On the binding affinity of macromolecular interactions: daring to ask why proteins interact

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.

Unraveling the folding-assisted unbinding mechanism of TCF with its binding partner β-catenin

This study utilizes molecular dynamics simulations aided with multiple walker parallel bias metadynamics to investigate the TCF unbinding mechanism from the β-catenin interface. The results, consistent with experimental binding affinity calculations, unveil a folding-assisted unbinding mechanism.

Computational Screening for the Dipeptidyl Peptidase-IV Inhibitory Peptides from Putative Hemp Seed Hydrolyzed Peptidome as a Potential Antidiabetic Agent

Dipeptidyl peptidase-IV (DPPIV) inhibitory peptides are a class of antihyperglycemic drugs used in the treatment of type 2 diabetes mellitus, a metabolic disorder resulting from reduced levels of the incretin hormone GLP-1. Given that DPPIV degrades incretin, a key regulator of blood sugar levels, various antidiabetic medications that inhibit DPPIV, such as vildagliptin, sitagliptin, and linagliptin, are employed. However, the potential side effects of these drugs remain a matter of debate. Therefore, we aimed to investigate food-derived peptides from Cannabis sativa (hemp) seeds. Our developed bioinformatics pipeline was used to identify the putative hydrolyzed peptidome of three highly abundant proteins: albumin, edestin, and vicilin. These proteins were subjected to in silico digestion by different proteases (trypsin, chymotrypsin, and pepsin) and then screened for DPPIV inhibitory peptides using IDPPIV-SCM. To assess potential adverse effects, several prediction tools, namely, TOXINpred, AllerCatPro, and HemoPred, were employed to evaluate toxicity, allergenicity, and hemolytic effects, respectively. COPID was used to determine the amino acid composition. Molecular docking was performed using GalaxyPepDock and HPEPDOCK, 3D visualizations were conducted using the UCSF Chimera program, and MD simulations were carried out with AMBER20 MD software. Based on the predictive outcomes, FNVDTE from edestin and EAQPST from vicilin emerged as promising candidates for DPPIV inhibitors. We anticipate that our findings may pave the way for the development of alternative DPPIV inhibitors.

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.

Immunoinformatics-Driven Strategies for Advancing Epitope-Based Vaccine Design for West Nile Virus

The West Nile virus (WNV) is the causative agent of West Nile disease (WND), which poses a potential risk of meningitis or encephalitis. The aim of the study was to design an epitope-based vaccine for WNV by utilizing computational analyses. The epitope-based vaccine design process encompassed WNV sequence collection, phylogenetic tree construction, and sequence alignment. Computational models identified B-cell and T-cell epitopes, followed by immunological property analysis. Epitopes were then modeled and docked with B-cell receptors, MHC I, and MHC II. Molecular dynamics simulations further explored dynamic interactions between epitopes and receptors. The findings indicated that the B-cell epitope QINHHWHKSGSSIG, along with three T-cell epitopes (FLVHREWFM for MHC I, NPFVSVATANAKVLI for MHC II, and NAYYVMTVGTKTFLV for MHC II), successfully passed the immunological evaluations. These four epitopes were further subjected to docking and molecular dynamics simulation studies. Although each demonstrated favorable affinities with their respective receptors, only NAYYVMTVGTKTFLV displayed a stable interaction with MHC II during MDS analysis, hence emerging as a potential candidate for a WNV epitope-based vaccine. This study demonstrates a comprehensive approach to epitope vaccine design, combining computational analyses, molecular modeling, and simulation techniques to identify potential vaccine candidates for WNV.

Assessing the performance of MM/PBSA and MM/GBSA methods. 10. Prediction reliability of binding affinities and binding poses for RNA-ligand complexes

Ribonucleic acid (RNA)-ligand interactions play a pivotal role in a wide spectrum of biological processes, ranging from protein biosynthesis to cellular reproduction. This recognition has prompted the broader acceptance of RNA as a viable candidate for drug targets. Delving into the atomic-scale understanding of RNA-ligand interactions holds paramount importance in unraveling intricate molecular mechanisms and further contributing to RNA-based drug discovery. Computational approaches, particularly molecular docking, offer an efficient way of predicting the interactions between RNA and small molecules. However, the accuracy and reliability of these predictions heavily depend on the performance of scoring functions (SFs). In contrast to the majority of SFs used in RNA-ligand docking, the end-point binding free energy calculation methods, such as molecular mechanics/generalized Born surface area (MM/GBSA) and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA), stand as theoretically more rigorous approaches. Yet, the evaluation of their effectiveness in predicting both binding affinities and binding poses within RNA-ligand systems remains unexplored. This study first reported the performance of MM/PBSA and MM/GBSA with diverse solvation models, interior dielectric constants (εin) and force fields in the context of binding affinity prediction for 29 RNA-ligand complexes. MM/GBSA is based on short (5 ns) molecular dynamics (MD) simulations in an explicit solvent with the YIL force field; the GBGBn2 model with higher interior dielectric constant (εin = 12, 16 or 20) yields the best correlation (Rp = -0.513), which outperforms the best correlation (Rp = -0.317, rDock) offered by various docking programs. Then, the efficacy of MM/GBSA in identifying the near-native binding poses from the decoys was assessed based on 56 RNA-ligand complexes. However, it is evident that MM/GBSA has limitations in accurately predicting binding poses for RNA-ligand systems, particularly compared with notably proficient docking programs like rDock and PLANTS. The best top-1 success rate achieved by MM/GBSA rescoring is 39.3%, which falls below the best results given by docking programs (50%, PLNATS). This study represents the first evaluation of MM/PBSA and MM/GBSA for RNA-ligand systems and is expected to provide valuable insights into their successful application to RNA targets.

