Motivation. Predicting the native state of a protein has long been considered a gateway problem for understanding protein folding. Recent advances in structural modeling driven by deep learning have achieved unprecedented success at predicting a protein’s crystal structure, but it is not clear if these models are learning the physics of how proteins dynamically fold into their equilibrium structure or are just accurate knowledge-based predictors of the final state. Results. In this work, we compare the pathways generated by state-of-the-art protein structure prediction methods to experimental data about protein folding pathways. The methods considered were AlphaFold 2, RoseTTAFold, trRosetta, RaptorX, DMPfold, EVfold, SAINT2 and Rosetta. We find evidence that their simulated dynamics capture some information about the folding pathway, but their predictive ability is worse than a trivial classifier using sequence-agnostic features like chain length. The folding trajectories produced are also uncorrelated with experimental observables such as intermediate structures and the folding rate constant. These results suggest that recent advances in structure prediction do not yet provide an enhanced understanding of protein folding.Availability. The data underlying this article are available in GitHub at https://github.com/oxpig/structure-vs-folding/Bioinformatics online. If it wProtein folding is often used as a misnomer for protein structure prediction, which is the prediction of the native state without regard to the pathway that the protein undergoes to attain it. The field of structure prediction has experienced significant progress over the past two decades, powered by the community-wide effort of the biennial CASP contest . This asThese dramatic advances raise the question of whether these methods have achieved better understanding of protein folding physics, or are just successful at leveraging statistical knowledge of crystal structures into a prediction. To the best of our knowledge, the ability of structure predictors to model folding pathways has not been evaluated previously. Related work has studied the search trajectories of fragment replacement methods  data. We observe that most of the structure prediction methods are not significantly better than an unbiased coin and some of them are consistently worse at reproducing experimental measurements.We compiled a dataset of 170 proteins for which experimental folding kinetics data is available. To produce this dataset, we collated entries from the Protein Folding Database (PFDB) of kinetic constants . The entWe collected available HDX data from Start2Fold and original papers see , to use Sequences and reference structures were downloaded from the RCSB PDB  with staWe generated protein folding trajectories using the latest versions, as of December 2020, of Rosetta . Despite the different initial states, all codes generate trajectories exhibiting complex folding dynamics.The pathways were analyzed using a method based on the fraction of native contacts between secondary structure elements. In a concerted, two-state mechanism, we expect a sudden change where most of the interactions between the secondary structure elements of a protein form in a single step, while in a multistate mechanisms, we expect several sets of interactions forming at disjoint points of the trajectory. Our analysis (see Section 2) identifies the steepest changes, and uses a statistical criterion to determine whether they should be considered as a single group (two-state) or multiple groups . N = 100 000), we determined that all the structure predictors are significantly superior to a random classifier , closely followed by EVfold (based on energy minimization subject to evolutionary constraints). EVfold could be considered the most physically realistic method of those tested, since it does not modify the energy function to bias it toward the predicted native state. DMPfold is similar to EVfold, as it uses the same simulation engine (CNS), but the former uses a different method for introducing distance restraints: in DMPfold they are predicted with deep learning, whereas EVfold uses a Potts model. EVfold is a better predictor of folding kinetics than DMPfold, and also comparable to or better than RaptorX and trRosetta, which rely on deep learning. This suggests that, with the exception of RoseTTAFold, which belongs to a novel family of methods with physical assumptions baked into the model’s architecture, deep learning models are performing worse.We also tested AlphaFold 2’s ability to predict folding kinetics, although in this case we had only one trajectory per protein. Using the method by Overall the quality of the structure prediction output does not appear to relate to the ability of the method to classify folding kinetics see . In the We examined one of the methods that use deep learning, DMPfold, in more detail. DMPfold uses an iterative process where prior predictions are used to refine the potential used in subsequent cycles. We compared the predictive power of multiple iterations, and observed that, while the area under the receiver-operating curve (AUROC) increases slightly with successive iterations, the overall accuracy is reduced see . The AURFinally, we found that some programs have an intrinsic bias toward predicting one or other folding mechanism. For example, for the majority of proteins, about 90% of the 200 DMPfold decoys exhibit two-state folding (hence the increase in AUROC from the 10 decoys sample to the 200 decoys sample), while RaptorX and EVfold tend toward predicting intermediates, and trRosetta presents a clear, but less marked bias toward two-state trajectories. These tendencies may explain the differences between unsupervised and supervised accuracy in Overall, these results suggest that protein structure prediction programs are not learning information about the folding mechanism.We next examined whether the protein structure prediction methods can predict the folding rate constant of the two-state processes. Our work follows that of Plaxco, Simons and Baker , show siWe tested whether we could predict the folding rate constants of 79 two-state folding proteins from the PFDB -equivariance might detect the footprint that folding mechanisms have left in the multiple sequence alignment. However, it is unlikely that AlphaFold 2 would outperform the length of the protein chain at predicting the folding rate constant.We also found that AlphaFold 2 behaves similarly to RoseTTAFold, as found in the previous section. The Spearman correlation coefficient between the relative position of the folding event and the logarithm of the These results reinforce the conclusion that the ability of protein structure prediction methods to model folding pathways is inferior to trivial baselines.As on occasion structure predictors do correctly identify folding kinetics, we next examine if in these cases, the structures predicted in the pathway are consistent with experimental data. We hypothesize that if the structure predictor has insight into the multistate process, it should (i) predict structures that are congruent with experimental measurements, and (ii) produce consistent predictions of the intermediates across independent replicas for the same protein. HDX experiments probe unfolded regions of a protein at different stages of the folding process and allow us to identify which regions of an intermediate are structured and which have not yet folded . The metrics of these classifiers are summarized in Intermediate structures are predicted with very low accuracy by all methods. A randomized permutation test shows that only one of the predictors, EVfold, exhibits predictive power superior to the random baseline. In contrast, RoseTTAFold is significantly worse than the random sample. This suggests that deep learning models are not learning the physics of folding, but rather collecting statistical information about crystal structures.As an additional sanity check, we considered whether the structures generated throughout the trajectories are consistent with basic physical rules. We computed the clashscore  and obtained a set of AlphaFold 2 trajectories, and used them to determine major features of folding using a simple set of statistical rules. We found that protein structure prediction methods can in some cases distinguish the folding kinetics (two-state versus multistate) of a chain better than a random baseline, but not significantly better, and often significantly worse, than a simple, sequence-agnostic linear classifier using only the number of amino acids in the chain.Using a similar approach, we examined the relationship between simulated trajectories and other experimental observables: the folding rate constant of two-state folders, and the structure of intermediates in multistate trajectories. The simulated trajectories were in most cases not better than random at predicting the contacts formed in an intermediate, and in the case of predicting folding rate constants, none of the methods was superior to a linear classifier using the length of the protein chain.Our results demonstrate that state-of-the-art protein structure prediction methods do not provide an enhanced understanding of the principles underpinning folding. Simulated trajectories from protein structure prediction methods are inconsistent with all available experimental data, in terms of folding mechanism, kinetics or structural data. In the general context of computational protein biophysics, our results suggests that current protein structure prediction programs, while now very successful at their primary role, are not an appropriate tool to investigate folding.There are some limitations to our study. First of all, the concepts of folding intermediate and folding formal kinetics are imprecise. For example, many proteins have a tendency to form compact, molten globule structures, that may then fold cooperatively in a process that is referred to as ‘two-state’ e.g.  or T4 lyWhile our results have shown the lack of consistency between the folding trajectories generated by protein structure prediction methods and experimental data, we have also seen that most structure predictors are better than random suggesting that a weak signal exists. The next stage will be to investigate how to extract the limited amount of folding information that is encoded in current protein structure prediction programs.btab881_Supplementary_DataClick here for additional data file.


Rtpca is an R package implementing methods for inferring protein–protein interactions (PPIs) based on thermal proteome profiling experiments of a single condition or in a differential setting via an approach called thermal proximity coaggregation. It offers user-friendly tools to explore datasets for their PPI predictive performance and easily integrates with available R packages.https://bioconductor.org/packages/Rtpca).Rtpca is available from Bioconductor (Bioinformatics online. Many crucial cellular processes are executed by ensembles of stably or dynamically associated proteins. Systematic profiling of protein–protein interactions (PPIs) has been performed using cell extracts and combining bait prey enrichment or coelution strategies with mass spectrometry . N.K. was supported by a fellowship of the EMBL International PhD programme. A.M. was supported by a fellowship of the EMBL International Postdoc programme (EI3POD) under Marie Skłodowska action found [664726]. Conflict of Interest: none declared. btaa682_Supplementary_DataClick here for additional data file.


