A structure-based benchmark for protein-protein binding affinity

Protein-RNA Docking Benchmark v3.0 Integrated With Binding Affinity

We introduce an updated non-redundant protein-RNA docking benchmark version 3.0 (PRDBv3.0) containing 197 test cases curated from 288 unique protein-RNA complexes available in the Protein Data Bank until July 2024. Among these, 27 are unbound-unbound (UU) type where both the binding partners are available in their unbound states, 160 are unbound-bound (UB) type where only the protein is available in unbound state and remaining 10 are bound-unbound (BU) type where only the RNA is available in unbound state. The benchmark is categorized into three classes based on the conformational flexibility of the protein interface: 117 rigid-body (R) complexes with minimal structural changes, 41 semi-flexible (S) complexes showing moderate conformational changes and 29 full-flexible (F) complexes with significant conformational changes. The current benchmark represents a 62% increase in the number of test cases compared to its previous version. Binding affinity (Kd) values for a subset of 105 protein-RNA complexes from PRDBv3.0 are catalogued along with additional experimental details to develop a comprehensive protein-RNA affinity benchmark. Moreover, a total of 255 unique RNA-binding domains, present in RNA-binding proteins, are also catalogued in this updated benchmark. PRDBv3.0 will facilitate the evaluation of both rigid-body and flexible docking methods as well as the methods that aim to predict binding affinity. The updated benchmark is freely available at http://www.csb.iitkgp.ac.in/applications/PRDBv3/PRDBv3.php.

SNX3 mediates heart failure by interacting with HMGB1 and subsequently facilitating its nuclear-cytoplasmic translocation

Sorting nexins (SNXs) as the key regulators of sorting cargo proteins are involved in diverse diseases. SNXs can form the specific reverse vesicle transport complex (SNXs-retromer) with vacuolar protein sortings (VPSs) to sort and modulate recovery and degradation of cargo proteins. Our previous study has shown that SNX3-retromer promotes both STAT3 activation and nuclear translocation in cardiomyocytes, suggesting that SNX3 might be a critical regulator in the heart. In this study we investigated the role of SNX3 in the development of pathological cardiac hypertrophy and heart failure. We generated abdominal aortic constriction (AAC) rat model and transverse aortic constriction (TAC) mouse model; hypertrophic neonatal rat cardiomyocytes (NRCMs) were induced by exposure to isoproterenol (10 μM). We showed that the expression of SNX3 was significantly upregulated in ISO-treated NRCMs and in the failing heart of AAC rats. Overexpression of SNX3 by intramyocardial injection of Ad-SNX3 induced heart failure in rats, and increased the susceptibility of NRCMs to ISO-induced myocardial injury in vitro. In contrast, conditional knockout of SNX3 in cardiac tissue in mice rescued the detrimental heart function in TAC mice, and knockdown of SNX3 protected against ISO-induced injury in NRCMs and AAC rats. We then conducted immunoprecipitation-based mass spectrometry and localized surface plasmon resonance, and demonstrated a direct interaction between SNX3-retromer and high mobility group box 1 (HMGB1), which mediated the efflux of nuclear HMGB1. Moreover, overexpression of HMGB1 in NRCMs inhibited the pro-hypertrophic effects of SNX3, whereas knockdown of HMGB1 abolished the protective effect of SNX3-deficiency. These results suggest that HMGB1 might be a direct cargo protein of SNX3-retromer, and its interaction with SNX3 promotes its efflux from the nucleus, leading to the pathological development of heart failure.

Flaxseed-derived peptide, Ile-Pro-Pro-Phe (IPPF), ameliorates hepatic cholesterol metabolism to treat metabolic dysfunction-associated steatotic liver disease by promoting cholesterol conversion and excretion

Flaxseed-derived peptide IPPF has been reported to effectively inhibit cholesterol micellization and reduce cholesterol accumulation in vitro. However, its effects on hepatic cholesterol accumulation and related dysfunction-associated steatotic liver disease (MASLD) in vivo, along with the underlying mechanisms and specific molecular targets, remain unclear. This study investigated the impact of IPPF on hepatic cholesterol accumulation to ameliorate MASLD and its potential mechanisms in vivo. Six-week-old male C57BL/6J mice were fed a high-cholesterol, high-fat diet and treated with different doses of IPPF via oral gavage for six weeks. IPPF intervention significantly reduced hepatic cholesterol levels and oxidative stress damage while increasing fecal cholesterol and bile acid excretion. Non-targeted metabolomics analysis revealed that IPPF primarily affected pathways related to ABC transporters and bile acid metabolism. IPPF intake upregulated the mRNA expression of Abcg5/8 and Cyp7a1 in the liver. Molecular docking, dynamics and Surface plasmon resonance (SPR) simulations demonstrated that IPPF binds strongly to ABCG5/8 and CYP7A1, forming stable complexes. Furthermore, cholesterol accumulation and MASLD in HepG2 cells induced by palmitic acid (PA) was alleviated by IPPF, but this effect was partly stopped when CYP7A1 or ABCG5/8 was inhibited. In conclusion, flaxseed-derived peptide IPPF targets CYP7A1 and ABCG5/8, promoting cholesterol conversion and excretion, thereby reducing hepatic cholesterol accumulation and offering a potential nutritional treatment for MASLD. IPPF can be used as a novel dietary cholesterol-lowering functional ingredient. This study provides a scientific basis and new perspective for the development of cholesterol-lowering functional foods and dietary supplements.

Estimating Absolute Protein-Protein Binding Free Energies by a Super Learner Model

Protein-protein binding is central to most biochemical processes of all living beings. Its importance underlies mechanisms ranging from cell interactions to metabolic control, but also to ex vivo biotechnology, such as the development of therapeutic monoclonal antibodies, the engineering of enzymes for industrial biocatalysis, the development of biosensors for disease detection, and the assembly of artificial protein complexes for drug screening. Therefore, predicting the strength of their association allows for understanding the molecular mechanisms and ultimately controlling them. We devised a machine learning ensemble model that uses Rosetta-based quantities to predict binding free energies of protein-protein complexes with accuracy rivaling both computationally demanding methods and currently available ML/DL tools. The method was encoded into an application Python pipeline named PBEE, which stands for Protein Binding Energy Estimator, allowing a rapid calculation of the absolute binding free energies of protein complexes from their PDB coordinates.

ProAffinity-GNN: A Novel Approach to Structure-Based Protein-Protein Binding Affinity Prediction via a Curated Data Set and Graph Neural Networks

Protein-protein interactions (PPIs) are crucial for understanding biological processes and disease mechanisms, contributing significantly to advances in protein engineering and drug discovery. The accurate determination of binding affinities, essential for decoding PPIs, faces challenges due to the substantial time and financial costs involved in experimental and theoretical methods. This situation underscores the urgent need for more effective and precise methodologies for predicting binding affinity. Despite the abundance of research on PPI modeling, the field of quantitative binding affinity prediction remains underexplored, mainly due to a lack of comprehensive data. This study seeks to address these needs by manually curating pairwise interaction labels on available 3D structures of protein complexes, with experimentally determined binding affinities, creating the largest data set for structure-based pairwise protein interaction with binding affinity to date. Subsequently, we introduce ProAffinity-GNN, a novel deep learning framework using protein language model and graph neural network (GNN) to improve the accuracy of prediction of structure-based protein-protein binding affinities. The evaluation results across several benchmark test sets and an additional case study demonstrate that ProAffinity-GNN not only outperforms existing models in terms of accuracy but also shows strong generalization capabilities.

PPB-Affinity: Protein-Protein Binding Affinity dataset for AI-based protein drug discovery

Prediction of protein-protein binding (PPB) affinity plays an important role in large-molecular drug discovery. Deep learning (DL) has been adopted to predict the changes of PPB binding affinities upon mutations, but there was a scarcity of studies predicting the PPB affinity itself. The major reason is the paucity of open-source dataset with PPB affinity data. To address this gap, the current study introduced a large comprehensive PPB affinity (PPB-Affinity) dataset. The PPB-Affinity dataset contains key information such as crystal structures of protein-protein complexes (with or without protein mutation patterns), PPB affinity, receptor protein chain, ligand protein chain, etc. To the best of our knowledge, this is the largest publicly available PPB affinity dataset, and we believe it will significantly advance drug discovery by streamlining the screening of potential large-molecule drugs. We also developed a deep-learning benchmark model with this dataset to predict the PPB affinity, providing a foundational comparison for the research community.

