Covalent labeling (CL) in combination with mass spectrometry can be used as an analytical tool to study and determine structural properties of protein-protein complexes. However, data from these experiments is sparse and does not unambiguously elucidate protein structure. Thus, computational algorithms are needed to deduce structure from the CL data. In this work, we present a hybrid method that combines models of protein complex subunits generated with AlphaFold with differential CL data via a CL-guided protein-protein docking in Rosetta. In a benchmark set, the RMSD (root-mean-square deviation) of the best-scoring models was below 3.6 Å for 5/5 complexes with inclusion of CL data, whereas the same quality was only achieved for 1/5 complexes without CL data. This study suggests that our integrated approach can successfully use data obtained from CL experiments to distinguish between nativelike and non-nativelike models. Covalent labeling (CL) from mass spectrometry experiments provides structural information of higher-order protein structure. Here, the authors develop an algorithm which integrates experimental CL data to predict protein complexes in the Rosetta molecular modeling suite using AlphaFold models. Protein-protein complexes are central in many crucial biological and cellular processes3, which makes their structural elucidation important. Currently, over 182,000 protein structures have been determined and archived in the Protein Data Bank (PDB), with around 114,000 of these being protein-protein complexes4. These high-resolution protein structures can be obtained using techniques such as nuclear magnetic resonance (NMR)5, cryo-electron microscopy (cryo-EM)6, and most notably X-ray crystallography7. However, structures at atomic resolution are not always obtainable due to limitations of the forementioned techniques in areas such as acceptable system size, required sample concentration, and excessive sample conformational heterogeneity.Mass spectrometry (MS) is a versatile analytical approach which has become a vital tool in structural biology, capable of probing the structure and dynamics of protein assemblies8, hydrogen-deuterium exchange (HDX)10, surface-induced dissociation (SID)12, ion mobility (IM)13, and covalent labeling of macromolecules (CL)15. Chemical cross-linking involves using chemical reagents that form covalent bonds to link specific functional groups within or across protein molecules, providing distance restraints. HDX methods can be used to study protein structure and dynamics using exchange between protein backbone amide protons and deuterium atoms from solution, which is sensitive to local solvent accessibility and flexibility. SID methods involve the soft ionization of native protein complexes into the gas phase which are then collided with a rigid surface where they can break apart into monomers or other intact subcomplexes. This method can provide information regarding the stoichiometry, connectivity, and interface strength of complexes. IM can offer structural information regarding the shape and size of a protein complex by analyzing the travel of a protein through a bath gas, providing an averaged cross-sectional area of the system. Finally, covalent labeling probes protein structure by exposing solvent-accessible amino acid side chains with either specific or nonspecific reagents that covalently bind. Differences in reactivity to labeling agents can distinguish between exposed and buried residues, as well as residues located at the surface of interacting domains in the case of protein complexes.Structural mass spectrometry is an alternative method which generally requires less time for sample preparation, can handle smaller sample sizes, is usable for a large range of protein sizes, and provides sparse biophysical data that can be used to gain insight into a variety of protein structural characteristics. Although MS experiments cannot comprehensively determine a high-resolution protein structure, insights into conformational states can be obtained, validation of existing models can be achieved, or the data can be supplemented with computational techniques for atomic-detail structure elucidation. A few common approaches in structural MS are chemical cross-linking16. Furthermore, due to the formation of stable, covalent bonds, the labeling of amino acids with labeling reagents are usually irreversible, unlike HDX where back-exchange frequently occurs, adding additional layers of complexity. Sparse structural data can be obtained from covalent labeling experiments with reagents such as hydroxyl radicals, carbenes, trifluoromethylations (CF3), diethylpyrocarbonate (DEPC), and sulfo-N-hydroxysuccinimide acetate (NHSA)17. These experiments provide metrics of modification that depend on the reactivity, solvent accessibility and potentially other structural features of the specific residues in solution. Structures of protein complexes can be further probed by comparing the degree of modification in the unbound and bound states, when possible. Interface residues are generally identified by examining large changes in modification rates between the unbound/bound state of a complex as solvent accessibility is likely to most dramatically change at the protein-protein interfaces. For example, a residue that gets labeled readily in the monomer, but not in the complex is likely part of the interface; although protein-protein binding could cause tertiary conformational changes, which might also result in changes in modification. The data obtained through a covalent labeling MS experiment can thus be used to probe higher orders structure of protein complexes.Covalent labeling offers several advantages over other MS techniques. For example, the challenging low abundance of specific interpeptide cross-links and complicated tandem MS fragmentation of chemically cross-linked peptides are not an issue for covalent labeling techniques19. The recent release of AlphaFold220  has resulted in a revolution in the accuracy of computational protein modeling. AlphaFold21 is a neural network-based model that takes advantage of sequence coevolution data which has shown remarkable success and has outperformed other prediction methods during the 13th and 14th (with AF2) Critical Assessment of Techniques for Protein Structure Prediction (CASP)23, a series of blind tests to gauge the current state of protein structure prediction. AlphaFold-Multimer24 was released in 2021 and uses the AF2 model but was trained to predict multimeric complexes from sequences of multiple chains. Similarly, traditional protein-protein docking algorithms are useful for analyzing and predicting models of complexes. In protein-protein docking, monomeric structures (which can be obtained in a variety of ways) are used as input, and structures of the complex are predicted, with favorable orientations of the different subunits. Existing protein-protein docking algorithms which have been successful include ClusPro25, HDOCK26, ZDOCK27, SwarmDock28, HADDOCK29, PIPER30, and RosettaDock31. RosettaDock is a part of the Rosetta32 molecular modeling software suite which contains a large variety of algorithms for computational modeling and analysis of protein structures.As an alternative to experimental methods, modern computational methods have seen great success in accurately predicting and modeling protein tertiary structure35. Information obtained from hydroxyl radical footprinting (HRF), HDX, and DEPC labeling experiments have been shown to improve tertiary structure prediction with Rosetta43 by using calculated solvent exposure metrics for models to select for experimentally accurate predictions. Similarly, protein shape and size information obtained through collisional cross-section data from IM experiments has also improved Rosetta structure prediction44. A method iSPOT45, which uses a combination of multiple biophysical methods , has been shown as a powerful approach for integrated modeling of multiprotein complexes. Isotope exchange using HDX-MS has been used to improve protein complex prediction by simulating complex isotope patterns and comparing to those obtained experimentally46. Similarly, differential HDX data has been incorporated into protein-protein docking to study the human uracil-DNA-gycosylase (hUNG) and its protein inhibitor (UGI)47. The use of differential covalent labeling has yet to be implemented within the RosettaDock framework. Although AF2 has proven to be an excellent and revolutionary method of protein structure prediction, there remain limitations, particularly for protein complexes48. Covalent labeling has the potential to help overcome some of these limitations and Rosetta is uniquely suited for the development of hybrid methods incorporating labeling data as additional scoring terms, for which there are many examples50. Here, we use RosettaDock to assemble protein complex subunits that were generated using AF2 and employ covalent labeling data to improve protein complex structure prediction.Incorporation of sparse experimental data into algorithms predicting protein structure can further improve computational predictionsIn this study, we develop the computational framework 52, and insulin 53. To establish the validity of the hypothesized relationship with experimental data, the native crystal structures of these complexes were used to analyze the proximity of residues (using inter-residue distance) to the interface as a function of modification rate in the monomer compared to the complex. We assumed large-scale conformational changes do not occur upon complex formation after examining all subunits of each complex which contained labeled residues and finding that the average RMSD of the unbound  to the bound subunit crystal structures was 2.1 Å. To quantify the amount of change in modification occurring between the unbound/bound state, a modification change was calculated from modification rates/extents of each state (see methods for full detail) with a larger positive value indicating a more significant decrease in modification from the unbound to bound state. We hypothesized that such a large decrease in modification from the unbound to bound state would likely be indicative of residues at the interface. The maximum modification change for a labeled residue observed across all three complexes was 80% and the average change was 18%. To isolate residues with large modification changes, we considered only residues that saw at least a 40% change in modification between unbound/bound states. Residues that were within 10 Å of the other chain were considered a part of the binding interface, which is consistent with the interface definition for the iRMSD calculations using DockQ57. From a total of 78 labeled residues across all complexes in the benchmark set, 38 of these residues were at the interface (distance < = 10 Å), and 40 of these residues were outside the interface (distance > 10 Å). The average modification percentage of residues in the interface was 38.59%, while the average modification percentage was -0.04% for residues outside the interface. We first used this criterion to compare the native structures to experimental data. Figure 37. In our benchmark, 91% of labeled residues with modification changes greater than or equal to 40% were close to protein-protein interfaces, resulting in a false positive rate of 9% which was within acceptable tolerances. This small preliminary analysis supported our hypotheses and indicated covalent labeling can be used to distinguish particular interface