Protein complexes are responsible for the enactment of most cellular functions. For the protein complex to form and function, its subunits often need to be present at defined quantitative ratios. Typically, global changes in protein complex composition are assessed with experimental approaches that tend to be time consuming. Here, we have developed a computational algorithm for the detection of altered protein complexes based on the systematic assessment of subunit ratios from quantitative proteomic measurements. We applied it to measurements from breast cancer cell lines and patient biopsies and were able to identify strong remodeling of HDAC2 epigenetic complexes in more aggressive forms of cancer. The presented algorithm is available as an R package and enables the inference of changes in protein complex states by extracting functionally relevant information from bottom-up proteomic datasets. AlteredPQR is a software tool, available as an R package, to infer remodeling of protein functional modules from whole-cell or tissue lysate proteomic measurements. MS-based proteomics is reaching speed, accuracy and consistency that make it suitable for clinical applications and biomarker assessments4. Notably, assessing not only individual protein quantities, but also the overall protein organization in the cell, could provide stronger associations to cellular and organism phenotypes. For establishing the cellular identity and response to stimuli, quantitative distribution and modular organization of cellular proteins are as important as the overall composition of its proteome. Core units of cellular functional modules are stable protein complexes. Due to the mechanism of complex assembly, individual subunits are often required to be expressed in defined quantitative relationships5. An implication of this is also that disease-associated deletion or inactivation of one complex member can directly influence quantities of other proteins in the same complex7, as well as the overall quantity and functional capacity of the complex. A growing body of evidence has demonstrated that proteomic data outperforms transcriptomic measurements in detecting correlated expression levels of protein complex members9. Moreover, protein expression correlation, when integrated with data on the composition of protein complexes, can provide a powerful means for defining stoichiometries of large complexes10 and detecting subunit exchange. A map of cellular functional modules is not static and formation and dissociation of functional modules can be essential for the enactment of condition- or disease-specific cellular processes; however, systematic probing of protein organization in the cell is time consuming and technologically challenging11.Thanks to recent developments in mass spectrometry (MS) and related software toolsHere we developed a software tool to infer remodeling of protein functional modules from whole-cell or tissue lysate proteomic measurements. This approach enables identification of disease-associated alterations in protein quantitative relationships (PQRs) and represents a new concept that can also be applied to biomarker studies. Changes in PQRs are expected to have stronger phenotypic effects when they disrupt finely tuned cellular processes, such as the formation of a protein complex or regulation of a signaling pathway. We made the method available as an R package called AlteredPQR  and appl15 (z score estimates (z scores (>3.5) are summed up and a P value for each pair is calculated by using a background score distribution for random protein pairs in which proteins were randomly assigned reference sample measurements of the studied proteins; and (4) finally, to ensure non-redundancy in the final list of significant protein pairs, each protein that was contributing to the signal is represented with the pair that had the highest total score. The last step ensures that in cases where the abundance of a single protein is strongly up- or downregulated, this change is still represented with only one pair. Together, this allows for identification of easily interpretable shifts in PQRs and confident detection of multiple outliers in tested samples.The AlteredPQR software tool is applicable to quantitative proteomic datasets generated with a wide range of available MS methodologies. Our aim was to design an approach that 1) can identify multiple outlier values in test samples; (2) is easily interpretable; and (3) is able to compare a priori biologically different samples, such as disease samples to corresponding healthy controls. For the latter, we use for instance the non-disease set to obtain an insight into the distribution of the reference PQRs, as well as the technical and biological variability in the measurements. Here, we focused only on protein pairs that are known or inferred to be associated through forming a protein complex. For this, we used a list of interacting proteins compiled from several publicly available databases and experimental datasets, including CORUM, Reactome and Interactome3D complex assignments and protein pairs found in multiple affinity-purification MS studies15 . It is i15 . They ra can idenstimates ; (3) sig9. BC is a heterogeneous disease where patient classification and treatment decisions are primarily based on the immunohistochemistry assays that assess expression of estrogen (ER) and progesterone (PR) hormone receptors, as well as of HER2 tyrosine kinase receptor. Severity of the disease also differs among the subtypes; patients with luminal A (defined as ER- and PR-positive and with a low proliferation rate) tend to have the most favorable prognosis, whereas patients with triple negative (TN)  lack evident drug targets and tend to experience aggressive disease progression  and that they had a substantial overlap of top hits  intuitive interpretation of results and quick assessment of the validity of identified outliers; (2) straightforward implementation of the nonparametric approach that is not sensitive to technical outliers in the reference set; and (3) predefined and widely accepted outlier threshold (absolute (Mi) > 3.5) that simplifies the definition of multiple outlier samples16. Of note, the 3.5 threshold can still be associated with >5% of false-positive outlier values, depending on the reference sample size16.We first compared the modified 17 and were more strongly enriched in cancer related roles when compared to significant hits from a standard protein-level comparison -adjusted P value < 3.1 × 10−5, Fisher’s exact test; Supplementary Note 9 to assess phenotypic associations of the identified altered PQRs. We found that PQRs of several protein pairs were predictive of a response to drugs that were previously discussed as candidates for TN BC treatments  BC cell lines as a reference set  and looked for significant outliers among the basal BC cell lines. Of note, the majority of TN cancers are of a basal origin and 22 out of the 24 basal cell lines used in the analysis were also TN. We found that by analyzing the altered PQRs, it was possible to obtain information on the plausible remodeling of protein interactions, which is not readily evident from studying individual protein quantities. Protein pairs with strongly altered PQRs in basal cell lines -methyltransferase proteins 3A and 1 (encoded by the DNMT3A and DNMT1 genes). Relative protein quantities across samples for the respective pairs are depicted in Fig. 18 and found that AlteredPQR scores for several signaling proteins strongly associated with activities of the associated downstream pathways  separately in luminal A and separately in basal samples. We considered that a protein pair had a strong correlation shift between the two BC subtypes only when the pairs significantly correlated in one, but not in the other subtype . We also required the shift in the correlation values between the two BC subtypes to be clearly evident . In this way, we identified in total 464 protein pairs with a strong correlation shift (Supplementary Table r > 0.6), but did not correlate at all, or had a negative correlation, in luminal A samples.For protein subunits of the same complex, a substantial change in the correlation of their expression values between different sample groups can indicate an assembly or disassembly of the respective protein complexesq value < 5 × 10−4, hypergeometric test; Fig. 26. Instances in which both proteins had an annotated role in epigenetic regulation are listed in Table 13, eight of these proteins  can be found within the HDAC2 complex, five proteins  are subunits of the nucleosome remodeling and deacetylation complex, whereas six are components of the related nucleosome remodeling and histone deacetylation (NuRD) complexes 1 and 2 . In addition, eight of these proteins  are subunits of the SIN3–ING1b complex. Some of the proteins have well-established roles in BC, such as protein encoded by the CHD4 gene, which can promote BC growth and progression27, protein encoded by the RBB4 gene, whose high expression levels associate with poor BC prognosis28 and protein encoded by the HDAC2 gene, which is found overexpressed in different cancers and considered an important target in cancer therapy29 . Following, for each protein pair, the log ratio values in the reference samples were used to estimate a background distribution that captures technical and biological variability, and log ratio of protein quantities in each test samples was assessed to identify outliers from the background distribution. This was performed using the modified z score statistic:The AlteredPQR algorithm relies on the availability of a reference set, which is used to estimate a background distribution of values and a test set, which is assessed to find outliers from this distribution. The input file for the algorithm is a data matrix with log-transformed protein quantitative measurements (rows are protein identifiers and columns represent studied samples). Columns in the matrix which correspond to reference and test samples need to be defined by the user (see R package Vignette for examples). In addition, the algorithm relies on a list of protein pairs that are known to form stable interactions, such as those within a protein complex. The list should be based on previous knowledge or on inferred complex assignments. The one we used in this study is also available with the package, but the list can be replaced by the user. It is composed of human protein pairs that belong to the same CORUM protein complexMi indicates a modified z scorei indicates a log ratio of protein quantities for each protein pair in an individual test sampleχ16). The threshold 3.5 is widely accepted in different implementations of the test. Of note, this threshold can still be associated with >5% of false-positive outlier values depending on the sample size. AlteredPQR scores were obtained by summing up significant outlier values. To associate the scores to P values, a background sample set composed of randomly sampled reference values was generated, analyzed proteins were grouped into random pairs, and AlteredPQR scores were calculated for these datasets. Distribution of the background scores was estimated with descdist and fitdist functions from the fitdistrplus R package (v.fitdistrplus_1.1-3). As this reported gamma distribution features, P values for the AlteredPQR scores obtained for the real datasets were calculated with the pgamma function using the estimated shape and rate of the simulated background distributions obtained by the fitdist function implementation of the matching moments method. The P values were adjusted for multiple testing with the BH method  value >3.5 in individual test samples were considered as significant outliers. This threshold was proposed by Iglewicz and Hoaglin, based on their simulation study in which they calculated a proportion of random pseudo-normal observations classified as outliers based on 10,000 replications, in sets of the sample sizes 10, 20 and 30 . Here, for individual proteins, we required an absolute modified In addition, proteins and protein pairs which were highly variable in BC cell line luminal samples, were excluded from the outliers list. Filtering for variability was used to overcome the limitation of a small reference set size and avoid noise. Criteria for this were:z scores higher than 3.5 and (2) pairs that included proteins which in at least three luminal samples had values higher or lower than the median of log protein quantity in luminal samples ±1.4826 × 2-times its MAD in luminal samples.