Understanding the binding behavior and conformational dynamics of intrinsically disordered proteins (IDPs) is crucial for unraveling their regulatory roles in biological processes. However, their lack of stable 3D structures poses challenges for analysis. To address this, we propose an algorithm that explores IDP binding behavior with protein complexes by extracting topological and geometric features from the protein surface model. Our algorithm identifies a geometrically favorable binding pose for the IDP and plans a feasible trajectory to evaluate its transition to the docking position. We focus on IDPs from Homo sapiens and Mus-musculus, investigating their interaction with the Plasmodium falciparum (PF) pathogen associated with malaria-related deaths. We compare our algorithm with HawkDock and HDOCK docking tools for quantitative (computation time) and qualitative (binding affinity) measures. Our results indicated that our method outperformed the compared methods in computation performance and binding affinity in experimental conformations. Intrinsically disordered proteins (IDPs) are involved in many biological processes, such as cell regulation and signaling, and their malfunction can become linked to severe pathologies ,2,3. UndPlasmodium falciparum (PF) is a protozoan parasite of humans that inflicts damage to the human immune system and is responsible for most malaria-related deaths [IDPs do not have distinct, well-defined secondary and tertiary structures because of their remarkable backbone flexibility . When and deaths . Plasmodd deaths . We consThe intrinsic disorder poses a challenge for both experimental analyses of the conformation and computational modeling due to the lack of stable structure. In spite of the instability, it is critical to understand the biological functionality during protein–protein interactions. Several rigid-body docking techniques have emerged as helpful tools to assess the prediction of possible interacting poses between two protein bio-molecules for global docking . These dIn this work, we propose a topology-based rigid-body docking algorithm that takes the protein surface models of the globular proteins to predict a binding conformation for the interacting IDPs. Our approach extracts the topological and geometric properties of the protein surface to generate random IDP conformation ensembles around it. It then ranks the conformation ensembles based on the docking score to find the geometrically favorable pose. The algorithm examines the score values to select the geometrically favorable binding position and plans a feasible trajectory from IDP’s initial location to it. Our method can be used as a tool to find the best docking position that geometrically fits the protein surface model when no information other than the individual structures is available. We performed experiments for nine globular proteins interacting with six IDP molecules in the conformation space. We considered the tertiary structure of the globular proteins, ranging between 173 and 1544 residues, as a stationary object and the rigid body of the IDPs as a moving object. Our results showed improved quantitative  and qualitative  analyses with our tool compared to two publicly available tools; i.e., HawkDock and HDOCK. We evaluated the interaction of all IDPs with all proteins over 1250 experiments with values averaged over 10 runs in each case and planned a path to the binding pose with the highest score among the top 10 predicted conformations.We obtained protein data from the Protein Data Bank (PDB) ,21 and cPlasmodium falciparum (PF) pathogen proteins , as shown in The proteins selected included nine We selected the 1KRN (88 residues), 2LE3 (42 residues), 5EJW (91 residues), and 7KPI (142 residues) proteins as IDPs based on their highly disordered behavior shown in the protein feature view plot available in the PDB database. The other IDP bio-molecules, AF-I1E4Y1-F1 (117 residues) and AF-P59773-F1 (190 residues), from the AlphaFold DB, were of the mus-musculus and homo sapiens species, respectively. The mean per-residue confidence score (pLDDT) for AF-I1E4Y1-F1 is 48, and for AF-P59773-F1, it is 59. The pLDDT measure estimates whether the predicted residue has similar distances to neighboring C-We performed experiments on a Dell Alienware Aurora desktop machine running the Ubuntu 20.4 LTS operating system and developed algorithms in C++ language using the PMPL library . We evalExtracting Geometric Features of the Protein Surface: Recall that our method constructs a surface mesh  around the considered protein surface models to abstract the topological and geometric information. During the execution, it randomly samples the IDP conformations around the protein surface model, thus constructing a manifold mesh representation to capture the topology of the protein’s surface. These topological features aid in identifying the geometric properties of the protein surface  for better approximations, as shown in Computational Time: We analyzed the computation time (in seconds) required for feature extraction and prediction of geometrically favorable docking conformations in these IDP–protein interactions. This study included all IDPs in nine PF pathogen protein conformation spaces. The feature extraction time measures the duration of the extraction of the topological and geometric features from the globular protein surface, while the ranking time finds the top 10 conformations. To assess our algorithm’s performance efficiency, we compared our total time to output the top 10 docking conformations with HawkDock and HDOCK, as depicted in Our method demonstrated faster prediction of IDP binding conformations compared to HawkDock and HDOCK in all PF pathogen protein conformation spaces except for 2LE3. The smaller size of the 2LE3 IDP led to a longer conformation sampling time, necessary for accurate feature capture of large- or complex-sized proteins. Proteins like 1SQ6, 6ZRY, and 7F9K have tetrahedral polygonal shapes rather than spherical surface structures, thus allowing the sampling of conformation ensembles in various fitting positions. As a result, it took extra time in the case of the 2LE3 IDP within the conformation space of these proteins. However, this difference did not affect our method’s performance significantly and resulted in lower time overhead compared to the baseline methods. In particular, HawkDock failed to find docking conformations for the 3NTJ protein due to its limitation to proteins with fewer than 1000 amino acids.We found that, using the geometric information for the protein surface, it was still possible to predict multiple structural arrangements of IDPs around the proteins to find the closest interacting binding pose between two bio-molecules without declining computation performance. Thus, we can conclude that the amount of data assessed by our method does not impact its surface approximation, and it still provides a quantitatively good performance.Selecting the Suitable Binding Conformation: As initially mentioned, our method predicted the top 10 docking conformations for an IDP across all PF pathogen protein conformation spaces. This process was iterated over 10 times, and for each iteration, we recorded the top 10 conformations to assess the likelihood of obtaining the same conformation from 10 random iterations. The recorded outputs were then further analyzed to identify the IDP conformation with the highest frequency as the most suitable docking position among the 10 experimental runs. This selected conformation was subsequently utilized as input for path planning. In Binding Affinity Measure: We compared the binding affinity of our IDP binding conformation with the binding affinity computed for the IDP conformations predicted by the HawkDock and HDOCK methods across all PF pathogen proteins. The molar Gibbs free energy ΔG was used to assess the relevance of the binding pose. Gibbs free energy is a thermodynamic potential that quantifies the maximum reversible work capacity of a thermodynamic system under constant temperature and pressure  conditions [nditions . ProteinWe utilized the molar Gibbs free energy ΔG to calculate the binding affinity of the top-ranked IDP conformation ensemble predicted by all three methods. As mentioned previously, HawkDock failed to predict the docking conformation for the 3NTJ protein, resulting in the absence of a binding affinity measure for this case.Based on the observations in Affinity Comparison to Known IDPs: We analyzed the binding affinity of protein–protein complexes specifically by focusing on folding-upon-binding [-binding . To vali-binding ,28,29, wIn addition to predicting binding conformations for rigid-body docking, our method also included feasible trajectory planning toward the selected finalized binding pose during re-scoring. We assessed the total time required for path planning to the predicted binding pose for all IDPs in the nine globular protein conformation spaces, as presented in The unpredictable behavior of IDPs around the studied proteins enabled us to analyze the feasibility of their interaction with specific proteins, particularly how easily they aligned around a protein structure for association. Path planning time provides insights into the locomotion of IDPs around proteins as they search for the most suitable binding pose for rigid-body docking. When used in conjunction with other tools to examine the conformational flexibility of IDPs during their motion around proteins, it can simplify flexible docking tasks by focusing computational methods solely on the dynamic structure of IDP conformations as they traverse the planned trajectory, facilitating future biological studies.We conclude that our approach successfully captures the geometric features of the protein surfaces and plans a path for IDP bio-molecules to the geometrically favorable binding pose, showing a higher affinity compared to affinity measures demonstrated by baseline methods. Thus, the work showed the significance of our approach for further biological studies.An important area of study includes understanding how a protein binds to another protein’s active site and what conformational changes both molecules undergo during docking to the active site or exit from it. Such information allows for predicting the possibility of an association between protein–protein pairs, the strength of this association, and the protein activity level. Protein function evaluation is a challenging task approached by various sequence-based and structural-based methods . HoweverAnother interesting docking conformation prediction tool in  integratMany studies have used a graph representation of a protein, indicative of its geometry and topology, to predict protein function . The topStudying the conformation of highly dynamic IDPs is a challenge in structural biology . NuclearThe authors of  used NMRdegrees of freedom . An abstract simplicial complex K  is a generalization of a graph that is useful in representing higher-than-pairwise connectivity relationships.The elements of the set are called vertices, and the set itself is a simplex. The vertices refer to the IDP’s conformation in the conformation space.Definition 2. (Vietoris–Rips complex). Given a set S of points in Euclidean space E, the Vietoris–Rips complex .S defines the group of all IDP conformations in the simplicial complex In this work, protein surface models were considered static objects. Definition 3. Let D be the Euclidean distance function that measures the distance between the point .Definition 4. Let  Γ counts all neighbors close to y in S within the