DeePNAP: A Deep Learning Method to Predict Protein-Nucleic Acid Binding Affinity from Their Sequences

Predicting the protein-nucleic acid (PNA) binding affinity solely from their sequences is of paramount importance for the experimental design and analysis of PNA interactions (PNAIs). A large number of currently developed models for binding affinity prediction are limited to specific PNAIs while also relying on the sequence and structural information of the PNA complexes for both training and testing, and also as inputs. As the PNA complex structures available are scarce, this significantly limits the diversity and generalizability due to the small training data set. Additionally, a majority of the tools predict a single parameter, such as binding affinity or free energy changes upon mutations, rendering a model less versatile for usage. Hence, we propose DeePNAP, a machine learning-based model built from a vast and heterogeneous data set with 14,401 entries (from both eukaryotes and prokaryotes) from the ProNAB database, consisting of wild-type and mutant PNA complex binding parameters. Our model precisely predicts the binding affinity and free energy changes due to the mutation(s) of PNAIs exclusively from their sequences. While other similar tools extract features from both sequence and structure information, DeePNAP employs sequence-based features to yield high correlation coefficients between the predicted and experimental values with low root mean squared errors for PNA complexes in predicting KD and ΔΔG, implying the generalizability of DeePNAP. Additionally, we have also developed a web interface hosting DeePNAP that can serve as a powerful tool to rapidly predict binding affinities for a myriad of PNAIs with high precision toward developing a deeper understanding of their implications in various biological systems. Web interface: http://14.139.174.41:8080/.

Antiplatelet mechanism of a subtilisin-like serine protease from Solanum tuberosum (StSBTc-3)

The aims of this study are to characterize the antiplatelet activity of StSBTc-3, a potato serine protease with fibrino (geno) lytic activity, and to provide information on its mechanism of action. The results obtained show that StSBTc-3 inhibits clot retraction and prevents platelet aggregation induced by thrombin, convulxin, and A23187. Platelet aggregation inhibition occurs in a dose-dependent manner and is not affected by inactivation of StSBTc-3 with the inhibitor of serine proteases phenylmethylsulfonyl fluoride (PMSF). In addition, StSBTc-3 reduces fibrinogen binding onto platelets. In-silico calculations show a high binding affinity between StSBTc-3 and human α2bβ3 integrin suggesting that the antiplatelet activity of StSBTc-3 could be associated with the fibronectin type III domain present in its amino acid sequence. Binding experiments show that StSBTc-3 binds to α2bβ3 preventing the interaction between α2bβ3 and fibrinogen and, consequently, inhibiting platelet aggregation. StSBTc-3 represents a promising compound to be considered as an alternative to commercially available drugs used in cardiovascular therapies.

The Diverse Nature of the Molecular Interactions That Govern the COV-2 Variants' Cell Receptor Affinity Ranking and Its Experimental Variability

A critical determinant of infectivity and virulence of the most infectious and or lethal variants of concern (VOCs): Wild Type, Delta and Omicron is related to the binding interactions between the receptor-binding domain of the spike and its host receptor, the initial step in cell infection. It is of the utmost importance to understand how mutations of a viral strain, especially those that are in the viral spike, affect the resulting infectivity of the emerging VOC, knowledge that could help us understand the variant virulence and inform the therapies applied or the vaccines developed. For this sake, we have applied a battery of computational protocols of increasing complexity to the calculation of the spike binding affinity for three variants of concern to the ACE2 cell receptor. The results clearly illustrate that the attachment of the spikes of the Delta and Omicron variants to the receptor originates through different molecular interaction mechanisms. All our protocols unanimously predict that the Delta variant has the highest receptor-binding affinity, while the Omicron variant displays a substantial variability in the binding affinity of the spike that relates to the structural plasticity of the Omicron spike-receptor complex. We suggest that the latter result could explain (at least in part) the variability of the in vitro binding results for this VOC and has led us to suggest a reason for the lower virulence of the Omicron variant as compared to earlier strains. Several hypotheses have been developed around this subject.

Self-assembly of peptide nanomaterials at biointerfaces: molecular design and biomedical applications

Self-assembly is an important strategy for constructing ordered structures and complex functions in nature. Based on this, people can imitate nature and artificially construct functional materials with novel structures through the supermolecular self-assembly pathway of biological interfaces. Among the many assembly units, peptide molecular self-assembly has received widespread attention in recent years. In this review, we introduce the interactions (hydrophobic interaction, hydrogen bond, and electrostatic interaction) between peptide nanomaterials and biological interfaces, summarizing the latest advancements in multifunctional self-assembling peptide materials. We systematically demonstrate the assembly mechanisms of peptides at biological interfaces, such as proteins and cell membranes, while highlighting their application potential and challenges in fields like drug delivery, antibacterial strategies, and cancer therapy.

Computational Studies on 6-Pyruvoyl Tetrahydropterin Synthase (6-PTPS) of Plasmodium falciparum

6-Pyruvoyl tetrahydropterin synthase (6-PTPS) is a lyase involved in the synthesis of tetrahydrobiopterin. In Plasmodium species where dihydroneopterin aldolase (DHNA) is absent, it acts in the folate biosynthetic pathway necessary for the growth and survival of the parasite. The 6-pyruvoyl tetrahydropterin synthase of Plasmodium falciparum (PfPTPS) has been identified as a potential antimalarial drug target. This study identified potential inhibitors of PfPTPS using molecular docking techniques. Molecular docking and virtual screening of 62 compounds including the control to the deposited Protein Data Bank (PDB) structure was carried out using AutoDock Vina in PyRx. Five of the compounds, N,N-dimethyl-N'-[4-oxo-6-(2,2,5-trimethyl-1,3-dioxolan-4-yl)-3H-pteridin-2-yl]methanimidamide (140296439), 2-amino-6-[(1R)-3-cyclohexyl-1-hydroxypropyl]-3H-pteridin-4-one (140296495), 2-(2,3-dihydroxypropyl)-8,9-dihydro-6H-pyrimido[2,1-b]pteridine-7,11-dione (144380406), 2-(dimethylamino)-6-[(2,2-dimethyl-1,3-dioxolan-4-yl)-hydroxymethyl]-3H-pteridin-4-one (135573878), and [1-acetyloxy-1-(2-methyl-4-oxo-3H-pteridin-6-yl)propan-2-yl] acetate (136075207), showed better binding affinity than the control ligand, biopterin (135449517), and were selected and screened. Three conformers of 140296439 with the binding energy of -7.2, -7.1, and -7.0 kcal/mol along with 140296495 were better than the control at -5.7 kcal/mol. In silico absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies predicted good pharmacokinetic properties of all the compounds while reporting a high risk of irritant toxicity in 140296439 and 144380406. The study highlights the five compounds, 140296439, 140296495, 144380406, 135573878 and 136075207, as potential inhibitors of PfPTPS and possible compounds for antimalarial drug development.

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.

