In recent years, improvements in protein function prediction methods have led to increased success in annotating protein sequences. However, the functions of over 30% of protein-coding genes remain unknown for many sequenced genomes. Protein functions vary widely, from catalyzing chemical reactions to binding DNA or RNA or forming structures in the cell, and some types of functions are challenging to predict due to the physical features associated with those functions. Other complications in understanding protein functions arise due to the fact that many proteins have more than one function or very small differences in sequence or structure that correspond to different functions. We will discuss some of the recent developments in predicting protein functions and some of the remaining challenges. During the past quarter century, advances in biochemistry, structural biology, proteomics, cell biology, genomics, and bioinformatics have yielded an explosion of information about protein functions. In addition to advancing our understanding of individual proteins, these advances have helped increase our understanding of macromolecular complexes, biochemical pathways, and the regulation of biological processes. This knowledge can be used to identify proteins and functions to target in developing novel therapeutics, for developing synthetic biological circuits or protein-based drug delivery systems, or for use in manufacturing and bioremediation. Our ability to predict protein functions has not kept up with the millions of protein sequences available from translating protein-coding genes from genome sequencing projects, as can be seen by the many protein entries listed as “predicted” or “probably” in the UniProt Knowledgebase . This peThe critical assessment of functional annotation (CAFA) community challenge compares the successes of function prediction algorithms and has found significant improvements over the past 10 years . The resAdvances in bioinformatics and other computer-based algorithms have also helped make possible the discovery of vast amounts of information about protein functions from biochemical, biophysical, cell biology, and other experimental approaches. Advances in computational methods have improved the ability to determine cellular localization when using microscopy, identify gene neighbors in genome sequences (which indicates proteins might be involved in the same pathway or structure), identify changing levels of protein expression under different cellular conditions , or co-expression with other proteins . Advances in bioinformatics have also aided in interpreting the results of proteomics studies, for example, in identifying protein fragments from mass spectrometry that help identify binding partners  and the presence of proteins in multiprotein complexes , although a protein’s function in those locations and complexes, whether regulatory, scaffold/structural, or catalytic, is often unknown.Advances in computational methods have also been important in determining or predicting protein structures, which can provide some information about a protein’s function. Advances in cryo-electron microscopy (Cryo-EM), X-ray crystallography, and NMR have yielded more protein structures and, importantly, structures of complexes with other proteins, DNA, RNA, and small-molecule ligands that provide key information about function. Recent successes in structure prediction using AI and homology modeling have yielded more models of protein structures that can also provide information about the overall fold; however, they do not always provide detailed information about active and interaction sites . CompariInformation about protein functions and other characteristics identified through diverse studies can be spread across many journal articles and other resources. Online databases are valuable tools that bring together this information. The UniProt Knowledgebase integrates and organizes information about protein sequences, structures, and functions for millions of proteins  The ProtAdvances in computational methods and projects for analyzing large quantities of protein structures, along with information about their functions, have been important for identifying features associated with specific functions. For example, the sub-classification of protein superfamilies into functional families (FunFams) and functional domains  or the iMany challenges remain in predicting protein functions, including the lack of characteristics or features correlated with some types of functions upon which to build an algorithm; the presence of many homologous proteins with small differences in sequence or structure that result in the proteins having different functions; the ability of proteins to have multiple functions; and a large number of proteins and types of proteins for which assays, representative structures, or other information about their functions are not known.For many biochemical or biophysical functions, a sequence, structural motif, or other characteristic has not been identified that correlates with that specific function. For some types of functions, it is because the physical requirements of the function are only weakly conserved. For example, protein–protein interaction sites can consist of a relatively smooth region on the surface that is not well conserved, so predictions of the locations of pockets on the protein surface or comparisons of amino acid motifs or constellations are not as successful as predicting the locations of catalytic sites. Hundreds of proteins have been found to bind to RNA but do not contain any of the canonical RNA-binding domains, and for most of these proteins, it is not known which parts of the proteins interact with RNA. In addition, relatively small parts of a protein can be involved in a function. Plasminogen-binding site requirements for several proteins have been found to mainly involve a lysine at the protein terminus. Intrinsically disordered proteins or domains (IDPs) do not have a folded three-dimensional structure and also frequently use small sequences and/or short helices to interact with other proteins. In addition, when a sequence or structural motif is found that correlates with a function of the protein, it might only be one aspect of the function. For example, binding to DNA might be the main function of some proteins, whereas for other proteins, DNA binding is just one aspect of a larger function, such as binding to and then catalyzing the cleavage of the DNA. Similarly, the binding of a small molecule or ion such as Ca++ could be a mechanism for regulating the protein’s function, for example, in tropomyosin in muscle contraction. Some of the best predictors of protein characteristics are programs that predict the presence of transmembrane alpha helices, for example, TMHMM . These pAnother challenge in predicting protein function is that predictions based on sequence or structural homology can be inaccurate because even proteins with significant amino acid sequence identity can have different functions. Proteins with small changes in the amino acid sequence in or near the active site might not share the catalytic activity of other members of an enzyme family but instead might have a different function, such as a different catalytic mechanism or a different substrate. The enolase superfamily contains evolutionarily related enzymes with similar structures, a (β/α)7β-barrel (TIM-barrel) fold, and similarities in their active sites and catalytic mechanisms, but the proteins in different subgroups catalyze different reactions. The superfamily contains enolases, which convert 2-phosphoglycerate to phosphoenolpyruvate in glycolysis; muconate lactonizing enzymes, which break down aromatics derived from lignin into citric acid cycle intermediates; mandelate racemases that interconvert the (S)-mandelate and (R)-mandelate enantiomers; 3-methylaspartate ammonia lyases that break down L-threo-3-methylaspartate to mesaconate and ammonia; and other enzymes . The amiMany members of enzyme families have three-dimensional folds or domains that resemble catalytically active members of the family but lack catalytic activity altogether. These pseudoenzymes are found in most enzyme superfamilies, including pseudokinases, pseudoubiquitin ligases, and pseudonucleases. Relatively small changes, such as lacking key catalytic amino acids in the active site, result in a protein that does not have catalytic activity but is instead involved in another function, for example, regulating a catalytically active subunit or acting as a scaffold for bringing together a multiprotein complex. Some pseudoenzymes still bind to a canonical ligand or cofactor, but instead of catalysis, they have a role in signaling pathways, transcription, or translation [reviewed in the work of Predictions of function are also complicated by moonlighting proteins, proteins that have two or more biochemical or biophysical functions . For exaMore generally, there are still protein functions that have not been identified or characterized. Proteins might be found in a location in the cell or associated with a cellular structure, but their presence there is not understood. A gene knockout of a protein might affect multiple biochemical pathways or cellular processes, but the actual biochemical or biophysical function of the protein is still unknown. Biochemical assays of activity, such as for catalytic activity, might not be available and could be challenging to develop, for example, for functions that involve regulating the function of another protein, forming part of the structure of a larger complex, or a scaffold protein whose main function is bringing other proteins together. Functions found only in few species, in a small number of cell types, or expressed under specific conditions are likely to be poorly characterized or not identified. In addition, some types of proteins have not been as thoroughly characterized as others, in part because it is not clear if they have functions. For example, there is a growing awareness that many microproteins, proteins with fewer than 100 amino acids, can have regulatory, structural, or other functions ? The experimentalists can help in identifying experiments to test the predictions and specific proteins that would be amenable to the needed experimental methods. What types of functions have been found experimentally that the current prediction methods tend to miss? What additional structural or functional data would help in providing the needed training set(s) for improving predictive algorithms? Which data should not be included in training sets for a specific algorithm? For example, are some of the structures not solved at a high enough resolution?In summary, in recent years, protein function prediction methods have seen significant advances with increasingly accurate protein function predictions. Challenges remain in identifying some types of functions, especially functions that do not correspond to known sequences or structural motifs, functions that vary even in very similar protein structures, functions of very small or intrinsically disordered proteins, functions that have not yet been identified or characterized, and proteins with combinations of multiple functions. In the future, the rapidly increasing amount of diverse kinds of experimental data, in combination with advances in computational methods that make use of these data, is likely to continue to improve the accuracy of function prediction and its applicability to more kinds of protein functions.