distance ϱ.Definition 5. Let f be a discrete Morse function on Please refer to  for our Definition 6. . The set of critical points is defined as the set of non-degenerate points on the surface of the protein when the given discrete Morse function f reaches its extreme values; i.e., local minima or maxima.Definition 7. . This set is defined as all possible IDP conformations in S at a radial distance of ϱ from a critical point on the protein surface. In other words, it is the union of intersections of vertices in S within the metric balls of radius ϱ centered at some critical point.Overall, our method first generates a simplicial complex ConstructComplex. Upon satisfying the sampling condition from [f from [f reaches its extremum.Algorithm 1 Sampling and planning path to binding poseInput: P: Protein surface model, R: A planned pathway to the binding site, s: initial IDP conformation, H: set of closest IDP conformations around the protein surface, g: best binding pose.1:Let 2:3:Refer to 4:5:6:for all x ∈ F do7:for alldo     8:if x closest to c then          9:               Equation 10:end if          11:end for     12:end for13:14:Refer to 15:return Algorithm 1 constructs a simplicial complex around the protein surface by sampling and connecting IDP conformations using the method ion from , the alg [f from  to this The algorithm then extracts the feasible critical points at radial distance Equation . From thThe method takes the conformation with the minimum Hausdorff distance as the final docking position from all the possible generated conformations. Finally, a path is planned for the IDP from the start conformation to the binding pose conformation, taking the other predicted IDP conformations as waypoints (line 14). The process of selecting a binding pose happens internally, with our method ranking and automatically choosing the binding conformation to plan the path during the interaction of protein–protein complexes. As a result, our algorithm outputs an extracted geometric information map consisting of critical points, feasible critical points (predicted IDP conformations), and a pathway from the start conformation to the binding pose conformation.The paper presented a framework that utilizes topological and geometric information from the structured protein surface to investigate the binding behavior of IDPs. The study assessed the performance efficiency and quality of the predicted experimental IDP conformations, comparing them to state-of-the-art methods. Additionally, it reported the path planning time required to determine a transition path to the docking position.The experimental results demonstrate that our method successfully predicts geometrically suitable binding poses for IDPs around protein surface models, specifically for rigid docking. Moreover, our approach outperformed the compared methods in computational performance and the predicted conformation quality. This research serves as an initial step towards further analyzing IDPs and their interactions with other biomolecules, leveraging geometric and topological representations of these entities. The future enhancement will incorporate scoring functions and binding affinity measures in our model by integrating it with computational methods designed to estimate binding affinities, with the benefit of determining the final association site for dynamically unstable IDPs more effectively. We plan to apply this idea to our future work and provide a prototype accessible to the research community. By achieving a geometrically suitable conformation with the lowest score and a high binding affinity, our approach presents the potential for advancing the development of structure-based vaccine design processes.


We describe a multi parametric-approach, YAPPIS-Finder, for predicting the PPI sites on protein surface. A non-redundant database of comprised of 2,265 protein-protein interaction interfaces (PPIIs) involving 4,530 protein-protein interacting partners(PPIPs) and depicting the interaction between protein-chains of experimentally determined PPCs was used in designing the YAPPIS-Finder. Parametric score obtained on analyzing these 4,530 PPIPs with respect to their residue interface propensity, theirhydrophobic content, and amount of solvation free energy associated with them provided the basis of YAPPIS-Finder. By applying YAPPIS-Finder on another dataset 4,290 PPIPs from 2,145 PPIIs, the optimal range of the parametric scores and protein-probe vander Waals energy of interaction was determined. Subsequently, taking the optimal range of PPIP parametric scores and threshold for protein-probe van der Waals energy of interaction into the consideration, the YAPPIS-Finder was tested on a blind dataset of554 protein-chains and it was found predicting 69.67% sites correctly. On predicting only one PPI site on each protein-chain, the YAPPIS-Finder found covering 22.91% of actually sites in the predicted site. Contrary to this, the sites predicted by SPPIDERcovered 22.7% of actual sites. However, on predicting two PPI sites for each protein-chain, the percentage coverage of actual sites in the predicted sites by YAPPIS-Finder exceeded two-fold (i.e. 41.81%), thus making the YAPPIS-Finder a better method. Proteins are the basic functional unit in cellular world of life . They arA non-redundant database protein-protein interaction interfaces (NRDB) depictinA non-redundant database of PPIIs All of the PPIPs from NRDB To determine the level of hydrophobicity (ϕ) associated with PPIPs, the hydrophobicity scale for amino acids given by Hessa et al. 2005 was used . For eacThe solvation free energy of PPIP was calculated by taking into account the solvation energy scale for amino acids given by  White etThe parametric scores obtained on analyzing the PPIPs from NRDB were set to provide foundation for the proposed scheme. However, the NRDB analysis revealed that the parametric scores for PPIPs contained a significant number of outliers in them. Therefore,a statistical approach of inter quartile range was adopted to remove the outliers. After outlier removal, the entire range of parametric score for each PPI sites parameter was divided into a number of bins with width calculated using Scott’'s rule (Eq. 4 - see PDF).Success of any prediction tool largely depends of quality of the datasets used in its designing. The dataset should be comprised of information related to both known interacting protein pairs (positive set) and non-interacting protein pairs (negative set).It is quite easy to obtain the experimental instances of PPI for the positive set while construction of negative set is not that much straightforward. In the proposed study, to develop a computational approach for PPI prediction, one training and one test setwas designed considering the presence or absence of SCOP superfamily pair in NRDB proposed by our research group previously The implementation of the proposed computational approach was completed in two phases; training and testing phase. During training phase, initially, using all of the PPIIs involved in NRDB  , the opThis was followed by the identification of the parametric scores and protein-probe interaction energy conforming Ƭ≥0.25(25%) against the actual PPI sites (demarcated on the basis ACP definition). The obtained parametric scores and threshold forprotein-probe van der Waals interaction energy were designated as the optimal range of the parameters. This way, the optimal ranges of all of the parameters were calculated.During the testing phase, the unbound forms of protein-chains extracted from the experimentally determined PPCs contained in testing dataset were examined using the similar strategy. The protein surface was examined corresponding to the optimal ranges of PPIsites parametric score and the optimal value of the van der Waals energy of interaction obtained during the training phase. For each PPI site parameter pair, the protein atoms with their parametric scores within the threshold values  wereclustered on the basis of geometric proximity and ranked using Eq. 5-7(see PDF). Here too, only the predicted sites with Ƭ≥0.25(25%) were termed as the correctly predicted sites. Following this, the best ranked predicted sites were compared against the actualPPI sites to evaluate the prediction efficacy.Protein-chains extracted from experimentally determined protein-protein complex.The input protein-chain was placed in a three-dimensional box, which was divided into a cubic grid of resolution 0.9 Å. At each grid point, a methyl (-CH3) probe was placed and van der Waals energy of interaction was calculated between the proteinatom and the methyl probe. The process of calculating the protein-probe van der Waals interaction energy is described in detail in Laurie and Jackson . The intClusters of atoms as the predicted PPI sites.The prediction power of YAPPIS-Finder was also compared against the SPPIDER  which isOptimization of protein-protein interaction sites parameters by running YAPPIS-Finder for the training datasetTo obtain the optimal range of PPI site parameters, the YAPPIS-Finder algorithm was applied on protein chains extracted from experimentally determined PPCs of training dataset as described in Step 4 and Figure 3(see PDF). The predicted and actual siteswere compared (demarcated using the definition of ACP) and overlap between the two was determined using Eq. 8 to 10(see PDF). Subsequently, the values of parametric scores and threshold of van der Waals energy for interaction were retrieved for which,Ƭ≥0.25(25%).The parametric scores obtained were termed as the optimistic range of the parameters under consideration.Prediction of interaction sites for the test dataset proteins by YAPPIS-Finder and performance analysis was done in similar manner.Only predicted sites with Ƭ≥0.25(25%) were termed as the correctly predicted sites.Prediction efficacy of YAPPIS-Finder and SPPIDER and comparison of their performance evaluation The prediction efficacy of YAPPIS-Finder was examined by running it on a blind dataset of 554 protein-chains from the test set. For these 554 protein-chains,their actual PPI sites were demarcated using the definition of ACP described in Materials and Methods section this section as well as in . Using YComparison between the performance evaluation of YAPPIS-Finder and SPPIDERFollowing to the determination of prediction efficacy of the YAPPIS-Finder and SPPIDER revealed that SPPIDER, the sites predicted by both of these approaches were compared against the actual sites. On predicting only one PPI site for each protein-chain,the YAPPIS-Finder found covering 22.91% of actually sites in the predicted site. Contrary to this, the sites predicted by SPPIDER covered 22.7% of actual sites. However, on predicting two PPI sites for each protein-chain, the percentage coverage of actualsites in the predicted sites by YAPPIS-Finder exceeded two-fold (i.e. 41.81%), thus making the YAPPIS-Finder a superior approach.This paper describes the development of a novel, multiparameteric-method YAPPIS-Finder to identify the protein-protein interaction sites. The YAPPIS-Finder was tested on a set of above 500 protein-protein complexes. The ability of the method in identifyingthe protein-protein interaction sites has been investigated. The YAPPIS-Finder method included the information of solvation. Residue propensity, hydrophobicity and van der Waals interaction energy in its ability to identify near-precise region of protein-proteininteractions whilst at the same time giving a higher degree of correlation in overlap between predicted and experimentally proved protein-protein interaction sites.