(1) pairs which could be classified as outliers in three or more luminal samples according to the same thresholds used for non-luminal samples; that is the absolute modified GO:0005200. In addition, ribosomal proteins  were also excluded, due to the lack of isoform-specific annotations and hence inability to annotate the functional impact of the altered PQRs. This included all proteins annotated with the GO term GO:0003735.Furthermore, for the following analyses, proteins associated with cytoskeleton functions were excluded to avoid instances where changes in protein expression reflect variable contribution of tumor stroma. These encompassed proteins annotated with the GO term For protein pairs detected as outliers, Pearson correlation of expression levels was calculated separately in reference samples and separately in all tested samples, as well as only in tested samples with altered PQRs.P value higher than 0.05, implying that the distribution of the data was not significantly different from normal distribution. Modified z scores require approximately normal distribution of values.Values in reference luminal samples were tested for normal distribution with the Shapiro–Wilk test. Overall, 92% and 84% of protein pairs in the analyzed BC cell line and patient samples, respectively, had a P values were corrected for multiple testing with the BH method. Enriched Reactome terms were obtained by uploading significant hits and background of analyzed proteins in the ConsensusPathDB database. There, significant P values are calculated with the hypergeometric test and corrected for multiple testing with FDR32.The AlteredPQR algorithm was applied on several quantitative proteomics datasets. To identify cellular functions which were most affected by the AlteredPQRs, proteins in the resulting lists were annotated with their KEGG and Reactome pathway assignments. To identify significantly enriched terms background sets were composed of proteins which were significant on the individual level, and all other measured proteins that entered the analysis, that is proteins that were listed as members of protein complexes. The former set of individually significant proteins was composed by performing Wilcoxon test between reference and test datasets and by considering only measured proteins that were also members of protein complexes. Enrichment of KEGG terms between proteins with altered PQRs and those in background sets was assessed using two-sided Fisher’s exact test. The obtained 33), BC-associated genes (obtained from DisGeNET annotations34 or Helios software assignments35) and BC subtype-specific essential genes (reported elsewhere36) were assessed with the chi-squared test.Differences among the three sets with respect to the fraction of known cancer drivers  values, which corresponded to BC cell survival and proliferation after drug treatment, were obtained from a published study13. Next, using a two-sided Fisher’s exact test, it was assessed whether any of the protein complexes were overrepresented on the resulting list.To identify protein complexes with multiple subunits affected by altered PQRs, proteins with significant PQR changes were grouped according to their CORUM assignmentsIn addition, it was assessed whether the whole protein complexes were also up- or downregulated in the tested samples. For this, only CORUM complex subunits measured across the majority of samples were considered and only samples in which all of the selected subunits were measured were considered further. Median expression levels of these subunits were used to represent protein complex expression. A procedure analogous to the one described above was used to identify outliers: median and MAD of protein complex expression levels in the reference samples were calculated and protein complex expression values in the individual test samples were compared to these values to identify significant outliers. Only complexes whose expression levels were categorized as outliers in 10% or more of the tested samples were considered further.18. For each protein pair with a significantly altered PQR, all studied samples were divided into two categories according to the quantitative ratio of the two proteins, which is based on whether this value was considered as an outlier in the respective sample or not. Pathway activities in the two categories were compared for all pathways with the assigned scores and, when any pathways with differences in their activities were found, the three most-significant pathways per pair were listed. To avoid redundancy, pathways whose proteins overlapped (>80%) with more significant pathways were omitted.Pathway activity scores in samples from patients with BC for which quantitative proteomics data were analyzed were obtained from the same studyProtein pairs with significantly altered PQRs between the patient samples with luminal A subtype and all other samples were further assessed to identify whether any of these were subtype specific. Their quantitative ratios were compared between the subtypes using a Wilcoxon test. Samples in which the tested pairs had most significant differences in PQRs between the luminal B and basal samples were clustered and visualized using the R heat map tool.It was considered that proteins have a role in epigenetic regulation if they were cataloged as ‘Chromatin modifying enzymes’ in Reactome or annotated either with a GO molecular function term ‘Chromatin binding’ or a GO biological process term ‘Chromatin remodeling’ Table .Further information on research design is available in the Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41592-023-02011-w.Supplementary InformationSupplementary Notes 1–4, Figs. 1–4, and legends of supplementary tables and description of supplementary software.Reporting SummarySupplementary SoftwareR package for the inference of protein complex states from quantitative proteomics data. The package takes information on known stable protein interactions (that is protein components of the same complex) and assesses how protein quantitative ratios change between different conditions. It reports protein pairs for which relative protein quantities to each other have been significantly altered in the tested condition.Supplementary TablesSupplementary Tables 1–4.


Integral membrane proteins are involved in a plethora of biological processes including cellular signalling, molecular transport, and catalysis. Many of these functions are mediated by non-covalent interactions with other proteins, substrates, metabolites, and surrounding lipids. Uncovering such interactions and deciphering their effect on protein activity is essential for understanding the regulatory mechanisms underlying integral membrane protein function. However, the detection of such dynamic complexes has proven to be challenging using traditional approaches in structural biology. Native mass spectrometry has emerged as a powerful technique for the structural characterisation of membrane proteins and their complexes, enabling the detection and identification of protein-binding partners. In this review, we discuss recent native mass spectrometry-based studies that have characterised non-covalent interactions of membrane proteins in the presence of detergents or membrane mimetics. We additionally highlight recent progress towards the study of membrane proteins within native membranes and provide our perspective on how these could be combined with recent developments in instrumentation to investigate increasingly complex biomolecular systems. Integral membrane proteins comprise approximately one-third of the human proteome  and undeUnderstanding how endogenous binding partners and drugs impact the structures and function of membrane proteins is a key step towards elucidating protein structure–function relationships. Advances in experimental and computational techniques have enabled access to hitherto elusive knowledge of membrane proteins’ working mechanisms and their interactions with other biomolecules. These methodological advancements range from developments in membrane protein crystallisation  and soluBy integrating native MS alongside other structural techniques, deep insights into the relationship between protein sequence, structure, dynamics, and function can be derived. The ease of coupling native MS with ion-mobility and hydrogen-deuterium exchange MS enables access to information regarding protein folding, conformation, and dynamics . SimilarThere has been a multitude of excellent, in-depth reviews focused on the study of membrane protein structure by native MS ,31–33. Hn-dodecyl β-D-maltoside) and LMNG  are often considered the preferred detergents for protein extraction and purification from cellular membranes as they create a stable environment for proteins [A fundamental challenge associated with handling membrane proteins outside of their cellular context is to maintain their native fold in aqueous buffers. This is often achieved using detergents, which mimic the phospholipid bilayer ,35. Deteproteins . Howeverproteins ,40. Therproteins ,42. TablExcitingly, new detergents with modified headgroups, linkers, and chain configurations continue to emerge. A notable example in this regard is the development of a ‘tailored’ OGD-based detergent that combines the membrane solubilisation and protein extraction efficacy of DDM with the ease of protein release from C8E4 micelles . SynthesDetergent-purified proteins can also be reconstituted into other membrane mimetics, such as bicelles, amphiphilic polymers, liposomes and nanodiscs, for native MS analyses ,46. HoweAlthough the use of native MS in probing the interactions of membrane proteins has been well described in recent reviews ,33, hereWhile native MS (not coupled with doing multiple rounds of MS/MS), does not provide localisation information on covalent modifications to proteins, it can still play a role in the characterisation of individual proteoforms. Such measurements can enable the effects of PTMs on protein interactions to be directly identified. For example, Harvey et al.  employedn), known as native top-down MS, can enable the identification and characterisation of specific proteoforms and post-translational modifications. In an elegant study, Ro et al. [On the other hand, coupling native MS with tandem MS (MSo et al.  recentlyA more common application of native MS is in the investigation of non-covalent biomolecular interactions, such as protein–protein interactions. Fantin et al.  comparedLipids can influence the structure and function of membrane proteins. For example, while some oligomeric membrane proteins contain strongly bound inter-subunit interfaces, other weakly oligomerise and readily dissociate into monomers or lower oligomeric forms upon extraction from their native lipid environment. It has been suggested  that in Another interesting area of research is the interaction of membrane proteins with drugs or antibiotics. Using native MS, the antibiotic chlorhexidine was shown to induce a change in the oligomeric state of the chlorhexidine efflux pump AceI . Chlorhe+. Such ions have been previously shown to regulate the assembly of several membrane protein complexes [+ binding in the presence of antagonists and suppressed Na+ binding in the presence of agonists. This result is consistent with previous structural studies that showed the presence of an allosteric Na+ binding pocket in inactive conformations of Class A GPCRs, which is absent in the active conformations. For GCGR, a more stable conformation was observed in NaCl/Tris buffer compared to ammonium acetate, as indicated by the lower charge state distribution and enhanced binding of the stabilising negative allosteric modulator, NNC0666, observed in the former buffer. These results highlight the applicability of native MS to study membrane proteins from physiologically relevant salt buffers. More recently, another native MS approach was developed to interrogate the propensity of β1-adrenergic receptor coupling to Gs and Gi. Upon capturing the G-protein-coupling complexes in presence of different ligands, it was observed that endogenous zinc ions specifically stabilise the complexes between G-proteins and receptors. Overall, this study uncovered a novel role for metal ions in modulating the selectivity of G-protein coupling [One type of interaction that is challenging to observe by native MS is the binding of proteins to non-volatile salt ions, such as Naomplexes . In natiomplexes . Neverthomplexes . This apomplexes  to studycoupling .To probe the interactions with ligands, in one study Gray et al.  combinedWhen using even the most ‘native-like’ membrane mimetic, there is always a risk of experimental artefacts caused by the extraction of the protein from its native membrane environment. Recently, membrane proteins and their complexes have been released into the mass spectrometer directly from their native membrane, marking a significant shift towards native MS of membrane proteins in the most native-like contexts. In 2018, Chorev et al.  used natIn a very recent study, Chen et al.  ejected Despite the numerous advantages and a recently published protocol , the praNative MS is a versatile and rapid technique for obtaining non-covalent interactions of membrane proteins that are often difficult or outright intractable to other techniques. It can be used to study membrane proteins in a varied range of membrane mimetic systems, notably in their own native lipid bilayer. Advances in the synthesis and commercialisation of mild and native MS-compatible detergents will continue to be relevant for investigating protein–drug/lipid binding where highly purified samples are required. Increasing the repertoire of activation regimes that enables release of intact oligomers from lipid bilayer nanodiscs will allow selective disruptions of the nanodiscs with minimal dissociation of the encapsulated protein complexes. Such an advance will pave the way for releasing intact proteins from more complex biological membranes and tissues for which strong lipid–protein interactions must be overcome.Recent advances in native MS have enabled protein assemblies to be released from intact lens tissue . For sucIn the modern era of structural biology, native MS has emerged as a powerful tool for defining the molecular sociology of a variety of membrane protein targets including transporters, channels, and receptors. Starting off with detergents and membrane mimetics, this area of research is now going through a transformational period towards more native environments.Although still in its infancy, analysis of complexes directly from native membranes by native MS has already managed to capture several key signalling events. However, the full potential and widespread use of this approach are yet to happen.In addition to optimising sample preparation strategies, there is a clear need for new instrumentation, which combines sensitivity, dynamic mass range, resolution, and access to diverse ion activation techniques including HCD, ECD, ETD, IRMPD and UVPD.