Using a curated collection of 254 available protein targets with both unbound and bound structures, we first demonstrate that AlphaFold confidence measures (pLDDT) can be repurposed for estimating protein flexibility and docking accuracy for multimers. We incorporate these metrics within our ReplicaDock 2.0 protocol2 to complete a robust in-silico pipeline for accurate protein complex structure prediction. AlphaRED (AlphaFold-initiated Replica Exchange Docking) successfully docks failed AF predictions including 97 failure cases in Docking Benchmark Set 5.5. AlphaRED generates CAPRI acceptable-quality or better predictions for 66% of benchmark targets. Further, on a subset of antigen-antibody targets, which is challenging for AFm (19% success rate), AlphaRED demonstrates a success rate of 51%. This new strategy demonstrates the success possible by integrating deep-learning based architectures trained on evolutionary information with physics-based enhanced sampling. The pipeline is available at github.com/Graylab/AlphaRED.Despite the recent breakthrough of AlphaFold (AF) in the field of protein sequence-to-structure prediction, modeling protein interfaces and predicting protein complex structures remains challenging, especially when there is a significant conformational change in one or both binding partners. Prior studies have demonstrated that AF-multimer (AFm) can predict accurate protein complexes in only up to 43% of cases. AlphaFold3 has brought a paradigm shift in the field of structural biology by intertwining deep-learning (DL) tools with evolutionary data to predict single-chain structures with high accuracy. Further, AlphaFold-multimer4 (AFm) and related work6 have demonstrated the utility of AlphaFold to predict protein complexes. The association of proteins to form transient or stable protein complexes often involves binding-induced conformational changes. Capturing conformational dynamics of protein-protein interactions is another grand challenge in structural biology, and many physics-based  approaches have been used to tackle this challenge.2 Computational tools have sampled the uncharted landscape of protein-protein interactions by emulating kinetic mechanisms such as conformer selection and induced-fit and by identifying energetically stable binding states. However, these tools are hampered by the accuracy of the energy functions and the limitations of time and length scales for sampling. In fact, AF-multimer (AFm) predicted accurate protein complexes in only 43% of cases in one recent study.1 As the development of DL-based tools have unveiled ground-breaking performance in structure prediction, integration of a biophysical context has potential to strengthen prediction of protein assemblies and binding pathways.In-silico protein structure prediction, 8 With the availability of AF and AFm, the CASP15-CAPRI experiment stood as its first blind assessment for prediction of protein complexes and higher-order assemblies.9 In this round, the docking community relied on AF and AFm for single-structure or complex predictions. Given that AlphaFold generates a static three-dimensional structure, it has been unclear whether conformational diversity could be captured by AlphaFold. In other terms, given a protein sequence, could AlphaFold generate ensembles of structures that include both unbound and bound conformations? Additionally, can AlphaFold reveal intrinsic conformational heterogeneity?Blind docking challenges prior to AF, particularly CASP13-CAPRI and CASP14-CAPRI experiments, reported high-quality predictions for only 8% targets.10 higher recycles on inference11 or modulating the MSA inputs13 with the amino acid sequence. While these approaches demonstrated the ability to generate broader conformational ensembles, AFm performance still worsens with a higher degree of conformational flexibility between unbound and bound targets.1 Prediction accuracies especially deteriorated in bound complex regions involving loop motions, concerted motions between domains, rearrangement of secondary structures, or hinge-like domain motions, i.e., large-scale conformational changes, which are also challenging for conventional docking methods.14To diversify model complexes generated with AlphaFold-multimer in the recent round of CASP15, predictors employed tuning parameters such as dropout,Unlike state-of-the-art docking algorithms, AlphaFold’s output models incorporate a residue-specific estimate of prediction accuracy. This suggests a few interesting questions: (1) Do the residue-specific estimates from AF/AFm relate to potential metrics demonstrating conformational flexibility? (2) Can AF/AFm metrics deduce information about docking accuracy? (3) Can we create a docking pipeline for in-silico complex structure prediction incorporating AFm to convert sequence to structure to docked complexes?2, large ensembles15, and fast-fourier transforms with improved energy functions16 to capture conformational changes and better dock protein structures. Coupling temperature replica exchange with induced-fit docking, ReplicaDock 2.02 achieved successful local docking predictions on 80% of rigid  and 61% medium (1.1 ≤ RMSDUB < 2.2 Å) targets in the Docking Benchmark 5.0 set17. However, like most state-of-the-art physics-based docking methods, ReplicaDock 2.0 performance was limited for highly flexible targets: 33% success rate on targets with RMSDUB ≥ 2.2 Å. Promisingly, by focusing backbone moves on known mobile residues , ReplicaDock 2.0 sampling substantially improved the docking accuracy. But the flexible residues must first, somehow, be identified. Additionally, physics-based docking is quite slow (6–8 hrs on a 24-core CPU cluster) compared to recent DL based docking tools (0.1–10 minutes on a single NVIDIA GPU). However, docking-specific DL tools such as EquiDock18 and dMASIF19 do not allow for protein flexibility, and recent tools like GeoDock,20 and DockGPT21 have very limited backbone flexibility. Further, all of these DL docking tools have low success rates on unbound docking targets such as those in Docking Benchmark 5.5.20Recent work in physics-based docking approaches tested induced-fit docking4) with physics-based docking schemes (ReplicaDock 2.02) to systematically dock protein interfaces. The overarching goal is to create a robust pipeline for easier, reproducible, and accurate modeling of protein complexes. We investigate the aforementioned questions and create a protocol to resolve AFm failures and capture binding-induced conformational changes. We first assess the utility of AFm confidence metrics to detect conformational flexibility and binding site confidence. Next, we feed these metrics and the AFm-generated structural template to ReplicaDock 2.0, creating a pipeline we call AlphaRED (AlphaFold-initiated Replica Exchange Docking). We test AlphaRED’s docking accuracy on a curated set of benchmark targets of bound and unbound protein structures of varying levels of binding-induced conformational change, including antibody-antigen interfaces, which additionally challenge AF2m due to the lack of evolutionary information across the interface.23 In summary, we to assess the promise of combining the best of deep learning and biophysical approaches for predicting challenging protein complexes.In this work, we combine the features of a top deep learning approach  structures of bound protein complexes and their corresponding unbound protein subunits. Each protein target (with unbound and bound structures) is classified based on their unbound-to-bound root-mean-square-deviation (RMSDUB) as rigid (RMSDUB ≤ 1.2 Å), medium (1.2 Å < RMSDUB ≤ 2.2 Å) or difficult (RMSDUB ≥ 2.2 Å). Further, owing to the poor performance of AlphaFold and other predictor groups in predicting antibody-antigen targets in the recent CASP15-CAPRI round24, we identified a subset comprising only antibody-antigen complexes  by extracting all the 67 antibody-antigen structures from the DB5.525 set. The comprehensive dataset includes 254 protein targets exhibiting binding-induced conformational changes.We curated a dataset for conformational flexibility from the Docking Benchmark Set 5.5 (DB5.5)github.com/YoshitakaMo/localcolabfold) of the AlphaFold-multimer v2.3.0 (released March 2023) for all 254 benchmark targets. Being trained on experimentally-characterized structures deposited in the PDB, AlphaFold is expected to produce models analogous to the PDB structures. However, since both unbound and bound structures exist for the benchmark targets in the PDB, we first investigated whether AFm exhibits any bias towards either unbound or bound forms for the same protein sequence. α-RMSD of all protein partners of the AFm predicted complex structures from the bound (B) and unbound (U) crystal structures on a log-log scale (a few AFm predicted models were 20 Å apart from both bound and unbound structures). As evident from For each protein target, we extracted the amino acid sequences from the bound structure and predicted a corresponding three-dimensional complex structure with the ColabFold implementation  and experimental (per-residue RMSD and LDDT) metrics against each other.AlphaFold employs multiple sequence alignments with a multi-track attention-based architecture to predict three-dimensional structures of proteins and complexes. Further, for each structural prediction, it provides a residue-level confidence measure: the predicted local-distance difference test (pLDDT), estimating the agreement between predicted model to an experimental structure based on the CDDT test . Tunyasuα deviations to determine the per-residue 27) and TGF-β receptor with FKBP12 domain (1B6C28). In both cases, pLDDT confidence scores correlate with the experimental measurements of binding: pLDDT decreases as red), the prediction is inaccurate, but higher confidence/pLDDT regions (highlighted in blue) have high accuracy of prediction with the bound form. The results for the entire benchmark set  show similar trends for most targets. The pLDDT, thus, can suggest protein residues that move upon binding.As a reference, we first superimposed the unbound partners over the bound structures and calculated residue-wise Cructures . In regii.e., even if the complex exhibits higher confidence, the docking interfaces could be incorrect. 