Systematic investigation of machine learning on limited data: A study on predicting protein-protein binding strength

The application of machine learning techniques in biological research, especially when dealing with limited data availability, poses significant challenges. In this study, we leveraged advancements in method development for predicting protein-protein binding strength to conduct a systematic investigation into the application of machine learning on limited data. The binding strength, quantitatively measured as binding affinity, is vital for understanding the processes of recognition, association, and dysfunction that occur within protein complexes. By incorporating transfer learning, integrating domain knowledge, and employing both deep learning and traditional machine learning algorithms, we mitigated the impact of data limitations and made significant advancements in predicting protein-protein binding affinity. In particular, we developed over 20 models, ultimately selecting three representative best-performing ones that belong to distinct categories. The first model is structure-based, consisting of a random forest regression and thirteen handcrafted features. The second model is sequence-based, employing an architecture that combines transferred embedding features with a multilayer perceptron. Finally, we created an ensemble model by averaging the predictions of the two aforementioned models. The comparison with other predictors on three independent datasets confirms the significant improvements achieved by our models in predicting protein-protein binding affinity. The programs for running these three models are available at https://github.com/minghuilab/BindPPI.

A Novel Confocal Scanning Protein-Protein Interaction Assay (PPI-CONA) Reveals Exceptional Selectivity and Specificity of CC0651, a Small Molecule Binding Enhancer of the Weak Interaction between the E2 Ubiquitin-Conjugating Enzyme CDC34A and Ubiquitin

Protein-protein interactions (PPIs) are some of the most challenging target classes in drug discovery. Highly sensitive detection techniques are required for the identification of chemical modulators of PPIs. Here, we introduce PPI confocal nanoscanning (PPI-CONA), a miniaturized, microbead based high-resolution fluorescence imaging assay. We demonstrate the capabilities of PPI-CONA by detecting low affinity ternary complex formation between the human CDC34A ubiquitin-conjugating (E2) enzyme, ubiquitin, and CC0651, a small molecule enhancer of the CDC34A-ubiquitin interaction. We further exemplify PPI-CONA with an E2 enzyme binding study on CC0651 and a CDC34A binding specificity study of a series of CC0651 analogues. Our results indicate that CC0651 is highly selective toward CDC34A. We further demonstrate how PPI-CONA can be applied to screening very low affinity interactions. PPI-CONA holds potential for high-throughput screening for modulators of PPI targets and characterization of their affinity, specificity, and selectivity.

Combining Complementarity and Binding Energetics in the Assessment of Protein Interactions: EnCPdock-A Practical Manual

The combined effect of shape and electrostatic complementarities (Sc, EC) at the interface of the interacting protein partners (PPI) serves as the physical basis for such associations and is a strong determinant of their binding energetics. EnCPdock (https://www.scinetmol.in/EnCPdock/) presents a comprehensive web platform for the direct conjoint comparative analyses of complementarity and binding energetics in PPIs. It elegantly interlinks the dual nature of local (Sc) and nonlocal complementarity (EC) in PPIs using the complementarity plot. It further derives an AI-based ΔGbinding with a prediction accuracy comparable to the state of the art. This book chapter presents a practical manual to conceptualize and implement EnCPdock with its various features and functionalities, collectively having the potential to serve as a valuable protein engineering tool in the design of novel protein interfaces.

Structural Basis of Conformational Dynamics in the PROTAC-Induced Protein Degradation

Pronounced conformational dynamics is unveiled upon analyzing multiple crystal structures of the same proteins recruited to the same E3 ligases by PROTACs, and yet, is largely permissive for targeted protein degradation due to the intrinsic mobility of E3 assemblies creating a large ubiquitylation zone. Mathematical modelling of ternary dynamics on ubiquitylation probability confirms the experimental finding that ternary complex rigidification need not correlate with enhanced protein degradation. Salt bridges are found to prevail in the PROTAC-induced ternary complexes, and may contribute to a positive cooperativity and prolonged half-life. The analysis highlights the importance of presenting lysines close to the active site of the E2 enzyme while constraining ternary dynamics in PROTAC design to achieve high degradation efficiency.

Identification and characterisation of a novel interaction between oestrogen receptor alpha and FOXP2

Forkhead box P2 (FOXP2) regulates expression of various genes and is associated with language, speech and neural development as well as cancer. Since there may be a putative link between sex and language and because transcription factors rarely function in isolation, this study aims to investigate whether FOXP2 directly associates with oestrogen receptor α (ER1), a nuclear receptor responsible for sexual differentiation that is also associated with cancer. Isothermal titration calorimetry and fluorescence anisotropy were used to investigate the interaction between the DNA-binding forkhead domain (FHD) of FOXP2, the N-terminal region (NT) of FOXP2, and the ligand-binding domain (LBD) of ER1. ER1 LBD does not interact with FOXP2 NT but associates with apo-FOXP2 FHD in an enthalpically favourable manner. The affinity of this interaction is inversely correlated to the salt concentration. Additionally, FOXP2 FHD that is bound to ER1 LBD, has reduced ability to interact with its cognate DNA. This research identifies a novel interaction between ER1 LBD and FOXP2 FHD and shows that the interaction is regulated by salt. Moreover, FOXP2 FHD cannot bind to both ER1 LBD and DNA simultaneously, suggesting that this interaction could be involved in regulating the transcriptional pathway of FOXP2 should the interaction be found in vivo. This study could serve as a foundation for uncovering the basis of sexual dimorphism in speech and language development and related disorders and potentially offers an alternate for targeted cancer therapies.

Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments

Investigating protein-protein interactions is crucial for understanding cellular biological processes because proteins often function within molecular complexes rather than in isolation. While experimental and computational methods have provided valuable insights into these interactions, they often overlook a critical factor: the crowded cellular environment. This environment significantly impacts protein behavior, including structural stability, diffusion, and ultimately the nature of binding. In this review, we discuss theoretical and computational approaches that allow the modeling of biological systems to guide and complement experiments and can thus significantly advance the investigation, and possibly the predictions, of protein-protein interactions in the crowded environment of cell cytoplasm. We explore topics such as statistical mechanics for lattice simulations, hydrodynamic interactions, diffusion processes in high-viscosity environments, and several methods based on molecular dynamics simulations. By synergistically leveraging methods from biophysics and computational biology, we review the state of the art of computational methods to study the impact of molecular crowding on protein-protein interactions and discuss its potential revolutionizing effects on the characterization of the human interactome.

DbGTi: Thermostable trypsin inhibitor from Dioscorea bulbifera L. ground tubers: assessment of antioxidant and antibacterial properties and cytotoxicity evaluation using zebrafish model

Oxidative stress disorders and diseases caused by drug-resistant bacteria have emerged as significant public health concerns. Plant-based medications like protease inhibitors are growing despite adverse effects therapies. Consecutively, in this study, trypsin inhibitors from Dioscorea bulbifera L. (DbGTi trypsin inhibitor) ground tubers were isolated, purified, characterized, and evaluated for their potential cytotoxicity, antibacterial, and antioxidant activities. DbGTi protein was purified by Q-Sepharose matrix, followed by trypsin inhibitory activity. The molecular weight of the DbGTi protein was found to be approximately 31 kDa by SDS-PAGE electrophoresis. The secondary structure analysis by circular dichroism (CD) spectroscopy revealed that the DbGTi protein predominantly comprises β sheets followed by α helix. DbGTi protein showed competitive type of inhibition with Vmax = 2.1372 × 10-1 μM/min, Km = 1.1805 × 102 μM, & Ki = 8.4 × 10-9 M and was stable up to 70 °C. DbGTi protein exhibited 58 % similarity with Dioscorin protein isolated from Dioscorea alata L. as revealed by LC-MS/MS analysis. DbGTi protein showed a non-toxic effect, analyzed by MTT, Haemolytic assay and in vivo studies on zebrafish model. DbGTi protein significantly inhibited K. pneumoniae and has excellent antioxidant properties, confirmed by various antioxidant assays. The results of anti-microbial, cytotoxicity and antioxidant assays demonstrate its bioactive potential and non-toxic nature.

