Serratia marcescens, a Gram-negative bacterium (Enterobacteriaceae) is a hospital-acquired opportunistic pathogen that infects the urinary and central nervous systems. The identification of new therapeutics against S. marcescens is crucial since it is now multi-drug resistant. Therefore, the current study was aimed to identify potential drug targets against S. marcescens strains i.e. WW4, SM39, and Db11 using comparative metabolic pathway analysis and subtractive genomics approach. The applied bioinformatics-based method was used to identify the unique metabolic pathways as the prioritized drug targets. The downstream analysis has led to the identification of three pathways that are specifically absent and/or present in the specific strain. Consequently, six proteins were identified through subtractive genomic analysis. The identified proteins were found as non-homologous and essential to the pathogen’s survival as well as unique to the WW4 strain. The estimated features proposed it as a potential drug target. The selected protein was further subjected to in-depth structural analysis for the structure modeling, structure validation, and protein-protein interaction analysis. Furthermore, the library of ~1500 approved compounds was screened against selected drug target to identify potential drug candidates. The current work may help in repurposing of the drug compounds as novel medication against S. marcescens. Serratia marcescens is an opportunistic nosocomial pathogen and causative agent of many infections including urinary and central nervous systems (meningitis) as well as heart (endocarditis) and wound infections [S. marcescens affects immunocompromised patients specifically those undergoing broad-spectrum antibiotic therapy. The bacteria spread mostly through invasive instrumentation such as intubation material, intravenous and urinary catheters [S. marcescens can be attributed to several virulence factors such as the pore-forming toxin hemolysin, the serralysin protease or a phospholipase [S. marcescens has been reported as an epidemic, primarily in The Neonatal Intensive Care Units (NICUs) and Intensive Care Units (ICUs) [S. marcescens are now generally multidrug-resistant [fections , 2. S. matheters , 4. The holipase –7. Preses (ICUs) , 9. S. mesistant .S. marcescens was empirically treated using aminoglycoside, piperacillin-tazobactam, carbapenem, or fluoroquinolone. Later, modification of the treatment was suggested based on the strain susceptibility test results [S. marcescens is intrinsically polymyxin resistant which is an antibiotic being used as a last resort to treat carbapenem-resistant Enterobacteriaceae [Serratia under the critical category as they are resistant to carbapenem [S. marcescens using public whole-genome datasets helped in the detection of 100 AMR  genotypes. Aminoglycoside and beta-lactam resistant genes including ESBL (Extended-spectrum beta-lactamases) and carbapenemase genes are highly prevalent in clinical isolates of S. marcescens [S. marcescens in recent times due to its antibiotic resistance capability [Currently, antibiotic resistance is a threat to antibacterial therapies . The ant results . A receneriaceae . The WHOrbapenem . Researcrcescens . The limrcescens , 16. Thercescens . There hpability .Different computational methods have been used earlier to search new chemical entities as potential drug candidates. In this context, comparative subtractive genomics coupled with metabolic pathway analysis produced robust data to prioritize unique essential proteins that may serve as drug targets. These essential proteins may act as putative targets for designing novel drugs. Previously, numerous such applications are reported in literature to propose new drug targets against deadly pathogens . ContrarS. marcescens has been extensively studied to identify potential drug targets yet the current applied method has not been reported to our knowledge. Furthermore, this work will help to identify potential drug targets.Even though S. marcescens as part of the analysis. The strains were analyzed to find potential drug targets retrieved from metabolic pathways of the pathogen. Metabolic pathways are a network of interconnected metabolic events that occur in a cell. Some of these metabolic pathways and metabolites are required essentially by the pathogens to survive. Therefore, it is prudent to target such metabolic pathways to eradicate the pathogen. Thus, the metabolic data of three available S. marcescens strains were retrieved. Metabolic pathways unique to the pathogen compared with Homo sapiens were selected using metabolic data. Essential proteins of these metabolic pathways were shortlisted using BLAST against the DEG database. Moreover, protein structural studies were conducted to model a 3D structure and locate the binding pockets of the protein. Finally, protein characterization and network topological analyses were performed. The current study is divided into two phases i.e. (1) Subtractive Genomics and (2) Structural Studies. The details of the study are provided in the workflow  was employed to sort the distinction among three strains graphically. Metabolic pathways that were uniquely present or absent among three pathogenic strains were visualized. Studies have reported the role of specific pathways and how their presence or absence impact the survival of the pathogen [A tool known as Diffuse  helped in the identification of proteins essential to the survival of bacteria [-5, identity more than 30%, and query length more than 70% to identify essential proteins.Annotation of the bacteria . The BLAThe characterization of protein’s subcellular location is a crucial aspect of its functional analysis. The PSORTb v 3.0  predicteThe physicochemical properties of the selected proteins were estimated through ProtParam server . The Provia a popular tool i.e., STRING [The protein interaction network analysis for the pigC protein known as prodigiosin synthetase that is involved in the synthesis of the antimicrobial compound prodigiosin was conducted , STRING . It predBLAST was implemented online against PDB using NCBI  to prediMoreover, DogSite Scorer was used to find protein binding pockets . This usNº of points in X, Y, and Z-dimensions were 126 and Center Grid Box: X center: 21.294, Y center: 47.682, and Z center: 25.044, respectively to allow the molecular docking to occur in the selected binding pocket having the active residues retrieved from DogSiteScorer . The AutoDock Vina was used to perform molecular docking.~1500 compounds in the approved drugs library were retrieved from the Drug Bank database to screen against the shortlisted drug targets. The analysis of the structural error of all target proteins was performed using ADT (Auto Dock Tool). Grid box parameters and configuration files of the drug target were individually prepared using the above docking parameters. Configuration files for the target covering the active site of the protein predicted through DogSiteScorer were set as The chemical descriptors and druggability of compounds were analyzed using the SWISSADME tool. The database was used to determine the pharmacokinetics of the compounds such as absorption, metabolism, druggability, rule of five, intestinal absorption, Caco2 permeability, and toxicity . Ames, cS. marcescens with the taxonomic ID of 615 and UniProt KB was selected as the target database.The extent of the pharmacological spectrum over the whole homologous bacterial population may be inferred through comparison of the predicted sequences’ conservation pattern with other strains that are used conventionally. Therefore, the online BLAST  was usedAfter retrieving data of the metabolic pathways, the differences among the strains were annotated and identification of unique or absent pathways among the strains was inferred.It was observed that the SM39 showed the greatest number of missing pathways and the Db11 showed the most additional number of pathways such as the Styrene and Polycyclic Aromatic Hydrocarbon Degradation pathways. The WW4 strain was found with an additional pathway unique to itself only i.e., the Prodigiosin Synthesis pathway.S. marcescens is shown  and predators (nematodes and protozoans) as its secretion causes membrane disruption of other organisms . A heatmis shown . S1 Tablis shown .The heatmap showed that the strains Db11 and WW4 were unique and hence subsequently used for further analysis. The proteins of those specific metabolic pathways were retrieved from the KEGG database. Among twenty proteins from both strains, two proteins were found in the Db11 strain and four in the WW4 strain that were non-homologous to the human proteome. BLASTp was then implemented against the DEG database using these six proteins, which resulted in only one protein i.e., the pigC protein as essential to the survival of the bacteria. The implemented BLASTp parameters and their respective outcomes have been mentioned in Details of these pathways and their proteins are mentioned BLASTp search against Drug Bank targets resulted in the identification of zero hits i.e., no hits were found using the set parameters for the pigC protein. Finding “No hits” does not necessarily mean that the protein cannot be used as a therapeutic target. However, the target was classified as novel target due to their absence in the Drug Bank database i.e., the protein in question could be a good target to be explored in further detail.S. marcescens that is vital for bacterial survival. The remaining five proteins without any hit were confirmed as non-essential and therefore, discarded. Hence, the identified essential protein was classified and selected as a novel drug target against S. marcescens i.e., prodigiosin synthetase.The main criterion that a potential drug target must satisfy is its essentiality for the survival of bacteria. Targeting such proteins may specifically kill bacteria. The essentiality of the non-homologous-druggable proteins was assessed by comparing them with the DEG database using BLASTp. This resulted in the identification of a single protein i.e, Prodigiosin synthetase as a novel drug target against pigC found in the WW4 strain of S. marcescens. This protein was found to be unique to the WW4 strain and is part of the accessory genome of the organism. The metabolic pathway in which it involved in is named Prodigiosin Biosynthesis (KEGG ID = 00333). A comprehensive literature review was conducted to find multiple roles of prodigiosin synthetase in pathogens and in other microbes.The selected protein was annotated as Prodigiosin synthetase encoded by the gene S. marcescens, particularly during interspecies competition. Targeting such a protein will inhibit the essential pathway, inhibit the synthesis of this antimicrobial agent and making the bacteria susceptible to the treatment.In brief, this enzyme is involved in the prodigiosin biosynthesis i.e., Prodigiosin has anticancer, antimalarial, antifungal and antibacterial properties. As the pathogen invests a huge amount of energy into the biosynthesis of prodigiosin, it can be inferred that it is critical to the survival of S. marcescens was compared with homologous proteins in Listeria monocytogenes and Neisseria meningitides by Clustal Omega 1.2.4 [S. marcescens and those of other bacterial species. The percentage similarity of the prodigiosin synthetase with different species is highlighted Sequence alignment of the selected protein was performed with its homologs in different species to analyze its phylogenic relationship. The alignment was performed using Clustal Omega V.1.2.4. The protein sequence of the selected protein in ga 1.2.4 . The resDe novo structural modeling was used as an alternative method in the absence of a suitable template by using the RoseTTA fold for the selected protein sequence.  