Common to all biological systems and living organisms are molecular interactions, which may lead to specific physiological events. Most often, a cascade of events occurs, establishing an equilibrium between possibly competing and/or synergistic processes. Biochemical pathways that sustain life depend on multiple intrinsic and extrinsic factors contributing to aging and/or diseases. This article deals with food antioxidants and human proteins from the circulation, their interaction, their effect on the structure, properties, and function of antioxidant-bound proteins, and the possible impact of complex formation on antioxidants. An overview of studies examining interactions between individual antioxidant compounds and major blood proteins is presented with findings. Investigating antioxidant/protein interactions at the level of the human organism and determining antioxidant distribution between proteins and involvement in the particular physiological role is a very complex and challenging task. However, by knowing the role of a particular protein in certain pathology or aging, and the effect exerted by a particular antioxidant bound to it, it is possible to recommend specific food intake or resistance to it to improve the condition or slow down the process. Antioxidants are substances able to reduce, prevent, or revert the oxidation of other substances. Alternatively, antioxidants are compounds that scavenge reactive oxygen species (ROS) or act to inhibit their production and/or stimulate the antioxidant defense system, thus reducing the cell-damaging effects of ROS . They arFood antioxidants can exhibit several modes of action in the body. They are (i) direct antioxidants of oxidized substances, (ii) metal ion chelators , (iii) cofactors of enzymes involved in antioxidant activity, (iv) activators of transcriptional factors which induce expression of genes involved in antioxidant activity, (v) modulators of signaling pathways, (vi) modulators of secondary and/or tertiary structure, thermal/proteolytic stability and primary function of bound proteins, (vii) participants in other physiological actions ,3.Only a few antioxidants, in the form of supplements, have been approved by the responsible authorities for use in human therapy. There is a great interest in synthesizing more potent derivatives or isolating them as natural products with improved absorption/bioavailability/antioxidant properties. However, supplements cannot completely substitute natural sources of antioxidants since a natural variety of derivatives and their possible synergistic or additive effects cannot be achieved in a lab.The antioxidant potential of foods can be determined by a number of methods: spectrometric, chromatographic, and electrochemical . SeveralBioavailability is a term that defines the amount of a nutrient that is digested in the gastrointestinal tract, absorbed in the intestine, and further metabolized in the body. This path depends on many factors, such as a chemical form or antioxidant derivative, food matrix, degree and type of transformation by host intestinal enzymes and microbial enzymes in colon microbiota, interaction with other molecules in the body, and transport . Food-deAntioxidants soluble in water can be directly absorbed in enterocytes by diffusion and/or via transporters and further translocated to blood. Lipophilic antioxidants, after digestion in the stomach, form micelles with exogenous lipids and host bile acids released from the gallbladder and further undergo diffusion through the epithelial surface of the intestine. Absorbed micelles are transported as lipoprotein particles via lymph . ModificThe food matrix plays a significant role in antioxidant availability. Unless free, antioxidants can be covalently bound to macromolecules , associated with other molecules via ionic bonds, mechanically entrapped in a food matrix, or physically associated with specific cell structures . The samContrary to that, co-ingestion of lipids may increase the bioavailability of lipophilic antioxidants, such as carotenoids, by facilitating their emulsification and absorption . The intA proper translation of mRNA, followed by the appropriate post-translational modifications, is essential for adequate protein folding, targeting, and specificity. Proteins are not stable molecules existing as just one well-defined structure. Usually, a population of several conformational states exists, although not necessarily at the same time and location. Protein conformers originate from molecular motions that do not require considerable energy input, giving rise to molecular states of similar stability . A dynamFood antioxidants can interact with proteins at several possible places: in a deep protein cavity, where accessibility of environmental solvent is low, in a cleft between protein units, at the shallow surface groove, or at the protein surface where charged amino acid residues are exposed. Bonds that form between the antioxidant molecule and protein are usually multiple, involving many amino acid residues, and mostly non-covalent: hydrogen bonds (H-bonds), electrostatic bonds , hydrophobic interactions, Van der Waals bonds, and π-π interactions. Amino acids that interact with a ligand can be polar or non-polar and belong to the α-helix, β-sheet, or loop region of the molecule. Sometimes, all structural elements are involved. Some interactions are weak, whereas others are strong. Structural adjustments of the protein to accommodate a ligand can affect its conformation, stability, and behavior.14C]quercetin was shown to bind highly selectively to HSA in vitro covalently [Covalent (irreversible) interactions require specific conditions and/or the involvement of specific reactive groups, which can chemically modify amino acid residues, enabling covalent binding. Such interaction in the case of human blood proteins is a rare event. An example of a covalent partnership is an interaction between epigallocatechin gallate (EGCG) and human serum albumin (HSA). Protein carbonylation occurs when these two molecules are incubated at pH 7.4 and 37 °C. EGCG oxidizes lysine residues at the EGCG-binding sites in HSA, generating an oxidatively deaminated product . EGCG cavalently ,26, and The effect of antioxidant binding to the protein (if there is an effect) can be activating, inhibiting, or modulating, which depends entirely on the nature of the binding couple. The effect of one antioxidant is usually of the same type on the number of interacting proteins. There are, however, antioxidants that can exert all three categories of effects depending on the particular binding protein. Resveratrol is an example of an antioxidant that can interact with numerous proteins that do not share primary sequence homology or common fold or have other similarities, exploiting all protein known binding places, forming all known types of bonds, with acidic, basic, or neutral amino acids and exerting no common effect . Due to cis and trans isomers, and their interaction with proteins can differ. Additionally, some antioxidants may exist as optical isomers—enantiomers (R and S). R, a naturally occurring form of α-lipoic acid (LA) in food, but not S, is an essential cofactor of enzymes involved in oxidative metabolism in mitochondria [One antioxidant molecule most often interacts with one molecule of protein. Sometimes, there are more binding places, for example, at more subunits. In addition, there may be several different binding places for the same antioxidant on one protein, as in the case of S-adenosylmethionine synthetase 2, which can bind two resveratrol molecules, one at the substrate binding place and the other at the interface between monomer units . Proteinchondria . A mixtuBesides common features of antioxidant activity, particular behavior of antioxidants may be characteristic just for some. For example, both LA and its reduced form, dihydro-LA (DHLA), act as antioxidants and, unlike other antioxidants, are effective in both hydrophilic  and lipophilic (plasma membrane) environments . LA/DHLAKa) and determine the mechanism of quenching (static or dynamic). Thermodynamic parameters of the interaction, such as enthalpy change (ΔH), entropy change (ΔS), and Gibbs free energy (ΔG), can be calculated by varying the experimental temperature. Negative values of ΔH and ΔG indicate a spontaneous process, as in the case of resveratrol/collagen binding [4 and 108 M−1, the protein fluorescence quenching approach can be reliably employed to determine constants between 104 and 106 M−1. An additional method to detect binding relies on the ability of an antioxidant to fluoresce, as in the case of resveratrol and phycocyanobilin (PCB), after excitation at 320 or 460 nm, respectively. The interaction of such antioxidants with proteins induces a conformational change, resulting in altered emission spectra [Investigation of protein fluorescence is one of the most commonly applied approaches to studying the binding of antioxidants. Protein intrinsic fluorescence arises from tryptophan and tyrosine side chains upon excitation at 280 nm. Ligand binding can alter the environment of aromatic side chains, causing changes in the fluorescence emission intensity  and a shift in the position of fluorescence maximum . Thus, e binding . Althoug spectra ,37.Ka, ΔG, ΔH, ΔS, and stoichiometry in a single experiment. However, the limited aqueous solubility of antioxidants imposes certain limitations and challenges for ITC experiments. For example, adding EGCG to the HSA solution results in enthalpy change originating not only from the binding event but also from the dissociation of initial EGCG aggregates upon dilution [Although fluorescence spectroscopy is widely used to characterize antioxidant/protein interaction, isothermal titration calorimetry (ITC) is a gold standard for the determination of thermodynamic parameters of ligand binding. The procedure assumes the addition of an antioxidant to a protein solution in a calorimeter cell held under isothermal conditions. The