Factors affecting product association as a mechanism of host-cell protein persistence in bioprocessing

Product association of host-cell proteins (HCPs) to monoclonal antibodies (mAbs) is widely regarded as a mechanism that can enable HCP persistence through multiple purification steps and even into the final drug substance. Discussion of this mechanism often implies that the existence or extent of persistence is directly related to the strength of binding but actual measurements of the binding affinity of such interactions remain sparse. Two separate avenues of investigation of HCP-mAb binding are reported here. One is the measurement of the affinity of binding of individual, commonly persistent Chinese hamster ovary (CHO) HCPs to each of a set of mAbs, and the other uses quantitative proteomic measurements to assess binding of HCPs in a null CHO harvested cell culture fluid (HCCF) to mAbs produced in the same cell line. The individual HCP measurements show that the binding affinities of individual HCPs to different mAbs can vary appreciably but are rarely very high, with only weak pH dependence. The measurements on the null HCCF allow estimation of individual HCP-mAb affinities; these are typically weaker than those seen in affinity measurements on isolated HCPs. Instead, the extent of binding appears correlated with the initial abundance of individual HCPs in the HCCF and the forms of the HCPs in the solution, i.e., whether HCPs are present as free molecules or as parts of large aggregates. Separate protein A chromatography experiments performed by feeding different fractions of a mAb-containing HCCF obtained by size-exclusion chromatography (SEC) showed clear differences in the number and identity of HCPs found in the protein A eluate. These results indicate a significant role for HCP-mAb association in determining HCP persistence through protein A chromatography, presumably through binding of HCP-mAb complexes to the resin. Overall, the results illustrate the importance of considering more fully the biophysical context of HCP-product association in assessing the factors that may affect the phenomenon and determine its implications. Knowledge of the abundances and the forms of individual or aggregated HCPs in HCCF are particularly significant, emphasizing the integration of upstream and downstream bioprocessing and the importance of understanding the collective properties of HCPs in addition to just the biophysical properties of individual HCPs.

Leucine Motifs Stabilize Residual Helical Structure in Disordered Proteins

Many examples are known of regions of intrinsically disordered proteins that fold into α-helices upon binding to their targets. These helical binding motifs (HBMs) can be partially helical also in the unbound state, and this so-called residual structure can affect binding affinity and kinetics. To investigate the underlying mechanisms governing the formation of residual helical structure, we assembled a dataset of experimental helix contents of 65 peptides containing HBM that fold-upon-binding. The average residual helicity is 17% and increases to 60% upon target binding. The helix contents of residual and target-bound structures do not correlate, however the relative location of helix elements in both states shows a strong overlap. Compared to the general disordered regions, HBMs are enriched in amino acids with high helix preference and these residues are typically involved in target binding, explaining the overlap in helix positions. In particular, we find that leucine residues and leucine motifs in HBMs are the major contributors to helix stabilization and target-binding. For the two model peptides, we show that substitution of leucine motifs to other hydrophobic residues (valine or isoleucine) leads to reduction of residual helicity, supporting the role of leucine as helix stabilizer. From the three hydrophobic residues only leucine can efficiently stabilize residual helical structure. We suggest that the high occurrence of leucine motifs and a general preference for leucine at binding interfaces in HBMs can be explained by its unique ability to stabilize helical elements.

Ebola virus VP35 interacts non-covalently with ubiquitin chains to promote viral replication

Ebolavirus (EBOV) belongs to a family of highly pathogenic viruses that cause severe hemorrhagic fever in humans. EBOV replication requires the activity of the viral polymerase complex, which includes the cofactor and Interferon antagonist VP35. We previously showed that the covalent ubiquitination of VP35 promotes virus replication by regulating interactions with the polymerase complex. In addition, VP35 can also interact non-covalently with ubiquitin (Ub); however, the function of this interaction is unknown. Here, we report that VP35 interacts with free (unanchored) K63-linked polyUb chains. Ectopic expression of Isopeptidase T (USP5), which is known to degrade unanchored polyUb chains, reduced VP35 association with Ub and correlated with diminished polymerase activity in a minigenome assay. Using computational methods, we modeled the VP35-Ub non-covalent interacting complex, identified the VP35-Ub interacting surface, and tested mutations to validate the interface. Docking simulations identified chemical compounds that can block VP35-Ub interactions leading to reduced viral polymerase activity. Treatment with the compounds reduced replication of infectious EBOV in cells and in vivo in a mouse model. In conclusion, we identified a novel role of unanchored polyUb in regulating Ebola virus polymerase function and discovered compounds that have promising anti-Ebola virus activity.

Bacterial flagella hijack type IV pili proteins to control motility

Bacterial flagella and type IV pili (TFP) are surface appendages that enable motility and mechanosensing through distinct mechanisms. These structures were previously thought to have no components in common. Here, we report that TFP and some flagella share proteins PilO, PilN, and PilM, which we identified as part of the Helicobacter pylori flagellar motor. H. pylori mutants lacking PilO or PilN migrated better than wild type in semisolid agar because they continued swimming rather than aggregated into microcolonies, mimicking the TFP-regulated surface response. Like their TFP homologs, flagellar PilO/PilN heterodimers formed a peripheral cage that encircled the flagellar motor. These results indicate that PilO and PilN act similarly in flagella and TFP by differentially regulating motility and microcolony formation when bacteria encounter surfaces.

Predictive Models and Impact of Interfacial Contacts and Amino Acids on Protein-Protein Binding Affinity

Protein-protein interactions (PPIs) play a central role in nearly all cellular processes. The strength of the binding in a PPI is characterized by the binding affinity (BA) and is a key factor in controlling protein-protein complex formation and defining the structure-function relationship. Despite advancements in understanding protein-protein binding, much remains unknown about the interfacial region and its association with BA. New models are needed to predict BA with improved accuracy for therapeutic design. Here, we use machine learning approaches to examine how well different types of interfacial contacts can be used to predict experimentally determined BA and to reveal the impact of the specific amino acids at the binding interface on BA. We create a series of multivariate linear regression models incorporating different contact features at both residue and atomic levels and examine how different methods of identifying and characterizing these properties impact the performance of these models. Particularly, we introduce a new and simple approach to predict BA based on the quantities of specific amino acids at the protein-protein interface. We found that the numbers of specific amino acids at the protein-protein interface were correlated with BA. We show that the interfacial numbers of amino acids can be used to produce models with consistently good performance across different data sets, indicating the importance of the identities of interfacial amino acids in underlying BA. When trained on a diverse set of complexes from two benchmark data sets, the best performing BA model was generated with an explicit linear equation involving six amino acids. Tyrosine, in particular, was identified as the key amino acid in controlling BA, as it had the strongest correlation with BA and was consistently identified as the most important amino acid in feature importance studies. Glycine and serine were identified as the next two most important amino acids in predicting BA. The results from this study further our understanding of PPIs and can be used to make improved predictions of BA, giving them implications for drug design and screening in the pharmaceutical industry.

Software for Predicting Binding Free Energy of Protein-Protein Complexes and Their Mutants

Protein-protein binding affinity prediction is important for understanding complex biochemical pathways and to uncover protein interaction networks. Quantitative estimation of the binding affinity changes caused by mutations can provide critical information for protein function annotation and genetic disease diagnoses. The binding free energies of protein-protein complexes can be predicted using several computational tools. This chapter is a summary of software developed for the prediction of binding free energies for protein-protein complexes and their mutants.

In silico prediction of interaction between Nipah virus attachment glycoprotein and host cell receptors Ephrin-B2 and Ephrin-B3 in domestic and peridomestic mammals

Nipah virus (NiV) is a lethal bat-borne zoonotic virus that causes mild to acute respiratory distress and neurological manifestations in humans with a high mortality rate. NiV transmission to humans occurs via consumption of bat-contaminated fruit and date palm sap (DPS), or through direct contact with infected individuals and livestock. Since NiV outbreaks were first reported in pigs from Malaysia and Singapore, non-neutralizing antibodies against NiV attachment Glycoprotein (G) have also been detected in a few domestic mammals. NiV infection is initiated after NiV G binds to the host cell receptors Ephrin-B2 and Ephrin-B3. In this study, we assessed the degree of NiV host tropism in domestic and peridomestic mammals commonly found in Bangladesh that may be crucial in the transmission of NiV by serving as intermediate hosts. We carried out a protein-protein docking analysis of NiV G complexes (n = 52) with Ephrin-B2 and B3 of 13 domestic and peridomestic species using bioinformatics tools. Protein models were generated by homology modelling and the structures were validated for model quality. The different protein-protein complexes in this study were stable, and their binding affinity (ΔG) scores ranged between -8.0 to -19.1 kcal/mol. NiV Bangladesh (NiV-B) strain displayed stronger binding to Ephrin receptors, especially with Ephrin-B3 than the NiV Malaysia (NiV-M) strain, correlating with the observed higher pathogenicity of NiV-B strains. From the docking result, we found that Ephrin receptors of domestic rat (R. norvegicus) had a higher binding affinity for NiV G, suggesting greater susceptibility to NiV infections compared to other study species. Investigations for NiV exposure to domestic/peridomestic animals will help us knowing more the possible role of rats and other animals as intermediate hosts of NiV and would improve future NiV outbreak control and prevention in humans and domestic animals.