29), medium (5VNW30) and flexible targets . In all the examples, the AFm model (highlighted in red to blue based on residue-wise pLDDT) is superimposed over an individual binding partner, and the bound structure is highlighted in pale-green. AFm models predict the individual subunits (protein partners) accurately in almost all scenarios, however the docking orientation is incorrect.When the prediction accuracy is lower, it is often evident from lower confidence metrics (such as average pLDDT or PAE). However, for AlphaFold-multimer complex predictions, the confidence metrics of the overall prediction do not correlate with the accuracy of the docked prediction, 33 DockQ β atoms within 5 Å); (c) Average pLDDT, determined by averaging over the per-residue LDDT score of the entire protein complex; and (d) Interface-pLDDT, determined by averaging the per-residue LDDT score only over the predicted interfacial residues (as identified in case a).We investigated whether any of the AlphaFold predictive metrics could be repurposed for distinguishing native-like binding sites from non-native ones. That is, can one could utilize pLDDT or PAE from AFm models to determine whether the predicted docked complex has the accurate binding orientation? Thus, we evaluated accuracy with the DockQ score, the standard metric for docking model quality.red). Finally, we show the trend between DockQ scores and interface-pLDDT for each target in dashed line) thus can serve as the AlphaFold-derived metric to distinguish acceptable quality docked predictions from incorrect models.2, to build a protocol for: (1) improving on incorrect AF docking predictions and producing alternate, near-native binding models and (2) capturing backbone conformational changes with our induced-fit protocol ReplicaDock2.02. We named the protocol AlphaRED (AlphaFold-initiated Replica Exchange Docking). AlphaRED uses AFm predicted structures as the primary template, estimates docking accuracy metrics, and initiates global docking or refinement protocols as required.With metrics to identify the flexible regions in the protein and the docking accuracy of generated docked models, we next fused AlphaFold-multimer (AFm) with our docking protocol, ReplicaDock 2.0For flexible local docking, we perform aggressive backbone moves , AlphaRED demonstrates a vast improvement in Irms for 93 out of 97 targets. Additionally, for targets where AFm prediction is considered acceptable (interface-pLDDT > 85), local docking slightly improves performance. AlphaRED captures lower interface-RMSDs (under 10 Å) for targets where AFm models dock at binding sites ~40 Å away. We investigated AlphaRED’s performance on all 254 benchmark targets . 97 targ34 However, antibodies have proven challenging for deep learning methods, especially those reliant on multiple sequence alignments, as each antibody evolves in a different organism, and their antigens evolve on a different timescale altogether. In our tests here, AFm predicted acceptable or better quality docked structures in only 19% of the 67 antibody cases. In contrast, the AlphaRED pipeline succeeds in 51% of the targets, a significant improvement.a) and local docking (b) example for targets 2FJU and 5C7X respectively. Starting from the incorrect AFm prediction (orange), AlphaRED samples over the conformational landscape to identify a top-scoring decoy (blue) with 2.6 Å Irms from the native (gray). blue) samples backbone closer to the bound form, improving model quality and docking accuracy.35 CASP15 (Summer 2022) provided multiple protein docking targets24 that were not included in AFm training, allowing an unbiased evaluation of our AlphaRED pipeline.9 Thus, we tested the protocol on the five heterodimeric nanobody-antigen complexes where most of the groups performed poorly  or CAPRI  competitions.d poorly .36 For representative target T205, our docking strategy improves the performance drastically (interface RMSD 11.4 Å for AFm model to 2.84 Å for AlphaRED) and binds in the correct site. The interface scores versus interface-RMSD plot shows a distinct funnel with low-energy medium-quality structures (top). Since the crystal structures are not yet released, the reference structure here is the top-model predicted for each category in CASP15.9 For all the targets, bottom shows similar improvements for other nanobody-antigen complexes. These cases validate our strategy for blind targets, and demonstrate the ability of AlphaRED to serve as a robust pipeline, integrating AlphaFold with biophysical attributes to better predict protein complex structures.For each target, we employed the AlphaRED strategy as described in ructures -top. Sin37 One of the key elements of its success was the ability to mine evolutionary links between amino acids across protein families and determine structural templates. This approach dramatically improves prediction accuracy for monomers as reflected from prior CASP rounds. However, across protein interfaces, the evolutionary constraints can be weak and often skew predictions to inaccurate binding sites. Here we demonstrated how augmenting the predictions of AlphaFold with an energy-function dependent sampling approach reveals better backbone conformational diversity and accurate prediction of protein complex structures. By utilizing the AlphaRED strategy, we show that failure cases in AFm predicted models are improved for all targets (lower Irms for 97 of 254 failed targets) with CAPRI acceptable-quality or better models generated for 66% of targets overall  utilized by AFSample, our docking routine fused with ColabFold uses 5–7 hours on our CPU cluster . The AlphaRED docking strategy demonstrates a new and better way to predict protein complex structures within feasible compute times.First, we showed that AlphaFold confidence measures can be repurposed for estimating flexibility and docking accuracy. Interface-pLDDT, an average of the per-residue pLDDT only for the interfacial residues, is a robust metric to determine whether AFm predicted binding interfaces are correct. Additionally, thresholds of per-residue pLDDT can ascertain regions of backbone flexibility upon binding. Thus, AFm predicted models can be used as input structures for ReplicaDock 2.0 guiding the choices of global or local sampling and identifying the mobile protein segments. With DL-methods for structure prediction and downstream sampling with a physics-based energy function, one can efficiently explore the protein energy landscape as demonstrated with AlphaRED’s performance on DB5.5. Finally, we evaluated recent CASP15 targets to investigate the extrapolation of this strategy over blind protein targets. CASP15 targets were absent from the training routine of AlphaFold and served as blind challenges to determine the efficacy of the protocol. With AlphaRED, we obtained DockQ scores over 0.23 for all five targets, with medium-quality models (DockQ > 0.49) for targets T205, T207, and T208 respectively. AFSample, a top-performing group in CASP15, employed stochastic perturbation with dropout and increased sampling to obtain medium and high-quality models for these targets. However, AFSample requires GPU simulations to produce ~240x models with compute time ~1000x more than the baseline AFm.34, but a lack of fast and accurate docking methods for antibodies prevent high-throughput computational screening. Additionally, this work is impactful because by integrating a physics-based method for refinement, the pipeline can potentially handle post-translationally modified proteins or non-canonical residue types that are not defined in ML approaches like AF.This work is particularly impactful for its success rate on antibody-antigen targets. Deep learning promises accurate design and optimization of antibody therapeutics38 With the paradigm shift in computational structural biology towards deep-learning approaches, integrating physics within these models has tremendous potential towards understanding protein dynamics, modulating protein-protein interactions, and downstream applications to protein design.With this work, we have built upon the recent advances in structural biology to develop a robust tool for protein docking. We fused deep-learning tools with conventional physics-based sampling tools to develop a pipeline that extracts the best outcomes of each methodology; where deep-learning methods generate accurate, static structures, and physics-based sampling provides diversity and better discrimination. The protein conformational landscape is vast and deep-learning tools such as AlphaFold provide a snapshot of relevant local minima that can aid in narrowing down the degrees of freedom in sampling.39 implementation of AlphaFold3 and AlphaFold-multimer (v.2.3.0)4. Each prediction run was performed without templates, with automatic alignments and the default number of recycles to generate five relaxed predictions. Each AlphaFold prediction includes a per-residue pLDDT (predicted LDDT) measurement40, a confidence measure in prediction accuracy, and predicted template alignment (pTM) score.41 The models were structurally compared with the unbound and bound structures (deposited in the PDB) for measuring flexibility, similarity and accuracy of docking prediction.For each target in the DB5.5 dataset, we obtained AlphaFold predicted models with the ColabFold v1.5.242 provide a static representation of the native-state of the protein. However, structural diversity has been captured by experimental techniques to identify different states of a protein in diverse physiological or chemical states, e.g. catalysis43, transport44, and ligand binding45. For protein docking challenges in particular, conformational changes are binding-induced, leading to structural differences between unbound and bound structures of protein targets.Structures of proteins deposited in the PDBα root-mean-square-deviation (RMSD) and local distance difference test (LDDT)40. To get a detailed representation of the intrinsic motion of a protein, we calculated RMSDs at a residue-level, i.e., per-residue Cα RMSD for each residue of a protein target. The sequences+structures of unbound and bound proteins were aligned and the RMSDs were calculated for the aligned residues. The total sequence lengths were also matched and lingering end-termini residues were trimmed to ensure structural and sequential similarity.To measure the conformational change in protein structures, we calculated two metrics: C40 Unlike the Global Distance Test (GDT)46 score based on rigid-body superimposition, the LDDT score measures the conserved local interactions in the protein model to the reference. For every residue, it computes the distance between all pair of atoms Local Distance Difference Test (LDDT) is a superimposition-free score that estimates local distance differences in a model relative to a reference structure.