shows thThe angstrom error estimate of the model showed an undulating pattern. The error estimate varied with 0.7 being the lowest and 6.02 is the highest, respectively .The modeled structure of the protein was evaluated using Verify 3D tool. This tool is used for the quantitative evaluation of the overall quality of the protein’s three-dimensional structure. Computationally, the modeled structure was classified with a quality score of 95.83% with at least 80% of the amino acids having a score of ≥ 0.2 .Additionally, the protein’s secondary structure was validated using PsiPred. It computationally estimates the secondary structural elements using the primary structure of the query protein. Yellow-colored bars represent strands while straight lines represent coils and pink colored bars represent helices. The Ramachandran plot was generated for the modeled protein to evaluate its quality. It showed that only 0.6% of the residues were found in the disallowed region. The ProCheck classified 91.5% residues in the core allowed region whereas 7.5% in the allowed and 0.4% in the generously allowed region. The Ramachandran Plot showed that five residues fall in disallowed region inclusive of asparagine, lysine, isoleucine and two serine residues, while showing the overall good quality of the structure as shown in The selected protein was characterized by studying its subcellular location, physicochemical characteristics, superfamily and interaction network. Details are provided in the following sections:The identification of protein sub-cellular localization is important to effectively characterize the protein’s function and chemical nature. Additionally, it aids in the evaluation of the molecular structure and druggability of the protein. Consequently, the selected protein was classified as cytoplasmic as 9.26% was computed as cytoplasmic while 0.24% was found in the cytoplasmic membrane. The bar chart presented in  shows thThe InterProScan is an online tool that computes a protein’s functional domains and predicts its super-family which function as diagnostic signatures for the classification of the proteins. The InterProScan predicted the protein’s ATP binding functional domain. This prediction serves as a diagnostic marker about the functional domain and the type of compounds it interacts with. This tool predicts that the protein is associated with the phosphorylation pathway within the cell. pI of the protein was 6.45 thus implying that the protein is acidic. As the protein’s instability index is higher than 40, the protein is considered unstable. The ProtParam computed the protein’s physicochemical properties. The Gravy index was estimated at -0.197 inferring that the drug target is hydrophilic. The theoretical C protein (red node) graphically with the top 11 nodes. The protein has 2721 edges, an expected 1126 edges, 199 nodes and an average of 27.3 nodes degree. The enrichment p-value for the predicted network is less than 1.0e-16, with an average local clustering coefficient of 0.701. These results showed that the protein is involved in various critical functions. Consequently, targeting such a protein can potentially cause the loss of function of the other associated proteins and thereby inhibit numerous biological pathways. Thus, it may be proposed as a potential therapeutic target. showed t2. The descriptive details of the binding pocket can be found in Furthermore, the DogSiteScorer was used to predict the protein’s binding pockets for the ligand for further protein structural characterization. Out of the 28 binding pockets identified, only one pocket was selected based on the highest average of simple and drug scores. The selected pocket was found with the highest drug score of 0.725 and a surface area of 1182.91 ÅAdditionally, inter-molecular hydrogen bonding interaction of the protein and the ligands are depicted. The protein-ligand interactions are visualized using Chimera and Ligplot. The 2D interactions of the Ligplot showed that the protein makes favorable hydrogen bonds with all three ligands as the bond lengths fall within 2.7 to 3.3 Å. The ATP forms the greatest number of hydrogen bonds with the amino acid residues of the protein, whereas MAP forms only a single hydrogen bond but numerous nonpolar interactions . FurtherVirtual screening was performed to identify compounds with binding energies similar to the docked ligands which were used as a reference. Among 1500 Approved drug library, 854 compounds were clustered in two peaks with binding energies in the range of -5.3 to -7.1 kcal/mol .Moreover, ADMET profiling was performed to shortlist compounds with potential binding affinity against Prodigiosin synthetase. Consequently, compounds DB00865, DB00821, DB00369, DB00423, DB00775 and DB00693 were shortlisted as favorable drug candidates Serratia marcescens strains. It was observed that six proteins from different S. marcescens strains were aligned to the reference protein pigC protein. The proteins having sequence identity > 90% can be used for the pigC conservancy analysis, details of these proteins are provided in the Furthermore, the results of the conservancy analysis showed that the pigC protein from the WW4 strain was locally aligned with the pigC protein of multiple different S. marcescens is a Gram-negative bacillus and an opportunistic nosocomial pathogen causing infections of the urinary tract, blood, central nervous system and pneumonia [S. marcescens harbor an inducible AmpC beta-lactamase present on the chromosome, which confers resistance to numerous antibiotics intrinsically [S. marcescens outbreaks have been documented dating back to the 1950s [S. marcescens strains are multi-drug resistant therefore effective therapies such as novel medications and vaccines are needed.neumonia . S. marcnsically . ApproxiComputational Biology is an emerging field of biological sciences that aids data analysis. New predictions are currently being made successfully using data mining techniques. The post-genomic era produced several genomic annotations that could not have been analyzed solely by humans. Computational Biology prioritizes potential drug targets to treat deadly diseases within the realm of infectious diseases. One such methodology is subtractive genomics and metabolic pathway analysis. The metabolic pathway analysis approach for the identification of novel drug targets is among the most cited strategies found in the literature. Environmental stress may cause upregulation of certain genes and the activation of selective metabolic pathways in bacteria. Such conditions may cause resistant pathogens to evolve with unique metabolic pathways in their dataset as shown in . ConsequS. marcescens. Using this technique, the pathogen’s unique metabolic pathways were identified, and essential proteins were prioritized as potential drug targets. Following this strategy, pathway 00333 (Prodigiosin biosynthesis) was found unique to a single strain  as shown in .Therefore, in the current study, a comparative metabolic pathway analysis along with subtractive genomics was applied to identify unique proteins that may serve as potential drug targets against shown in . We propg target . FurtherS. marcescens and those belonging to the Streptomycetaceae and Pseudoalteromonadaceae families produce Prodiginines [S. marcescens and elevated Prodigiosin production [Various bacteria including iginines . The awaoduction .S. marcescens infection. For example, this bacterium may pose to our body’s natural flora as they are antimicrobial in nature. Knowledge of prodigiosin synthetase that synthesizes prodigiosin may help alleviate prevalence of infections by Serratia marcescens. A study has revealed that mutagenesis, particularly a 17 bp deletion in the phosphoenolpyruvate (PEP) domain of the protein resulted in the truncation and loss of function of the domain. As a result, the bacteria (i.e. S. marcescens) not only failed to synthesize but also failed to secrete Prodigiosin since the macrovesicle formation was inhibited [Thus, these studies concentrated on the benefits of this alkaloid for humans. However, it is also important to understand the threats associated with a nhibited . This shAdditionally, de novo modeling was used to model the structure of Prodigiosin synthetase as shown . The strSignificantly, the docking studies showed that ATP binds with the protein with a binding affinity of -4.64 kcal/mol, MBC with -6.04 kcal/mol and MAP with -4.83 kcal/mol. The same parameters that were used for the docking of these ligands were used for the virtual screening of ~1500 approved compounds library. About 854 compounds out of 1500 were found with binding energies in the range of -5.3 to -7.1kcal/mol. Compounds shortlisted for further analysis are highlighted as yellow bars in . A totalSerratia marcescens strains from different geographical locations. The predicted protein should be conserved among the bacterial strains of specific pathogen to ensure that it can serve as an effective drug or vaccine target across multiple strains [Serratia marcescens.The results of the conservancy analysis part of the study showed that Prodigiosin Synthetase (PigC) displayed high conservancy pattern with homologues of  strains . Hence, In vitro and in vivo studies are the suggested follow-up for future collaborative research among the scientific community.Nevertheless, the study has some limitations as all methods were carried out on a computational approach. Further complementary experimental studies are recommended to validate these findings. S. marcescens. Consequently, the protein Prodigiosin synthetase was proposed as a potential drug target. This protein is involved in a network with 198 other proteins, therefore targeting it may help eradicate the pathogen from the respective host. Moreover, using the molecular docking approach for the identified drug target, one can identify and select molecules such as DB00423 and DB00775 as the ones with the most favorable binding affinities with the target active site residues. This study may enable future researchers to produce efficacious drugs and vaccines against strain-specific S. marcescens.Pathogenic genome and proteome analysis helped in drug target identification. In this research, a subtractive genomic and metabolic pathway analysis technique was performed for the prediction of a non-homologous, essential, druggable protein against S1 Appendix(DOCX)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(A) The image shows the accession numbers of the functional domains while (B) image tabulates the names of the domains.(TIF)Click here for additional data file.S4 FigThe ligand binding pocket in protein predicted through DoGSite Scorer. Detailed description related to binding pocket and amino acids found in active are mentioned.(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 TableTable showing the detailed annotation of the direct interaction formed by the hub protein (PigC).(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S1 Graphical abstract(TIF)Click here for additional data file.