Quantification of biases in predictions of protein-protein binding affinity changes upon mutations

Understanding the impact of mutations on protein-protein binding affinity is a key objective for a wide range of biotechnological applications and for shedding light on disease-causing mutations, which are often located at protein-protein interfaces. Over the past decade, many computational methods using physics-based and/or machine learning approaches have been developed to predict how protein binding affinity changes upon mutations. They all claim to achieve astonishing accuracy on both training and test sets, with performances on standard benchmarks such as SKEMPI 2.0 that seem overly optimistic. Here we benchmarked eight well-known and well-used predictors and identified their biases and dataset dependencies, using not only SKEMPI 2.0 as a test set but also deep mutagenesis data on the severe acute respiratory syndrome coronavirus 2 spike protein in complex with the human angiotensin-converting enzyme 2. We showed that, even though most of the tested methods reach a significant degree of robustness and accuracy, they suffer from limited generalizability properties and struggle to predict unseen mutations. Interestingly, the generalizability problems are more severe for pure machine learning approaches, while physics-based methods are less affected by this issue. Moreover, undesirable prediction biases toward specific mutation properties, the most marked being toward destabilizing mutations, are also observed and should be carefully considered by method developers. We conclude from our analyses that there is room for improvement in the prediction models and suggest ways to check, assess and improve their generalizability and robustness.

DG-Affinity: predicting antigen-antibody affinity with language models from sequences

BACKGROUND

Antibody-mediated immune responses play a crucial role in the immune defense of human body. The evolution of bioengineering has led the progress of antibody-derived drugs, showing promising efficacy in cancer and autoimmune disease therapy. A critical step of this development process is obtaining the affinity between antibodies and their binding antigens.

RESULTS

In this study, we introduce a novel sequence-based antigen-antibody affinity prediction method, named DG-Affinity. DG-Affinity uses deep neural networks to efficiently and accurately predict the affinity between antibodies and antigens from sequences, without the need for structural information. The sequences of both the antigen and the antibody are first transformed into embedding vectors by two pre-trained language models, then these embeddings are concatenated into an ConvNeXt framework with a regression task. The results demonstrate the superiority of DG-Affinity over the existing structure-based prediction methods and the sequence-based tools, achieving a Pearson's correlation of over 0.65 on an independent test dataset.

CONCLUSIONS

Compared to the baseline methods, DG-Affinity achieves the best performance and can advance the development of antibody design. It is freely available as an easy-to-use web server at https://www.digitalgeneai.tech/solution/affinity .

The polymer and materials science of the bacterial fimbriae Caf1

Fimbriae are long filamentous polymeric protein structures located upon the surface of bacteria. Often implicated in pathogenicity, the biosynthesis and function of fimbriae has been a productive topic of study for many decades. Evolutionary pressures have ensured that fimbriae possess unique structural and mechanical properties which are advantageous to bacteria. These properties are also difficult to engineer with well-known synthetic and natural fibres, and this has raised an intriguing question: can we exploit the unique properties of bacterial fimbriae in useful ways? Initial work has set out to explore this question by using Capsular antigen fragment 1 (Caf1), a fimbriae expressed naturally by Yersina pestis. These fibres have evolved to 'shield' the bacterium from the immune system of an infected host, and thus are rather bioinert in nature. Caf1 is, however, very amenable to structural mutagenesis which allows the incorporation of useful bioactive functions and the modulation of the fibre's mechanical properties. Its high-yielding recombinant synthesis also ensures plentiful quantities of polymer are available to drive development. These advantageous features make Caf1 an archetype for the development of new polymers and materials based upon bacterial fimbriae. Here, we cover recent advances in this new field, and look to future possibilities of this promising biopolymer.

Pseudomonas aeruginosa AlgF is a protein-protein interaction mediator required for acetylation of the alginate exopolysaccharide

Enzymatic modifications of bacterial exopolysaccharides enhance immune evasion and persistence during infection. In the Gram-negative opportunistic pathogen Pseudomonas aeruginosa, acetylation of alginate reduces opsonic killing by phagocytes and improves reactive oxygen species scavenging. Although it is well known that alginate acetylation in P. aeruginosa requires AlgI, AlgJ, AlgF, and AlgX, how these proteins coordinate polymer modification at a molecular level remains unclear. Here, we describe the structural characterization of AlgF and its protein interaction network. We characterize direct interactions between AlgF and both AlgJ and AlgX in vitro and demonstrate an association between AlgF and AlgX, as well as AlgJ and AlgI, in P. aeruginosa. We determine that AlgF does not exhibit acetylesterase activity and is unable to bind to polymannuronate in vitro. Therefore, we propose that AlgF functions to mediate protein-protein interactions between alginate acetylation enzymes, forming the periplasmic AlgJFXK (AlgJ-AlgF-AlgX-AlgK) acetylation and export complex required for robust biofilm formation.

Deep learning-based method for predicting and classifying the binding affinity of protein-protein complexes

Protein-protein interactions (PPIs) play a critical role in various biological processes. Accurately estimating the binding affinity of PPIs is essential for understanding the underlying molecular recognition mechanisms. In this study, we employed a deep learning approach to predict the binding affinity (ΔG) of protein-protein complexes. To this end, we compiled a dataset of 903 protein-protein complexes, each with its corresponding experimental binding affinity, which belong to six functional classes. We extracted 8 to 20 non-redundant features from the sequence information as well as the predicted three-dimensional structures using feature selection methods for each protein functional class. Our method showed an overall mean absolute error of 1.05 kcal/mol and a correlation of 0.79 between experimental and predicted ΔG values. Additionally, we evaluated our model for discriminating high and low affinity protein-protein complexes and it achieved an accuracy of 87% with an F1 score of 0.86 using 10-fold cross-validation on the selected features. Our approach presents an efficient tool for studying PPIs and provides crucial insights into the underlying mechanisms of the molecular recognition process. The web server can be freely accessed at https://web.iitm.ac.in/bioinfo2/DeepPPAPred/index.html.

Computed Protein-Protein Enthalpy Signatures as a Tool for Identifying Conformation Sampling Problems

Understanding the thermodynamic signature of protein-peptide binding events is a major challenge in computational chemistry. The complexity generated by both components possessing many degrees of freedom poses a significant issue for methods that attempt to directly compute the enthalpic contribution to binding. Indeed, the prevailing assumption has been that the errors associated with such approaches would be too large for them to be meaningful. Nevertheless, we currently have no indication of how well the present methods would perform in terms of predicting the enthalpy of binding for protein-peptide complexes. To that end, we carefully assembled and curated a set of 11 protein-peptide complexes where there is structural and isothermal titration calorimetry data available and then computed the absolute enthalpy of binding. The initial "out of the box" calculations were, as expected, very modest in terms of agreement with the experiment. However, careful inspection of the outliers allows for the identification of key sampling problems such as distinct conformations of peptide termini not being sampled or suboptimal cofactor parameters. Additional simulations guided by these aspects can lead to a respectable correlation with isothermal titration calorimetry (ITC) experiments (R2 of 0.88 and an RMSE of 1.48 kcal/mol overall). Although one cannot know prospectively whether computed ITC values will be correct or not, this work shows that if experimental ITC data are available, then this in conjunction with computed ITC, can be used as a tool to know if the ensemble being simulated is representative of the true ensemble or not. That is important for allowing the correct interpretation of the detailed dynamics of the system with respect to the measured enthalpy. The results also suggest that computational calorimetry is becoming increasingly feasible. We provide the data set as a resource for the community, which could be used as a benchmark to help further progress in this area.