The fragment-centric design promises a means to develop complex xenobiotic protein surface mimetics, but it is challenging to find locally biomimetic structures. To address this issue, foldameric local surface mimetic (LSM) libraries were constructed. Protein affinity patterns, ligand promiscuity and protein druggability were evaluated using pull-down data for targets with various interaction tendencies and levels of homology. LSM probes based on H14 helices exhibited sufficient binding affinities for the detection of both orthosteric and non-orthosteric spots, and overall binding tendencies correlated with the magnitude of the target interactome. Binding was driven by two proteinogenic side chains and LSM probes could distinguish structurally similar proteins with different functions, indicating limited promiscuity. Binding patterns displayed similar side chain enrichment values to those for native protein–protein interfaces implying locally biomimetic behavior. These analyses suggest that in a fragment-centric approach foldameric LSMs can serve as useful probes and building blocks for undruggable protein interfaces. Foldameric local surface mimetics (LSMs) detect spots at protein surfaces and are promising building blocks in a fragment-centric design of xenobiotic structures and protein–protein interaction inhibitors. Dissociation constants below 100 μM, the fragment-based drug design limit,38 would facilitate observation of protein-specific binding patterns and their potential application as surface mimetic building blocks.Protein surface mimetics are chemical tools that target protein–protein interactions (PPIs) that are undruggable with small molecules.39–41 These sequences display only two proteinogenic side chains on the same side that can be strongly shielded from the solvent by the bulky scaffold and the cyclic side chains. While longer foldameric sequences have been experimentally shown to recognize protein surfaces and inhibit protein–protein interactions (PPIs) for targets such as hDM2,42,43 the Bcl-xL family,44 gp-41,45,46 the VEGF-receptor,47 and β-amyloid oligomers,22 the general druggability of protein interfaces with foldameric secondary structures has not been established. This might cast doubt on the general compatibility with protein surfaces that unnatural backbone segments are often not tolerated at protein–foldamer interfaces for ligands derived from natural templates in a top-down design scenario.48,49 In contrast, compatibility was displayed toward some bottom-up designed foldameric sequences,50 which led to high-affinity ligands.Our hypothesis was that the shortest foldamer sequences with folding tendency in water can be used as LSM probes . The sho51 It is especially challenging to handle protein interface similarity and ligand promiscuity when exposed protein surfaces are targeted. The relevance of this point for foldamers is indicated by the fact that the chemical space of non-natural residues in bioactive sequences is focused on aliphatic and aromatic hydrophobic side chains that are primarily responsible for ligand promiscuity and non-specific binding.52,53 Literature examples exemplify the existence of this phenomenon for peptidic54 and aromatic oligoamide foldamers.25,55 For acceptable LSM probes, druggability and promiscuity properties should be in balance, mimicking the behaviour of natural proteins. The number of known interactions, and therefore, current knowledge, is limited for druggability–promiscuity properties of foldamers. This situation calls for a systematic approach that generates data on protein–foldamer interactions and facilitates an understanding of molecular recognition patterns depending on the side chain composition, secondary structures of foldamer segments, and target protein surfaces.Besides sufficient affinity (druggability), ligand promiscuity is an equally important problem for the design of specific surface mimetic ligands.In this work, the druggability–promiscuity problem for foldameric LSMs is addressed with a bottom-up fragment-centric pull-down methodology. To test these parameters, target proteins that display different levels of interface similarity and promiscuity in their own PPI network were selected. After mapping LSM binding fingerprints, we found that proposed probes displayed biomimetic features on the protein test set, suggesting the suitability of short foldameric sequences to probe protein surfaces and serve as surface mimetic building blocks.41,56 Here, helices H14 and H12 were utilized. The closely packed side chain distances in α-helices (Cαi–Cαi+3 ≈ 5 Å and Cαi–Cαi+4 ≈ 6 Å) and in β-sheets (Cαi–Cαi+2 ≈ 6 Å and “sideways” Cαi–Cαi+n≈ 5 Å) are comparable with the closest side chain distances in foldameric helices H14 (Cβi–Cβi+3 ≈ 5 Å) and H12 (Cβi–Cβi+2 ≈ 6 Å). Helix H14 exhibits a strong tendency to fold and its side chains are oriented in parallel. Helix H12 is conformationally less stable in aqueous medium, demonstrating an elongated geometry, and side chains are juxtaposed with a small tilt angle.41Peptide sequences constructed from β-amino acid residues exhibit a strong tendency to fold into secondary structures at short chain lengths, and the specific geometry is controlled by the backbone stereochemistry pattern and topology of structure-inducing side chains.S,2S)-2-aminocyclohexanecarboxylic acid, ACHC) and H12 -2-aminocyclopentanecarboxylic acid, ACPC), respectively. Such oligomers allow two proteinogenic side chains that point to the identical face of the helix to be incorporated into the sequence  (These secondary structures can be stabilized by cyclic side chain residues so that they display folding at lengths of hexamers and octamers for H14 ((1sequence . Sixteen (L1–L4) .32 These structural units can display a surface area of ca. 500 Å2 toward a protein surface, which can provide the binding free energy for affinities in the range of 1–500 μM. Such affinity is sufficient for probing the surface and is useful for subsequent fragment-based design.This library includes a considerable fraction of peptide foldamers that fold at such a short chain length. These peptides can be considered as minimally folded building blocks in the peptide foldamer space.57 a highly pleiotropic molecule that plays a vital role in every eukaryotic cell. It displays EF-hand motifs and mediates an extremely large number of signal pathways via control of enzyme and ion channel activities in a Ca2+-dependent manner. Two additional EF-hand proteins with high structural homology were selected from the S100 family: S100A4 and S100B,58 both important pharmacological targets for cancer treatment.59 S100A4 is involved in tumour progression and metastasis and has many interacting partners. S100B is expressed in melanocyte-derived tumours and mature astrocytes,60 where it plays an important role in neurite extension. Numbers of its known protein partners are significantly less than that for S100A4. Galectin-1  and the winged helix domain of RecQ helicase (RecQ-WH) were used as target models with low tendency to form PPIs. Gal-1 has an immunosuppressive effect, promoting cancer progression and metastasis through recognition of β-galactoside motifs on cell surface glycoproteins.66 Gal-1 has a β-sandwich structure with a jelly roll topology,67–69 that may interact with proteins in a carbohydrate independent manner.66,70–72 RecQ is a prokaryotic intracellular ATP-dependent DNA helicase, which plays important roles in DNA damage response, DNA recombination, replication, and repair.73,74 RecQ interacts with the disordered C-terminal of the single-stranded DNA-binding protein (SSB) through its WH domain, which is the single PPI reported for RecQ-WH.75 All selected targets exhibit solvent exposed shallow binding sites; however, CaM is able to wrap around α-helices with special side chain motifs, thereby forming a hydrophobic pocket.76,77 For competitive binding studies, peptide motifs of PPI partners were selected from the following proteins: transient receptor potential cation channel subfamily V member 1 (TRPV1), non-muscle myosin IIA (NMIIA), ribosomal S6 kinase 1 (RSK1), and SSB, for CaM, S100A4, S100B, and RecQ-WH, respectively.Five proteins  were selFB) were determined from the unbound fractions measured by HPLC-MS methods. Hits were corrected with background binding of the resin. The protocol allowed all binding events to equilibrate irrespective of affinity, specificity, and binding kinetics. This was essential to judge overall druggability and promiscuity. Importantly, the concentration of protein and the total concentration of foldameric LSMs were equimolar, which minimized competition between the probes. This experimental setup approximated independent binding of individual library members. Results were visualized as heat maps of dissociation constants  and the average bound fraction values obtained for the H14 helical LSM probe library . If the sheer hydrophobic surface of these residues drove interaction with the test proteins, higher uniform baseline binding would have been observed. Many sequences displayed no significant binding. Interestingly, certain levels of diagonal symmetry could be observed in the affinity maps , suggestwj) of the residues at protein-LSM contacts were analysed. The literature definition of wj37 is given in fj is the number of interface residues of type j, and normalization factor vj is the overall prevalence of the amino acid of type j. Index m denotes the residue type. To approximate residue frequencies based on bound fractions for the H14 LSM library, the following formula was applied:Focusing on the native-like anchor points, normalized frequencies  computational Ala scanning categorized by secondary structure types37 and (ii) protein–protein interface analysis, defining interface residues with a maximum distance of 6 Å for the contacting residues84  changes were observed for W, F, I, L, D, E, N, S and T . This segment fits into a narrow and slightly bent site on RecQ-WH. As expected, surface mapping of RecQ-WH with bulky LSM probes led to a low number of hits. Even the propensity of the protein to bind anionic sequences did not drive anionic LSM probes to the binding site, possibly because of steric incompatibility.Gal-1 and the RecQ-WH domain were models for proteins with a low tendency to form direct protein–protein complexes. Gal-1 does not exhibit preference for specific hydrophilic side chains, and accordingly, its LSM affinity pattern did not show a tendency to bind other than hydrophobic side chains. The binding cleft of RecQ-WH includes a hydrophobic graft flanked by polar and positively charged residuesThese findings suggest that the protein interfaces studied select preferred side chain chemistries from the LSM probe library in a manner similar to selection of natural partners. On this ground, we concluded that LSM probes exhibit biomimetic features in presenting proteinogenic side chains for their protein partners.32 Despite the limited structural complexity of LSM probes, affinity fingerprints were characteristic of target proteins. The overall tendency to bind LSM probes correlates with the number of PPI partners identified for the protein. Pairwise correlations between orthosteric binding patterns remained low even for proteins with high structural homology. LSM probes