Functions of intrinsically disordered proteins do not require structure. Such structure-independent functionality has melted away the classic rigid “lock and key” representation of structure–function relationships in proteins, opening a new page in protein science, where molten keys operate on melted locks and where conformational flexibility and intrinsic disorder, structural plasticity and extreme malleability, multifunctionality and binding promiscuity represent a new-fangled reality. Analysis and understanding of this new reality require novel tools, and some of the techniques elaborated for the examination of intrinsically disordered protein functions are outlined in this review. In line with this hypothesis were numerous pieces of evidence generated by the crystal structures of proteins solved by x-ray diffraction, careful analysis of protein denaturation and unfolding, and many other observations, all indicating that specific functionality of a given protein is defined by a unique spatial positioning of its amino acid side chains and prosthetic groups, suggesting that such a specific spatial arrangement of functional groups in biologically active proteins is defined by their unique 3D structures predetermined by the unique amino acid sequences encoded in unique genes. These correlations were in line with the famous “one gene–one enzyme” hypothesis, where a gene encodes a single enzyme that affects a single step in a metabolic pathway3. It is recognized now that the aforementioned “one gene–one enzyme” hypothesis is an oversimplification, and numerous observations fail to fit into or be explained by this model4. Accumulated data challenged both the functional requirement of a unique structure in a biologically active protein and the absolute validity of the “one gene–one enzyme” conjecture, suggesting that the related paradigms should be changed8. In line with these considerations, it is recognized now that the complexity of biological systems is determined by protein diversification and not by the existence of a large number of distinct genes each encoding a unique protein9. In fact, multiple means cause the dramatic and efficient increase in the size of a functional proteome in comparison with the size of a corresponding genome. These proteome-diversifying factors include the allelic variations , different pre-translational mechanisms affecting genes , and changes induced in proteins by numerous post-translational modifications (PTMs)14. The result of this multilevel diversification that combines allelic variations, pre-translational alterations, and PTMs is the generation of multiple proteoforms, which are distinct protein molecules with different structures and diverse functions, from a single gene15.For more than a hundred years, the dominant model describing the molecular mechanism of protein functionality was the classic structure–function paradigm. This paradigm considered protein function in light of the “lock and key” hypothesis, where a unique biological function of a protein was considered to be the consequence of the presence of a unique and highly organized structure in its active site and where, in order to exert a chemical effect on each other, both a substrate and an enzyme have specific geometric shapes that fit exactly into each other, like a key specifically and uniquely fits to a lock22. These proteins, which were originally considered unique exceptions to the “lock and key” rule, are extremely common in nature; all proteomes of living organisms and viruses analysed so far possess noticeable levels of intrinsic disorder41; and the penetrance of disorder increases with the increase in the organism complexity42. As an example, the fraction of proteins predicted to have long IDPRs  increases from Bacteria and Archaea to Eukaryota43. The increased amount of disorder in eukaryotes is attributed to the increased roles of their cellular signalling that often relies on IDPs/IDPRs47. Also, just a small fraction of proteins with known crystal structures in the Protein Data Bank are entirely devoid of disorder49. An important feature of IDPs/IDPRs is their exceptional spatiotemporal heterogeneity, where different regions of a given protein can be ordered (or disordered) to a different degree51. Therefore, the overall structure of functional proteins represents a continuous spectrum of conformations with a different degree and depth of disorder50, thereby generating a complex protein structural space that defines a structure-disorder continuum with no clear boundary between ordered and disordered proteins/regions50. The presence of the aforementioned different levels and depths of intrinsic disorder delineates the mosaic structure of proteins, which typically contain foldons , inducible foldons (disordered regions that can fold at interaction with a binding partner), morphing inducible foldons (disordered regions that can fold differently at interaction with a different binding partner), semi-foldons , non-foldons (IDPRs with entropic chain activities), and unfoldons 50. Obviously, the presence of intrinsic disorder and conformational flexibility in proteins contributes to their structural and functional heterogeneity, representing additional means for generating proteoforms52. In fact, since any protein exists as a dynamic conformational ensemble, members of which have different structures  and potentially different functions, it can be considered a basic  proteoform. Such a conformational proteoform is different from the inducible proteoform that originates from the various alterations  of the canonical protein sequence and that represents a mixture of these various forms. Obviously, since it also represents a structural ensemble, any member of the inducible or modified proteoform  is itself a conformational proteoform52. Finally, since protein function, interaction with specific partners, or placement inside the natural cellular environment can also affect the structural ensemble of both basic and induced proteoforms, functionality per se can be considered a factor generating new functioning proteoforms. As a result, instead of being depicted as an oversimplified “one gene–one protein” view, the actual gene–protein relationship is much more complex, being described by the “one gene–many proteins–many functions” model53. Therefore, a correlation between protein structure and function represents a “protein structure–function continuum”, where at any given moment, any given protein exists as a dynamic conformational ensemble containing multiple proteoforms  characterized by diverse structural features and various functions52.Furthermore, it is also recognized now that many protein functions do not require unique structure. These structure-less biologically active proteins carrying structure-independent functions are currently known as intrinsically disordered proteins (IDPs) or hybrid proteins containing ordered domains and IDP regions (IDPRs)Concluding this section, we need to emphasize that the presence of intrinsic disorder and conformational flexibility in proteins changed the rigid “lock and key” model proposed for the description of the general molecular mechanisms of protein function. Although “lock and key” (or its modification in a form of induced fit) can be used for the description of catalytic activities of some enzymes, many other protein functions  do not fit into this rigid view since, owing to the presence of disorder and flexibility, the locks are melted and the keys are molten. This also suggests that some novel approaches are needed to analyse intrinsic disorder-based functionality. The goal of this article is to shed some light on this problem by presenting the most recent advances in the analysis of protein disorder-based functionality.in vitro andin vivo and development of related molecular mechanisms. Although enzymatic catalysis is not among the disorder-based protein functions, some of the techniques elaborated for the analysis of the interactivity of ordered proteins can be successfully used for the functional characterization of IDPs. Biophysical techniques that are typically used to study protein–partner interactions are designed either to investigate thermodynamics or kinetics (or both) of the binding or to characterize the structural changes associated with the interactions. Many of these techniques are suitable for the analysis of both order-based and disorder-based protein interactions although the IDP-centred interactions involve a variety of binding modes, ranging from the folding upon binding mechanism to the formation of dynamic fuzzy complexes. Thermodynamic-focused techniques for the analysis of protein–partner interactions include isothermal titration calorimetry55 and surface plasmon resonance (SPR)56, whereas dissociation constants can be measured by dynamic light scattering57 and analytical ultracentrifugation58. All of these techniques can determine dissociation constants. In addition, SPR can determine kon and koff of binding events56. Although, traditionally, the major technique for the analysis of binding-induced structural changes in proteins was x-ray crystallography, this tool provides a static 3D picture of a protein complex and therefore has rather limited application to IDPs/IDPRs (with the obvious exception of the cases when disordered protein or region folds at interaction with the specific partner). Among other experimental techniques for the analysis of binding-induced structural changes are small-angle x-ray scattering (SAXS)60, single-molecule Förster resonance energy transfer (smFRET) 65, electron paramagnetic resonance (EPR)67, and hydrogen/deuterium exchange (HDX) mass spectrometry71. Although IDPs/IDPRs are commonly involved in transient protein–protein interactions , which are crucial for cell signalling, characterization of such interactions at the atomic-resolution level is rather challenging by the majority of conventional techniques. However, such interactions can be analysed by using solution nuclear magnetic resonance (NMR) spectroscopy76, including diamagnetic and paramagnetic  techniques77. Peculiarities of the application of NMR for the analysis of IDPs/IDPRs and disorder-based protein complexes are detailed in several recent reviews75. Importantly, smFRET79 and NMR82 can be successfully used for the in-cell analysis of IDPs and their interactions. It was also pointed out that the most appropriate and eloquent description of the structure and dynamics of IDPs and IDP-based complexes could be achieved via the combined use of several aforementioned techniques, such as NMR, smFRET, and SAXS enhanced