However, learning to walk trajectories on a network to identify protein complexes leads naturally to a reinforcement learning (RL) approach, a strategy not extensively explored for community detection. Here, we develop and evaluate a reinforcement learning pipeline for community detection on weighted protein–protein interaction networks to detect new protein complexes. The algorithm is trained to calculate the value of different subgraphs encountered while walking on the network to reconstruct known complexes. A distributed prediction algorithm then scales the RL pipeline to search for novel protein complexes on large PPI networks.Proteins often assemble into higher-order complexes to perform their biological functions. Such protein–protein interactions (PPI) are often experimentally measured for pairs of proteins and summarized in a weighted PPI network, to which community detection algorithms can be applied to define the various higher-order protein complexes. Current methods include unsupervised and supervised approaches, often assuming that protein complexes manifest only as dense subgraphs. Utilizing supervised approaches, the focus is not on The reinforcement learning pipeline is applied to a human PPI network consisting of 8k proteins and 60k PPI, which results in 1,157 protein complexes. The method demonstrated competitive accuracy with improved speed compared to previous algorithms. We highlight protein complexes such as C4orf19, C18orf21, and KIAA1522 which are currently minimally characterized. Additionally, the results suggest TMC04 be a putative additional subunit of the KICSTOR complex and confirm the involvement of C15orf41 in a higher-order complex with HIRA, CDAN1, ASF1A, and by 3D structural modeling.Reinforcement learning offers several distinct advantages for community detection, including scalability and knowledge of the walk trajectories defining those communities. Applied to currently available human protein interaction networks, this method had comparable accuracy with other algorithms and notable savings in computational time, and in turn, led to clear predictions of protein function and interactions for several uncharacterized human proteins.The online version contains supplementary material available at 10.1186/s12859-023-05425-7. Protein–protein interactions (PPIs) are essential to nearly all cellular functions and biological processes. From antibodies binding antigens to block infections, to protein filaments comprising cellular cytoskeletons, protein interactions are an important organizational principle across biological scales and organisms. Such multi-protein complexes may additionally bind other molecules, such as DNA, RNA, or metabolites, and play critical roles in cellular processes ranging from DNA replication to transcription, multicellular interactions, and tissue organization.e.g. the networks hu.MAP 1.0 [i.e., protein complexes [As a consequence, a growing variety of experimental techniques have been developed to determine PPIs at a large scale, notably including affinity purification/mass spectroscopy (AP/MS), co-fractionation/mass spectrometry (CF/MS), cross-linking/mass spectrometry (XL/MS), proximity labeling, and yeast two-hybrid assays (Y2H), which are collectively reviewed in –5. The r.MAP 1.0  and hu.M.MAP 1.0  that int.MAP 1.0 –11 and C.MAP 1.0 –15, can omplexes .i.e., not use any information from known communities in a network and instead rely only on the network topology to cluster it into its communities. Currently, existing unsupervised community detection algorithms tend to rely on many assumptions regarding the topological structures of communities. MCODE (Molecular COmplex DEtection) is an unsupervised method of detecting protein complexes running on the assumption that dense regions of a network represent complexes [Community detection methods can be unsupervised, omplexes . Anotheromplexes . This paomplexes , Clusteromplexes , and GCEomplexes , among oThe FCAN-MOPSO algorithm is an enhanced clustering method that optimizes communities based on fuzzy clustering logic and multi-objective particle swarm optimization (MOPSO) techniques . It clusi.e., the probability of being a community) from known complexes using different learning algorithms. One such approach uses a support vector machine (SCI-SVM) and a Bayesian network (SCI-BN) [On the other hand, supervised community detection methods do consider different topological features of communities apart from density, and learn a community fitness function ((SCI-BN) . For bot(SCI-BN) . All thr(SCI-BN)  with ~ 8(SCI-BN)  with ~ 1(SCI-BN) . By usinWhile current supervised learning methods learn community fitness functions, they do not learn trajectories on the network that can lead to a protein complex, potentially missing complexes that cannot be traversed using the heuristics employed, for instance in a greedy setting where the community fitness function is maximized at each step. We can apply reinforcement learning to learn paths traversed on a graph to recall known complexes with high accuracy. MARL (Multi-Agent Reinforcement Learning) uses Q-learning to form clusters in networks based on a multi-agent environment . In thisRather than using a multi-agent approach with a team reward based on modularity, an unsupervised measure, we use a single-agent approach (where a subgraph is viewed as an agent) with a supervised reward from training communities based on whether the agent selects the right neighbor to grow the subgraph. The rewards are used to train a value iteration algorithm to learn an optimal value function that is used to predict new communities. During the prediction phase, we implement a parallel method with a single agent on each core (process) to increase the speed of the algorithm by predicting communities in a distributed fashion. Applying the reinforcement-learning (RL) algorithm on a human PPI network after learning from experimentally characterized complexes, we can identify candidate protein complexes, and these candidates can then be experimentally characterized. We can create reliable models of protein complexes that allow us to extract more information about their stability, affinity, and specificity.In the current work, we formulate community detection as a reinforcement learning task and implement a value iteration algorithm, learning from known communities. The RL algorithm