method sensitively measures enthalpy change induced by ligand binding . ITC is dilution . Since adilution .Another technique used for binding studies is surface plasmon resonance (SPR). Although it is usually employed to examine protein/protein interactions, ligand/protein associations can also be detected . The proMass spectrometry (MS) can also detect ligand/protein interactions, providing that they are sufficiently strong to withstand vacuum and ionization conditions. Hydrogen bonds, electrostatic interactions, and Van der Waals forces are strengthened or unchanged in a vacuum, whereas hydrophobic interactions are weakened. Several modifications are usually required for the ligand/protein complex to remain stable . This teUV/VIS absorption spectroscopy is one of the most straightforward techniques for studying protein conformational change and detecting antioxidant/protein complex formation. The method captures the effects of the environmental change close to aromatic side chains. Another technique, near-UV circular dichroism (CD) spectroscopy, reveals changes occurring specifically in the chiral environment around aromatic structures (protein tertiary structure). Since a protein binding pocket usually contains aromatic moieties, the near-UV CD spectrum sensitively monitors ligand binding . LimitatFar-UV CD spectroscopy is commonly used to evaluate the effects of antioxidant binding on protein secondary structure. Since experiments require a smaller amount of protein (approximately 0.1 mg), this type of analysis is more frequently employed than near-UV CD measurements. Several software packages have been developed to quantify secondary protein structures, such as α-helix, β-sheet, β-turn, and random coil, facilitating data management . Seconda2O molecules in the amide I region, thus requesting signal correction for the buffer contribution. Conduction of experiments in D2O would make data treatment much easier since D2O does not absorb in this spectral region. However, one should keep in mind that due to the H-D exchange, protein secondary structures may be altered by the replacement of H2O by D2O. If ligand binding provokes discrete changes in the secondary structure of the protein, employment of both FTIR and far-UV CD is suggested to confirm the effect, as was performed to define an increase of 5% of α-helical structures upon PCB binding to HSA [Fourier-transform infrared spectroscopy (FTIR) is an alternative technique for studying the secondary structures of proteins. Unlike far-UV CD, FTIR is not sensitive to light scattering, providing a valuable tool for protein aggregation examination. Several bands characterize the IR spectra of proteins, but an amide I band, arising from C=O stretching frequency, is the most convenient for evaluating . A limitg to HSA .The formation of the antioxidant/protein complex may also be traced via the absorption and CD spectra of an antioxidant. For example, PCB binding to HSA induces stereoselective interaction only with the M conformer of the ligand, as can be observed by the negative Cotton effect in the CD spectrum of the complex. Furthermore, increased absorbance of PCB in the presence of HSA indicates the transition of this tetrapyrrole from cyclic to more extended conformation upon binding . IncreasSmall-angle X-ray and neutron scattering methods (SAXS and SANS) provide global information on protein size, shape, and folding state in the solution. Software packages for the analysis of SAXS and SANS data enable the determination of the 3D structure of proteins alone and in complexes with ligands, although at a much lower resolution than crystallographic methods ,51. LigaThe formation of the antioxidant/protein complex can alter the thermal and/or proteolytic stability of the protein . Ligand binding can change the melting temperature (Tm) of the protein as well as the shape of the denaturation curve. There are, however, interesting cases when the trend of the melting curve of the complex is different from the trend of the curve originating from the native protein, yet the melting temperatures may be the same. Such an example can be seen in the case of PCB binding to catalase . IncreasDocking simulations predict antioxidant binding site(s) to a particular protein by matching known structural data on binding partners and processing them through appropriate software. Out of many produced ligand/protein complexes in a model system, the best docking orientation, the number of ligand/protein interactions, and the docking energy score nominate the most probable binding site and conformation of the ligand. The competition between different antioxidants for the same binding site can also be studied using docking simulations  and confEach protein inside the body performs its specific roles, which may be affected by ligand binding. When the ligand is an antioxidant, three aspects are to be considered—the primary functions of the protein, a possible antioxidant effect of the ligand on the protein, and the reducing power of the complex compared to protein or antioxidant alone. Primary roles of the protein can be investigated via functional tests that simulate specific physiological events, for example, enzyme activity, antigen/antibody interaction, receptor binding, cell internalization, cell signaling, coagulation, interaction with other ligands or metal ions, etc. Accordingly, an array of methods is employed for such examination. In vitro assays with cells confirm the biological effects of antioxidant/protein complexes . HoweverThe binding of polyphenols to protein receptors on immune cells can exert an immunomodulatory effect . For exaSince all proteins are prone to oxidation, antioxidant binding may affect this susceptibility. One should bear in mind that one protein  can be oxidatively modified in many ways, at different sites, by different oxidizing agents . Thus, aA decay curve of the native protein molecule results from the reduction in its intrinsic fluorescence with time exposed to a free radical inducer. An antioxidant bound to protein may exert either no protective effect, which can be monitored as a steep decay curve, the same as for the native protein submitted to free radical oxidation, or exhibit some degree of protection, resulting in a smoother decay (oxidation) process. General examples of the effects are shown in Not only protein but an antioxidant itself can be oxidatively stabilized in a ligand/protein complex, increasing its bioactive potential. The effect originates from the antioxidative activity of the protein. Examples of such proteins are phycocyanin and BSA ,68. ManyThe knowledge collected by examining interactions discussed so far was further applied to form complexes on purpose with additional covalent bonds, providing more stable associations that affect protein significantly. Since this article intends to review interactions that can occur in circulation, artificial modifications will be briefly mentioned. Only certain amino acid residues can form covalent bonds but can also be chemically modified to introduce new reactive groups ,74,75. CFor the purpose of this review article, interactions between antioxidants classified according to their chemical structure and major human proteins in circulation, together with the observed effects, were examined in the literature. SCOPUS database was used to collect information for the period 2000–2023 (February). Major proteins were defined as those whose referent concentration in the blood reaches two g/L or more. Data on plasma proteins: albumin (38–51 g/L), immunoglobulin (Ig) G (8–18 g/L), IgA (0.9–4.5 g/L), IgM (0.6–2.5 g/L), transferrin (1.8–3.8 g/L), α2–macroglobulin (2–4 g/L), and fibrinogen (2–4 g/L), as well as whole blood protein hemoglobin (115–170 g/L), were searched for. No results were found for the interaction between immunoglobulin molecules and any antioxidants. As for other antioxidant/protein interactions, only data for proteins that could be undoubtedly identified as human were included in this article. Known interactions between food antioxidants and chosen human proteins  are listed in Ka values for the same interacting pair vary between studies, most likely due to different experimental conditions applied. Therefore, the research field on antioxidant/protein interactions remains widely open.The most frequently studied interactions are between antioxidant molecules and HSA . InteracCassia fistula leaf extract-based gold nanoparticles were shown to interact with HSA inducing minor changes in its structure [Some data found during the literature search were conflicting. For example, Zhang and coauthors reported an increase in the α-helix content of HSA upon interaction with gallic acid , whereastructure . EffectsBovine, porcine, or sheep protein interactions with antioxidants were also examined. Sometimes there are only data for a particular antioxidant and animal protein complex. For example, the binding of thymol , limonenOxidative stress underlines numerous chronic and degenerative impairments and participates in aging, apoptosis, cancer, pathogen infection, diabetes, cardiovascular, neurological, and other diseases. A human organism has its defense mechanisms, but that capacity is often overcome, and exogenous antioxidants from food are needed to support the combat. Redox equilibrium in the body plays a critical role in health maintenance, especially in scavenging free radicals and free metal ions and modifying cell signaling, host immunity, and gene expression .Many antioxidants have been recognized to exert beneficial health effects . AlthougLactobacillus, Enterococci, and Bifidobacterium species) and inhibiting pathogens (such as Clostridium). However, each antioxidant exerts a positive/negative effect on a specific set of bacteria [Antioxidants protect neurons against oxidation, particularly lipid components of cell membranes contributing to cell integrity and proper neurotransmission. Some neurodegenerative disorders, such as Alzheimer’s disease, are accompanied by the