High‐resolution experimental structural determination of protein–protein interactions has led to valuable mechanistic insights, yet due to the massive number of interactions and experimental limitations there is a need for computational methods that can accurately model their structures. Here we explore the use of the recently developed deep learning method, AlphaFold, to predict structures of protein complexes from sequence. With a benchmark of 152 diverse heterodimeric protein complexes, multiple implementations and parameters of AlphaFold were tested for accuracy. Remarkably, many cases (43%) had near‐native models  generated as top‐ranked predictions by AlphaFold, greatly surpassing the performance of unbound protein–protein docking (9% success rate for near‐native top‐ranked models), however AlphaFold modeling of antibody–antigen complexes within our set was unsuccessful. We identified sequence and structural features associated with lack of AlphaFold success, and we also investigated the impact of multiple sequence alignment input. Benchmarking of a multimer‐optimized version of AlphaFold (AlphaFold‐Multimer) with a set of recently released antibody–antigen structures confirmed a low rate of success for antibody–antigen complexes (11% success), and we found that T cell receptor–antigen complexes are likewise not accurately modeled by that algorithm, showing that adaptive immune recognition poses a challenge for the current AlphaFold algorithm and model. Overall, our study demonstrates that end‐to‐end deep learning can accurately model many transient protein complexes, and highlights areas of improvement for future developments to reliably model any protein–protein interaction of interest. High‐resolution structural characterization of these interactions provides insights into their molecular basis, as well as structure‐guided design of binding affinities and identification of inhibitors. However, structures for large numbers of molecular interactions remain undetermined experimentally, due to limitations in resources, and the challenges of structural determination techniques.In response to this need, numerous predictive computational methods to model structures of protein–protein complexes have been developed over several decades, including protein docking methods that use unbound or modeled component structures as input to perform rigid‐body global searches in six dimensions,The recently developed AlphaFold algorithm (AlphaFold v.2.0) performs end‐to‐end modeling with a deep neural network to generate structural models from sequence,The reported capability to model homomultimers,Here we report a systematic assessment of the accuracy of AlphaFold in performing end‐to‐end modeling of transient protein complexes, using 152 heterodimeric test cases from Protein–Protein Docking Benchmark version 5.5 (BM5.5)22.1To assess the accuracy of AlphaFold in predicting structures of transient protein–protein complexes, we used Protein–Protein Docking Benchmark 5.5 (BM5.5),fnat) metrics. While acceptable accuracy models can include moderate deviation from known structures (including models with up to 10 Å L‐RMSD), medium and high‐accuracy models are more reflective of previously utilized model accuracy cutoffs, such as the 2.5 Å I‐RMSD cutoff used for near‐native models by Chen and WengEscherichia coli TolA/Phage G3B complex) in Figure The performance of AlphaFold, ColabFold, and ZDOCK was assessed by comparison of models with experimentally determined structures of the bound complexes; overall success rate comparisons are shown in Figure 2.2To investigate the determinants of successful performance for AlphaFold, we compared performance across subsets of cases divided by various biological and structural properties Figure . As expePerformance across benchmark cases was also assessed by complex category, as well as protein source Figure . Notablyp = .007; Table p ≤ .001; Figure We performed analysis of a series of geometric and other protein complex properties to identify possible relationships with AlphaFold modeling success. Computed interface features were assessed for association with incorrectly modeled cases versus cases with near‐native AlphaFold complex models (medium and/or high CAPRI accuracy) Figure . GreaterNeff]) were observed, in most cases the individual ligand and receptor chains were modeled accurately (backbone RMSD with bound component chains <2.5 Å). Protein complex model accuracies based on interface residue RMSD (I‐RMSD) and CAPRI criteria did not show a relationship with maximum subunit chain RMSD  and MSA ensemble size (Nensemble) in AlphaFold, for a subset of the docking test cases selected to represent the antibody, enzyme, and “other” protein complex types, and observed very little effect on predictive performance  and global accuracy (pTM),While pTM and pLDDT showed some capability to identify correct versus incorrect complex structural models, the overlap in scores between accuracy categories Figure  led us t2.5Due to the lack of any successful structural prediction of 11 antibody–antigen complexes from the BM5.5 set, we assembled a set of 20 additional nonredundant antibody–antigen complexes with known structures to assess AlphaFold accuracy Table . These cInspection of AlphaFold models of antibody–antigen complexes indicated that many of the inaccurate models had few or no contacts between antibody and antigen chains; one example is shown in Figure 2.6After confirming the limited success of AlphaFold in predicting antibody–antigen complex structures, we performed additional modeling assessments in AlphaFold to identify factors responsible for that performance. While smaller interface size and larger complex structure size were found to be associated with lower AlphaFold success for the overall set of cases Figure , additio2.7After observing limited success of AlphaFold for modeling of antibody–antigen complexes, we tested modeling of that class of complexes with AlphaFold‐Multimer.Results from this assessment, shown in Table Due to the relatively limited number of antibody–antigen cases tested initially with AlphaFold‐Multimer Table , we asseHaving determined the predictive performance of AlphaFold‐Multimer for antibody–antigen complexes, we tested that algorithm for its capability to model T cell receptor‐peptide–major histocompatibility complex (TCR‐pMHC) structures, to further delineate its modeling accuracy for adaptive immune recognition. Although most TCRs share a general binding site and orientation over the pMHC,3Our extensive testing of AlphaFold performance on a nonredundant benchmark of protein–protein complexes indicates that AlphaFold is largely successful at predicting binary transient protein complex structures. However, some complexes were not successfully modeled, most notably antibody–antigen and other adaptive immune interactions, while other categories of test cases showed upper limits of success as well. The limited number of antibody–antigen complex structures that were successfully modeled show that antibody complex modeling can be performed in some cases with the AlphaFold framework, while many of the incorrect models seem to be readily identifiable based on AlphaFold metrics of predicted interface residues, or previously developed energy‐based scoring functions.While protein complex and interface size showed some associations with AlphaFold success for complexes in general, we found that the lack of useful coevolution signals in the MSAs for antibody–antigen complexes was likely responsible for the limited success of those cases, as shown by the lack of effect on performance when removing the MSA input signal. We tested this using a recently optimized version of AlphaFold for protein–protein interfaces, named AlphaFold‐Multimer,Although protein–protein interfaces were not used for training of AlphaFold v.2.0, it is possible that individual chain structures from BM5.5 complex structures were part of the AlphaFold training set, which could influence the predicted conformations of the subunits and indirectly influence the complex structure models. Benchmarking of this AlphaFold model with recently released complex structures that have no related complexes released prior to the AlphaFold training date addresses this concern, and results with such a set were reported by Evans et al. in the AlphaFold‐Multimer study,While this manuscript was under review, a separate studyAlphaFold's end‐to‐end modeling approach represents a major advance and performance improvement over traditional protein–protein docking methods, serving as a proof of concept and a possible framework for optimization to accurately model most or all protein–protein interactions. While optimization of AlphaFold for protein complexes (AlphaFold‐Multimer) was recently reported and released by the DeepMind team44.1https://zlab.umassmed.edu/benchmark/), and are based on the extent of binding conformational changes for each complex.A test set of heterodimeric protein complexes was obtained from Protein–Protein Docking Benchmark 5.5 (BM5.5).Twenty additional antibody–antigen modeling test cases Table  were selα or β variable domain) with any Class I TCR‐pMHC complex structures released before May 2018, and no redundancy with any of the complex structures in the benchmark set. Complexes with noncanonical or modified amino acids in peptides were excluded, and a resolution cutoff of 3.25 Å was applied (in accordance with the other benchmarks in this study), except for the 7RM4 TCR‐pMHC complex structure which was retained due to its resolution being close to the cutoff (3.33 Å). TCR α, TCR β, peptide, and MHC chains were input as separate sequences to AlphaFold‐Multimer. For efficiency, only TCR variable domain sequences, and peptide‐binding domains of MHCs (α1 and α2 domains), were used for modeling.TCR‐pMHC complex structures for AlphaFold‐Multimer benchmarking were identified from Class I TCR‐pMHC complex structures in the TCR3d database,4.2https://github.com/deepmind/alphafold) and installed on a local computing cluster. Sequences of protein chains for the protein–protein complexes were obtained from the PDB “seqres” file and used as input for each complex modeling job. Raw MSAs were prepared for each chain with the downloaded published AlphaFold pipeline,AlphaFold was downloaded from Github (Nensemble) and recycling (Ncycle) parameters on complex modeling accuracy, we increased Nensemble or Ncycle by modifying those parameters in the AlphaFold Python code, while keeping the input MSAs and sequences/features the same.To introduce chain breaks, a residue index shift of 200 was added to the junction of interacting chains, as