accurately and efficiently predicts candidate complexes by learning and using a value function from known communities, which maps the density of a subgraph to the probability (score) that traversing the subgraph will yield a protein complex. The algorithm trains on known complexes that have nodes and edges from the network, to accurately optimize scores for the various densities that could occur on the network. Then, the RL pipeline uses these scores to traverse the network by starting with different seed nodes to create candidate complexes in parallel.The RL pipeline for community detection presents various advantages when compared to its unsupervised and supervised counterparts. Unlike unsupervised methods that rely on assumptions regarding communities such as ‘communities are dense subgraphs of a network’, the RL algorithm takes a more flexible approach by learning trajectories on the network to find known communities having different topologies. To sample candidate communities from the network (an NP-hard problem), while supervised methods have previously used pre-defined heuristics, the RL algorithm learns the correct heuristic to use. Compared to supervised machine learning methods such as neural networks and AutoML methods which require sufficient hyper-parameter tuning and have high training time, the RL pipeline uses the simple value iteration algorithm with few parameters, achieving comparable results in a fraction of the time. Combined with its parallel implementation utilizing multiple cores, the RL pipeline is a fast community detection algorithm enabling efficiency and scalability to large networks. In this paper, we demonstrate the algorithm’s utility by applying it to a high-quality human PPI network (consisting of 8k proteins and 60k pairwise protein interactions) and learn 1,157 candidate protein complexes. These candidates include complexes containing uncharacterized proteins, for which we can suggest possible functions based on ‘honor by association’ with their co-complex members.Reinforcement learning (RL) uses machine learning to enable an agent to make a sequence of decisions based on rewarding desired behaviors and punishing undesired ones. These rewards reinforce the right decisions so that the agent repeats them. Over time, the algorithm finds the best possible decision or action to take in each situation. Thus, RL is an intuitive method for community detection, as decision-making for the long-term goal of finding a protein complex is needed during the process of traversing a network to grow a complex from a node.agent created to perform certain actions on the tic tac toe grid (the environment) based on what state the environment is in, in real-time. After each action taken, the agent receives a reward based on the four possible outcomes of what the state could result in: it wins, the opposing player wins, a draw, or continues the game. If the agent wins, it will receive a positive reward , and if the opposing player wins or if there is a draw—it will receive a negative reward , and if the game continues there will be no reward . The AI player continues to make moves based on what state it currently is in to maximize its cumulative reward or return. This cycle, also known as an episode, terminates when the game ends. While trying to maximize its rewards, the agent will learn the optimal policy, i.e., the best action to be taken in a particular state. The optimal policy is learned by starting with an initial policy and adapting it based on its experience encountering various states.There are three main variables in RL: a state, value function, and reward. A state is a “position” the agent is in, in an environment. A value function is a score given to a state that estimates how beneficial it is for the agent to be in that state to achieve the goal. Lastly, a reward is an incentive that tells the agent if the decision made was correct or not. For example, consider a computer playing against a human in a game of tic tac toe. The AI player is an i.e., a map of the states to their values can be learned using the value iteration algorithm, a classic reinforcement learning method for problems where a model of the environment dynamics is known, usually with a small number of discrete states. The algorithm is a dynamic programming method that solves the Bellman Optimality Equation  is the value function of the current state (at time t in the episode) in the current iteration k  and Vk-1 (st +1) is the value function (from the previous iteration k-1) of the next possible state.HereTo build an RL pipeline for the problem of community detection, the algorithm is first trained on known training communities or complexes. Once the training is deemed to be successful, the learned value function from the training is then used to find complexes on a network.i.e., the current subgraph by its topological feature, density. The state (density d) of the current subgraph is the ratio of the actual number of edges in a subgraph to the total possible number of edges and can be calculated with Eq. , is only dependent on the previous state (st), the current subgraph. It does not depend on any other states previously encountered by the agent, satisfying the Markov property  and solved using reinforcement learning methods.Here, on the left-hand side, The value iteration update rule with our formulation of the community detection problem is given by Eq. :5\documeVk (st) is the value function of the current state (at time t in the episode) in the current iteration k , R is the defined reward, γ is the discount factor (0.5), and Vk-1 (st +1) is the value function (from the previous iteration k-1) of the next possible state. We obtain this simple update rule, derived from the Bellman optimality equation  of 1 in Eq.  encountered in the process.Finding candidate complexes by using the learned value function to walk on the protein–protein interaction network.Benchmarking learned complexes against known complexes.There are three main steps for community detection on a network using reinforcement learning:i.e., the current subgraph expands by adding, at each step, the neighbor that yields the highest value for the current subgraph , we perform a post-processing step to merge highly overlapping complexes. Adapting the pairwise merging algorithm in Super. Complex , if the sure Eq.  and choot is the user-specified