formation of protein aggregates; oxidative stress favors protein modification and cross-linking into insoluble fibrils . Food anbacteria ,195. TheUnderstanding interactions between antioxidants and proteins gained increased attention in the last two decades, although most investigations still employ in vitro experiments. Relatively simple models are needed to capture the effect of a particular antioxidant binding to a specific protein. Most research work has been performed using one protein and one antioxidant. Examination of the binding event of several antioxidants and one protein was rarely conducted. Such investigation, for example, includes follow-up of the effects when the whole plant extracts are assayed, as mentioned previously. Examination of the binding effect of a particular antioxidant with several proteins is an even rarer study design. It was employed only with simple systems, not having too many protein species. For example, phenolic compounds were isolated from the albumin, glutelin-1, glutelin-2, prolamin, and globulin wheat protein fractions and were identified by HPLC/LC-MS . The binAntioxidants behave according to their structure and nature, and their antioxidant capacity, when bound to proteins, may or may not be affected, depending on the structure and nature of proteins. It is also worth mentioning that analytical conditions in assays employed for in vitro measurement of an antioxidant capacity are not the same as conditions in vivo, suggesting caution in extrapolating in vitro results to living beings. There are hundreds of antioxidant derivatives in food, many more metabolites, and only a fraction have been studied. Likewise, few proteins were assayed as antioxidant-binding partners and very few were human proteins. According to the Plasma Proteome Database, more than 10.000 plasma proteins exist, or several billion in the entire human proteome; most have yet to be quantified due to very low concentrations . They arMost articles report that the interaction was recorded, evaluated as a structural alteration of the protein, and sometimes characterized as a change in the function of the protein or antioxidant. As mentioned, some results do not agree between studies and may be conflicting. Future work should be directed into a deeper analysis of the already known complexes and search for the new associations and their examination in relation to cell/extracellular milieu defined by specific environmental features and molecular species, such as pH, free radicals, redox-reactive small molecules, redox enzymes, etc. Antioxidant/protein complexes should also be examined concerning food habits and (patho)physiological conditions. It is certainly intriguing to learn about mechanisms that govern and regulate the distribution of many antioxidants between many proteins in the body, and not only between proteins but other molecules as well. Methodological approaches for these investigations are, however, limited. Separation of individual proteins from a complex mixture, such as human plasma, without disturbing antioxidant/protein interaction and detection of bound individual antioxidants is still an issue to resolve. Improvements in the existing analytical methods and the developing of more sensitive and specific techniques to handle that task are needed.Some proteins bind antioxidants, and specific ligands only bind to some proteins. Some antioxidants are present in forms that may directly bind to proteins, some after processing, and some are quickly transformed into derivatives with characteristics different from parent molecules. Some antioxidants compete, and some act in synergy. Only some protein functions are affected by antioxidant binding, and different antioxidants exert different effects. There are hundreds of food antioxidants and thousands of plasma proteins, which may form a coupling pair. Finally, there are severe methodological limitations of analytical techniques that can capture antioxidant/protein interactions that occur in a complex system such as the human body and regarding interferences from other biochemical processes. Having in mind all said, the investigation of antioxidant/protein interactions at the level of the human organism, the determination of antioxidant distribution between proteins, and involvement in the particular physiological role is a very complex and challenging task. However, by knowing the role of a particular protein in certain pathology or aging, and the effect exerted by a particular antioxidant bound to it, it is possible to recommend specific food intake or resistance to it to improve the condition or slow down the process.


Identifying the functional sites of a protein, such as the binding sites of proteins, peptides, or other biological components, is crucial for understanding related biological processes and drug design. However, existing sequence-based methods have limited predictive accuracy, as they only consider sequence-adjacent contextual features and lack structural information.In this study, DeepProSite is presented as a new framework for identifying protein binding site that utilizes protein structure and sequence information. DeepProSite first generates protein structures from ESMFold and sequence representations from pretrained language models. It then uses Graph Transformer and formulates binding site predictions as graph node classifications. In predicting protein–protein/peptide binding sites, DeepProSite outperforms state-of-the-art sequence- and structure-based methods on most metrics. Moreover, DeepProSite maintains its performance when predicting unbound structures, in contrast to competing structure-based prediction methods. DeepProSite is also extended to the prediction of binding sites for nucleic acids and other ligands, verifying its generalization capability. Finally, an online server for predicting multiple types of residue is established as the implementation of the proposed DeepProSite.https://github.com/WeiLab-Biology/DeepProSite. The proposed DeepProSite can be accessed at https://inner.wei-group.net/DeepProSite/.The datasets and source codes can be accessed at  Interactions between proteins and various biological molecules form the basis of protein function in most biological processes, such as gene expression regulation, signal transduction, and metabolic pathway regulation , conformIn regard to predicting protein binding sites, computational methods can generally be divided into two categories: those that rely on the protein sequence and those that rely on the protein structure. Sequence-based methods are based on the primary amino acid sequences of proteins for binding site prediction, and some notable examples are DLPred . As a reDespite the recent progress made using computational methods, their application in large-scale high-throughput prediction is constrained by several issues. First, structure-based methods require known tertiary structures as a prerequisite. However, because the structures of most proteins are still unknown, this approach is not applicable to proteins without a known structure. Moreover, experimental determination of protein structures is a time-consuming and challenging task. Second, sequence-based methods often use evolutionary information, which is typically generated by comparing the query protein to a large-scale protein database in terms of sequence. However, the performance of this approach is poor when predicting proteins with insufficient sequence similarity. Extracting evolutionary features also takes a long time. Third, existing methods rely heavily on hand-designed features to construct models, which may fail to consider key biological features.In recent years, the field of protein structure prediction has witnessed remarkable advancements due to the application of deep learning techniques. These include AlphaFold2  and does not rely on explicit parameterization of any physicochemical properties. Finally, we developed a user-friendly and comprehensive web server for site prediction that can be conveniently accessed and used by biologists.In this study, we use the same benchmark datasets as in previous studies to train and evaluate our method. 2 of absolute solvent accessibility after protein–protein complex formation. A previous study  in the protein graph, we also adopted the relative spatial and positional encodings proposed in where Finally, the rotationally and translationally invariant spatial edge features i and j.where the first feature is a radial-based distance encoding (we apply 16 anisotropic Gaussian RBFs spaced from 0 to 20 Å), the second feature represents the relative direction of the neighbour node entation . In addik-nearest mask based on the distance matrix to exclude amino acids that are spatially remote when calculating attention scores. However, this framework cannot capture the geometric edge features between residues. Here, we integrated the edge features when updating the target node byThe standard Transformer encoder includes two components: multi-head self-attention and position-wise feed-forward network. The self-attention module globally calculates the attention scores for all nodes to the target node and then aggregates them to update the target node. To incorporate protein structure information, GraphSite  leverages the output from the last layer of the Graph Transformer to estimate the residuewise probability of protein–protein/peptide binding viaLth Graph Transformer layer; n residues in a query protein. The sigmoid function converts the raw network outputs into protein/peptide-binding probabilities between 0 and 1.where To evaluate our model’s performance, we randomly divided our training samples into five parts and employed the 5-fold cross-validation (CV) process. In each round, we trained a model on four parts of the data while using the remaining part for model evaluation. We repeated this procedure five times and used the mean validation performance to choose the model’s hyperparameters . To makeβ1 of 0.9, β2 of 0.98, and ε