recently implemented in ColabFold.Three out of the 152 test cases failed to complete in the AlphaFold pipeline due to GPU memory limits during structure prediction, or errors during feature preparation: 1ZM4, 2OZA, and 1B6C. AlphaFold structure prediction runs were performed on an NVIDIA Titan RTX or NVIDIA Quadro 6,000 GPU.4.3https://colab.research.google.com/github/sokrypton/ColabFold/blob/main/beta/AlphaFold_advanced.ipynb). Input protein sequences were identical to those used for AlphaFold modeling. The MMseqs2 methodProtein–protein complex predictions were generated in ColabFoldhttps://robetta.bakerlab.org/submit.php) to generate complex models.ColabFold enables users to pair alignments for different protein sequences based on UniProt accession numbers; this is a selectable option on the ColabFold interface.https://piercelab.ibbr.umd.edu/af_complex_models.html.All models generated with AlphaFold, ColabFold and RoseTTAFold are made available to the public at: 4.4https://github.com/deepmind/alphafold on November 2, 2021, and local ColabFold downloaded from https://github.com/sokrypton/ColabFold on January 12, 2021. Input MSA features were generated by either the AlphaFold‐Multimer pipeline described in Evans et al.,AlphaFold‐Multimer4.5To enable comparison against a rigid‐body docking algorithm, we generated docking models using ZDOCK version 3.0.2.4.6fnat. Backbone atoms were used in the I‐RMSD and L‐RMSD calculations.Docking models were assessed using the CAPRI criteria4.7To calculate the interface pLDDT, we averaged the per‐residue pLDDT of interface residues. Interface residues are defined as residues with atomic contacts across the interface within the specified distance cutoff. Interface PAE was calculated by averaging the PAE of cross‐interface residue pairs with atomic contacts within a given distance cutoff. The interface distance cutoffs tested range from 4 to 10 Å. An interface pLDDT score of 0 and an interface PAE score of 35 was assigned to models without any interface contacts within the distance cutoff.4.8To resolve possible unfavorable geometries or clashes in experimentally determined complex structures and AlphaFold models, the Rosetta FastRelax protocol‐relax:constrain_relax_to_start_coords‐relax:coord_constrain_sidechains‐relax:ramp_constraints false‐ex1‐ex2‐use_input_sc‐no_optH false‐flip_HNQ‐nstruct 14.9Post‐relax complex structures were used as inputs to obtain IRAD,4.10Neff is used as a measure of the MSA depth. Neff score is defined as the number of clusters after the raw MSA inputs were clustered at the 62% sequence identity using CD‐HITThe 4.11TM‐scores were calculated using TM‐score executable4.12r-project.org), and heatmaps were generated with the ComplexHeatmap packagep values were calculated with ggpubr package in R. Wilcoxon rank‐sum test was performed using ggsignif package in R. Binary and multi‐class ROC curves with AUC values were calculated with the “pROC” and “multiROC” packages, respectively, in R. 95% CI values for binary ROC AUC values were calculated using the “ci.auc” function in pROC, with 2000 stratified boostrap replicates, and multi‐class ROC AUC confidence interval values were calculated using the “boot” R packageFigures of structures were generated using PyMOL version 2.4 . Box plots, line plots and bar plots were generated with the ggplot2 packageRui Yin: Conceptualization; data curation; formal analysis; investigation; writing – original draft; writing – review and editing. Brandon Y Feng: Writing – original draft; writing – review and editing. Amitabh Varshney: Writing – review and editing. Brian G Pierce: Conceptualization; funding acquisition; supervision; writing – original draft; writing – review and editing.The authors declare no competing interests.Appendix S1 Supporting Information.Click here for additional data file.


The oxidative stress response pathway is one of the hotspots of current pharmaceutical research. Many proteins involved in these pathways work through protein–protein interactions (PPIs). Hence, targeting PPI to develop drugs for an oxidative stress response is a promising strategy. In recent years, small molecules targeting protein–protein interactions (PPIs), which provide efficient methods for drug discovery, are being investigated by an increasing number of studies. However, unlike the enzyme–ligand binding mode, PPIs usually exhibit large and dynamic binding interfaces, which raise additional challenges for the discovery and optimization of small molecules and for the biochemical techniques used to screen compounds and study structure–activity relationships (SARs). Currently, multiple types of PPIs have been clustered into different classes, which make it difficult to design stationary methods for small molecules. Deficient experimental methods are plaguing medicinal chemists and are becoming a major challenge in the discovery of PPI inhibitors. In this review, we present current methods that are specifically used in the discovery and identification of small molecules that target oxidative stress-related PPIs, including proximity-based, affinity-based, competition-based, structure-guided, and function-based methods. Our aim is to introduce feasible methods and their characteristics that are implemented in the discovery of small molecules for different types of PPIs. For each of these methods, we highlight successful examples of PPI inhibitors associated with oxidative stress to illustrate the strategies and provide insights for further design. Oxidative stress (OS) is a state of imbalance between the production of reactive oxygen species  and antioxidant defenses, that can become a main trigger of inflammation, aging, and chronic diseases including cancer ,2. Free Proteins work by forming different complexes, which involve multiple protein–protein interactions (PPIs) and play important roles in the regulation of biological systems . HoweverCurrently, many methods, ranging from protein-based molecular methods to cell-based high-content screening strategies, have been developed to identify compounds that interfere with PPIs of interest . IdeallyBased on the nature of PPI binding modes, the rational design of proximity-based methods is becoming a promising strategy for discovering PPI inhibitors. All reported assays have been developed based on the interactions between two proteins, and the principle of proximity-based methods is shown in 2) into singlet oxygen (1O2) upon excitation by light at 680 nm. Each donor bead can produce about 60,000 singlet oxygen molecules, which can diffuse to a distance of 200 nm in solution, within a half-life of 4 μs   108] whi6-XL665 were added and the cell and the samples were incubated. The donor emission was measured at 620 nm, followed by the acceptor emission at 665 nm. Data were analyzed using the TR-FRET ratio (665 nm emission/620 nm emission). Finally, compound 11 (3), was identified, and DDO-5936 (4) and 18H (5) were obtained for further structural optimization (Heat shock protein 90 (HSP90) is a chaperone that maintains cellular protein homeostasis. HSP90 and its cochaperones  help mature nascent or misfolded peptides. CDC37 is a kinase-specific chaperone that recognizes and delivers kinases to HSP90, and the HSP90/CDC37 complex forms a type I PPI. CDC37 is overexpressed in tumor cells, compared to normal cells, and CDC37/HSP90 synergistically facilitates the maturation of oncogenesis. Hence, targeting CDC37/HSP90 PPI is a robust strategy for developing a novel cancer therapy . In 2019mization B 111]..6-XL665 5, IC50 = 3–6 μM). Finally, 33i , with favorable in vitro and in vivo pharmacokinetic properties, was obtained after structural optimizations (Interleukin-2 (IL-2) is a proinflammatory cytokine that plays an important role in the growth of active T cells, through mediating the T-helper 1 (Th1) immune response ,115,116.izations C.7, KD = 3.59 nM (BLI), EC50 = 28.6 nM (FP)) was identified as the most potent inhibitor in subsequent functional experiments (The Nrf2 transcription factor is a member of the CNC protein family. Nrf2 is ubiquitously expressed in tissues and is a major mediator of cellular adaptation to oxidative stress. Kelch-like ECH-associated protein 1 (Keap1) is a component of the ubiquitin ligase complex that is specially targeted for inhibition by both chemopreventive agents and oxidative stress. Nrf2 forms a cytoplasmic complex with Keap1. This complex prevents Nrf2 activation and nuclear translocation by promoting its ubiquitination and proteolysis ,123,124.eriments D.2O2-mediated cell death. The Wnt/β-catenin signaling pathway  participates in multiple physiological processes, including cell proliferation and differentiation, stem cell self-renewal, and tissue homeostasis [6, to attach the proteins to streptavidin-coated donor beads and nickel chelate acceptor beads, respectively. When the donor and acceptor beads were brought within 200 nm, emission light at 570 nm was detected. If a β-catenin/BCL9 PPI inhibitor completely disrupted the PPI, no fluorescence signal would be observed. Finally, the hit compound CP-868388 , which selectively inhibits the β-catenin/BCL9 PPI with a Ki of 3.6 μM in AlphaScreen competitive inhibition assays [In 2014, Zhang et al. discovered a β-catenin/BCL9 PPI inhibitor, CP-868388, using the AlphaScreen method . Oxidatieostasis ,127,128.eostasis ,130. β-Ceostasis . As showP-868388 E was disn assays .10), with an HTRF IC50 of 3.88 μM, was discovered [D: ~11 μM), and the cocrystal structure of compound 1 bound to MDM2 was obtained. Based on this hit, structure-based rational design and synthesis were performed to obtain potent MDM2/p53 PPI inhibitors with good pharmacokinetic properties and in vivo efficacy [11), has entered into clinical trials and is in phase I for the treatment of cancer [The Wnt/β-catenin signaling pathway induces DNA damage response by regulating multiple proteins such as p53. The transcription factor p53, also known as a tumor suppressor, plays a crucial role in cell growth regulation, progression through the cell cycle, apoptosis, and genomic stability. In 50% of human cancers, p53 is defective due to functional mutations or inhibition by its negative regulator, murine double minute 2 (MDM2). Hence, development of an MDM2/p53 PPI inhibitor is a promising strategy for cancer therapy. The MDM2/p53 complex, a type IV PPI, and the α-helical region of MDM2 binds to the N-terminal transactivation domain of p53. In 2009, Allen et al. carried out an HTRF-based high-throughput screen to discover an MDM2/p53 PPI inhibitor. A hit, compound 1  was identified as the most potent PPI inhibitor  is overexpressed in almost all cancers and has become