Fungal enzyme degradation of lignin-PLA composites: Insights from experiments and molecular docking simulations

Fungal enzymes are effective in degrading various polymeric materials. In this study, we assessed the initial degradation of composites consisting of lignin-poly(lactic acid) (PLA) with both unmodified lignin (LIG) and oxypropylated lignin (oLIG) incorporated at 10 % and 40 % weight within the PLA matrix in a fungal environment. Trametes versicolor fungi were used, and the samples were treated only for eight weeks. Although there was no significant difference in weight loss, the degradation process impacted the chemical and thermal properties of the composites, as shown by Fourier transform infrared spectroscopy (FTIR) and Differential scanning calorimetry (DSC) analyses. After the degradation process, the carbonyl index values decreased for all composites and the hydroxyl index values increased for LIG/PLA and a reverse trend was observed for oLIG/PLA composites. The first heating scan from DSC results showed that the melting peak and the cold crystallization peak disappeared after the degradation process. Microscopic analysis revealed that LIG/PLA exhibited higher roughness than oLIG/PLA. Molecular docking simulations were carried out using guaiacylglycerol-β-guaiacyl ether (GGE) and lactic acid (LA) as model compounds for lignin and PLA, respectively, with laccase (Lac) enzyme for Trametes versicolor. The docking results showed that GGE had the strongest binding interaction and affinity with Lac than lactic acid and oxypropylated GGE. The oxypropylated GGE formed a shorter hydrogen bonding with the Lac enzyme than GGE and LA. The trend associated with the degradation of composites from experimental and molecular docking findings was consistent. This combined approach provided insights into the degradation process using fungi and had the potential to be applied to different polymeric composites.

Elucidation of the structural dynamics of mutations in PHB2 protein associated with growth suppression and cancer progression

Prohibitin is a multifunctional protein that plays an important role in numerous cellular processes. Membrane-associated mitochondrial prohibitin complex is made up of two subunits, PHB1 and PHB2 which are ubiquitously expressed and analogous to each other. High levels of prohibitin expression have consequently been found in esophageal cancer, endometrial adenocarcinoma, gastric cancer, hepatocellular carcinoma, breast cancer and bladder cancer. The aim of this study is to analyse two-point mutation PHB2_MT1(I → A) and PHB2_MT2(I → P), their effect on PHB2 protein and its effect on formation of mitochondrial complex. It is a residual level study, based on current experimental validation. To establish the effects of the two-point mutations, computational approaches such as molecular modelling, molecular docking, normal mode simulation, molecular dynamics simulations and MM/GBSA were used. An analysis of the energy dynamics of both unbound and complex proteins was conducted to elucidate how mutations impact the energy distribution of PHB2. Our study confirmed that the two mutations decreased the overall stability of PHB2. This was evidenced by heightened atomic fluctuations within the mutated region, accompanied by elevated deviations observed in RMSD and Rg values. Furthermore, these mutations were correlated with a decline in the organization of secondary structural elements. The mutations in PHB2_MT1 and PHB2_MT2 resulted in formation a less stable prohibitin complex. Thus, PHB1 and PHB2 may act as molecular target or novel biomarkers for therapeutic intervention in numerous forms of malignancies.

Decision tree-based identification of important molecular fragments for protein-ligand binding

Fragment-based drug design is an emerging technology in pharmaceutical research and development. One of the key aspects of this technology is the identification and quantitative characterization of molecular fragments. This study presents a strategy for identifying important molecular fragments based on molecular fingerprints and decision tree algorithms and verifies its feasibility in predicting protein-ligand binding affinity. Specifically, the three-dimensional (3D) structures of protein-ligand complexes are encoded using extended-connectivity fingerprints (ECFP), and three decision tree models, namely Random Forest, XGBoost, and LightGBM, are used to quantitatively characterize the feature importance, thereby extracting important molecular fragments with high reliability. Few-shot learning reveals that the extracted molecular fragments contribute significantly and consistently to the binding affinity even with a small sample size. Despite the absence of location and distance information for molecular fragments in ECFP, 3D visualization, in combination with the reverse ECFP process, shows that the majority of the extracted fragments are located at the binding interface of the protein and the ligand. This alignment with the distance constraints critical for binding affinity further supports the reliability of the strategy for identifying important molecular fragments.

Cryo-EM of a heterogeneous biochemical fraction elucidates multiple protein complexes from a multicellular thermophilic eukaryote

Biomolecular complexes and their interactions govern cellular structure and function. Understanding their architecture is a prerequisite for dissecting the cell's inner workings, but their higher-order assembly is often transient and challenging for structural analysis. Here, we performed cryo-EM on a single, highly heterogeneous biochemical fraction derived from Chaetomium thermophilum cell extracts to visualize the biomolecular content of the multicellular eukaryote. After cryo-EM single-particle image processing, results showed that a simultaneous three-dimensional structural characterization of multiple chemically diverse biomacromolecules is feasible. Namely, the thermophilic, eukaryotic complexes of (a) ATP citrate-lyase, (b) Hsp90, (c) 20S proteasome, (d) Hsp60 and (e) UDP-glucose pyrophosphorylase were characterized. In total, all five complexes have been structurally dissected in a thermophilic eukaryote in a total imaged sample area of 190.64 μm2, and two, in particular, 20S proteasome and Hsp60, exhibit side-chain resolution features. The C. thermophilum Hsp60 near-atomic model was resolved at 3.46 Å (FSC = 0.143) and shows a hinge-like conformational change of its equatorial domain, highly similar to the one previously shown for its bacterial orthologue, GroEL. This work demonstrates that cryo-EM of cell extracts will greatly accelerate the structural analysis of cellular complexes and provide unprecedented opportunities to annotate architectures of biomolecules in a holistic approach.

The Thermodynamic Fingerprints of Ultra-Tight Nanobody-Antigen Binding Probed via Two-Color Single-Molecule Coincidence Detection

Life on the molecular scale is based on a versatile interplay of biomolecules, a feature that is relevant for the formation of macromolecular complexes. Fluorescence-based two-color coincidence detection is widely used to characterize molecular binding and was recently improved by a brightness-gated version which gives more accurate results. We developed and established protocols which make use of coincidence detection to quantify binding fractions between interaction partners labeled with fluorescence dyes of different colors. Since the applied technique is intrinsically related to single-molecule detection, the concentration of diffusing molecules for confocal detection is typically in the low picomolar regime. This makes the approach a powerful tool for determining bi-molecular binding affinities, in terms of KD values, in this regime. We demonstrated the reliability of our approach by analyzing very strong nanobody-EGFP binding. By measuring the affinity at different temperatures, we were able to determine the thermodynamic parameters of the binding interaction. The results show that the ultra-tight binding is dominated by entropic contributions.