of Finally, we utilized a four-layer graph transformer model with four attention heads, 64 hidden units, and a dropout rate of 0.2. The Adam optimizer with F1 score (F1), Matthews correlation coefficient (MCC), area under the receiver operating characteristic curve (AUC), and area under the precision-recall curve (AUPRC). The formulas for computing these metrics are as follows:The distribution of positive and negative samples in the dataset used here is highly unbalanced. To comprehensively evaluate the effectiveness of the proposed method, we used a range of standard evaluation metrics, including accuracy (ACC), precision (Pre), recall rate (Rec), specificity (Spe), where true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) represent the number of correctly predicted binding sites, correctly predicted nonbinding sites, incorrectly predicted binding sites, and incorrectly predicted nonbinding sites, respectively. AUC and AUPRC are threshold-independent metrics that provide a comprehensive evaluation of model performance. In contrast, other metrics require the use of a threshold and the conversion of predicted probabilities to binary predictions. The optimal threshold is determined by maximizing the MCC for each model. AUPRC is used for hyperparameter selection because it is more sensitive than other selectors, provides a comprehensive analysis when dealing with imbalanced datasets, and emphasizes few classes in imbalanced binary classification problems.F1, MCC, AUC, and AUPRC values on Pep_Test_125, Pep_Test_639, Pro_Test_60, and Pro_Test_315  DSSP, (ii) ProtT5, (iii) DSSP + ProtT5, (iv) EVO (PSSM + HMM), (v) DSSP + EVO, (vi) ProtT5 + EVO, and (vii) DSSP + ProtT5 + EVO. As shown in  similar . In addiTo assess the predictive capability of DeepProSite, we conducted a comparative analysis considering various other sequence-based and structure-based methods. Specifically, we evaluated the performance of DeepProSite on Pep_Test_125 and Pep_Test_639, and compared it against five sequence-based methods  and four structure-based methods [PepSite  and 4. SWe compared the performance of DeepProSite with that of state-of-the-art methods on two protein datasets: Pro_Test_60 and Pro_Test_315. The methods evaluated included five sequence-based methods [PSIVER , SCRIBERU-test results  . Moreover, our analysis revealed a negative correlation between the prediction error of ESMFold at the amino acid level, as measured by the distance between predicted and natural amino acids after structure alignment, and the performance of DeepProSite   between pProSite . OverallF1 score, MCC, and AUPRC of 0.458, 0.419, and 0.492, respectively, even as a structure-based prediction method. Similar results were obtained for PepBCL and PepBind. In protein 4BVX_A, there are a total of 203 residues with 33 protein binding residues. DeepProSite predicted 34 binding residues, 25 of which are true positives, resulting in an F1 score of 0.758, MCC of 0.711, and AUPRC of 0.754, whereas Transformer predicted only 10 correct binding residues, resulting in a lower F1 score, MCC, and AUPRC of 0.507, 0.410, and 0.470, respectively. GraphPPIS predicted 24 correct binding residues, resulting in F1 score, MCC and AUPRC of 0.676, 0.610, and 0.612, respectively, even as a structural prediction method. Similar results were obtained for SPPIDER and ProNA2020. The results indicate that incorporating the spatial information captured from the graph module can enhance the precision of our approach in recognizing binding sites and minimizing the chances of false positives. As shown in To visually depict DeepProSite’s predicted ability, two protein sequences, PDB ID: 4L3O (chain A) and 4BVX (chain A), were randomly selected from the Pep_Test_125 and Pro_Test_315 datasets for illustrative purposes. 2+, Ca2+, and Mn2+. Details of these benchmark datasets are provided in 2+, Ca2+, and Mn2+, respectively, compared with GraphBind. The above results indicate that DeepProSite has strong robustness and generalization ability, and can serve as a reliable tool for predicting ligand binding sites based on protein sequences.To investigate the generalizability of our model, we analyzed how DeepProSite performed when detecting various binding sites for different ligands. We retrained DeepProSite on datasets of different ligand types and compared it with the results of various methods, such as TargetS  the high-quality structure predicted by ESMFold, (ii) the pretrained protein language model offers a powerful representation that enhances the quality of predictions, and (iii) the structure-aware Graph Transformer effectively identifies and predicts binding residue patterns, thus contributing to the overall efficiency of the approach.Despite the benefits of the DeepProSite method, certain aspects of our approach can be improved. For example, by utilizing protein primary sequences to construct heterogeneity maps, the model’s robustness to varying structure prediction qualities can be enhanced. In addition, our method is limited to identifying possible protein binding residues based only on protein-related information, and cannot predict the binding pattern of specific ligands. These issues will be improved in future work.In summary, our method can provide valuable insights for studying protein–protein/peptide binding patterns, pathogenic mechanisms of mutations, and drug development. For instance, some diseases are caused by mutations resulting in alterations in protein–protein/peptide binding sites, which lead to abnormal protein functions. Understanding the characteristics of these sites and their effects upon mutation can help reveal these pathogenic mechanisms. In addition, the prediction of protein–protein/peptide binding sites can provide valuable information for drug development, including designing more precise targets and improving drug selectivity and affinity. Such predictions can also be used to study protein interaction networks and biological signalling to further understand the biological functions of proteins. In the future, we intend to enhance the Graph Transformer’s design and incorporate multitask learning to expand its application to various other domains. This involves predicting the binding sites of proteins with other ligands and identifying functional sites of proteins, such as methylation sites, phosphorylation sites, and allosteric sites.btad718_Supplementary_DataClick here for additional data file.


The SBILib Python library provides an integrated platform for the analysis of macromolecular structures and interactions. It combines simple 3D file parsing and workup methods with more advanced analytical tools. SBILib includes modules for macromolecular interactions, loops, super-secondary structures, and biological sequences, as well as wrappers for external tools with which to integrate their results and facilitate the comparative analysis of protein structures and their complexes. The library can handle macromolecular complexes formed by proteins and/or nucleic acid molecules (i.e. DNA and RNA). It is uniquely capable of parsing and calculating protein super-secondary structure and loop geometry. We have compiled a list of example scenarios which SBILib may be applied to and provided access to these within the library.https://github.com/structuralbioinformatics/SBILib.SBILib is made available on Github at  Macromolecular structure and interaction profiles are essential for the understanding of protein function in health and disease , or on mThe SBILib library can be installed through pip  or downloaded from GitHub. Installation instructions are provided in the README file. SBILib dependencies include: BLAST, CD-HIT, DSSP, NumPy (mmCIF formats. This information can be retrieved directly from the PDB (The library is designed to process protein and nucleic acid (i.e. DNA and RNA) structural information both in Protein Data Bank (PDB) and The library can handle the results from external software  and integrates them as internal objects or methods. External software must be installed locally, and users can manually specify their system address in the configuration file .SBILib provides PDB, DSSP, and BLAST parsing. The SBILib’s BLAST module takes amino acid sequences as input to search for potential homologs. The results are stored as a Python object. Automatic parsing and storage of BLAST results are handled by additional modules. In addition to filtering based on BLAST statistics , users may automate the selection of hits above the Rost’s curve of significant sequence identity , such asProtein sequences and BLAST objects can also be used to generate alignments in different formats. For instance, alignments in PIR format for each hit from searching a query sequence in a database of template structures using BLAST can be used as input for MODELLER  are handled as lists of objects. Each object has references to positions in the chain object of a PDB object. Distances are calculated using SciPy.The GitHub repository includes several examples describing the use of the library. These include reading protein files in various formats, analyzing basic protein information, handling the results from a BLAST search, viewing the geometry and conformation of smotifs (i.e. features that were used in the classification of loops and their prediction from sequence), as well as investigating protein interactions with other biomolecules (Github: Scenarios.ipynb & README.md).SBILib is an open-source Python library for the analysis of protein folds, as well as macromolecular structures and interactions. It can be both implemented programmatically in large bioinformatic projects or used as a stand-alone tool for routine protein structure analyses. The loop handling features of the library can be used in the field of structure-based computational protein design for loop grafting or remodeling purposes. The SBILib package is integral to various current projects and as such is expected to see updates and additions as its functionality within those projects evolve.