residues based off large changes in labeling.To test the proposed hypotheses, we used a benchmark set of protein complexes with publicly accessible differential labeling data and a native crystal structure. This dataset consisted of actin bound to gelsolin segment 1 2 = 0.36 and a normalized root-mean-square error (NRMSE) of 1.5 was observed between modification change  and the interface distances  for labeled residues. A larger distance between a labeled residue and the other subunits in the bound form correlated with generally smaller changes in modification. This linear correlation observed was similar to previous work comparing solvent exposure metrics (solvent accessible surface area and neighbor count) and covalent labeling38. We used this correlation to predict an expected modification change from any structural model . The linear parameters of slope and intercept  accounts for interactions at the binding interface and can be supplemented with additional score terms to predict more nativelike poses. Here we aimed to explore whether covalent labeling MS data can meaningfully improve model quality. Due to the accuracy of AlphaFold2 (AF2) for monomer prediction, models generated by AF2 were used to provide the input to RosettaDock and a covalent labeling-based score term was used to rescore the oligomeric structures of modeled protein complexes and predict the native structure.RosettaDock has had many successes in modeling quaternary protein structureThe parameters obtained from the correlation Fig. . The onlTo simulate a more realistic situation, we then used AF2 to generate components (subunits or sub-complexes) of the complexes, which were then input into docking simulations. The top-ranked AF2 models were all accurate with respect to the native structure with RMSD values of 1.2 Å and 0.7 Å for actin and gs1 A and G chains respectively, 1.6 Å for β-2-microglobulin chains, and 1.5 Å for insulin heterodimer. Scoring of the docked sets using covalent labeling data was performed by combining the covalent labeling score term produced by our method with Isc, as previously described. The score versus RMSD plots without using covalent labeling data are shown in Fig. The assessment of additional metrics further demonstrated the benefits of including covalent labeling in scoring. As shown in Table As a comparison to state-of-the-art methodology, we also used AlphaFold-Multimer to predict the full structure of the complexes from our benchmark set without including the native structure as a homolog. Figure Sparse experimental data can bolster the effectiveness of existing computational techniques. In this current study, we have proposed a hybrid technique utilizing the combination of state-of-the-art computational methods (AlphaFold and RosettaDock) with covalent labeling mass spectrometry data to address cases when the computational tools fail to model accurate complexes. Covalent labeling reagents modify residues based on features such as solvent accessibility, and we have demonstrated that changes in modification of residues in covalent labeling experiments can be used to determine the likely proximity of these residues to the binding interface within protein complexes Fig. . As the 51, β-2-microglobulin 52, and insulin 53. Crystal structures were available for each for the purpose of benchmarking predicted models. Residue-resolved differential covalent labeling data were also obtained for each system in both the unbound and bound states53. The labeling reagent used for the actin/gs1 and insulin complexes was hydroxyl radicals and for β-2-microglobulin, the labeling reagents were diethyl pyrocarbonate (DEPC) and sulfo-N-hydroxysuccinimide (NHSA). We previously showed that labeling a subset of ‘optimal’ residues  provides the highest amount of structural information useful in structure prediction37. HRF reliably labels L and F  and DEPC/NHSA labels R, K, T, and S . There were 41 labeled residues for both the unbound and bound states for actin/gs1, 20 for β-2-microglobulin, and 17 for insulin. For benchmarking purposes, interface residues were defined as any residue with a heavy atom within 10 Å of a heavy atom in another protein subunit. Although each labeled residue had a measure for the frequency of modification in both the unbound and bound states separately, we wanted to directly quantify the change in modification between these states, hypothesizing that residues with large changes would likely be part of the protein-protein interface. For each complex in the data set, the modification change between different states of the complex was computed from the data, as shown in Eq. The three protein complexes used in the benchmark dataset were actin bound to gelsolin segment 1 20. For insulin, the base subunit is a heterodimer, so AlphaFold-Multimer24 was used. The default settings for both AlphaFold methods were used along with the addition of all genetic databases (–db_preset=full_dbs flag). Since traditional docking consists of two docking partners, the insulin complex was broken up into three separate structures to model all unique interfaces, where AB_CD, ABCD_EFGH, ABCDEFGH_IJKL define what chains make up each docking partner, separated by an underscore  for a more realistic prediction protocol20. We modified the AlphaFold-Multimer input to only use PDB templates of structures that were deposited prior to the date of the first published structure of each complex to prevent any biased homology modeling based on existing crystal structures of the complexes.As a comparison to the docked models produced by RosettaDock, we also used AlphaFold-Multimer to predict full structures of each complex. To generate a more fair, blind prediction using AlphaFold-Multimer, restrictions were placed on which templates were used during model construction, as recommended by AlphaFold developersIn this study, we proposed that differential covalent labeling data (comparing the unbound and bound states of a complex) could be used to indicate the proximity of a labeled residue to the binding interface of protein complexes and subsequently be used to assess model quality based on agreement with the experimental data. This was accomplished by comparing the modification change Eq.  of labelTherefore, to integrate the information regarding the modification change and interface distance into Rosetta to improve model scoring, a covalent labeling score term  or high fold similarity (0.5 <TM-score <1.00) to the native structure. DockQ is a protein-protein docking quality measure which ranges between 0.0 and 1.0 with a perfect match being equal to 1.0. Similar to TM-score, DockQ uses four categories for classifying models: incorrect (0 <DockQ score <0.23), acceptable quality (0.23 < = DockQ score <0.49), medium quality (0.49 < = DockQ <0.80), and high quality (DockQ score > 0.80).The quality of models was assessed quantitively using alpha-carbon root-mean-squared deviation (RMSD), template modeling score (TM-score)Python v.3.7.3 was used for data analysis. Matplotlib v.3.1.2 was used for the creation of all scatter plots. PyMol v.2.0.7 was used to generate the figures of all proteins.Further information on research design is available in the Supplementary InformationReporting SummaryDescription of Additional Supplementary FilesSupplementary Data 1


Proteins exist in several different conformations. These structural changes are often associated with fluctuations at the residue level. Recent findings show that co-evolutionary analysis coupled with machine-learning techniques improves the precision by providing quantitative distance predictions between pairs of residues. The predicted statistical distance distribution from Multi Sequence Analysis reveals the presence of different local maxima suggesting the flexibility of key residue pairs. Here we investigate the ability of the residue-residue distance prediction to provide insights into the protein conformational ensemble. We combine deep learning approaches with mechanistic modeling to a set of proteins that experimentally showed conformational changes. The predicted protein models were filtered based on energy scores, RMSD clustering, and the centroids selected as the lowest energy structure per cluster. These models were compared to the experimental-Molecular Dynamics (MD) relaxed structure by analyzing the backbone residue torsional distribution and the sidechain orientations. Our pipeline allows to retrieve the experimental structural dynamics experimentally represented by different X-ray conformations for the same sequence as well the conformational space observed with the MD simulations. We show the potential correlation between the experimental structure dynamics and the predicted model ensemble demonstrating the susceptibility of the current state-of-the-art methods in protein folding and dynamics prediction and pointing out the areas of improvement. From a kinetic perspective, there is an almost linear correlation (R = 0.75) between the natural logarithm of folding rate and the percentage of Relative Contact Order (%RCO)3 which shows the dependency of the kinetic folding from the overall protein topology. According to the Levinthal paradox4, despite the huge number of potential conformations, a protein can fold to its one conformation, namely native structure, following folding pathways characterized by folding intermediates6. Studies of the polymer chain entropies revealed that low-energy intermediate conformational ensembles are less populated compared to high-energy ensembles indicating that the protein-folding energy landscapes are funnel-shaped10. The funneled formulation offers insights into proteins conformational heterogeneity and protein chains entropy providing a microscopic framework for folding kinetics.Understanding the relationship between protein 3D structure and function is the key to unveil the protein biological mechanism and therefore the ability to modulate it. The Anfinsen’s thermodynamic hypothesis states that all the information driving the protein folding is encoded in the protein first sequence11. This approach is limited by the heterogeneity of the experimentally available structures in the PDB which, up to date, count only 1200 different folds12. The FM approach, also known as ab initio prediction, is required for proteins that lack any statistically significant similar protein sequences with known structures. Among the plethora of possible FM approaches extensively used ones are the fragment assembly 16 or the first principle physics-based methods19. These methods are based on physics-based force fields to search for low energy states following the Anfinsen’s hypothesis. However, they suffer from the inaccuracy of the force fields potential in the description of the thermodynamic protein stability which lead to an inaccurate description of the atomic interaction and folds. Moreover, the high degree of freedom prevents an exhaustive investigation of the free energy landscape using high resolution interactions.There are two main computational approaches used to predict 3D protein structures: (i) the template-based modeling (TBM) and (ii) the free-modeling approaches (FM). TBM is based on the observation that homologous proteins have similar structure so a known protein structure can be used as template for homologous protein20. Indeed, these methods showed that evolutionary information captured in MSA of homologous sequences not only offers the template of known structure but also inter-residue correlations that can be decoded into contact