An idea to explore: Engaging high school students in structure-function studies of bacterial sortase enzymes and inhibitors - A comprehensive computational experimental pipeline

High school science fairs provide an exceptional opportunity for students to gain experience with scientific research, and participation has positive outcomes with respect to chosen careers in the sciences. However, it can be challenging to engage high school students in university-level research outside of formal internship programs. Here, we describe an experimental pipeline for a computational structural biology project that engages high school students. Students are involved at every step of the investigation and utilize freely available software to dock inhibitors onto protein homologues, and then analyze the resulting complexes. Bacterial sortases are transpeptidases on the cell surface of Gram-positive bacteria and are a potential target for the development of antibiotics. Students modeled inhibitors bound to sortases from several organisms, asking questions about affinity and selectivity. Their project was ranked in the top 10% at both regional and state science fairs. This project design is easily adaptable to countless other protein systems and provides a pipeline for collaborative high school student/university professor inquiry.

How does the same ligand activate signaling of different receptors in TNFR superfamily: a computational study

TNFα is a highly pleiotropic cytokine inducing inflammatory signaling pathways. It is initially presented on plasma membrane of cells (mTNFα), and also exists in a soluble variant (sTNFα) after cleavage. The ligand is shared by two structurally similar receptors, TNFR1 and TNFR2. Interestingly, while sTNFα preferentially stimulates TNFR1, TNFR2 signaling can only be activated by mTNFα. How can two similar receptors respond to the same ligand in such a different way? We employed computational simulations in multiple scales to address this question. We found that both mTNFα and sTNFα can trigger the clustering of TNFR1. The size of clusters induced by sTNFα is constantly larger than the clusters induced by mTNFα. The systems of TNFR2, on the other hand, show very different behaviors. Only when the interactions between TNFR2 are very weak, mTNFα can trigger the receptors to form very large clusters. Given the same weak binding affinity, only small oligomers were obtained in the system of sTNFα. Considering that TNF-mediated signaling is modulated by the ligand-induced clustering of receptors on cell surface, our study provided the mechanistic foundation to the phenomenon that different isoforms of the ligand can lead to highly distinctive signaling patterns for its receptors.

Exploring the Binding Affinity of the ARR2 GARP DNA Binding Domain via Comparative Methods

Plants have evolved signaling mechanisms such as the multi-step phosphorelay (MSP) to respond to different internal and external stimuli. MSP responses often result in gene transcription regulation that is modulated through transcription factors such as B-type Arabidopsis response regulator (ARR) proteins. Among these proteins, ARR2 is a key component that is expressed ubiquitously and is involved in many aspects of plant development. Although it has been noted that B-type ARRs bind to their cognate genes through a DNA-binding domain termed the GARP domain, little is known about the structure and function of this type of DNA-binding domain; thus, how ARRs bind to DNA at a structural level is still poorly understood. In order to understand how the MSP functions in planta, it is crucial to unravel both the kinetics as well as the structural identity of the components involved in such interactions. For this reason, this work focusses on resolving how the GARP domain of ARR2 (GARP2) binds to the promoter region of ARR5, one of its native target genes in cytokinin signaling. We have established that GARP2 specifically binds to the ARR5 promoter with three different bi-molecular interaction systems-qDPI-ELISA, FCS, and MST-and we also determined the KD of this interaction. In addition, structural modeling of the GARP2 domain confirms that GARP2 entails a HTH motif, and that protein-DNA interaction most likely occurs via the α3-helix and the N-terminal arm of this domain since mutations in this region hinder ARR2's ability to activate transcription.

Phage-based peptides for pancreatic cancer diagnosis and treatment: alternative approach

Pancreatic cancer is a devastating disease with a high mortality rate and a lack of effective therapies. The challenges associated with early detection and the highly aggressive nature of pancreatic cancer have limited treatment options, underscoring the urgent need for better disease-modifying therapies. Peptide-based biotherapeutics have become an attractive area of research due to their favorable properties such as high selectivity and affinity, chemical modifiability, good tissue permeability, and easy metabolism and excretion. Phage display, a powerful technique for identifying peptides with high affinity and specificity for their target molecules, has emerged as a key tool in the discovery of peptide-based drugs. Phage display technology involves the use of bacteriophages to express peptide libraries, which are then screened against a target of interest to identify peptides with desired properties. This approach has shown great promise in cancer diagnosis and treatment, with potential applications in targeting cancer cells and developing new therapies. In this comprehensive review, we provide an overview of the basic biology of phage vectors, the principles of phage library construction, and various methods for binding affinity assessment. We then describe the applications of phage display in pancreatic cancer therapy, targeted drug delivery, and early detection. Despite its promising potential, there are still challenges to be addressed, such as optimizing the selection process and improving the pharmacokinetic properties of phage-based drugs. Nevertheless, phage display represents a promising approach for the development of novel targeted therapies in pancreatic cancer and other tumors.

Aquaporin 9 is involved in CRC metastasis through DVL2-dependent Wnt/β-catenin signaling activation

Background

Aquaporin 9 (AQP9) is permeable to water or other small molecules, and plays an important role in various cancers. We previously found that AQP9 was related to the efficacy of chemotherapy in patients with colorectal cancer (CRC). This study aimed to identify the role and regulatory mechanism of AQP9 in CRC metastasis.

Methods

The clinical significance of AQP9 was analysed by using bioinformatics and tissue microarray. Transcriptome sequencing, Dual-Luciferase Reporter Assay, Biacore, and co-immunoprecipitation were employed to demonstrate the regulatory mechanism of AQP9 in CRC. The relationship between AQP9 and CRC metastasis was verified in vitro and in vivo by using real-time cell analysis assay, high content screening, and liver metastasis models of nude mice.