overlap threshold, Cp is a predicted complex and Ck is a known complex.Here, To gauge the accuracy of the RL algorithm, we use different evaluation measures to compare learned complexes with known complexes. The learned complexes are compared with the known complexes after removing nodes missing in the set of known complexes. We employ a variety of evaluation measures such as the FMMF, Community-wise Maximum F-similarity-based F-score (CMMF), and Unbiased Sn-PPV Accuracy (UnSPA) defined in Super.Complex , in addiAs discussed in , one of Cp is a derived complex and Ck is a known complex.Here, To demonstrate the effectiveness of the RL algorithm, we show the algorithm’s performance on synthetic and real datasets and discuss the human protein complexes learned by the algorithm. We first evaluate the algorithm on a synthetic toy dataset to help provide intuition for how the RL process works on a simple system that can be fully visualized. Then, we apply RL to the current full-scale human protein interaction network, focusing here specifically on the discovery of higher-order physical protein assemblies. For this purpose, we analyzed the hu.MAP networks  comprisFor a perfect comparison, we use the same pre-processing steps as in Super.Complex for both the network and complexes, as well as the same training and testing complexes . Since The RL algorithm is also tested on hu.MAP 2.0 , a humanIn this section, we examine the value functions learned in the experiments, as these signal the key heuristic learned to identify trajectories on the network leading to communities. During the training phase, we track the value for each encountered state (density) over time. Once the value of each of the states starts to converge, it can be assumed that the value has reached its optimum. Figure We also investigate the relationship between a state’s density and its value. Figure The learned value functions for the synthetic dataset and hu.MAP 1.0 enable us to accurately predict complexes on the respective networks, as shown in the next section. Further, employing transfer learning, we use the value function learned on hu.MAP 1.0 to accurately predict complexes on hu.MAP 2.0  encountered in the process of reconstructing known communities, we now use the value functions learned to find communities on the whole network. Recall the synthetic dataset containing 14 communities used to evaluate the RL algorithm. The performance of the algorithm across different evaluation measures is excellent as summarized in Table For a perfect comparison, Super.Complex is evaluated on hu.MAP 1.0 using only the subgraph feature density. The best results from Super.Complex are obtained using a k-nearest neighbors’ classifier (with k = 76) to train a community fitness function, and from a search process for candidate complexes using maximal cliques as starting seeds and a pseudo-metropolis heuristic (with a probability of 0.1) for complex growth (with an exploration probability ϵ of 0.01). The candidate complexes are then merged with an overlap threshold of 0.2 Jaccard coefficient to yield 798 final complexes. In contrast, the RL algorithm predicts a higher number (1157) of complexes possibly explaining the slightly higher (FMM) recall measure . For Super.Complex with all subgraph features, the time complexity of the prediction phase is X). The time complexity of the RL training algorithm is T is the number of training complexes. In contrast, the training complexity of Super.Complex is The average time complexity of the prediction phase of the RL algorithm is We note that the RL algorithm can be especially useful in community detection problems with a small number of known complexes, as demonstrated in our experiments . Even if the number of known complexes is small, for each complex, the value iteration procedure in the RL algorithm explores several trajectories to learn the complex, incidentally, increasing the size of the training dataset used to learn the complexes. On the other hand, existing supervised community detection methods train on a dataset with a size equal to the number of training complexes. Other benefits of the RL algorithm include the lack of need for extensive hyperparameter tuning and the ability to predict complexes that do not contain smaller complexes. For comparison, in Super.Complex, at each stage of growth in a candidate complex, the pipeline seeks to yield a final protein complex, attempting 4 different heuristics, each with 1–2 hyperparameters. Contrastingly, the RL pipeline learns and traverses the optimal trajectory to find a complex without optimizing for intermediate complexes, and without the need for heuristics, thus saving on searching for parameters in the candidate complex growth step. Thus, the RL algorithm finds the best sequence of steps to grow a complex, while also being efficient.In summary, relative to more sophisticated supervised ML strategies, the simplicity of the value iteration algorithm and the comparable accuracy along with improved efficiency demonstrates the great potential of the RL algorithm for solving community detection problems.Importantly, the RL algorithm returns many well-known human protein complexes accurately (as would be expected from the precision measurements on withheld test complexes), several of which are illustrated in Fig. Moreover, a noteworthy feature of the RL algorithm is that it adds a protein to a complex only if the protein increases or maintains the value function of the complex. For example, this can be seen when building the learned complex in Fig. Of particular interest are complexes corresponding to proteins with low annotation scores, as finding the proteins in complexes with better-annotated proteins may help suggest potential functions for these otherwise minimally characterized proteins . We searFor example, in Fig. As another example of a minimally characterized protein, C18orf21 (chromosome 18 open reading frame 21) is reported to possibly regulate the Rnase/Mrp complex, a ribonucleoprotein complex involved in RNA processing . Both RLSomewhat more information can be gleaned for C15orf41 (chromosome 15 open reading frame 41), which, while minimally characterized, has recently been detected to interact with Codanin-1 (CDAN1) in human cells, and this interaction forms a tight, near stoichiometric complex . MoreoveTo obtain more support for the overall