maps. These contact maps are then translated into distograms (distance probability distribution plots) providing quantitative information that can be used as potential restraints to improve the accuracy of de novo structure prediction of static protein structure.The application of deep learning methods to the protein structure prediction field is a game changer. In 2019, during the CASP13 competition, several groups  showed that by applying deep learning based methods it was possible to gain fold-level accuracy for proteins lacking homologues in the PDB. By combining deep learning approaches with inputs from coevolutionary coupling features derived from Multi Sequence Alignment (MSA), the level of accuracy (GDT-TS) reached was ≈ 10% higher (for the easy targets) and ≈ 20% higher (for the difficult targets) than in the previous version of CASP21 in CASP14 demonstrated that deep learning techniques have reached a point where the accuracy of the predicted model, in many cases, is comparable with that of the experimental structure. With this new generation of computational structure prediction tools we can focus on many other questions with the highest biological relevance such as (i) small-molecule docking to protein models, (ii) protein–protein interaction predictions and (iii) protein conformational ensemble prediction. The latter is the focus of the discussion reported in this article.The impressive performance of AlphaFold212 one can find entries with similar or even identical sequences that have different structures. Since, deep learning methods can be trained over the entire PDB and it has been proven that MSA can encode information about functional conformations22, it is reasonable to hypothesize that the current prediction methods would be able to propose biological relevant conformation of a query protein. In CASP14, AlphaFold2 predicted the accurate structure of 75 targets within the submitted 5 models. Masrati et al.23 superimposed and visually inspected all the 5 models for each of the 75 predicted targets finding that 80% showed the same conformations while the remaining 20% have more than one single distinct one..Multiple conformations are often linked to different protein functions, e.g. active and inactive conformation in enzymes, and inward versus outward facing upon substrate transport binding in transporters. It has been long shown that in the PDB24 as a core. By combining the current state-of-the-art ML technique for MSA (DeepMSA) with deep residual-convolutional network transform-restrain Rosetta (trRosetta) and the AWSEM force field, we observed that not only the X-ray structures of the different protein states were predicted but also the similar intermediate states explored by MD simulation run on the experimental structures.To test the ability of exploring the conformational space of a query protein we built up a pipeline with trRosettahttps://github.com/deepmind/alphafold). A Jupyter Notebook version, namely ColabFold25, was recently developed. It combines AlphaFold2 with Google Colab25 providing a free and accessible on-line platform for protein folding that does not require any installation. We therefore decided to adopt the ColabFold pipeline to generate five models and to compare them with the predicted structural ensembles generated by our pipeline.Currently, it is difficult to assess the ability of AlphaFold2 in predicting multiple biologically relevant conformations for several different proteins. Although the code is available, it requires expensive hardware and a storage data capability of five TB as reported in the alphafold github 27.Multi Sequence Alignment (MSA) provides useful information about the evolutionary conserved positions and motifs that can hardly be derived from a single query sequence. In protein structure prediction, MSA is the principal source to retrieve local feature and residue-residue contacts which becomes critical for ab initio protein structure prediction28. HHblits29 from the HH-suite has recently become the general method for profile hidden Markov model (HMM) construction, and HMM search tools from HMMER suite30 are valid alternatives for the applications. However, only few pipelines efficiently provide sensitive MSA profiles from the query sequence by scanning multiple large sequence databases. DeepMSA31 is an open-source method for constructing sensitive and highly diverse MSA by merging sequence from three whole genome and metagenome databases. It combines HHblits and modified version of Jackhammer/HHsearch32 to perform homologous sequence search and further refine the alignment with a custom HHblits database reconstruction step. It has been proven that DeepMSA consistently improves the accuracy of contact and secondary structure prediction which are particularly important for protein-structure prediction31.Several sequence construction methods have been derived over the past years. PSI-BLAST is one of the most used approaches to query sequence specific profile generation24, (ii) the default DeepMSA pipeline and (iii) HHblits by iteratively scanning three times (− n 3) the Uniref30 database33 for homologous sequence with a minimum master sequence coverage (-cov) of 75% and default e-value cutoff. The quality of the MSA profile was determined by comparing the probability distance prediction generated by trRosetta (see "d≈6 Å) and close (d≈8 Å) conformation. The distance prediction with trRosetta-MSA showed a shoulder at 7 Å and a pick at 9 Å (S6.A) while with DeepMSA (S6.B) we have a separation between the pick at 6 Å and 8 Å. HHblits-MSA distance prediction showed a completely different shift in the trend with only one pick at roughly 8 Å. Due to the importance of the MSA quality in the final protein structure prediction we opted for DeepMSA to generate the MSA profile input.We compared the trRosetta MSA profile generation with HHblits and DeepMSA to ensure the accuracy of the MSA input. As a test case we considered the myoglobin protein (PDB: 2EB8) and generated the MSA profile with: (i) the default trRosetta MSA pipelinee) is a small phosphotransferase enzyme in which the conversion between active (open) and inactive (closed) conformations is rate limiting for catalysis. AKe enzyme consists of a CORE subdomain (from N79 to G214 residue), AMP- (from V121 to Q160 residue) and ATP- (from I26 to R78 residue) binding subdomains which undergo significant conformational changes upon substrate binding. This enzyme catalyzes a reversible phosphoryl transfer reaction with the primary purpose of maintaining the energy balance in cells, and is ubiquitously expressed in many different organisms36. From a functional point of view, the conformational shift causes the rearrangement of the backbone H-bonding patterns without unfolding the entire protein.Adenylate kinase isolated from Escherichia coli 35 and inhibitor-bound conformations 34. It has been experimentally shown that at 35 °C the ATPlid region unfolds selectively. This state is denoted as “binding incompetent” state and appears to be an intermediate state on the global folding pathway. Recently, Olsson et al.37 experimentally confirmed that the interconversion between open and closed state involves partial folding/unfolding of the ATPlid subdomain. In addition, the ATPlid and AMPlid conformational changes occur in absence of substrates39.The structure of AKe structure prediction the main challenge comes from the lack of small molecules that lock the protein in either the open or closed conformation. We selected the AK sequence range for which we have crystallographic information for both the apo (open) and the holo (close) conformations and generated 1000 models with our pipeline . The 189 remaining models were clustered based on their backbone RMSD for a total of two clusters  analysis reported in Fig. lid for cluster-2 (green) is roughly double (≈ 0.5 nm) compared to the one observed for cluster-1  and the 4AKE-MD . Regarding the ATPlid fluctuation, a similar displacement intensity is observed in both clusters (≈ 0.7 nm) which is roughly double the one observed in 4AKE-MD (≈ 0.4 nm). Principal Component Analysis (PCA) performed on cluster-1 and cluster-2 showed that the direction of the first principal component (PC1) involves the typical rearrangement of ATPlid and AMP lid regions experimentally reported41 and confirmed in the PCA performed on 4AKE-MD simulation frames  intermediate state conformations observed in the 4AKE-MD simulation (gray). Cluster-2 PC1 density distribution range (green) included the AKe open conformation. By correlating the aperture angle with the distance between ATPlid and AMP lid regions it can be observed that cluster-1 (orange) and cluster-2 (green) explored both the close and open conformations respectively  reviewed close and intermediate AKe conformations, while the AlphaFold2 prediction deposited in the AlphaFold Protein Structure Database (red star)21 provides only the close AKe conformation.In the AK45. Many integrins do not normally exhibit high affinity for ligands and must be activated in order to observe the binding48. This has led to the hypothesis that the change into the high affinity state is resulted in a conformational change. In response to several mechanical50 and biochemical signals51, integrins undergo global and local conformational changes and ligand binding affinity variations. Under physiological conditions, LFA-1 ectodomains may assume a global bent conformation with a low binding affinity (PDB:5ES452). Changing the metal ion from Ca2+/Mg2+ to Mn2+ results in an extended integrin conformation accompanied by a higher ligand binding affinity53 (PDB:5E6U52). In addition, local conformational changes occur at the level of the αI domain influencing the ligand binding affinity of LFA-153. The αI domain is the ligand binding domain of LFA-1 with a gradually decreasing affinity for metal ions in the order Mn2+  > Mg2+  > Ca2+54. Indeed, structural studies on LFA-1 revealed a metal ion binding site called Metal Ion55 Dependent Adhesion Site (MIDAS) located at the top of the αI domain 61. The MIDAS directly coordinates the sidechains of polar/acidic residues  discriminating between open and close conformations which correspond to high and low binding affinities respectively 65. In both open and close conformations the MIDAS ion shared S139 as primary coordinator. In the closed structure,T206 and D239 form direct bonds to MIDAS metal ion59 while in the open conformations the metal ion is directly coordinated by T206 and the MIDAS ion undergoes inward movement by about 0.2 nm66. Surface plasmon resonance experiments suggest an inverse relationship between αI ligand affinities and metal binding: the closest conformation corresponding to the highest-ligand affinity54. Steering Molecular Dynamics simulation in explicit solvent confirms that the position of the metal ion is coupled with the α7-helix location (S283-T300): when the α7-helix is in the middle down position, the MIDAS ion has a strong tendency to move inward to its open position binding ligands with high affinity66. Indeed structural analysis on the αI domains61 shows that in the open conformation the metal ion coordination translates in a downward displacement of the α7-helix highlighting the presence of a transmission signal from the MIDAS of αI to the other integrin domains69. Lastly, the key residues in the “rachet ”-like αI region  have also been observed to be correlated with the movement of the α7-helix.Lymphocyte function-associated antigen 1  is a metal binding seven domain