Results

We found that AQP9 was highly expressed in metastatic CRC. AQP9 overexpression reduced cell roundness and enhanced cell motility in CRC. We further showed that AQP9 interacted with Dishevelled 2 (DVL2) via the C-terminal SVIM motif, resulting in DVL2 stabilization and the Wnt/β-catenin pathway activation. Additionally, we identified the E3 ligase neural precursor cell expressed developmentally downregulated 4-like (NEDD4L) as a modulator regulating the ubiquitination and degradation of AQP9.

Conclusions

Collectively, our study revealed the important role of AQP9 in regulating DVL2 stabilization and Wnt/β-catenin signaling to promote CRC metastasis. Targeting the NEDD4L-AQP9-DVL2 axis might have therapeutic usefulness in metastatic CRC treatment.

Performance of wild, tailed, humidity-robust phage on a surface-scanning magnetoelastic biosensor for Salmonella Typhimurium detection

A wild, tailed phage (TST) was compared with a genetically modified, filamentous phage (FST) for S. Typhimurium (ST) detection. When both phages were introduced into oppositely charged MUA and MUAM sensors, the RU values of TST showed an obvious increase on the MUAM sensor. The sensitivity of TST [54.78 ΔRU/(log PFU/mL)] was greater than that of FST [48.05 ΔRU/(log PFU/mL)]. The binding affinity (K_D = 1.75 × 10^-13 M) of TST on MUAM sensor was greater than that of FST. Both phages were specific to only ST, and TST exhibited a persistent binding capability at 50 % RH. When each phage-immobilized sensor was employed on chili pepper, the sensitivity [880.80 Hz/(log CFU/mL)] and detection limit (1.31 ± 0.27 log CFU/mL) of TST were significantly greater than those of FST. The orientation of TST on sensor promoted the uniform capture of bacteria and enhanced the reliable performance of a surface-scanning magnetoelastic biosensor.

AREA-AFFINITY: A Web Server for Machine Learning-Based Prediction of Protein-Protein and Antibody-Protein Antigen Binding Affinities

Protein-Protein binding affinity reflects the binding strength between the binding partners. The prediction of protein-protein binding affinity is important for elucidating protein functions and also for designing protein-based therapeutics. The geometric characteristics such as area (both interface and surface areas) in the structure of a protein-protein complex play an important role in determining protein-protein interactions and their binding affinity. Here, we present a free web server for academic use, AREA-AFFINITY, for prediction of protein-protein or antibody-protein antigen binding affinity based on interface and surface areas in the structure of a protein-protein complex. AREA-AFFINITY implements 60 effective area-based protein-protein affinity predictive models and 37 effective area-based models specific for antibody-protein antigen binding affinity prediction developed in our recent studies. These models take into consideration the roles of interface and surface areas in binding affinity by using areas classified according to different amino acid types with different biophysical nature. The models with the best performances integrate machine learning methods such as neural network or random forest. These newly developed models have superior or comparable performance compared to the commonly used existing methods. AREA-AFFINITY is available for free at: https://affinity.cuhk.edu.cn/.

Plasma membrane preassociation drives β-arrestin coupling to receptors and activation

β-arrestin plays a key role in G protein-coupled receptor (GPCR) signaling and desensitization. Despite recent structural advances, the mechanisms that govern receptor-β-arrestin interactions at the plasma membrane of living cells remain elusive. Here, we combine single-molecule microscopy with molecular dynamics simulations to dissect the complex sequence of events involved in β-arrestin interactions with both receptors and the lipid bilayer. Unexpectedly, our results reveal that β-arrestin spontaneously inserts into the lipid bilayer and transiently interacts with receptors via lateral diffusion on the plasma membrane. Moreover, they indicate that, following receptor interaction, the plasma membrane stabilizes β-arrestin in a longer-lived, membrane-bound state, allowing it to diffuse to clathrin-coated pits separately from the activating receptor. These results expand our current understanding of β-arrestin function at the plasma membrane, revealing a critical role for β-arrestin preassociation with the lipid bilayer in facilitating its interactions with receptors and subsequent activation.

Highly Cytotoxic Copper(II) Mixed-Ligand Quinolinonato Complexes: Pharmacokinetic Properties and Interactions with Drug Metabolizing Cytochromes P450

The effects of two anticancer active copper(II) mixed-ligand complexes of the type [Cu(qui)(mphen)]Y·H₂O, where Hqui = 2-phenyl-3-hydroxy- 1H-quinolin-4-one, mphen = bathophenanthroline, and Y = NO₃ (complex 1) or BF₄ (complex 2) on the activities of different isoenzymes of cytochrome P450 (CYP) have been evaluated. The screening revealed significant inhibitory effects of the complexes on CYP3A4/5 (IC₅₀ values were 2.46 and 4.88 μM), CYP2C9 (IC₅₀ values were 16.34 and 37.25 μM), and CYP2C19 (IC₅₀ values were 61.21 and 77.07 μM). Further, the analysis of mechanisms of action uncovered a non-competitive type of inhibition for both the studied compounds. Consequent studies of pharmacokinetic properties proved good stability of both the complexes in phosphate buffer saline (>96% stability) and human plasma (>91% stability) after 2 h of incubation. Both compounds are moderately metabolised by human liver microsomes (<30% after 1 h of incubation), and over 90% of the complexes bind to plasma proteins. The obtained results showed the potential of complexes 1 and 2 to interact with major metabolic pathways of drugs and, as a consequence of this finding, their apparent incompatibility in combination therapy with most chemotherapeutic agents.

Understanding the in silico aspects of bacterial catabolic cascade for styrene degradation

Styrene is a nonpolar organic compound used in very high volume for the industrial scale production of commercially important polymers such as polystyrene resins as well as copolymers like acrylonitrile butadiene styrene, latex, and rubber. These resins are widely used in the manufacturing of various products including single-use plastics such as disposable cups and containers, protective packaging, heat insulation, and so forth. The large-scale utilization leads to the over-accumulation of styrene waste in the environment causing deleterious health risks including cancer, neurological impairment, dysbiosis of central nervous system, and respiratory problems. To eliminate the accumulating waste. Microbial enzyme-based system represents the most environmental friendly and sustainable approach for elimination of styrene waste. However, comprehensive understanding of the enzyme-substrate interaction and associated pathways would be crucial for developing large-scale disposal systems. This study aims to understand the molecular interaction between the protein-ligand complexes of the styrene catabolic reactions by bacterial enzymes of sty operon. Molecular docking analysis for catalytic enzymes namely, styrene monooxygenase (SMO), styrene oxide isomerase (SOI), and phenylacetaldehyde dehydrogenase (PAD) of the bacterial sty operon was carried out with their individual substrates, that is, styrene, styrene oxide, and phenylacetic acid, respectively. The binding energy, amino acids forming binding cavity, and binding interactions between the protein-ligand binding sites were calculated for each case. The obtained binding energies showed a stable association of these complexes indicating the future scope of their utilization for large-scale bioremediation of styrene, and its commercially used polymers and copolymers.

