molecules. These three features were fed into the model to identify whether the protein and molecules can effectively bind. The model in our work does not rely on the binding conformation of the protein and the molecule, and the experimental results show that the model reaches an AUC value of 0.975, which is better than current deep learning models.