Protein–protein interactions (PPIs) play a crucial role in many biological processes, including antibody–antigen binding, cell apoptosis and signal transduction . AlteratThe change in binding affinity caused by AA mutations, or omplexes . Over thocument} . Early \mentclasspt{minimaas FoldX  and BeAtas FoldX . These mas FoldX .In recent years, the rapid expansion of experimental data and computational resources led to the emergence of machine learning methods, especially those mainly focusing on characterizing geometric properties of protein structures . The strocument} . For insg et al.  developes et al. . Furthermentclass2pt{minimocument} , 21.In addition to traditional machine learning methods, the deep learning-based geometric method graph neural network (GNN) could be a more efficient way for extracting structural representations of protein–protein complexes. This is because it can directly take the geometric graph which can be easily constructed from raw protein coordinates as model inputs [22]. A typical processing pipeline for such methods involves acquiring a protein–protein complex graph after AA site mutations, then inputting the WT and mutant protein–protein complex graphs into a GNN to obtain the respective graph structure representations. The Most machine learning and deep learning methods currently rely on a combination of energy-based and structural-based features to predict the mutation . HoweverMoreover, transfer learning, which aims to capture general regulations of downstream samples through pre-training the model on more accessible other data (not involving the type of labels in downstream tasks) , can heltructure . Mutant mentclasspt{minimas  and dees (FoldX ), among To summarize, a more efficient and sufficient characterization and learning of comprehensive geometric relationships within PPI systems (in which the mutant structures are unknown), under insufficient labeled PPI We conducted extensive experiments on MpbPPI to evaluate its performance in conjunction with three distinct mutation structure generation tools: FoldX, MODELLER and AlphaFold2. Our approach achieved state-of-the-art performance on three single mutation datasets and one more challenging multiple mutations dataset under two realistic evaluation settings. The results demonstrate that MpbPPI is a promising approach for predicting protein–protein binding affinity alterations caused by AA mutations.The rest of the paper is organized as follows. We first provide the overall description of our proposed framework in Overview of MpbPPI section. The details of our experiments are given in the Results section. The analysis about the extension and flexibility of MpbPPI is elucidated in the Discussion section, and the detailed model implementation information is laid out in the Methods section.As an overview , MpbPPI Next, to better facilitate the learning of geometric regulations of the PPI structures, we devised four pre-training tasks (to be performed simultaneously) for the protein geometric equivariant encoder (GEE) of MpbPPI, including (residue) backbone denoising, side chain denoising, solvent-accessible surface area (SASA) prediction and AA type prediction. Intuitively, multiple pre-training objectives aiming at learning geometric properties of individual residues themselves from different perspectives will encourage the GEE to capture the comprehensive interactive relationships within a complex, which is ultimately beneficial to generate high-quality PPI structural representations for downstream analyses. Based on this intuition, we corrupted the contact graphs of each pre-training protein–protein complex by adding noise to the 3D coordinates of the randomly chosen residue backbone and side chain atoms, and the corrupted graphs were sent to the GEE to perform the multi-task pre-training.After the pre-training, MpbPPI first generates the aforementioned contact graphs for the WT and corresponding mutant complexes (which are from various mutant generation tools) of each sample point in the downstream t graphs , GEE encThe overall diagram further illustrating the data flow of MpbPPI and how its components are linked is shown in To comprehensively compare MpbPPI with other existing advanced approaches, we considered three single AA site mutation benchmark datasets, including single-point mutations in the AB-Bind dataset  (S645), Based on the above four datasets, following the convention, we first performed the 10-fold cross-validation splitting data based on mutation sample points and used mean Pearson’s correlation coefficient , when compared with GeoPPI, another geometric property pre-training-based method, under the strict pre-training sample screening and the same GBT predictor (see Methods), MpbPPI still overall outperformed it on the relatively larger single- and multiple-mutation datasets. This further demonstrated the effectiveness of the proposed different-scale geometric encoding framework and corresponding pre-training tasks. Interestingly, MpbPPI-FoldX consistently outperformed MpbPPI-MODELLER and MpbPPI-AlphaFold2, which indicated that under current PPI ormation .Based on the best-performing model MpbPPI-FoldX, under the same experimental and evaluation setting as the last section, we conducted the ablation study to discuss the effectiveness of our devised geometric characteristics and corresponding pre-training tasks. Specifically, we compared different variants of MpbPPI-FoldX, including MpbPPI (Backb+AA), MpbPPI (Backb+Sidec+AA), MpbPPI (Backb+SASA+AA) and MpbPPI (Backb+Sidec+SASA+AA), in which the suffixes represent different combinations of the used geometric characteristics plus their pre-training techniques . Besides, to demonstrate the effectiveness of the auxiliary AA prediction task, we removed this prediction task from MpbPPI (Backb+Sidec+SASA+AA) and also put this variant into the comparison [denoted as MpbPPI (w/o AA)]. The evaluation results of these variants on each dataset are shown in From the results we found that, compared with MpbPPI (Backb+AA) that did not use any explicit characteristics depicting geometric relationships between side chain atoms and between side chain and residue backbone atoms, the inclusion of side chain denoising and SASA prediction pre-training tasks  i.e. MpbPPI (Backb+Sidec+AA) and MpbPPI (Backb+SASA+AA) improved the performance. This clearly demonstrated the importance of our designed two types of side chain-related geometric learning pre-training tasks (and corresponding features). Besides, the performance margin between MpbPPI (w/o AA) and MpbPPI (Backb+Sidec+SASA+AA) illustrates the effectiveness of the auxiliary AA prediction task. Furthermore, as for the best-performing combination under the current experimental setting, it could vary for different datasets e.g. the combination of Backb+SASA+AA achieved the best result on S4169, but for S645, M1101 and S1131, the best combinations were Backb+Sidec+SASA+AA, Backb+Sidec+SASA+AA and Backb+Sidec+AA, respectively.In this section, we further tested the generalization capability of MpbPPI through a WT protein–protein complex type-based data splitting setting. Specifically, to better examine the model performance on previously unseen WT protein–protein complexes , we devised a data-splitting method in which original sample points were split into five folds and there was no intersection of WT protein–protein complex types between any of the two folds. We strived to make the both sample total number and WT protein–protein complex type number assigned to each fold to be as close as possible. Furthermore, we performed 5-fold cross-validation based on this data splitting scheme, in this case, the model cannot see any WT protein–protein complexes (types) in the test set during the training phase, which enables model performance examination in the more realistic application scenario. We performed such data splitting five times, for each repeat, making different WT protein–protein complex types enter each fold, and we reported the averaging results of these five times of independent (5-fold) cross-validation runs. A detailed description of the above data splitting can be found in Our comparative study showed that see , MpbPPI Mutations can alter the formation of protein complexes by enhancing or inhibiting interactions between proteins. This, in turn, affects various functions in the body, including catalyzing chemical reactions, transporting molecules and transmitting signals. Gaining insights into how mutations influence protein–protein interactions can help us understand the development and progression of diseases, ultimately leading to the creation of more effective treatments  in both the pre-training and downstream sets. At the same time, all H atoms were removed from these PDB files.We then modeled a given PPI complex as a residue contact graph In order to capture more comprehensive interactive relationships for residue nodes in ibutions ), we defIn order to generate  residue node and edge features The centroid of side chain atoms (vector)The center of mass of side chain atoms (vector)The maximum coordinate value of side chain atoms (vector)The unit vector of CαThe normalized Cα coordinate (vector)Solvent-accessible surface area (SASA) Residue interface information The detailed calculation description and meaning of these features are provided in Other than the above seven types of residue node features, we also added five additional node features for providing AA sequence information and close neighboring relationships of a residue, in which The amino acid (AA) type, given as a one-hot representation Chain index Sine–cosine encoded dihedral angles Unit vector encoding of the Unit vector encoding of the To create edge features The unit vector in the direction of Euclidean distance encoding between Sine–cosine encoded For the sine–cosine and Gaussian radial basis encoding methods, we suggest referring Jing et al. for the detailed description .For the scalar and vector features of a residue node The equivariance property is of importance in protein-related representation learning based on machine learning. Specifically, for a protein structure, its learned representation should not be sensitive to the imposition of protein’s rigid motions  because the physical law controlling the dynamics of molecules is not influenced by such motions . In ordeHere, we adopted the notation  motions . \documementclass2pt{minimBased on the above GEE encoder and the designed contact graphs for each protein–protein complex in our strictly screened pre-training set, our multi-task pre-training strategy was devised, in which the backbone denoising, side chain denoising, SASA prediction and AA type prediction tasks were included, which will be performed simultaneously. Intuitively, setting multiple pre-training tasks, which share closely related geometric information of the residues themselves, could be beneficial to capturing comprehensive interactive contact relationships (regulations) within a protein–protein complex. Due to the page limitation, please see the detailed description and meaning of these tasks in Besides, according to the ground truth to be recovered, the loss functions measuring the model reconstruction errors were mean square error MSE  To facilitate MpbPPI learning multi-scale geometric regulations of protein–protein complexes, we devise four pre-training tasks aiming at learning the geometric properties of individual residues themselves from different perspectives, for forcing MpbPPI to capture the comprehensive geometric regulations based on a strictly screened protein complex pre-training dataset.MpbPPI can flexibly accept the mutant structures from various generation tools  as the model input, allowing the evaluation and comparison of different generation tools to choose the most appropriate method for a given PPI system.The effectiveness of MpbPPI on predicting PPI Supplementary_Material_bbad310Click here for additional data file.