by the molecular dynamic simulations, since complementary experimental data from these techniques ensure important and meaningful constraints for computational simulations84. In line with these developments, several groups are developing new approaches for the computational descriptions of disordered ensembles99. Furthermore, an openly accessible database of structural ensembles of intrinsically disordered and unfolded proteins, pE-DB (http://pedb.vib.be), was created to promote the elaboration of novel modelling approaches and to allow a better understanding of disorder-based functionality101.Traditional analysis of protein functionality was mostly centred on the development of means for accurate characterization of enzymatic activity or ligand binding (or both) of a protein102. Although these proteins formed a highly specific complex with picomolar affinity, they completely retained their highly disordered nature, long-range flexibility, and overall highly dynamic character102. This complex is an extreme case of an IDP-driven polyelectrostatic binding mechanism proposed as a result of the NMR-based analysis of a complex between the polyvalent intrinsically disordered cyclin-dependent kinase inhibitor Sic1 and its ordered partner, SCF ubiquitin ligase subunit Cdc4103. This Sic1–Cdc4 complex is held together by cumulative electrostatic interactions between the numerous phosphorylated sites of Sic1 and a single binding site of Cdc4; the binding strength is dependent on the phosphorylation degree of Sic1, and Sic1 remains largely disordered in its Cdc4-bound state103.Illustrating the remarkable power of NMR spectroscopy when applied to the functional and structural analysis of disorder-based interactions, a recent study provided a structural characterization of an intriguing complex formed between two IDPs: human histone H1 and its nuclear chaperone prothymosin-alpha105. PMLOs are related to various biological processes compartmentalized in diverse regions of the cell106, are able to facilitate and respond to various biological functions and stimuli107, and therefore are considered important players in cellular life. PMLOs are highly dynamic but stable, protein-only or protein–nucleic acid assemblages characterized by cell size–dependent dimensions108, whose structural integrity and biogenesis are exclusively determined by protein–protein, protein–RNA, or protein–DNA interactions or a combination of these110. These liquid droplets are formed via the intracellular liquid–liquid phase transitions (LLPTs) or the intracellular liquid–liquid demixing phase separation111 initiated by the colocalization of molecules at high concentrations within a small cellular micro-domain113. Biogenesis of PMLOs is a highly controllable and reversible process, and formation of PMLOs is triggered by changes in the concentrations of proteins undergoing LLPT, changes in the concentrations of specific small molecules or salts, changes in osmolarity, and changes in the pH or temperature (or both) of the solution or by various PTMs and alternative splicing of the phase-forming proteins, by the binding of these proteins to some definite partners, or by changes in other environmental conditions that affect the protein–protein or protein–nucleic acid interactions116. PMLOs are very large (detectable by light microscope), liquid-like assemblages which are not covered by the membranes and whose components are involved in direct contact and exchange with the PMLO environment113. As a result, PMLOs are characterized by liquid-like behaviour, being capable of wetting, dripping, and forming spherical structures upon fusion119. Since proteins driving LLPTs are intrinsically disordered or contain IDPRs120, PMLOs represent an intricate form of the disorder-based protein complexes121, which are commonly formed without noticeable structural changes in the proteins undergoing LLPTs122. This conclusion is supported by the NMR analysis of several PMLOs or liquid droplets such as in the case of the Alzheimer-related protein tau124, elastin-like polypeptides (ELPs)125, the low-complexity domain of the RNA-binding protein fused in sarcoma (FUS)126, heterogeneous nuclear ribonucleoprotein A2 (hnRNPA2)127, and the intrinsically disordered N-terminal 236 residues of the germ-granule protein Ddx4128. Techniques that can be used for the analysis of the dynamics, structure, morphology, and rheology of phase-separated droplets and PMLOs and their componentsin vitro and in live cells were systematically analysed in a recent review129. Special emphasis was put on the suitability of single-molecule fluorescence methods for the characterization of functional dynamics of PMLOs130, on the use of fluorescence recovery after photobleaching (FRAP) as a technique of first choice for assessing fluidity of PMLOs and phase-separated droplets and to estimate protein diffusion coefficients131, and dual-colour fluorescence cross-correlation spectroscopy (FCCS) for the analysis of concentrations, diffusion characteristics and interactions of two fluorescent species in solution132.Multivalent interactions between IDPs that are not accompanied by noticeable structural changes are directly linked to the biogenesis of the proteinaceous membrane-less organelles (PMLOs), which are abundant in cytoplasm, nucleus, and mitochondria of various cells and which play a number of important roles in the organization of various intracellular processesXenopus oocyte nucleus was shown to determine the access to the macromolecules within these PMLOs from the nucleoplasm133. PMLOs can also act as liquid-phase micro-reactors, where the cytoplasmic reactions are accelerated because of the increased concentrations of related RNA and protein components135. PMLOs can also serve as a means for recruitment and concentration of specific proteins, as seen in Negri bodies (NBs), which are cytoplasmic liquid organelles in which viral RNAs (mRNAs as well as genomic and antigenomic RNAs) are synthesized136. Neuronal ribonucleoprotein (RNP) particles, or granules that concentrate specific sets of mRNAs and regulatory proteins, serve as dynamic sensors of localized signals and play a dual role in the translation of associated mRNAs, preventing mRNA translation at rest and ensuring local protein synthesis upon activation137.The liquid-like nature of PMLOs and phase-separated droplets can affect and modulate functions of their constituents, which are accumulated within droplets at high concentrations but remain dynamic. In line with this hypothesis, the low-density structure of PMLOs in the140. Another example of the emergent behaviour is given by the spatiotemporal oscillations of the Min protein system  that moves from pole to pole of the rod-shapedEscherichia coli cells with the intrinsic wavelength comparable to the size of theE. coli cell141. Oscillating movements of this system are required for the spatial regulation of the positioning of the cytokinetic Z ring that determines the division plane144. Such oscillations can be visualized if the components of this system are fluorescently labelled147. Furthermore, on the supported lipid bilayersin vitro, these Min proteins self-organize to form traveling protein surface waves emerging from the repetitive binding-detaching cycles of proteins to the membrane149. Also, depending on the peculiarities of their environment, MinD and MinE were shown to self-organize into a wide variety of patterns150.LLPTs and PMLOs are illustrative examples of the disorder-based emergent behaviour of proteins151. Colocalization is evaluated by pixel-based methods or object-based methods151. In the first case, the image generated by the fluorescence microscope is analysed to measure global correlation coefficients between pixel intensities in different colour channels that allow finding and quantification of overlapping pixel intensities in different channels152. In the second case, the objects (molecules) are first segmented and then represented as points through coordinates of their mass centre in the delimited FOV and then their spatial distributions are analysed154. A systematic study published in 2015 compared pixel-based and object-based methods for finding colocalization in synthetic and biological images and revealed that data generated by the object-based methods are more statistically robust than the results of pixel-based approaches151.Bioimaging is a commonly used technique for the quantification of intracellular protein–protein interactions (PPIs). Here, the presence of molecular interactions is judged by the analysis of spatial colocalization between the different populations of differently labelled molecules in the field of view (FOV) of dual- or multiple-channel fluorescence microscopein vitro andin vivo are traditionally analysed by using the affinity purification-based pull-down assays155 or co-immunoprecipitation (coIP) experiments156 allowing the direct detection of physical interactions. Here, either purified and tagged protein is used as a “bait” to bind any interacting proteins (pull-down assays) or antibody against a target protein is used to immunoprecipitate the complexes containing the target protein (coIP). Although CoIP and pull-down assays are typically used as “yes-no” tools for showing the presence or absence of PPIs, it was recently shown that the dissociation constant (KD) of complexes formed by two purified proteins can be measured by using the quantitative pull-down assay157. However, these two techniques are typically limited to the high-affinity binding and therefore are not easily transferable to the analysis of disorder-based interactions, which are often weak. This caveat can be overcome by using chemical158 or photo-affinity159 cross-linking of samples before conducting pull-down and CoIP assays. Chemical and photo-affinity cross-linking combined with mass spectrometry (XL-MS) is another technique for the analysis of weak and transient PPIs164. The use of genetically encoded photo-crosslinkers using natural amino acid analogues that contain a photo-affinity group as the warhead and that can be site-specifically incorporated into a protein of interest to covalently trap non-covalent PPIs under living conditions represents a promising development in this area165.PPIs168. Here, interaction between two proteins, called bait and prey, activates reporter genes that enable yeast growth on specific media or a colour reaction168. In 2015, high-affinity binders to transiently structured IDP, the prokaryotic ubiquitin-like protein Pup, and its unstructured segments were identified and characterized at atomic resolution by using the