are thus sensitive to the function encoded by PPI interfaces. LSM probes achieve such biomimetic behaviour by presenting proteinogenic side chains in a manner similar to natural PPIs. This behaviour is reflected in protein-like enrichment and depletion values obtained for the side chain chemistries. Moreover, test proteins were able to select key residue types needed for specific PPI recognition patterns from the libraries.Foldameric LSM probe libraries were constructed with H14 and H12 scaffolds and were tested with five proteins. Affinity patterns of 512 LSM probes were recorded, and the resulting 2560 protein–foldamer interactions were analysed using a fragment-centric approach. LSM affinities were secondary structure dependent, and a sufficient number of hits in the affinity range of 1–150 μM was obtained for the hexameric H14 library. Binding was driven by two proteinogenic side chains displayed. The hydrophobic foldamer scaffold served as a template and solvent shield. The better performance of H14 LSM probes may be explained by their ability to geometrically adapt their stable and bulky skeleton to the local environment at protein hot spots. Foldameric LSMs sensitively probed protein surfaces, and thus, detected both orthosteric and non-orthosteric hot spots, based on competition experiments. Orthosteric hits identified in this way along with their geometrically confined binding to the PPI interface offer the possibility of linking them to a high-affinity ligand. This concept was demonstrated earlier in a dynamic covalent system with CaM.98 His chemistry-inspired concept did not explain the operation of the adaptive immune system, but Pauling's ingenious idea is still there mostly untapped to offer a route to synthetic biopolymers that mimic molecular recognition properties of natural antibodies. This work takes a step toward Pauling's concept, using self-organizing protein mimetic LSM probes as promising candidates for recognition segments. An additional understanding of dynamic covalent chemistry may eventually solve the problem of in situ coupling.In the early days of chemical biology, Linus Pauling proposed target-induced self-organization of biopolymers as a possible mechanism to generate high-affinity ligands against proteins.There are no conflicts to declare.SC-011-D0SC03525D-s001


Protein homo-oligomerization is a very common phenomenon, and approximately half of proteins form homo-oligomeric assemblies composed of identical subunits. The vast majority of such assemblies possess internal symmetry which can be either exploited to help or poses challenges during structure determination. Moreover, aspects of symmetry are critical in the modeling of protein homo-oligomers either by docking or by homology-based approaches. Here, we first provide a brief overview of the nature of protein homo-oligomerization. Next, we describe how the symmetry of homo-oligomers is addressed by crystallographic and non-crystallographic symmetry operations, and how biologically relevant intermolecular interactions can be deciphered from the ordered array of molecules within protein crystals. Additionally, we describe the most important aspects of protein homo-oligomerization in structure determination by NMR. Finally, we give an overview of approaches aimed at modeling homo-oligomers using computational methods that specifically address their internal symmetry and allow the incorporation of other experimental data as spatial restraints to achieve higher model reliability. Many proteins have a natural tendency to self-associate into homo-oligomeric protein complexes, also termed homomers, which are composed of two or more identical subunits. According to the estimation, 30–50% of all proteins oligomerize . In addiHomo-oligomerization is believed to be nature’s solution to form large proteins by avoiding efficiency problems with the synthesis of long polypeptide chains. In comparison to small proteins, larger ones are more favorable due to their higher stability and smaller solvent-exposed surface percentage. Moreover, building larger protein complexes from smaller subunits has several benefits. Such complexes are less prone to translational errors, as only the defected subunits can be discarded and replaced in contrast to the whole large single polypeptide-chain protein. Next, coding efficiency is higher because less information needs to be stored to build a large protein. Furthermore, assembly of (homo)-oligomeric proteins can be triggered and/or fine-tuned and thus provides an additional layer of regulation, which is crucial in dynamic processes such as actin filament assembly  and micrAs these advantages are almost intuitive, it is often assumed they should also provide a clear evolutionary benefit. However, Lynch suggested that homo-oligomers could have arisen from stochastic, non-adaptive processes ,16 and tSymmetry is an inherent property of almost all homo-oligomers characterized up to date. Although homo-oligomers often exhibit at least some degree of local asymmetry , this asn in Schönflies notation) with a single axis of rotation, and dihedral symmetries (Dn) with at least one additional axis of rotation, perpendicular to the first one , local (limited to a part of the molecule) or helical . SymmetrCurrently, the main methods for high-resolution structure determination of proteins are X-ray crystallography and nuclear magnetic resonance (NMR), with cryo-electron microscopy (cryo-EM) joining the group, fueled by technological advances in recent years enabling sub-1.5 Å resolution . Of thes2 per subunit , while crystal contacts bury, on average, less than 400 Å2 per subunit [2, and in some instances even more. Here, the main question with which a crystallographer is faced during structure analysis and interpretation is: Which, if any, of these inter-molecular contacts are stable also in solution and hence could be of biological relevance? Below, we provide an overview of the approaches aimed to address this question, first by considering the most important aspects of protein crystallization relevant for homo-oligomers.A critical requirement for X-ray structure determination is a crystal of the molecule/complex of interest. A crystal represents an ordered array of molecules, and the smallest unit from which the complete crystal can be re-created by application of translation and rotation is the asymmetric unit. Therefore, the crystal structure of a protein is determined and reported as the structure(s) of the protein molecule(s) and their ligands within one copy of the asymmetric unit. Next, symmetry operations of the crystal space group, determined already during initial diffraction data processing and later confirmed during phasing and structure refinement process, can be applied to generate neighboring asymmetric units, the unit cell and, by translation, the complete crystal. Application of these operations produces copies of protein molecules, which form a network of inter-molecular contacts stabilizing the crystal . Typical subunit . It can Since, in protein crystals, solvent typically occupies approximately half of the volume of the crystal , proteinWithin the crystal, two types of symmetry operations are possible—crystallographic and non-crystallographic symmetry (NCS) operations. The crystallographic symmetry operations relate to neighboring asymmetric units, while the NCS operations work on molecules within the asymmetric unit. An extensive overview of crystal symmetry is given in the International Tables for Crystallography , particuα atoms [In case that the rotation axis/axes of symmetric homo-oligomeric complexes coincide with the crystallographic rotation axis/axes, the asymmetric unit will contain only part of the homo-oligomer, and the whole assembly can be re-created by application of symmetry operations of the crystal space group producing identical copies of the initial part. Indeed, analysis of a subset of structures in the Protein Data Bank showed that trimers, tetramers and hexamers preferentially crystallized in systems where the homo-oligomer symmetry was incorporated into crystal symmetry—in this case, the asymmetric unit does not contain the full oligomer . Howeverα atoms .To determine which molecular assembly, either containing purely crystallographic symmetry, non-crystallographic symmetry or the combination of both, is biologically relevant, an inspection of all different inter-molecular contacts within the crystal is necessary. This is equally true for all protein complexes—for both hetero- as well as for symmetric and asymmetric homo-oligomers.Various computational tools are available for this purpose and have already been extensively reviewed, for example, by Capitani and coworkers  and receiG) and associated estimation of interface specificity  as the measure of the probability of obtaining lower ∆iG from randomly picked atoms. Interface surface area is calculated as the difference between accessible surface area (ASA) of separated and complexed protein molecules, divided by the number of molecules in the proposed complex. ASA is commonly calculated by rolling a probe of the radius of 1.4 Å (corresponding to a water molecule) around the protein atoms with slightly increased radii to account for hydrogen atoms [iG (corresponding to hydrophobic interfaces), with higher P-value and with larger interface area are considered as mediating a stable oligomeric assembly. The significance of the observed interaction is reported as complexation significance score (CSS), which is the maximal fraction of the analyzed interface in terms of free energy of binding—higher value (up to 1) corresponds to higher significance. Another energy-based approach is that of ClusPro-DC [Of the thermodynamics-based methods, the most commonly used tool is PISA  ,59. PISAen atoms . AdditiousPro-DC , which uusPro-DC  docking Empirical approaches work by analyzing specific features of each of the distinct inter-molecular interfaces within the crystal and classifying it as stable/biologically relevant in light of knowledge on already thoroughly analyzed interfaces. For example, EPPIC (evolutionary protein-protein interface classifier) relies on evolutionary analysis considering surface entropy of homologous sequences and few other parameters. However, for clear distinction, a high sequence similarity between query and homologs is needed . The autThe group of approaches incorporating machine learning (ML) is much broader. One of the newer tools is PIACO , which is based on covariance calculated from multiple sequence alignment and few other parameters such as amino acid composition and pair frequency . The preTo finally confirm the in-solution and also biological relevance of the proposed homo-oligomeric assembly, further experiments are needed. For example, insight into stoichiometry in the solution can be provided by size exclusion chromatography, static and dynamic laser light scattering experiments, analytical ultracentrifugation, mass spectrometry under native conditions and other methods. Further insight, also in terms of overall structural features, possibly of help in distinguishing between non-relevant and relevant assemblies, can be provided by small-angle X-ray scattering (SAXS). Examples are the crystal structures of human aldehyde dehydrogenase 7A1  and of fNuclear