protein composed of a head piece  and two leg parts . It belongs to the Integrin family of cell surface receptors whose functions are associated with leukocyte diapedesis, migration within tissue and the cell-adhesion recognition process. Precisely, LFA-1 binding to intercellular adhesion molecule 1 (ICAM-1) mediates the adhesion of leukocytes to blood vessels or antigen presenting cells (APC)In this study we focused on the αI domain of the LFA-1 protein. The main challenge in the structure prediction of the αI/LFA-1 protein comes from the absence of the metal ion located at the MIDAS ion site which position is tied with the α7-helix location. All the MD simulations on the X-ray structures have been performed without the metal ions.58 and 3HI641 respectively. We selected the αI sequence for which we have crystallographic information for both the open and close conformations and generated 1000 models with our pipeline  filtered out. The 212 remaining models were clustered based on their backbone RMSD for a total of one cluster  resembled a close LFA1 conformation  performed on cluster-1 highlighted the stability of both α7-helix and MIDAS residues confirming the adoption of a close LFA-1 conformation rather than the flexible open conformations Fig. C. The PC72. Its function involves (i) the reversible binding of oxygen to facilitate its diffusion73 and (ii) the scavenging of nitric oxide to prevent the inhibition of the mitochondrial enzyme cytochrome C-oxidase75. Myoglobin has a molecular mass of roughly 17 kDa and consists of a single polypeptide of 153 amino acids with a secondary structure of eight α-helices. Between the fifth and the sixth α-helix there is a hydrophobic region that accommodates the heme moiety.Myoglobin is a heme-containing globular protein that is abundant in myocyte cells of heart and skeletal muscle78 showing that the main variation is due to the location of the α6-helix (from H82 to K98) where the heme pocket is located. The heme is bound to H95 providing extra stabilization and steric bulk that prevents collapse79. Previous protein structure prediction of the Myoglobin conformation showed that the absence of the heme made it impossible to provide stabilization and leading to greater structural heterogeneity of the α6-helix80.Several crystallographic structures have been solved for both the apo- and holo- Myoglobin81, and the holo form containing the cofactor (PDB: 2JHO)82. The overlap between the two X-ray structures showed a deviation in the α6-helix region of 4.3 Å: in the apo-form the α-helix is unfolded occupying the heme-pocket and coordinating the copper complex ), while in the holo-form the α6-helix is folded establishing the side of the heme pocket. The main challenge in the structure prediction of Myoglobin comes from the absence of both the heme cofactor and the metal coordinator. Our atomistic MD simulations on the X-ray structures have been performed without the heme and the copper complex CuII showing that the α6-helix of the apo conformation assumed the same location of the α6-helix of the holo conformation during the first 10 ns of simulation (S3.A). Indeed, backbone RMSD analysis on the MD simulations revealed an increment of a factor of 2 Å for 2EB8 confirming the structural deviation from the initial X-ray position (S3.B).We considered two forms of Myoglobin, the apo structure without its heme cofactor (PDB: 2EB8)40 and models with energy higher than − 664 kcal/mol were filtered out. The 212 remaining models were clustered based on their backbone RMSD for a total of one cluster ). This conformational conversion revealed that in the absence of any metal complex only one heme pocket arrangement is observed, which translates into one models. Cluster-1 (orange) explored the conformational space observed in the MD simulations (light and dark gray). RMSD clustering analysis on the current available experimental structures deposited in the PDB showed that 99% of the myoglobin structures have a holo-like conformation and 1% displayed an apo-like conformation. The α6-loop arrangement is observed only in two PDB structures (2EB8 and 2EB9). For this example, we were able to compare our results only with the ColabFold pipeline models25 (red dot) since the no prediction is available for myoglobinin the AlphaFold Protein Structure Database. ColabFold pipeline25 predictions explored only a limited region of the conformational space covered by the MD simulations in between the X-ray structures. It is worth mentioning that from this analysis we observed that the two clusters obtained are not representative of the two experimental conformations but reflect the flexibility observed in the region between L40 and D60.The same sequence coverage for both the apo- and holo-conformation was selected as input for our pipeline to generate up to 1000 models  and the C-terminal (residue 80–164). The active site cleft is located at the interface between the two domains. An occluded active site were observed in early X-ray structures suggesting the hypothesis that relative movements between the N-and C-terminal domains enable substrate access84. Later crystal structures of point mutants (e.g. M6I) showed conformations where the active site is more open, suggesting that the two domains undergo a ‘hinge-bending’ motion86. Direct evidence of the hinge-bending transformation was obtained by Electron Magnetic Resonance (EPR) analysis showing an equilibrium between open and close conformation in solution87. EPR distance analysis confirmed that the open structures are not purely distortions caused by the mutations and crystal packing but represent the consequence of intrinsic hinge-bending motions. This motion was furthermore supported by NMR83 data and MD89 simulations, corroborating the presence of ‘hinge-bending’ motion in T4 lysozyme. Moreover, Fluorescence Correlation Microscopy (FSC) established the presence of multiple intermediate conformations, in contrast to the two state model90.T4 lysozyme (T4L) is a globular protein of roughly 18 kDa that helps release mature phages by breaking down the Escherichia coli cell wall during infectionWe considered two X-ray structures representing the open (PDB:150L) and closed (PDB:256L) T4-lysozyme conformations. The main challenge in the structure prediction of the T4-lysozyme comes from considering the mutated protein sequence M6I as starting point of our prediction to test the ability of our pipeline in predicting conformational changes in the presence of point mutation.40 and the ones with energy higher than − 537 kcal/mol (S4.A) filtered out. The 182 remaining models were clustered based on their backbone RMSD for a total of two clusters  since the AlphaFold Protein Structure Database prediction is not available. ColabFold pipeline25 predictions explore only the closed region of the conformational space covered by the 256L-MD simulations.The same sequence coverage for both the open- and closed-conformation was selected as input for our pipeline to generate up to 1000 models (see "Tetrahymena thermophila (BUBL) where two bacterial-intein like (BIL) domains are flanked by two independent ubiquitin like domains (ubl4*ubl5). Generally, inteins are protein sequences known to perform protein splicing producing an intein domain and an inteinless host protein without insertion91. On the contrary, the BIL2 domain does not disrupt the structural continuity of the protein but rather interposes between two independently folded domains92. Their ability of acting as promiscuous catalytic elements that exercise protein splicing between several different domains has been extensively used in protein engineering and synthetic biology95. From a biological function perspective, the intein protein-splicing translates into a benefit for the host by constituting a switch from inactive to active native protein. In particular, BIL2 presents another interesting aspect: the presence of a highly conserved motif, RGG, that represents the biological hallmark for ubiquitin activation and conjugation96. It has been experimentally observed that T. thermophila BIL domains are able to provide a perfect activated ubl protein without energy consumption97.BIL2 is a part of the polyubiquitin locus of 100. The BIL2 displayed a well recognizable horseshoe-like fold typical for HINT domains, but in contrast to the majority of them, no α-helix structural elements are present. At the moment, only these two structures are publicly available representing both the inactive and unprecedent zinc-induced active forms. Structural comparison between the apo (PDB:6TMM) and the zinc-bound form (PDB:6Y75) allowed the elucidation of the role of zinc for protein splicing . The zinc is bound away from the catalytic center to which it is connected only through a H-bond network. It is coordinated by H48 and H125 disrupting the secondary structural arrangement between the two exteins. Based on the latest model, the zinc-binding regulates the splicing reaction by inhibiting the premature C-cleavage through the H125 interaction and by inducing a conformational change upon activation of N-S acyl shift which induces the thio-ester bond formation with the N-extein.Recently, two structures of the splicing element TthBIL2  have been deposited in the PDB shedding light on the protein splicing reaction and the role of the metal ion as reaction promoter rather than inhibitor40 and the models with energy higher than − 564 kcal/mol (S5.A) filtered out. The 179 remaining models were clustered based on their backbone RMSD for a total of one cluster  sampled as well an orientation space compatible with both the apo and Zn-bounded, while the five models generated by ColabFold pipeline25 explored orientation closer to the apo-form (red dots)  than the one covered by the trRosetta ensemble (ΔRMSD ≈ 7 Å) indicating the higher precision of AlphaFold in describing the experimental structure as well as the limited protein dynamic space explored. It is important to mention that with ColabFold implementation of AlphaFold we were only able to generate five models rather than the 1000 models generated with our pipeline. Further investigation on the influence of number of models onto the AlphaFold conformational exploration needs to be performed.In this study we compared the performance of trRosetta with the current state-of-the-art method AlphaFold from ColabFold Google implementationThe depth of the initial MSA as well as the choice of the energy function represent key points in protein conformational prediction. We observed that the number of effective sequences considered and the choice of the approach used to generate the MSA profile are fundamentals in the ability of deep learning algorithms as trRosetta to retrieve flexible information. The accuracy of the energy functions is an essential component in computational protein structure prediction. Assuming that we have a good discrimination between folded and unfolded structures we should satisfy both low energy and low RMSD. The native state of the protein is expected to be found at the minimum of the energy function in order to discriminate between native and non-native structure. We adopted the AWSEM forcefield which has been trained to differentiate decoys and identify the near-native structure as the one with low energy. The correlation between AWSEM energy function and RMSD shows the ability of the forcefield to discriminate between conformations allowing to energy filter the models. The energy evaluation of the experimental structures showed the presence of decoys