Proteins mediate their functions through chemical interactions; modeling these interactions, which are typically through sidechains, is an important need in protein design. However, constructing an all-atom generative model requires an appropriate scheme for managing the jointly continuous and discrete nature of proteins encoded in the structure and sequence. We describe an all-atom diffusion model of protein structure, Protpardelle, which instantiates a “superposition” over the possible sidechain states, and collapses it to conduct reverse diffusion for sample generation. When combined with sequence design methods, our model is able to co-design all-atom protein structure and sequence. Generated proteins are of good quality under the typical quality, diversity, and novelty metrics, and sidechains reproduce the chemical features and behavior of natural proteins. Finally, we explore the potential of our model conduct all-atom protein design and scaffold functional motifs in a backbone- and rotamer-free way. Therefore, the ability to design proteins for novel functions often involves some modeling of structure, implicitly or otherwise. In order to have the most control over the arrangement of structural elements, it is often beneficial to design proteins  scratch , 2.Within a protein structure, sidechains are the primary functional effectors and define both the intrinsic properties of the protein and the types of interactions a protein can make. While it is well known that the sequence (and therefore the set of sidechain) determines the global fold of the protein, the interactions of sidechains with other sidechains can also influence local packing and structure, as well as distal allosteric effects. Sidechains also mediate most unique protein functions, ranging from protein-protein interactions to enzyme catalysis and metal coordination. Their precise placement in designed proteins is essential for downstream function. Combined with the backbone atoms, the ability to model the full atomic protein structure is important for advancing protein design.de novo design methods rarely model the sidechains at all when specifying the sequence or structure, but instead sample the backbone structure and sequence separately, and then build the sidechains afterwards [However, modeling sidechains in protein design is difficult: to specify which sidechains need to be modeled, the sequence must be known, at which point the protein is already fully determined. Thus, current terwards –21. Someterwards , 22–24. terwards –29. Whilhttps://github.com/alexechu/protpardelle.Here we describe an all-atom protein generative model, Protpardelle, which co-designs the backbone, sequence, and sidechains of a protein together. Our method enables a key capacity in protein design: generating complete proteins with coherent sequences and all-atom structures. By co-designing the structure and sequence, the all-atom modeling approach allows the sequence to influence the backbone conformation through the sidechains, and vice versa. We accomplish this by developing a way to manage all of the sidechains at once during the generation process, which we dub “superposition” in reference to how quantum wavefunctions exist in multiple states before “collapsing” into a single state when observed. Model samples are of good quality, both in terms of consistency between structure and sequence as well as chemical fidelity of the sidechains. Preliminary exploration of design applications suggests that our model can be used to design new proteins in an all-atom context, as well as when conditioned on only the functional groups of protein sidechains. We also describe a performant backbone generative model as a special case of our model. Both models are computationally lightweight, which aids exploratory research; in service of this, we intend to make our code available at 22.1Diffusion or score-based generative models –33 have p0(x)) to a tractable distribution ) [To review the basic approach of diffusion-based generative models, we can define (1) forward and (2) reverse SDEs that connect an interesting distribution ) . The forg(t) is a diffusion coefficient, w is the standard Wiener process, and f is a drift term which is typically of the form f(t)x and describes a time-dependent scaling of the data. Different choices for the drift and diffusion coefficients give rise to the various variance-preserving and variance-exploding noise process formulations [x log p(x), we can produce solutions to the reverse SDE, allowing us to generate data from noise. This score model is typically a neural network trained with denoising score matching, which we call Dθ [Here ulations , 32. Giv call Dθ , 41.σ(t) = t so that the noise level increases at the same rate as time, or diffusion progress [In place of the reverse SDE, we can also choose to integrate the equivalent probability flow ODE, which recovers the same marginal distributions . One parprogress .σ in this direction  We will use x0 to denote a sample from p0(x), i.e. unnoised data, and xt to denote samples from the marginal distributions pt(x), i.e. noised data.The marginals associated with this ODE are ffective , 43. Intirection .  is straightforward, and we will also discuss our results with a backbone-only generative model. However, all-atom protein modeling presents an interesting challenge not only because of the dual continuous and discrete nature (structure and sequence) of proteins, but also because the discrete sequence directly defines which atoms are present in the 3D structure. For example, serine has five heavy atoms, N, CA, C, O, CB, and OG, whereas histidine has ten atoms, N, CA, C, O, CB, CG, CD2, ND1, CE1, and NE2. Five of these atoms  are common to both amino acids, but serine has an atom missing in histidine, and histidine has five atoms missing in serine. This creates a chicken-and-egg problem: for each position, we cannot know which sidechain atoms to build without knowing the amino acid identity, but if we know all the amino acid identities, then the protein is already specified. Nearly all current generative modeling paradigms utilize deep neural networks which work with fixed-size input and output, with length and shape differences between unique data points handled by masking. For all-atom protein generation where both the protein structure and its sequence are unknown at the beginning of sampling, the estimated sequence evolves with time and therefore the structure consists of different atoms at each time step. Practically speaking, this means that not only does the data change due to the noise process, but the mask itself also changes with each diffusion timestep. This remains the case whether sidechains are represented as sets of atoms or as sequences of chi angles. This makes it difficult to define a diffusion process which transforms data smoothly with time, if the data is disappearing and reappearing at each step.X0) and the current noisy state (Xt). At each denoising step, we collapse the Xt superposition to produce a single noisy protein structure xt which we can use to predict the denoised data x0 with the score network. This predicted x0 can be used to update our X0 estimate and to predict a new sequence. Then, the actual denoising step occurs: we collapse Xt and X0using the new sequence to get xt and x0, and then we integrate the ODE using these two quantities (see Algorithms 1 and 2 for the full pseudocode). We use notations xt and the denoising step input xt, respectively: note that the score network prediction and the denoising step are decoupled in our method. The output of the denoising step xt−1 at timestep t is exactly the t − 1; this arrangement is so that inputs to the network are always at the same noise level in the ODE solution trajectory.To address this challenge, we define our noising process to act on a “superposition” of protein structure states - that is, the protein backbone and the coordinates of each of the twenty possible sidechains at once. This is clearly an unrealistic model of protein structure, but allows us to handle the uncertainty associated with changes to the sequence in time. Given a sequence, we can “collapse” this superposition by selecting the sidechain states that correspond to this sequence to yield an all-atom protein structure. During structure generation, we maintain both an estimate of the fully denoised superposition state  predictor of protein sequence given structure can fill this role. For this we used the ProteinMPNN graph neural network architecture which has been shown to capture an efficient locality-based inductive bias and produces strong sequence design results when used to parameterize an autoregressive model . We adapitioning . With thIt is possible for the sequence to influence the structure in two ways. One is the intended behavior where the positions of sidechains in space induce changes to the backbone to accommodate these sidechains. The second is for the model to infer the sequence from the atom mask and memorize the structure given this sequence, or “sequence leakage”. This creates a distribution shift issue during sampling where if the sequence is not plausible, the network is asked to denoise unusual inputs and struggles to produce valid structures. We remedy this by obscuring the atom mask by noising all 37 unique atom position inputs instead of only the atoms corresponding to the sequence. Later in the diffusion process as the structure information becomes clearer, the sequence predictions also improve and the problem recedes.33.1We first sought to establish the feasibility of the general ODE and denoising scheme by training the model on protein backbones only, i.e. generating the N, CA, C, and O atoms. Initially, we used a convolutional U-Net as the denoiser network and found helical structures to be easy to generate, but beta sheets to be more difficult to properly pair, likely due to their non-local nature. We found that changing the neural network to a U-ViT, removing the convolutional up- and down-sampling layers, and training with self-conditioning , 49 all With a baseline structure generation model in place, we next explored the capacity of the miniMPNN model to co-design the sequence during the structure diffusion. Following similar training procedures as for the original model, we found that miniMPNN was adequate as a structure-conditioned sequence predictor, achieving ~38% sequence recovery on a validation set and a mean scRMSD of ~8 in 1–5 sampling steps. We found that the base performance of the model as well as the many design steps (100s) used for structure diffusion led to lower quality sequences. To resolve this issue, we replaced the sequence prediction at the final step with a prediction from the full pre-trained ProteinMPNN . This imWith a basic approach in place for structure and sequence co-design, to enable all-atom protein generation, we needed to also diffuse the sidechains. The simplicity of our training scheme made this very straightforward: since our forward diffusion process is identical for each atom, we simply increased the number of atoms per residue. The model was able to generalize to this extension easily. However, when examining more closely some of the early failure modes for sidechain generation, we noticed that most of the sidechains were generated to a reasonable degree of chemical fidelity, but a small percentage of sidechains showed some divergence during sampling, producing bond lengths that were wrong by several angstroms. We hypothesized this was because the sidechains that were infrequently selected during sampling had either too few denoising steps overall, or perhaps a single denoising step that was too large. We did not find a significant association between the largest denoising step and sidechain divergence, but we did note that chemically invalid sidechains seemed to be sampled less frequently on average. To resolve this