An updated dataset and a structure-based prediction model for protein-RNA binding affinity

Understanding the process of protein-RNA interaction is essential for structural biology. The thermodynamic process is an important part to uncover the protein-RNA interaction mechanism. The regulatory networks between protein and RNA in organisms are dominated by the binding or dissociation in the cells. Therefore, determining the binding affinity for protein-RNA complexes can help us to understand the regulation mechanism of protein-RNA interaction. Since it is time-consuming and labor-intensive to determine the binding affinity for protein-RNA complexes by experimental methods, it is necessary and urgent to develop computational methods to predict that. To develop a binding affinity prediction model, first we update the dataset of protein-RNA binding affinity benchmark (PRBAB), which includes 145 complexes now. Second, we extract the structural features based on complex structure, and then we analyze and select the representative structural features to train the regression model. Third, we random select the subset from the PRBAB2.0 to fit the protein-RNA binding affinity determined by experiment. In the end, we tested our model on the nonredundant PDBbind dataset, and the results showed that Pearson correlation coefficient r = .57 and RMSE = 2.51 kcal/mol. The Pearson correlation coefficient achieves 0.7 while removing 5 complex structures with modified residues/nucleotides and metal ions. While testing on ProNAB, the results showed that 71.60% of the prediction achieves Pearson correlation coefficient r = .61 and RMSE = 1.56 kcal/mol with experiment values.

EnCPdock: a web-interface for direct conjoint comparative analyses of complementarity and binding energetics in inter-protein associations

CONTEXT

Protein-protein interaction (PPI) is a key component linked to virtually all cellular processes. Be it an enzyme catalysis ('classic type functions' of proteins) or a signal transduction ('non-classic'), proteins generally function involving stable or quasi-stable multi-protein associations. The physical basis for such associations is inherent in the combined effect of shape and electrostatic complementarities (Sc, EC) of the interacting protein partners at their interface, which provides indirect probabilistic estimates of the stability and affinity of the interaction. While Sc is a necessary criterion for inter-protein associations, EC can be favorable as well as disfavored (e.g., in transient interactions). Estimating equilibrium thermodynamic parameters (∆G_binding, K_d) by experimental means is costly and time consuming, thereby opening windows for computational structural interventions. Attempts to empirically probe ∆G_binding from coarse-grain structural descriptors (primarily, surface area based terms) have lately been overtaken by physics-based, knowledge-based and their hybrid approaches (MM/PBSA, FoldX, etc.) that directly compute ∆G_binding without involving intermediate structural descriptors.

METHODS

Here, we present EnCPdock ( https://www.scinetmol.in/EnCPdock/ ), a user-friendly web-interface for the direct conjoint comparative analyses of complementarity and binding energetics in proteins. EnCPdock returns an AI-predicted ∆G_binding computed by combining complementarity (Sc, EC) and other high-level structural descriptors (input feature vectors), and renders a prediction accuracy comparable to the state-of-the-art. EnCPdock further locates a PPI complex in terms of its {Sc, EC} values (taken as an ordered pair) in the two-dimensional complementarity plot (CP). In addition, it also generates mobile molecular graphics of the interfacial atomic contact network for further analyses. EnCPdock also furnishes individual feature trends along with the relative probability estimates (Pr_fmax) of the obtained feature-scores with respect to the events of their highest observed frequencies. Together, these functionalities are of real practical use for structural tinkering and intervention as might be relevant in the design of targeted protein-interfaces. Combining all its features and applications, EnCPdock presents a unique online tool that should be beneficial to structural biologists and researchers across related fraternities.

Prediction of transient and permanent protein interactions using AI methods

Protein-protein interactions (PPIs) can be classified as permanent or transient interactions based on their stability or lifetime. Understanding the precise details of such protein interactions will pave the way for the discovery of inhibitors and for understanding the nature and function of PPIs. In the present work, 43 relevant physicochemical, geometrical and structural features were calculated for a curated dataset from the literature, comprising of 402 protein-protein complexes of permanent and transient categories, and 5 different Supervised Machine Learning models were developed with Scikit-learn to predict transient and permanent PPI. Additionally, deep learning method with Artificial Neural Network was also performed using Tensor Flow and Keras. Predicted models achieved accuracy ranging from 76.54% to 82.71% and k-NN has achieved the highest accuracy. Detailed analysis of these methods revealed that Interface areas such as Percent interface accessible area, Interface accessible area and Total interface area and the parameters defining the shape of the PPI interface such as Planarity, Eccentricity and Circularity are the most discriminating factors between these two categories. The present method could serve as an effective tool to understand the mechanism of protein association and to predict the transient and permanent interactions, which could supplement the costly and time-consuming experimental techniques.

Structural Changes, Biological Consequences, and Repurposing of Colchicine Site Ligands

Microtubule-targeting agents (MTAs) bind to one of several distinct sites in the tubulin dimer, the subunit of microtubules. The binding affinities of MTAs may vary by several orders of magnitude, even for MTAs that specifically bind to a particular site. The first drug binding site discovered in tubulin was the colchicine binding site (CBS), which has been known since the discovery of the tubulin protein. Although highly conserved throughout eukaryotic evolution, tubulins show diversity in their sequences between tubulin orthologs (inter-species sequence differences) and paralogs (intraspecies differences, such as tubulin isotypes). The CBS is promiscuous and binds to a broad range of structurally distinct molecules that can vary in size, shape, and affinity. This site remains a popular target for the development of new drugs to treat human diseases (including cancer) and parasitic infections in plants and animals. Despite the rich knowledge about the diversity of tubulin sequences and the structurally distinct molecules that bind to the CBS, a pattern has yet to be found to predict the affinity of new molecules that bind to the CBS. In this commentary, we briefly discuss the literature evidencing the coexistence of the varying binding affinities for drugs that bind to the CBS of tubulins from different species and within species. We also comment on the structural data that aim to explain the experimental differences observed in colchicine binding to the CBS of β-tubulin class VI (TUBB1) compared to other isotypes.

An artificial neural network model to predict structure-based protein-protein free energy of binding from Rosetta-calculated properties

The prediction of the free energy (ΔG) of binding for protein-protein complexes is of general scientific interest as it has a variety of applications in the fields of molecular and chemical biology, materials science, and biotechnology. Despite its centrality in understanding protein association phenomena and protein engineering, the ΔG of binding is a daunting quantity to obtain theoretically. In this work, we devise a novel Artificial Neural Network (ANN) model to predict the ΔG of binding for a given three-dimensional structure of a protein-protein complex with Rosetta-calculated properties. Our model was tested using two data sets, and it presented a root-mean-square error ranging from 1.67 kcal mol^-1 to 2.45 kcal mol^-1, showing a better performance compared to the available state-of-the-art tools. Validation of the model for a variety of protein-protein complexes is showcased.

IRAA: A statistical tool for investigating a protein-protein interaction interface from multiple structures

Understanding protein-protein interactions (PPIs) is fundamental to infer how different molecular systems work. A major component to model molecular recognition is the buried surface area (BSA), that is, the area that becomes inaccessible to solvent upon complex formation. To date, many attempts tried to connect BSA to molecular recognition principles, and in particular, to the underlying binding affinity. However, the most popular approach to calculate BSA is to use a single (or in some cases few) bound structures, consequently neglecting a wealth of structural information of the interacting proteins derived from ensembles corresponding to their unbound and bound states. Moreover, the most popular method inherently assumes the component proteins to bind as rigid entities. To address the above shortcomings, we developed a Monte Carlo method-based Interface Residue Assessment Algorithm (IRAA), to calculate a combined distribution of BSA for a given complex. Further, we apply our algorithm to human ACE2 and SARS-CoV-2 Spike protein complex, a system of prime importance. Results show a much broader distribution of BSA compared to that obtained from only the bound structure or structures and extended residue members of the interface with implications to the underlying biomolecular recognition. We derive that specific interface residues of ACE2 and of S-protein are consistently highly flexible, whereas other residues systematically show minor conformational variations. In effect, IRAA facilitates the use of all available structural data for any biomolecular complex of interest, extracting quantitative parameters with statistical significance, thereby providing a deeper biophysical understanding of the molecular system under investigation.

Modulation of interaction of BRCA1-RAD51 and BRCA1-AURKA protein complexes by natural metabolites using as possible therapeutic intervention toward cardiotoxic effects of cancer drugs: an in-silico approach

Breast cancer type 1 susceptibility protein (BRCA1) plays an important role in maintaining genome stability and is known to interact with several proteins involved in cellular pathways, gene transcription regulation and DNA damage response. More than 40% of inherited breast cancer cases are due to BRCA1 mutation. It is also a prognostic marker in non-small cell lung cancer patients as well as a gatekeeper of cardiac function. Interaction of mutant BRCA1 with other proteins is known to disrupt the tumor suppression mechanism. Two directly interacting proteins with BRCA1 namely, DNA repair protein RAD51 (RAD51) and Aurora kinase A (AURKA), known to regulate homologous recombination (HR) and G/M cell cycle transition, respectively, form protein complex with both wild and mutant BRCA1. To analyze the interactions, protein-protein complexes were generated for each pair of proteins. In order to combat the cardiotoxic effects of cancer drugs, pharmacokinetically screened natural metabolites derived from plant, marine and bacterial sources and along with FDA-approved cancer drugs as control, were subjected to molecular docking. Piperoleine B and dihydrocircumin were the best docked natural metabolites in both RAD51 and AURKA complexes, respectively. Molecular dynamics simulation (MDS) analysis and binding free energy calculations for the best docked natural metabolite and drug for both the mutant BRCA1 complexes suggested better stability for the natural metabolites piperolein B and dihydrocurcumin as compared to drug. Thus, both natural metabolites could be further analyzed for their role against the cardiotoxic effects of cancer drugs through wet lab experiments.Communicated by Ramaswamy H. Sarma.