YTH-selected peptide aptamers and in-cell NMR169. Similarly, a combination of YTH screenings with NMR spectroscopy, cross-linking experiments, and competition-binding assays was recently used to characterize the interactivity of a long IDPR linking the KIX domain (kinase-inducible domain [KID] interacting domain) and bromodomain of CBP (cAMP response element-binding [CREB]-binding protein) termed ID3 and to show that ID3 binds to the intrinsically disordered RNA-binding Zinc-finger protein 106 (ZFP106), and both interactors maintained disorder in their bound states170. Recently, YTH assay was used to compare mutational robustness of the intrinsically disordered viral protein VPg and of its interactor eIF4E using libraries of mutant forms of both VPg and eIF4E171. This study revealed that VPg was significantly more robust against mutations than eIF4E171.One of the commonly used approaches for investigating PPIs in living systems is a genetic approach: yeast-two-hybrid (YTH) screening173. BiFC was successfully used for thein planta analysis of homo- and hetero-dimerization of the intrinsically disordered dehydrins fromArabidopsis thaliana, AtCOR47, AtERD10 and AtRAB18174, and for the analysis of interactivity of another Arabidopsis protein, histone deacetylase complex 1 (HDC1) protein175.Another tool for the analysis of weak PPIs is the bimolecular fluorescence complementation (BiFC) assay, which uses the ability of two non-fluorescent fragments of a fluorescent protein to associate and form a fluorescent complex, and association is facilitated when they are fused to two interacting proteins178. One of these PDL systems is a proximity-dependent biotin identification (BioID) approach that uses biotin ligase BirA as an enzyme catalysing the biotinylation of target protein in the presence of biotin and that uses subsequent streptavidin-mediated pull-down and mass spectrometry analysis for the identification of interacting proteins180. Recently, it was shown that biotinylation-based proximity labelling is biased by structural features of target proteins, causing enrichment of cellular biotinylation events within the IDPRs of protein targets181. In addition to biotin ligase, proximity labelling can be conducted by some peroxidase enzymes, which, in the presence hydrogen peroxide, can generate short-lived free radicals  that represent the enzyme-generated reagents that can covalently label neighbouring proteins182.Finally, among other experimental tools used for the analysis of PPIs are various proximity-dependent labelling (PDL) approaches, where the target protein has to be fused with an enzyme capable of catalytic attachment of a reactive molecule to the interacting partners in a distance-dependent manner 189 and the capability to bind to multiple partners and gain very different structures in the bound state196, which increases complexity of the disorder-based interactomes197. Often, such foldable IDPRs are engaged in recognition function of IDPs and therefore are known as molecular recognition features201. Since such molecular recognition features (MoRFs)  are characterized by specific features , they can be rather accurately predicted from the protein amino acid sequence202. There are numerous computational tools for finding disorder-based interactions sites in proteins, which are grouped into three major classes: tools looking for MoRFs ; algorithms such as PepBindPred210, ANCHOR212 and disoRDPbind213 to find generic disordered protein-binding regions; and a method for finding short linear sequence motifs (SLiMs), SLiMpred214. Although all of these tools analyse the capability of a target protein to be engaged in PPIs, disoRDPbind also predicts the protein region capable of binding to DNA and RNA213. There is also a tool for finding disordered flexible linker regions that serve as linkers/spacers in multi-domain proteins or between structured constituents in protein domains: the DFLpred method215. Peculiarities, advantages and disadvantages of all of these techniques, together with the 32 tools for the prediction of intrinsic disorder predisposition of a query protein, were carefully analysed and compared in a recent comprehensive review202. Recently, Zarinet al. did a comprehensive evolutionary computational analysis to search for molecular features that are preserved in the amino acid sequences of orthologous IDPRs216. This analysis revealed that orthologous IDPRs frequently contain multiple “evolutionary signatures” . Based on these observations, it was suggested that such evolutionary signatures could be used for the prediction of functionality of IDPRs from their amino acid sequences216.Among the important features of IDPs/IDPRs associated with their functionality are the ability to undergo at least partial folding at interaction with specific partners218. Therefore, prediction of localization of PTM sites within the amino acid sequences of IDPs and IDPRs represents an important direction in computational analysis of disorder-based functionality. In fact, systematic bioinformatic analyses of the peculiarities of the IDP/IDPR-located display sites targeted for PTMs and their adjacent regions demonstrated that their sequence attributes  are rather similar to those of IDPRs. These observations define the potential predictability of such disorder-centred PTM sites and were used for the development of disorder-focused predictors of protein phosphorylation217, methylation219, ubiquitination220, and S-palmitoylation221, a unified sequence-based predictor of 23 types of PTM sites, which can be used for finding protein regions that undergo multiple homologous or heterologous PTM events and for finding shared PTM sites 218.Another important feature of disorder-based functions is their regulation by numerous PTMs2P2 database (http://d2p2.pro/)222, which is a resource of pre-computed disorder predictions for a large library of proteins from completely sequenced genomes222. In a visually attractive form, D2P2 generates a functional disorder profile of a query protein that includes outputs of nine per-residue disorder predictors, represents positions of functional domains, shows a gradient bar reflecting the consensus of nine disorder predictors, where the increase in strength of correlation is shown by colour change from white to dark green, and also indicates location of the predicted disorder-based binding sites (MoRFs) and positions of various PTMs222.Disorder status and potential disorder-related information for a query protein can be retrieved from the Dhttp://elm.eu.org/), which is a collection of manually annotated SLiM instances curated from experimental literature224. SLiMs are composed of short stretches of adjacent amino acids and can be found in IDPRs of many proteins. They are short, compact, degenerate peptide segments that act as protein interaction sites and are essential for almost all cellular processes223. An ELM resource can also be used for finding potential SLiMs in a query protein. It filters out globular domains and retains predicted SLiMs associated with various functions224. There are six types of annotations for the SLiMs that are described by the ELM server224: motifs for targeting to subcellular compartments (ELM_TRG), degron motifs that play a role in polyubiquitylation and targeting of proteins to proteasomal degradation (ELM_DEG), motifs that act as proteolytic cleavage sites (ELM_CLV), ligand binding motifs (ELM_LIG), docking motifs (ELM_DOC), and sites for PTMs (ELM_MOD)224.Finally, localization of various functional short linear motifs, SLiMs, in a query protein can be assessed by the eukaryotic linear motif (ELM) resource  tool for finding PSPs- 122;A tool for finding PSPs on the basis of the similarity of sequence composition and residue spacing  to fingerprints of PMLO-forming features of a disordered nuage protein Ddx4- 228;PScore that evaluates the expected number of long-range π–π interactions involving non-aromatic groups in a query protein- 229;LARKS tool for finding, in query proteins, low-complexity aromatic-rich kinked segments that are potentially related to the formation of subcellular membrane-less assemblies- 230;R+Y model for the evaluation of the content in a query protein of arginine and tyrosine residues that can be engaged in cation–π interactions serving as drivers of phase separation- catGRANULE algorithm that predicts PCPs by evaluating intrinsic disorder and nucleic acid binding propensities; sequence length; and arginine, glycine and phenylalanine content , which are known to be enriched in some PCPs231;the- 232;PSPer uses the hidden Markov model for prediction of PSPs and considers the presence in a query protein of prion-like domains, IDPRs, arginine-rich domains, RNA recognition motifs, and other features- 233.PSPredictor, which is a machine learning tool for sequence-based prediction of PSPsOne more important recent direction in the elaboration of computational tools for functional analysis of IDPs and IDPRs is related to the development of methods for prediction of liquid–liquid phase separation (LLPS) and finding phase-separating proteins (PSPs). In fact, although the analysis of LLPTs and PMLOs is a rapidly developing field that clearly attracts significant attention of multiple researchers, general knowledge of the prevalence and distribution of PSPs is still rather rudimentary. Therefore, tools for LLPS and PSP predictions are in high demand. Recently, information on the first-generation PSP predictors and their basic principles was summarized by Vernonhttp://bio-comp.org.cn/llpsdb) that provides comprehensive information on proteins undergoing LLPSin vitro and contains 1182 entries describing 273 independent proteins and 2394 specific conditions234.Another illustration of the interest of researchers in LLPS and PMLOs is the recent development of an LLPSDB database  activities of ordered proteins, structure and disorder represent a unity of opposites orcoincidentia oppositorum. On the other hand, an actual line between order and disorder is elusive and structural and un-structural biology should not be opposed but united since they clearly complement one other235. Therefore, a complete understanding of the biological functionality at the proteome level requires careful consideration of both order- and disorder-based protein functions and only such a united approach can ensure the previously unattainable comprehension of biological complexity. On the other hand, structural and functional characterization of ordered and disordered proteins requires very different methodological approaches, and an analysis of hybrid proteins