with energies comparable to the experimental structure for all the test cases except the αI-domains of LFA-1 where only the energy of the experimental apo structure is considered in the energy filtered ensemble.A further limitation of the current deep learning techniques is the uncertainty in the prediction accuracy of the protein active site. AlphaFold and trRosetta have been trained with the apo protein structures and only consider X-ray structures limiting the data set to static information. We investigated the ability of these deep learning algorithms to predict the side chain orientations for the test case Tetrahymena thermophila-BIL2 which experimentally discriminates the holo- and apo-form on the basis of H48 and H125 orientations. Our pipeline was able to predict the presence of the only global fold experimentally observed as well as to sample the several histidine orientations. ColabFold predicted the apo conformation and AlphaFold the Zn-bound structure. However, we failed on clustering out the different orientations showing the limitations of the current pipeline in discriminating between side chain conformations. It is important to notice that if the conformational transition depends on the presence of coordinated metal ions, the prediction is challenging and only one conformation was generated by our pipeline as well as ColabFold platform.Proteins are not static entities but in motion in the cell. Switching between structurally distinct states is common in proteins carrying out functions such as catalysis or molecular recognition. X-ray crystallography provides a static snapshot of the possible conformations assumed by the protein in a crystallization setting that is not representative of the physiological environment. NMR structures offer dynamic information of the protein in solution but the protein size is a limit difficult to overcome. Cryo-EM delivers an overview of the protein orientations but current technical limitations  hamper high-throughput experiments preventing high numbers of structures to be available at high resolution. Hence the necessity of providing new approaches that allow to accurately and robustly predict conformational space and motions that are critical for protein function.24 with.Alternative protein states are still a largely open question in the field of protein folding. With the advent of machine learning techniques for protein structure predictions as AlphaFold or trRosetta we reach accuracies of the overall fold prediction comparable with the experimental structures provided in the PDB. However the quality of the prediction is still evaluated on the basis of static experimental structure. Here, we tested the ability of the current state-of-the-art machine learning methods in predicting protein conformational space. We have observed a potential correlation between residue-residue distance prediction and protein flexibility. MSA seems to encode information regarding the conformational flexibility of the protein which can in turn translate into bimodal behavior of the probability distance and angle distribution used in machine learning approaches as trRosetta. By combining trRosetta31 and AWSEM force field40 for rescoring the predicted models, we observed that the conformational space explored by the predicted models was similar to the one observed during the MD simulations of the target experimental protein structure. In particular, for the adenylate kinase and the T4 lysozyme predictions it was possible to predict the active and inactive structures as well as the intermediate conformations observed during the MD simulations. Although the trRosetta algorithm has been trained on static PDB structures, it was able to a certain extent to reproduce the protein flexibility.DeepMSA for MSA profile generation104 to the variation in MSA profile confirming the low correlation between MSA depth and model accuracy previous observed104. On the contrary, trRosetta showed higher correlation between MSA depth and model accuracy that could in principle be translated in higher robustness of the method and ability to predict relevant conformational change upon mutation.Limitations of the technique have been observed in the case where metal ions (LFA1 test case) and cofactor (Myoglobin test case) were influencing the conformational equilibrium. The clear conformational distinction observed experimentally was also lost during the MD simulations without the protein counterpart. It is important to notice that the ability to predict protein flexibility is correlated to the number of available structures for the different protein conformations in the PDB, and this is the strongest limitation of the current machine learning techniques. Another important aspect is the quality of the initial MSA profile. We observed in our study that the choice of the MSA algorithm can hamper the model prediction by favoring one conformation . Not only the MSA profile quality but also mutations in the initial target sequence could have an impact in the performance of the final prediction with the current deep learning methods. A recent study on the end-to-end RoseTTAFold protein prediction showed that small perturbation in the input sequence lead to radically different predicted protein in contrast with the observation that similar sequence lead to similar structure. By modifying 5 residues in the target sequence the RMSD was spanning from 0.9 A to 34.2 A. This highlighted the sensitivity of RoseTTAFoldWith our study we only begin to explore the capability of deep learning methods in conformational protein structure prediction. We observed the potential to extrapolate flexibility from MSA that translates into restraints providing models with biologically relevant conformations. However, all these methods currently rely strongly on the current structures available in the PDB—limiting the prediction performance on novel fold and unseen conformations. Introducing dynamic information from FRET experiments, conformational heterogeneity data from cross-linking Mass Spectrometry as well as increased numbers of cryo-EM structures in the training data set would bring a new set of information that would enhance the description of protein flexibility. A complete and accurate description of the protein conformational space would impact drug discovery by providing initial starting points for active and inactive conformations changing the way to approach the drug discovery campaign and allowing to focus on biologically relevant questions rather than methodological issues.For each of the 5 test cases  the following pipeline has been applied to generate the initial coarse models Fig. .Figure 6The key components of our pipeline include (1) the deep residual convolutional network transform-restrained Rosetta (trRosetta) and (2) the rescoring step with ASWEM force field.https://github.com/gjoni/trRosetta24. The procedure adopted in our study does not involve the use of templates to test the ability of the deep residual network to predict structural information for proteins that lack structural homologues in the PDB (https://github.com/audagnotto/trRosetta_xml).1000 models have been generated with the XML version of transform-restrained Rosetta pipeline (trRosetta) used during CASP13 and available at Initially, the Multi Sequence Alignment (MSA) profile has been generated following the open-source pipeline DeepMSA which takes as input the target sequence and iterates over 3 different databases . This consecutive approach adopts a cutoff based on the number of effective sequence (Nf) to quantify the homologous sequence coverage allowing the collection of a deep and sensitive MSA from metagenome sequence databases.105 as well as the Associative memory, Water mediated, Structure and Energy Model (AWSEM) force field40 . AWSEM is a coarse-grained protein force field used for de novo protein structure prediction which contains physical motivated terms and bioinformatically based local structure biasing terms that take into account many-body effects modulated by the local sequence. For each of the model generated by trRosetta a short NPT minimization at 300 K was running for 2000 steps with LAMMPS engine and AWSEM force field following the guideline provided by the Papoian Lab (https://github.com/adavtyan/awsemmd/wiki).The MSA profile generated is provided as input to trRosetta convolutional network delivering the probability distributions for Cβ-Cβ distance and inter residue orientations and converts them into spline restraints. Furthermore, the Rosetta model building minimization protocol guided by the restrains generates final predictions which are scored with the Rosetta score function ref2015The comparison between the RMSD/Rosetta-Energy plot with the RMSD/AWSEM plot (S8) showed a higher correlation for the latter consenting to filter out the models with energy higher than the mean Energy and proceed with the further analysis.106. The number of clusters that best represent the heterogeneity of the data set has been chosen with the Elbow method107. For each of the generated clusters a representative model is selected as the one with the lowest energy among all the other models in the cluster. Furthermore each of the lowest energy models per cluster is refined.The energy filtered models have been hierarchically clustered using pairwise RMSD implemented in MDAnalysis Toolsgmx covar and gmx anaeig utility toolkits. In all the studies, the first eigenvectors captured on average 80% of the total motions for both the apo and holo conformations indicating that this vector represent the essential subspace of the system. The visual representation and the density plot of the first principal component captures and describes in detail the overall dynamics of a system while relative residue fluctuations are examined by the Root Mean-Square Fluctuation GROMACS toolkit (gmx rmsf). We considered as ground truth the conformational space explored by the Molecular Dynamics simulation of the experimental X-ray structure.For each of the test cases we performed a series of analysis to evaluate the conformational space explored by the models. The Principal Component Analysis (PCA) has been adopted to characterize the overall motion changes of apo and holo conformations over the simulated time. The PCA calculation has been performed with GROMACS 2020.3 on the MD trajectories using 108 in a water box of the dimension 80 × 80 × 80 Å and neutralized by the addition of NaCl at a concentration of 150 mM. Amber99SB force field109 was used for running the simulation. MD simulations were performed in GROMACS.2019 software110. The system was minimized for 1,000 steps and equilibrated in the isothermal-isobaric (NPT) ensemble for 2 ns with a timestep of 2 fs to bring the temperature and the pressure in a range of 300 K and 1 atm respectively. The system was simulated for on average 300 ns in the NPT ensemble . The temperature was controlled using the modified Berendsen thermostat with a coupling time of 0.1 ps while the pressure was controlled by Parrinello-Rahman with a reference pressure of 1.0 bar and a compressibility of 4.5Xe−5 bar −1. The periodic electrostatic interactions were computed using particle mesh Ewald (PME) summation with a grid spacing smaller than 1 Å. The protein was unconstrained during the MD simulation.The initial experimental structure is downloaded from the PDB and only the protein coordinates are considered for the simulation. The resulting cleaned-model was fully solvated with TIP3P water modelsSupplementary Information.