magnetic resonance or NMR spectroscopy—shortly, NMR—is based on collecting various types of spectra, mainly 1D and 2D. Neighboring atoms, covalent bond lengths and other distances, dihedral angles and similar structure characteristics can be estimated from these spectra and are then used as restraints to calculate a convergent ensemble of structural models. Typically, around 20 models, representing different possible conformers, all satisfying the initial restraints, are obtained . ContrarAn advancement in oligomer structure determination by NMR is represented by the approach using residual dipolar couplings, which provide domain orientation restraints , togetheInterestingly, by using NMR, it has been shown that several homo-oligomers that were described as symmetric using X-ray crystallography display a certain degree of symmetry deviation, mainly due to hydrophilic amino acid residues at the subunit interface . The autTo determine protein structure, a special variant of cryo-EM is employed—the single particle analysis (SPA). Here, a high number of two-dimensional low-resolution images of the single macromolecule or an assembly—hence, single particle—in various orientations is combined to reconstruct its three-dimensional model . InternaIn the process of cryo-EM structure determination, symmetry of macromolecular assemblies is generally detected during the initial analysis and classification of single particles. The most commonly used approach is based on a multivariate statistical analysis (MSA), which was initially used to process noisy images of randomly oriented biological macromolecules . An imprHigh-resolution structure determination of protein homo-oligomers using X-ray crystallography or NMR is often time- and resource-consuming, and sometimes a structure of the monomer is available or can be modeled using homology approaches easily. In these cases, a structural model of homo-oligomer can be generated using computational approaches. For higher model reliability and relevance, additional data on the oligomerization interfaces obtained from other experiments can be used as spatial restraints. The success of modeling, especially of the docking approach, is critically dependent on the starting monomer structure—more success can be expected when their conformation closely resembles the one within the oligomer. Therefore, since due to computational complexity large conformational changes are inherently problematic to model, special care must be taken when selecting the input structure. This is even more critical when domain swapping is expected .Here, we provide an overview of approaches and available computational tools, which are of special interest when modeling homo-oligomers , first b2 symmetry in symmetrical homo-dimers. There are several tools available where the user can impose such symmetry in docking of subunits—some allow modeling of only a small assembly such as dimer or trimer, while others are less restricted in the number of subunits.Cyclic symmetry is the most frequently encountered symmetry type in protein homo-oligomers, especially in its simplest form—the Cn symmetric. Imposing symmetry at the initial sampling instead of filtering the results at the end also leads to improvements in both the accuracy and computational time [M-ZDOCK  is an exnal time . Althougnal time , which dnal time .2 symmetry [A similar approach is also employed in SymmDock ,88 whichsymmetry . To furtsymmetry  for fittClusPro, another software that uses FFT for sampling ,90,91, aSymmetry modeling of up to eight subunits can also be defined in GRAMM-X ,93. SymmAlthough dihedral, tetrahedral, octahedral and icosahedral symmetric complexes can be in principle generated with additional transformations of protein complexes with cyclic symmetry, the software that incorporate this option in their ab initio docking workflow are rare. However, some were designed specifically for this purpose.2 protein complexes [2 symmetric docking solutions, provided by MolFit, to assemble the tetramer. The second combines two different C2 docking solutions (ab and ac), each representing one interaction surface between subunits in the tetramer. Later, the algorithm was extended to generate cyclic and dihedral symmetric complexes with a higher number of subunits [MolFit was the first algorithm to employ FFT to calculate the correlation function  and was omplexes  by utilisubunits .3 trimers.Symmetry assembler (SAM)  employs 2 symmetric docking of a previously predicted Cn complex.HSYMDOCK  also enaGalaxyTongDock , similarProtein complexes with dihedral symmetry can also be modeled with HADDOCK ,105, one2 pairs, C3 triplets, C4 quadruplets and/or C5 quintuplets of subunits. By combining these options, complexes with dihedral symmetry can also be assembled. Symmetry is imposed at every stage of the HADDOCK protocol by requiring the intermolecular distances between symmetric Cα to be the same.Multimer docking was introduced to HADDOCK in 2010 . CurrentA protocol for symmetry docking with Rosetta SymDock, another very popular software for molecular docking, was demonstrated to be successful for modeling cyclic, dihedral, helical and icosahedral complexes . It usesThe accuracy of modeling predictions can be further improved by including restraints based on experimental data, such as NMR , cross-lThe increasing number of solved 3D structures of protein complexes, deposited to PDB, enabled the development of homology-based algorithms for homo-oligomer structure prediction.The first homology-based modeling software that was available to users as an automated pipeline for protein structure prediction was SWISS-MODEL ,111,112.One of the first software, designed specifically for template-based homo-oligomeric modeling, was GalaxyGemini, developed by the Chaok Seok group . GalaxyGn symmetries are considered. The key advantages of GalaxyHomomer over its predecessor GalaxyGemini are the final terminal region and loop-remodeling, using GalaxyLoop GalaxyLoop [The same group later also developed another software homo-oligomer structure prediction—GalaxyHomomer ,115, whilaxyLoop ,150,151 laxyLoop . Symmetric docking of cyclic and dihedral complexes was also introduced to the HDOCK webserver . HDOCK aRecently, ClusPro template-based modeling (TBM) webserver was launched . HoweverSymmetry constraints can also be used in Rosetta’s comparative modeling protocol, Rosetta CM . Target Although not designed for handling symmetric complexes, several docking algorithms and servers, not described above, were successfully employed for docking homo-oligomeric complexes in the recent CASP-CAPRI experiments ,154: SWAMycobacterium abscessus proteome as a proof-of-concept as part of the Mabellini project [To tackle homo-oligomer modeling on a proteome scale, the ProtCHOIR tool was recently developed and applied to the  project .https://robetta.bakerlab.org (accessed on 20 August 2021), also holds great promise.The development of deep learning methods led to a dramatic improvement in structure prediction of uncomplexed proteins, especially by the AlphaFold2 algorithm . On the Identification of protein homo-oligomers, and other assemblies in general, from crystal structures has a long history and continues to be an important aspect of protein structure determination. In the last decades, computational methods are becoming more and more relevant due to the high number of experimental template structures for homology modeling and for deciphering the nature of inter-subunit contacts. However, despite significant advancement in several algorithms designed to specifically tackle modeling of homo-oligomers, and advances in protein–protein docking algorithms in general, there is still plenty of room for improvement.Highly accurate models can be predicted for homodimers, especially when good templates are available. However, predictions are poorer for higher-order oligomers or cases without suitable homology-template of the complex, as can be seen from CASP -CAPRI  experiments ,154. ForEvaluation of protein assembly predictions in CASP13 led to similar conclusions . The aut


Viral infections are causing significant morbidity and mortality worldwide. Understanding the interaction patterns between a particular virus and human proteins plays a crucial role in unveiling the underlying mechanism of viral infection and pathogenesis. This could further help in prevention and treatment of virus-related diseases. However, the task of predicting protein–protein interactions between a new virus and human cells is extremely challenging due to scarce data on virus-human interactions and fast mutation rates of most viruses.multitask transfer learning approach that exploits the information of around 24 million protein sequences and the interaction patterns from the human interactome to counter the problem of small training datasets. Instead of using hand-crafted protein features, we utilize statistically rich protein representations learned by a deep language modeling approach from a massive source of protein sequences. Additionally, we employ an additional objective which aims to maximize the probability of observing human protein–protein interactions. This additional task objective acts as a regularizer and also allows to incorporate domain knowledge to inform the virus-human protein–protein interaction prediction model.We developed a SARS-CoV-2 virus receptor. Experimental results show that our proposed model works effectively for both virus-human and bacteria-human protein–protein interaction prediction tasks. We share our code for reproducibility and future research at https://git.l3s.uni-hannover.de/dong/multitask-transfer.Our approach achieved competitive results on 13 benchmark datasets and the case study for the  Virus infections cause an enormous and ever increasing burden on healthcare systems worldwide. The ongoing COVID-19 pandemic caused by the zoonotic virus, SARS-CoV-2, has resulted in enormous socio-economic losses . Besidesin vitro experimentation by providing a list of most probable interactions, which actual biological experimentation techniques can falsify or verify.In vitro experiments based on yeast-two hybrid (Y2H), ligand-based capture MS, proximity labeling MS, and protein arrays have identified tens of thousands of virus-human protein interactions –14. ThesIn this work, we cast the problem of predicting virus-human protein interactions as a binary classification problem and focus specifically on emerging viruses that has limited experimentally verified interaction data.Limited interaction data. One of the main challenges in tackling the current task as a learning problem is the limited training data. Towards predicting virus-host PPI, some known interactions of other human viruses collected from wet-lab experiments are employed as training data. The number of known PPIs is usually too small and thus, not representative enough to ensure the generalizability of trained models. In effect, the trained models might overfit the training data and would give inaccurate predictions for any given new virus.Difference to other pathogens. A natural strategy to overcome the limitation posed by scarce virus protein interaction data is to employ transfer learning from available intra-species PPI or PPI data for other types of pathogens. This may, in