an important oncotarget ,138. Ovenhibitor F.Oxidative stress is a kind of negative effect produced by free radicals in the body, and is considered to be an important factor leading to aging and disease. The research on oxidative stress has always been a hotspot of basic research. Oxidative stress can cause changes in a variety of cytokines, and the antioxidant defense mechanism will mobilize a variety of signaling factors to fight oxidative stress, such as Nrf2, HSP90, IL-2, etc. Therefore, these proteins are promising targets for the development of antioxidants. In this review, we first divided proteins into five categories according to their modes of action: type I, two proteins interact through preformed surfaces; type II, proteins with preformed globular structures that adapt upon interaction to form a complex with a novel conformation; type III, a rigid globular protein interacts with a peptide; type IV, a flexible globular protein interacts with a peptide; type V, interaction between two peptide chains. Then, we classified PPI analysis methods according to different mechanisms. We described in detail the principles, advantages, and disadvantages of these methods, as well as the types of PPI applicable to each method. Finally, we listed the classic cases of oxidative stress-related protein inhibitor development, and described their development process and analysis methods. We hope this review provides ideas for the selection of methods for the subsequent development of PPI inhibitors.During the past few decades, increasing research efforts toward the development of small molecules that target PPIs have emerged and resulted in multiple successful drug candidates entering the clinic. Unlike traditional enzyme-ligand binding modes, PPIs usually exhibit dynamic features that can be classified into five types, based on the binding modes and epitopes of the interaction interface. Although some common biological methods can be used to screen compounds, it is important to distinguish between various methods when designing specific small molecules to target different types of PPIs. In both academia and industry, an increased demand for PPI targets will require rational methods for the development and optimization of small molecules. There is an urgent need for medicinal chemists to understand the current methods that can be applied to multiple types of PPIs. Recognizing the principles and applications of the different methods will benefit the design of PPI inhibitor discovery campaigns and methods for addressing current challenges. Thus, we provide a review of the current methods that are specifically used in the discovery of small molecules that target oxidative stress-related PPIs, including proximity-based, affinity-based, competition-based, and function-based methods. In the future, we expect that the scientific community will develop even better methods for use in the discovery of small molecule inhibitors of different types of PPIs. Overall, with an increasing number of successful examples, new approaches and concepts for the discovery of small molecules that target oxidative stress-related PPIs will be developed and applied and will provide additional insights into the development of PPI inhibitors.


The design of new therapeutic molecules can be significantly informed by studying protein-ligand interactions using biophysical approaches directly after purification of the protein-ligand complex. Well-established techniques utilized in drug discovery include isothermal titration calorimetry, surface plasmon resonance, nuclear magnetic resonance spectroscopy, and structure-based drug discovery which mainly rely on protein crystallography and, more recently, cryo-electron microscopy. Protein-ligand complexes are dynamic, heterogeneous, and challenging systems that are best studied with several complementary techniques. Native mass spectrometry (MS) is a versatile method used to study proteins and their non-covalently driven assemblies in a native-like folded state, providing information on binding thermodynamics and stoichiometry as well as insights on ternary and quaternary protein structure. Here, we discuss the basic principles of native mass spectrometry, the field’s recent progress, how native MS is integrated into a drug discovery pipeline, and its future developments in drug discovery. The investigation of non-covalent interactions between a biological macromolecule and small molecule, which are driven by a myriad of forces including hydrogen bonds, Van der Waals forces, electrostatic and hydrophobic interactions, play a crucial role in the development of drug candidates. A variety of analytical methods are utilized to identify and quantify protein-ligand interactions, including isothermal titration calorimetry (ITC) , surfaceThe mass spectrometry (MS) analytical toolbox contains numerous biophysical techniques , but onld) of the same complex over a range of temperatures (Native MS exploits the gentle nature of electrospray ionization (ESI)  to transeratures . Furthereratures , the teteratures  and the eratures  has alsoeratures  were receratures  could preratures . Even aleratures . For exaeratures . Anothereratures . Therefoeratures .in situ.Over the last 30 years, numerous examples have proven the analytical advantages of native MS  in addreESI  is the cm/z). The collection of charge states that represent a single protein moiety, is referred as the charge state distribution (CSD) of gas-phase protein ions, and usually resembles one or more Gaussian distributions   and Orbitrap mass analyzers, have also been adapted for native MS   mass anaative MS .Many modern MS-instruments used for native MS combine these mass analyzers with either a quadrupole or an ion trap mass analyzer, in a single configuration. One of the most common hybrid instruments extensively used in studies of biomolecular assemblies is the Quadrupole Time–of–flight (QTof) , which cpro), using over 1,250 fragments from a compound library that yielded 48 high-value covalent fragments  with a known binding site is available, this can be used to quickly assess the specificity for that site for a different ligand (L). Native MS competitive binding experiments consist in keeping the protein and L concentrations constant, while increasing the HL concentrations. The relative abundances of the different species in the mass spectra allow assessing whether the displacement of L with HL is competitive or not. If the HL is competing with the L for the same site, the fraction of the complex with the L decreases and the fraction of the complex with the HL increases as the HL concentration is increased as shown in m/z range of the mass spectrometer, allowing them to determine high-affinity binding constants in the picomolar range.The ion intensity ratio between the free protein and the protein−ligand complex is measured for affinity measurements, and ideally, the target protein concentration should be below the expected KAdditional to affinity measurements, biomolecules’ thermodynamic and kinetic properties can be investigated by following the abundance of different species as a function of temperature and over a time course . ThermodEscherichia coli. Their approach allowed them to determine the thermodynamics of individual binding events for lipids with variable chain lengths, resolving unique thermodynamic properties. Another study by off) from the tetrameric protein streptavidin  was quantified via the titration approach  stabilizes complex formation whereas an inverse agonist (S32212) disrupts the receptor-mini-Gs complex, showing the potential of native MS in investigating the effect of small molecules on GPCR coupling. By exploring these effects with experimental GPCR-targeting drugs, it may be possible to examine bias toward particular signaling pathways, thereby facilitating the development of highly selective therapeutic agents. Native MS has shown a significant advantage in studying membrane protein-lipid interactions. The resolution of this technique enables the detailed interrogations of a wide range of membrane proteins bound to different lipids, and the impact of acyl chain lengths and degrees of saturation on lipid-binding  . To enabThe application of native MS to compound library screening has only recently emerged, as outlined in the following examples. The native MS screening approach shows a particular potential for fragment-based drug discovery (FBDD). The ability to capture the weak binding of fragments at a millimolar range of affinity and the low sample consumption widens the accessibility even for compounds with poor solubility . In orded values, compounds with the desirable affinity, for example, Kd<50 μM, are chosen for further validation. The validation will include the specificity of binding to the protein-target by screening these compounds to a different protein or in a competition format by using a hot ligand. Moreover, the Kd values measured with the direct approach are confirmed with the titration method   is the aEscherichia coli molecular chaperone DnaK in response to nucleotide and substrate binding. Unique conformational states that arose due to the allosteric effects of small molecule interactions were identified and combined with results from double electron−electron resonance spectroscopy to confirm structural data derived from both NMR and X-crystallography experiments  is a collisional activation method in which the unfolding of protein complexes in the gas-phase is monitored with IM-MS. During IM-MS screening, CIU fingerprints can be uniquely related to specific protein-ligand binding modes . The unfm/z dimensions and the accuracy of computational models of protein structure and dynamics increase the use of IM-MS for studying the impact of ligand binding on protein structures and assemblies will also significantly increase.It is expected that as the resolution of IM-MS methods in both the mobility and Technological advances continue to emerge, and the recent introduction of orbitrap mass analyzers modified for protein assemblies affords excellent resolution and sensitivity, enabling facile definition of concurrent binding of small molecules in a MDa complex . Taking The current mass spectrometers allow high throughput screening of small molecules for binding against a protein target and determining the dissociation constant. As shown and discussed above, the thermodynamic and kinetic properties of biomolecules can also be measured with MS using modified instrumentation . Therefo3D models of macromolecular complexes have been deduced by combining MS experiments with data from crystallography, NMR, small-angle X-ray