The goal of CASP experiments is to monitor the progress in the protein structure prediction field. During the 14th CASP edition we aimed to test our capabilities of predicting structures of protein complexes. Our protocol for modeling protein assemblies included both template‐based modeling and free docking. Structural templates were identified using sensitive sequence‐based searches. If sequence‐based searches failed, we performed structure‐based template searches using selected CASP server models. In the absence of reliable templates we applied free docking starting from monomers generated by CASP servers. We evaluated and ranked models of protein complexes using an improved version of our protein structure quality assessment method, VoroMQA, taking into account both interaction interface and global structure scores. If reliable templates could be identified, generally accurate models of protein assemblies were generated with the exception of an antibody‐antigen interaction. The success of free docking mainly depended on the accuracy of initial subunit models and on the scoring of docking solutions. To put our overall results in perspective, we analyzed our performance in the context of other CASP groups. Although the subunits in our assembly models often were not of the top quality, these models had, overall, the best‐predicted intersubunit interfaces according to several accuracy measures. We attribute our relative success primarily to the emphasis on the interaction interface when modeling and scoring. Template‐based modeling is based on the observation that homologous proteins often interact in the same way.The field of protein structure prediction is monitored in the Critical Assessment of Structure Prediction (CASP) experiments that explore every aspect of protein structure modeling.We participated in recent CASP and CAPRI experiments, aiming to test our abilities to predict structures of protein complexes using template‐based modeling and free docking.To explore these questions in detail, we participated in the CASP14 experiment, where our group (“Venclovas”) performed relatively well, particularly in the interface prediction. In this article we describe our modeling methods and analyze our results in detail aiming to understand what went right, what went wrong and why.22.1The outline of our modeling workflow in CASP14 is shown in Figure 2.2The outline of multimeric comparative modeling pipeline is provided in 2.3For some targets neither sequence‐based nor structure‐based searches produced alignments with multimeric homologs of sufficient reliability and/or coverage such that these alignments could be used for multimeric comparative modeling. However, in some cases we observed that the proteins identified as potential multimeric templates had similar functional annotations as the CASP targets. If the alignment of CASP server models to these multimeric structures using TM‐align2.4Several targets were predicted to be coiled coils using MultiCoil22.5When no templates could be found for protein complexes, free docking of top five selected monomeric CASP server models was done by Hex2.6For some large target protein complexes structural templates were available only for some of the subunits or domains. In these cases, a hybrid modeling strategy was used, that is, part of the complex was modeled using comparative modeling, whereas remaining subunits were docked to it either by template‐based docking using TM‐align2.7For model selection we used both global structure scores and interface scores. This approach was described previously2.8https://kliment-olechnovic.github.io/voronota/expansion_js/.VoroMQA‐dark is a new model quality assessment method based on the previously published VoroMQA33.1To analyze our performance in CASP14 we used several accuracy measures designed to evaluate various features of multimeric models. For the overall model evaluation we used QS‐score, a distance‐based measure of interface accuracy.The summary of our modeling results based on the QS‐score is presented in Table 3.2z scores of two interface accuracy measures (ICS and IPS) and two global structure accuracy measures (lDDT and TM‐score).The results, presented in Table The comparison, shown in Figure z scores. z Score values were accumulated progressively for targets ordered by the maximum ICS value of all the models produced by any group for a given target. Such an ordering may be interpreted as an estimate of the target difficulty. Figure To look at different features in more detail, we examined per‐target z score differences resulted from small structural improvements  having two different interfaces (T1099v1 and T1099v2). Our models contained high‐accuracy interface 2, yet the interface 1 was incorrect . The rea3.4When modeling a large protein complex templates might be available just for some parts of it. During CASP14 this was the case for heteromeric targets H1060 and H1097. For both of these targets more accurate models were generated for those parts that had templates.H1060 was a viral protein complex containing 27 subunits  using a higher number of diverse input monomers, for example, generated by modeling domain motions and by remodeling flexible loops and tails; (2) ensuring that the docking software always performs a sufficiently exhaustive sampling of conformations; (3) producing structural variations of each oligomeric docking solution using molecular dynamics or other sampling techniques. These enhancements would allow to explore the conformational space more thoroughly, possibly leading to better results.Despite the limitations of our CASP14 modeling protocol, our strong performance suggests that the prediction of interchain contacts using coevolution and deep learning methods still has little impact on modeling of protein‐protein interactions. Why is that? Apparently, there are multiple reasons why interchain contact prediction is harder than intrachain. For example, contact prediction for heteromeric protein complexes requires generating joined multiple sequence alignments. The interacting proteins in the alignment are inferred by genomic distances or by phylogeny,In addition to the issues related to obtaining and analyzing the multiple sequence alignments, training of supervised learning‐based methods for contact prediction using the structures of protein complexes may be limited by the availability of experimental structural data. The number of possible protein complexes is believed to outnumber the number of possible protein folds,Modeling of structures for individual proteins is highly automated, and multiple structure prediction servers are available in CASPTo conclude, the progress in monomeric protein structure prediction has not yet translated into similar breakthrough in structural modeling of protein complexes. A number of issues of both technical and fundamental nature have to be solved to make a leap in producing reliable structural models of protein interactions, and it will be exciting to see what developments will occur in this research area in the nearest future.https://publons.com/publon/10.1002/prot.26167.The peer review history for this article is available at Appendix S1: Supporting informationClick here for additional data file.


Ligand–protein interactions play a key role in defining protein function, and detecting natural ligands for a given protein is thus a very important bioengineering task. In particular, with the rapid development of AI-based structure prediction algorithms, batch structural models with high reliability and accuracy can be obtained at low cost, giving rise to the urgent requirement for the prediction of natural ligands based on protein structures. In recent years, although several structure-based methods have been developed to predict ligand-binding pockets and ligand-binding sites, accurate and rapid methods are still lacking, especially for the prediction of ligand-binding regions and the spatial extension of ligands in the pockets.In this paper, we proposed a multilayer dynamics perturbation analysis (MDPA) method for predicting ligand-binding regions based solely on protein structure, which is an extended version of our previously developed fast dynamic perturbation analysis (FDPA) method. In MDPA/FDPA, ligand binding tends to occur in regions that cause large changes in protein conformational dynamics. MDPA, examined using a standard validation dataset of ligand-protein complexes, yielded an averaged ligand-binding site prediction Matthews coefficient of 0.40, with a prediction precision of at least 50% for 71% of the cases. In particular, for 80% of the cases, the predicted ligand-binding region overlaps the natural ligand by at least 50%. The method was also compared with other state-of-the-art structure-based methods.https://github.com/mingdengming/mdpa.MDPA is a structure-based method to detect ligand-binding regions on protein surface. Our calculations suggested that a range of spaces inside the protein pockets has subtle interactions with the protein, which can significantly impact on the overall dynamics of the protein. This work provides a valuable tool as a starting point upon which further docking and analysis methods can be used for natural ligand detection in protein functional annotation. The source code of MDPA method is freely available at: The online version contains supplementary material available at 10.1186/s12859-022-04995-2. Proteins are ubiquitous in the cellular environment. They are not only the main components of cells but also the vital workforce to maintain the biochemical function of life. Proteins achieve their functions by interacting with a variety of molecules in cells, including some macromolecules  and small molecules  , 2. ThesOver the years, biological experiments have provided a wealth of protein structural data spanning all domains of life, which are available from the Protein Data Bank (PDB ), providAs an early geometric method, POCKET  and the Dx [Dx values were selected and clustered to form prediction regions. In DPA, proteins were treated as some elastic network structures, and the external interactions between test points and neighboring protein atoms were simulated by connecting springs. The method was evaluated using 305 proteins in GOLD test set [We have developed a template-free, structure-based and stand-alone method to predict protein LBS based on the protein dynamics perturbation analysis  , 47. TheDx , which iDx  was usedtest set  of the ttest set  and theitest set –55.In this study, we presented a new version of DPA, called the multilayer dynamics perturbation analysis (MDPA), to predict both the protein LBSs and the spatial extension of ligands within the predicted pocket. The method was evaluated with a popular CCDC/Astex dataset for ligand-binding site prediction  and compjth atom. In DPA/FDPA, the potential energy , where U is usually valid because, under normal conditions, biomacromolecules must keep their structural fluctuations within a narrow range to perform their function correctly [MDPA is an extension of the previously developed fast DPA or FDPA method. See the original work for detaorrectly .D-value: Da)P(a)(X) . In pracrk model , 63, and program , and thohe OPTICS clusteriD-values D-values were screened out, and grouped into clusters In DPA/FDPA, the set of test points generated by MSMS usually forms a closed surface parallel to the protein surface, which in turn makes the predicted pocket consisting of selected surface points very close (1.5 Å) to protein residues in any case. This often differs from what we observed when small molecules bind to proteins. In fact, except for a few marginal atoms, the entire small molecule tends to be located in