its simplest fashion, not be a viable strategy as virus proteins can differ substantially from human or bacterial proteins. Typically, they are highly structurally and functionally dynamic. Virus proteins often have multiple independent functions so that they cannot be easily detected by common sequence-structure comparison , epochs from 0 to 200 with a step of 2 and the learning rate lr in Novel H1N1 and Novel Ebola datasets, we test each with 160 different combinations of negative training and negative testing. Therefore, we fix the hidden dimension to 16, We use Pytorch  to impleDoc2vec model, we use the released code shared by the authors with the given parameters. For the Generalized and Denovo models, we re-implement the methods in Python using all the parameters and feature set as described in the original papers. For Barman and DeepViral, the results are taken from the original papers or calculated from the given model prediction scores.For the AUC) and the area under the precision-recall curve (AP), the Precision, Recall, and F1 scores.For all benchmark datasets except the case study, we report five metrics: the Area under Receiver Operating Characteristic curve  methods employing hand-crafted input features, (ii) sequence embedding-based methods, (iii) an approach that uses protein domain information, (iv) simpler variants of MTT as ablation studies respectively. All statistical test results present in this section are those from the pair-wise t-test [In the following four subsections, we provide a detailed comparison of e t-test  on the FGeneralized [Denovo [Denovo and Generalized are 686 and 1175, respectively. They both employ SVM for the classification task. Since SVM scales quadratically with the number of data points, Denovo and Generalized are not scalable to larger datasets.eralized  and Deno [Denovo  are the MTT on small testing datasets. Detailed scores are given in Table MTT significantly outperforms Denovo in all benchmarked datasets with a confidence score of at least Generalized, MTT has higher performance in six out of seven datasets (except Denovo_slim). The difference is the most significant on the Barman, Zhou’s H1N1, and Zhou’s Ebola datasets. On Denovo_slim dataset, MTT ’s F1 score is lower than Generalized and only 2% higher than Denovo. This is expected since Denovo_slim is a specialized dataset favoring methods using local sequence motif features, which are exploited by Denovo and Generalized.Figure Hybrid is one recently proposed, deep learning-based method. Despite that, the input features are still manually extracted from the protein sequence. Since the code is not publicly available, we only have the AUC score corresponding to the Zhou’s H1N1 dataset, which is also taken from the original paper as listed in Table Hybrid, MTT has higher AUC score. Though comparison on the AUC for one dataset does not bring much insight, we include this method here for completeness.Doc2vec and MotifTransformer are state-of-the-art methods based on sequence embeddings or representations. Doc2vec utilizes the embeddings learned from the extracted k-mer features while MTT and MotifTransformer employ the embedding directly learned from the amino acid sequences. In addition, MTT is a multitask-based approach that incorporates additional information on human protein–protein interaction into the learning process.MTT and Doc2vec over all benchmarked datasets. Detailed scores are presented in Table MotifTransformer model is not publicly available, we only have the corresponding results available for the Zhou’s H1N1 and Zhou’s Ebola datasets, which are also taken from the original paper. ‘-’ denotes the score is not available. Compared with MotifTransformer, MTT has a slightly worse F1 score on Zhou’s H1N1 and significantly better F1 score on Zhou’s Ebola datasets.Figure Comparison withDoc2vec. MTT out-performs Doc2vec in 5 out of 9 benchmark datasets, and the performance gap is statistically significant with a p-value smaller than 0.05. MTT is significantly better than Doc2vec on the Novel Ebola dataset, while on the Novel H1N1 dataset, the reverse holds true. Doc2vec outperforms MTT in three testing datasets whose negative samples were drawn from a sequence dissimilarity method. We also note that these datasets might be biased since in the ideal testing scenario, we do not have knowledge about the set of human proteins that interacted with the virus. Therefore, such dissimilarity-based negative sampling is infeasible.Barman features set is constructed from the domain-domain association and the hand-crafted feature extracted from the protein sequences. Since the protein domain information is not available for all viral proteins, the Barman method has restricted application. A comparison between Barman and MTT is presented in Table Barman model on one dataset. From reported results, we could clearly see that MTT outperforms its competitor for a large margin in all available metrics.DeepViral exploited that disease phenotypes, the viral taxonomies, and proteins’ GO annotation to enrich its protein embeddings. Table MTT and DeepViral on the four datasets released by DeepViral ’s authors. The reported results on each dataset are the average after five experimental runs for DeepViral and ten experimental runs for MTT. We observe MTT and STT significantly supersede their competitor regarding the averaged F1 score. The gain is more significant on smaller datasets (644788 and 333761)STT and the Naive baseline baseline models. STT is the MTT model without the human PPI prediction task. Naive baseline concatenates the learned embeddings for the virus and human proteins to form the input to a Logistic Regression model. Figure MTT and its variants on our benchmarked datasets. Table MTT is significantly better than STT in five out of nine benchmarked and the four DeepViral datasets with a p-value smaller than 0.05. While in the remaining four datasets, the difference is not statistically significant. This confirms that the learned patterns from the human PPI network bring additional benefits to the virus-human PPI prediction task.We compare our method with two of its simpler variants: the Naive baseline, MTT wins in eight out of nine benchmarked and the four DeepViral datasets. On the remaining dataset (Novel H1N1), the difference is not statistically different. STT significantly outperforms Naive baseline in eight out of nine datasets. This claims the effectiveness of our chosen architecture.Compare with The virus binding to cells or the interaction between viral attachment proteins and host cell receptors is the first and decisive step in the virus replication cycle. Identifying the host receptor(s) for a particular virus is often fundamental in unveiling the virus pathogenesis and its species tropism.SARS-CoV-2. Our virus-human PPI dataset is retrieved from the InAct Molecular Interaction database [SARS-CoV-2 binding partners.Here we present a case study for detecting the human protein binding partners for database   appears in the human surfacetome [CC-plasma membrane, CC-cell junction}.facetome  list or facetome , 69:{CC-indirect (interactions that are not marked as direct in the database) between the human proteins and any virus except SARS-CoV and SARS-CoV-2. The indirect interactions can be a physical association (two proteins are detected in the same protein complex at the same point of time) or an association in which two proteins that may participate in the formation of one or more physical complexes without additional evidence whether the proteins are directly binding to specific members of such a complex).The negative samples for training data contain Validation and test sets. As established in studies [SARS-CoV [SARS-CoV-2 viruses [SARS-CoV and SARS-CoV-2, respectively. Our negative validation and testing set encapsulate of all possible combinations the two viral spike proteins and 52 human proteins that meet our filtering criteria. studies –72, angiSARS-CoV  and SARSSince we are interested in only the direct interaction between virus and human proteins, we also customize our intra human PPI training set. Our intra human PPI dataset is also retrieved from the InAct  databaseFinally, we here evaluate the prediction methods on how effective they are in ranking human protein candidates for binding to an emerging virus envelope protein. Figure SARS-CoV-2 receptor proven in in vivo and in vitro studies [MTT in each of the ten experimental runs. We observe a significant difference between the highest ranked performance of MTT and its competitors. The performance gain shown by MTT over STT is quite substantial after ten runs and supports the superiority of our multitask framework. The next highest nine hits presented in both models have not been shown to interact with SARS-CoV-2 in in vitro studies. Interestingly, dipeptidyl peptidase 4 (DDP4), a receptor for another betacoronavirus MERS-CoV [Using this method we find that ACE2, the only  studies , 74, 75,MERS-CoV  also scoMERS-CoV , it is yMERS-CoV , appeareMERS-CoV .MTT and Doc2vec models. Specifically, the proteins are ranked based on the average (over 10 runs) confidence scores as predicted by the two models. While for MTT, the receptor ACE2 always occurs at the top of the list with average confidence score of more than 0.70 (which is more than 11% higher than the confidence score assigned to the second hit), Doc2vec assigns it a score of less than 0.44 where ACE2 is ranked 2nd based on average scores. Moreover, there is negligible difference between the prediction scores for ACE2 and the first predicted hit ANPEP in case of Doc2vec.In Figures MTT can provide high-quality prediction results and can help biologists to restrict the search space for the virus interaction partner effectively. This case study showcases the effectiveness of our method in solving virus-human PPI prediction problem and aims to convince biologists of the potential application of our prediction framework.These results indicate that SARS-CoV-2 virus spike protein and its human receptor indicates that our model can be used as an effective tool to reduce the search space for evaluating host protein candidates as interacting partners for emerging viruses. In future work, we will enhance our multitask approach by incorporating more domain information including structural protein prediction tools [We presented a thorough overview of state-of-the-art models and their limitations for the task of virus-human PPI prediction. Our proposed approach exploits powerful statistical protein representations derived from a corpus of around 24 Million protein sequences in a multitask framework. Noting the fact that virus proteins tend to mimic human proteins towards interacting with the host proteins, we use the prediction of human PPI as a side task to regularize our model and improve generalization. The comparison of our method with a variety of state-of-the-art models on several datasets showcase the superiority of our approach. Ablation study results suggest that the human PPI prediction side task brings additional benefits and helps boost the model performance. A case study on the interaction of the on tools  as well