scattering and EM  and it iin situ native MS will add another dimension to the 3D views of large macromolecular assemblies currently imaged in their native frozen-hydrated state by electron cryotomography (ECT)  . The so-hy (ECT)  will offd determinations by direct-ESI assay or titration experiments have provided accurate values and can be used to quickly assess the compounds’ affinity to the target protein during a screening campaign. Native MS can also efficiently assess compound specificity for a particular binding site in competitive binding experiments. It is possible to distinguish multiple binding sites with the appropriate instrumental parameters, revealing complex allosteric mechanisms. In addition, IMS studies during screening have provided insight into conformational changes of a protein upon binding to a compound. The continuous improvements in mass spectrometry hardware and software are expanding the limits of native MS applications. In addition, native MS is expected to be fully integrated with other structural biology techniques, such as X-ray crystallography, cryo-EM, and in a not distant future cryo-ET, in the drug discovery pipeline, providing unprecedented insights on protein-ligand binding and ligands screening that could significantly impact the drug discovery process.In the last few years, native MS has become a well-established technique for drug discovery. Its high sensitivity, simplicity, speed, wide dynamic range, low protein and ligand consumption, and the possibility of automation and high throughput makes it an integral component of the biophysical toolkit commonly used for primary screening, adding to techniques such as NMR and SPR. Moreover, the ability to explore all the aspects of protein-ligand interactions and dynamics is the basis of the unique potential of native MS in the fragment hit identification. The majority of published applications of native MS in drug discovery are on soluble proteins. Nevertheless, in recent years the frontier conquered by native MS in membrane proteins broadens the repertoire of protein targets screened by this technique. K


The rapid accumulation of high-throughput sequence data demands the development of effective and efficient data-driven computational methods to functionally annotate proteins. However, most current approaches used for functional annotation simply focus on the use of protein-level information but ignore inter-relationships among annotations.Here, we established PFresGO, an attention-based deep-learning approach that incorporates hierarchical structures in Gene Ontology (GO) graphs and advances in natural language processing algorithms for the functional annotation of proteins. PFresGO employs a self-attention operation to capture the inter-relationships of GO terms, updates its embedding accordingly and uses a cross-attention operation to project protein representations and GO embedding into a common latent space to identify global protein sequence patterns and local functional residues. We demonstrate that PFresGO consistently achieves superior performance across GO categories when compared with ‘state-of-the-art’ methods. Importantly, we show that PFresGO can identify functionally important residues in protein sequences by assessing the distribution of attention weightings. PFresGO should serve as an effective tool for the accurate functional annotation of proteins and functional domains within proteins.https://github.com/BioColLab/PFresGO.PFresGO is available for academic purposes at Bioinformatics online.Supplementary data are available at  Proteins are indispensable macromolecules that play fundamental roles in many activities and biological functions in living cells, such as maintaining normal metabolism, transporting nutrients, transducing signals and catalyzing biochemistry interactions , Kyoto EThe rapid accumulation of protein datasets through the use of genomic, transcriptomic and proteomic techniques has resulted in an exponential growth in demand for high-throughput and reliable functional annotation of such datasets  containsCompared with homology-based methods, conventional machine-learning approaches, such as support vector machines , and deeIn this article, we propose a novel, attention-based approach, termed PFresGO, for protein function annotation by leveraging both protein residual-level representations and GO architecture. PFresGO uses sequence information of the query proteins as input; it takes the protein sequence embedding encoded by the pre-trained language model as well as GO terms embedding as inputs and delivers a probability of protein function by calculating the correlation between protein features and individual GO terms via an attention mechanism. Our findings show that PFresGO performs better than existing methods across all GO categories and benefits markedly from the incorporation of GO hierarchical structure information. The interpretation of annotation results is enhanced through the location of functionally relevant residues/domains in protein sequences via the analysis of attention weights.n = 489 terms), BP (1943) and CC (320), with each GO term linked to >50 non-redundant Protein Data Bank (PDB) chains. This dataset was further divided into training , validation  and test  datasets for the training, optimization and evaluation of the model, respectively. For the test dataset, only proteins with at least one trusted functional annotation in each of the three GO categories was selected. All annotations represented in the test dataset were experimentally validated, and the maximum length of protein chains was limited to 1000. CD-HIT  . The one-hot encoding procedure was followed by a fully connected layer with hidden dimension S with l residues into the residue-level protein sequence feature embedding Given a protein sequence Gene ontology (GO) is a commonly used classification scheme in terms of annotating protein functions. Here, we applied the pre-trained model Anc2vec .A higher IC value of the GO term corresponds to a higher specificity (i.e. a rarer occurrence). We separately stratified the GO terms within the MF, BP and CC categories into three groups according to their IC values and then compared the performance of PFresGO with that of other methods using these GO terms with distinct IC values. We recorded the AUPRC values of different methods across all IC cutoffs . AlthougFurthermore, we analyzed the performance of PFresGO in cases where the proteins in test set did not share any homologous domains with those in training set. More specifically, we applied the ECOD classifier   . AlthougWe demonstrated the effectiveness of integrating a pre-trained deep-learning language model and the hierarchical structure of GO terms for function annotation. On the other hand, there is a caveat when engaging the attention-based mechanism, the use of which can result in substantial memory consumption, which can further limit the number of GO terms that can be annotated; however, the annotation performance using GO terms of high specificity significantly benefits from the integration of structure information from GO graphs. Significantly, PFresGO can also infer functional sites in protein by assessing the attention weightings of individual amino acid residues. A case study showed that the distribution of attention weights along a protein sequence is readily interpretable in relation to functionally relevant amino acid residues or domains. We conducted a multiple attention analysis of the functions of select proteins and illustrated that important residues identified by PFresGO with high attention weights accord well experimental data in the BioLip database. Based on these findings, we expect that PFresGO will serve as a useful tool for the functional annotation of proteins and the identification of functional sites in proteins, which will be beneficial given the ever-expanding genomic, proteomic and transcriptomic datasets.btad094_Supplementary_DataClick here for additional data file.


Proteins perform many essential functions in biological systems and can be successfully developed as bio-therapeutics. It is invaluable to be able to predict their properties based on a proposed sequence and structure. In this study, we developed a novel generalizable deep learning framework, LM-GVP, composed of a protein Language Model (LM) and Graph Neural Network (GNN) to leverage information from both 1D amino acid sequences and 3D structures of proteins. Our approach outperformed the state-of-the-art protein LMs on a variety of property prediction tasks including fluorescence, protease stability, and protein functions from Gene Ontology (GO). We also illustrated insights into how a GNN prediction head can inform the fine-tuning of protein LMs to better leverage structural information. We envision that our deep learning framework will be generalizable to many protein property prediction problems to greatly accelerate protein engineering and drug development. Artificially designed proteins or polypeptide chains can also be synthesized to exhibit desired biological functions for research and therapeutic applications.Proteins are the major macromolecules carrying out the essential functions in biology. Composed of a sequence of amino acids (AAs) connected by peptide bonds, a natural protein folds into its tertiary (3D) structure during biosynthesis on the ribosome3, trRosetta4 and a three-track deep neural net approach by Baek et al.5, have exceeded performance based on traditional approaches for modeling the folding processes.An important problem researchers have been studying intensively for over five decades is the complete determination of 3D protein structures, which ultimately enhances our understanding of many of their other properties such as biological function, druggability, and stability against physical or enzymatic stress. Protein structures can be determined experimentally using methods such as nuclear magnetic resonance (NMR), X-ray crystallography, and cryogenic electron microscopy. Computational methods for predicting unknown structures have also been developed via software like Rosetta. Recent breakthroughs in deep learning approaches including AlphaFold3 study has demonstrated that sequence alone can predict 3D structures with outstanding performance, thus is it still necessary to explicitly incorporate known structural information into protein models? Just as phenotypes are not fully determined by genotype, so too are protein sequences not always folded into the same 3D structures when subjected to different physiological conditions. An extreme example is the mis-folded forms of Amyloid beta, which are implicated