issue, we annealed the sequence resampling rate with time to increase the rate at which likely sidechains were selected; this improved the overall quality of the generated sidechains and structures. Additionally, we found that running a second sampling stage conditioned on both the backbone and sequence  entirely removed any faulty sidechains. This second stage added very little sampling time since it did not involve running miniMPNN or ProteinMPNN. Together, these modifications and improvements allowed us to consistently generate high-quality all-atom protein structures.3.2We evaluated our model on three main properties which are relevant for generative models, all related to sampling: the quality (broadly defined), diversity, and novelty of model samples. Intuitively, we desire our model to generate “good” samples that exhibit structural diversity and can generalize beyond the training set. The plausibility (or designability) of sampled proteins is clearly important because we want our designs to fold successfully in solution, and can be evaluated using self-consistency metrics, which predict the structure of a designed sequence for a protein and assess the agreement between the predicted structure and sampled structure. The agreement is typically scored with either the RMSD metric or the TM-score metric calculated on the alpha-carbon atoms (referred to as scRMSD and scTM), and these metrics have been suggested to correlate with experimental success , 18, 21.The diversity of model samples is an important quality not only because we want to avoid artifacts of generative modeling , but also because we want to draw deeply on protein structural space to produce solutions for different design problems. Diversity can be examined by clustering samples and counting them or measuring the maximum TM-score between clusters, but can be a difficult property to assess in general since it is closely tied with the number of samples drawn. We chose to cluster our samples and count the number of clusters, following the previous approach of Yim et al. . We alsoThe backbone model, when combined with ProteinMPNN as the sequence design module, achieves strong performance on these properties. Our sampling-time tuning and optimization allows us to unconditionally generate proteins of length up to 300 with comparable designability to current methods, at a much lower computational cost –C. Our sWe assessed the all-atom model on the same metrics to evaluate its ability for unconditional protein generation. Sampling is fairly robust at lengths up to 150, with a success rate of ~60% on proteins in this range when assessed by scRMSD ,B. We arModel samples also exhibit diversity and novelty. When we draw more and more samples uniformly over the length range, we are able to continue to produce new structural clusters, although the ratio drops off at larger numbers of samples . SecondaThe previously discussed metrics can be computed solely on a structure (i.e. backbone) and the corresponding sequence. Since our model generates the atoms of the sidechains independently and does not enforce idealized bond geometries, we further evaluated the chemical quality of the samples. When compared to the ground truth training data, we find that samples from the model generally follow the same distribution with the same modes for bond lengths and bond angles, but with greater variance, as is often the case with free-atom generation methods ,B. We cofa_dun Rosetta energy term . Without relax, most residues score within a tolerable range for the fa_dun energy, and closely follow the distribution of (unrelaxed) natural protein structures . Since this protein is designed de novo by the Sculptor algorithm [To test our ability to design new protein-protein interactions, we explored scaffolding a protein-binding motif in an all-atom manner, providing as input the backbone, sidechains, and sequence of only the motif. For this, we selected a lgorithm  and and A method to generate protein structures in an all-atom manner also enables new ways to generate proteins conditioned on functional motifs. Most current machine learning-based methods rely on models to first infer backbone conformations that seem mutually probable with the binding targets, and then to design the sequence and model the sidechains. We wanted to explore whether it is possible to generate complete proteins conditioned directly on the chemical groups which mediate the interaction. A metal-binding interaction is primarily dependent on the ligand interactions formed by the polarizable groups on the surrounding sidechains. We extracted only these polarizable groups for a single metal from a natural diiron-binding protein, cytochrome b1 (PDB: 1BCF); these groups included the carboxylates on three glutamate residues and the imidazole ring from the histidine residue. With only the atoms of these specific groups as conditioning input to the model, the model was able to design a protein scaffold that hosts these groups, together with the remaining sidechain atoms and rotamers needed . Some deIn these cases, we are able to obtain solutions resembling plausible proteins and indicate that this method could be a promising new approach for protein design. However, fidelity to conditioning, sampling success rate, and overall quality of these conditional generations remains to be improved. While standard replacement guidance was sufficient for conditioning tasks with strong input information, such as loop inpainting, we found that for tasks such as motif scaffolding with much less input information, the model was prone to ignoring the conditioning during generation. For these tasks, reconstruction guidance was needed to improve the coherence of generated designs with the provided conditioning information . Many pr4Our results describe a number of contributions that we hope will advance the field of protein design. We have applied a new diffusion process and network architecture, both originally developed in computer vision, to protein structures in a way that allows for high-quality sampling of proteins. We describe a way to alter this process during sampling with a sidechain “superposition” so that a model trained only on standard protein structures is able to denoise sidechains for arbitrary sequences that any sequence design model might choose. This enables all-atom protein generation in a way where the model is able to reason about the sidechains jointly with the backbone, since both are noised and denoised together. Finally, we discuss potential extensions of this model to enable new ways to design proteins without dependence on modeling structural interactions implicitly through the backbone.We stress that while our model is capable of co-designing sequence and structure, it remains a structure-primary generative model that produces estimates of the sequence during its sampling trajectory. It does not define any noising process on the sequence; nor is it a joint model in the sense that we are able to marginalize and condition in some way to produce solutions to the sub-tasks of structure and sequence generation and forward and inverse folding. However, structure-primary approaches have shown ever increasing capabilities to generate proteins with novel functions . We hopeWhile we have primarily explored applications for generating new proteins and designing proteins conditioned on explicit sidechain interactions, we note that our model automatically incorporates new ways of solving old protein design problems. In particular, we note that rotamer packing is an easy and efficient special conditioning case of this model. This capacity is demonstrated briefly in the second-stage design of our sampling process, where the sequence and backbone are fixed but the sidechains are re-sampled. We believe this to also be an interesting direction for future methods development.Supplement 1


The 2023 Albert Lasker Basic Medical Research Award has been given to Demis Hassabis and John Jumper of DeepMind for the invention of AlphaFold, the artificial intelligence (AI) system that solved the long-standing challenge of predicting the three-dimensional (3D) structure of proteins from the one-dimensional (1D) sequence of their amino acids.The expression “I alphafolded it” is now something I hear almost every day, whether in the lab, during a thesis defense, or at a scientific conference. This transformation of a noun into a verb, akin to the phrase, “I googled it,” mirrors the revolution that has occurred in biological sciences over the past two and a half years. This change began with the announcement on November 30, 2020 , of the Knowledge of a protein’s atomic level structure is essential for a meaningful understanding of how it functions. Since virtually every biological process involves proteins, this knowledge is critical. These biomolecules can be enzymes, antibodies, or motors found in our muscles, participating in signaling, sensing, motility, immune response, translation, compartmentalization, and regulation. However, the determination of even a single protein structure can be highly laborious. The AI-based breakthrough of providing millions of structures with an accuracy likely comparable to that of experimental methods  greatly Not surprisingly, considerable effort has been put into developing prediction methods for protein structure. To this end, John Moult and Krzysztof Fidelis founded the Critical Assessment of Structure Prediction (CASP) in 1994 . CASP’s Results (once corrected for a larger database) largely plateaued by the 11th round of CASP in 2014 (referred to as “CASP11”) . EarlierMeanwhile, Dr. Hassabis, a renowned polymath with expertise in computer science and neuroscience, as well as game design (being an expert game player himself), cofounded DeepMind in 2011. DeepMind developed AlphaGo, an AI program that beat the world Go champion in 2016, an achievement that is considered one of the major successes of AI  method and a new machine learning approach for simultaneously training all the energy function parameters . In the Dr. Jumper was part of the DeepMind team that participated in CASP13 in 2018. The team, along with other groups, applied AI methods that were becoming increasingly powerful . DeepMinTo achieve this success, they developed a whole new AI architecture, recognizing that they were predicting protein structures going from a 1D sequence to a 3D physical structure , 15. TheOther factors contributed to AlphaFold’s success. A one-by-one deletion of the various elements in the prediction pipeline produced only a slight decrease in the performance . SurprisWhile the physical principles of protein structure are included throughout AlphaFold, there are instances where biophysics was sacrificed for expediency , 15. FunVery commendably, DeepMind made the AlphaFold code publicly available at the time of the original 2021 publication . This haAlphaFold’s influence goes well beyond the prediction of the structure of individual proteins. A potentially more important area is the prediction of protein-protein interactions. Although not designed for this task, AlphaFold was adapted to docking  by simplAlphaFold also works on membrane proteins , proteinWhile the structure prediction challenge is effectively solved, many aspects of protein folding, such as dynamics and thermodynamics, remain active areas of research . It willFollowing the July 2021 online publication of AlphaFold, I sent my colleagues an email with the subject line “Revolution in structural biology” and text “…I believe the impact of this work may rival that of genomics and sequencing as the tools start to be applied to protein-protein interactions, membrane protein structure prediction, protein design, and drug discovery… We shall see.” Two years later, I can confidently say that we have seen, and it is clear: AlphaFold has transformed biological and biomedical research for the better.