A Novel Mathematical Model That Predicts the Protection Time of SARS-CoV-2 Antibodies

Infectious diseases such as SARS-CoV-2 pose a considerable threat to public health. Constructing a reliable mathematical model helps us quantitatively explain the kinetic characteristics of antibody-virus interactions. A novel and robust model is developed to integrate antibody dynamics with virus dynamics based on a comprehensive understanding of immunology principles. This model explicitly formulizes the pernicious effect of the antibody, together with a positive feedback stimulation of the virus-antibody complex on the antibody regeneration. Besides providing quantitative insights into antibody and virus dynamics, it demonstrates good adaptivity in recapturing the virus-antibody interaction. It is proposed that the environmental antigenic substances help maintain the memory cell level and the corresponding neutralizing antibodies secreted by those memory cells. A broader application is also visualized in predicting the antibody protection time caused by a natural infection. Suitable binding antibodies and the presence of massive environmental antigenic substances would prolong the protection time against breakthrough infection. The model also displays excellent fitness and provides good explanations for antibody selection, antibody interference, and self-reinfection. It helps elucidate how our immune system efficiently develops neutralizing antibodies with good binding kinetics. It provides a reasonable explanation for the lower SARS-CoV-2 mortality in the population that was vaccinated with other vaccines. It is inferred that the best strategy for prolonging the vaccine protection time is not repeated inoculation but a directed induction of fast-binding antibodies. Eventually, this model will inform the future construction of an optimal mathematical model and help us fight against those infectious diseases.

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.

Graphene surface plasmon sensor for ultra-low-level SARS-CoV-2 detection

Precisely detecting the ultra-low-level severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is crucial. The detection mechanism must be sensitive, low-cost, portable, fast, and easy to operate to tackle coronavirus disease 19 (COVID-19). This work proposes a sensor exploiting graphene surface plasmon resonance to detect SARS-CoV-2. The graphene layer functionalized with angiotensin-converting enzyme 2 (ACE2) antibodies will help efficient adsorption of the SARS-CoV-2. In addition to the graphene layer, ultra-thin layers of novel two-dimensional materials tungsten disulfide (WS2), potassium niobate (KNbO3), and black phosphorus (BP) or blue phosphorus (BlueP) used in the proposed sensor will increase the light absorption to detect an ultra-low SARS-CoV-2 concentration. The analysis presented in this work shows that the proposed sensor will detect SARS-CoV-2 as small as ∼1 fM. The proposed sensor also offers a minimum sensitivity of 201 degrees/RIU, a figure-of-merit of 140 RIU-1, and enhanced binding kinetics of the SARS-CoV-2 to the sensor surface.

Analytical Method for Experimental Validation of Computer-Designed Antibody

In the computational design of antibodies, the interaction analysis between target antigen and antibody is an essential process to obtain feedback for validation and optimization of the design. Kinetic and thermodynamic parameters as well as binding affinity (K_D) allow for a more detailed evaluation and understanding of the molecular recognition. In this chapter, we summarize the conventional experimental methods which can calculate K_D value (ELISA, FP), analyze a binding activity to actual cells (FCM), and evaluate the kinetic and thermodynamic parameters (ITC, SPR, BLI), including high-throughput analysis and a recently developed experimental technique.

Native holdup (nHU) to measure binding affinities from cell extracts

Characterizing macromolecular interactions is essential for understanding cellular processes, yet most methods currently used to detect protein interactions from cells are qualitative. Here, we introduce the native holdup (nHU) approach to estimate equilibrium binding constants of protein interactions directly from cell extracts. Compared to other pull-down-based assays, nHU requires less sample preparation and can be coupled to any analytical methods as readouts, such as Western blotting or mass spectrometry. We use nHU to explore interactions of SNX27, a cargo adaptor of the retromer complex and find good agreement between in vitro affinities and those measured directly from cell extracts using nHU. We discuss the strengths and limitations of nHU and provide simple protocols that can be implemented in most laboratories.

Explicit versus implicit consideration of binding partners in protein-protein complex to elucidate intrinsic dynamics

The binding of many proteins to their protein partners is tightly regulated via control of their relative intrinsic dynamics during the binding process, a phenomenon which can in turn be modulated. Therefore, investigating the intrinsic dynamics of proteins is necessary to understand function in a comprehensive way. By intrinsic dynamics herein, we principally refer to the vibrational signature of a protein molecule popularly obtained from normal modes or essential modes. For normal modes, one often considers that the molecule under investigation is a collection of springs in a solvent-free or implicit-solvent medium. In the context of a protein-binding partner, the analysis of vibration of the target protein is often complicated due to molecular interaction within the complex. Generally, it is assumed that the isolated bound conformation of the target protein captures the implicit effect of the binding partner on the intrinsic dynamics, therefore suggesting that any influence of the partner molecule is also already integrated. Such an assumption allows large-scale studies of the conservation of protein flexibility. However, in cases where a partner protein directly influences the vibration of the target via critical contacts at the protein-protein interface, the above assumption falls short of providing a detailed view. In this review article, we discuss the implications of considering the dynamics of a protein in a protein-protein complex, as modelled implicitly and explicitly with methods dependent on elastic network models. We further propose how such an explicit consideration can be applied to understand critical protein-protein contacts that can be targeted in future studies.

Picomolar or beyond Limit of Detection Using Molecularly Imprinted Polymer-Based Electrochemical Sensors: A Review

Over the last decades, molecularly imprinted polymers (MIPs) have emerged as selective synthetic receptors that have a selective binding site for specific analytes/target molecules. MIPs are synthetic analogues to the natural biological antigen-antibody system. Owing to the advantages they exhibit, such as high stability, simple synthetic procedure, and cost-effectiveness, MIPs have been widely used as receptors/sensors for the detection and monitoring of a variety of analytes. Moreover, integrating electrochemical sensors with MIPs offers a promising approach and demonstrates greater potential over traditional MIPs. In this review, we have compiled the methods and techniques for the production of MIP-based electrochemical sensors along with the applications of reported MIP sensors for a variety of analytes. A comprehensive in-depth analysis of recent trends reported on picomolar (pM/10^-12 M)) and beyond picomolar concentration LOD (≥pM) achieved using MIPs sensors is reported. Finally, we discuss the challenges faced and put forward future perspectives along with our conclusion.