physical association of proteins in this cluster, we modeled the 3D structure of the C15orf41-CDAN1 interaction using AlphaFold-Multimer , as implFinally, C11orf42 was found as a subunit in a complex  that corresponds to a subcomplex of the Retromer or SNX/BAR complex (e.g. as in ), with sIn conclusion, we asked if reinforcement learning could be applied to learn to walk trajectories on a protein interaction network, and in this way more accurately determine protein complexes. Application of the method to currently available human protein interaction networks performed competitively with other algorithms, with comparable accuracy but notable savings in computational time, and in turn led to clear predictions of protein function and interactions for several uncharacterized human proteins. We could support at least one of these, C15orf41, with independent evidence from 3D structural modeling.Three main avenues can be explored to improve the RL community detection algorithm: improving the subgraph representations, the RL formulation, and the candidate community search process.We currently represent a subgraph by a single feature, its density, which gives a problem formulation with a small state space. While performance may be negatively impacted, to improve accuracy, more subgraph features could be included in addition to density. Examples of other subgraph features that could be added include edge weight statistics, node clustering coefficient statistics, and degree correlation statistics. However, as the number of features increases, the state space increases exponentially. For instance, if we incorporate 18 topological features with a discretization of each feature into 10 bins, we increase the number of states to The RL formulation could be modified to better accommodate overlapping community detection, by giving a positive reward if, while growing a seed node, a neighbor is present in any of the training complexes that can be built, rather than considering only one training community at a time. Note that in this scenario, the reward for a neighbor can change dynamically based on the possible communities that can be built from that step. Due to the dynamic rewards that need to be computed for each neighbor at each iteration, by checking whether the new subgraph is a part of any of the training communities, computational time would increase significantly compared to the current static reward system, however, it may improve accuracy. Also, different rewards and discount factors can be experimented with in the training phase of the algorithm.Finally, in the candidate community search process, there may be scenarios where there are multiple highest-scoring neighbors to add at an iteration in the growth process of a subgraph. Currently, we have only added one of the highest-scoring neighbors to grow the complex. Each of the other highest-scoring neighbors can be added as well to grow complexes we may have missed in the current method.Additional file 1: This file contains the following supplementary figures and tables. Figure S1. Convergence of other scores from the training RL algorithm. Table S1. RL algorithm performance on training and testing toy complexes. Table S2. RL algorithm performance on training and testing hu.MAP 1.0 complexes. Table S3. RL algorithm performance on hu.MAP 2.0 complexes.


Proteins are essential molecular building blocks of life, responsible for most biological functions as a result of their specific molecular interactions. However, predicting their  binding  interfaces remains a challenge. In this study, we present a geometric transformer that acts directly on atomic coordinates labeled only with element names. The resulting model—the Protein Structure Transformer, PeSTo—surpasses the current state of the art in predicting protein-protein interfaces and can also predict and differentiate between interfaces involving nucleic acids, lipids, ions, and small molecules with high confidence. Its low computational cost enables processing high volumes of structural data, such as molecular dynamics ensembles allowing for the discovery of interfaces that remain otherwise inconspicuous in static experimentally solved structures. Moreover, the growing foldome provided by de novo structural predictions can be easily analyzed, providing new opportunities to uncover unexplored biology. Predicting protein interactions is crucial for understanding biological functions. Here, authors introduce a geometric transformer that accurately identifies protein binding interfaces, enabling new insights into unexplored biology. Especially so around proteins, which perform their functions by interacting with other proteins as well as with nucleic acids, membranes, and molecules and ions of various kinds.Molecular interfaces are ubiquitous in biology and of utmost relevance beyond their central role in establishing cell boundaries and intracellular organization7. The most modern methods for predicting protein interactions currently target the prediction of either specific pairs of interacting residues/atoms, relying intensively on the analysis of residue–residue coevolution patterns and thus limited to protein–protein interactions, or predicting only which regions of a protein are prone to interaction14. Even the latter, presumably a simpler problem, is yet far from solved, and most methods aim mainly at discovering protein interfaces tailored to interact with other proteins, with a strong focus on features of the protein surface and in some cases also exploiting their sequence conservation. These methods are thus limited, because calculation of protein surfaces and mapping of their properties are time-consuming, complicating their high-throughput application at the proteome scale; besides, they require parametrizations and are very sensitive to details and errors of the 3D structure or model15. Meanwhile, methods that rely on sequence conservation or residue coevolution often perform poorly for shallow sequence alignments. Approaches based on folding protein complexes de novo simultaneously discovering the interaction interfaces and subunit conformations, such as AlphaFold-multimer16, are limited to protein–protein interactions, are far slower than predicting the interaction interface from structures and will fail if the folding protocol itself fails.Predicting the interactions that a given protein