Protein solubility is based on the compatibility of the specific protein surface with the polar aquatic environment. The exposure of polar residues to the protein surface promotes the protein’s solubility in the polar environment. The aquatic environment also influences the folding process by favoring the centralization of hydrophobic residues with the simultaneous exposure to polar residues. The degree of compatibility of the residue distribution, with the model of the concentration of hydrophobic residues in the center of the molecule, with the simultaneous exposure of polar residues is determined by the sequence of amino acids in the chain. The fuzzy oil drop model enables the quantification of the degree of compatibility of the hydrophobicity distribution observed in the protein to a form fully consistent with the Gaussian 3D function, which expresses an idealized distribution that meets the preferences of the polar water environment. The varied degrees of compatibility of the distribution observed with the idealized one allow the prediction of preferences to interactions with molecules of different polarity, including water molecules in particular. This paper analyzes a set of proteins with different levels of hydrophobicity distribution in the context of the solubility of a given protein and the possibility of complex formation. Escherica coli [There are two environments of biological activity for proteins: an aquatic environment, and a membrane environment. Hence, the possibility of protein solubility is critical to protein activity. Experimental analysis of the solubility of proteins is supported by theoretical models, which is why many methods and computational tools aimed at determining the degree of solubility are available in the network ,2. Howevica coli ,21. In the present study, a model called the fuzzy oil drop (FOD)  was usedThe FOD model is based on the assumption that the idealized hydrophobicity distribution for a fully soluble system of bipolar molecules is the distribution expressed by the 3D Gaussian function, reflecting the hydrophobicity distribution in the spherical micelle. The values of the function spanned on the protein (sigma parameters adjusted to the size of the molecule) express the expected level of hydrophobicity at a given point. Each deviation—whether local, or covering the entire molecule of a protein or complex—identified on the basis of differences between the idealized distribution and that observed in a given protein—assesses its degree of maladjustment to the assumed distribution. The type of mismatch—local exposure of hydrophobic residues and/or local hydrophobic deficit—is interpreted either as a potential possibility of complexing another protein , or as tIn the present study, the status of the complexes, chains, and domains present in proteins with a gradually increasing degree of mismatch in the distribution observed with the idealized one was assessed using an analysis based on the fuzzy oil drop model. The influence of the environment was demonstrated using the example of membrane proteins, where the external conditions for the part directed to the cytoplasm and the part anchored in the membrane were radically different.The proteins were selected for analysis to represent a possibly large spectrum of different solubility and complexation tendencies ,34,35,36The proteins discussed here were selected taking into account the criteria of gradually developed structures: from a monomer with high solubility; through a chain with a domain structure, complexes with various forms of their stabilization, and domain-swapping; and further through complex quaternary structures with ligands present; to a multi-chain complex anchored in a membrane that acts as a channel for the transport of molecules.Type III antifreeze proteins are proteins whose biological activity is related to their solubility. Their presence prevents water from taking the structural form of ice ,41. Thiszoarces viviparus—zvafp13 (PDB ID 4UR4). The O distribution in this protein is very close to that of the T distribution, which guarantees the high solubility of this protein. It should be noted that the compatibility of the O distribution with the T distribution indicates the presence of a centric concentration of hydrophobicity with the presence of the polar surface of the protein. Similarly, the double-length protein—the Ca2+-dependent type II antifreeze protein (PDB ID 6JK4)—also shows a distribution matching the idealized one. This agreement indicates exposure of polar residues to the surface. Type II and III antifreeze proteins represent structures with very low RD values. Here, an example of this group of proteins is a 66-amino-acid chain in a fish antifreeze protein from zoarces viviparus—zvafp6 (PDB ID 4UR6)—deposited in the PDB does not appear to be the form found in nature. The monomer status shows very low RD values, which means full surface coverage by polar residues. The globular  structure of each chain of this protein is accompanied by very little intermolecular contact. Intermolecular contact relies mainly on several interactions, based on the interactions of polar groups. The impact of the electrostatic type in the aquatic environment, in the form of limited contact, is highly unlikely. At the same time, a very low RD value for single structures indicates the full availability of the surface for interactions with water.The dimeric structural form of type III fish antifreeze protein from The intramolecular dimer antifreeze protein rd3 (PDB ID 1C8A) has an RD value of >0.5 . The strSalmonella enterica SafA pilin in complex with a 19-residue SafA Nte peptide shows the status of the idealized distribution. This means that it can function as an independent structural unit in the aquatic environment. Complexation of the 16-amino-acid polypeptide chain, however, results in an additional reduction in the RD value, which means that the status of the complex is more favorable for the structure as a whole.The domain-swapping phenomenon is the exchange of structural elements between proteins, resulting in the formation of dimeric (or polymeric) forms. A chain fragment of one protein interacts with another protein in order to complete its super-secondary structure. This fragment, leaving its position in the native protein, opens the site for complexation of a fragment of the chain of another protein. The reaction can cause a domino effect. The domain-swapping phenomenon is observed relatively often ,44,45. TThis is also evidenced by the change in the status of the system itself: a slightly deformed beta-barrel shows an RD of 0.428, and in the composition extended by the presence of an additional polypeptide shows an RD of 0.416. Similarly, the status of the adjacent segments to the incorporated peptide shows an RD of 0.664, while the status of the same system in the presence of a polypeptide decreases to an RD of 0.533. The presence of the polypeptide in each of the considered systems turns out to be advantageous in reducing the RD value, which means obtaining a distribution close to the idealized distribution. This, in turn, favors obtaining a micelle-like structure, which is a favorable system in the water environment for bi-polar molecules, with different degrees of differentiation of the polar to the hydrophobic parts. Thus, a micelle-like structure is the optimal solution here. The status of the components of this complex is shown in The core pilin domain (PDB ID 3CRF) structure is more complex. It consists of a system of two chains  and a C peptide. The B domain provides fragments 0–19 of the A domain. However, the B domain also includes the C 0–19 peptide. The structure of the beta-structural system forms a deformed beta-barrel. Fragments 0–19 are part of these deformed beta-barrels. The complex does not exhibit an ordered structure of the fuzzy oil drop model. This is mainly due to the absence of a common hydrophobic core. In a central part of the complex, where the hydrophobic core is expected, the loosely packed interface is present. Chains A and B, treated as individual structural units, show a status minimally exceeding the adopted threshold level (RD = 0.5). Connecting fragments 1–19 results in the reduction of the RD value to 0.500 . In the Why is the four-chain protein with a high RD value the example for protein solubility considerations? This question will be answered via the analysis of the structure of human hemoglobin (PDB ID 1A3N). According to the fuzzy oil drop model, protein solubility is represented by the protein’s RD being less than 0.5. This value means that the surface of the protein is covered with polar groups with centrally concentrated hydrophobic residues. The full agreement of the O and T distributions expresses a very high solubility, eliminating any interaction with other molecules except water, ions, or other molecules of a polar nature. The presence of a biological function requires exceptions to this rule. The specificity of the local deviation determines the specificity of the biological activity. The exposure to hydrophobic residues on the surface implies the formation of protein–protein complexes. Contact with hydrophobic surfaces eliminates the unfavorable—in terms of entropy, contact with water. Identification of local exposure suggests the complexation site of another protein, or binding to a bipolar ligand. The local hydrophobicity deficit, often accompanying the presence of cavity, suggests a site of interaction with the ligand or substrate. Therefore, the identification of the part of the protein involved in the complexation of a ligand or another protein allows for the prediction of the site of complexation. The fragments of the polypeptide chain eliminated from the RD analysis make it possible to identify that part of the protein, the hydrophobicity arrangement of which is consistent with the assumed model, and thus indicates the locations of the parts of the protein responsible for its solubility. Such operations were performed for hemoglobin. The RD parameter values for the four-chain complex show high mismatch. The status of individual chains shows compatibility with an idealized distribution  .The structure of individual chains is close to the idealized distribution;The presence of a ligand-binding cavity affects the incompatibility of the O and T distributions;The difference between the α and β chain characteristics, from the point of view of both P–P interaction and ligand binding; andThe dominant role of the interface structure on the complex stabilization, with the presence of the ligand-binding cavity having a much smaller effect.A comparison of RD values for the complex and the individual chains indicates:KL) results in obtaining the status expressed as RD < 0.5.The comparison of the RD values suggests the differentiation of the characteristics of A chains versus the B chain, which is observed in the experimental studies ,47. The The analysis of the results also suggests the presence of distortions in the hydrophobicity distribution resulting from the presence of the ligand-binding cavity. The identified part (after the elimination of residues showing large differences between the T and O distributions) allows for the extraction of the part of the complex that, showing the order proper to the aqueous environment, ensures the solubility of the complex. This indicates the presence of local inconsistencies with the idealized distribution, in the