In recent years, there has been a breakthrough in protein structure prediction, and the AlphaFold2 model of the DeepMind team has improved the accuracy of protein structure prediction to the atomic level. Currently, deep learning-based protein function prediction models usually extract features from protein sequences and combine them with protein–protein interaction networks to achieve good results. However, for newly sequenced proteins that are not in the protein–protein interaction network, such models cannot make effective predictions. To address this, this article proposes the Struct2GO model, which combines protein structure and sequence data to enhance the precision of protein function prediction and the generality of the model.We obtain amino acid residue embeddings in protein structure through graph representation learning, utilize the graph pooling algorithm based on a self-attention mechanism to obtain the whole graph structure features, and fuse them with sequence features obtained from the protein language model. The results demonstrate that compared with the traditional protein sequence-based function prediction model, the Struct2GO model achieves better results.https://github.com/lyjps/Struct2GO.The data underlying this article are available at  As the expression products of genes and macromolecules in organisms, proteins are the main material basis of life activities, widely existing in various cells, providing many functions such as catalysis, cell signal, and structural support, playing a key role in life activities and functional execution. At the same time, the study of proteins can better grasp life activities on a molecular level, which has important practical significance for the management of diseases, the creation of new medications and the improvement of crops. Because of the progressing high-throughput sequencing technology, protein sequence data are increasing exponentially. At present, more than 100 000 proteins have been obtained by biological experiments in the Universal Protein (UniProt)  databaseThe protein function prediction problem can be viewed as a multi-label binary classification problem, that is, by extracting the features of the given protein and mapping it to the space of protein function labels. A variety of data sources can be tapped to obtain protein function prediction features, such as protein sequence, protein structure, protein family, and protein–protein interaction network, etc. The most commonly used information source is protein sequence and interaction network. The protein function labels can be standardized through k nearest neighbors  of “IDA,” “IPI,” “EXP,” “IGI,” “IMP,” “IEP,” “IC,” or “TA” were extracted, among which the human dataset included 20 395 data. Concurrently, we downloaded and parsed the most recent gene ontology data released by the official gene ontology website, construct the directed acyclic graph of gene ontology according to the terms of BPO, CCO, and MFO branches parsed, and complete the labels according to the above true path rules. It should be noted that most of the functional terms do not appear in the dataset or only annotate a few proteins, so this article filters out gene ontology nouns with a frequency lower than a certain threshold for each branch to reduce the sparsity of labels. After completion, the number of BPO, MFO, and CCO labels is 809, 273, and 298 respectively see .Protein structure and function are closely related. To better infer protein-related functions from protein structure information, we transform the three-dimensional protein structure into a two-dimensional protein contact map, construct a protein structure network to aggregate the information of adjacent residues, and finally obtain the protein structure features.In terms of a specific implementation, we can obtain the three-dimensional atomic coordinates of the protein structure through AlphaFold2, and then calculate the relative distance between amino acid residues. If the Cα atom between them is less than 10 Å, it is considered that there is an edge directly connected between the two residues. We employed two distinct methods for generating contact maps: ANY-ANY and NBR, see v, the likelihood of visiting the subsequent vertex x isIn the protein structure network, each node is an amino acid residue. To obtain the features of the node, the most intuitive method is to use the one-hot encoding of 20 different amino acids, but this method cannot capture the position information of the same amino acid in different protein networks. Therefore, we utilize graph representation learning to acquire the structural information of the node in the protein network. Among the current algorithm, DeepWalk  is optimv and vertex x, and Z is the normalization constant. Node2vec introduces two hyperparameters p and q to regulate the random walk strategy. Assuming that the current random walk passes through  to reach vertex v, letwhere t and vertex x.where p to be 0.8, q to be 1.2, combined with the one-hot encoding of each residue, and finally, we generate 1 × 50 dimensional feature vectors for each residue in the protein.In terms of a specific implementation, this experiment is based on the open-source distributed machine learning platform Spark-On-Angel of Tencent and uses the efficient data storage, update, and sharing services provided by Spark to implement the node2vec algorithm for graph computing. In the proteins we input, the number of amino acid residues is below 1500, so we choose the length of the walk-in node2vec to be 30, In the natural language domain, there has been a rapid advancement of pre-trained models such as Bert  , Chebyshlth layer nodes, where In recent years, the self-attention mechanism has been extensively employed in deep learning models, resulting in noteworthy outcomes, and allowing the model to focus more on significant features. where N is the number of nodes in this layer, ith node, and where To validate the effectiveness of Struct2Go, we divided the human protein dataset into training set, validation set, and test set in a ratio of 8:1:1 respectively to conduct experiments with three different prediction models. We compared the predicted results of the test set with those of the current mainstream models, including Naïve, BLAST, DeepGO, DeepGOA, DeepFRI, and GAT-GO. Naïve algorithm annotates GO terms according to the frequency, and BLAST is a protein sequence comparison technique that utilizes sequence similarity and dynamic programming to predict gene labels. DeepGO leverages both protein sequence information and PPI network data to infer gene ontology tags. DeepGOA innovatively introduces GCN to obtain knowledge guidance prediction in GO, DeepFRI transforms protein three-dimensional structure into a contact map and uses GCN to extract structural features for protein function prediction, and GAT-GO changes the aggregation function GCN to GAT based on DeepFRI and verifies it through experiments.In this article, AUC, AUPR, and Fmax are selected as metrics to evaluate the accuracy of protein function prediction from different perspectives.(The definition of specific parameters and formulas can be found in the Then, we perform ablation experiments to assess the impact of each component in the Struct2GO model on the enhancement of performance, as shown in The experiments involved extracting protein semantic features from individual sequences using the SeqVec pre-trained model, obtaining contact maps based on AlphaFold2’s atomic-level protein three-dimensional coordinates, and extracting protein structural features through hierarchical graph pooling. From the experimental data, it can be seen that the removal of any component will lead to the loss of model performance, which fully proves that all components of our model are effective. The ablation experiments reveal that, compared to protein semantic features obtained solely from single sequences, protein structural features have a significant impact on downstream function prediction tasks. Analogous to the findings of We compare the different variants of each component in the model and verify through experiments that our model achieves the best results in each variant, as shown in Then, we examined the effects of parameters such as dropout, learning rate, pooling ratio, and conv number on the model. We employed the control variable method, varying a single parameter at a time for multiple comparison experiments, and evaluated the actual impact of the parameter on the model performance by observing the performance of the model after training, to identify the optimal parameter value. The scope of hyperparameter comparison experiments is presented in The utilization of dropout in deep neural networks can mitigate overfitting and enhance the generalization capacity. From the experiments in From the experimental data depicted in From the experimental data depicted in The pooled ratio represents the ratio of the number of nodes in the subgraph generated in the next layer in the hierarchical process to the original graph, that is, the pooling ratio. In In this article, we propose a powerful end-to-end graph deep learning model Struct2Go, which can effectively and quickly annotate protein functions based on protein structure and sequence. Specifically, we adopt a graph pooling model to acquire structural features from the three-dimensional protein structure predicted by AlphaFold2 and integrate the sequence features extracted by Seqvec to train the protein function classifier. AlphaFold2 predicted three-dimensional protein structure data provides strong support for our functional prediction, which can enable us to abandon the constraints of PPI networks in previous works and effectively improve the generality of the model. At the same time, compared with the previous methods for predicting protein function based on experimentally determined protein structure, AlphaFold2 provides sufficient high-resolution structure information, which enables our model to perceive more homologous information and effectively improve the accuracy of prediction. The comparative experiments demonstrate that Struct2Go has attained the most advanced performance, thereby conclusively demonstrating the effective support of structural information for protein function prediction.In our future work, we will continue to investigate novel methods and enhance the generality and precision of the Struct2Go model. In addition, the AlphaFold2 website provides us with 217 million protein structure datasets of multiple species, which can be used in future research to try large-scale cross-species protein function model training, which can effectively improve the generality of the model.At the same time, in order to focus more on the influence of subtle structural changes on protein function prediction in future work, we can explore new approaches in protein structure feature extraction. For instance, we can investigate embedding the amino acid features extracted from sequence models into protein structural networks and explore novel random walk models to more comprehensively unearth valuable information within protein structures. In addition, we can also build a protein network based on structural similarity, with a single protein as the node, and use the effective information of homologous proteins in network propagation to improve the accuracy of the model prediction.btad637_Supplementary_DataClick here for additional data file.