α coordinates of the α atom in the For a protein structure with α atom in the Finally, the distances to score (The advantage of the LDDT measurement lies in the estimation of relative domain orientations in multi-domain proteins or concerted motions (e.g.: hinge-like moves in closed and apo proteins). In these cases, the RMSDs would be relatively high for all residues in the mobile domain, however, since the inter-residue distances within the domains are conserved, they would provide an inaccurate depiction of flexibility for the protein. Estimating both RMSDs and LDDT scores allows us to obtain a nuanced perspective of flexibility during protein association based on experimental structures.2. Each simulation initiates 8 trajectories across 3 temperature replicas with inverse temperatures set to 1.5−1 kcal−1.mol, 3−1 kcal−1.mol and 5−1 kcal−1.mol, respectively. Across each replica within each trajectory, rigid body perturbations (4 Å translations and 8° rotations) are performed for an exhaustive global search. Next, we perform an energy-based clustering of the models to obtain diverse and energetically favourable clusters. Five cluster centers (decoys) are selected and passed to the flexible local docking stage to sample conformational changes.Using AlphaFold2 as a structural module, we built a pipeline for protein-protein docking to better predict protein complex structures with relatively higher accuracy. As illustrated in Backrub47, Balanced Kinematic Closure  and Sidechain. The sampling weights are biased such that backbone and side-chain movers are weighted higher than rigid body moves (3:1 weightage for backbone:rigid-body moves). We perform directed backbone sampling by focusing on predicted mobile residues (per residue pLDDT < 80). This is automated with the BFactorResidueSelector that selects contiguous sets of residues below the specified pLDDT threshold.On other hand, if the interface-pLDDT > 85, the binding orientation has higher confidence and the protocol directly performs a flexible local docking simulation skipping the rigid, global docking. In this stage, we perform smaller rigid-body perturbations (1 Å translations and 3° rotations) and aggressive backbone moves using a set of backbone and side-chain movers: Rosetta 2, we perform backbone sampling directed only on the mobile residues (with per residue pLDDT < 80) identified from the AlphaFold model. We automate it using the BFactorResidueSelector to select contiguous sets of residues below the specified pLDDT threshold in the prior section. This residue subset is passed along to the backbone movers to sample backbone moves along with small rigid-body moves. Sampled decoyed are then refined, i.e. undergo side-chain packing and minimization, to output docked decoys. The best ranked decoys based on interface scores are then identified as the top-scoring structures.However, unlike the induced-fit strategy in ReplicaDockSupplement 1


We combined information from Differentially Expressed (DE) protein-coding transcripts from cancer genomics, together with domain architecture, protein interaction network, and gene enrichment analysis to provide an easy-to-interpret view of the effects of protein-coding splice variants. We retrieved all the protein-coding Ensembl transcripts and mapped Interpro domains and post-translational modifications on canonical sequences to identify functionally relevant splicing events. We also retrieved isoform-specific protein–protein interactions and binding regions from IntAct to uncover isoform-specific functions via gene-set over-representation analysis. Through EXPANSION, users can analyze precalculated or user-inputted DE transcript datasets, to easily gain functional insights on any protein spliceform of interest.EXPANSION (http://expansion.bioinfolab.sns.it/. The code of the scripts used for EXPASION is available at: https://github.com/raimondilab/expansion. Datasets associated to this resource are available at the following URL: https://doi.org/10.5281/zenodo.8229120. The web-server was developed using Apache2 (https://https.apache.org/) and Flask (v2.0.2) (http://flask.pocoo.org/) for the web frontend and for the internal pipeline to handle back-end processes. We additionally used the following Python and JavaScript libraries at both back- and front-ends: D3 (v4), jQuery (v3.2.1), DataTables (v2.3.2), biopython (v1.79), gprofiler-officia l(v1.0.0), Mysql-connector-python (v8.0.31). To construct the API, Fast API library (v0.95.1) was used.EXPANSION is freely available at  Alternative splicing is a widespread and complex process in eukaryotic cells that plays a critical role in regulating gene expression and generating protein diversity . The proSeveral methods and associated webservers have integrated the information of protein structures, domains, post-translational modifications (PTMs) and interaction networks to explore the consequences of genetic variants [e.g. Mechismo  by using ClustalOmega   to identify specific biological processes affected by alternative splicing. The g: profiler-official (v1.0.0)  which were processed through the TOIL pipeline  were used to construct the API. The API documentation website (https://expansion.bioinfolab.sns.it/api/) offers information about how to use the API and the supported methods. The EXPANSION API is intended for multi-gene queries of the information integrated in the EXPANSION webserver and allows for retrieval of either individual, or combined, analyses generated for the queried transcript sets.Through an application programming interface (API), we enable direct open access to data. The Python programming language and the Fast API library  of functional categories for the genes in the network. User can specify whether to calculate ORA for either the entire, or isoform-specific networks, allowing comparison of distinct biological processes mediated by isoform-specific interactors . The resPRKACB, a protein kinase A catalytic subunit, is characterized by alternative splicing isoforms, which are expressed in a tissue specific fashion and are associated to a number of disorders, including cancer  are most likely to be affected in terms of splice-variants . Interes−5) and pathways such as ‘CREB1 phosphorylation through the activation of Adenylate Cyclase’ (FDR = 3E−4), while the alternative isoform P22694-2 is associated to cellular compartments such as ‘Cyliary base’ (FDR = 1E−10) and processes such as ‘Lipid metabolism pathway’ (FDR = 1E−10).Furthermore, we discovered that the splicing-affected binding regions (210–218) within these three isoforms also exhibit an otherwise missing ubiquitination at position ‘214’ . This abOverall, EXPANSION web server takes gene-centric queries, and provides a multi-modal annotation of alternative splicing isoforms to help predicting the functional impact of such variation. This analysis encompasses the identification of variants with respect to aligned sequences, domain architecture, PTMs, and interaction networks mediated by the protein isoform considered. Furthermore, over-representation analysis of functional categories is performed for each isoform-specific group to compare distinct biological processes mediated by isoform-specific interactors. These findings are then mapped onto the DE transcripts across cancers to obtain an integrated view of the functional consequences of DE spliceforms, whilst giving the possibility to process with the same pipeline additional DE transcript datasets. We also provide access to our webserver through an API interface, which allows for multi-gene queries.EXPANSION currently includes only human isoforms and a possible area of improvement will be considering protein isoforms from additional model organisms. It would also be interesting to integrate experimental, isoform-specific interactions from IntAct with computationally predicted ones. This will be particularly useful for protein isoforms that are currently not reported in IntAct. Furthermore, incorporating methods into the webserver to predict the effect of splicing on PPIs would be intriguing [e.g. based on protein language models , others, such as IntAct isoform specific networks are not. We therefore believe that our web-toolkit will offer researchers a comprehensive exploration of protein AS-forms and their potential functional consequences which is not fully available elsewhere, providing valuable insights for the study of these complex phenomena.