Machine learning methods for protein-protein binding affinity prediction in protein design

Protein-protein interactions govern a wide range of biological activity. A proper estimation of the protein-protein binding affinity is vital to design proteins with high specificity and binding affinity toward a target protein, which has a variety of applications including antibody design in immunotherapy, enzyme engineering for reaction optimization, and construction of biosensors. However, experimental and theoretical modelling methods are time-consuming, hinder the exploration of the entire protein space, and deter the identification of optimal proteins that meet the requirements of practical applications. In recent years, the rapid development in machine learning methods for protein-protein binding affinity prediction has revealed the potential of a paradigm shift in protein design. Here, we review the prediction methods and associated datasets and discuss the requirements and construction methods of binding affinity prediction models for protein design.

Intrinsic protein disorder uncouples affinity from binding specificity

Intrinsically disordered proteins (IDPs) and intrinsically disordered regions (IDRs) of proteins often function by molecular recognition, in which they undergo induced folding. Based on prior generalizations, the idea prevails in the IDP field that due to the entropic penalty of induced folding, the major functional advantage associated with this binding mode is "uncoupling" specificity from binding strength. Nevertheless, both weaker binding and high specificity of IDPs/IDRs rest on limited experimental observations, making these assumptions more speculations than evidence-supported facts. The issue is also complicated by the rather vague concept of specificity that lacks an exact measure, such as the Kd for binding strength. We addressed these issues by creating and analyzing a comprehensive dataset of well-characterized ID/globular protein complexes, for which both the atomic structure of the complex and free energy (ΔG, Kd ) of interaction is known. Through this analysis, we provide evidence that the affinity distributions of IDP/globular and globular/globular complexes show different trends, whereas specificity does not connote to weaker binding strength of IDPs/IDRs. Furthermore, protein disorder extends the spectrum in the direction of very weak interactions, which may have important regulatory consequences and suggest that, in a biological sense, strict correlation of specificity and binding strength are uncoupled by structural disorder.

An Experimental Framework for Developing Point-of-Need Biosensors: Connecting Bio-Layer Interferometry and Electrochemical Impedance Spectroscopy

Biolayer interferometry (BLI) is a well-established laboratory technique for studying biomolecular interactions important for applications such as drug development. Currently, there are interesting opportunities for expanding the use of BLI in other fields, including the development of rapid diagnostic tools. To date, there are no detailed frameworks for implementing BLI in target-recognition studies that are pivotal for developing point-of-need biosensors. Here, we attempt to bridge these domains by providing a framework that connects output(s) of molecular interaction studies with key performance indicators used in the development of point-of-need biosensors. First, we briefly review the governing theory for protein-ligand interactions, and we then summarize the approach for real-time kinetic quantification using various techniques. The 2020 PRISMA guideline was used for all governing theory reviews and meta-analyses. Using the information from the meta-analysis, we introduce an experimental framework for connecting outcomes from BLI experiments (KD, kon, koff) with electrochemical (capacitive) biosensor design. As a first step in the development of a larger framework, we specifically focus on mapping BLI outcomes to five biosensor key performance indicators (sensitivity, selectivity, response time, hysteresis, operating range). The applicability of our framework was demonstrated in a study of case based on published literature related to SARS-CoV-2 spike protein to show the development of a capacitive biosensor based on truncated angiotensin-converting enzyme 2 (ACE2) as the receptor. The case study focuses on non-specific binding and selectivity as research goals. The proposed framework proved to be an important first step toward modeling/simulation efforts that map molecular interactions to sensor design.

Differential nuclear import sets the timing of protein access to the embryonic genome

The development of a fertilized egg to an embryo requires the proper temporal control of gene expression. During cell differentiation, timing is often controlled via cascades of transcription factors (TFs). However, in early development, transcription is often inactive, and many TF levels stay constant, suggesting that alternative mechanisms govern the observed rapid and ordered onset of gene expression. Here, we find that in early embryonic development access of maternally deposited nuclear proteins to the genome is temporally ordered via importin affinities, thereby timing the expression of downstream targets. We quantify changes in the nuclear proteome during early development and find that nuclear proteins, such as TFs and RNA polymerases, enter the nucleus sequentially. Moreover, we find that the timing of nuclear proteins' access to the genome corresponds to the timing of downstream gene activation. We show that the affinity of proteins to importin is a major determinant in the timing of protein entry into embryonic nuclei. Thus, we propose a mechanism by which embryos encode the timing of gene expression in early development via biochemical affinities. This process could be critical for embryos to organize themselves before deploying the regulatory cascades that control cell identities.

A dynamical view of protein-protein complexes: Studies by molecular dynamics simulations

Protein-protein interactions are at the basis of many protein functions, and the knowledge of 3D structures of protein-protein complexes provides structural, mechanical and dynamical pieces of information essential to understand these functions. Protein-protein interfaces can be seen as stable, organized regions where residues from different partners form non-covalent interactions that are responsible for interaction specificity and strength. They are commonly described as a peripheral region, whose role is to protect the core region that concentrates the most contributing interactions, from the solvent. To get insights into the dynamics of protein-protein complexes, we carried out all-atom molecular dynamics simulations in explicit solvent on eight different protein-protein complexes of different functional class and interface size by taking into account the bound and unbound forms. On the one hand, we characterized structural changes upon binding of the proteins, and on the other hand we extensively analyzed the interfaces and the structural waters involved in the binding. Based on our analysis, in 6 cases out of 8, the interfaces rearranged during the simulation time, in stable and long-lived substates with alternative residue-residue contacts. These rearrangements are not restricted to side-chain fluctuations in the periphery but also affect the core interface. Finally, the analysis of the waters at the interface and involved in the binding pointed out the importance to take into account their role in the estimation of the interaction strength.

Cold spots are universal in protein-protein interactions

Proteins interact with each other through binding interfaces that differ greatly in size and physico-chemical properties. Within the binding interface, a few residues called hot spots contribute the majority of the binding free energy and are hence irreplaceable. In contrast, cold spots are occupied by suboptimal amino acids, providing possibility for affinity enhancement through mutations. In this study, we identify cold spots due to cavities and unfavorable charge interactions in multiple protein-protein interactions (PPIs). For our cold spot analysis, we first use a small affinity database of PPIs with known structures and affinities and then expand our search to nearly 4000 homo- and heterodimers in the Protein Data Bank (PDB). We observe that cold spots due to cavities are present in nearly all PPIs unrelated to their binding affinity, while unfavorable charge interactions are relatively rare. We also find that most cold spots are located in the periphery of the binding interface, with high-affinity complexes showing fewer centrally located colds spots than low-affinity complexes. A larger number of cold spots is also found in non-cognate interactions compared to their cognate counterparts. Furthermore, our analysis reveals that cold spots are more frequent in homo-dimeric complexes compared to hetero-complexes, likely due to symmetry constraints imposed on sequences of homodimers. Finally, we find that glycines, glutamates, and arginines are the most frequent amino acids appearing at cold spot positions. Our analysis emphasizes the importance of cold spot positions to protein evolution and facilitates protein engineering studies directed at enhancing binding affinity and specificity in a wide range of applications.

Understanding Supramolecular Assembly of Supercharged Proteins

Ordered supramolecular assemblies have recently been created using electrostatic interactions between oppositely charged proteins. Despite recent progress, the fundamental mechanisms governing the assembly of oppositely supercharged proteins are not fully understood. Here, we use a combination of experiments and computational modeling to systematically study the supramolecular assembly process for a series of oppositely supercharged green fluorescent protein variants. We show that net charge is a sufficient molecular descriptor to predict the interaction fate of oppositely charged proteins under a given set of solution conditions (e.g., ionic strength), but the assembled supramolecular structures critically depend on surface charge distributions. Interestingly, our results show that a large excess of charge is necessary to nucleate assembly and that charged residues not directly involved in interprotein interactions contribute to a substantial fraction (∼30%) of the interaction energy between oppositely charged proteins via long-range electrostatic interactions. Dynamic subunit exchange experiments further show that relatively small, 16-subunit assemblies of oppositely charged proteins have kinetic lifetimes on the order of ∼10-40 min, which is governed by protein composition and solution conditions. Broadly, our results inform how protein supercharging can be used to create different ordered supramolecular assemblies from a single parent protein building block.