AlphaFold changed the field of structural biology by achieving three-dimensional (3D) structure prediction from protein sequence at experimental quality. The astounding success even led to claims that the protein folding problem is “solved”. However, protein folding problem is more than just structure prediction from sequence. Presently, it is unknown if the AlphaFold-triggered revolution could help to solve other problems related to protein folding. Here we assay the ability of AlphaFold to predict the impact of single mutations on protein stability (ΔΔG) and function. To study the question we extracted the pLDDT and <pLDDT> metrics from AlphaFold predictions before and after single mutation in a protein and correlated the predicted change with the experimentally known ΔΔG values. Additionally, we correlated the same AlphaFold pLDDT metrics with the impact of a single mutation on structure using a large scale dataset of single mutations in GFP with the experimentally assayed levels of fluorescence. We found a very weak or no correlation between AlphaFold output metrics and change of protein stability or fluorescence. Our results imply that AlphaFold may not be immediately applied to other problems or applications in protein folding. AlphaFold is widely claimed to have revolutized protein 3D structure prediction from protein sequence, a 50-years long-standing challenge of protein physics and structural bioinformatics . The fou‘It will change everything’, said Andrei Lupas in an interview to Nature . One of https://alphafold.ebi.ac.uk/faq), the expectations of AlphaFold are so high that we judged it prudent to check how well AlphaFold predictions could work for estimation of ΔΔG values. Therefore, given that pLDDT score reflects confidence of the location of the residue in the structure, it may be expected that this measure correlates with ΔΔG or protein function. We found that the difference between pLDDT scores, the only local AlphaFold prediction metric reported in the output PDB file, had a very weak correlation with experimentally determined ΔΔG values . The difference in the global AlphaFold metric—the pLDDT averaged for all residues—shows no correlation, both isolated and in combination with the mutated residue’s pLDDT score. Similarly, the same AlphaFold metrics had a very weak correlation with the impact of single mutations on protein function, fluorescence, of GFP. Recent results [Guided by the expected immediate impact of AlphaFold for the solution of a wide range of problems in structural bioinformatics, we explored the capacity of AlphaFold predictions to serve as a proxy for the impact of mutations on protein stability change (ΔΔG). Although AlphaFold provides a disclaimer that it “has not been validated for predicting the effect of mutations”   by theirThe per-residue local distance difference test (pLDDT) confidence scores for the protein structure models downloaded from the AlphaFold DB were retrieved from the B-factor field of the coordinate section of the pdb file. The pLDDT confidence scores for the protein structure models that we predicted by standalone AlphaFold were extracted from the pickle file, from “plddt” array. By default, AlphaFold produces five models. The differences in pLDDT and <pLDDT> were statistically significant within the group of five produced models (both for wildtype and mutant); we used in our analysis only the best one, i.e., having the highest value of <pLDDT>.To identify the sequence identities between the proteins in the dataset of mutations we performed protein BLAST  search oWe divided the dataset into training and test sets for linear regression model based on the arbitrary sequence identity threshold of 50%. Mutations in proteins above the threshold comprised the training set, and the rest of mutations were used as the test set. The training and test sets resulted in 423 mutations in 50 proteins and 731 mutations in 23 proteins, respectively.Multiple linear regression fit with two parameters was performed using the linear_model module of Sklearn library with default parameters.Mutated amino acids were annotated by relative solvent accessibility, effect of mutation on stability, hydrophobicity, polarity, and side chain size.ASA is the solvent accessible surface area and maxASA is the maximum possible solvent accessible surface area of an amino acid [Information on solvent accessibility was taken from Stride . The relino acid . Followiino acid  we used http://www.imgt.org/IMGTeducation/Aide-memoire/_UK/aminoacids/IMGTclasses.html. The side chain sizes were annotated as very small (1), small (2), medium (3), large (4), very large (5). We defined ‘no’, ‘small’, and ‘large’ change in size chain volume equal to difference of 0, 1 or 2, and 3 or 4 in absolute values, respectively.The rest of the properties were assigned according to All correlations were adjusted for multiple hypotheses testing by Benjamini-Hochberg correction .We used experimental data on protein stability changes upon single-point variations from ThermoMutDB Database . After tα score (pLDDT) per residue” [-10) and <pLDDT> . For each mutation in the dataset, we calculated the difference in pLDDT between the wild type and mutated structures in the mutated position as well as the difference in <pLDDT> between wild type and mutant protein structure models. By checking ΔpLDDT and Δ<pLDDT> values as potential proxies for the change of protein stability we explored the hypothesis that the change of protein stability due to mutation is somehow reflected in the difference of AlphaFold confidence between wild type and mutant structures.Along with coordinates of all heavy atoms for a protein, AlphaFold model contains “its confidence in form of a predicted lDDT-Cresidue” . LDDT raresidue” . The pLD-8) for ΔpLDDT and 0.02 ± 0.03  for the Δ<pLDDT>.First, we studied the relationship between the effect of mutation on protein structure stability and the difference in the accuracy of protein structure prediction by AlphaFold for the wild-type and mutant proteins. We did not observe a pronounced correlation between the mutation effect and the difference in confidence metrics . The cor-12) we then explored how their combination correlates with the effect of mutation. Multiple linear regression model resulted in the dependence ΔΔG = -0.99–0.13 ⋅ ΔpLDDT + 0.03 ⋅ Δ<pLDDT>. We did not obtain any pronounced correlation either for training  or test sets .Since the confidence metrics for a given amino acid and whole protein are weakly correlated . However, there was no correlation between ΔpLDDT and ΔΔG for that 87% of mutations with negative ΔpLDDT.We explored the outliers with high absolute values of ΔpLDDT in We explored the correlation between AlphaFold prediction metrics and ΔΔG for different categories of mutations . The correlation remained poor  and for mutations changing the side chain size . However, the correlation between ΔpLDDT or Δ<pLDDT> and ΔΔG for different types of mutations within these categories was not strong  for ΔpLDDT and 0.16 ± 0.04  for the Δ<pLDDT> , it is so weak that it cannot be used for accurate ΔΔG predictions . However, the only reason for a protein to fold into the distinct native structure is the stability of this structure, so the protein 3D structure and its stability are closely connected. Logically, an algorithm predicting protein 3D structure from sequence should search for the most stable 3D state under the native (or standard) conditions. If a compact structure becomes unstable  then we might expect that the algorithm shifts its predictions toward an unfolded state. Evidence in favor of this point of view is the successful prediction of natively disordered protein regions by AlphaFold and the correlation between the decrease of pLDDT and propensity to be in a disordered region [Taken together, our data indicate that AlphaFold predictions cannot be used directly to reliably estimate the impact of mutation on protein stability or function. But why should we have expected such a correlation in the first place? Indeed, AlphaFold was not designed to predict the change of protein stability or function due to mutation. In the words of the authors “AlphaFold is not expected to produce an unfolded protein structure given a sequence containing a destabilising point mutation”  the difference in the pLDDT score before and after mutation in the mutated position, (ii) the difference in the averaged pLDDT score across all positions before and after mutation. We found that the correlation was weak or absent, and, therefore, AlphaFold predictions are unlikely to be useful for ΔΔG predictions. Taken together with our recent result that AlphaFold models are not better for ΔΔG predictions than best templates , we see S1 TableThe list of 1154 mutations in 73 proteins randomly chosen for ΔΔG analysis as described in Materials and methods).(XLSX)Click here for additional data file.S2 TableThe list of 796 single mutants of GFP randomly chosen for our analysis as described in Materials and methods).(XLSX)Click here for additional data file.S3 TablePearson (PCC) and Spearman (SCC) correlation coefficients for different categories of mutations. P-values were calculated by Kruskal-Wallis H-test.(XLSX)Click here for additional data file.