in Alzheimer’s disease6. The conformation of proteins’ 3D structures, such as G-protein-coupled receptors (GPCRs)7 and hemoglobin, also change with the presence or absence of allosteric modulators, which directly affects protein functions such as ligand binding and oxygen binding, respectively. Therefore, there is a well-established biochemical foundation supporting the additive value of 3D protein structure in protein property prediction.However, accurately predicting the 3D structures is merely the first step in protein modeling. The ultimate goal is to predict proteins’ properties. The AlphaFold210, various types of LMs have also been adapted to protein modeling by treating protein sequences as the language of life, where tokens are AAs. Prominent examples include transformer-based LMs such as those developed in ProtTrans11 and ESM12 as well as long short-term memory (LSTM)-based LMs from Alley et al.13 and Heinzinger et al.14. Trained with billions of natural protein sequences alone in self-supervised fashion, protein LMs have been shown to achieve state-of-the-art performance on various residue-level and protein-level tasks12.With the recent advances in large pretrained language models (LMs) from natural languagesarchaea, bacteria, and eukarya)11 and within protein families, an evolutionary vector field can trace back the protein’s evolution15. More interestingly, a recent study found that protein LMs trained on mostly wild-type (WT) sequences with masked LM objective, can be used to quantify mutational effects using the LM likelihood without further training16.Mechanistically, researchers found LMs are able to learn evolutionary information embedded in billions of protein sequences across many species. Concretely, protein LMs can embed proteins from different domains of life 19 developed a bidirectional LSTM model with a residue contact prediction objective to incorporate structural information. For protein property prediction tasks, protein 3D structures have been mostly treated as a graph of AA residues, which can then be fed into graph neural networks (GNNs). By representing protein 3D structures as AA graphs based on contact maps built from C-alpha distances, Villegas-Morcillo et al.20 found a graph convolutional net (GCN)21 underperformed a sequence-only baseline where AA embeddings from protein LMs are used to predict protein functions. Gligorijević et al. introduced DeepFRI22, a GCN-based architecture to combine the information from sequence and structure by incorporating AA embeddings from protein LMs as node features. DeepFRI achieved state-of-the-art performance on various protein function prediction tasks.Protein 3D structures have also been explicitly used for both general-purpose protein LMs and property prediction tasks. Bepler and Berger23 added residue directions and orientations as edge features on the protein graph to improve the generative modeling of protein sequences from this structural representation. By predicting inter-residue orientations in addition to distances, Yang et al.4 also improved their protein structure prediction algorithm. More recently, Jing et al.24 developed a novel neural module geometric vector perceptrons (GVP) for learning vector-valued and scalar-valued functions over 3D Euclidean space. The output of GVP is equivariant or invariant to rotations and reflections in 3D Euclidean space. When processing protein graphs with rich node and edge features including dihedral angles, backbone directions, inter-residue distances, GVP achieved state-of-the-art performance on protein design and model quality assessment tasks.However, representing 3D structure by building AA graphs from the contact map is a reductionist approach: it only captures inter-residue distances and interactions while disregarding fine-grained details in protein structures such as residue orientations. Studies explicitly incorporating structural information have achieved improved performance on protein structural modeling. For instance, Ingraham et al.22 performs the feature extractions for protein sequence and structures in isolation. Therefore, we reason that the potential synergistic predictive values between structure and sequence cannot be fully exploited by those methods.Moreover, protein property prediction methods such as DeepFRI22, or do not incorporate structural information12. We also demonstrated that the structural fine-tuning of protein LM improves its ability in assessing mutational effects in a zero-shot fashion.In this study, we developed a novel end-to-end deep learning framework for protein property prediction by leveraging information from both protein sequences and 3D structures. Our end-to-end neural architecture organically connects a protein LM and a GNN, allowing gradients to back propagate into both the GNN and the LM. Our approach can be considered a novel fine-tuning procedure for protein LMs by injecting inductive bias from 3D structures, which outperformed previous approaches on protein property prediction tasks that either keep the protein LM fixed24: the protein LM takes protein sequences as input to compute embeddings for individual AAs, which are then concatenated into the node scalar features in the AA graph representation of protein structures. The GVP network is responsible for learning complex structure–function relationships from the AA graphs, with help from the LM. In the training phase, LM-GVP is trained in an end-to-end fashion: the gradients are back-propagated from the GVP network to the transformer blocks of the protein LM , Molecular Function (MF), and Biological Process (BP), as well as two protein engineering datasets, Fluorescence and Protease stability from Tasks Assessing Protein Embeddings (TAPE)27. To prove the synergistic predictive value between protein sequences and structures, we set up baseline models using sequence-only and structure-only information. The sequence-only baseline fine-tunes the protein LM by connecting a linear layer to the pooled output of the classification token, whereas the structure-only baseline is a GNN network (GVP or GAT) trained on AA graphs with one-hot encoded identity of the AA as scalar node features. We also compared LM-GVP with 2-stage architectures developed in DeepFRI22 where protein LM and GNN are trained independently. Overall, we found that the LM-GVP achieved the best performances on all three subsets of the GO dataset et Table  and compts Table . In addits Table ,2, indic29. Protein functions that are more predictable by structures than sequences are enriched for components of large protein complexes such as viral envelope (GO:0019031), photosystem I reaction center (GO:0009538), hemoglobin complex (GO: 0005833), and MHC protein complex (GO:0042611)  Table . Compone1) Table , S4. Fur30, a model-agnostic method for interpreting neural models. For each protein, IG generates a saliency map where each residue is associated with an attribution score, indicating how much the residue contributes to the model’s prediction. We calculated AUROC to quantify the agreement between the saliency scores and binding sites. We found that LM-GVP can identify the active sites on proteins responsible for the binding ATP, GTP and Heme me Table  more accme Table . This is31, followed by DBSCAN32 to detect and visualize families of proteins , within this cluster on the MSA with distinct sequence and an additional high saliency region at positions 315–320 . We replicate this analysis for six additional models: for each of the three available GO classification tasks, we fine-tune ProtBERT directly in addition to fine-tuning LM-GVP. As expected, most of the transformer models fine-tuned directly on the GO data subsequently produce lower values of rho for a given mutational screen than ProtBERT, whereas the likelihoods produced by the LM-GVP models generally retain or exceed their correlation with the target values. This is the expected result of relative over-fitting of the transformer weights to the specific GO task during fine-tuning with a simple linear prediction head. In contrast, the geometrically-aware prediction head of LM-GVP is able to leverage the protein structure associated with a given sequence to obtain better classification performance on each task while simultaneously preserving important information about the amino acid distributions underlying the language model. This is likely the direct result of the fact that the protein’s tertiary structure is the mediating factor through which all proteins ultimately perform their function. Critically, we see that the transformer fine-tuned directly on the GO-MF labels perform worse than their out-of-the-box counterparts in terms of the rank correlation on all 6 zero-shot analyses . However, due to the limited availability of high-resolution 3D structures, protein LMs trained solely on 1D sequence information dominate many predictive applications related to proteins.19, where contact maps were explicitly used when training the LM. Graph transformers34 could also be leveraged to learn from rich attributed graphs constructed from 3D structures in self-supervised settings. However, researchers still need to tackle the challenge of relatively fewer available protein structures (~ 180 K) compared to sequences (> 300B).In this study, we demonstrated the additive value of incorporating structural information on top of protein LMs for property prediction tasks. Our LM-GVP leverages fine-grained structural information beyond just binary contact maps, providing a novel inductive bias to instruct the pretrained protein LM to jointly learn with its GVP head to optimize the predictive performance for protein properties. Our observation that structural information is complementary to protein LMs also suggests that the representation capacity of protein LMs for structure is limited. After all, most protein LMs are trained on protein sequences alone and the objective functions  may not encourage the LMs to learn complex evolutionary sequence-structure relationships. One potential solution to increase the structural representation of protein LMs is to jointly learn from sequence and structure as pioneered by Bepler and Berger35. On text classification tasks, LM-GVP can be adopted to back propagate the gradients from a GNN operating on graphs of tokens to LMs to potentially improve predictive performance. Other applications of natural language processing (NLP) such as question-answering have also seen a similar approaches36 that organically combine GNNs with LMs.LM-GVP can be also considered a novel fine-tuning procedure for protein LMs vis-à-vis building upon the notion that LMs capture “the language