remains a challenging task. In fact, as was pointed out, the current literature is focused mostly on fully ordered or fully disordered proteins, generating an immense “grey” area, where order and disorder are mixed and resulting in an incomplete understanding of the diverse mechanisms and functions used by hybrid proteins235.Although IDPs/IDPRs were largely ignored for most of the existence of protein science, it is now clear that IDPs and disorder-based functions represent a new reality. Originally, the field of un-structural biology stood up as an attempt to explain many cases of rare exceptions . However, in light of the broad acceptance of the new un-structural biology paradigm, one should keep in mind that it would be a clear mistake to continue contradistinguishing and opposing ordered proteins and IDPs, as they work together in a living cell, indicating that understanding and explanation of the protein dynamics and functionality require a tandem action of the disciplines of structural and un-structural biologyBiFC, bimolecular fluorescence complementation; coIP, co-immunoprecipitation; ELM, eukaryotic linear motif; FOV, field of view; ID3, IDPR linking the KIX domain and bromodomain of CBP; IDP, intrinsically disordered protein; IDPR, intrinsically disordered protein region; LLPS, liquid–liquid phase separation; LLPT, liquid–liquid phase transition; MoRF, molecular recognition feature; NMR, nuclear magnetic resonance; PDL, proximity-dependent labelling; PMLO, proteinaceous membrane-less organelle; PPI, protein–protein interaction; PSP, phase-separating protein; PTM, post-translational modification; SAXS, small-angle x-ray scattering; SLiM, short linear sequence motif; smFRET single-molecule Förster resonance energy transfer; SPR, surface plasmon resonance; YTH, yeast-two-hybrid


Decrypting the interface residues of the protein complexes provides insight into the functions of the proteins and, hence, the overall cellular machinery. Computational methods have been devised in the past to predict the interface residues using amino acid sequence information, but all these methods have been majorly applied to predict for prokaryotic protein complexes. Since the composition and rate of evolution of the primary sequence is different between prokaryotes and eukaryotes, it is important to develop a method specifically for eukaryotic complexes. Here, we report a new hybrid pipeline for predicting the protein-protein interaction interfaces in a pairwise manner from the amino acid sequence information of the interacting proteins. It is based on the framework of Co-evolution, machine learning (Random Forest), and Network Analysis named CoRNeA trained specifically on eukaryotic protein complexes. We use Co-evolution, physicochemical properties, and contact potential as major group of features to train the Random Forest classifier. We also incorporate the intra-contact information of the individual proteins to eliminate false positives from the predictions keeping in mind that the amino acid sequence of a protein also holds information for its own folding and not only the interface propensities. Our prediction on example datasets shows that CoRNeA not only enhances the prediction of true interface residues but also reduces false positive rates significantly. The biological machinery performs its cellular functions when its basic units, such as DNA, RNA, and proteins, interact with each other. To understand the overall functioning of the cell, it is important to delineate the pairwise interactions of these basic units such as DNA-protein, RNA-protein, and protein-protein. Of these, the inter-protein interactions that a cell possesses play a very crucial role in understanding the various cellular processes and hence also their functioning or misfunctioning in the disease models. There are various experimental methods known for examining these interactions such as yeast two hybrid Y2H) , co-immuH , co-imA number of in silico methods have been described earlier to predict these PPII based on available data, such as (1) homology-, (2) machine learning-, and (3) Co-evolution-based. Homology based methods are generally applied when confident homologs of both the interacting proteins are available, followed by protein-protein docking for visualizing the protein interaction interfaces, such as PredUS , PS-HomPThe third class, Co-evolution-based methods which were originally formulated to predict contact forming residues within a single protein and therefore for the prediction of the structure of the protein. These methods have been extrapolated to also predict the inter-protein interaction interfaces based on the multiple sequence alignments (MSA) of the proteins. Concatenating the MSA of an interacting pair and using the same statistical formulae as described for intra pairs have been implemented to predict the co-evolving contact forming pairs by various methods, such as DCA , EvComplAlthough the low predictability of the methods available to date for eukaryotic protein complexes can be attributed to the differences in the rate of evolution of the proteins in the two lineages. It has been reported that there is a difference in the composition of the type of amino acids present in prokaryotic versus eukaryotic proteins and also in the radius of gyration and planarity in the interaction interface. Since the eukaryotic proteins are not exclusive to only one set of function, it has been perceived that most of the eukaryotic protein interactions are transient, having smaller interaction hotspot zones and more planar binding sites consisting of more polar and aromatic residues. These properties of the eukaryotic protein interactions make them essential part of cell signaling pathways .Hence, to delineate the vast PPII network of eukaryote lineage, e.g., human protein interaction network, which contains about one hundred and fifty thousand  interactions (with only about 10% of known structures of these protein complexes) , it is iThe overall pipeline to predict pairwise contact forming residues from sequence derived data can be divided into three distinct parts as depicted in The Affinity Database version 2.0  was usedThe datasets for each interacting pair of proteins having identical species were subjected to structure-guided multiple sequence alignments using PROMALS3D . The aliwww.rcsb.org) and any missing regions reported in the structure were removed from the sequence data. All the features for training and testing were compiled as all versus all residue pairs between sequence of the interacting pair of protein (Protein A and Protein B) in form of M × N matrix (M = length of Protein A and N = length of Protein B). All the feature values were scaled between 0 and 1   where thhttps://github.com/mlampros/OpenImageR) package in R and the size of the matrices were kept same to avoid any loss of information. Additionally, various other kernel matrices were also used to train and test different datasets varying from 3 × 3 to 7 × 7 with varying percentage decrease in the weights from 10% to 25%. Hence, for each independent training/testing cycle, 18 feature matrices were used for each pair of interacting protein for training the Random Forest classifier .To include residue environment information for training the machine learning algorithm, a kernel matrix of size 5 × 5 was defined and convolved over the nine feature matrices as described above. The convoluted features were generated by using OpenImageR  was down sampled. A number of ratios for negative to positive samples were tested iteratively  and best evaluation statistics were obtained when the negative sample size was five times that of positive samples (5:1). This was used as training set for the supervised classification model.http://www.rstudio.com/) and machine learning was performed using Python version 3.7 via anaconda-navigator (https://anaconda.com).The Random Forest classifier  was traiTo determine the predictability of the classifier models, leave-one-out method of cross validation was applied were the training dataset consisted of sampled pairs from 41 complexes and the 42nd protein complex was treated as test sample. To maintain homogeneity for comparing the results of all 42 protein complexes, the 5 × 5 feature kernel matrix was used to derive the environmental features. Owing to the imbalance nature of the number of interacting pairs versus non interacting pairs for any given protein complex used for testing, the evaluation parameters used for determining the predictability of the model was both macro and weighted averages of precision, recall and F1-score. The macro averages for each of these parameters gives equal importance to both the data classes despite the imbalance nature of the dataset, whereas the weighted averages considers class imbalance while calculating the evaluation parameters.To reduce the number of false positives obtained from the Random Forest classifier, a holistic approach was adopted as described in The predictions from the Random Forest classifier were used to plot the inter-protein residue network as a bipartite graph using the igraph package in Rstudio. Since the RSA for residues present in the core of the protein should be 0, these residues were extracted from SPIDER3  for bothThe residual inter-protein network obtained were then plotted as a binary matrix of Protein A versus Protein B where 0 represented predicted non interface pairs and 1 represented predicted interface pairs. To identify the most probable interaction interfaces, cluster of 1′s was identified by convolving a unitary matrix of size equal to that of kernel matrix used for deriving environmental features  over the prediction matrix. Subsections having the maximum number of 1′s hence obtained the highest score (score of 9 for 3 × 3 matrix and 25 for 5 × 5 matrix). A cut off value of 2 for 3 × 3 matrix and 6 for 5 × 5 matrix was selected to sort the high scoring pairs considering that at least 25% of the 3 × 3 or 5 × 5 subsections of the prediction matrix are populated with 1′s. These high scoring pairs were then extracted and mapped onto the test dataset structures to identify the true positives such that they also occur in the group of 3 residues at a stretch in both the proteins.2, 37 °C at 110 rpm, were transfected with plasmid DNA using Polyethylenimine . Cells were harvested after 60 h and lysed with lysis buffer (1X DPBS (Gibco), 0.2% tween 20, protease inhibitor cocktail, 1mM PMSF) by incubating the cells on ice for 30 min followed sonication and centrifugation. 