can establish with other molecules remains a major challenge in biology, still open despite numerous developments along various fronts20 to various problems in natural language processing and protein structure prediction, we developed a rotation-equivariant transformer-based neural network that acts directly on protein atoms predicting interaction interfaces with high confidence, without the need for parameterization of the system’s physics, running fast enough to process large structural datasets such as ensembles from molecular dynamics simulations or entire foldomes. We build on this transformer to develop PeSTo—the Protein Structure Transformer—a generalized predictor of protein  binding  interfaces.Here, building on the recent successful application of transformershttps://pesto.epfl.ch/]. PeSTo runs fast enough to allow processing of large volumes of structural data, such as molecular dynamics trajectories, enabling the discovery of cryptic interacting interfaces22, and the continuously growing foldome provided by AlphaFold predictions, which allows us to perform a detailed analysis of the human interfaceome.Trained to predict protein–protein interaction interfaces, PeSTo outperforms the state of the art. Training to predict other kinds of  binding interfaces was straightforward as the method does not depend on any explicit parametrization of physicochemical features. Therefore, confident predictions of protein interactions with nucleic acids, lipids, ligands and ions are also easily produced. Given the computational performance of the method, we could provide it not only as standalone code but also implemented in a user-friendly webserver [18 and geometric deep learning7 representing structures as graphs or point clouds and integrate the requirement of the invariance or equivariance of the neural network29. The major breakthroughs come from the field of protein folding30, where AlphaFold19 integrates attention in the Evoformer blocks and the structure module and the third track of the RoseTTAFold20 model uses a SE(3)-Transformer31 to refine the atom coordinates during folding. Moreover, the recurrent geometric network32 (RGN2) leverages the Frenet-Serret formulas to represent the backbone of proteins, and the geometric vector perceptron33 (GVP) uses linear operations to compose vector features with gating34. Multiple other machine learning-based protein–protein interaction site prediction methods have been developed37.Many successful methods combine transformersq) and a vector state (p) encoding the properties of the structure. We define a geometric transformer operation acting on this cloud of points to update these states using the states and geometry in their local neighborhood as shown in Fig. nn) is encoded using the geometry  and the state of the pair of atoms involved. A multi-head attention layer eventually decodes and regulates the propagation of the information . However, in practice, the operation is much more computationally efficient with a fixed number of nearest neighbors. In the same fashion as applying convolution operations on an image, chaining geometric transformers can propagate information at a longer range than the local context of a single operation. Therefore, the main architecture is based on a bottom-up approach, starting from a small context of 8 nearest neighbors (≈3.4 Å radius) up to long-range interactions with 64 nearest neighbors -transformerWe trained a PeSTo model using over 300’000 protein chains from the PDB (see “Methods”) to predict which residues are involved in a protein–protein interface as flagged by an output value ranging from 0 to 1 Fig. . Zero me15. We used a benchmark dataset of 417 structures commonly shared by the two methods (see “Methods”). On this benchmark PeSTo outperforms ScanNet without multiple sequence alignment (MSA) with a median receiving operating characteristic (ROC) area under the curve (AUC) of 0.93 against 0.87 , which we excluded from our training set at 30% sequence identity. PeSTo reaches a median receiving operating characteristic (ROC) area under the curve (AUC) of 0.92 against 0.8 for MaSIF-site followed by SPPIDER35 and PSIVER37  from the structures within the validation set of PeSTo and AlphaFold (see “Methods”). We observed that PeSTo performs almost as well as AlphaFold-multimer (see Supplementary Table 38 (PPDB5) dataset in their unbound conformations. The example in Fig. To further showcase the quality of the predictions in real-world applications, we tested proteins from the Protein-Protein Docking Benchmark 5.0Importantly, the short time needed to run the model  Fig. . The unbIn light of the results for protein–protein interface predictions, we extended the model to find and identify more types of interfaces, resulting in a generalized PeSTo model that predicts protein interaction interfaces with other proteins as well as with nucleic acids, ions, ligands, and lipids. We trained a generalized PeSTo model with PDB structures featuring all the kinds of expected interactions, as described in Methods. The interface predictions for protein–nucleic acid interfaces are almost as good as for protein–protein interfaces, reaching ROC AUC of 0.89 for the testing set Fig. . The genWe next illustrate the generalized PeSTo model showcasing five examples from the testing set that attest to its capacity to discern among various interfaces, even when they are overlapping or under-represented in the PDB. The first example Fig.  correspoAlthough on interfaces with lipids the generalized PeSTo performs less well, in practice we observe that the model is able to accurately detect lipid-binding pockets for soluble proteins  for which an X-ray structure of the DNA-bound form was available Fig. . This st41. The database currently includes highly accurate structures, many actually containing domains with experimentally solved structures, models with no structures in the PDB or with little homology to PDB structures yet highly accurate as judged by AlphaFold predicted local distance difference test (pLDDT) and predicted alignment error (PAE), and also several models of very low pLDDT and PAE scores. We selected 7464 high-quality models for further analysis from the total of 20504 entries based on their pLDDT and PAE scores, as described in Methods.We sought to explore the whole human proteome and analyze what we call hereafter the