Proteochemometric (PCM) modelling is a powerful computational drug discovery tool used in bioactivity prediction of potential drug candidates relying on both chemical and protein information. In PCM features are computed to describe small molecules and proteins, which directly impact the quality of the predictive models. State-of-the-art protein descriptors, however, are calculated from the protein sequence and neglect the dynamic nature of proteins. This dynamic nature can be computationally simulated with molecular dynamics (MD). Here, novel 3D dynamic protein descriptors (3DDPDs) were designed to be applied in bioactivity prediction tasks with PCM models. As a test case, publicly available G protein-coupled receptor (GPCR) MD data from GPCRmd was used. GPCRs are membrane-bound proteins, which are activated by hormones and neurotransmitters, and constitute an important target family for drug discovery. GPCRs exist in different conformational states that allow the transmission of diverse signals and that can be modified by ligand interactions, among other factors. To translate the MD-encoded protein dynamics two types of 3DDPDs were considered: one-hot encoded residue-specific (rs) and embedding-like protein-specific (ps) 3DDPDs. The descriptors were developed by calculating distributions of trajectory coordinates and partial charges, applying dimensionality reduction, and subsequently condensing them into vectors per residue or protein, respectively. 3DDPDs were benchmarked on several PCM tasks against state-of-the-art non-dynamic protein descriptors. Our rs- and ps3DDPDs outperformed non-dynamic descriptors in regression tasks using a temporal split and showed comparable performance with a random split and in all classification tasks. Combinations of non-dynamic descriptors with 3DDPDs did not result in increased performance. Finally, the power of 3DDPDs to capture dynamic fluctuations in mutant GPCRs was explored. The results presented here show the potential of including protein dynamic information on machine learning tasks, specifically bioactivity prediction, and open opportunities for applications in drug discovery, including oncology.The online version contains supplementary material available at 10.1186/s13321-023-00745-5. Proteins are complex biological units that constitute the basis for cellular function. As such, studying their structure and interaction with the environment is a key aspect of preclinical drug discovery . In compSeveral types of protein descriptors are available for PCM modelling and similar applications –9. TheseWhile the goal of protein descriptors is to capture the full complexity of the protein, they largely fail to depict protein dynamism. At physiological temperatures, proteins exist in an equilibrium of structural conformations, which can be studied experimentally or simulated with Molecular Dynamics (MD) . ChangesG protein-coupled receptors (GPCRs) have extensively been explored as targets in bioactivity prediction, including PCM, due to their biological and therapeutic relevance , 20. GPCHere, 3D dynamic protein descriptors (3DDPDs) were developed leveraging atom coordinates and partial charges from publicly available single replicate MD simulations from GPCRmd. Two descriptor architectures were explored: embedding-like (protein specific—ps3DDPD), and one-hot encodings (residue specific—rs3DDPD). The performance in PCM GPCR bioactivity prediction of these novel protein descriptors was benchmarked against and in combination with a panel of state-of-the-art protein descriptors. Finally, the ability of our 3DDPDs to capture dynamic changes driven by (cancer-related) somatic point mutations in GPCRs was tested. These results highlight 3DDPDs as a stepping stone for further research on protein descriptors used for predicting drug-target interactions based on protein dynamics.3D dynamic protein descriptors (3DDPDs) were designed to capture the dynamic behavior of proteins in MD simulations. For this purpose, atomic coordinates were first extracted from the MD trajectories and their variability over a certain number of frames calculated. As proof of concept, 3DDPDs were conceived for single MD trajectory replicates in this work. In order to account not only for the position but also for the type of atoms in the protein, atomic partial charges were computed. Next, two strategies were developed to condense the dense atomic information into protein descriptors Fig. . These sThe 3DDPD generation strategy described above was optimized by comparing the descriptors’ performance on PCM modelling tasks. GPCRs were selected as the protein family for this case study given the availability of a large number of MD trajectories freely in the GPCRmd database . ParticuFirst, the “dynamic” properties derived from atomic coordinates were optimized. Here, the use of mean, median, and standard deviation from the mean (SD) or just the SD, representing the “rigidity” of each atomic coordinate was benchmarked. For rs3DDPDs, using SD resulted in better performance Fig. a, contra1 receptor. To ensure a consistent number of features per descriptor, in rs3DDPDs only the first two options could be tested, where the class A binding pocket performed significantly worse than the full sequence  values to a GPCR class A MSA  and nucleotide adenosine A1 receptor (AA1R) in their overall dynamic behavior), (ii) members of the same family but different subfamilies (e.g. nucleotide receptors adenosine A2A (AA2AR) and P2Y purinoceptor 1 (P2RY1) in TM2, ICL2, ECL2, ICL3, and C-terminus), or (iii) even members of the same subfamily (e.g. 5-hydroxytryptamine receptors 5HT1B and 2B (5HT2B) in N-terminus, TM3, TM4, ECL2, ICL3, and ECL3). Importantly, the main dynamic patterns described above were highly conserved for the three different replicates of the same system available on GPCRmd  models was benchmarked using 3DDPDs in combination with ECFP6 molecular fingerprints against models using as protein descriptors one of five other one-hot encoded descriptors  or one protein embedding (i.e. Unirep). The benchmark was carried out for classification and regression tasks using two different types of training-test splits: 80:20 random split and temporal split with 2013 as a cutoff year for the test set. The temporal split was introduced as a more accurate representation of a drug discovery campaign where data from the past is used to predict novel chemical entities developed later in time and indeed showed a considerable decrease in chemical bias compared to the random split (0.051 vs. 0.279).The bioactivity dataset compiled for bioactivity modelling contained 38,701 bioactivity datapoints heterogeneously distributed across the 26 targets  schema , which cAA223_PC3, corresponded to the BW position 3.32 in TM3. For further interpretability, this generic position can also be directly mapped to a specific residue in a protein of interest. As an example, in AA1R 3.32 it translated to Val 87 . The two other ECL positions are only available in other receptors , beta-2 adrenergic receptor (ADRB2), and CC chemokine receptor 5 (CCR5). The selection of mutations was done for the original set of 26 GPCRs when there was available mutagenesis data in GPCRdb (Table 7.42). Most mutant trajectories showed deviations from wild type trajectories in terms of RMSF , but most commonly spawned across the whole sequence or altered stability in distant regions. For example, in AA2AR L85A3.33 increased flexibility in ICL2 and ECL2 and S91A3.39 in ICL3 and TM6. Moreover, adjacent mutations that triggered different effects were observed. For example, in ADRB2, S203A5.43 decreased stability in TM1, ICL2, and ECL3, while S204A5.44 decreased stability in TM2 and TM4 while increasing stability in ICL3. Of note, in ACM2 D103E3.32 and D103N3.32 triggered similar higher flexibility in ECL1 and ECL2, with an overall differential pattern of lower stability