1 mg of supernatant was incubated with glutathione beads  pre-bound with GST tagged Anti-GFP nanobody  [Human Nup93 (KIAA0095) fragments (full length (1–819), 1–150, 1–82, 96–150) were cloned in pEGFP-C1 expression vector  fused with GFP at N-terminus. HEK293F cells  cultured in freestyle media  in a humidified incubator maintained with 8% COMA, USA)  for 4 h The predictability of any supervised machine learning method is dependent on the nature of features used for training. Random Forest classifier is a tree-structure based algorithm where the classification rules are learned based on the feature values and their target class provided while training. Various features generated for training the Random Forest classifier were divided into three categories viz evolution-based, structure-based  and contact potential-based features. For the evolution-based feature, a new Co-evolution algorithm was derived as explained in The nature of physicochemical properties of the residue interaction in the protein interface is somewhere in between their properties when present in the core or on the surface of the protein. It has been reported that the interface environment is closer to that exhibited on the outside in contact with the solvent as opposed to that present in the core of the protein . For exaThe knowledge-based statistical potentials have also been used previously to mimic the interactions between the amino acids in a protein. One of such knowledge-based potential is the contact potential derived by Miyazawa and Jernigan based on statistical analysis of the protein structures. These contact potentials are widely used in the computational prediction for protein folding. The contact potentials for the residue lying in the PPI interface should ideally lie in between those of buried and exposed residues. To assess their applicability in identifying interface residues of the interacting proteins three approximations of these contact potentials were used as features.The contacts between two residues of the interacting proteins also depend on its neighboring residues by creating a favorable niche for the interaction to take place. Hence, the properties governing the interaction (as described above) of the neighboring residues will also have an impact on the overall predictability of the Random Forest classifier. To address this, the Random Forest classifier was trained in two different modes, i.e., with and without environment features, the results of which are explained below.To validate the effect of the environment features on the Random Forest classifier, the classifier was trained both with and without the environment features. The overall accuracy obtained for the dataset trained with the environment features was 86% as opposed to that for without environment features was 80%. In terms of evaluation statistics, such as precision and recall, there an increase of 5% when environmental features are used to train the model  as opposed to the one without environmental features  as depicted in One of the marked features of Random Forest classifier is that it is able to decipher the importance of every feature used for training, which can be used to determine the over-fitting of a model, as well as to gain insights about the physical relevance of the features in predicting the PPI interface. The feature importance plot for the dataset without the environment features  depicts The interaction interfaces of the proteins can be classified into 6 possible categories based on the secondary structure compositions of the interface hotspot regions, such as α-α, α-β, α-l, β-β, β-l, and l-l . Since the residue environment features were identified as the most critical features in the training of Random Forest classifier model, it is important to consider the role of the size of kernel matrix used for training the classifier. The residue environment for any protein can range from n − 1 to n + 1 position and up to n − 3 to n + 3 positions; thus, all such variations were tested by training different classifiers. For every different size and weight of the feature kernel matrix, the derived features were generated and used to train different Random Forest models. For each of the test dataset, all these different models were tested to determine a relationship between the nature of interaction in terms of secondary structure pairs and the size and weight of feature kernel matrices. The optimized models were then utilized to test for pair of interacting proteins with known crystal structure which were not a part of the training dataset to validate the predictability of the method. As observed from Leave-one out method as explained in Four eukaryotic protein complexes with known 3D crystal structures were used to assess the predictability of the complete pipeline CoRNeA. The interface residues of these protein complexes were predicted using model trained on 42 protein complexes of the affinity database. The combined amino acid length of the two proteins in these hetero dimers ranged from 127 amino acids to 986 amino acids. Additionally, owing to the variability in terms of secondary structure composition of these complexes, the derived features were generated using different sizes and weights of the kernel matrices for each test case. The model which predicted with the best evaluation statistics for each test case was considered for the downstream network analysis and final prediction matrix processing. Moreover, CoRNeA was used to predict the interaction interface of a known interacting pair of protein from the nuclear pore complex to assess the applicability of the pipeline to reduce false positive pairs in absence of structural information.The first test dataset used for assessing the predictability of CoRNeA was the crystal structure of Vav and Grb2 Sh3 domain (PDB ID: 1GCQ)  which coTo further reduce the number of false-positive pairs, network analysis was performed. The intra-protein contact forming residue pairs for Chain A (Protein A) and Chain C (Protein B) of 1GCQ were obtained from Co-evolution analysis where only top 5% pairwise values were considered to be true cases. The length of Chain A is 56 amino acids which would lead to 1540 intra pairs. The highest scoring 157 pairs were considered while constructing the intra-protein contact forming residue network of Chain A of 1GCQ, as depicted in The structure of the alpha gamma heterodimer of human IDH3 (PDB ID: 5YVT)  Figure B was useThe crystal structure of ubiquitin-like activating enzyme E1A and E1B (PDB ID: 1Y8R ) having The crystal structure of Nup107-Nup133 complex  consists of the C-terminal region of both the proteins was used as another test dataset. The residual network consisting of 540 pairs was generated after removing the nodes which are a part of the intra network in either of the proteins. The total number of points were further reduced to 240 after performing convolution on the final prediction matrix using a unitary 3 × 3 matrix and keeping a cut off of more than 2. Of the 240 pairs, 6 pairs were identified as true positives within the distance of 10 Å D.To test the applicability of the pipeline on the dataset without known structural information, hNup93-hNup205 interaction interface was explored. Nup93 is a linker protein of the Nup93-subcomplex of the NPC. It is known to connect the adaptor/ inner ring of the spoke region with the central channel pore of the NPC . The adaCoRNeA was employed to identify the interaction interface of Nup93-Nup205 complex by utilizing full length sequence information of both the proteins (Nup93: 819 amino acids and Nup205: 2012 amino acids). Since, the secondary structure prediction of both these proteins depicts α-helices, the 3 × 3 kernel matrix derived Random Forest model was utilized to predict the interface pairs. The resultant high scoring pairs, which pertained to specifically the R2 region of Nup93 (96–150) with the C-terminal region of Nup205 obtained from CoRNeA B, are inFurther, validation of the interacting interface between Nup93 and Nup205 predicted with CoRNeA analysis was done by in vitro pull-down experiment using Nup93 deletion constructs C. Upon pTo assess the predictability of CoRNeA, the results obtained from it for the four test cases described above were compared to the predictions of recently published method BIPSPI  and CompSince comparable predictions in terms of number of true positives were only obtained for one test dataset (PDB ID: 5YVT), the results obtained from both CoRNeA and BIPSPI for this protein complex were mapped onto the structure of 5YVT as shown in CoRNeA can, however, be further optimized to reduce the false-positive rates, as well as improve the true positive predictions, by increasing the training dataset. As it is evident that the environmental features play a very important role in training the classifier and that there is a correlation between the type of secondary structures and kernel matrices used to derive these environmental features, different training sub-datasets can be used to train specifically on various combinations of secondary structures to decrease the false positive prediction by Random Forest classifiers and hence increase the specificity of the overall pipeline.https://github.com/krits04/cornea).Predicting the pairwise interacting residues for any two-given pair of proteins from only the amino acid sequence still remains a challenging problem. In this study, the newly designed pipeline CoRNeA addresses some of the challenges for predicting the PPI interfaces, such as applicability to eukaryotic PPI and high false-positive rates, by incorporating Co-evolution information and intra-contacts for improving the precision and recall of the pipeline. Overall, by including the intra-contact information through network analysis, the false positive rates reduced by 10-fold, hence improving the true negatives for the 4 examples tested with the known crystal structure in this study. Thus, this pipeline can be utilized to predict the interface residues as a pairwise entity and also to understand folding of the individual proteins though intra-contact predictions. Obtaining the structural information of proteins individually, as well as in complex with their interacting partners, is a tremendously challenging problem especially for large multimeric complexes. CoRNeA can be utilized to identify the minimal interacting regions in the heterodimers for its biochemical reconstitution, which can then be utilized in structure elucidation studies. The Random Forest output in terms of probabilities obtained from CoRNeA can also be used as a starting point for protein docking studies in cases where 3D structure models  are available. The web server is currently under development and the R codes, along with the trained models, are available on GitHub (