form of a local hydrophobicity deficit (in the case of the cavity) and a local excess (in the case of interaction with other chains). Eliminating the residues showing these mismatches from the computation results in an RD of <0.5. This part of the complex is responsible for the micelle-like organization and, therefore, also for solubility.The durability of the complex can be achieved by generating an additional domain in the interface area. Such a domain, which consists of fragments derived from both complexed chains and dimers, is an example of synergy in attaining the goal of complex stability. Such a solution was identified earlier in dystrophin . The preThe protein discussed here is an example of a complex formation mechanism involving chain sections not only in interface creation, but also in the structuring of a new separate domain that fully functions as an interface. An exemplary object for the analysis of a multi-chain complex is pilin ,34. ThisThe pilin monomer molecule (PDB ID 2PIL) consists of a long helix in the N-terminal part of the chain and a globular part (PDB ID 1HPW) . Analysiignicoccus hospitalis show diversity of packing in proteins containing N-terminal type IV pilin helices, arranged conically to form a geometrically ordered system with an axial structure in the presence of symmetry (PDB ID 3J1R)  a. The status of this complex is expressed by a very high RD value see .Chain A exhibits a significant excess of hydrophobicity in the complex, coming from not one but several surrounding helices derived from adjacent monomers. This is closely related to the need to anchor the complex in the cell membrane—i.e., in a highly hydrophobic environment . Chain FMoving on to the analysis of the complete pilin complex (PDB ID 5VXY)  composedChain K, in the central part of the complex, shows the closest approximation of the T and O distributions, but this convergence is only due to the fact that it is located appropriately in relation to the assumed 3D Gaussian distribution.The pilin complex as a channel (apart from anchoring in the membrane), and thus representing a system with a fairly high structural and functional complexity, requires in-depth analysis. This complex is an object of independent analysis. Here, only the high readability of the analysis, based on the fuzzy oil drop model, in relation to the prediction of the solubility or complexity of the molecules in question, was demonstrated. A separate issue is the assessment of the status of a single chain in such a composite complex as pilin. All of the monomers in the complex showing the same sequence get different roles to play within the complex. The mechanism of creating quaternary structures is very diverse, and depends on the desired biological function. Usually, the method of obtaining two- or multi-chain complexes has one goal: to ensure specific biological activity. Nevertheless, the means of achieving these goals can vary. Obtaining highly complex systems cannot be simple and similar for many different systems. The examples presented here provide one amongst many possible mechanisms. The analysis presented here is intended as a background reference for the study of the formation of misfolding complexes. The repeatability of O versus T profiles in the case of, for example, pilin—a multicomponent fibril—helps to identify the specificity of amyloid structures, where the repeatability of hydrophobicity distributions is also very high. There is a question as to why amyloid propagates indefinitely, while piline has a defined, limited, and repeatable length (limited number of chains in the complex). The solutions presented here do not exhaust the spectrum of possibilities leading to the formation of quaternary structures. Nevertheless, it is impossible to eliminate the aquatic environment as a factor which may play a decisive and active role in the complexation process. Antifreeze and hemoglobin proteins present in the analyzed collection act as reference structures. Antifreeze proteins show high solubility, representing a high degree of similarity to the T and O distributions, which indicates the concentration of hydrophobic residues in the center of the molecule, with the exposure to polar residues on the surface. As a result, the surface is covered with polar systems that constitute objects for beneficial interaction with water molecules in the environment. The presence of hemoglobin results from the need to demonstrate the presence of inter-chain complexation and the complexation of large ligands. The presence of ligands is closely related to the performance of biological activity. The structure of hemoglobin, despite significant deviations of the O distribution from the T distribution resulting from the biological function performed, shows the presence of some chains that meet the criteria qualifying them as guaranteeing solubility. The analysis of the multi-chain association with a fibrillary structure can be an example for comparisons with the structures of amyloid fibrils. The lack of a hydrophobic core in both cases is common, expressed by the deviation of the O distribution from the T distribution. The main difference, however, lies in the maintenance of at least some of the globular form of the chains constituting the canal, while the amyloid chains show a 2D Gaussian structure, representing flat structures. The consequence is that all residues present are involved in the inter-chain interactions in amyloids, whereas in pilin this occurs only for amino acids representing the appropriate status to the rest of the molecule. The 2D Gaussian form promotes and even guarantees unlimited growth of the fibril. A review of proteins with diversified structures and biological activities, in terms of the increasing amount of information carried by a given protein and the degree of complexity of its structure, is discussed in detail in ,23. The The formation of the multi-molecule system as a channel is similar to the observation of amyloids in the form of a cyclically repeated deviation in the distribution observed in the protein versus an idealized distribution. The analysis carried out here is one approach to determining the differences that enable the identification of amyloid complexing versus other fibrillar forms of multi-molecular complexes. This differentiation is based on the presence of 3D Gaussian globular structures in the case of single chains included in functional complexes, while amyloid fibrillary complexes represent a form with 2D Gaussian distribution with respect to a single chain of the fibril component. This means that biologically active protein complexes are constructed from globular units , while in the case of amyloids, specific flat surfaces in the form of successive patches are observed.The present analysis is focused on the complexation of small proteins. The problems of the relationship between the size of monomer interfaces in oligomers, as well as the identification of large hydrophobic cavities in proteins, are the main issues under consideration in the current analysis .The proteins were selected for analysis to represent possibly highly different proteins with respect to their solubility and complexation . The set of selected proteins is presented in KL) [The applied model (fuzzy oil drop) assumes the hydrophobicity distribution in the form expressed by the 3D Gaussian function. This function reflects the distribution of hydrophobicity with a centric concentration of hydrophobicity, and its gradual reduction with distance from the center, reaching values close to zero on the protein surface. The size of the ellipsoid expressed by this function depends on the size of the analyzed molecule. This function, spread over the protein molecule, is defined by the values of the parameters σX, σY, and σZ, determined according to the size of the protein. The level of idealized hydrophobicity for a given residue (Ti) expresses the value of the 3D Gaussian function in the position of the so-called effective atom (the average position of the atoms making up a given amino acid). This idealized distribution is confronted with the observed distribution (O), which is the result of hydrophobic interactions between the residues. This value depends on the distance between the effective atoms, and on the intrinsic hydrophobicity of the interacting amino acids . The degKL) .KL for the O distribution, taking the T distribution as the reference distribution, while the O–R distance expresses the DKL for the O distribution, taking the R distribution as the reference distribution. An RD value of less than 0.5 indicates the presence of a hydrophobic core. The concept of a hydrophobic core, apart from the concentration of hydrophobicity in the center of the molecule, also expresses the presence of a polar surface. Identified amino acids showing a deviation between the T and O distributions are analyzed for their contribution to a biological activity. This model has been described many times [The second reference distribution against which the O distribution is assessed is the R distribution, which is the unified distribution, where each residue (effective atom) is assigned a constant hydrophobicity level equal to 1/N, where N is equal to the number of amino acids in the chain. Comparing the distance between the O and T distributions with the distance between the O and R distributions enables us to determine the similarity of the O distribution to each of the reference distributions. The lower distance from the O distribution to the T distribution is interpreted as the presence of a hydrophobic core in the analyzed protein. It is expressed by the parameter RD (Relative Distance). RD is equal to the ratio of the O–T distances to the sum of the O–T and O–R distances. The O–T distance is the value of Dny times .The paper demonstrates the possibility of quantifying environmental preference for protein solubility using the fuzzy oil drop model. The diverse nature of the proteins in question reveals the consistency of the measure expressed by the RD parameter, with the presence of a polar surface guaranteeing solubility with the presence of local disorders in the distribution of hydrophobicity observed in the investigated protein against an idealized distribution. These local variations of high specificity determine the specificity of the biological activity of a given protein. The use of the FOD model makes it possible to assess the status of the chain in relation to the hydrophobic environment of the membrane proteins. Exposure to the hydrophobic surface identified in the protein structure is a factor that promotes interaction with the membrane. The model used introduces the possibility of quantifying this specificity. The present analysis recognizes the conditions that guarantee the solubility of proteins against the phenomenon of amyloid insolubility. In the search for differences between amyloids and complex proteins with biological activity, the solubility phenomenon is of critical importance. The analysis of the list of proteins with different characteristics, including the membrane protein, reveals a whole spectrum of possibilities of identifying the types and the degree of differentness of characteristics in relation to the aquatic and non-aqueous environments expressed by the fuzzy oil drop model.