in D103E3.32. In general, the mutations with smaller dynamic fluctuations from the wild type also corresponded to those with a smaller effect in bioactivity, such as AA1R R291C7.56 and R296C8.51, and ADRB2 V317A7.43 . These results supported the ability of rs3DDPDs to capture dynamic fluctuations in mutants. Nevertheless, the mutant discriminatory power of rs3DDPDs did not correlate directly to that of using directly RMSF  were developed that capture the dynamic fluctuations of GPCRs as observed in MD simulations. Our one-hot encoding (rs3DDPDs) and embedding-like (ps3DDPDs) descriptors matched the performance in PCM tasks of non-dynamic state-of-the-art protein descriptors, outperforming them in regression tasks with a more challenging temporal split validation. Moreover, by mapping the most important rs3DDPD features in regression models to their GPCR generic positions it was shown that 3DDPDs represent biologically relevant information for ligand binding and activation. Finally, 3DDPDs were employed to discriminate mutant GPCRs based on their dynamic behavior with promising results that could be translated to the field of oncological drug discovery.The MD simulations for the construction of 3D dynamic protein descriptors (3DDPDs) were obtained from GPCRmd  followinPython library MDtraj  was usedf, and the mean, median, and SD of the x, y, and z coordinates were calculated for each sub-trajectory. Additionally, atomic partial charges were generated for each atom in the system with RDkit Gasteiger charges calculator [Atomic coordinates were extracted from GPCRmd trajectories with MDtraj. Each trajectory was divided into sub-trajectories of a defined number of frames, lculator . The nexFor rs3DDPDs, coordinate statistics and partial charges per atom were collected for each target and standardized between 0 and 1. Subsequently, dimensionality reduction was applied in the form of PCA. A number of PCs for each atom were selected and grouped per residue as average and SD. A second dimensionality reduction step was applied to residue data and the selected PCs were placed in their matching sections corresponding to an MSA of the targets of interest.Protein-specific ps3DDPDs were generated similarly to rs3DDPDs with some differences. Firstly, coordinate statistics and partial charges per atom were collected for all targets together and standardized between 0 and 1. Secondly, atom PCA was not grouped per residue and no second PCA was applied. Instead, the PCs selected per atom were grouped per target as average, median, and SD, constituting the final descriptor.i)Trajectory data: the use of all statistical values derived from the x, y, and z coordinates was compared to just the SD, representing the “rigidity” of each atomic coordinate.ii)Frame split: number of frames included in each trajectory split, for which 10, 50, 100 and 500 frames were tested. This parameter was optimized on rs3DDPDs and the results were applied to ps3DDPDs.iii)Residue PCA (only for rs3DDPDs): number of PCs selected after residue data PCA, either 3, 5, 7, or 10.iv)Atom PCA coverage: variance explained by the selected number of PCs on atom data, either 95% or 99%.v)Atom selection: inclusion of atomic data from all heavy atoms or just non-carbon atoms.vi)Residue selection: strategies to focus the 3DDPDs on the protein binding site. These selections were based on structural-driven MSAs at different protein family levels, starting from using the full sequence, then the binding pocket of class A GPCRs, then of specific GPCR families, then GPCR subfamilies, and finally, target-specific binding pocket. To ensure a consistent number of features per descriptor, in rs3DDPDs only the first two options were tested.vii)Combination with classical protein descriptors: tested sequentially and, for the case of rs3DDPDs also embedded on the descriptor via the residue PCA.The generation parameters for the descriptors were randomly initialized and sequentially optimized. The parameters optimized included (in the following order):The optimization of 3DDPDs was done by comparing their performance with different parameters on PCM Bioactivity regression modelling on a temporal split.Hierarchical clustering dendrograms were computed to visualize similarities and differences between 3DDPD descriptors and dynamic behavior (represented by MD’s RMSF) across targets. Python package Scipy  was usedThe bioactivity dataset for PCM modelling was constructed starting from the highly curated Papyrus dataset version 5.50 . For thePCM modelling was implemented in Python 3.8  using thThe compound descriptors used were Morgan fingerprints of radius 3 (ECFP6) and length 1024 , pre-calTwo methods were used to split the PCM dataset into training and test sets. Firstly a random split was used, where 80% of the data was allocated to the training set and 20% of the data to the test set. Data for all targets was present in both the training and the test set. Secondly, a temporal split was used to provide the model with a more challenging validation task than the random split, where compound-target pairs first recorded before 2013 were allocated to the training set, and newer datapoints to the test set. The cutoff year was selected to make sure that all targets were represented in the test set. This resulted in a test set with 39% of the data, which was not equally distributed per target but showed considerably reduced chemical bias between training and test set compared to the random split. Chemical bias was computed as the asymmetric validation embedding (AVE) bias defined by Wallach & Heifets  using asr and RMSE for regression tasks. Other metrics are available in the additional data. For comparison purposes, a single average performance metric was calculated for QSAR RF models trained and tested on each target of the set independently.All RF models were trained using fivefold cross-validation, and the performance of the models was evaluated on the test set. The evaluation metrics reported were MCC for classification and Pearson Ten model replicates were trained for each protein descriptor benchmarked with random seeds 1234, 2345, 3456, 4567, 5678, 6879, 7890, 8901, 9012, and 9999. The seed was used for resampling, booth in the form of K-Fold shuffling in cross-validation and train/test splitting, the latter only in the case of a random split. Moreover, each model was initialized with a random seed as per default in Scikit-learn RF. The statistical significance of the differences in performance when using different protein descriptors was calculated by performing an independent T-test of the average performance metrics in the pool of model replicates. Differences were considered significant when p-value < 0.05. Performance comparison plots were generated in Python using the packages Matplotlib .In order to test the usage of 3DDPDs in mutants, several mutations for the GPCRs in the 3DDPD set were selected. To simulate a real application scenario, a mutant PCM dataset was created, gathering available mutagenesis data from GPCRdb for the GPCR 3DDPD set. Mutations with datapoints available for more than ten different ligands were selected.To extend the applicability domain, somatic mutations in cancer patients were extracted from the GDC database v22.0  for the Mutant MD simulations were performed according to the GPCRmd pipeline . EquilibRepresentations of proteins in 3D were generated using PyMOL 2.5.2 .Mutant MD simulations were computed both on a local Rocky Linux 8 server and the Leiden University High-Performance Computing cluster ALICE. The local server contains Dual Xeon(R) E5-2650 v4 12 core CPU, 512 G DDR4 memory, 7 Nvidia GTX 1080/8 Gb mem, and 1 GeForce RTX 2080 Ti/11 Gb mem. MD simulations were computed on one GPU node each. PCM modelling and data analysis was done in the aforementioned local Rocky8 system.Additional file 1: Tables S1-S3.Additional file 2: Figures S1-S8.