Spike protein of SARS-CoV-2 Omicron variant: An in-silico study evaluating spike interactions and immune evasion

Background

The fundamentals of the infectivity and immune evasion of the SARS-CoV-2 Omicron variant are not yet fully understood. Here, we carried out an in-silico study analyzing the spike protein, the protein electrostatic potential, and the potential immune evasion.

Methods

The analysis was based on the structure of the spike protein from two SARS-CoV-2 variants, the original Wuhan and the Botswana (Omicron). The full-length genome sequences and protein sequences were obtained from databanks. The interaction of the spike proteins with the human Angiotensin Converting Enzyme 2 (ACE2) receptor was evaluated through the open-source software. The Immune Epitope Database was used to analyze the potential immune evasion of the viruses.

Results

Our data show that the Omicron spike protein resulted in 37 amino acid changes. The physicochemical properties of the spike had changed, and the electrostatic potentials differed between both variants. This resulted in a decrease in protein interactions, which does not establish a greater interaction with the ACE2 receptor. These changes compromise key receptor-binding motif residues in the SARS-CoV-2 spike protein that interact with neutralizing antibodies and ACE2.

Conclusions

These mutations appear to confer enhanced properties of infectivity. The Omicron variant appears to be more effective at evading immune responses.

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.

Designing an Epitope-Based Peptide Vaccine Derived from RNA-Dependent RNA Polymerase (RdRp) against Dengue Virus Serotype 2

Dengue fever (DF) continues to be one of the tropical and subtropical health concerns. Its prevalence tends to increase in some places in these regions. This disease is caused by the dengue virus (DENV), which is transmitted through the mosquitoes Aedes aegypti and A. albopictus. The treatment of DF to date is only supportive and there is no definitive vaccine to prevent this disease. The non-structural DENV protein, RNA-dependent RNA Polymerase (RdRp), is involved in viral replication. The RdRp-derived peptides can be used in the construction of a universal dengue vaccine. These peptides can be utilized as epitopes to induce immunity. This study was an in silico evaluation of the affinity of the potential epitope for the universal dengue vaccine to dendritic cells and the bonds between the epitope and the dendritic cell receptor. The peptide sequence MGKREKKLGEFGKAKG generated from dengue virus subtype 2 (DENV-2) RdRp was antigenic, did not produce allergies, was non-toxic, and had no homology with the human genome. The potential epitope-based vaccine MGKREKKLGEFGKAKG binds stably to dendritic cell receptors with a binding free energy of −474,4 kcal/mol. This epitope is anticipated to induce an immunological response and has the potential to serve as a universal dengue virus vaccine candidate.

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.

Critical Design Factors for Electrochemical Aptasensors Based on Target-Induced Conformational Changes: The Case of Small-Molecule Targets

Nucleic-acid aptamers consisting in single-stranded DNA oligonucleotides emerged as very promising biorecognition elements for electrochemical biosensors applied in various fields such as medicine, environmental, and food safety. Despite their outstanding features, such as high-binding affinity for a broad range of targets, high stability, low cost and ease of modification, numerous challenges had to be overcome from the aptamer selection process on the design of functioning biosensing devices. Moreover, in the case of small molecules such as metabolites, toxins, drugs, etc., obtaining efficient binding aptamer sequences proved a challenging task given their small molecular surface and limited interactions between their functional groups and aptamer sequences. Thus, establishing consistent evaluation standards for aptamer affinity is crucial for the success of these aptamers in biosensing applications. In this context, this article will give an overview on the thermodynamic and structural aspects of the aptamer-target interaction, its specificity and selectivity, and will also highlight the current methods employed for determining the aptamer-binding affinity and the structural characterization of the aptamer-target complex. The critical aspects regarding the generation of aptamer-modified electrodes suitable for electrochemical sensing, such as appropriate bioreceptor immobilization strategy and experimental conditions which facilitate a convenient anchoring and stability of the aptamer, are also discussed. The review also summarizes some effective small molecule aptasensing platforms from the recent literature.

Symmetrization in the Calculation Pipeline of Gauss Function-Based Modeling of Hydrophobicity in Protein Structures

In this paper, we show, discuss, and compare the effects of symmetrization in two calculation subroutines of the Fuzzy Oil Drop model, a coarse-grained model of density of hydrophobicity in proteins. In the FOD model, an input structure is enclosed in an axis-aligned ellipsoid called a drop. Two profiles of hydrophobicity are then calculated for its residues: theoretical (based on the 3D Gauss function) and observed (based on pairwise hydrophobic interactions). Condition of the hydrophobic core is revealed by comparing those profiles through relative entropy, while analysis of their local differences allows, in particular, determination of the starting location for the search for protein–protein and protein–ligand interaction areas. Here, we improve the baseline workflow of the FOD model by introducing symmetry to the hydrophobicity profile comparison and ellipsoid bounding procedures. In the first modification (FOD–JS), Kullback–Leibler divergence is enhanced with its Jensen–Shannon variant. In the second modification (FOD-PCA), the molecule is optimally aligned with the axes of the coordinate system via principal component analysis, and the size of its drop is determined by the standard deviation of all its effective atoms, making it less susceptible to structural outliers. Tests on several molecules with various shapes and functions confirm that the proposed modifications improve the accuracy, robustness, speed, and usability of Gauss function-based modeling of the density of hydrophobicity in protein structures.

Unveiling the underpinnings of various non-conventional ELISA variants: a review article

INTRODUCTION

Enzyme-linked immunosorbent assay (ELISA) is a key bio-analytical technique used for the detection of a large array of antigenic substances of scientific, clinical, food safety, and environmental importance. The assay primarily involves capturing and detecting target analytes using specific antigen-antibody interactions. The wide usage of ELISA shoulders on its high specificity and reproducibility. Notwithstanding, the conventional microwell plate-based format of ELISA has some major drawbacks, such as long assay time (4 - 18 h), large sample volumes requirement (100 - 200 μL), lack of multiplicity, and burdensome procedures that limit its utility in rapid and affordable diagnostics.