The tool can perform a multiple-site query in a simple way, and the whole dataset is available for download as well as incorporated into our own accessible pipeline. To create this resource, National Center for Biotechnology Information protein data were retrieved using the Entrez Programming Utilities. After processing all human protein domains, residue positions were reverse translated and mapped to the reference genome hg19 and stored in a MySQL database. In total, 760 487 protein domains from 42 371 protein models were mapped to hg19 coordinates and made publicly available for search or download (www.prot2hg.com). In addition, this annotation was implemented into the genomics research platform GENESIS in order to query nearly 8000 exomes and genomes of families with rare Mendelian disorders (tgp-foundation.org). When applied to patient genetic data, we found that rare (<1%) variants in the Genome Aggregation Database were significantly more annotated onto a protein domain in comparison to common (>1%) variants. Similarly, variants described as pathogenic or likely pathogenic in ClinVar were more likely to be annotated onto a domain. In addition, we tested a dataset consisting of 60 causal variants in a cohort of patients with epileptic encephalopathy and found that 71% of them (43 variants) were propagated onto protein domains. In summary, we developed a resource that annotates variants in the coding part of the genome onto conserved protein domains in order to increase variant prioritization efficiency.Genetic variation occurring within conserved functional protein domains warrants special attention when examining DNA variation in the context of disease causation. Here we introduce a resource, freely available at Database URL:www.prot2hg.com The widespread application of whole-exome sequencing (WES) and whole-genome sequencing (WGS) requires interpretation of individual sequence variants. Recommendations exist for variants in genes already associated with a disease . HoweverTo look for sequences conserved across species is an efficient strategy, and many tools were developed to search for evolutionarily conserved elements . The undProtein domains are conserved structural entities, and information about a protein domain may provide clues as to the structure and/or function of the protein studied. Furthermore, it may help to establish evolutionary relationships across protein families . Last, bTo be able to determine whether specific human genetic variants fall onto annotated conserved protein domains might be helpful for studies of variant mapping in rare diseases, the global evaluation of genomic variation in relationship to protein architecture and the comprehensive evaluation of the population-level proteome as derived from genetically diverse human populations.https://www.rcsb.org/pdb/chromosome.do)   provideshttp://grch37.ensembl.org/Homo_sapiens/Tools/VEP)  (ols/VEP) , the useHere we present a tool that can perform multiple queries (batch of 100 variants) in a simple way and give a quick, easily readable answer. Moreover, it is possible to download the entire dataset and integrate this annotation into a local pipeline.https://www.ncbi.nlm.nih.gov/refseq/) and is an essential resource for genomic, genetic and proteomic research  provides a comprehensive source of the human reference genome assembly (research . For prowww.prot2hg.com) or as a downloadable dataset. Moreover, data were incorporated into the GENESIS platform for analysis and matchmaking of exome and genome data from rare diseases (tgp-foundation.org).We developed a resource that annotates variants in the coding part of the genome onto conserved protein domains in an easy-to-use way. This could help to make variant interpretation more efficient. The resource thus provides this missing connection for large-scale genome/proteome studies and surpasses prioritization of variants. Our data are freely accessible through a dedicated open web-based query tool . In this analysis, two types of protein domains were used: sites and regions.A protein site is a protein domain with a shorter sequence and, often, specifically known functions. Many protein sites reside within larger protein domains. Examples of such protein sites are transmembrane regions and nitrosylation or phosphorylation sites.A protein region is part of a protein with a known function, usually with a length of over 100 amino acids. It is a part of a protein that can adopt a particular 3D structure . TypicalThe cDNA and protein sequences, feature tables and RefSeq Gene identification from the NCBI protein database for each protein were downloaded via the EZ Utilities application programming interface (API) using custom Python scripts .Custom Python scripts were used to transform the NCBI data structure. Data were split into blocks of structured text for each protein followed by extraction of the amino acid sequence, cDNA sequence, RefSeq identifier (NM_ID) and coding sequence information to determine sequence shift. The shift (stored in the ‘coded_by’ line in A double-verification system was implemented to ensure that amino acid sequences were properly mapped to the cDNA sequence. The input for this step is a custom data structure (from the previous step), with protein subsequence, complete sequence of a gene and the location of the start and end of the protein domain. To begin mapping, each protein subsequence (amino acid) was converted into its nucleic acid sequence. Due to codon degeneracy, with 64 possible triplets coding for only 20 different amino acids, most amino acids are inserted into a growing polypeptide chain in response to two or more different triplets in the mRNA . To resohttps://genome.ucsc.edu/) to annotate each protein with a chromosomal location. The exon table containing the number of exons, exon chromosomal location and coding strand location for each NM_ID was retrieved. The UCSC exon table information was combined with our computed cDNA start/end locations to determine which exons are translated into domains. The location of each exon within the domain interval was stored. Lastly, the Prot2HG database (MySQL) was created to store the annotated data with web-based query tools, enabling the user to find an annotation for individual variants.All chromosomal intervals for each exon from the University of California Santa Cruz (UCSC) RefSeq gene database were downloaded via the MySQL API . Sites were shorter with a mean length of 7.3 bp .For each record, the following details are provided: gene name; RefSeq ID—protein (NP_) and gene (NM_); mapped strand (+/−); type of domain (site/region); domain name; reverse mapping score (0.00–1.00); chromosomal location; and the feature table note with further information. The number of records in the database is significantly higher than the number of protein domains due to splitting into exons.www.prot2hg.com. The website is built on a PHP server using the Twitter Bootstrap HTML template and connected to the MySQL database. In the home tab  exome database , in addiStatistical evaluation with a chi-square test was done to test the random distribution of the data . The indP < 0.01, odds ratio 1.59). Further, for ClinVar pathogenic/likely pathogenic, 66% of variants fall on to an annotated domain, while only 32% of variants without pathogenic/likely pathogenic status were annotated. Again, these results showed a significant and dependent relationship between ClinVar pathogenic status and annotation onto a protein domain was observed . Chi-square statistics are summarized in Significantly more rare than common variants were annotated onto a domain  were propagated onto protein domains. From the functional point of view, these were mostly ion transport regions (14x), transmembrane regions (6x) and ion-channel transmembrane regions (5x). Many of the variants were annotated onto multiple domains (43 variants onto 93 domains).in silico prediction tools are helpful. Moreover, phenotype driven analysis . We observed a significant difference between a proportion of variants that were annotated onto a protein domain , and a chi-square test showed this difference to be significant. As protein domains are conserved regions of the genome, these regions are thus less likely to be hypervariable, and this may explain the difference in the distribution.Similarly, data from ClinVar were extracted. Variants designed as pathogenic/likely pathogenic in ClinVar were annotated onto a domain in 66%, while only 32% remaining variants (not pathogenic/likely pathogenic) were annotated onto a domain. The chi-square test was used for analysis; the difference was evaluated as significant and may support the hypothesis that pathogenic/likely pathogenic variants are more often distributed through known protein domains.These results highlight the utility of such annotations. For variants in genes with known disease associations, this approach could help make variant analysis more efficient. Moreover, when searching for novel disease-to-gene associations, such prioritization of variants might be useful, because it provides clues as to the function of the protein. And for certain types of disorders , it is a fundamental piece of information to know that a variant is annotated onto an ion transport or transmembrane protein domain. We demonstrated the utility of Prot2HG and its impact on variant interpretation through retrospective analysis of clinical Next Generation Sequencing dataset consisting of 60 previously confirmed pathogenic or likely pathogenic variants in a cohort of patients with epileptic encephalopathy. It was found that most of the variants (71%) were truly propagated onto protein domains.www.prot2hg.com. The tool will expand variant annotation tools available, especially for rare diseases. Functional analysis and interpretation of variants from NGS based on possible functional consequences is the future. We suggest this annotation is particularly useful when analyzing/re-analyzing data from WES while searching for novel gene-to-disease associations.Here we introduce an annotation with NCBI protein features that addresses the question of whether a particular variant falls onto an annotated (known) protein domain. The database is available at This work was supported by the Charles University, project GA UK [grant number 388217] and supported by Ministry of Health of the Czech Republic [grant number 16-30206].Conflict of interest. None declared.Supplementary_file_for_baz161Click here for additional data file.