Millions of protein sequences have been generated by numerous genome and transcriptome sequencing projects. However, experimentally determining the function of the proteins is still a time consuming, low-throughput, and expensive process, leading to a large protein sequence-function gap. Therefore, it is important to develop computational methods to accurately predict protein function to fill the gap. Even though many methods have been developed to use protein sequences as input to predict function, much fewer methods leverage protein structures in protein function prediction because there was lack of accurate protein structures for most proteins until recently.We developed TransFun—a method using a transformer-based protein language model and 3D-equivariant graph neural networks to distill information from both protein sequences and structures to predict protein function. It extracts feature embeddings from protein sequences using a pre-trained protein language model (ESM) via transfer learning and combines them with https://github.com/jianlin-cheng/TransFun.The source code of TransFun is available at  Proteins are essential macromolecules that carry out critical functions such as catalyzing chemical reactions, regulating gene expression, and passing molecular signals in living systems. It is critical to elucidate the function of proteins. However, even though various next-generation genome and transcriptome sequencing projects have generated millions of protein sequences, the experimental determination of protein function is still a low-throughput, expensive and time-consuming process. Thus, there is a huge gap between the number of proteins with known sequence and the number of proteins with known function, and this gap keeps increasing. As a result, it is important to develop computational methods to accurately predict the function of proteins.Given the sequence of a protein and/or other information as input, protein function prediction methods aim to assign the protein to one or more function terms defined by Gene Ontology (GO) . GO orgaMany protein function prediction methods use sequence or structure similarity to predict function, assuming proteins with similar sequences and structures likely have similar function. For example, GOtcha, Blast2GO , PDCN W, and DIATo improve the generalization capability of protein function prediction, advanced machine learning-based methods such as FFPred and labeler  have beeInspired by the recent advance, we develop a method to use a pre-trained protein language transformer model to create embeddings from protein sequences and combine them with a graph representation constructed from AlphaFold predicted ge model  trained We collected protein sequences with function annotations from the UniProt/Swiss-Prot database, released by 23 February 2022, amounting to a total of 566 996 proteins. We gathered their functional annotations from UniProt and the ontology graph data from the Open Biological and Biomedical Ontology (OBO) Foundry data repository. We also collected predicted structures of 542 380 proteins from the AlphaFold Protein Structure Database (AlphaFoldDB) published on 12 January 2022. To ensure consistency between the predicted structures from AlphaFoldDB and the corresponding proteins from UniProt, we compare their sequences and UniProt ID. All but 301 proteins have the same sequence. For the ones with different sequences, they usually only differ in a few residues. To make them consistent, we use the sequences extracted from the predicted structures as the final sequences.The protein function annotations are described in the Gene Ontology (GO) terms. GO uses directed acyclic graphs (DAGs) to model the relationship between GO terms. The nodes represent the GO terms, and the links represent the relationship between the terms. GO provides three separate directed acyclic graphs (DAG) for each of the three ontologies . For each protein, the specific GO terms provided in the UniProt function annotation file were first gathered. Then, their parent and ancestor terms in the GO DAG were also collected. The terms with the evidence codes  were used as the function label for the protein according to the standard used in the Critical Assessment of Protein Function Annotation (CAFA) .The entire dataset above was filtered to retain only proteins with sequence length between To compare our method with existing methods, we use the CAFA3  dataset CAFA3_test_dataset) from AlphaFoldDB in the same way as for our curated dataset. If no predicted structure was found for a protein, we used AlphaFold2 to predict its structure. During the input feature generation, for a protein sequence with length >1022 in the CAFA3 benchmark dataset, we divided it into smaller chunks of 1022 residues except for the last chunk for the language model to generate sequence embeddings that were concatenated together as the sequence embeddings for the entire protein sequence.We collected the predicted structures for the proteins in the CAFA3 test dataset . This dataset has 702, 705, and 1084 proteins in CC, MF and BP respectively.We also collected new proteins released between March 2022 and November 2022 in UniProt as our second test dataset . TransFun takes as input the sequence and predicted 3D structure of a protein and predicts the probabilities of GO terms for it in each GO category . TransFun × n adjacency matrix, where K nearest neighbors of node n is the number of residues. Since both thresholds produce similar results, we use We construct a graph from the structure of a protein under consideration, represented as a  protein . The nodThe graph constructed from a protein structure is stored in a binary adjacency matrix, where rd layer. The per-residue embedding for all the residues of a protein is an We generate embeddings for the sequence of a protein using the ESM-1b  pre-traiESM-1b was trained with the The deep graph neural network architecture of TransFun is composed of four blocks of rotation- and translation-equivariant graph neural networks  Fig. 1)Fig. 1), x, y, z) coordinates of each residue. We tested two optional features for the edges: (1) the distance between the two nodes of the edge; and (2) a binary number 0/1 indicating if the two residues are two adjacent residues connected by a peptide bond in the protein. However, the edge features do not improve the prediction accuracy on top of the node features and therefore are excluded in the final model of TransFun.Each EGNN block accepts a graph as input to update its features. The initial node features include the per-residue embeddings, and the (C/2 from EGNN2 to generate an output of dimension of C/4.In The output embeddings (features) from each of the three blocks  are aggregated by using a global mean pooling on its node features to obtain the overall features for each protein, separately. The overall pooled features of EGNN1, EGNN2, and EGNN4 are then concatenated with the per-sequence output of EGNN3, resulting in We performed an ablation study on different numbers of EGNN blocks, different ways of combining the features of the EGNN blocks, and a multi-layer perceptron that uses only sequence information as input (see the Supplementary Section “An ablation study of the deep learning architecture of TransFun”). The final architecture in The numbers of examples for different GO terms are very different. We use class weights to scale the training loss for GO terms appropriately to weigh less-represented GO terms (classes) more. The size of protein clusters in the training dataset is also imbalanced, where some clusters are very large, but some are very small. To reduce over the representation of proteins in a large cluster during training, we randomly sample one representative protein from each cluster for each training epoch. Although the representative protein sampled is similar in sequence to all the other proteins in the same cluster, their functional annotation may differ, especially when the sequence similarity is low. Therefore, we recompute the class weights per training epoch so that classes represented in the epoch are weighed appropriately.ab initio deep learning prediction method and a homology sequence similarity-based method such as DIAMONDScore  method to combine the predictions from DIAMONDScore and the predictions of TransFun, which is called TransFun+. The score that TransFun+ predicts for a GO term is the weighted average of the score predicted by TransFun and the score predicted by DIAMONDScore. The weights were optimized on our curated validation dataset.In this work, we also designed such a Fmax and the Area Under the Precision-Recall curve (AUPR)—to evaluate the performance of our methods. The Fmax is the maximum F-measure computed over all the prediction thresholds. The F-measure for each threshold is computed as the harmonic mean of the precision (TP/(TP+FP)) and recall (TP/(TP+FN)), where TP is the number of true positives, FP the number of false positives, and FN the number of false negatives. The AUPR is computed by using the trapezoidal rule to approximate the region under the precision–recall curve.We mainly use the two widely used metrics—After training and optimizing TransFun on our curated training and validation datasets, we blindly evaluated it on the new test dataset (new_test_dataset) and the CAFA3 test dataset (CAFA3_test_dataset) together with other methods.Fmax score and AUPR  and cellular component (CC), while the Fmax and AUPR for BP slightly decreases. This change may be also partially due to the difference in the test datasets at the two different sequence identity thresholds. However, the largely consistent results show that TransFun can work well when the sequence identity between the test protein and the training proteins is ≤30%. When the sequence identity threshold is increased from 50% to 90%, the performance is very similar, indicating when the sequence identity is higher than 50%, further increase sequence identity may not have a significant impact on the prediction accuracy.We compare the performance of TransFun on our curated validation datasets created using sequence identity thresholds of composite (meta) methods—DIAMONDScore—DeepGOPlus, TALE+, and TransFun+ on the new test dataset in the three ontology categories  in terms of the Fmax score and AUPR score. Naïve, DIAMONDScore, DeepGOCNN, TALE, DeepFRI, and TransFun are individual methods. DeepGOPlus, TALE+ and TransFun+ are composite (or meta) methods that combine the predictions of two individual methods .Fmax score of 0.628, 0.608, and 0.413 for CC, MF, and BP, the highest AUPR score of 0.569 and 0.366 for MF and BP, and the second highest AUPR score of 0.603 for CC. TALE has the highest AUPR score of 0.621 for CC.Among the five individual methods , TransFun has the highest Fmax score (i.e. 0.628) for CC. This indicates that combining the deep learning methods and sequence-similarity based methods can improve prediction accuracy. Among the three composite methods, TransFun+ performs best for CC and MF in terms of Fmax and for MF and BP in terms of AUPR, while DeepGOPlus performs best for BP in terms of Fmax and TALE+ performs best for CC in terms of AUPR.The three composite methods  generally performs better than their individual counterpart  in all the function categories in terms of both Fmax and AUPR except that TransFun and TransFun+ has the same The precision-recall curves of these methods on the new test dataset are plotted in Fmax and AUPR, we also evaluate the six individual methods using In addition to ing Smin   of a target protein for the MSA-based language model (e.g. ESM-MSA) to generate extra embedding features for TransFun to see if they can further improve prediction accuracy. Another limitation of TransFun and other protein function prediction methods is the lower prediction accuracy for more specific GO terms (the nodes at the lower levels of the gene ontology directed acyclic graph) because these terms have much fewer proteins associated with them than more general GO terms. More machine learning techniques and data preparation techniques are needed to address this imbalance problem because accurately predicting more specific GO terms is more useful for biological research than more general GO terms. Finally, we plan to incorporate the protein–protein interaction information relevant to protein function prediction into TransFun.btad208_Supplementary_DataClick here for additional data file.