of life”. Such a procedure can be extrapolated to natural languages as well. Natural language can also be represented as graphs in various ways, such as dependency graphs (e.g. syntactic dependency parsing tree or semantic dependency parsing tree) and co-occurrence graphsIn conclusion, we described the novel method of LM-GVP, a generalizable deep learning framework for protein property prediction, harnessing the representation power from pretrained protein LMs and fine-grained structural information to achieve the state-of-the-art performance on various property prediction tasks. We have also provided mechanistic insights into the impact of structurally-instructed fine-tuning for protein LMs and believe there could be beneficial implications in natural languages.We experiment with many machine learning models for protein property prediction tasks, including sequence-only and structure-only baselines, 2-stage methods for combining of sequence and structural information, as well as our LM-GVP. Here we describe those models in great details.11 with a dense layer with number of output units corresponding to different tasks. The dense layer is connected to the classification token [CLS] of ProtBERT, which is the last layer hidden-state of [CLS] further processed by a linear layer and a Tanh activation function. During fine-tuning, the gradients are back-propagated the to all the layers in ProtBERT except for the embedding layer.Protein property prediction is analogous to document classification tasks in natural language. To use protein LMs for property prediction, we fine-tune ProtBERT22. Here we describe our structure-only baselines based on two types of GNNs: GAT and GVP.Graph neural networks (GNNs) have been used for protein property predictions23 and Jing et al.24. Each node For both GAT and GVP, the 3D structure of protein is transformed to a proximity graph Node features 8, representing the AA distance alone the 1D protein sequence. The unit vector in the direction of connecting C-alpha atoms is used as vector features Edge features 37 where messages are computed from neighboring nodes and edges, which are subsequently used to update node embeddings at each graph propagation step. Generically, a message on a given edge Both GAT and GVP follow the message passing paradigmNext, the node embedding is updated by aggregating the messages from all of its edges:22, the GAT network only uses the one-hot encoding of the AA’s identity as node features and its message function is defined by:GAT and GVP differ by the choice of message function 3 layers of GAT convolutions are used on the protein graphs with different number of AAs. To aggregate the node embeddings to the final protein-level representation, we first concatenate node features from all layers into a single feature matrix, i.e., 24. It is composed of three consecutive GVP convolution layers, which operates on the tuple of scalar and vector features:The GVP network is modified from the model quality assessment (MQA) network described by Jing et al.The GVP network also take advantage of the both node and edge features described above when computing the message:The update function of GVP convolution layer is defined by:22 to combine information from protein sequence and structure using AA embeddings from protein LM and GNNs, respectively. This is a 2-stage method works by calculating the AA embeddings from a pretrained the protein LM in the first stage:11 as the protein LM across all experiments.we implement 2-stage models following Gligorijević et al.38 by first learn the parameters in the GVP network while keeping the parameters in the LM frozen until converge. Then we unfreeze the parameters in the LM’s transformer layers to fine-tune the parameters in both the LM and the GVP network.LM-GVP is our novel method with the same architecture as the 2-stage model where GVP network is used as the GNN module, but trained in a 1-stage end-to-end fashion. That is, we initialize the protein LM layers with weights from a pretrained protein LM, then the gradients are back-propagated into the protein LM’s transformer layers. In the training phase, we adopted the gradual unfreezing technique developed by Howard and RuderWe use mean squared error (MSE) and weighted cross-entropy loss function for regression and multi-label classification tasks, respectively. To account for class imbalance in multi-label classification settings, we weight the binary cross-entropy losses from each label 39 with Key hyperparameters including learning rate and batch size across experiments are determined through grid search in the choices of  for learning rate and batch size of  based on model’s loss function on validation set. To avoid overfitting, we employ an early stopping criterion with patience = 10 epochs and trained for a maximum of 200 epochs. ADAM optimizerhttps://github.com/flatironinstitute/DeepFRI/tree/master/preprocessing/data provided in DeepFRI22. The construction, preparation, and train/valid/test splitting strategy for of this dataset are comprehensively described in DeepFRI22. We downloaded the protein structures in PDB format for proteins in the GO dataset from RCSB PDB40 and transform the protein structure to attributed graphs as described in a previous section (Structure-only baselines). The three tasks within the GO dataset are multi-label classification.We obtain 3 datasets covering different tasks for protein property prediction. The Gene Ontology (GO) dataset contains three hierarchies of protein functions: biological processes (BP), molecular functions (MF), and cellular components (CC). This dataset is the downloaded from 27. The Fluorescence dataset contains green fluorescent protein (GFP) with mutations and corresponding log-fluorescence intensity. The goal of this task is to predict the log-fluorescence intensity from the sequence and/or structure of the GFP mutants. We adopt the same train/valid/test splitting strategy from TAPE27. The Protease stability dataset contains proteins and measurement of their intrinsic stability in terms of maintaining its fold above a protease concentration threshold. We split the train/valid/test sets for the Protease stability dataset by stratifying the regression target. Both the Fluorescence and Protease stability datasets are regression tasks with single target. The 3D structures of proteins in the Fluorescence and Protease datasets are generated by Rosetta first by introducing the mutations with the fixbb protocol41 and then relaxing42 the resulting structure.We also obtain two protein engineering datasets, Fluorescence and Protease stability from TAPE30 is a model-agnostic interpretation technique that attributes the prediction of a model to its input features. It can be applied to any differentiable model and does not require modification of the model structure. The method has been widely used in interpreting DNNs for natural language understanding and motivated by its success, we also apply it here to obtain residue-level importance for predicting molecular functions.Integrated Gradients (IG)IG integrates the gradient along a straight-line path between a baseline input and the original input to obtain the feature attributions/saliency scores. More specifically, if we denote the original input as To analyze the LM-GVP model, we used the embedding of a sequence that consists of entirely neutral tokens ([SEP]) as the baseline input. We denote the original AA sequence as 43 (https://zhanglab.dcmb.med.umich.edu/BioLiP/download.html). Where data was not available, a sphere of 5.0 Å was created around the ligand in the binding pocket and all sidechain interactions with the ligand were included. We use the known binding sites to obtain a binary profile for each protein. Each residue is associated with a 0 or 1 ground truth label indicating whether it is known binding sites. Our hypothesis is that if a residue has a higher saliency score, it is more likely to be a binding site. We compute the area under the ROC curve (AUROC) to analyze the alignment between the saliency score obtained via IG and the ground truth binding sites for different molecular functions include ATP binding, GTP binding, heme binding and cytokine receptor binding. See Supplementary Table We analyzed the resulted saliency score by comparing it with known binding sites retrieved from BioLiP31 is a non-linear dimension reduction technique that can be used to visualize the clusters within high-dimensional data. We applied UMAP to analyze the latent representation at the penultimate layer of LM-GVP (with 400 dimension) and identified families of proteins with similar structural/sequence motifs that are related to their molecular functions (GO-MF terms). We use DBSCAN32 to extract and analyze selected protein clusters in more detail. Figure 15.Uniform Manifold Approximation and Projection (UMAP)40 and load it into PyMOL44. We then used the spectrum coloring command in PyMOL to assign color to each of the residues based on their saliency scores with the most salient residues colored in shades of red and the least salient residues in shades of blue.We obtained each protein’s 3D structure from the Protein Data Bank45 and the fourth as part of TAPE27. For each non-wildtype protein sequence, we mask the mutated amino acid(s) and pass the tokenized representation through the transformer component of LM-GVP to generate probability distributions over all possible amino acids at the masked positions. As in Meier et al.16, we then compute the masked marginal probability score asWe analyze zero-shot performance of our fine-tuned language models on four separate datasets of mutational scans for individual proteins, three of which were provided as part of the DeepSequence GitHub repository18. Briefly, we first calculate the self-attention maps from the pretrained ProtBERT without any fine-tuning on 20 randomly selected proteins with more than 30 AAs, to predict residue contacts defined by C-alpha distance <  = 10 Å between residues at least 6 AAs apart to ignore local contacts. The self-attention maps from all layers of the transformer heads were used as features to learn a logistic regression model with L1 penalty to predict contacts. We then fit parameters in the logistic regression model via scikit-learn46. In the inference phase, we predict the contact maps for 500 randomly sampled proteins in the test sets of the five datasets. Then compute the precision score between the contact maps predicted from attention maps and the ground truth.To assess the structural information intrinsic to protein LMs, we adopt the few-shot learning approach described in Rao et alSupplementary Information.