Pulse dipolar EPR for determining nanomolar binding affinities

Protein interaction studies often require very low concentrations and highly sensitive biophysical methods. Here, we demonstrate that pulse dipolar electron paramagnetic resonance spectroscopy allows measuring dissociation constants in the nanomolar range. This approach is appealing for concentration-limited biomolecular systems and medium-to-high-affinity binding studies, demonstrated here at 50 nanomolar protein concentration.

Cu^II-nitroxide RIDME measurements at 100 nM protein concentration allow reliable extraction of dissociation constants and distances, while measurements at 50 nM protein concentration allow reliable extraction of dissociation constants only.

The point mutation of the cholesterol trafficking membrane protein NPC1 may affect its proper function in more than a single step: Molecular dynamics simulation study

The Niemann-Pick type C1 (NPC1) protein is one of the key players of cholesterol trafficking from the lysosome and its function is closely coupled with the Niemann-Pick type C2 (NPC2) protein. The dysfunction of one of these proteins can cause problems in the overall cholesterol homeostasis and leads to a disease, which is called the Niemann-Pick type C (NPC) disease. The parts of the cholesterol transport mechanism by NPC1 have begun to recently emerge, especially after the full-length NPC1 structure was determined from a cryo-EM study. However, many details about the overall cholesterol trafficking process by NPC1 still remain to be elucidated. Notably, the NPC1 could act as one of the target proteins for the control of infectious diseases due to its role as the virus entry point into the cells as well as for cancer treatment due to the inhibitory effect of tumor growth. A mutation of NPC1 can leads to dysfunctions and understanding this process can provide valuable insights into the mechanisms of the corresponding protein and the therapeutic strategies against the disease that are caused by the mutation. It has been found that patients with the point mutation R518W (or R518Q) on the NPC1 show the accumulation of lipids within the lysosomal lumen. In this paper, we report how the corresponding mutation can affect the cholesterol transport process by NPC1 in the different stages by the molecular dynamics simulations. The simulation results show that the point mutation intervenes at least at two different steps during the cholesterol transport by NPC1 and NPC2 in combination, which includes the association step of NPC2 with the NPC1, the cholesterol transfer step from NPC2 to NPC1-NTD while the cholesterol passage within the NPC1 via a channel is relatively unaffected by R518W mutation. The detailed analysis of the resulting simulation trajectories reveals the important structural features that are essential for the proper functioning of the NPC1 for the cholesterol transport, and it shows how the overall structure, which thereby includes the function, can be affected by a single mutation.

Tight Complex Formation of the Fumarate Sensing DcuS-DcuR Two-Component System at the Membrane and Target Promoter Search by Free DcuR Diffusion

Signaling of two-component systems by phosphoryl transfer requires interaction of the sensor kinase with the response regulator. Interaction of the C4-dicarboxylate-responsive and membrane-integral sensor kinase DcuS with the response regulator DcuR was studied. In vitro, the cytoplasmic part of DcuS (PAS_C-Kin) was employed. Stable complexes were formed, when either DcuS or DcuR were phosphorylated (K_d 22 ± 11 and 28 ± 7 nM, respectively). The unphosphorylated proteins produced a more labile complex (K_d 1380 ± 395 nM). Bacterial two-hybrid studies confirm interaction of DcuR with DcuS (and PAS_C-Kin) in vivo. The absolute contents of DcuR (197-979 pmol mg⁻¹ protein) in the bacteria exceeded those of DcuS by more than 1 order of magnitude. According to the K_d values, DcuS exists in complex, with phosphorylated but also unphosphorylated DcuR. In live cell imaging, the predominantly freely diffusing DcuR becomes markedly less mobile after phosphorylation and activation of DcuS by fumarate. Portions of the low mobility fraction accumulated at the cell poles, the preferred location of DcuS, and other portions within the cell, representing phosphorylated DcuR bound to promoters. In the model, acitvation of DcuS increases the affinity toward DcuR, leading to DcuS-P × DcuR formation and phosphorylation of DcuR. The complex is stable enough for phosphate-transfer, but labile enough to allow exchange between DcuR from the cytosol and DcuR-P of the complex. Released DcuR-P diffuses to target promoters and binds. Uncomplexed DcuR-P in the cytosol binds to nonactivated DcuS and becomes dephosphorylated. The lower affinity between DcuR and DcuS avoids blocking of DcuS and allows rapid exchange of DcuR.

IMPORTANCE Complex formation of membrane-bound sensor kinases with the response regulators represents an inherent step of signaling from the membrane to the promoters on the DNA. In the C4-dicarboxylate-sensing DcuS-DcuR two-component system, complex formation is strengthened by activation (phosphorylation) in vitro and in vivo, with trapping of the response regulator DcuR at the membrane. Single-molecule tracking of DcuR in the bacterial cell demonstrates two populations of DcuR with decreased mobility in the bacteria after activation: one at the membrane, but a second in the cytosol, likely representing DNA-bound DcuR. The data suggest a model with binding of DcuR to DcuS-P for phosphorylation, and of DcuR-P to DcuS for dephosphorylation, allowing rapid adaptation of the DcuR phosphorylation state. DcuR-P is released and transferred to DNA by 3D diffusion.

Protein-Protein Binding Free Energy Predictions with the MM/PBSA Approach Complemented with the Gaussian-Based Method for Entropy Estimation

Here, we present a Gaussian-based method for estimation of protein-protein binding entropy to augment the molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) method for computational prediction of binding free energy (ΔG). The method is termed f5-MM/PBSA/E, where "E" stands for entropy and f5 for five adjustable parameters. The enthalpy components of ΔG (molecular mechanics, polar and non-polar solvation energies) are computed from a single implicit solvent generalized Born (GB) energy minimized structure of a protein-protein complex, while the binding entropy is computed using independently GB energy minimized unbound and bound structures. It should be emphasized that the f5-MM/PBSA/E method does not use snapshots, just energy minimized structures, and is thus very fast and computationally efficient. The method is trained and benchmarked in 5-fold validation test over a data set consisting of 46 protein-protein binding cases with experimentally determined dissociation constant K _d values. This data set has been used for benchmarking in recently published protein-protein binding studies that apply conventional MM/PBSA and MM/PBSA with an enhanced sampling method. The f5-MM/PBSA/E tested on the same data set achieves similar or better performance than these computationally demanding approaches, making it an excellent choice for high throughput protein-protein binding affinity prediction studies.

Integrated unsupervised-supervised modeling and prediction of protein-peptide affinities at structural level

Cell signal networks are orchestrated directly or indirectly by various peptide-mediated protein-protein interactions, which are normally weak and transient and thus ideal for biological regulation and medicinal intervention. Here, we develop a general-purpose method for modeling and predicting the binding affinities of protein-peptide interactions (PpIs) at the structural level. The method is a hybrid strategy that employs an unsupervised approach to derive a layered PpI atom-residue interaction (ulPpI[a-r]) potential between different protein atom types and peptide residue types from thousands of solved PpI complex structures and then statistically correlates the potential descriptors with experimental affinities (KD values) over hundreds of known PpI samples in a supervised manner to create an integrated unsupervised-supervised PpI affinity (usPpIA) predictor. Although both the ulPpI[a-r] potential and usPpIA predictor can be used to calculate PpI affinities from their complex structures, the latter seems to perform much better than the former, suggesting that the unsupervised potential can be improved substantially with a further correction by supervised statistical learning. We examine the robustness and fault-tolerance of usPpIA predictor when applied to treat the coarse-grained PpI complex structures modeled computationally by sophisticated peptide docking and dynamics simulation. It is revealed that, despite developed solely based on solved structures, the integrated unsupervised-supervised method is also applicable for locally docked structures to reach a quantitative prediction but can only give a qualitative prediction on globally docked structures. The dynamics refinement seems not to change (or improve) the predictive results essentially, although it is computationally expensive and time-consuming relative to peptide docking. We also perform extrapolation of usPpIA predictor to the indirect affinity quantities of HLA-A*0201 binding epitope peptides and NHERF PDZ binding scaffold peptides, consequently resulting in a good and moderate correlation of the predicted KD with experimental IC50 and BLU on the two peptide sets, with Pearson's correlation coefficients Rp = 0.635 and 0.406, respectively.