Sequenced Protein–Protein Interaction (PPI) prediction represents a pivotal area of study in biology, playing a crucial role in elucidating the mechanistic underpinnings of diseases and facilitating the design of novel therapeutic interventions. Conventional methods for extracting features through experimental processes have proven to be both costly and exceedingly complex. In light of these challenges, the scientific community has turned to computational approaches, particularly those grounded in deep learning methodologies. Despite the progress achieved by current deep learning technologies, their effectiveness diminishes when applied to larger, unfamiliar datasets.In this study, the paper introduces a novel deep learning framework, termed DL-PPI, for predicting PPIs based on sequence data. The proposed framework comprises two key components aimed at improving the accuracy of feature extraction from individual protein sequences and capturing relationships between proteins in unfamiliar datasets. 1. Protein Node Feature Extraction Module: To enhance the accuracy of feature extraction from individual protein sequences and facilitate the understanding of relationships between proteins in unknown datasets, the paper devised a novel protein node feature extraction module utilizing the Inception method. This module efficiently captures relevant patterns and representations within protein sequences, enabling more informative feature extraction. 2. Feature-Relational Reasoning Network (FRN): In the Global Feature Extraction module of our model, the paper developed a novel FRN that leveraged Graph Neural Networks to determine interactions between pairs of input proteins. The FRN effectively captures the underlying relational information between proteins, contributing to improved PPI predictions. DL-PPI framework demonstrates state-of-the-art performance in the realm of sequence-based PPI prediction.The online version contains supplementary material available at 10.1186/s12859-023-05594-5. Proteins constitute a central focus of investigation across multiple research domains, given their critical role as the primary organic components of cells. Rather than functioning in isolation, proteins engage in intricate interactions, catalyzing reactions between multiple proteins to accomplish specific tasks . These iThe prediction of protein interaction relationships offers valuable insights into shared functionalities and processes among different types of cancers , as wellAmong the protein interaction prediction methodologies in the domain of computer science, three principal categories are distinguished based on the biological information of proteins: structure-based models –11, geneThese approaches rely on several feature extraction processes for the protein sequences. In the research conducted by Shen et al. , this meThe application of computer methods for predicting Protein–Protein Interactions (PPI) can be divided into two main stages. The initial phase was dominated by Machine Learning technologies , involviIn recent years, Deep Learning has emerged as a promising technology capable of learning protein features more accurately and automatically, thereby enhancing the accuracy of PPI prediction. Numerous studies have employed Convolutional Neural Networks (CNN) to extract features from protein amino acid sequences –40. In 2However, it is important to acknowledge that GCN is still in the early stages of development, and many shallow models may struggle to capture features from higher-order neighbors. Additionally, some GCN models have shown limited effectiveness when applied to unknown PPI datasets . FurtherSince the introduction of AlexNet , Convoluitecture . This \dIn the subsequent paper, Inception V3 , the conThe Attention Mechanism is a technique that involves assigning weights to input elements to highlight the importance of certain features while de-emphasizing others, ultimately generating a weighted sum for a specific target. This mechanism is particularly useful when dealing with sets of input vectors and output vectors, both of varying lengths (N). By applying the self-attention mechanism, the problem of establishing correlations for multiple correlated inputs, which cannot be fully addressed by traditional fully connected neural networks, can be effectively resolved. The self-attention mechanism enables the model to recognize the interconnections between different components within the entire input.The Attention Mechanism initially emerged in the 1990s, finding applications in computer vision. In 2014, Google Mnih et al.  integratThis research paper introduces DL-PPI: Graph Neural Network for sequence-based Protein–Protein Interaction (PPI) Prediction, which presents an end-to-end deep learning framework tailored for sequence-based PPI prediction tasks. In the approach, proteins are treated as nodes, and protein interactions are represented as edges, resulting in the construction of undirected graphs. PPI prediction is framed as a link prediction problem, wherein the matrix X is formed using the features of each protein node, and the relationship between each node (PPI) is used to construct the adjacency matrix A, serving as the input to our model. By processing the graph with DL-PPI and leveraging Graph Neural Networks (GNNs), the paper aims to extract relationship features between proteins and consequently infer interactions involving unknown proteins.In summary, this paper contributes to three main aspects. Firstly, the paper proposes an end-to-end graph neural network-based model, specifically designed for predicting Protein–Protein Interactions between novel proteins. This model takes the sequence features of two proteins as input and predicts whether an interaction will occur. Secondly, we introduce a novel graph similarity algorithm that enhances the interaction representation between two protein node features, leading to more accurate relationship classification in the prediction module. Lastly, the proposed DL-PPI model demonstrates superior performance across different datasets, outperforming the GNN-PPI baseline  and provSTRING STRING version 10.5 [ion 10.5  is a pubFor the PPI prediction task, the paper adopted two datasets utilized in the PIPR study . The firFurthermore, the entire protein sequence from the Homo sapiens subset of STRING was utilized as the third dataset, namely STRING, which encompasses a total of 593,397 PPIs .Yeast dataset The Yeast PPI dataset is a widely recognized benchmark dataset extensively utilized in state-of-the-art methods [ methods , 32, 53. methods . The mod methods . NegativThese datasets were used to perform the Protein–Protein Interaction prediction task in the study.Data Pre-processing PPI data is pre-processed using a pre-trained embedding model to encode the protein information effectively.Protein Node Feature Extraction This step focuses on capturing protein features encoded in a one-dimensional space through sequence feature learning. Each protein undergoes processing using Inception methods to derive protein node-level features.Global Feature Extraction Protein maps are constructed, and a Graph Neural Network (GNN) is employed to learn the topology and relationships between proteins at a global level.PPI Prediction The features obtained in the previous step are input into a self-attention mechanism module, resulting in two protein features enriched with essential information. Subsequently, the interaction features of the two nodes are fused, and the type of interaction relationship between them is predicted using a Feature Relational Reasoning Network (FRN).The overall architecture of the DL-PPI learning process is illustrated in Fig. This paper introduces DL-PPI, a comprehensive end-to-end deep learning framework specifically tailored for addressing PPI prediction tasks. In this context, the PPI prediction problem is formulated as a multivariate classification task with seven distinct classes, all based on protein sequence data. DL-PPI is designed to capitalize on extensive protein and PPI datasets during the training process, empowering it to effectively predict interactions between two input proteins and ascertain the corresponding types of interactions based on the inherent sequence characteristics of the proteins. The DL-PPI method involves four main steps: The use of amino acid embedding enables the transformation of proteins into distinct dimensional spaces, allowing for the extraction of multidimensional information about these proteins. By representing proteins in these embedded spaces, the model can capture intricate relationships and patterns among amino acids, ultimately encoding a comprehensive representation of the protein’s characteristics.Amino acid embedding leverages techniques inspired by word embedding in natural language processing, where words are mapped to continuous vectors in a lower-dimensional space. Similarly, in protein sequences, each amino acid is represented by a unique vector in the embedded space, preserving relevant information while reducing the dimensionality of the data. This process enhances the model’s ability to recognize similarities, correlations, and functional characteristics among different proteins based on their embedded representations.Amino acid embeddingsa, is represented as the sum of two subembeddings, i.e., In our approach, each amino acid vector, denoted as am model  on proteThe second part, operties . Additiooperties .By employing this dual subembedding representation, it is effective to capture both co-occurrence similarity and categorization information of amino acids, facilitating a comprehensive and informative representation of proteins in a lower-dimensional space. This enhanced representation is crucial for improving the performance of the DL-PPI framework in predicting Protein–Protein Interactions and gaining valuable insights into the complex interactions among proteins.In previous studies , 52, it Inception modulea represents the feature dimension (set as 13) obtained from the previous step. Inception V3 is utilized to capture the distinctive features of the protein sequence. This model comprises four sub-modules, structured as depicted in Fig. In the context of the Inception module, the matrix dimension is denoted as entclass1pt{minimaBy integrating the Inception module into the DL-PPI framework, this paper aims to leverage the power of multi-scale feature extraction to better capture the intricate characteristics of protein sequences and improve the accuracy of PPI prediction. This approach enhances the model’s ability to discern subtle patterns and correlations between amino acid sequences, thus facilitating more precise and reliable predictions of protein interactions.P represents the proteins and X represents the interactions between proteins.In this section, the researchers explore the methodologies employed in previous studies  for learThe protein network graph is constructed utilizing all the training sets. For enhanced generalization capability and improved performance in predicting unknown proteins, it is advisable not to include all proteins. This is especially relevant since, for unknown proteins, their neighboring nodes are typically unknown. The protein node features obtained from the protein node feature extraction module are employed as input for the protein graph. Within this graph, protein data serve as nodes, protein interaction data function as edges, and the graph is systematically built by identifying and amalgamating common protein interaction types among different proteins as links to the edges. This approach ensures a more robust representation of the protein network, enhancing its utility in predicting interactions involving previously uncharacterized proteins.p at the kth iteration. k represents the iterations, Through this construction process, the model demonstrates robustness in predicting interactions involving unknown proteins. Using the Graph Isomorphic Network (GIN) approach  for learBy leveraging the GIN model to update node features within the PPI network, the model