Scoring is a challenging step in protein–protein docking, where typically thousands of solutions are generated. In this study, we ought to investigate the contribution of consensus-rescoring, as introduced by  These relative frequencies are then averaged, i.e., normalized by the interface size, to compute the CONSRANK score of each pose P:where Ncont(P) denotes the number of interface contacts in docking pose P.where First, we considered the un-normalized version of CONSRANK scores , denotedS(Cij) by relative frequencies of residues:Then, we implemented two other variations, by replacing relative frequencies of contacts F(Ri) denotes the frequency of residue i at the protein–protein interface (distance between heavy atoms lower than 5 Å) in the set of N decoys. The two related scores are respectively defined by:where Nres(P) denotes the number of interface residues in pose P. Here, interface residues are simply those involved in contacts at the interface.where Note that is it possible to compute the contact and residue frequencies (Eqs 1 and 4) on a given set of docking poses and then to evaluate another set of docking poses .We implemented the BSAS clustering procedure   to reducThe top 2,000 solutions according to the ZDOCK native scoring function were rescored using the rescoring schemes detailed below. We monitored the presence of near-native docking hits (interface Cα RMSD < 2.5 Å) in the top 10 solutions after re-ranking. Each protein–protein complex with a near-native docking hit in the top 10 solutions is counted as a success.https://github.com/MMSB-MOBI/CHOKO.The consensus-based rescoring functions are implemented in python code accessible on GitHub, which operates directly on ZDOCK output files, and allows to treat rapidly thousands of docking poses . In addition, the code allows to compute statistics on a given set of poses and re-score another of structures (see “Results” section). All the scripts necessary to reproduce the results shown in this article are available at: In this study, we compare four consensus-based scores to identify the near-native solutions among the ensembles generated by ZDOCK. The traditional CONSRANK score  is consiIn the initial data set of 176 protein–protein complexes, ZDOCK was able to generate at least one near-native docking hit (interface Cα RMSD < 2.5 Å) in the first top 2,000 solutions for 90 protein–protein complexes. These 90 protein–protein complexes thus constitute our reference data set for the rest of the study. We explore if and how consensus-based rescoring is efficient at scoring the decoys of these 90 protein–protein complexes.First, we compare the four versions of consensus-based rescoring functions: either contact-based [following the CONSRANK  scheme] versus 20 for the CONSRANK scheme. It is thus possible to rescore large sets of docking poses using consensus-based scoring functions, with better performance than the commonly used CONSRANK scheme.The results of this evaluation are shown in In this section, we explore how to combine rescoring functions with the native scoring function of ZDOCK. Out of the 90 protein–protein complexes with at least one near-native docking hit in the top 2,000 solutions, the ZDOCK native scoring function identifies 29 successes, i.e., 29 complexes with at least one near-native docking hit in the top 10, see We first analyzed the overlap of the successful cases by ZDOCK and each rescoring function, and found that many successful cases achieved by rescoring are well predicted by the ZDOCK scoring function, see N1 poses of ZDOCK with the first N2 poses after rescoring, with N1 + N2 = 10, and no redundancy. Again, we vary the subset of docking poses used to compute the contact and residue frequencies with Eqs 1 and 4  and in any case, we rescore the first 2,000 solutions provided by ZDOCK using Eqs 2, 3, 5, and 6. We estimate the performance by counting the number of successes, i.e., number of complexes with at least one near-native docking hit (interface Cα RMSD < 2.5 Å) in the first 10 solutions.We then tested a combination of ZDOCK poses and rescored poses by combining the first The results of this evaluation are shown in Clustering is classically used to improve the performance, by reducing the structural redundancy of docking solutions . Here, wAs shown in N1 ZDOCK clusters, with the representative poses of the first N2 poses after rescoring, with N1 + N2 = 10. We estimate the performance by counting the number of successes, i.e., number of complexes with at least one near-native hit (interface Cα RMSD < 2.5 Å) in the first 10 solutions.We then explore how to combine ZDOCK results and rescoring results. We have tested a combination of clusters. On the one hand, we computed clusters from the poses ranked by their initial ZDOCK scores. On the other hand, we computed clusters from poses reordered after consensus rescoring. We then combine the representative poses of the first The results of this evaluation are shown in To complete this study, in this section, we present examples to illustrate the asset of consensus rescoring when used in combination with the native ZDOCK scoring function. We used the results generated using the CONSRANK_U function, with frequencies estimated on the first 1,000 poses, and combined five ZDOCK clusters and five consensus clusters. As explained in the previous section, this setting allows to reach 38 successes. We present four examples from the ZDOCK decoy set where the use of consensus rescoring is critical in For the 38 successful complexes in this experiment, we systematically computed the number of near-native poses coming from ZDOCK clusters and the number of near-native poses coming from CONSRANK_U clusters. Detailed results are provided in We have implemented four variants of consensus-based rescoring functions: the CONSRANK score, the CONSRANK un-normalized score, and their equivalents based on residue frequencies and tested them on the rescoring of large sets of docking poses of the ZDOCK benchmark. In this context, un-normalized scores that do take into account the size of the interfaces are in general more efficient than normalized scores. When used alone, consensus-based scoring functions degraded the initial performance of the physics-based ZDOCK scoring function. However, when both physics-based and consensus-based scoring functions were used in combination, we observed a marginal improvement. This calls for calibration when using consensus-based scoring functions to re-rank large sets of docking decoys, since they are, by definition, highly dependent on the docking decoy population.All datasets presented in this study are included in the article/GL, JS, CH, and JM contributed to software. GL, YM, and NU contributed to investigation. MO and JM contributed to the methodology. TH contributed to the data curation. JM conceptualized and wrote the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Protein solubility is a key aspect for many biotechnological, biomedical and industrial processes, such as the production of active proteins and antibodies. In addition, understanding the molecular determinants of the solubility of proteins may be crucial to shed light on the molecular mechanisms of diseases caused by aggregation processes such as amyloidosis. Here we present SKADE, a novel Neural Network protein solubility predictor and we show how it can provide novel insight into the protein solubility mechanisms, thanks to its neural attention architecture. First, we show that SKADE positively compares with state of the art tools while using just the protein sequence as input. Then, thanks to the neural attention mechanism, we use SKADE to investigate the patterns learned during training and we analyse its decision process. We use this peculiarity to show that, while the attention profiles do not correlate with obvious sequence aspects such as biophysical properties of the aminoacids, they suggest that N- and C-termini are the most relevant regions for solubility prediction and are predictive for complex emergent properties such as aggregation-prone regions involved in beta-amyloidosis and contact density. Moreover, SKADE is able to identify mutations that increase or decrease the overall solubility of the protein, allowing it to be used to perform large scale in-silico mutagenesis of proteins in order to maximize their solubility. neural attention to predict protein solubility while “opening” the model itself to interpretability, even though Machine Learning models are usually considered black boxes. Thank to the attention mechanism, we show that i) our model implicitly learns complex patterns related to emergent, protein folding-related, aspects such as to recognize β-amyloidosis regions and that ii) the N-and C-termini are the regions with the highes signal fro solubility prediction. When it comes to enhancing the solubility of proteins, we, for the first time, propose to investigate the synergistic effects of tandem mutations instead of “single” mutations, suggesting that this could minimize the number of required proposed mutations.The solubility of proteins is a crucial biophysical aspect when it comes to understanding many human diseases and to improve the industrial processes for protein production. Due to its relevance, computational methods have been devised in order to study and possibly optimize the solubility of proteins. In this work we apply a deep-learning technique, called  Proteins have evolved within the different cellular environments to improve or preserve their functions, maintaining at the same time a degree of hydrophobicity necessary to proper fold, and enough solubility to prevent protein precipitation. Solubility is then a fundamental ingredient that must be properly balanced to maintain the protein functions and not aggregating . ProteinSince the soluble expression of proteins is crucial for protein production  for bothglobal features such as sequence length and the fraction of residues exposed to the solvent.The methods developed so far in both categories use various Machine Learning (ML) approaches, such as Neural Networks (NN) , Gradienshrunk into a single predictive scalar value.A common issue that the methods predicting the solubility of proteins