Protein engineering is an emerging field in biotechnology that has the potential to revolutionize various areas, such as antibody design, drug discovery, food security, ecology, and more. However, the mutational space involved is too vast to be handled through experimental means alone. Leveraging accumulative protein databases, machine learning (ML) models, particularly those based on natural language processing (NLP), have considerably expedited protein engineering. Moreover, advances in topological data analysis (TDA) and artificial intelligence-based protein structure prediction, such as AlphaFold2, have made more powerful structure-based ML-assisted protein engineering strategies possible. This review aims to offer a comprehensive, systematic, and indispensable set of methodological components, including TDA and NLP, for protein engineering and to facilitate their future development. Protein engineering aims to design and discover proteins with desirable functions, such as improving the phenotype of living organisms, enhancing enzyme catalysis, and boosting antibody efficacy . It has Two traditional protein engineering approaches include directed evolution  and ratiractable . As a reRecently, data-driven machine learning has emerged as a new approach for directed evolution and protein engineering , 9. MachThe process of data-driven MLPE typically involves several elements, including data collection and preprocessing, model design, feature extraction and selection, algorithm selection and design, model training and validation, experimental validation, and iterative model optimization. Driven by technological advancements in high-throughput sequencing and screening technologies, there has been a substantial accumulation of general-purpose experimental datasets on protein sequences, structures, and functions , 14. TheData representation and feature extraction are crucial steps in the design of machine learning models, as they help to reduce the complexity of biological data and enable more effective model training and prediction. There are several typical types of feature embedding methods, including sequence-based, structure-based , 17, phyThe designs and selections of MLPE algorithms depend on the availability of data and efficiency of experiments. In real-world scenarios, small labeled training datasets are prevalent, and as a result, simple machine learning algorithms such as support vector machines and ensemble methods are often employed for small training datasets. In contrast, deep neural networks are more suitable for large training datasets. In addition to regression models, unsupervised zero-shot learning methods can also be utilized to address scenarios with limited labeled data availability , 34. TheMLPE has the potential to significantly accelerate the development of new and improved proteins, revolutionizing numerous areas of science and technology . DespiteIn artificial intelligence, natural language processing (NLP) has recently gained much attention for representing and analyzing human language computationally . NLP covSimilar to human language, the primary structure of a protein is also represented by a string of amino acids, with 20 canonical amino acids. The analogy between protein sequences and human languages has inspired the development of computational methods for analyzing and understanding proteins using models adopted from NLP . The selTo train a local evolutionary model, MSAs search strategies such as jackhmmer  and EvCoWith large-size data, global evolutionary models usually adopt the large NLP models. Convolutional Neural networks (CNNs)  models aAlthough global evolutionary models can learn a variety of sequences derived from natural evolution, they face challenges in concentrating on local information when predicting the effects of site-specific mutations of a target protein. To enhance the performance of global evolutionary models, fine-tuning strategies are subsequently implemented. Specifically, fine-tune strategy further refines the pre-trained global models with local information using MSAs or target training data. The fine-tuned eUniRep  shows siWith various language models proposed, comprehensive studies on various models and the strategy in building downstream model is necessary. A study explored different approaches utilizing the sequence embedding to build downstream models . Two othAided by advanced NLP algorithms, sequence-based models have become the dominant approach in MLPE , 12. HowTDA is a mathematical framework based on algebraic topology , 63, whiThe basic idea behind TDA is to represent the data as a point cloud in a high-dimensional topological space, and then study the topological invariants of this space, such as the genus number, Betti number, and Euler characteristic. Among them, the Betti numbers, specifically Betti zero, Betti one, and Betti two, can be interpreted as representing connectedness, holes, and voids, respectively , 77. TheHomology groups are algebraic structures that are associated with topological spaces . They prHomology theory has many applications in mathematics and science. It is used to classify topological spaces in category theory, to study the properties of manifolds in differential geometry and algebraic geometry, and to analyze data in various scientific fields . HoweverPersistent homology is a relatively new tool in algebraic topology that is designed to incorporate multiscale topological analysis of data , 63. ThePersistent homology is the most important approach in TDA  . The motHowever, aforementioned TDA methods are still limited in describing complex data, such as its lack of description of non-topological changes (i.e. homotopic shape evolution) , its incPersistent topological Laplacians (PTLs) are a new class of mathematical tools designed to overcome the aforementioned challenges in TDA . One of Like persistent homology, persistent Laplacians are limited in their ability to handle directed networks and atomic polarizations. To address these limitations, persistent path Laplacians have been developed . Their hNone of the PTLs mentioned above are capable of handling different types of elements in a molecule as persistent cohomology does. To overcome this limitation, persistent sheaf Laplacians  were desPersistent hypergraph Laplacians enable the topological description of internal structures or organizations in data . PersistFinally, unlike all the aforementioned PTLs, evolutionary de Rham-Hodge Laplacians or persistent Hodge Laplacians are defined on a family of filtration-induced differentiable manifolds . They arSimilar to topological data analysis, graph- and topology-based deep learning models have been proposed to capture connectivity and shape information of protein structure data. Graph neural networks (GNNs) consider the low-order interactions between vertices by aggregating information from neighbor vertices. A variety of popular graph neural network layers has been proposed, such as convolution graph networks (GCN) , graph aWith variety of architectures of GNN layers, self-supervised learning models are widely used for representation learning of graph-based data. Graph autoencoder (GAE) and variational graph autoencoder (VGAE) consist of both encoder and decoder, where the decoder employ a linear inner product to reconstruct adjacent matrix . While mIn applications to learning protein structures, GCNs have been widely applied to building structure-to-function map of proteins , 102. MoTopological deep learning, proposed by Cang and Wei in 2017 , is an ent}-GNNs  and simpnt}-GNNs .Protein engineering is a typical black-box optimization problem, which focuses on finding the optimal solution without explicitly knowing the objective function and its gradient. In protein engineering, the goal in designing algorithms for this problem is to efficiently search for the best sequence within a large search space: where There are two practical challenges in protein engineering. First, the fitness landscape is usually epistatic , 112, whre found . Either re found , making With enriched data-driven protein modeling approaches from protein sequences to structures, recent advanced machine learning methods have been widely developed to accelerate protein engineering in silico  1, 11, , 115. UtUsing a limited number of experimentally labeled sequences, machine learning models can carry out zero-shot or few-shot predictions . The accMLPE methods need to take the experimental capacity into account when attempt to balance the exploitation-exploration. In this section, we discuss different strategies upon the number of experimental capacity. First, we discuss zero-shot strategy when no labeled experimental data is available. Second, we discuss supervised models for performing greedy search (i.e. exploitation). Last, we discuss uncertainty quantification models that balance exploration and exploitation trade-off.First, we review the zero-shot strategy that interrogates protein fitness with an unsupervised manner . This isZero-shot predictions rely on the model’s ability to recognize patterns in naturally observed proteins, enabling it to make informed predictions for new sequences without having direct training data for the target protein. As discussed in Section VAEs are popular local evolutionary models for zero-shot predictions such as DeepSequence  and EVE nference  providesELBO is taken as the scoring function to quantify the mutational likelihood of each query sequence. The ELBO-based zero-shot predictions show advanced performance reported in multiple works , 33, 55.Transformer is the currently state-of-the-art model which has been used in many supervised tasks . It learsequence , 34: The PPLs assume the independence between amino acids. To consider the dependence between amino acids, one can calculate the conditional probability by summing up all possible factorization . But thiFurthermore, many different strategies have been employed to make zero-shot predictions. Fine-tune model can improve the predictions by combining both local and global evolutionary models . TrancepSupervised regression models are among the most prevalent approaches used in guiding protein engineering, as they enable greedy search strategies to maximize protein fitness . These mA variety of supervised models have been applied to predict protein fitness. In general, statistical models