can effectively learn comprehensive graph-level representations that encapsulate the complex relationships and interactions between proteins. This approach empowers the DL-PPI framework to achieve improved predictive performance, particularly when dealing with interactions involving previously unseen proteins.The Attention mechanism involves processing the input raw data in the form of <Key, Value> pairs. Given a specific Query, the goal is to calculate the similarity coefficient between the Key and Query, resulting in the corresponding weight coefficient for the associated Value. These weight coefficients are then used to weight and sum the Values, yielding the final output.Q,K and V to represent the Query, Key, and Value, respectively. The formula for calculating the attention weight factor, denoted as W, is shown in Eq. In the context of the DL-PPI framework, we use Q,K, and V are derived from the same matrix by different linear transformations. This design choice enables the self-attention mechanism to focus on the connections within the inputs, thereby extracting more comprehensive feature information from the protein nodes. This attribute makes it particularly suitable for predicting interactions involving unknown proteins.The self-attention mechanism, represented as Eq. X represents the input matrix, and The self-attention mechanism is mathematically formulated as follows Eq. Indeed, by incorporating the attention mechanism, the DL-PPI framework can effectively concentrate on the most relevant information and features within the data. This allows the model to capture and exploit the intrinsic relationships and dependencies present in the protein nodes, while reducing the emphasis on less important elements. The attention-weighted aggregation process enhances the feature extraction capabilities of our framework and leads to improved prediction accuracy, particularly for interactions involving unknown proteins.R. Let The objective of Neural Tensor Networks (NTN)  is to deTo calculate a likelihood score for the existence of a relationship between the two entities, the NTN-based function is formulated as Eq. R are represented by The parameters specific to relation By utilizing NTN, the model can effectively capture complex relationships and interactions between entities in multiple dimensions, enabling more expressive representations and improving the capability of the model to reason about relationships and make accurate predictions. The NTN-based function plays a critical role in enhancing the overall performance of the model in relation prediction tasks.The NTN has demonstrated superior performance in computing relationship scores compared to traditional methods. However, its reliance solely on parameter updates during training overlooks the internal correlation between pairs of input vectors, potentially affecting the task of fine similarity computation.To address this limitation, this paper proposed a new method called the Feature Relational Reasoning Network (FRN). The structure of the FRN module is illustrated in Fig. In the redesigned FRN module, the paper visualizes the interactions as follows in Eq. Additionally, to capture the cosine similarity between In the final step of the DL-PPI framework, the model utilizes a fully connected layer (FC) as a classifier for multi-label PPI prediction. The activation function used in this layer is the Rectified Linear Unit (RELU).For the multi-label PPI prediction task, the paper employs the Multi-task Binary Cross-Entropy Loss . By employing this loss function, the model can effectively learn the relationships between proteins and make accurate predictions for various types of interactions. The FC layer with RELU activation function serves as a crucial component in the classification process, contributing to the model’s capability to handle multi-label PPI prediction tasks.In this section, the model presents the results of the proposed method on three distinct datasets: SHS27k, SHS148k, and STRING. The paper evaluated the performance using various evaluation measures to assess the effectiveness of the DL-PPI framework in predicting PPI. In addition, the performance of the proposed method was compared with the methods previously reported in the literature. Finally, a generalizability analysis is carried out to explore how well the model performs on unseen data and conduct ablation experiments to understand the contribution of individual component to the overall performance.The evaluation of the method involves measuring its accuracy, precision, recall, F1 score, and other relevant metrics on the three datasets. The paper analyzes the results to assess the model’s ability to predict PPI and identify the most informative features contributing to accurate predictions.Overall, the Results section provides a comprehensive evaluation of the proposed DL-PPI framework, its comparison with existing methods, and a detailed analysis of its generalizability and key features. These findings contribute to a better understanding of the model’s capabilities and potential implications for future research and practical applications in the domain of Protein–Protein Interaction prediction.In the experimental setup, the paper utilized four different datasets: SHS27k, SHS148k, STRING and Yeast datasets as the basis for comparison with four state-of-the-art protein interaction prediction models: DNN-PPI , PIPR 5, TAGPPI In the evaluation of the proposed DL-PPI method, the primary criterion employed was the Micro-F1 metric. The choice of Micro-F1 is suitable to this problem, as it considers data distribution in situations where imbalanced data is present, and assigns equal importance to each sample.The evaluation metrics used to compute the Micro-F1 score included Precision, Recall, and F1-score, defined by Eq. Micro-F1 is computed from the point of view of the samples, considering each sample as equal and ignoring the category weights between the samples. By calculating the sum of Precision and Recall for all categories (here 7 categories), the Micro-F1 score was computed based on the Precision, Recall, and F1-score values, providing a comprehensive evaluation of the DL-PPI model’s effectiveness in predicting Protein–Protein Interactions and its capability to handle imbalanced data distributions across various datasets.In the experimental settings, the Breath-First Search (BFS) and Depth-First Search (DFS) methods proposed in a previous paper  was usedRegarding protein features, the authors utilized amino acid sequences  and reprFor optimization during training, the authors opted for the Adam algorithm , a widelDuring the training process, a batch size of 2048 was employed, and the model was trained for 300 epochs. Each epoch represented one complete iteration through the entire training dataset.These chosen experimental settings ensured a consistent and rigorous evaluation of the DL-PPI model’s performance on the selected datasets. This approach provided a robust basis for comparing the results with other state-of-the-art models and assessing the model’s capabilities in predicting protein–protein interactions.In this study, we conduct a comprehensive benchmark analysis to showcase the effectiveness and superiority of the proposed DL-PPI framework by comparing its performance with other state-of-the-art methods commonly used in similar prediction tasks. This comparative evaluation aims to highlight the advancements and advantages of the proposed approach in the field of PPI prediction.The results of the benchmark analysis are presented in Table Under the Random assessment, most of the models demonstrate a significant performance advantage over the BFS and DFS methods. This observation suggests that these models exhibit a strong ability to effectively learn protein models with discretely distributed features.Furthermore, the DL-PPI model stands out as it does not experience a substantial decrease in performance under the BFS and DFS evaluations compared to the other methods. This indicates that the DL-PPI model is adept at learning more informative features from the protein neighbourhood nodes, leading to higher accuracy in predicting novel protein interactions.Fig. In addition, the evaluation of the method was conducted using the Yeast dataset, and DL-PPI was compared to four other models. All models listed in Table Overall, these findings further validate the effectiveness of the DL-PPI model and highlight its potential for accurately predicting protein–protein interactions, especially in scenarios involving unknown proteins.The researchers conducted ablation experiments to systematically remove or modify specific components of the DL-PPI framework. This enabled them to gain valuable insights into the individual contributions and impact of these components on the overall predictive performance.To assess the model’s generalization ability on an unknown dataset, a comparison experiment was designed, as presented in Table This module were conducted to assess the individual contributions of each component in the DL-PPI model. The experiments were performed on the SHS27k, SHS148k, and STRING datasets using the random classification method with an epoch of 200. Multiple combinations of Inception, Attention, and FRN were examined to determine their impact on the overall performance.The results of the ablation studies are presented in Table The objective of this experiment is to conduct a comparative analysis between the FRN  module in DL-PPI and the method NTN proposed in the paper Neural Tensor Networks . The expIn this section, we enhance the comprehensiveness and persuasiveness of our experiments through horizontal validation. Subsequently, Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted on the predicted proteins within the STRING database, as illustrated in Figs. GO is a database created by the GeneOntology Consortium to establish a standard semantic vocabulary for qualifying and describing the functions of genes and proteins across a wide range of species. GO is one of several biological ontology languages that provide a systematic way of defining a three-layer structure for describing the functions of gene products. They divide the function of a gene into three parts: Cellular Component (CC), Molecular Function (MF), and Biological Process (BP).KEGG was established in 1995 as a database integrating genomic, chemical, and systemic-functional information. KEGG combines gene catalogs derived from genomes that have been completely sequenced with higher level cellular, species, and ecosystem-level system function associations.Horizontal coordinate suggests the three basic GO categories  and the next level of TERMs for each category, from which you can see which specific TERMs describe BP, CC or MF. The vertical coordinate indicates the number of genes annotated to a term (the term and its subterms). The results in Fig. The results in Fig. In conclusion, this paper have introduced DL-PPI, a novel deep learning-based model for protein–protein interaction prediction that solely relies on protein sequences as input data. Unlike existing methods with limited generalization ability for unknown proteins, DL-PPI leverages a more profound understanding of protein feature interactions to predict interactions involving novel proteins. The model involves preprocessing of protein data, node feature extraction, graph level feature extraction, attention module, and FRN module, culminating in prediction and classification. Through extensive experiments, we have demonstrated the effectiveness of the Attention and FRN modules in improving the model’s performance.The experimental results have shown that DL-PPI surpasses state-of-the-art PPI prediction methods, particularly in predicting interactions with previously unseen proteins. The ability to accurately predict novel protein–protein interactions makes DL-PPI a valuable tool for advancing the field of protein interaction prediction and opens up possibilities for various applications in the life sciences.Additional file 1:  Comparison of Precision and Recall.