Comparative Reverse Vaccinology of Piscirickettsia salmonis, Aeromonas salmonicida, Yersinia ruckeri, Vibrio anguillarum and Moritella viscosa, Frequent Pathogens of Atlantic Salmon and Lumpfish Aquaculture

Marine finfish aquaculture is affected by diverse infectious diseases, and they commonly occur as co-infection. Some of the most frequent and prevalent Gram-negative bacterial pathogens of the finfish aquaculture include Piscirickettsia salmonis, Aeromonas salmonicida, Yersinia ruckeri, Vibrio anguillarum and Moritella viscosa. To prevent co-infections in aquaculture, polyvalent or universal vaccines would be ideal. Commercial polyvalent vaccines against some of these pathogens are based on whole inactivated microbes and their efficacy is controversial. Identification of common antigens can contribute to the development of effective universal or polyvalent vaccines. In this study, we identified common and unique antigens of P. salmonis, A. salmonicida, Y. ruckeri, V. anguillarum and M. viscosa based on a reverse vaccinology pipeline. We screened the proteome of several strains using complete available genomes and identified a total of 154 potential antigens, 74 of these identified antigens corresponded to secreted proteins, and 80 corresponded to exposed outer membrane proteins (OMPs). Further analysis revealed the outer membrane antigens TonB-dependent siderophore receptor, OMP assembly factor BamA, the LPS assembly protein LptD and secreted antigens flagellar hook assembly protein FlgD and flagellar basal body rod protein FlgG are present in all pathogens used in this study. Sequence and structural alignment of these antigens showed relatively low percentage sequence identity but good structural homology. Common domains harboring several B-cells and T-cell epitopes binding to major histocompatibility (MHC) class I and II were identified. Selected peptides were evaluated for docking with Atlantic salmon (Salmo salar) and Lumpfish MHC class II. Interaction of common peptide-MHC class II showed good in-silico binding affinities and dissociation constants between −10.3 to −6.5 kcal mol−1 and 5.10 × 10−9 to 9.4 × 10−6 M. This study provided the first list of antigens that can be used for the development of polyvalent or universal vaccines against these Gram-negative bacterial pathogens affecting finfish aquaculture.

Theoretical Development of DnaG Primase as a Novel Narrow-Spectrum Antibiotic Target

The widespread use of antibiotics to treat infections is one of the reasons that global mortality rates have fallen over the past 80 years. However, antibiotic use is also responsible for the concomitant rise in antibiotic resistance because it results in dysbiosis in which commensal and pathogenic bacteria are both greatly reduced. Therefore, narrow-range antibiotics are a promising direction for reducing antibiotic resistance because they are more discriminate. As a step toward addressing this problem, the goal of this study was to identify sites on DnaG primase that are conserved within Gram-positive bacteria and different from the equivalent sites in Gram-negative bacteria. Based on sequence and structural analysis, the primase C-terminal helicase-binding domain (CTD) was identified as most promising. Although the primase CTD sequences are very poorly conserved, they have highly conserved protein folds, and Gram-positive bacterial primases fold into a compact state that creates a small molecule binding site adjacent to a groove. The small molecule would stabilize the protein in its compact state, which would interfere with the helicase binding. This is important because primase CTD must be in its open conformation to bind to its cognate helicase at the replication fork.

The Large Nonstructural Protein (NS1) of Human Bocavirus 1 Directly Interacts with Ku70, Which Plays an Important Role in Virus Replication in Human Airway Epithelia

Human bocavirus 1 (HBoV1), an autonomous human parvovirus, causes acute respiratory tract infections in young children. HBoV1 infects well-differentiated (polarized) human airway epithelium cultured at an air-liquid interface (HAE-ALI). HBoV1 expresses a large nonstructural protein, NS1, that is essential for viral DNA replication. HBoV1 infection of polarized human airway epithelial cells induces a DNA damage response (DDR) that is critical to viral DNA replication involving DNA repair with error-free Y-family DNA polymerases. HBoV1 NS1 or the isoform NS1-70 per se induces a DDR. In this study, using the second-generation proximity-dependent biotin identification (BioID2) approach, we identified that Ku70 is associated with the NS1-BioID2 pulldown complex through a direct interaction with NS1. Biolayer interferometry (BLI) assay determined a high binding affinity of NS1 with Ku70, which has an equilibrium dissociation constant (KD) value of 0.16 μM and processes the strongest interaction at the C-terminal domain. The association of Ku70 with NS1 was also revealed during HBoV1 infection of HAE-ALI. Knockdown of Ku70 and overexpression of the C-terminal domain of Ku70 significantly decreased HBoV1 replication in HAE-ALI. Thus, our study provides, for the first time, a direct interaction of parvovirus large nonstructural protein NS1 with Ku70. IMPORTANCE Parvovirus infection induces a DNA damage response (DDR) that plays a pivotal role in viral DNA replication. The DDR includes activation of ATM (ataxia telangiectasia mutated), ATR (ATM- and RAD3-related), and DNA-PKcs (DNA-dependent protein kinase catalytic subunit). The large nonstructural protein (NS1) often plays a role in the induction of DDR; however, how the DDR is induced during parvovirus infection or simply by the NS1 is not well studied. Activation of DNA-PKcs has been shown as one of the key DDR pathways in DNA replication of HBoV1. We identified that HBoV1 NS1 directly interacts with Ku70, but not Ku80, of the Ku70/Ku80 heterodimer at high affinity. This interaction is also important for HBoV1 replication in HAE-ALI. We propose that the interaction of NS1 with Ku70 recruits the Ku70/Ku80 complex to the viral DNA replication center, which activates DNA-PKcs and facilitates viral DNA replication.

Wash-Free Amperometric Escherichia coli Detection via Rapid and Specific Proteolytic Cleavage by Its Outer Membrane OmpT

Various methods have been developed for the detection of Escherichia coli (E. coli); however, they are complex and time-consuming. OmpT─a cell membrane endopeptidase of E. coli─strongly embedded in the outer membrane of only E. coli, exposed to external solutions, with high proteolytic activity, could be a suitable target molecule for the rapid and straightforward detection of E. coli. Herein, a wash-free, sensitive, and selective amperometric method for E. coli detection, based on rapid and specific proteolytic cleavage by OmpT, has been reported. The method involved (i) rapid proteolytic cleavage of consecutive amino acids, after cleavage by OmpT, linked to an electrochemical species (4-aminophenol, AP), by leucine aminopeptidase (LAP, an exopeptidase), (ii) affinity binding of E. coli on an electrode, and (iii) electrochemical-enzymatic (EN) redox cycling. OmpT cleaved the intermediate peptide bond of a peptide substrate containing alanine-arginine-arginine-leucine-AP (-A-R-R-L-AP), forming R-L-AP, followed by the cleavage of two peptide bonds of R-L-AP sequentially by LAP, to liberate an electroactive AP. Affinity binding and EN redox cycling, in addition to rapid proteolytic cleavage by OmpT and LAP, enabled high electrochemical signal amplification. Two-sequential-cleavage was employed for the first time in protease-based detection. The calculated detection limit for E. coli cells in tap water (approximately 10³ CFU/mL after 1 h incubation) was lower than those obtained without affinity binding and EN redox cycling. The detection method was highly selective to E. coli as OmpT is present in only E. coli. High sensitivity, selectivity, and the absence of wash steps make the developed detection method practically promising.

A highly accurate metadynamics-based Dissociation Free Energy method to calculate protein-protein and protein-ligand binding potencies

Although seeking to develop a general and accurate binding free energy calculation method for protein-protein and protein-ligand interactions has been a continuous effort for decades, only limited successes have been obtained so far. Here, we report the development of a metadynamics-based procedure that calculates Dissociation Free Energy (DFE) and its application to 19 non-congeneric protein-protein complexes and hundreds of protein-ligand complexes covering eight targets. We achieved very high correlations in comparison to experimental binding free energies for these diverse sets of systems, demonstrating the generality and accuracy of the method. Since structures of most proteins are available owing to the recent success of prediction by artificial intelligence, a general free energy method such as DFE, combined with other methods, can make structure-based drug design a widely viable and reliable solution to develop both traditional small molecule drugs and biologic drugs as well as PROTACS.