AREAS COVERED

Here, we reviewed microfluidic-ELISA, paper-ELISA, aptamer-ELISA, and those based on novel incubation such as heat-ELISA, pressure-ELISA, microwave-ELISA, and sound-ELISA. Further, the current trends and future prospects of these ELISA protocols in clinical diagnostics are discussed.

EXPERT OPINION

The reviewed non-conventional ELISA formats are relatively rapid, require low reagent volumes, are multiplexable, and could be performed in a low-cost setup. In our opinion, these non-conventional variants of ELISA are on a par with the conventional format for clinical diagnostics and fundamental biological research and hold added clinical translational potential for quick, inexpensive, and convenient measurements.

Repertoire-scale measures of antigen binding

Antibodies and T cell receptors (TCRs) are the fundamental building blocks of adaptive immunity. Repertoire-scale functionality derives from their epitope-binding properties, just as macroscopic properties like temperature derive from microscopic molecular properties. However, most approaches to repertoire-scale measurement, including sequence diversity and entropy, are not based on antibody or TCR function in this way. Thus, they potentially overlook key features of immunological function. Here we present a framework that describes repertoires in terms of the epitope-binding properties of their constituent antibodies and TCRs, based on analysis of thousands of antibody-antigen and TCR-peptide-major-histocompatibility-complex binding interactions and over 400 high-throughput repertoires. We show that repertoires consist of loose overlapping classes of antibodies and TCRs with similar binding properties. We demonstrate the potential of this framework to distinguish specific responses vs. bystander activation in influenza vaccinees, stratify cytomegalovirus (CMV)-infected cohorts, and identify potential immunological "super-agers." Classes add a valuable dimension to the assessment of immune function.

ppdx: Automated modeling of protein-protein interaction descriptors for use with machine learning

This paper describes ppdx, a python workflow tool that combines protein sequence alignment, homology modeling, and structural refinement, to compute a broad array of descriptors for characterizing protein-protein interactions. The descriptors can be used to predict various properties of interest, such as protein-protein binding affinities, or inhibitory concentrations (IC₅₀ ), using approaches that range from simple regression to more complex machine learning models. The software is highly modular. It supports different protocols for generating structures, and 95 descriptors can be currently computed. More protocols and descriptors can be easily added. The implementation is highly parallel and can fully exploit the available cores in a single workstation, or multiple nodes on a supercomputer, allowing many systems to be analyzed simultaneously. As an illustrative application, ppdx is used to parametrize a model that predicts the IC₅₀ of a set of antigens and a class of antibodies directed to the influenza hemagglutinin stalk.

An Electrostatically-steered Conformational Selection Mechanism Promotes SARS-CoV-2 Spike Protein Variation

After two years since the outbreak, the COVID-19 pandemic remains a global public health emergency. SARS-CoV-2 variants with substitutions on the spike (S) protein emerge increasing the risk of immune evasion and cross-species transmission. Here, we analyzed the evolution of the S protein as recorded in 276,712 samples collected before the start of vaccination efforts. Our analysis shows that most variants destabilize the S protein trimer, increase its conformational heterogeneity and improve the odds of the recognition by the host cell receptor. Most frequent substitutions promote overall hydrophobicity by replacing charged amino acids, reducing stabilizing local interactions in the unbound S protein trimer. Moreover, our results identify "forbidden" regions that rarely show any sequence variation, and which are related to conformational changes occurring upon fusion. These results are significant for understanding the structure and function of SARS-CoV-2 related proteins which is a critical step in vaccine development and epidemiological surveillance.

In Silico Approach in the Evaluation of Pro-Inflammatory Potential of Polycyclic Aromatic Hydrocarbons and Volatile Organic Compounds through Binding Affinity to the Human Toll-Like Receptor 4

Polycyclic aromatic hydrocarbons (PAHs) and volatile organic compounds (VOCs) are widespread across the globe, existing in the environment in complex mixtures potentially capable of initiating respiratory illnesses. Here, we use an in silico approach to evaluate the potential pro-inflammatory effects of various carcinogenic PAHs and VOCs through their binding affinity towards the human toll-like receptor 4 (TLR4). For receptors and ligands, RCSB Protein Data Bank and PubChem were used in obtaining their 3D structures, respectively. Autodock Vina was utilized to obtain the best docking poses and binding affinities of each PAH and VOC. Out of the 14 PAHs included in this study, indeno(1,2,3-cd)pyrene, benzo(ghi)perylene, and benzo[a]pyrene had the highest binding affinity values of −10, −9, and −8.9 kcal/mol, respectively. For the VOCs, out of the 10 compounds studied, benzene, 1,4-dichlorobenzene, and styrene had the highest binding affinity values of −3.6, −3.9, and −4.6 kcal/mol, respectively. Compounds with higher affinity than LPS (−4.1 kcal/com) could potentially induce inflammation, while compounds with lower affinity would be less likely to induce an inflammatory response. Meanwhile, molecular dynamics simulation and RMSF statistical analysis proved that the protein, TLR4, stably preserve its conformation despite ligand interactions. Overall, the structure of the TLR4 was considered inflexible.

Artificial intelligence channelizing protein-peptide interactions pipeline for host-parasite paradigm in IL-10 and IL-12 reciprocity by SHP-1

Identification of molecular targets in any cellular phenomena is a challenge and a path that one endeavors upon independently. We have identified a phosphatase SHP-1 as a point of intervention of IL-10 and IL-12 reciprocity in leishmaniasis. The therapeutic model that we have developed uniquely targets this protein but the pipeline in general can be used by the researchers for their unique targets. Naturally occurring peptides are well known for their biochemical participation in cellular functions hence we were motivated to use this uniqueness of physico-chemical properties of peptides conferred by amino acids through machine learning to channelize a mode of therapeutic exploration in infectious disease. Using computational approaches, we identified high order sequence conservation and similarity in SHP-1 sequence which was also evolutionarily conserved, complete structure of Mouse SHP-1 was predicted and validated, a unique motif of the same was identified against which library of synthetic peptides were designed and validated followed by screening the library by docking them with MuSHP-1 protein structure. Our findings showed 3 peptides had high binding affinity and in future can be validated using cell based and in vivo assays.