the center of the binding pocket, some distance from the surface residues. Considering the importance of this ligand spatial extension in pockets, here we introduce a simple generalization of FDPA, called multilayer DPA or MDPA, for predicting protein LBSs and ligand spatial extension in pockets. MDPA treats the surface points To verify the accuracy of the algorithm in predicting ligand-binding pockets, the CCDC/Astex dataset of 85 protein-ligand complexes were used in this study . The datRp and RL defined a set of residues To evaluate the prediction accuracy, we selected the set of protein residues SL be the set of atoms of a ligand found in the complex, and Sl be the subset of SL whose elements are ligand atoms located within 3.5 Å of any test point of the predicted pocket Compared with DPA/FDPA, the new output of MDPA was a prediction of the spatial extension of ligands inside the predicted binding pocket. To evaluate the quality of predictions, we defined an evaluation strategy similar to evaluating LBS predictions. Let As a fast geometric stand-alone method, FPOCKET has become one of the most popular tools for analyzing protein structural pockets in recent years. The method is based on the determination of alpha spheres by Voronoi tessellation . An alphThe lysozyme from turkey egg-white (PDB code: 1JEF ) has beepro) of coronaviruses has been identified as a key drug target for the development of inhibitors to block viral RNA protein processing. The overall structure of the protease  can be code 6Y2F, 69, in One of the key features of protein pockets is the distribution of ligand-binding sites within them, which largely determines protein function. To evaluate the ability of MDPA to predict ligand-binding sites, MDPA was applied to the protein structures of the CCDC/Astex dataset of 85 protein-ligand complexes. The residues identified by 3-layered MDPA predictions were then compared to the ligand-binding sites listed in the SITE records for each complex structure Table . MDPA maCompared with previous DPA studies, the development of the MDPA algorithm aims to improve the prediction of the spatial extension of ligands in protein pockets and provide useful information for subsequent ligand prediction and ligand-protein docking calculations. Any medium to large-sized ligand usually adopts a specific spatial distribution within the pocket, with parts of the ligand structure close to the protein and others away from the protein. Both DPA and MPA identify surface point clusters that impose high perturbation on the overall dynamics of the protein through interactions with neighboring protein amino acids, and according to this method, it is easier to detect interacting point sets closer to the protein surface. A key step in MDPA is to expand the collection of highly perturbing points at increasing distances from the protein surface in a layer-by-layer manner. Therefore, it would be interesting to verify the correlation between the direction of the predicted region increase and the spatial extension of ligands in the pocket. As an example, Fig. For comparison, we selected two free, standalone protein pocket prediction programs, FPOCKET  and CAVIData-driven methods have long been used to predict important ligand binding residues in proteins and physicochemical properties involving ligand-protein interactions . Recent Just as flexible docking or flexible ligand recognition , 79 mustA is an arbitrary value. To determine the maximum possible value of A, the Procheck[A value was chosen under the condition that the Procheck program reports that at least 90% of the dihedral angles are within a reasonable area. MDPA was then applied to each conformation, and the predictions were compared with ligands for which binding sites were found  was developed to predict ligand-binding pockets in proteins. This structural-based method is a direct improvement on our previously established method. The method has been validated using a standard ligand-protein complex dataset, with an LBS prediction accuracy of at least 30% in 65% of cases and recall of at least 30% in 87% of cases. One of the key features of MDPA is that MDPA is designed to predict the spatial extension of ligands within the binding pocket. The predicted binding region overlapped with the natural ligand in 82% of the complexes in the test dataset, with a prediction accuracy of at least 30% in 87% of the cases and a recall of at least 30% in 95% of cases, indicating that MDPA can give a reasonable prediction for protein pickets.The combination of MDPA with deep learning methods based on ligand-protein interaction big data (such as DeepbindPoc) provides a good starting point for MDPA to study further biomolecular functions, including the prediction of natural ligands and their involved physicochemical interactions.Additional file 1: Table S1. Prediction results of DPA for protein structures in CCDC/Asterx dataset; Table S2. Prediction results of FPOCKET for protein structures in CCDC/Asterx dataset; Table S3. Prediction results of CAVIAR for protein structures in CCDC/Asterx dataset; Table S4. Selection of optimal pockets for protein structures in CCDC/Asterx dataset by combining DeepbindPoc; Figure S1. MDPA calculation results of SARS-COV-2.


Recent breakthroughs in protein structure prediction demarcate the start of a new era in structural bioinformatics. Combined with various advances in experimental structure determination and the uninterrupted pace at which new structures are published, this promises an age in which protein structure information is as prevalent and ubiquitous as sequence. Machine learning in protein bioinformatics has been dominated by sequence-based methods, but this is now changing to make use of the deluge of rich structural information as input. Machine learning methods making use of structures are scattered across literature and cover a number of different applications and scopes; while some try to address questions and tasks within a single protein family, others aim to capture characteristics across all available proteins. In this review, we look at the variety of structure-based machine learning approaches, how structures can be used as input, and typical applications of these approaches in protein biology. We also discuss current challenges and opportunities in this all-important and increasingly popular field.  With the increase in protein sequence data obtained from large-scale high-throughput sequencing technology, machine learning (ML) has become a key methodology in protein bioinformatics. In protein structure prediction, be it secondary structure, backbone angles, contacts, folds, or full-atom structure, ML has become indispensable and forms the basis of a number of popular tools and algorithms. ML has also successfully been applied to predict protein function, protein-protein interactions, drug-target binding, enzyme substrate specificity, thermostability, catalytic rates, binding affinity, variant and mutant effects and more. ML is data-driven and attempts to identify patterns in existing data to predict properties of new, unseen data. Given ML’s requirement of large amounts of diverse data, the overwhelming majority of ML applications on proteins use sequences as input, some of which are powering different aspects of popular resources such as Ensembl sequence-structure knowledge gapde novo prediction techniques (template-free or de novo modelling). Given that homology modelling performs well when using templates with> 30% sequence identity to the protein of interest, accurate structural models can be obtained for over 60% of the genes in the top 12 most accessed genomes on UniProt Historically, structural biology depends primarily on experimental structure determination methods including X-ray crystallography, nuclear magnetic resonance (NMR), small-angle scattering, and cryo-electron microscopy (cryo-EM). The Protein Data Bank (PDB) With these advances, we are at the brink of a structural revolution with millions of newly modelled structures at our disposal. Thus ML applications in protein bioinformatics, already shown to be very powerful in shedding light on biological problems, now have a wealth of structural information to exploit as input instead of, or along with, the typically used protein sequences. These sequence- and structure-based ML methods (hereafter referred to as “structure-based”) can greatly outperform purely sequence-based approaches, as demonstrated in studies where the same ML architecture is validated using only sequence and both sequence and structure information 2Machine learning (ML) is defined as “the study of computer algorithms that improve automatically through experience and by the use of data” Supervised ML attempts to predict a certain response by learning patterns from labelled data. In the case of classification, this response is the membership of the data point in a particular grouping or class. Regression, on the other hand, predicts a real-valued numeric outcome. Unsupervised ML attempts to find clusters or learn reduced representations from data without any labels. See protein family based and protein universe based techniques. These two categories differ in the kinds of prediction problems they are applied to, the kinds of algorithms used, and the kinds of representations used as input.ML has been used widely across biology for decades, with reviews outlining its usage in the fields of omics 2.1Protein family based ML is used to predict properties of the members of individual protein families or sub-families, usually consisting of hundreds to thousands of experimentally characterised training proteins. Some of the questions in protein family supervised ML include specificity prediction of substrates, intermediates, products, and inhibitors; state prediction in the context of engineering thermostability, binding affinity and activity; and prediction of the effects of mutations. In many cases, such as the immensely diverse lipocalins The superfamily of G protein-coupled receptors (GPCRs) is the largest family of targets for approved drugs in modern drug discovery, and hence also a popular target for ML approaches to drive exploration and understanding. GPCRs play an essential role in physiological processes such as vision, olfaction, neuronal signal transmission, cell differentiation, pain, muscle contraction, and hormone secretion Another important class of drug targets are the kinases In the field of natural products and specialised metabolism in plants, bacteria, and fungi, ML has slowly been gaining popularity over more traditional approaches involving similarity search or analysis of a few, closely related proteins. ML has been used for successful prediction of substrate Though computationally predicted structures are shown to be highly accurate at the backbone level, tasks such as the ones described above which involve small molecule binding may need further family-specific processing and ML-based approaches to harness the structural information specifically related to ligand interaction. For example, et al.Unsupervised ML in the protein family space hosts a new sub-field of structural bioinformatics, dubbed “comparative structuromics" by Mohammed AlQuraishi. This is concerned with tools, algorithms, and techniques to compare and contrast assorted datasets of protein structures to answer a variety of biological questions - the evolutionary relationships between structural orthologs, interaction networks and how they are affected by structural changes, folding and changes within different cellular contexts and organisms, and how structure and folding are coupled with different functional characteristics. Zebra3D a small number of data