had to face is the fact that the input protein sequences may have very different lengths, and indeed building ML models able to work with protein sequences is a common task in structural bioinformatics. From the ML standpoint, this task is not trivial because the variable length of proteins poses some issues to conventional ML methods, such SVM or Random Forests. This problem is usually addressed by using sliding window techniques to predict each residue independently , 17, butNeural Networks (NN) are flexible models that can elegantly address this issue. The classical approaches consist in building a pyramid-like architecture  that takHere we propose a novel solution to this issue, which has been inspired by the neural attention mechanisms developed for Natural Language Processing and machine translation , 20. Ourinterpreted, showing that the learned patterns correlate with complex sequence aspects such as the presence of aggregating regions or the protein contact density, while it does not correlate with more trivial aspects such as biophysical propensity scales or solvent accessibility.Additionally, the use of neural attention allows our model to be tandem mutations (> 2 × 106 pairs). This allowed us to investigate the possible effects of synergistic interactions between mutations, indicating that, in certain regions of the proteins, the implementation of pairs of mutations could have a larger effect that the sum of the effects of independent mutations. Finally, we show that the predicted synergistic effects have a significant correlation with the average contact distances between residues, extracted from the protein PDB structure, suggesting that SKADE is able to catch a glimpse of complex emergent properties such as the contact density.We also show that, even if it has not been specifically trained for the task, SKADE can distinguish between mutations that increase or decrease the proteins’ solubility. This, coupled with the fact that it can generate hundreds of predictions per second, makes it ideal to perform in-silico optimization of protein solubility. To show its potential in this sense, we performed a complete in-silico mutagenesis of the UPF0235 protein MTH_637, computing both the effect of all the possible single mutations and all possible pairs of To train and test our model, we used the protein solubility datasets adopted in , 11. UsiTo validate the ability of SKADE to distinguish between variants increasing or decreasing the overall solubility of the protein, we adopted the dataset used in CamSol , which cP and the attention network A.SKADE is a NN model which consists in two sub-networks: the predictor network The NN takes as input just a the target proteins sequences, without using evolutionary information or other kinds of annotations. As a first step, each residue in the input sequence is translated into a trainable 20-dimensional embedding encoding the 20 possible amino acids. The embedded sequences are then sorted and padded, in order to maximize the efficiency of the NN computations on GPUs.A and P are constituted by 2 layers of bidirectional Gated Recurrent Unit (GRU) Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: This is an interesting manuscript in the burgeoning field of protein solubility prediction. It is based on machine learning models, and produces results in line with other available methods. The authors describe their use of ML methods as novel in the field and this may be the case, I am afraid that I am not expert in ML methods. What I regard as novel are a couple of observations made in the manuscript, and that is where I would like to see more work carried out to clarify the results.First, a statement is made to the effect that the predictions / model are not based on the commonly used biophysical properties (of amino acids). But, I do not see how this can be asserted if amino acids themselves are central to the prediction scheme - as I see it, sequence is the input to the training algorithm.Second, and of the most interest to me, is the observation that N-t and C-t seem to possess  more or less all the predictive power. I would not be surprised to find a tendency towards greater predictive power, but a flat line  over the central 70% of the sequence I find amazing. Is it possible that the method is learning sequences (i.e. homology) and that this is best expressed at NT and CT when sequences of variable lengths are mapped to percentiles?My own speculation is probably wrong, but I feel that the authors need more work here. e.g. What happens if the standard bioinformatics methods for reducing to non-redundant training set is used?In the data reported for SKADE analysis of the CAMSOL mutation set, are all the mutations that affect solubility in the NT and CT regions - as might be expected given the results of Fig. 2?Similarly, the synergistic mutations in later figures, are they also in NT and CT regions?Reviewer #2: The paper presents a novel computational approach to predicting protein and peptide solubility from amino acid sequence alone SKADE. The model behind the method is a Recurrent Neural Network with soft self-attention and dot-product score function, built using Gated Recurrent Units. It is implemented in Python using Pytorch framework and supplies a pre-trained network for immediate applications.SKADE is a great example of a neural network design without explicit feature engineering. The resulting model is comparable in performance or outperforming (depending on metrics) state of the art methods such as PaRSnIP and DeepSol that rely on knowledge-based and engineered features. The distributions of weights and scores along protein sequences suggest that N- and C-termini are most important determinants of protein and peptide solubility.In addition, SKADE is able to identify mutations that increase or decrease the overall solubility of the protein, making it a useful and fast method for scanning mutagenesis, not limited to single mutations. Testing the effects of protein truncation on solubility is also possible with SKADE.Major:1. Although the model provides per-residue attention values and scores, interpretability of the methods remains limited. The only conclusion is that N- and C- termini are more important, but that is about it. Besides, these are the output layers of the network, what is inside is still a “black box” in terms of interpretability, I suggest deemphasizing this point in abstract and conclusions. The lack of correlations with biophysical properties contradicts this conclusion.2. It is quite surprising to see that there is no correlation of attention and solubility profiles to any physico-chemical and/or structural properties of amino acids. Particularly, because these features work quite well in other solubility prediction methods. I think Table 1 in supporting information files does not comprehensively cover the biophysical features, for instance aggregation propensity, SASA. Besides, it is not clear how the correlations were calculated. Could the lack of correlation be related to scalability of attention/profile with sequence length? I.e. values of a short protein could be incomparable to values of a large protein.3. According to neural network architecture it seems that the Embedding for converting 20 amino acid categories into a 20-dimensional tensor acts simply as a One Hot encoder. If there is any actual embedding that is learnt during training could you visualize the resulting amino acid similarities? Otherwise I suggest renaming embedding to encoding.https://devguide.python.org/#status-of-python-branches . It looks like there should not be any reasons not to update the code to Python 3.4. Python 2.7 is not acceptable by any means. This Python branch reached end of life on January 1 2020, is no longer supported. Moreover, is currently being removed from many computer systems for security concerns because it is unsupported. See https://www.python.org/dev/peps/pep-0008/ Automatic conversion with autopep8 --aggressive could be used, for instance. Note that there is a lot of imported but unused modules, variables, and other issues that a lint program could fix, please also run pylint to get rid of the software issues.5. Please format code according to PEP8 Minor:1. There is an imbalance between soluble and insoluble classes, since you have not used weighted sampling. To what extent do you think this imbalance affected the results? I suspect this could be the reason behind performance gap with respect to positive/negative class prediction separating SKADE and PaRSnIP.2. The details of model convergence should be provided in supporting information. I could see that tensorboard was used in the program, so it should be possible to include those plots.3. Line 98, why i<0 ?https://www.uniprot.org/uniprot/O267344. Line 298: According to uniprot the protein should be 104, not 103 residues long 5. The equation in line 300 is not clear, were the pairs of mutations generated including two mutations on the same position? Shouldn’t it be something like 104x19x19x103/2?6. Current Pytorch v 1.3.1, version was 1.0.1 used in the paper. Model needs to be recalculatedkipoi.org repository, that would make it much easier to run it7. Please submit the model to 8. Peptide N- and C-termini are effectively charged residues in terms of their physico-chemical characteristics affecting solubility, i.e. Ala is hydrophobic, but will have a charge on the N-termini. However, it is not treated/encoded in any way different than any other amino acid. Was the reasoning behind it to an unbiased representation in the model or something else?9. What is BAC in table 2?10. Line 65, correlation is reported as AUC, which is not convenient11. Line 319: “my” > “by”12. In my opinion it is better to show dependence of synergetic effect on spatial distance directly as a scatter plot to emphasize on the correlation, particularly in proximity13. In Figure 3 according to heatmap the first WT residue in O26734 should be V (the only white shaded residue), however this is not the case. What gives?**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Computational Biologydata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article . 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