The network will aid influenza A disease modeling by providing strain-specific virulence levels with interacting protein domains. It can possibly contribute to computational methods for uncovering influenza infection mechanisms mediated through protein domain interactions between viral and host proteins. It is available at https://iav-ppi.onrender.com/home.There exist several databases that provide virus-host protein interactions. While most provide curated records of interacting virus-host protein pairs, information on the strain-specific virulence factors or protein domains involved, is lacking. Some databases offer incomplete coverage of influenza strains because of the need to sift through vast amounts of literature . None have offered complete, strain specific protein-protein interaction records for the influenza A group of viruses. In this paper, we present a comprehensive network of predicted domain-domain interaction(s) (DDI) between influenza A virus (IAV) and mouse host proteins, that will allow the systematic study of disease factors by taking the virulence information  into account. From a previously published dataset of lethal dose studies of IAV infection in mice, we constructed an interacting domain network of mouse and viral protein domains as nodes with weighted edges. The edges were scored with the Domain Interaction Statistical Potential (DISPOT) to indicate putative DDI. The virulence network can be easily navigated  Influenza A virus (IAV) is a single stranded, positive ribonucleic acid (RNA) virus that is a respiratory pathogen across many species such as humans, swine, and wild waterfowl. It consists of eight genomic segments which encode at least 11 proteins. The structure and organization of the virus particle is shown in Hemagglutinin (HA) and neuraminidase (NA) on the viral particle surface, are the proteins responsible for mediating entry into and cleavage from the host cell, respectively. Matrix protein 1 (M1) is a component of the viral envelop while matrix protein 2 (M2) is found below the lipid bilayer of the viral membrane, strengthening it. Together with the nucleoprotein (NP), they form the ribonucleoprotein complex , 1957 (H2N2) and 1968 (H3N2) pandemics are 50 million , 1.1 mil50)  . By comp50) .On one hand, databases such as HPIDB  and STRIIn-vitro interactions found to be occurring amongst target proteins and IAV (via biochemistry assays or cell cultures) could assist in designing knock-out factors or drug targets that will allow in-vivo validation of the interaction in mouse models.On the other hand, it is challenging to directly study the effect of influenza A virulence in human hosts owing to ethical considerations. Mice have been used to infer disease pathology of IAV in humans . While m50 categories) and protein-protein interactions could prove beneficial in understanding the factors contributing to IAV virulence. Domain-domain interaction(s) DDI can be particularly useful because a protein domain is often a discrete functional unit that is modular, and protein-protein interactions rely on combinations of DDI   was deve50 values and interacting protein domains from the C57BL/6J mouse strain as identified by DISPOT. The virulence network of interacting protein domains will assist studies of IAV disease modeling by providing data of putative interacting protein domains that are associated with their LD50 values.The protein network is presented in a clear graphical user interface (GUI) that easily shows the LDThe rest of this manuscript is organized as follows. https://iav-ppi.onrender.com/home.50 value was the key information needed to identify the virulence class of a specific IAV strain. Virulence was classified as two-class (avirulent/virulent) and three-class (low/intermediate/high) (shown in50 thresholds of 103.0 and 106.0 were applied (shown in50 infection unit) (50 infection units include Plaque-forming Unit (PFU), Focus-forming Unit (FFU), 50% Egg Infective Dose (EID50), 50% Tissue Culture Infectious Dose (TCID50) and 50% Cell Culture Infectious Dose (CCID50), where the equality across all units was assumed.This work built on initial data records of mouse model infectious studies collected in the previous work by F.X. Ivan and C.K. Kwoh . Their sshown in. Essentishown in, referenon unit) . LD50 inhttps://supfam.org/sequence/search) (SUPERFAMILY 2.0 sequence search (/search)  was used/search) .http://dispot.korkinlab.org/home/pairs)   served ae/pairs) , an in-d2 is the natural logarithm of observed frequencies of interactions between domains in the DOMMINO database. Mmean is the average number of interactions for a pair of domain families, calculated from the non-redundant DOMMINO dataset. Non-redundant refers to two corresponding pairs of domains that do not share 95% or more sequence identity  PubMed , where Lr papers  and (Shir papers  were dochttps://github.com/tengann/IAV-Host-PPI-Database/blob/main/RawData_2022.xlsm) holds 488 infection records involving wild-type, laboratory, mouse-adapted, recombinant or mutant IAV strains. IAV genomes of wild-type strains are in their natural and non-mutated form while laboratory strains were prepared by means of reverse genetics. Mouse-adapted strains were derived from serial lung-to-lung passages of virus in mice. Genetic amino acid sequences of mutant virus were changed through point mutations via single amino acid substitutions. Recombinant strains were formed by the combination of protein segments from at least two different IAV strains.The preliminary dataset presented in this work     ID numbeaxonomy) .https://www.uniprot.org/) (https://www.uniprot.org/uniprotkb?query= UP000000589) were retrieved. Swiss-Prot reviewed refers to records with information fully and manually extracted from literature or curator-evaluated computational analysis (Amino acid sequences of both IAV and mouse proteins were retrieved from the UniProt (release 2021_03) protein knowledgebase (UniProtKB) (ot.org/) . IAV proanalysis . It striAs an average protein consists of two or more domains, domain start and end residue numbers, corresponding to regions of protein sequences matching each assigned SCOP domain (by SUPERFAMILY 2.0) were independently noted for every protein sequence retrieved from UniProtKB. Since each domain has its distinct structure and biological function, only a subset of domains constituting each protein are involved in the interaction between a pair of proteins. Thus, this enhances the complexity of host-pathogen protein interaction analysis .Overall, IAV strains in the dataset were found to comprise proteins domains belonging to 13 SCOP superfamilies . Then, tvia subtype and strain to view information collected from literature searches, an interactive network graph with accompanying information on node and edge attributes as well as amino acid sequences extracted from UniProt. IAV-Mouse PPI web server GUI has a comprehensible interface, made up of two pages, with various features, including browsing https://dashboard. render.com/) web service with Python web server gateway interface HTTP server, Gunicorn V20.1.0 was utilized to build and run the web interface entirely in the cloud.Firstly, Microsoft Excel 2016 was used to store and organize data collected from literature. Secondly, the web interface was developed on the code editor Visual Studio Code V1.71.2, with Python V3.7.4 as the programming language. Python libraries used were Pandas V1.3.5, for transforming comma-separated values (csv) from Excel files to dataframes. Dash V2.3.1 was the framework for designing the application’s functionalities, described as follows: Dash bootstrap components V0.3.0 for building the application’s layout, graph visualization component Dash Cytoscape V0.3.0 for constructing the interactive network graph. Beautiful Soup V4.11.1 was the HTML parser used for pulling protein sequences from the UniProt database. Lastly, cloud hosting application, Render  belonging to the selected subtype group.The main page features three main sections—header, body and footer shown in .The header section consists of two components—pathogen header text component and infection records information table.This text component displays the IAV subtype, pathogen name and Taxonomy ID, based on selections made by the user in the home page.This section presents infection records information collected directly from literature and additional information collected from web tools, NCBI Taxonomy and UniProt databases in the form of a table. Information is filtered to present only those relevant to the user’s selections in the home page.The body section consists of two panels, where the left panel displays the network graph and edge properties. The right panel is divided into two subsections and displays the virus and mouse node properties, respectively.The network graph was designed such that the user can clearly differentiate between IAV and mouse nodes by colors, where pink was assigned to IAV nodes and blue to mouse nodes. User can differentiate interaction statistical potentials by edge weights, where a thicker edge line represents a higher possibility of interaction. Also, the edge color will change to blue upon clicking, to highlight the edge selection.Node properties include either the IAV protein segment or name of mouse protein, UniProt ID, SCOP superfamily ID, superfamily name and SCOP start/end residue(s). Edge properties comprise IAV and mouse SCOP superfamily ID and name with the matching DISPOT statistical potential score. All IAV node properties will be populated upon clicking of any pink IAV node while only the mouse SCOP superfamily ID field will be populated upon clicking of any blue mouse node in the network graph. Similarly, the former, together with its respective edge property will be presented upon click on any edge. To display all mouse node properties, a mouse protein first needs to be selected from the “Browse Proteins” dropdown under the mouse node properties subsection.The footer provides the user with the following supplementary information—protein sequence, non-interacting viral segments and abbreviations.Web scraping was applied to extract amino acid sequences of IAV and mouse proteins from the UniProt database.Non-interacting IAV protein segments consist of the following cases: 1) The UniProt ID could not be found. Therefore, the protein sequence for input to SUPERFAMILY 2.0 is unknown and no domain information could be retrieved. In this case, the UniProt ID field was indicated with “Not Found” and remaining information was labelled as “N/A”. 2) Some protein sequences retrieved were not mapped to any SCOP superfamily based on the SUPERFAMILY 2.0 database. As such, there was no protein domain information for input to DISPOT. 