points. A wide range of algorithms are at our disposal for these tasks, including but not limited to k-nearest neighbours algorithms (k-NNs) 2.2de novo all atom structure modelling. In fact, all the top-performing Critical Assessment of Structure Prediction  lack a fixed or ordered three-dimensional structure. This widespread phenomenon, thought to occur in over 33% of eukaryotic proteins, has been linked with allosteric regulation, enzyme catalysis, and a variety of diseases Unsupervised techniques in the protein universe support tasks such as structure query and retrieval, clustering for motif and hotspot discovery, and structure-based fold annotation. For the former task, an array of fast techniques that allows near-instant retrieval of structures matching an input structure 3feature matrix. Another approach is to generate reduced fixed-dimensional protein representations, referred to as embeddings. Both these approaches  have predictive power and can even perform as well as experimental structures on specific tasks 3.1Broadly, protein structures are compared at the residue level, where features are extracted from each individual residue in the structure, or at a structural environment level, where features are extracted from well-defined portions of the structure (or the entire structure) containing relevant and localised properties. The former approach is commonly used in structurally conserved protein family ML tasks involving the entire protein, and the latter is used for more divergent proteins or for more specific tasks involving the corresponding structural environments. Both approaches use a range of techniques to align or arrange the extracted features into the fixed dimensional feature matrix format.3.1.1Many different features can be extracted from each residue in a protein structure using a plethora of computational tools, as listed in When the proteins under consideration are evolutionarily closely related, multiple protein alignment is commonly used to generate the input feature matrix. While sequence alignment has generally been much more popular than structure alignment, the existence of protein families which share the same structural fold despite having little sequence similarity necessitates the use of structure-based alignment methods. This has driven the development of fast multiple structure aligners capable of scaling to the numbers of proteins required to train ML algorithms et al.An alternative to the tabular format is a (dis)similarity matrix, often used as input to kernel-based methods such as SVMs or in unsupervised dimensionality reduction. For instance, de Lima 3.1.2Fig. Representations of the molecular surface D are useProteins often interact with other molecules - other proteins, peptides, nucleic acids and small molecule ligands - so computational representations of these binding regions or interfaces are necessary for a number of tasks. Graph 3.2A complementary approach to generate the tabular input required for ML is by using end-to-end or pre-trained embedding algorithms. These typically make use of unsupervised DL methods trained on a large dataset of proteins to produce a series of values representing a given protein in a fixed high-dimensional space, often without the need for explicitly handcrafted features. Due to the training process, these values place similar proteins closer together in this space thus capturing overall protein variation and relationships between individual proteins. For example, recent global sequence embeddings have been shown to capture amino acid characteristics and other physiological properties of proteins as a whole End-to-end learning is popular in this area, covering techniques which start from the raw protein structure with minimal processing and automatically extract features based on optimising prediction accuracy in a given end task - thus the intermediate feature representations or embeddings learned are more applicable to the task at hand and can be retrained to adapt better to different tasks. ContactLib-ATT GeoPPI Another interesting and relevant approach is structure-guided sequence embeddings 4Despite rapid progress in the direction of structure-based ML, there are challenges to address before it can become as ubiquitously used as sequence-based ML. Just as there exists a wide variety of tools for answering questions from a sequence perspective, there need to be tools in structural bioinformatics that are as easy to use, as intuitive to interpret, as optimised, and as feature-rich.4.1The universal and widespread use of protein sequence data, combined with its one-dimensional nature, has resulted in a diverse landscape of highly optimised sequence-based tools and algorithms. Many of these, including clustering algorithms, aligners, feature extractors etc., scale to hundreds of thousands of sequences with ease. This cannot be said for structure-based approaches yet, both due to their relative newness and to structural data being much more complex than sequence data.Often this resource intensiveness starts from the very first step - i.e. generating structural models. Template-based or homology modelling approaches take a matter of minutes to hours for generating a single model, often exacerbated by the need to infer multiple models for better robustness and expensive additions such as loop modelling for special cases. Recent template-free methods such as AlphaFold2 and RosettaFold run in minutes, though scaling very poorly with the number of residues, and require GPUs and high amounts of memory and disk space. Memory and space requirements for both are somewhat alleviated by the presence of servers such as SWISS-MODEL Once a dataset of structures is gathered or generated, the next steps often involve structural comparison and feature extraction. Alignment-free structural comparison techniques are relatively fast already, but structural aligners that scale to the sizes of datasets required for ML have only recently started to appear. These are still a far cry from the highly optimised sequence aligners, but many of these optimisation techniques can be transferred to structure-based approaches and represent a logical next step as ML on structures grows in popularity. Extraction of many of the features detailed in 4.2End-to-end learning, where a DL model learns a mathematical function to map an input to a complex output End-to-end learning is becoming popular for a number of tasks as large models trained once on huge datasets of structures can then be reused for smaller sets of proteins and adapted to similar tasks with much less resource consumption and, at the same time, a great increase in performance for even sparse amounts of data However, these learners do need huge initial training sets of diverse data and careful architecture engineering to avoid overfitting as well as large amounts of computational resources for training and inference. In addition, results from such approaches are difficult to interpret in terms of which kinds of protein properties are being used to make certain decisions, which is a useful property of more handcrafted ML techniques to hypothesise about the underlying biology.4.3Since proteins are inherently dynamic in nature, their true “structure” is much more than the rigid three-dimensional coordinates which serve as the basis for many of the approaches detailed in the previous sections. Instead, a protein is an ensemble of possible conformations, with some areas displaying more flexibility than others. This is further influenced by the constant interaction of proteins with the surrounding solvent, small molecules, nucleic acids, peptides and of course other proteins, all of which drive conformational changes within the protein. Protein biological activity often involves adopting specific conformations, contributions from local fluctuations, and even large-scale structural transitions between different conformations. In fact, the old paradigm that sequence encodes structure, and structure determines function can now be rephrased as sequence encodes structure, structure determines dynamics, and dynamics encodes function Protein flexibility and conformational diversity can be modelled in multiple ways. One of the most common approaches is using molecular dynamics (MD) simulations, which calculates the force exerted on each atom by all other atoms as a function of time using a molecular mechanics force field Together, these computational techniques can provide information about globular protein flexibility and mutations 4.4A major limitation of DL-based structure prediction techniques, where prediction acts merely as an alternative to an experimental technique, is that they do not immediately provide us with a deeper understanding of the processes behind the folding of proteins as this is not their aim Interpretable ML is a crucial concept in bioinformatics, as often we are as interested in the how and why of a prediction as we are in the what. Thus an important next step in structure-based ML is to couple predictions with an understanding of protein biology in terms of folding, interaction, function, and the interplay between the three. From a protein universe perspective, interpretation becomes dependent on the model inspection techniques specific to DL approaches. While this is a nascent field, techniques such as integrated gradients, saliency and class activation maps exist for this purpose, though they are rarely used yet in structure-based ML tasks 4.5Biological function is only partly determined by an individual protein – its genomic and cellular contexts also play a big role. Each protein is determined by an underlying gene sequence, but the mapping from gene to protein is not so straightforward, complicated by the existence of alternatively spliced transcript variants One area of study focused on integrating these different contexts of proteins and their complex interactions is network biology. This field is crucial for the accurate modelling of biological systems, and given the influx of data from high-throughput interaction assays and large-scale multi-omics studies, a great target for ML and DL methods. The future holds an increasing number of opportunities for this combination of network biology and ML 5Protein structure is a central component to understanding biological processes, and thus a great addition to ML approaches in the protein bioinformatics field. In this review we described the space of structure-based ML in terms of the tasks it can be applied to, and the kinds of input representations and algorithms used with a number of examples demonstrating the powerful predictions that can be obtained. Mainly due to the recent breakthroughs in computational structure prediction, the field of structure-based ML is expanding very rapidly, with a high number of actively cited preprints in this review attesting to this. At the moment, sequence-based features, aligners, representations, and ML approaches still far outnumber structure-based ones and they are generally much faster as well. However, the power of structural information to improve computational prediction of protein biology is alluring, and the growth of structural databases, algorithms for alignment and representation, and increasing accessibility of relevant DL approaches and architectures will foster a new generation of protein bioinformatics in which structure will play a starring role.Janani Durairaj: Conceptualization, Investigation, Visualization, Writing – original draft, Writing – review & editing. Dick de Ridder: Writing – review & editing, Supervision. Aalt D.J. van Dijk: Writing – review & editing, Supervision, Project administration.We have no conflicts of interest to disclose.