SARS-CoV-2 genomic sequencing efforts have scaled dramatically to address the current global pandemic and aid public health. However, autonomous genome annotation of SARS-CoV-2 genes, proteins, and domains is not readily accomplished by existing methods and results in missing or incorrect sequences. To overcome this limitation, we developed a novel semi-supervised pipeline for automated gene, protein, and functional domain annotation of SARS-CoV-2 genomes that differentiates itself by not relying on the use of a single reference genome and by overcoming atypical genomic traits that challenge traditional bioinformatic methods. We analyzed an initial corpus of 66,000 SARS-CoV-2 genome sequences collected from labs across the world using our method and identified the comprehensive set of known proteins with 98.5% set membership accuracy and 99.1% accuracy in length prediction, compared to proteome references, including Replicase polyprotein 1ab . Compared to other published tools, such as Prokka (base) and VAPiD, we yielded a 6.4- and 1.8-fold increase in protein annotations. Our method generated 13,000,000 gene, protein, and domain sequences—some conserved across time and geography and others representing emerging variants. We observed 3362 non-redundant sequences per protein on average within this corpus and described key D614G and N501Y variants spatiotemporally in the initial genome corpus. For spike glycoprotein domains, we achieved greater than 97.9% sequence identity to references and characterized receptor binding domain variants. We further demonstrated the robustness and extensibility of our method on an additional 4000 variant diverse genomes containing all named variants of concern and interest as of August 2021. In this cohort, we successfully identified all keystone spike glycoprotein mutations in our predicted protein sequences with greater than 99% accuracy as well as demonstrating high accuracy of the protein and domain annotations. This work comprehensively presents the molecular targets to refine biomedical interventions for SARS-CoV-2 with a scalable, high-accuracy method to analyze newly sequenced infections as they arise. The ongoing SARS-CoV-2 pandemic has undoubtedly shaped our lives as one of the most significant global health challenges of the 21st century. However, unlike previous pandemics, we now have sequencing technology with tremendous throughput to analyze the genomic content of SARS-CoV-2. As labs around the world sequence isolates from infected individuals, we can track and characterize the viral genome evolution for near real-time surveillance.The first sequenced SARS-CoV-2 genome  was submCoronaviridae traits, such as gene order consistency and transcriptional slippage [In comparison to other coronaviruses, SARS-CoV-2 differs phenotypically with its significant increase in transmissibility and asymptomatic or presymptomatic transmission, as well as genotypically from its polybasic furin cleavage site insertion in the S protein . Howeverslippage . The −1 slippage . This phslippage  and may There are several viral genome annotation methods, such as VAPiD, Prokka, InterProScan, and others ,8,9, thaAs an alternative to an autonomous genome annotation method, alignments to the Wuhan reference genome  can be cIn this work, we present a semi-supervised custom pipeline to annotate all genes, proteins, and functional domains for SARS-CoV-2. These sequences comprise the molecular targets to develop more accurate diagnostics, antivirals, and vaccines. Our semi-supervised method was applied to 66,905 SARS-CoV-2 genomes collected from NCBI GenBank  and GISAhttps://ibm.biz/functional-genomics, accessed on 23 March 2021). Ultimately, we present a comprehensive comparative analysis and data resource of 13,000,000 publicly available SARS-CoV-2 viral sequences with the aim of identifying potential targets to aid in vaccine, diagnostic, or therapeutic development.The complete collection of SARS-CoV-2 genes, proteins, and functional domains continues to be updated and can be accessed via a web browser user interface or our developer toolkit   and NCBIhttps://github.com/IBM/omxware-getting-started/tree/master/SARS-CoV-2_parser, accessed on 23 March 2021. The customizations for the gene and protein annotations are described briefly below and in further detail in Specific refinements to our previously described genome annotation pipeline  were madTo accommodate the nascent state of the SARS-CoV-2 reference data, the evidence codes required by Prokka to build the reference index used in gene and protein naming were loosened. Additional control surrounding the annotation mode, e.g., single or metagenome mode, were made to enable the single genome mode when the input genome was less than 100,000 bp in length to accommodate the length of SARS-CoV-2 . A targeted search of intermediate gene coordinate data generated by Prodigal (within Prokka) for high sequence similarity to known protein references was added to augment genome annotations to include ORF9b, ORF10, and Envelope small membrane protein, which were otherwise missing from the annotations. For Replicase polyprotein 1ab, nucleotide sequences were harvested from intermediate gene coordinates and extended or fused to ensure full length gene products with correct start and stop codons. These sequences were verified to contain the slippery site  with nuchttps://www.ebi.ac.uk/interpro/proteome/uniprot/UP000464024/, accessed on 23 March 2021.Unique protein sequences were processed with InterProScan v5.48-83  to ident–r) NC_045512.2. Protein names and sequences were extracted from VAPiD output files using BioPython’s parser. Prokka version 1.14.5 [–kingdom Viruses. The resulting protein sequences from each tool were compared for set membership per genome, protein sequence truncations and overall sequence similarity. Here, protein set membership accuracy is defined as ability to detect the set of expected proteins per genome.To compare our method against other published viral genome annotation tools, VAPiD (v1.2 with Python3) was run on a set of 100 randomly selected SARS-CoV-2 genomes above the quality control thresholds previously defined in n 1.14.5  was run To evaluate our method in the context of emerging and shifting SARS-CoV-2 variants, we expanded the initial genome corpus analyzed to include representation of all four variants of concern (VoC) and four variants of interest (VoI) defined by the Wold Health Organization and U.S. Centers for Disease Control as of 19 August 2021. This includes the following variants: B.1.1.7 Alpha, B.1.351 Beta, B.1.617.2 Delta, P.1 Gamma as well as B.1.525 Eta, B.1.526 Iota, B.1.617.1 Kappa, C.37 Lamda for VoC and VoI, respectively. For each variant listed, 500 genomes were selected randomly from the list of all SARS-CoV-2 genomes present in NCBI GenBank on 19 August 2021 for a total of 4000 additional diverse variant genomes. These genomes were subsequently annotated as described in Protein annotations were evaluated against the SARS-CoV-2 proteome reference sequences indicated in ViralZone, SIB Swiss Institute of Bioinformatics  for comphttps://www.ebi.ac.uk/interpro/protein/reviewed/P0DTC2/, accessed on 12 February 2021). Additionally, where predicted domain sequences were assigned an IPR code , the predicted domain sequence was compared against the reference sequence to yield a percent identity. Reference domain sequences were extracted from the S protein amino acid sequence (UniProt: P0DTC2) based on domain start and stop sites indicated at the link above. The amino acid percent identity was calculated with considerations for insertions, deletions, or substitutions.For domain accuracy comparative analysis, our predicted domains identified in the spike glycoprotein (S protein) were analyzed for set membership completeness against the expected InterPro domain architecture for UniProt reference sequence P0DTC2  of all genome sequences to track duplicates. The rate of ‘duplicated’ SARS-CoV-2 genome sequences within a single data source or between data sources is indicated in For an autonomous COVID-19 genome annotation pipeline to achieve clinical and biological relevance, it must accurately identify all known molecular targets within a genome. The SARS-CoV-2 proteome  is definp < 2.2 × 10−16) and are 8.75-fold more similar (D = 0.0617) than those predicted from genomes not passing our quality thresholds, i.e., low quality genomes.Furthermore, not only must the complete set of named genes and proteins be identified for accurate genome annotation, but the generated sequences must also be grounded in biological reality. Specifically, in silico predicted sequences should not be truncated with respect to the length of known references, and the mutational density must be low, considering the temporally recent emergence of SARS-CoV-2 and observed lower mutation rate, compared to other RNA viruses . Using oIn addition, certain gene and protein sequences required us to develop additional targeted methodological advances for identification . SpecifiUltimately from this genome corpus, we were able to identify over 13 million gene, protein, and functional domain sequences in total . Our sysWith regard to pipeline accuracy, we evaluated our pipeline against VAPiD , which cAs SARS-CoV-2 undergoes mutation events, a comprehensive catalog of variants is essential for developing molecular interventions with sufficient specificity and binding efficiency. In our initial genome corpus, we observed a median of 425 unique sequences with non-synonymous mutational differences per protein  per protein. The S protein, which is involved in the invasion of human cells through interaction with ACE2 , is obseSince the S protein is key in the recognition of the host cellular receptor to initiate virus entry and host cell invasion and the target of multiple vaccines, antivirals, and diagnostics, we further examined its observed variants. By querying the S protein from the Functional Genomics Platform and accessing 4548 unique sequences, including metadata, such as collection date and exposure region, we observed two predominant S protein variants that shift in their cumulative frequency over time . Initialhttps://www.ebi.ac.uk/interpro/protein/reviewed/P0DTC2/, accessed on 12 February 2021). The S protein is canonically annotated to have nine domains necessary for its function (based on InterPro reference data at time of manuscript submission). From 5702 distinct spike protein sequences, our annotation method yielded an average 94.4 ± 4.4% domain set membership accuracy (Related to understanding S protein function and interaction with host proteins, we further evaluated our predicted protein domains for their identification and sequence accuracy. Protein domains are sub-regions in the amino acid chain that are observed to determine biological activity, binding efficacy, and key structural components of a protein . In the accuracy a with miaccuracy b and theBeyond the initial corpus used to develop this method, we aimed to assess the robustness of the method against the more recent variants that have emerged. A random sampling of SARS-CoV-2 genomes were retrieved from NCBI GenBank  across tSince the start of the SARS-CoV-2 global pandemic, there have been immense efforts globally to sequence with near real-time efficiency the viral genomes observed in infected patients. In order to capitalize on this large and growing corpus of data, high throughput computational methods must be developed for rapid, high accuracy analysis to deliver the molecular targets that are actually under evaluation for drug development, vaccine specificity, and diagnostic testing. The method described here provides one such avenue to accomplish this goal. The protein and domain data generated as part of this work provide these molecular targets in an efficient manner with very high accuracy across the entire SARS-CoV-2 proteome and for all genomes analyzed in this corpus, spanning multiple countries and lineages. Beyond this, our semi-supervised pipeline does not require the use of a single reference genome, which better allows the detection of novel or mutating gene, protein, and respective domain sequences as they emerge. The method described here was integrated with our Functional Genomics Platform and applied to hundreds of thousands of SARS-CoV-2 genomes. As the vaccination rates rise and the pandemic continues, this method can be used to rapidly monitor and track emerging protein variants across hosts and sampling niches, e.g., aerosol, wastewater, and surfaces, to inform disease understanding, vaccine specificity, and host protein binding affinity. Additionally as future work, further confirming the in silico predicted sequences, using a structural model will allow for refinement of the protein sequences and key domains to expand our understanding of interaction with host proteins, antivirals, or diagnostics. Overall, the data generated as part of this work provide a comprehensive set of protein and domain variants observed globally and support the research community as we continue to aim to understand and control the COVID-19 pandemic.