interfaceome, namely all the potential protein interfaces able to bind other proteins, nucleic acids, lipids, ligands and ions. For this task, we obtained all the structures and models for human proteins in the AlphaFold-European Bioinformatics Institute (AF-EBI) databaseWe could immediately notice that our model produces robust results that further validate the quality of interface predictions. In particular, the amino acid distributions for specific molecular interfaces recapitulates known biochemistry  and large lipid coronas surrounding transmembrane protein domains , interactions with proteins and nucleic acids involve the largest areas with 32 ± 22 and 29 ± 23 nm42. Also in this case, we observed a very good correlation between sets of residues involved in interfaces with the ROC AUC steadily increasing as the analysis is limited to regions of the models of higher quality , next to a predicted interaction site that corresponds to a known kinase binding site (JAK2). On the extracellular side of the membrane, a binding site expected for the carrier retinol-binding protein (RBP) is also predicted. Therefore, residues with high protein interaction scores  and to a model of the dimer built with AlphaFold-multimer. PeSTo predicts in both cases interfaces prone to interact with other proteins and with lipids. In the monomeric model, part of the interface predicted to interact with lipids overlaps with an interface predicted for proteins, suggesting this is the region for homodimerization within the membrane. Accordingly, this interface is not predicted for the dimer, and the new set of residues predicted to interact with lipids makes perfect sense as the membrane-spanning region  and returns them with additional information reporting on the confidence of the prediction on a per-residue basis. Output files can be downloaded or visualized right within the website. Furthermore, we provide the source code (https://github.com/LBM-EPFL/PeSTo) as to facilitate application to large structural ensembles as done here for the human interfaceome.To make PeSTo-based predictions for proteins available to the community, we implemented it in a webserver at Provided that sufficient training data are available, the method can be easily upgraded (as for instance to improve further protein–lipid predictions) and is reusable for other specific applications. In fact, the parameter-free PeSTo architecture is general enough that could be easily accommodated to pursue other structure-based problems such as docking or modeling interactions with materials. The description is totally agnostic to the exact physicochemical properties of the atoms in the structure, thus easily extendable to other materials and fields, and is probably also less sensitive to problems related to the starting structures such as missing atoms as compared to methods that require intermediate calculations of surfaces and volumes.Given the ever-growing accumulation of structural information and rapid expansion of predicted foldome data, PeSTo stands as an accurate, flexible, fast, and user-friendly solution to dissect the vast and dynamic interaction landscape of proteins and can be readily used to discover new and richer biological insights.44. The subunits are clustered using a maximum of 30% sequence identity between clusters. The clusters of subunits are grouped into approximately 70% training set (376216 chains), 15% validation set (101700 chains), and 15% testing set (97424 chains). We selected the best hyperparameters by evaluating the model on the validation set. The testing set is composed of the clusters containing any of the 53 subunits from the MaSIF-site benchmark dataset or 230 structures from the Protein-Protein Docking Benchmark 5.038 (PPDB5) dataset. Additionally, we extracted a subset 417 structures common in the benchmark dataset of ScanNet15 and the testing dataset of PeSTo. Unless specified, all the examples selected to assess the quality of the predictions from the model belong to the testing set.The dataset is composed of all the biological assemblies from the Protein Data BankAll models of the structures are loaded as a single structure. The chain name is tagged with the model identifier to distinguish subunits from different models. Moreover, the chain name of all non-polymer chemical molecules is tagged to have them in separate subunits. Duplicated subunits, molecules, and ions generated when concatenating multiple models are removed. The first alternate location of the atoms is kept. Water, heavy water, hydrogen, and deuterium atoms are removed from the structures.We identified the 30 most common atomic elements on PDB. The element is used as the only feature as a one-hot encoding. The input vectorial features are initially set to zero. The distances matrices and normalized displacement vector matrices are used as geometrical features. Amino acids, nucleic acid, ions, ligands, and lipids are selected from a list of 20, 8, 16, 31, and 4 most common molecules, respectively. Non-native molecules used to help solving the structure are ignored. An interface is defined as a residue–residue contact within 5 Å. All protein–protein interfaces as well as protein–nucleic acid, protein–ion, protein–ligand, and protein–lipids interfaces are identified. The details of the interface for each subunit are stored in the dataset as an interactions types matrix (79×79). This enables the selection of specific interfaces as labels at the start of the training session without having to rebuild the whole dataset. The interfaces targets can be selected from any combinations of subsets from the 79 molecules available.S = 32 with a 3 layers neural network with hidden layer size of 32. Each geometric transformer is composed of 5 neural networks of 3 layers to perform the multi-head self-attention  as described in Supplementary Algorithm nn), the additional non-existent interactions are sent to a sink node with a scalar and vector state set to zero. 4 sets of 8 geometric transformers with an increasing number of nearest neighbors for each set  are applied in succession. The geometric residue pooling module aggregates the encoding at the atomic-level of the structure to a residue-level description by using a local multi-head mask on the atoms forming each residues  as described in Supplementary Algorithm S = 32 decoding the state of all residues and computing the