Characterizing thestructural dynamics of proteins with heterogeneousconformational landscapes is crucial to understanding complex biomolecularprocesses. To this end, dimensionality reduction algorithms are usedto produce low-dimensional embeddings of the high-dimensional conformationalphase space. However, identifying a compact and informative set ofinput features for the embedding remains an ongoing challenge. Here,we propose to harness the power of Residue Interaction Networks (RINs)and their centrality measures, established tools to provide a graphtheoretical view on molecular structure. Specifically, we combinethe closeness centrality, which captures global features of the proteinconformation at residue-wise resolution, with EncoderMap, a hybridneural-network autoencoder/multidimensional-scaling like dimensionalityreduction algorithm. We find that the resulting low-dimensional embeddingis a meaningful visualization of the residue interaction landscapethat resolves structural details of the protein behavior while retainingglobal interpretability. This feature-based graph embedding of temporalprotein graphs makes it possible to apply the general descriptivepower of RIN formalisms to the analysis of protein simulations ofcomplex processes such as protein folding and multidomain interactionsrequiring no protein-specific input. We demonstrate this on simulationsof the fast folding protein Trp-Cage and the multidomain signalingprotein FAT10. Due to its generality and modularity, the presentedapproach can easily be transferred to other protein systems. This is confounded by the fact that theconformational ensemble of a protein often comprises highly nonlinear,concerted dynamics on multiple scales. Large, global conformationalmotions of entire domains can be intertwined with subtle, local fluctuationsin flexible regions of the protein. At both ends of that range, andin-between, these dynamics can critically determine the function ofthe protein.3 Finding a meaningful andinterpretable representation of heterogeneous conformational ensemblesat different scales is a crucial, yet challenging step in extractinginsights from protein MD data sets.Molecular dynamics (MD) simulations offer a powerful tool for studyingincreasingly complex biomolecular systems at a high temporal and spatialresolution as they evolve under the forces from classical mechanics.Advances in computational power and methods make it possible to produceever-longer simulations of ever-larger systems.Rg) or the fraction of native contacts.4 Used as coordinates of a map, such collective variables (CVs) canprovide a global view of protein dynamics, omitting details on a localscale, or, if specific atom distances or dihedral angles are selectedas CVs, a bespoke, narrow view of local behavior can be obtained.4 If, on the other hand, multiple conformationalprocesses contribute to the conformational ensemble, then it is oftenuseful to take some high-dimensional numerical description of theprotein structure and obtain a low-dimensional map or embedding ofthat description with the help of dimensionality reduction algorithms.6 One typically does not directly use the full set of Cartesian coordinatesof the trajectory as the high-dimensional input descriptors. Instead,each protein conformation is translated into a feature vector in aprocess called featurization. The featurization step crucially determineswhich aspects of the underlying protein dynamics are reflected inthe embedding and which are discarded.6 For example, one can focus on the local dynamics of the proteinbackbone by using its backbone dihedral angles as a feature set, discardinginformation on the side chain dynamics and global behaviors. Usingpairwise distances metrics between amino acid side chains, such ascontact matrices, one can retain more comprehensive details, yet theresulting data sets can become prohibitively large and often containredundant information, which can necessitate the selection of a subsetof pairwise distances one considers representative of relevant dynamics.7 The intermediate step of featurization thus offersthe opportunity to adjust the scope of the analysis and put a focuson specific protein behaviors.8 However,this often means that it needs to be tailored to the protein systemwith system-specific additional input. This could be a native structure,for methods based on native contacts,9 orprior knowledge on the system dynamics for hand-selecting specificfeatures of interest. For an embedding algorithm for complex proteindynamics that does not require protein-specific input or tailoring,we need a general protein description that captures the relevant conformationalbehavior of different protein systems in a compact and meaningfulway.6For the visualization anddownstream analysis of complex proteinensembles, low-dimensional maps of the protein’s conformationallandscape are often indispensable. One can obtain such maps by hand-selectingspecific scalar descriptors derived from the protein structure, forexample, the root-mean-square deviation (RMSD) from a given referenceconformation, the radius of gyration  contain nodes and edges, where the nodes represent the aminoacid residues of the protein, and the connecting edges represent interactionsor contacts between the residues. This focus on residue contacts providesa mesoscopic perspective on protein dynamics, connecting behaviorson a local and global scale.18 The maps resulting from a low-dimensionalembedding of the nodes’ closeness centralities resolve meaningfuldetails of the protein behaviors while retaining global structureand interpretability. The method can be applied to different proteinsystems without protein-specific input. We demonstrate this on thesmall and fast folding protein Trp-Cage23 and on the multidomain signaling protein FAT10.24In the present work, we want to combine the expressivenessandgenerality of the RIN formalism with the interpretability and dynamicresolution of a landscape embedding. The goal is to obtain an interpretable,temporal graph embedding for RINs from full-length MD trajectoriesof complex protein systems. By arranging the conformations of theprotein in a two-dimensional map based on their residue interactions,we obtain a visualization of the protein’s “residueinteraction landscape”. To extract a feature set from the RINsthat can be used for the embedding, network theory opens up a straightforwardavenue: Node centralities provide a mathematically rigorous formalismto capture the role of amino acid residues in protein structure andfunction.22.116 Here, we areusing a simple geometric criterion based on the spatial distance betweenresidues. Two residues are connected by an edge if the minimum distancebetween any two atoms in the residues is below a cutoff of 6 Å,with the exception of interactions between directly neighboring residuesin the protein sequence. This produces a discretized protein contactmap, which is then translated into an undirected and unweighted RINgraph describing the residue interactions of an individual, staticprotein structure. From the RINs, we can calculate the node centralityof each residue based on various centrality metrics from network theory.30 Here, we propose to use the closeness centrality as a “natural”centrality metric,31 which describes howclose or distant, in the network, each residue is to all other residues.In physical terms, this means that a residue’s closeness centralityis connected with that residue’s “aptitude to participatein signal transmission” throughout the RIN.15 It is calculated as the reciprocal mean length of the shortestpaths from that node to all other nodes in the RIN according to vi is the node of interest and vi to any other node vj.31N is the total number of nodes/residues in the network,used here for normalization. It has been shown that the closenesscentrality is an important network topological metric for predicting“functionally important residues”,29 such as protein–protein interaction sites,27 catalytic sites,28 and binding sites.30 Here, rather than focusing onthe role of individual residues, the N closenesscentralities, in the order of the protein’s amino acid sequence,are used as an N-dimensional fingerprint describingthe connectivity of the graph representing the RIN. Because the closenesscentrality for each residue takes into account network paths acrossthe entire RIN, it integrates global information on the conformationof the protein and local information on the residue’s neighborhood.32 With N dimensions, it is still too high-dimensionalto visualize. To project the high-dimensional feature vector intoa low-dimensional map, we employ the dimensionality reduction algorithmEncoderMap, which couples a neural network autoencoder with a multidimensional-scaling-likeembedding based on pairwise distances between data points. This feature-basedgraph embedding uses the Euclidean distances in the high-dimensionalmetric space of the closeness fingerprints to place protein structuresthat are close in this feature space close to each other in the low-dimensionalmap. In addition, the distances are transformed using a sigmoid functionin order to suppress the impact of small distances between very similarpoints and of very large distances that would be hard to reproduceaccurately, a transformation first applied for multidimensional scalingsuch as the Sketch-map algorithm33 andadapted for EncoderMap. The overview of the workflow , a 3–10 helix, and an C-terminalproline helix, which cage a hydrophobic tryptophan residue (TRP6)in the center.42 Coloring the maps using these observables shows that they captureimportant, known features of this process 43 The transition region betweenthe far ends of the map resolves the folding transitions from fullyunfolded (low-contact) conformations to higher-contact conformationswith an increasing similarity to the folded state. This is reflectedin the smooth gradient of the radius of gyration (Rg) , which bear similarity to previously described foldingintermediates.41 Coloration according to the helix-RMSD from the N-terminal αhelix (residues 2–8) in 44 The nucleation-condensation pathway 3c and thion (Rg) 3d along ion (Rg) 3e I1, I2 pathway3eA, in w pathway3eB, the We have shown that themap of the residue interaction landscapeof Trp-Cage recovers meaningful descriptors of Trp-Cage’s conformationalbehavior, resolving relevant details of its folding. Together withits continuous global structure, this makes the map a meaningful andinterpretable visualization of the conformational behavior of thissmall, fast-folding protein. Since the approach was applied withoutspecific prior information about the protein or its folding characteristics,it should in principle be generalizable. This makes it particularlyinteresting for complex protein systems that do not have a known nativereference conformation or little available structural information.An example of such a case are proteins that adopt a dynamic conformationalensemble with multiple metastable substates and changing intramolecularinteractions. This could be because their function depends on flexible,disordered regions in their structure or because they have multipledomains whose interactions change dynamically.2.346 Both the N-terminus and the C-terminus haveflexible tails. FAT10 gets isopeptide-linked via its C-terminal diglycinemotif to its substrate proteins and labels them for degradation bythe 