3) IAV domain information was available but the DOMMINO database did not have any entry between the IAV domain and all of 17, 120 retrieved mouse proteins, hence no interaction information was returned by DISPOT.This section conveys extra information to the user; specifically, the definitions and expansions of abbreviations used as well as notes targeted to help the user better comprehend the network graph.This section lists domain pairs identified with high scoring interaction potentials. In According to DISPOT scores obtained, the top 10 domain pairs with strongest statistical interaction potentials are as listed in Of 109 unique IAV strains presented in this database, the PB1 gene segment of three strains  were assThe M1 gene segment of eight IAV strains were assigned a Methyl-accepting chemotaxis protein (MCP) signaling domain instead of alpha-catenin/vinculin-like. No interaction was found between the MCP signaling domain with all domains present in mouse proteins. Comparing results from experiments carried out in H3N2 IAV strains , 7, espeEZRI_MOUSE (UniProt accession number: P26040)] has a PH-like domain. Based on meta-analysis of IAV interactome studies on the M1 gene segment conducted by (Pleckstrin homology (PH) domain-like is a short peptide module often found in cytoskeletal proteins . Althougucted by , Ezrin wSYCC_MOUSE (UniProt accession number: Q9ER72)], an interactor of NS1, catalyzes the ATP-dependent ligation of cysteine to tRNA (Cys) and plays a role in translation . ARSs play a crucial role in protein synthesis by attaching amino acids to their cognate transfer RNAs (tRNAs) . Specifinslation . Furthernslation , which sRS27A_MOUSE (UniProt accession number: P62983)], is a protein with the Zn-binding ribosomal protein domain. It is an NS1-interacting host protein node, classified as belonging to the apoptosis pathway . Mouse protein, cleavage and polyadenylation specificity factor subunit 4 (CPSF4) [CPSF4_MOUSE (UniProt accession number: Q8BQZ5)] contains the CCCH zinc finger domain. The interaction between NS1 and CPSF4 controls the alternative splicing of tumor protein p53 (TP53) transcripts, and alters the expression of TP53 isoforms in parallel. As a result, cellular innate response, particularly via type I interferon secretion is regulated, leading to efficient viral replication  domain, otherwise known as antibodies is the sole SCOP protein domain that interacts with three IAV domains, namely DNA/RNA polymerases, Viral protein domain and Sialidases in the PB1, HA, and NA segments respectively. Immunoglobulin is the most abundant domain found among the 2419 unique mouse proteins containing interacting SCOP domains . During The non-interacting influenza hemagglutinin  SCOP domain present in the HA is an example that not all domains constituting a protein are involved in interaction between a pair of proteins. The stalk evolves slower than the receptor binding head and it is suggested that it has to remain structurally conserved owing to its role in membrane fusion . StudiesGenerally, DISPOT works as a tool to streamline the PPI prediction problem through providing insight on the possibility of specific DDIs in a given physical PPI. However, it is not a classification method and statistical potentials returned are useful for ranking DDIs but do not directly translate to the probability score. DISPOT which solely uses information about interactions between protein domain should not be used as a standalone PPI prediction tool to identify virulence factors responsible for IAV infections . Based oMAVS_MOUSE (UniProt accession number: Q8VCF0)] protein, a component of the retinoic acid-inducible gene I (RIG-I) antiviral pathway. This pathway along with multiple others, is essential for combating and resolving viral infection, repair of damaged tissues, and generating adaptive immune response. It has been revealed that PB1-F2 inhibits antiviral cytokines and enhances expression of inflammatory cytokines through direct interaction with MAVS and other components of the RIG-I/MAVS system  [S system . Howeverrounded to 3 d.p.). However a study conducted by ], containing the homeodomain-like domain, was identified to be interacting with IAV gene segments PB1, PA, NA, and M2 but not HA. By comparison, for DISPOT no interaction was detected between the homeodomain-like domain and segments PB1 and NA. Likewise, as protein sequences of segments PA and M2 were not assigned any SCOP domain by SUPERFAMILY 2.0, DISPOT could not be used to ascertain these interactions. Further, the cellular localisation for proteins with homeodomain-like domains was found to be in the nucleus according to UniProt, which indicates its interaction with HA would be unlikely, given that HA, mediates cell-surface recognition and viral entry.A homeodomain-like domain was identified by DISPOT to be interacting with the viral protein domain present in the HA gene segment of IAV, with a statistical potential score of −3.203  are not available in any public database. Translation of strain specific genomic sequences to whole proteomes is a challenging task needing extensive experimental effort. In this work, obtaining these proteomes by means of experimental protein sequencing was not possible as the necessary materials and labor were not available.doc2vec) to represent protein sequences as low-dimensional rich feature vectors. Then, a random forest classifier was trained using a training dataset that covers known PPIs between human and all viruses to predict human-virus PPIs. Lastly, the HVPPI web server automatically calculates the interaction probability of a query protein pair. The data to be used as input to HVPPI can be constructed as follows: Firstly, human protein sequences can be obtained using mouse and human homologs. Next, all protein sequences with SCOP domain(s) assigned to them can be trimmed, following the collected start and end residue numbers. Subsequently, trimmed protein sequences can be paired corresponding to DDIs recognized by DISPOT. Interaction probabilities provided as predicted outputs by HVPPI for each IAV-human protein pair can then be used to detect false positives. For protein sequences not assigned to any SCOP domain, complete protein sequences can be used, which will in turn aid with the detection of false negatives.To bridge the gaps in this work, sequence-based PPI prediction methods can be employed to substantiate DDIs identified by DISPOT. An example is the Human-Virus Protein-Protein Interactions (HVPPI) web server, developed by X.Yang and colleagues . HVPPI avia manually changing the protein sequences of wild-type or laboratory IAV strains that are available in UniProt. This enriches the dataset further.As an extension of this work from the raw dataset, instead of ignoring non-standard strains, the protein sequences of recombinant or mutant IAV strains can be reproduced 50 values can be incorporated to represent the PPI network as a weighted undirected graph. Later, graph embedding methods can be applied to this weighted graph to learn low-dimensional node representations (The DISPOT statistical potentials, HVPPI interaction probabilities and LDntations . Structuntations .As IAV is a significant danger to global human health and life, it is critical to have deeper, accurate as well as reliable insights and knowledge on the virulence factors responsible for IAV infections to counteract potential outbreaks . This wo


Protein–protein interactions (PPIs), such as protein–protein inhibitor, antibody–antigen complex, and supercomplexes play diverse and important roles in cells. Recent advances in structural analysis methods, including cryo-EM, for the determination of protein complex structures are remarkable. Nevertheless, much room remains for improvement and utilization of computational methods to predict PPIs because of the large number and great diversity of unresolved complex structures. This review introduces a wide array of computational methods, including our own, for estimating PPIs including antibody–antigen interactions, offering both historical and forward-looking perspectives. Protein–protein interactions (PPIs) play fundamentally important roles in cellular functions and biological processes, and structural understanding of the PPIs is important for the elucidation of those functions , participated in CAPRI about 10 years ago  must usually be selected using some method such as scoring function, clustering, or consensus (Lensink and Wodak Machine-learning (ML) and AI-based scoring methods have been developed in the past few years  convolutional neural networks   for the modeling of monomeric structures of protein in CASP14 was announced, it was assumed that AF2 would be useful for the modeling of protein–protein complexes, even though it was originally trained on individual protein chains  instead of MSA. Among the follow-up replicative studies of AF2, such as OpenFold . Four high-precision docking software suites ClusPro (Brenke et al. The AI-based prediction of antibody–antigen interfaces has been developed as PECAN (Pittala and Bailey-Kellogg To obtain information about antibody-specific epitopes, some improvements in the accuracy of docking and affinity predictions must be achieved. For this purpose, a large and non-redundant benchmark set for antibody–antigen docking and affinity prediction was constructed. It includes camelid nanobodies, therapeutic monoclonal antibodies, and broadly neutralizing antibodies that target viral glycoproteins (Guest et al. Another possible approach is the search of similar regions on proteins to known antibody-binding epitopes. This approach is based on the fact that some antibodies can cross-reactively recognize different antigen proteins having similar surface regions in the structures and properties (Vieths et al. Here we have introduced and discussed a diverse range of computational methods, from docking-based to AI-based approaches, for PPI prediction. Along with the increase in the amount of information related to protein sequences and structures, AI-based approaches, especially those based on evolutionary information and templates, are expected to become more powerful and useful. Yet, prediction of hetero PPIs leaves room for improvement. Particularly, antibody–antigen interaction predictions remain very limited in terms of their accuracy, although many sophisticated prediction methods have been developed (Ambrosetti et al.


Over 300 000 protein–protein interaction (PPI) pairs have been identified in the human proteome and targeting these is fast becoming the next frontier in drug design. Predicting PPI sites, however, is a challenging task that traditionally requires computationally expensive and time-consuming docking simulations. A major weakness of modern protein docking algorithms is the inability to account for protein flexibility, which ultimately leads to relatively poor results.Here, we propose DockNet, an efficient Siamese graph-based neural network method which predicts contact residues between two interacting proteins. Unlike other methods that only utilize a protein’s surface or treat the protein structure as a rigid body, DockNet incorporates the entire protein structure and places no limits on protein flexibility during an interaction. Predictions are modeled at the residue level, based on a diverse set of input node features including residue type, surface accessibility, residue depth, secondary structure, pharmacophore and torsional angles. DockNet is comparable to current state-of-the-art methods, achieving an area under the curve (AUC) value of up to 0.84 on an independent test set (DB5), can be applied to a variety of different protein structures and can be utilized in situations where accurate unbound protein structures cannot be obtained.https://github.com/npwilliams09/docknet and an easy-to-use webserver at https://biosig.lab.uq.edu.au/docknet. All other data underlying this article are available in the article and in its online supplementary material.DockNet is available at Bioinformatics online. Over 300 000 protein–protein interaction (PPI) pairs have been identified in the human proteome. Technological advances, such as yeast two-hybrid screening  and affiGenerally, protein docking algorithms are good at utilizing the structural and physicochemical properties to predict PPIs. However, due to computational expense, most algorithms treat each protein as a rigid body and optimize the relative conformation of each protein to promote shape complementation and minimize intermolecular energies . RecentlDockNet was constructed utilizing two datasets [DIPS ] to avoiThe best-performing model featured a base of 128 convolution filters, two residual graph convolution layers, a dropout rate of 0.2, four wave blocks and contained 579 073 parameters. We attempted numerous augmentations of the model ; howeverStaphylococcus aureus Staphopain B bound to its inhibitor Staphostatin B (Comparison of our method to three other algorithms trained on the DIPS dataset, and therefore were direct comparisons to DockNet; Graph average , BIPSPI For the webserver, users upload a file in PDB format or provide a valid PDB accession code with the structure for two protein partners. The output page  summarizIn summary, DockNet is an efficient neural network architecture which can predict contact residues between two interacting protein structures. DockNet captures the full context of the protein, leading to a prediction of interaction between 3D structures, rather than 2D graphs.btac797_Supplementary_DataClick here for additional data file.