prediction, returning a confidence score from 0 to 1.The input features are embedded to an input state size of The model is trained to predict protein interfaces with protein, nucleic acid, ligand, ion, or lipid. The best neural networks architecture was trained for 8 days on a single NVIDIA V100 (32 GB) GPU. Subunits with a maximum of 8192 atoms (≈100 kDa) without hydrogens are used to limit the memory requirement during training. Subunits with less than 48 amino acids are ignored during training. We trained only on the first bioassembly provided by the PDB database. The effective generalized protein interfaces dataset after filtering is composed of 113805 subunits for training and 29786 subunits for testing.15, MaSIF-site36, SPPIDER35 and PSIVER37. ScanNet is the most recent geometry-based deep learning method for protein–protein interface prediction. MaSIF-site is the best available surface-based deep learning method for protein–protein interface prediction. SPPIDER is a long-standing and well-tested method used as a reference for protein–protein interface prediction. PSIVER only uses sequence information and is benchmarked to show the difference in performance between structure-based and sequence-based methods. The benchmarking of PeSTo was performed using structures taken from the testing dataset exclusively. We use 512 structures per interface type for the protein, ion and ligand interfaces predictions. The low amount of structures available limits the testing dataset to 391 and 161 structures for the nucleic acid and lipid interfaces prediction, respectively.Our method was compared with ScanNet16. We modeled the protein complexes using the implementation of AlphaFold-multimer by ColabFold with MMseqs245 with the default parameters of 10 recycles and 5 predicted models. We extracted the protein–protein interfaces of the AlphaFold-multimer models  and computed the average interfaces over the 5 predicted models. PeSTo was used to predict the protein–protein interfaces for the 46 subunits. Lastly, we computed the accuracy, precision, Matthews correlation coefficient (MCC), receiver operating characteristic (ROC) and precision-recall (PR) area under the curve (AUC) on the PeSTo predicted protein–protein interfaces and the average protein–protein interfaces of the AlphaFold-multimer predicted models, which are reported in Supplementary Table We identified 23 dimers  not present in the training set of PeSTo or of AlphaFold and with a maximum of 20% sequence identity with the AlphaFold-multimer training set 46) of the subunits alone for the bound receptor (bR), unbound receptor (uR), bound ligand (bL), and unbound ligand (uL). All systems were setup using CHARMM36m47 and its recommended TIP3P water model, and MD simulations were run with Gromacs 202048. For the general analysis, 500 frames per simulation are used to evaluate PeSTo for a total of 400,000 frames . The analyzed dataset is composed of 7464 quality predicted structures from a total of 20,504.We downloaded all the available 20’504 (at the time of writing) AlphaFold predicted structures version 2 for human sequences from the AlphaFold-European Bioinformatics Institute (AF-EBI) database50 implemented by MDTraj51.PeSTo was applied to all models. For the analysis of interface residue composition and UniProt-annotated sequence regions, we considered only predicted interfaces with high confidence (>0.8) at well-folded regions (pLDDT > 70). Interface residues are grouped into interfaces by connecting all residues at well-folded regions (pLDDT > 70) and at a predicted interface (>0.5) with alpha carbon within 10 Å. We selected only the predicted interfaces of quality with average predicted interface confidence above 0.8 for all analyses. Two quality interfaces of different types are overlapping if they share at least 5 residues. Solvent-accessible surface area per atom of all models was computed using the Shrake and Rupley algorithm52. The features analyzed include a curated list of annotated features, the subcellular localization, the mutation sites, natural variants, and the GO biological process and molecular function. The pathogenicity of natural variants was extracted from the clinical significance of genetic variations available at dbSNP53.The UniProt-annotated features and GO terms for all corresponding 20’504 AlphaFold models were downloaded from UniProt website42, and extracted the interfaces from the predicted complexes with an interface defined as a residue–residue contact within 5 Å. PeSTo was applied to predict the protein–protein interfaces on the subunits of the predicted complexes alone.We downloaded all the 1102 predicted yeast protein complexes with AlphaFold and RoseTTAFold by Humphreys et al.Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Reporting Summary


AlphaFold2 in reliable protein three-dimensional (3D) structure prediction, assists the move of structural biology toward studies of protein dynamics and mutational impact on structure and function. This transition needs tools that qualitatively assess alternative 3D conformations.The success of MutAmore, a bioinformatics tool that renders individual images of protein 3D structures for, e.g., sequence mutations into a visually intuitive movie format. MutAmore streamlines a pipeline casting single amino-acid variations (SAVs) into a dynamic 3D mutation movie providing a qualitative perspective on the mutational landscape of a protein. By default, the tool first generates all possible variants of the sequence reachable through SAVs (L*19 for proteins with L residues). Next, it predicts the structural conformation for all L*19 variants using state-of-the-art models. Finally, it visualizes the mutation matrix and produces a color-coded 3D animation. Alternatively, users can input other types of variants, e.g., from experimental structures.We introduce MutAmore samples alternative protein configurations to study the dynamical space accessible from SAVs in the post-AlphaFold2 era of structural biology. As the field shifts towards the exploration of alternative conformations of proteins, MutAmore aids in the understanding of the structural impact of mutations by providing a flexible pipeline for the generation of protein mutation movies using current and future structure prediction models.The online version contains supplementary material available at 10.1186/s12859-023-05610-8. AlphaFold2 [AlphaFold2 combines advanced Artificial Intelligence (AI) with evolutionary information from multiple sequence alignments (MSAs). Reliable 3D predictions for over 200 million proteins [ESMFold [AlphaFold2.The remarkable success of phaFold2  in effecproteins . Downstrproteins ) find ne[ESMFold . Embeddi[ESMFold , 8, ofte[ESMFold  using th[ESMFold  and ecliAlphaFold2 era, structural biology moves toward a deeper exploration of alternative protein conformations and mutational landscapes [In the post-ndscapes . Many prndscapes –14. UndeAlphaFold2 often fails to correctly predict the effect of missense mutations upon 3D structure [Despite its immense success, tructure , 8, 15. tructure , 8. Strutructure  offer boMany methods have been developed to predict the effects of sequence variants upon protein function, including SIFT , PolyPheMutAmore (MutAtion movie renderer), a tool that provides a pipeline rendering a mutated protein sequence into a dynamic 3D protein mutation movie (PMM), thereby making the analysis of mutational landscapes accessible and tangible. The implementation and results presented highlight the potential of MutAmore to fill a growing need within the structural biology field. Its efficient visualizations could aid research of protein dynamics, function, evolution, and disease mechanisms.Imagine we had fast and accurate structure predictions for all L*19 SAV mutants of a protein with L residues. How to visualize those data? Today, no comprehensive and accessible visualization tools for alternative protein conformations are available. To fill this void, we introduced MutAmore is designed to create an animated visualization of the mutational landscape of proteins. Inputting a protein amino acid sequence (or a set thereof) as a FASTA file [MutAmore first generates all possible single amino acid variants (SAVs) for this sequence(s) and uses a structure prediction model to predict 3D structure for each SAV  wild-type structure. The local Distance Difference Test (lDDT) [t (lDDT)  tallies t (lDDT) .AlphaFold2 standard). All recent 3D structure prediction methods include predicted lDDT values as confidence scores, including systems which do not build on top of the AlphaFold2 architecture, such as RoseTTAFold [3D visualizations of all variants are created via the PyMOL API  and coloeTTAFold  and EMBEeTTAFold . PyMOL aMutAmore then assembles the final frames of the animation in residue index order (from first at the N-terminus to the last at the C-terminus), merging the 3D renderings with the mutation profile to create the PMM with ffmpeg [h ffmpeg . RendereWhen users provide experimental structures—or otherwise their own labels—in a PDB-formatted file, the visual output highlights the differences between predictions and experimental models by showing the latter at full opacity while applying slight transparency to the former, giving the visual impression of “filling in the gaps” between known structures with predictions.MutAmore lets experienced users override the zoom level manually where needed.Users can adapt the resolution of the final animation to their needs, e.g., choosing high-quality for publications or lower resolution clips for web sharing. The mutation profile automatically scales to the specified vertical resolution for optimal visual interpretability. The 3D visualization rendered by PyMOL automatically chooses a zoom level, which allows enough space to accommodate structural changes caused by mutations. MutAmore also lets users render subsets of the most impactful mutations, e.g., top-50: those with the highest effect upon 3D. In this mode, the framerate is slightly reduced (slowed down) for better visual comparison.MutAmore provides an option for a two-step process: computation of all mutation predictions using a server machine, and subsequent processing and rendering of the animated visualization on a desktop computer.Many advanced structure prediction systems require robust and substantial GPU resources. Therefore, MutAmore on an Intel Xeon Gold 6248 CPU with a NVidia Quadro RTX 8000 GPU (48 GB) using twelve proteins ranging in lengths from 72 to 639 residues, including both globular and membrane proteins. Although AlphaFold2 [ColabFold [ESMFold [We evaluated phaFold2  or its folabFold  outperfoThe time required for structure predictions substantially varies with the method used. ColabFold, utilized for the five smallest proteins only, accumulated over a week of GPU time, given the non-linear scale with protein length, this rapidly becomes infeasible for longer proteins. ESMFold obtained predictions for all twelve proteins in about 16 days, with the bulk of the computation time dedicated to the longest samples (11 days for the protein with 639 residues). In contrast, the predictions for the nine smaller proteins were computed by ESMFold in 30 h. The tremendous increase in runtime by protein length  of single amino-acid substitutions (SAVs), MutAmore enhances the exploration and understanding of alternative protein conformations brought on by mutations. This is particularly significant in translating the depth and complexity of the protein mutational landscape. Our benchmark demonstrated the efficiency and versatility of MutAmore even for high-definition 4 K video settings. The tool effectively balances computational load by allowing multi-step operation across multiple systems, ensuring usability across varied system capabilities. We hope that structural biology increasingly shifts toward routine analysis of alternate protein conformations. MutAmore supports such a more dynamic perspective on protein structures and might aid analyzing protein function and studying protein evolution.We designed MutAmore is publicly available and is free for all users.Project name: MutAmore.https://github.com/kWeissenow/MutAmoreProject home page: Operating systems: Linux.Programming language: Python.Other requirements: ffmpeg 4.1 or higher, PyMOL 2.2 or higher.License: MIT.Any restrictions to use by non-academics: None.Additional file 1. In figures S1-S12 we show screenshots of all protein mutation movies used for benchmarking. Corresponding tables S1-S12 indicate runtimes for all individual samples, including prediction time using ESMFold and all MutAmore rendering pipeline steps. Figure S13 visualizes the scaling of runtime for prediction and rendering steps with sequence length.