26S proteasome.24 Therefore, it playsa role in cellular signaling and proteostasis and is associated withnumerous types of cancer47 and Parkinson’sdisease.48 The conformational ensembleof FAT10 crucially determines its function, i.e., interactions withsubstrates and the proteasome, and has thus far not been characterizedin detail. This includes the interactions between the two domainsand the dynamics of the flexible regions of FAT10: the N-terminaltail, the C-terminal tail and the linker.24 Here, we analyzed 50 × 50 ns of atomistic simulationsof FAT10, corresponding to 250, 050 simulation frames. Eachof the 50 simulation trajectories was started in an “open”conformation, in which the two domains were separated and had no nonconvalentcontacts with each other. In the course of the simulations, the twodomains would commonly collapse onto each other, forming “closed”conformations with different noncovalent contacts at the interfacebetween them. A closed structure of FAT10 is shown in Rg over the timeseries of the closeness fingerprint. In the Supporting Information, we further illustrate howthe closeness fingerprint integrates information on the global proteinconformation and the local residue environment.A protein that exhibits both functionallyrelevant flexible regions and changing domain–domain interactionsis the ubiquitin-like modifier FAT10 (human leukocyte antigen (HLA)-Fadjacent transcript 10). FAT10 is a 165-residue signaling proteinthat consists of two ubiquitin-like domains joined by a flexible linker.Rg, 1–C4) with the red section of the N-terminaldomain, blue means many contacts with the blue section, gray meanslow contact count overall or equal contacts with red and blue section.At an even finer level, the map resolves the dynamic behavior of theflexible regions of FAT10. As an example of this, two closed statesof FAT10 are shown in f, the C-terminal tailmoves freely, exposed to the solvent, while in the other state (Tb), the C-terminal tail is in close contact with the rest ofFAT10, shielded from solvent exposure and forming a part of the domaininterface. Whether FAT10 is open or closed, how its domains interactand how its flexible regions behave are crucial determinants of thefunction of FAT10. FAT10 has a plethora of protein–proteininteractions49 and different closed conformationsoffer different interaction sites for binding proteins. The N-terminaland C-terminal tails are essential for binding to the proteasome andthe substrate, respectively. Deletion mutants for the flexible linkerdisplay complete loss of activity.46 Amap that resolves these conformational dynamics of this protein whileretaining an interpretable global structure is a useful tool for understandingthe high-dimensional MD data on this complex protein system.To visualize the conformational dynamics of FAT10 over all 50 simulationtrajectories, the 165-dimensional closeness vector of all simulationframes was projected into a two-dimensional map of the residue interactionlandscape, colored by different structural properties see 5. Colori3Describing proteins as graphs is a naturaland powerful way tocapture their three-dimensional structure. By projecting the graphsfrom a protein MD trajectory into a low-dimensional embedding, weobtain a map of the protein’s residue interaction landscape,providing a mesoscopic view of its conformational ensemble. Here,we have shown that the closeness fingerprint offers a compact andexpressive feature set to perform the graph embedding with the dimensionalityreduction algorithm EncoderMap. The resulting workflow for a feature-based,temporal graph embedding for RINs can be generalized to differentprotein systems without system-specific prior input. We have shownthis for two different protein systems with different sizes and differentconformational behaviors: the 20-residue fast-folding protein Trp-Cageand the 165-residue multidomain protein FAT10.16 making it possible to adjustthe information content of the description, for example, by includingdetails on the interaction types50 or energies17 or by describing the protein at higher (atomistic)51 or lower (coarse-grained)20 resolution. It is important that the formalism for the protein-graphtranslation is chosen appropriately for the system under study andthe underlying scientific question.52 Transformingthe Cartesian coordinates of the protein into a relational data setof its interactions is bound to reduce the information content inthe resulting description. For example, local details in the dynamicsof otherwise rigid, globular proteins may get lost in the transformation.This means that the presented workflow is most suited for situationswhere protein systems undergo strong changes in their residue interactions.In principle,any protein can be translated into a graph, makingthe presented workflow easily generalizable to different proteins.Due to its modularity, it can incorporate any type of protein graphthat allows for the calculation of node centralities. There are manydifferent tools and formalisms to translate proteins into graphs,54 In many settings, theseapproaches have to accommodate graphs of different sizes and unknownnode identities, for example, in representation learning for smallmolecules.57 In contrast, our specific task of embedding temporalgraphs from protein MD trajectories is more focused, making it possibleto rely on a deterministic graph representation and forgoing a trainingor message passing process. The graphs from the RINs have a fixedsize, a known node identity and a known node ordering naturally definedby the protein sequence. This means we can take advantage of usingnode-specific features as a graph representation for further downstreamprocessing.Due to the noneuclidean nature of graph data structures, applyingmachine learning algorithms on graphs is challenging. Recently, therehas been a surge of various deep learning approaches, in particulargraph neural networks, being investigated to learn suitable representationsof graph structured data.58 Its clear mathematicaldefinition (requiring no training) and close correspondence to distance-basedfeaturizations in Cartesian space  make it a robust protein descriptionwith a high ante-hoc interpretability.59 This robustness and interpretability set thecloseness fingerprint apart from other RIN representations using graphneural networks or other centrality metrics.60 Falling between local feature sets like backbone dihedral anglesand global metrics like the fraction of native contacts, the closenessfingerprint from protein RINs offers a mesoscopic perspective on proteinconformation. For illustration, embeddings for FAT10 using a numberof local feature sets are included in the Supporting Information . Because the closeness centralityfor each residue takes into account the paths from that residue toall other residues, it connects local information on the neighborhoodof the individual residues with global information on the contacttopology of the entire protein. With this, the closeness fingerprintextracts a compact, N-dimensional feature set fromthe full residue–residue contact map.32The closeness fingerprint used here as the nodefeature set hasseveral properties that make it suitable for the use as an RIN graphrepresentation and in our view also as a general feature set for proteins:Like other featurizations of protein conformations, the closenessfingerprint is a set of internal features, invariant to rotation andtranslation. It is a simple metric that is implemented in variousnetwork analysis tools.32 This wouldbe different using a global embedding like principal component analysisor more local algorithms like UMAP61 . The fact that this mesoscopiclandscape embedding retains a global structure and is constructedwith a focus on residue interactions means that we can qualitativelyinterpret the dimensions of the map and extract information aboutthe relationships between structures from different map regions. Thiscan come at the cost of losing fine structural resolution and discrete,local clustering. For example, different low-contact structures that may be separated, e.g., in dihedral space, but donot differ with respect to their residue interactions, will not beseparated in the map of the residue interaction landscape. They willbe tightly grouped in a region that can be recognized as a low-contactregion. This means that one needs to be cautious when applying clusteringalgorithms directly to the low-dimensional embedding.We retainthe mesoscopic character of the closeness fingerprintby embedding it with EncoderMap. Combining a neural network autoencoderwith a tunable Euclidean distance cost function, it offers a nonlinear,MDS-like behavior with the ability to handle large amounts of data.This allows us to set a focus on intermediate distances and to usethe closeness fingerprint for a mesoscopic graph distance metric asproposed by Donnat and Holmes.62 one could build a tool toseparate, aggregate, and select protein conformations from a heterogeneousensemble for further, more detailed downstream analyses based in graphtheory. This could include investigations of allostery,64 community detection algorithms65 or graph spectral methods.66 It could also augment kinetic models, such as finely resolvedMarkov state models,67 with a visualizationmethod. Because the graph formalism is such a flexible protein representation,the could also be used in scale-bridging applications to improve samplingbased on the low-dimensional embedding.68 Since the presented method is purely based on graph topology, itcould also be suited to visualize the interaction dynamics of othercomplex dynamic systems that can be described by networks.70Ratherthan resolving the fine, local details in protein structures,the strength of the presented approach is to provide a coherent, interpretablevisualization of a protein’s residue interaction landscape.By combining it with an algorithm dedicated to finely resolved structure-basedclustering,44.14.1.1The 208 μslong folding simulation of Trp-Cage (K8A mutant of the thermostableTrp-Cage TC10b) as reported in ref  has been4.1.246 50 simulations were performed for 50 nseach, with 25 different starting conformations of FAT10 and 2 differention concentrations . The starting structures of FAT10 were constructedfrom the two separate, experimentally solved protein data bank (PDB)structures of the two domains.46 ChainB from the crystal structure 6GF1 was used for the N-terminal domain (ND), and chain1 from the NMR (nuclear magnetic resonance) structure bundle 6GF2was used for the C-terminal domain (CD). Both were mutated to restoretheir wild-type sequence. The two domains were subsequently joinedin a maximally extended conformation. In the resulting fully extendedmolecule, the ψ angle in residue D85 was rotated 360° in25 steps to produce the 25 starting structures. All MD simulationsfor FAT10 were carried out using the GROMACS simulation package (version2018.5)71 with the GROMOS96 54/A7 forcefield72 and the SPC/E water model.73 The temperature was kept at 300 K witha velocity rescaling thermostat74 and thepressure at 1 bar with the Parinello-Rahman barostat.75 The default md leap-frog integrator was used with a timestep of 2 fs and a cutoff for van der Waals interactions of1.4 nm. Bonds were constrained using the LINCS algorithm76 and electrostatic interactions were treatedwith the particle mesh Ewald scheme77 anda cutoff of 