Accessing this potential has been challenging for both computation and experiments because the space of possible protein molecules is much larger than the space of those likely to have functions. Here we introduce Chroma, a generative model for proteins and protein complexes that can directly sample novel protein structures and sequences, and that can be conditioned to steer the generative process towards desired properties and functions. To enable this, we introduce a diffusion process that respects the conformational statistics of polymer ensembles, an efficient neural architecture for molecular systems that enables long-range reasoning with sub-quadratic scaling, layers for efficiently synthesizing three-dimensional structures of proteins from predicted inter-residue geometries and a general low-temperature sampling algorithm for diffusion models. Chroma achieves protein design as Bayesian inference under external constraints, which can involve symmetries, substructure, shape, semantics and even natural-language prompts. The experimental characterization of 310 proteins shows that sampling from Chroma results in proteins that are highly expressed, fold and have favourable biophysical properties. The crystal structures of two designed proteins exhibit atomistic agreement with Chroma samples (a backbone root-mean-square deviation of around 1.0 Å). With this unified approach to protein design, we hope to accelerate the programming of protein matter to benefit human health, materials science and synthetic biology.Three billion years of evolution has produced a tremendous diversity of protein molecules Evolution has produced a range of diverse proteins, and now a generative model called Chroma can expand that set by allowing the user to design new proteins and protein complexes with desired properties and functions. Reasons for this include the fact that modelling the relationship between sequence, structure and function is difficult, and most methods of computational design rely on iterative search and sampling processes that, just like evolution, must navigate a rugged fitness landscape incrementally8. Although many computational techniques have been developed to accelerate this search3 and to improve the prediction of natural protein structures9, the space of possible proteins remains combinatorially large and is only partly accessible to conventional computational methods. Determining how to efficiently explore the space of designable protein structures remains an open challenge.Protein molecules perform most of the biological functions necessary for life, but creating them is a complicated task that has taken billions of years of evolution. The field of computational protein design aims to shorten this process by automating the design of functional proteins in a programmable manner. Although there has been considerable progress towards this goal over the past three decades12. For this reason, there has been considerable work to develop generative models of protein space, applied to both protein sequences19 and structures26.An alternative and potentially appealing approach to protein design is to sample directly from the space of proteins that is compatible with a set of desired functions. Although this approach could address the fundamental limitation of iterative search methods, it would require a way to parameterize the a priori ‘plausible’ protein space, a way to draw samples from this space, and a way to bias this sampling towards desired properties and functions. Deep generative models have proven successful in solving these kinds of high-dimensional modelling and inference problems in other domains, for example in the text-conditioned generation of photorealistic imagesN-body methods for the efficient simulation of everything from stellar systems to molecular ones27. Finally, the requirement to sample from a model without having to retrain it on new target functions is of considerable interest because protein design projects often involve many complex and composite requirements that may vary over time.Despite recent advances in generative models for proteins, we argue that there are three properties that have yet to be realized simultaneously in one system. These are: modelling the joint, all-atom likelihood of sequences and three-dimensional structures of full protein complexes; achieving this with computation that scales sub-quadratically with the size of the protein system; and enabling conditional sampling under diverse design constraints without retraining. The first of these, generating full complexes, is important because proteins function by interacting with other molecules, including other proteins. The second, the sub-quadratic scaling of computation, is important because it has been an essential ingredient for managing complexity in other modelling disciplines, such as computer vision, in which convolutional neural networks scale linearly with the number of pixels in an image, and in computational physics, which uses fast 29, which model high-dimensional distributions by learning to gradually transform them into simple distributions in a reversible manner, and of graph neural networks31, which can efficiently process geometric information in complex molecular systems. We show that Chroma generates high-quality, diverse and innovative structures that refold both in silico and in crystallographic experiments, and that it enables the programmable generation of proteins conditioned on diverse properties such as symmetry, shape, protein class and even textual input. We anticipate that scalable generative models such as Chroma will enable a widespread and rapid increase in our ability to design and build protein systems that are fit for function.Here we introduce Chroma, a generative model for proteins that achieves all three of these requirements by modelling full complexes with quasi-linear computational scaling and by allowing arbitrary conditional sampling at generation time. It builds on the framework of diffusion modelsO(N2) (refs. 25), or cubically, O(N3) (refs. 23), in the number of residues N. This has either limited their application to small systems or required large amounts of computation for modestly sized systems. To overcome this problem, Chroma introduces a novel neural-network architecture  or O(Nlog[N]); Supplementary Fig. 16 that have seen further refinement and experimental validation34.Chroma achieves high-fidelity, efficient generation of proteins by introducing a new diffusion process, neural-network architecture, and sampling algorithm based on principles from contemporary generative modelling and biophysical knowledge. Diffusion models generate data by learning to reverse a ‘noising’ process, which for previous image-modelling applications has typically been uncorrelated Gaussian noise. By contrast, our model learns to reverse a correlated noise process to match the distance statistics of natural proteins, which have scaling laws that are well understood from biophysics , but in silico evidence can be gathered more systematically. We sought to evaluate the fidelity of sequence–structure pairs generated by Chroma by measuring their agreement with three state-of-the-art methods for structure prediction42 domains required to greedily cover 80% of the residues in a protein at a TM score above 0.5, normalized by protein length  that are compiled into a set of sampling conditioners that bias the diffusion process towards these properties Fig. . To demo47, but it has generally been difficult to simultaneously optimize for both the desired overall symmetry and the molecular interaction details between protomers. Symmetry conditioning within the generation process in Chroma should make it simpler to sample structures that simultaneously meet both requirements.We began by considering analytic conditioners that can control protein backbone geometry. We found that conditioning on the symmetry of protein complexes can readily generate samples under arbitrary symmetry groups Fig. . Figure 1DRF) into two halves with a plane, remove one of the halves and regenerate the missing half. The cut plane introduces multiple discontinuities in the chain simultaneously, and the generative process must sample a solution that simultaneously satisfies these boundary conditions while being biophysically plausible. Nevertheless, the samples achieve both goals and, interestingly, do so in a manner very different from each other and from natural DHFR. In the second row of Fig. We next explored substructure conditioning Fig. –P, which48 between atoms in the structures and user-provided point clouds  that was intended to generate high-likelihood samples drawn from the model. Specifically, the protocol involved three steps: generate backbones by drawing independent samples from Chroma at low temperature; design sequences for each backbone using the Chroma design network; and automatically select a subset for experimental characterization to match the desired experimental scale, driven primarily by sequence and/or structure likelihood  for attempted expression and structural characterization Fig. . We firsoli Fig. . We confoli Fig.  and corroli Fig. . We creaoli Fig. , which poli Fig. . In thisoli Fig. .Fig. 5Ex4NH2, chain E) having query and target TM scores of 0.7 and 0.3, respectively, and the top hit to UNC_239  having query and target TM scores of 0.5 and 0.23, respectively . Differential scanning calorimetry data for these proteins were consistent with relatively stable folding, with melting temperatures ranging from 64 °C to 78 °C  and on CATH topology  on a commodity graphics processing unit (such as an NVIDIA V100) in a few minutes.We reasoned that the best way to determine the plausibility of the protein space parameterized by Chroma was to draw independent samples from the model and test them experimentally. Note that this is a departure from the prototypical protein-design protocol, in which initial proposal designs are down-selected using a custom set of filters intended to avoid known or hypothesized model deficiencies and help focus on designs that are more likely to work experimentally. Although the latter practice, which is broadly adopted in the field, can be effective at increasing design success rates, it does require a custom set of filters for each design project and makes fully automated design difficult to achieve. Furthermore, such an approach would detract from our intention of characterizing the distribution learned by Chroma.Our experimental validation shows that Chroma has learnt a sufficiently accurate distribution such that sampling from it results in proteins that express, fold, have favourable biophysical properties and conform to intended structures at non-trivial rates. Even under the highly conservative view that only the proteins we purified and characterized individually in solution constitute successful designs , Chroma would still have a 3% success rate. Moreover, the two designs with experimentally determined crystal structures demonstrate that a non-trivial fraction of this distribution should be expected to be atomistically accurate. Given the breadth and novelty of the structure space learned by Chroma Fig.  and 13, 51 and a few other cases that have high symmetry52—and in these cases, design efforts have benefited tremendously, creating possibilities that are not available in other systems53. For all other structure types, however, a great amount of computational time has been spent on the search for reasonable backbones, often leaving the focus on actual functional specifications out of reach. Chroma has the potential to address this problem, enabling a shift from focusing on generating feasible structures towards a focus on the specific task at hand—namely, what the protein is intended to do. By leveraging proteins sampled over more than 3 billion years of evolution, and by finding new ways to assemble stable protein matter, generative models such as Chroma are well poised to drive another expansion of biomolecular diversity with benefits for human health and bioengineering.The task of exploring protein structure space in a way that can produce physically reasonable and designable conformations has been a long-standing challenge in protein design. In a few protein systems, it has been possible to parameterize the backbone conformation space mathematically—most notably the α-helical coiled coilFurther information on research design is available in the Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-023-06728-8.Supplementary InformationExtended descriptions of the Chroma model, sampling algorithms, conditioners, in silico evaluations, an ablation study, training pipelines and the experimental validation. It includes appendices, Supplementary figures and Supplementary tables.Reporting SummaryPeer Review File