and machine learning models such as linear regression , ridge rRather than the architectures of supervised models, the predictive accuracy highly rely on the amount of information obtained from the featurization process . The phyWith the extensive accurate protein-to-fitness machine learning models, active learning further designs iterative strategy between models and experiments to sequentially optimize fitness with the consideration of exploitation-exploration trade-off D–E 115]115].To balance the exploitation-exploration trade-off, the supervised models require to predict not only the protein fitness but also quantify the uncertainty of the given protein . The mosThe tree-based search strategy is also efficient by building a hierarchical search path, such as the hierarchical optimistic optimization (HOO) , the detRecently, researchers have turned to generative models to quantify uncertainty in protein engineering, employing methods such as Variational Autoencoders (VAEs) , 40, 54,In this review, we have discussed the advanced deep protein language models for protein modeling. We further provided an introduction of topological data analysis methods and their applications in protein modeling. Relying on both structure-based and sequence-based models, MLPE methods were widely developed to accelerate protein engineering. In the future, various machine learning and deep learning will have potential perspectives in protein engineering.Comparing to sequence data, three-dimensional protein structural data offer more comprehensive and explicit descriptions of the biophysical properties of a protein and its fitness. As a result, structure-based models usually provide superb performance than sequence-based models for supervised tasks with small training set , 120.As protein sequence databases continue to grow, self-supervised models demonstrate their ability to effectively model proteins using large-scale data. The protein sequence database provides a vast amount of resources for building sequence-based models, such as UniProt  databaseTo address the limited availability of structure data, researchers have focused on developing highly accurate deep learning techniques aimed at enabling large-scale structure predictions. These state-of-the-art methodologies have the potential to significantly expand the database of known protein structures. Two prominent methods are AlphaFold2  and RoseBoth AlphaFold2 and RosettaFold are alignment-based, which rely on MSAs of the target protein for structure prediction. Alignment-based approaches can be highly accurate when there are sufficient number of homologous sequences  in the database. Therefore, these methods may have reduced accuracy with low MSAs depth in database. In addition, the MSAs search is time consuming which slows down the prediction speed. Alternatively, alignment-free methods have also been proposed to tackle these limitations . An earlIn building protein fitness model, the structural TDA-based model has exemplified that the AlphaFold2 structure is as reliable as the experimental structure . The zerCurrent MLPE methods are usually designed for limited training set. The ensemble regression is an effective approach to accurately learn the fitness landscape with small but increasing size of training sets from deep mutational scanning .The breakthrough biotechnology, next-generation sequencing (NGS)  largely Machine learning and deep learning techniques are revolutionizing protein engineering.Topological data analysis enables advanced structure-based machine learning-assisted protein engineering approaches.Deep protein language models extract critical evolutionary information from large-scale sequence databases.supplement_bbad289Click here for additional data file.


Computational methods for the quantification and visualization of the relative contribution of molecular interactions to the stability of biomolecular structures and complexes are fundamental to understand, modulate and engineer biological processes. Here, we present Surfaces, an easy to use, fast and customizable software for quantification and visualization of molecular interactions based on the calculation of surface areas in contact. Surfaces calculations shows equivalent or better correlations with experimental data as computationally expensive methods based on molecular dynamics.https://github.com/NRGLab/Surfaces. Surface’s documentation is available at https://surfaces-tutorial.readthedocs.io/en/latest/index.html.All scripts are available at  Molecular interactions determine all aspects of biological processes. Understanding the relative contributions of individual molecular interactions can guide the design of effective drugs to modulate biological processes as well as help understand the effect of mutations in natural processes or guide their introduction in protein engineering.ab initio approaches, are often computationally expensive, limiting the extent to which they can be used to explore this expansive searchable space .Protein engineering is an important tool in biotechnology that can produce proteins with improved therapeutic and industrial profiles . In receWith the decreasing cost of computational power, it is possible to simulate biological systems considering more realistically the underlying physical processes at the core of molecular interactions. Molecular dynamics (MD) simulations are at the forefront of such efforts, but such methods are computationally expensive and often also difficult to implement and thus remain impractical for high-throughput applications or broad adoption. Among these methods, we can highlight well known methods for modeling and analysis of protein structures, FoldX  that aimLigand-Protein Contacts) and CSU    . TherefoIn this application note, we present Surfaces, a fast method that utilizes atomic surface areas in contact, user-defined atom-type definitions, and pairwise pseudo-energetic matrices as a proxy for highlighting favorable and unfavorable interactions within and between proteins, as well as between proteins and other biomolecules.Surfaces quantifies atomic interactions using two measures. The first measure is the area in contact between atoms as described and calculated by Vcontacts using a Voronoi procedure .N is the total of atoms of unit 1, M is the total of atoms of unit 2, i and j, and i and j. Units can correspond to residues, ligands or ligand atoms.The complementarity function (CF) that accounts for these two measures is described by The CF calculation is based on the scoring function utilized by FlexAID , a liganTo visually represent the results of interaction analysis, Surfaces offers functions created using the PyMOL API (pymol.org). These functions are also customizable and allow for the visualization of the overall contribution of residues to interaction surfaces, as well as specific interactions between structural units, as PyMOL sessions.Surfaces was built as a set of python scripts designed to evaluate various types of interactions in proteins. The scripts are user-friendly, fast, and can be easily customized and automated, offering a scriptable way of generating data and visual representations for the interactions of interest.The specific types of interactions that can be analyzed by Surfaces are protein-protein, protein-ligand and residue interactions. In all cases, the input is a .PDB formatted file while the output of the quantitative analysis is saved as a .CSV file. The output for visualization is saved as a .PSE PyMOL session file. The visual output shows the surfaces of the relevant units of interest colored according to the net value of interactions and the specific pairwise interactions to the net total are represented as colored dash lines with a customizable color scale. In all cases, the input .PDB structure needs to be cleaned of any non-defined atoms in a pre-processing step, which can be done using scripts that are provided with the Surfaces software. The pre-processing is not done automatically to offer the use the possibility to customize. For example, if the user wishes to ignore specific hetero-atoms, these need to be removed but otherwise, defined. Surfaces offers a general script for atom type assignment mapping element types to the default SYBYL 40 atom types.Protein–protein interfaces: For this application, two groups of protein chains are required as input, Surfaces calculates the interactions between residues of the first group and residues of the second group  involving those residues /ACE2 binding ΔΔG values measured by Surface Plasmon Resonance . SurfaceLastly, gRINN  is a widIn order to consider structural variations, captured when using MD-based approaches, Surfaces can be used to analyze protein ensembles, generated with tools such as the NRGTEN package . Such a Surfaces provides a simple, fast, and easy to use method to analyze and visualize biomolecular interactions. Surfaces performs equivalently or better than widely used methods such as Rosetta, FoldX, MM/GBSA and MM/PBSA, and, for specific datasets, comparable even to computationally expensive and cumbersome to implement methods such as gRINN and FEP. The use of variations in solvent accessible surface areas or those in contact to estimate free-energy contributions has been established previously  but somehow fell out of use over the past 20 years within the broader community. However, this work shows that such calculation can provide data of the same quality as more computationally expensive methods based on molecular dynamics, currently available methods based on machine learning, or other mean-field based statistical approaches.As far as small-molecule protein interactions are concerned, docking simulations can be used to predict the binding pose, the relative ranking of molecules in virtual screening and the actual binding affinity. Until recently, no docking scoring function was able to tackle all three applications at once . The CF The scriptable and customizable nature of Surfaces makes it a valuable tool for researchers seeking to analyze large structural datasets, such as in virtual screening or protein engineering, thus permitting a broader exploration of search space. Lastly, the ease of use of Surfaces and low demand on computational resources makes this type of analysis accessible to a broader audience.btad608_Supplementary_DataClick here for additional data file.