1.4 nm. Protonation states according to a pH of7 were used. The starting structures were placed into a dodecahedralsimulation box  with periodic boundaryconditions. An energy minimization was performed in vacuum for 100 psbefore solvation and the addition of ions. Six Cl– ions were added for all simulations for neutralization. An additional119 Na+ and Cl– ions were added for 25of the 50 simulations to achieve an approximately physiological NaClconcentration of 150 mmol. After this, another energy minimizationwas done with position restraints for 100 ps. Here, for thesimulations with additional NaCl, a time step of 1 fs was chosenfor the energy minimization steps. Equilibration was done in threesimulation runs of 100 ps, first under constant temperature(NVT) with position restraints, then under constant pressure (NpT)with position restraints and again under constant pressure (NpT) withoutposition restraints. The potential energy and temperature during thefinal equilibration runs were checked to ensure stability. Productionsimulation runs were performed for 50 ns each, totaling 2.5 μsof simulation time. After reducing the number of frames of each resultingtrajectory to 5001 ,the protein was centered, made whole and checked for secondary structureinstabilities using functions of the GROMACS suite. The resultingsimulation data set consisted of 50 trajectories with a total of 250,050simulation frames. It should be noted that the presented data doesnot (yet) represent a fully sampled, atomistic phase space of FAT10.It does offer a wide range of interactions between two loosely connectedfolded protein domains and additional intrinsically disordered proteinregions, which is why we chose the system and this data set as anexample.Human leukocyte antigen (HLA)-Fadjacent transcript 10 (FAT10) was simulated atomistically in fulllength and with a procedure similar to that in Aichem et al.4.2G bea graph of the protein residue interaction network. The graph consistsof a set of nodes (vertices) V with size N = |V|, the number of residues in theprotein, and a set of edges (interactions) E of size M = |E|. Each node vi in V represents one aminoacid in the protein sequence, and each edge ei,j in E represents an interaction between two amino acids. The closenesscentrality ci of eachnode vi is then givenby the reciprocal of the mean shortest path length d from that node to all other nodes vj≠i, normalized by the number of nodes N:d is an integer count of thelowest number of edges separating vi and vj. Here,a simple geometric criterion is used to determine the residue interaction:Two nodes are connected by an edge, if the smallest distance betweenany atoms of the two corresponding amino acids 78 is below acutoff of 6.0 Å, with the exception of interactions betweendirectly neighboring residues in the protein sequence. We have foundthe approach to be fairly robust toward the choice of the cutoff,when the graphs stay connected. Nonetheless, the method of RIN constructionis a free parameter in the presented approach and should be chosenwith care.52 The graphs were constructedusing the python package NetworkX.35 Thecloseness centrality was calculated using the python package NetworKit.58 The N-dimensional vector ofthe N closeness centralities ci, ordered by the amino acid sequence of theprotein, then forms the closeness fingerprint describing each proteinconformation from the simulation trajectory. It was transformed tobe in a range of 0–1 by dividing by its maximum over all simulationframes. The fact that the RINs of proteins studied here have a relativelylow number of nodes and the high-performance implementation of thecloseness centrality algorithm in NetworKit means that this fairlydemanding calculation only takes on the order of a few minutes ona desktop computer with an Intel Core i7-8700K 12 core 3.70 GHzCPU for the large data sets investigated here. For very large proteins,where scaling might become an issue, NetworKit also offers an approximatecloseness centrality algorithm with better scaling behavior. We wantto note that other path-based centrality metrics such as betweennesscentrality or eigenvector centrality60 could providea suitable picture of protein behavior, when projected into a low-dimensionalembedding, likely highlighting other aspects of a protein’sdynamics.Let 4.3N-dimensional closeness fingerprint was used as input forthe nonlinear dimensionality reduction algorithm EncoderMap.80 EncoderMap combines a neural network autoencoder with a sigmoidcost function to produce a meaningful low-dimensional representationof a high-dimensional feature space. It consists of an encoder partwith an input layer and several hidden layers, a bottleneck layerwith few neurons, and a decoder part with several more hidden layersand an output layer. It is trained to accurately reproduce the informationfrom the input layer in the output layer. Because it has to pass theinformation from the input through the bottleneck layer before decodingit into the output, it learns to encode it into a low-dimensionalrepresentation of the inputs. This learned mapping or code can thenbe used to project the points from high-dimensional space into thelow-dimensional map. In addition to the cost function of the autoencoder Cauto, which reflects the differencebetween the input and the output, there is an additional pairwisedistance cost function Csketch, which forces the autoencoder to arrange the points in thelow-dimensional map so that the arrangement of points in low-dimensionalspace reflects the distances between the points in high-dimensionalspace:m is the numberofprojected frames and Rij and rij are the pairdistances between the frames in the high- and low-dimensional space,respectively. Using the sigmoid function:a and b. It was found that the qualitative results of several EncoderMapruns with different parameters were fairly robust toward parameterselection. The parameters  changes in a graph topology,32 which is why they are applied here. Should adifferent focus be desired, the closeness fingerprint can in principleserve as input for other embedding algorithms. We have included embeddingsfor Trp-Cage and FAT10 obtained by the more global principal componentanalysis and the more local UMAP algorithm in the Supporting Information.The shown in 1 were th4.485 and pandas.86 Structural analyses, such calculations of RMSD, Rg and distances were performedusing MDTraj.78 The contacts between FAT10sdomains were calculated by separating the CD into two sections (redand blue) at residue 124 and summing up the number of contacts, determinedby the above distance criterion, of the ND with the respective sectionsof the CD. The two contact counts were combined by counting the contactswith the blue section negatively. Data visualization was performedusing Matplotlib,87 plotly and plotly dash.Visualization of molecular structures was performed using ChimeraX.89Alldata analysis tasks were performed using Python 3. General computationswere performed using NumPy


Molecular-level classification of protein–protein interfaces can greatly assist in functional characterization and rational drug design. The most accurate protein interface predictions rely on finding homologous proteins with known interfaces since most interfaces are conserved within the same protein family. The accuracy of these template-based prediction approaches depends on the correct choice of suitable templates. Choosing the right templates in the immunoglobulin superfamily (IgSF) is challenging because its members share low sequence identity and display a wide range of alternative binding sites despite structural homology.F-score of 0.64 and 0.78, respectively. We also provide a way to assess the confidence of the results. The average and median F-scores increase to 0.8 and 0.81, respectively, if 27% of low confidence cases and 17% of medium confidence cases are removed. Lastly, we provide residue level interface predictions, protein complexes, and confidence measurements for singletons in the IgSF.We present a new approach to predict protein interfaces. First, template-specific, informative evolutionary profiles are established using a mutual information-based approach. Next, based on the similarity of residue level conservation scores derived from the evolutionary profiles, a query protein is hierarchically clustered with all available template proteins in its superfamily with known interface definitions. Once clustered, a subset of the most closely related templates is selected, and an interface prediction is made. These initial interface predictions are subsequently refined by extensive docking. This method was benchmarked on 51 IgSF proteins and can predict nontrivial interfaces of IgSF proteins with an average and median https://gitlab.com/fiserlab.org/interdct_with_refinement.Source code is freely available at:  Protein–protein interactions are the primary facilitators of most cellular functions, including but not limited to signal transduction, molecular transport, and immune function . CharactF-scores between 0.38 and 0.41, while ISPIP generated an average F-score of 0.52 when using XGBoost   program is then applied with default parameters to determine the binding interface residues of each complex . ConservLER 10.2  assigninLER 10.2 , are assrotein i . To quanrotein i  are summrotein i  resultinrotein i . Next, arotein i . Each seLER 10.2  and all LER 10.2  and haveobe size , composetemplate . For theobe size .F-score which punishes missing true positive residues more than having extra false positive residues.InterDCT predictions are generally slightly larger (average interface size = 17.9 residues) than cognate IgSF interfaces (average interface size = 15.8 residues). To home in on a more accurate interface prediction that precisely accommodates a single ligand, we proceed to dock our query with 13 different, unrelated protein probes . Six of The program INTERCAAT was used with default settings to determine protein interfaces . INTERCAAT employs Voronoi tessellation to identify atoms that share a hyperplane and whose distance is less than the sum of each atom’s Van der Waals radii plus the diameter of a solvent molecule. Interacting atoms are then classified and interactions are filtered based on compatibility.F-scores were calculated using the standard F1-score formula: F-scores were used to evaluate interface predictions by comparing our predictions with the templates actual interface definition according to INTERCAAT   (F-scores of 0.73 versus 0.64) since our method is unable to optimally cluster and select the best templates for about 1/3 of the nontrivial proteins. To address some of the failures in our method, we sought to distinguish interface predictions by establishing a confidence measure.To establish a baseline for our approach, we calculated naïve interface predictions for each nontrivial protein using a single template with the highest sequence identity to the query. The average and median dictions . After rs (0.79)  and the s (0.78)  perform Due to the limited number of templates available and the heterogeneity of the IgSF superfamily, it is possible that no suitable template(s) currently exists that can accurately predict the interfaces for certain queries. It is also possible that our method may sometimes fail to cluster a query to the correct subset of templates. As such, we created a confidence measurement that can assign interDCT predictions with either a high, medium, or low confidence. The concept behind the confidence measure is that templates that are clustered with the query are expected to be informative about each other as well. To gauge this, we can measure how consistently the templates predict each other’s interface. A high success rate of predicting interfaces among the templates would suggest that these selected templates are more consistent and more likely that they will deliver a good prediction to the query as well. Besides overall average success rate we also monitor the standard deviation of these predictions to account for outliers among templates.F-score. We then average and take the standard deviations of all interface prediction F-scores over all clustered templates. This is repeated 10 times selecting 10 alternatively clustered interDCT templates. The resulting average of F-scores and average of standard deviations are separately averaged, which we will define as template reliability and template inconsistency, respectively. If template reliability >0.525 and template inconsistency <0.175, the interDCT prediction is deemed to have high confidence. If template reliability is either <0.525 or template inconsistency >0.175, the interDCT prediction is deemed to have medium confidence. If template reliability <0.525 and template inconsistency >0.175, the interDCT prediction is deemed to have low confidence  and all low confidence (28%) cases are removed  . This go of 0.78 . After e to 0.81 .ab initio interface prediction method; however, these results reinforce the fact that template-based interface prediction methods are far superior when it is possible to use them.In addition to outperforming meta methods for interface prediction, such as the recently published ISPIP method  and PSIVi onto protein j is not equivalent to mapping the interface residues of protein j onto protein i. For this reason, the rank matrix is not symmetric. By comparing the rows of the matrix, some proteins can immediately be seen as being more related than others. For example, template 1, template 2, and the query can easily be recognized as being related due to the rank similarities between corresponding rows  pioneered the concept by utilizing NMR-derived restraints to concentrate docking around relevant regions of a protein, effectively reducing the docking sampling space (https://gitlab.com/fiserlab.org/interdct_with_refinement in the bestComplexes directory).As a practical result, we provide interface predictions, along with confidence measures, for 78 of the extracellular IgSF singletons identified by the Protein Interactions Calibrated hidden Markov model Tree (PICTree) method . Singletbtad510_Supplementary_DataClick here for additional data file.


The biological functions of proteins are determined by the chemical and geometric properties of their surfaces. Recently, with the booming progress of deep learning, a series of learning-based surface descriptors have been proposed and achieved inspirational performance in many tasks such as protein design, protein–protein interaction prediction, etc. However, they are still limited by the problem of label scarcity, since the labels are typically obtained through wet experiments. Inspired by the great success of self-supervised learning in natural language processing and computer vision, we introduce ProteinMAE, a self-supervised framework specifically designed for protein surface representation to mitigate label scarcity. Specifically, we propose an efficient network and utilize a large number of accessible unlabeled protein data to pretrain it by self-supervised learning. Then we use the pretrained weights as initialization and fine-tune the network on downstream tasks. To demonstrate the effectiveness of our method, we conduct experiments on three different downstream tasks including binding site identification in protein surface, ligand-binding protein pocket classification, and protein–protein interaction prediction. The extensive experiments show that our method not only successfully improves the network’s performance on all downstream tasks, but also achieves competitive performance with state-of-the-art methods. Moreover, our proposed network also exhibits significant advantages in terms of computational cost, which only requires less than a tenth of memory cost of previous methods.https://github.com/phdymz/ProteinMAE. Protein surface is a high-level representation of protein, which encodes the external structures involved in interaction with molecules and is highly correlated with the properties of the protein. The representation of protein surface is widely used in applications such as protein–protein interactions prediction , a serieHowever, these learning-based methods are highly reliant on training data. Insufficient training data tend to cause overfitting, resulting in limited performance. In the case of protein representation learning, the acquisition of labeled data such as protein properties is challenging and expensive, because the labels are often obtained through wet experiments. Although the labeled data are scarce, unlabeled data such as raw protein structures  , which are also the center points of g patches. Then, we find i-th patch in the point cloud, m is empirically set to 60%. In the subsequent experiments (details in Section 4.5), we will also discuss in detail the effects of different mask ratios on downstream task performance. During fine-tuning and inference, we will no longer mask any patches.As shown in As shown in d-dimension token embeddings i-th patch.Each unmasked patch then gathers the concatenated features of all points within the patch and uses a simple MLP and pooling layer to convert the patch into a series of Since the relative spatial position between protein structures has a significant impact on protein properties, position encoding is added to the unmasked tokens to introduce relative spatial position information. We use a learnable position encoding approach, i.e. an MLP that embeds the center coordinate of each patch into the embedding space. The position encoding is added to the token embedding. The following transformer takes features In addition, we also incorporate a specialized token, i.e. the CLS Token in i-th masked patches, and i-th masked patches. During the pretraining stage, we select the optimal epoch based on the reconstruction loss and utilize the weights with minimal reconstruction loss for downstream tasks.During the pretraining stage, our decoder takes the encoded features As shown in Binding site identification aims at classifying the protein surfaces into interaction sites and noninteraction sites. Identifying the location of binding site is crucial for ligand binding, as small molecules only inhibit or activate specific biological functions by binding to specific protein pockets. Setting up the downstream task of predicting binding site can evaluate whether the pretrained network has learned geometric and chemical information on the protein surface. For this downstream task, we add a head to identify the binding site, which takes encoded features as inputs and outputs the point-level binary prediction. During fine-tuning, we use the same balanced cross-entropy loss as dMaSIF , coenzyme A (CoA), and HEME]. As this downstream task is a classification task, we add a classification head for fine-tuning. The classification head takes the encoded features of the CLS Token and the pooled features of the other tokens as inputs and outputs the probabilities of affinity for the seven cofactors. We use a cross-entropy loss  for fineThe prediction of protein–protein interaction is essential in protein design tasks, as it serves as a starting point for protein docking. In this task, given two protein surface patches, we extract two descriptors for them, respectively. The similarity between the descriptors measures the probability of their binding. For this task, we utilize an identity network as our head, which is equivalent to directly using the outputs of the encoder as the descriptors. During fine-tuning, we use same balanced metric loss as dMaSIF (We utilize the Pytorch  optimizeWe utilize the dataset of binding site identification task in dMaSIF (We mainly compare our method with two advanced learning-based competitors, namely MaSIF  cannot aFollowing the setting in We present the performance of our method and the competitors in We utilize the dataset of ligand-binding pocket classification task in MaSIF (Similar to the binding site identification task, we utilize dMaSIF  as compeSince the category distribution of the dataset is not exactly balanced, we utilize the balanced accuracy  as our eWe present our method and competitors’ performance in We utilize the dataset of protein–protein interaction prediction task in dMaSIF (We utilize the same competitors as in the binding site identification task.This task can be regarded as a binary classification task, whose objective is to classify the match and mismatch patches. Therefore, we follow the settings in We present the performance of our method and the competitors in We conduct an ablation experiment on the binding site identification task to evaluate importance of chemical features in protein surface representation. The results of this experiment are depicted in m. To evaluate model’s sensitivity to different mask ratios, we have conducted an experiment to compare the performance of pretraining on the binding site identification task under different mask ratios. As shown in Our self-supervised learning involves the hyper-parameter of mask ratio We visualize the results of masked reconstruction. As shown in Since tasks such as protein design often need implementation on very large datasets, lightweight algorithms can significantly reduce costs. Although previous learning-based methods have shown great advantages in computational cost compared to traditional methods, they still face large computational costs due to the large amount of data. However, our transformer-based network, which applies self-attention at the patch level rather than the point level, has a significant advantage in computational cost over existing surface-based methods. As shown in In this article, we propose a self-supervised learning framework called ProteinMAE to address the issue of data scarcity in protein representation learning. Based on a proxy task of masked reconstruction, we can leverage a large amount of unlabeled data to improve the model’s performance on downstream tasks. To validate its effectiveness, we pretrain a network on the unlabeled PDB dataset and test it on three downstream tasks: binding site identification in protein surface, ligand-binding protein pocket classification, and protein–protein interaction prediction. The results demonstrate that our self-supervised learning not only improves the network’s performance on all downstream tasks but also helps the network achieve competitive performance to state-of-the-art models. Additionally, our ProteinMAE framework is quite efficient, with computational costs far lower than other representation learning methods, making it highly advantageous in practical applications. In the future, we plan to extend this framework to molecular representation learning and explore more downstream tasks.btad724_Supplementary_DataClick here for additional data file.