Accurate prediction of change in protein stability due to point mutations is an attractive goal that remains unachieved. Despite the high interest in this area, little consideration has been given to the transformer architecture, which is dominant in many fields of machine learning.In this work, we introduce PROSTATA, a predictive model built in a knowledge-transfer fashion on a new curated dataset. PROSTATA demonstrates advantage over existing solutions based on neural networks. We show that the large improvement margin is due to both the architecture of the model and the quality of the new training dataset. This work opens up opportunities to develop new lightweight and accurate models for protein stability assessment.https://github.com/AIRI-Institute/PROSTATA and https://prostata.airi.net.PROSTATA is available at  Quantitative prediction of the effects of single amino acid substitutions on protein stability is a major problem that remains unresolved. Protein stability is related to its structure, function, and molecular evolution. The prediction of protein stability is part of a broader issue of predicting evolutionary fitness and the phenotypic effects of genomic variations.Accurate predictions of changes in protein stability caused by mutations provide crucial insight into how proteins fold and function and also have important applications in the bioindustry. Amino acid substitutions in protein sequences can be stabilizing, destabilizing, or neutral, depending on whether the folded or unfolded states are favored compared to wild-type protein, or there is no effect . The appMachine learning has irreversibly changed the landscape of computational biology and molecular modeling over the last few years. A plethora of tools designed to predict protein stability perfectly illustrate this change . We can Classic machine learning models are by far the most populous category of tools for predicting protein stability . PancottRecently, methods from the last category based on neural network (NN) approaches became popular. This category includes methods such as DeepDDG  ID, PDB chain, and mutation code (position and residues in it before and after mutation), from now on referred to as ID. Data were averaged over experimental pH and temperature (T) and pooled in five steps.Split the data. All samples were divided into two groups according to whether pH and T were available (Group I) or not (Group II).Select core samples. The samples in Group I with pH and T closest to the standard values , HEME C (HEC), or BILIVERDINE IX ALPHA (BLA) within their corresponding PDB structures.The “oligomerization” test set was constructed by selecting samples from the PROSTATA dataset that correspond to proteins crystallized in a homo-oligomeric state with over 30% of their residues within a 4.5 Å distance from adjacent subunits.de novo designed miniproteins from the mega dataset  and mutated (MT) embeddings into a joint representation used as input for a linear regression head. A widely used approach in transformer models is using CLS token embeddings for sequence classification. We explored several ways to combine these vectors into a single representation:Concatenation of WT and MT embeddings of the mutation position .The outer product of WT and MT embeddings of the mutation position .Linear combination WT and MT embeddings of the mutation position .Linear combination of CLS embeddings .Linear combination of CLS embeddings concatenated with WT and MT embeddings of mutation position .All the models were trained with the ADAM optimizer and a batch size of one for three epochs. The learning rate was increased linearly from 0 to 1e−5 during the first 30% of the examples and then linearly decreased to 0 for the remaining examples. We did not freeze the transformer backbone and trained all model weights in an end-to-end manner. The hyperparameters were selected based on , root mean square error (RMSE), and mean absolute error (MAE) to evaluate PROSTATA performance and compare it with published methods [INPS-Seq  designed miniproteins with limited or no homological proteins available in the UniRef30 database  are the most common secondary structure elements within the experimentally resolved structures. PROSTATA demonstrated the best performance for beta-strand regions, with a slightly lower performance for alpha-helices and coils.Several proteins included in the dataset have a well-packed tertiary fold under biologically relevant conditions only in the oligomeric form. In particular, amyloid peptides are known to be disordered as monomers. Other proteins could be prone to form homodimers or other states of homoligomerization . To analAs expected, the correlation between the experimental and predicted neficial .Other challenging cases might include predicting the effect of mutations in the binding sites of small molecules and cofactors. Cofactor binding usually stabilizes the protein fold, but the location of binding sites is not explicitly encoded. The protein language models might capture the effect implicitly due to evolutionary traits.To study the performance of PROSTATA, we split the dataset on all hemoglobin or hemoglobin derivative binding proteins and other proteins as a test and training set, respectively. Surprisingly, PROSTATA shows above-average precision in this class of proteins, indicating that the protein language model is able to distinguish well between classes of proteins . This made novo designed miniproteins suggests that PROSTATA has acquired broad domain knowledge through transfer learning.In this paper, we used the transfer learning approach to build a predictive model based on combinations of embeddings from the pre-trained protein language model ESM-2. The model PROSTATA is an ensemble of five models with different regression heads. PROSTATA achieves high correlation and low error performance compared to other models being trained on their respective datasets, showing the presented architecture’s benefits. PROSTATA, trained on the dataset presented in the current work, demonstrates the highest performance on the S669 test sets among all other models. Analysis of the performance of our new model on several test sets, including protein classes known to be challenging in Overall, this work offers a new framework based on large pre-trained protein language models for stability changes prediction.btad671_Supplementary_DataClick here for additional data file.


AlphaFold-like systems are rapidly expanding the scale of proteome structuring, and MineProt provides an effective solution for custom curation of these novel high-throughput data. It enables researchers to build their own server in simple steps, run almost out-of-the-box scripts to annotate and curate their proteins, analyze their data via a user-friendly online interface, and utilize plugins to extend the functionality of server. It is expected to support researcher productivity and facilitate data sharing in the new era of structural proteomics.Database URLMineProt is open-source software available at https://github.com/huiwenke/MineProt. AI-based protein structure prediction systems represented by RoseTTAFold  and AlphWhen AI systems are rapidly generating structure predictions, problems arise about how to curate these novel high-throughput data. It is difficult for public databases like AlphaFold DB to curate all new data without delays, while it might be hard for most researchers to develop a similar website from scratch to publish their data in a more intuitive way . In the MineProt is designed as server–client architecture. The server supports container deployment including Docker and Podman. It provides a graphical interface for users to curate and analyze their protein structures, which are grouped into different repositories for better management. Users will be able to access the MineProt Search Page  in a webUsers are also enabled to retrieve similar structures via the Salign Page  powered The MineProt interface is reinforced by a Browse Page and an Import Page as well . The BroThe client named MineProt Toolkit provides several Python and Shell scripts for the remote management of protein repositories. Its core function can be summarized as transforming results of AI systems to protein repositories compatible with MineProt’s modules, as illustrated in In addition, MineProt Toolkit provides plugins for functional extension. For example, given that researchers may expect to search their proteins via BLAST, we have developed a JavaScript plugin linking SequenceServer hits to MineProt Search Page . It can It is expected for MineProt to support researcher productivity and facilitate data sharing in the high-throughput era of structural proteomics. Nevertheless, since MineProt was born upon the arrival of this new era, countless challenges will ambush them both. For example, the compatibility of CIF format emerges to be a problem, for it seems to be too flexible while different software has different processing protocols. The PDB-CIF convertor implemented by MineProt works well for down-stream applications including Mol* and AlphaFill , which iFurther development of MineProt will continue following the trends in this field. It will support more AI systems, provide more plugins to extend its functionality , update its modules for more efficiency, and become more user-friendly. Such efforts will continue to improve the ability of researchers to analyze and interpret structural proteomics data.setup.sh is provided for one-click installation. The dependencies include Elasticsearch (7.12.1), US-align (20 220 924) and MAXIT (11.100). Elasticsearch is employed as the full-text search engine, where protein annotations serve as keywords in its indices. US-align is the key module for structure alignment, supporting PDB structure searching beyond keyword-searching or BLAST. MAXIT is used for PDB-CIF format converter, which is essential for Mol* visualization. Various APIs (application programming interface) are developed for data management as well as interaction among different modules .The MineProt server is implemented in PHP (5.5.9+ with cURL module) and requires one basic web-server platform such as Nginx and Apache. It supports container deployment including Docker and Podman, and a shell script MineProt deposits protein-related files in different directories called protein repositories. In the repository, PDB and CIF files storing structure information are required for one protein, while A3M files storing co-evolutionary information and JSON files storing model scores such as pLDDT, predicted template modeling (pTM) and predicted aligned error can also be deposited. Files related to the same protein share the same prefix. Each protein repository has an Elasticsearch index of the same name, which stores descriptions of each of its proteins for searching. Repositories without indices will not be displayed on the MineProt interface, neither searchable nor browsable.The Salign Page is designed as a SequenceServer-like interface. It supports PDB–PDB alignment currently. Once receiving query structure and other parameters, MineProt server sends selected repositories and keywords to its search engine to get all candidate structures. After that it creates a background process for US-align calculation between the query structure and candidate structures while returning a job ID to the user. Computing efficiency is affected by hardware, for example, it takes 93.3 s to align 3FM0 from RCSB PDB to 194 WD40-containing proteins from CP-8382 dataset on AMD Ryzen 5 5600 G and over 4 min on Intel Xeon Processor E5-2660 v2. When the job is done, it picks out hit structures with TM-score >0.5 and sort the results by RMSD. Users can access the result page through the returned job ID within 24 h.<AI_system>/transform.py). CIF files are usually generated via MAXIT API of MineProt server. Then, it creates Elasticsearch indices and invokes UniProt API for protein annotation (import2es.py). For each protein, homologs in its A3M file are enumerated with multi-threads and sent to UniProt API until registered UniProtKB/UniParc accessions are returned. All available UniProt, InterPro and GO annotations are extracted from the response and added to corresponding Elasticsearch indices. Finally, it uploads all files, which are compressed during transmission, to the target protein repository (import2repo.py). Shell scripts integrating all these functions are provided for AlphaFold and ColabFold; meanwhile, a formatting script ppdb/transform.py is applicable to most predictions using pTM models.MineProt Toolkit is developed in Python (3.6+) with dependencies of three third-party packages, Requests, NumPy and Biopython. Its workflow can be divided into three steps. First, it transforms raw outputs of AI-based protein structure prediction systems into eligible PDB, CIF, A3M and JSON files (baad059_SuppClick here for additional data file.