Analysis of Protein-Protein Interactions for Intermolecular Bond Prediction

Bitter taste receptors establish a stable binding affinity with the SARS-CoV-2-spike 1 protein akin to ACE2

COVID-19 is caused by the highly contagious SARS-CoV-2 virus, which originated in Wuhan, China, resulting in the highest worldwide mortality rate. Gustatory dysfunction is common among individuals infected with the Wild-type Wuhan strain. However, there are no reported cases of gustatory dysfunction among patients infected with the mutant delta variant. The reason behind this remains elusive to date. This in-silico-based study aims to unravel this clinical factor by evaluating the overall binding affinity of predominant bitter taste receptors associated with gustatory function (T2R-4, 10, 14, 19, 31, 38, 43, and 46) with the Receptor Binding Domain (RBD) of spike 1 (S1) protein of Wuhan (Wild)/delta-SARS-CoV-2 (mut1-T478K; mut2-E484K) variants. Based on docking and MM/PBSA free binding energy scores, the Wild RBD showed a stronger interaction with T2R-46 compared to the ACE2 protein. However, both delta variant mutants (mut1 and mut2) could not establish a stronger binding affinity with bitter taste receptor proteins, except for T2R-43 against mut1. In conclusion, the delta variants could not establish a better binding affinity with bitter taste receptors, contradicting the Wild variant that determines the severity of gustatory dysfunction among patients exposed to the delta and Wild SARS-CoV-2 variants. The study's inference also proposes T2R-46 as an alternate binding receptor target for RBD-S1 of Wild SARS-CoV-2, augmenting its virulence in all functional organs with compromised α-gustducin interaction and bitter sensitization. This in-silico-based study needs further wet-lab-based validation for a better understanding of the role of T2R-46-based viral entry in the human host.

Biochemical and physiological characterization of Aedes aegypti midgut chymotrypsin

The Aedes aegypti mosquito is a vector of dengue, Zika, and chikungunya. The mosquito's reliance on blood facilitates the transmission of these viral pathogens to humans. Digestion of blood proteins depends on the biphasic expression of serine proteases, with trypsin-like activity contributing to most of the activity in the midgut. Other proteases found (serine collagenase- and chymotrypsin-like) are thought to contribute to digestion, but their roles are largely understudied. Thus, elucidating the activity and specific roles of all midgut proteases will help understand the complexity of the digestion process and help validate them as potential targets for the development of a new vector control strategy. Herein, we focused on characterizing the activity profile and role of Ae. aegypti chymotrypsin (AaCHYMO). Knockdown studies resulted in elimination and significant reduction of chymotrypsin-like activity in blood fed midgut extracts, while in vitro fluorescent and blood protein digestion assays revealed important substrate specificity differences. Interestingly, knockdown of AaCHYMO did not impact fecundity, indicating the presence of an intricate network of proteases working collectively to degrade blood proteins. Further, knockdown of the ecdysone receptor (EcR) led to a decrease in overall AaCHYMO expression and activity in the mosquito, which may play an important regulatory role.

Progress on the development of prediction tools for detecting disease causing mutations in proteins

Proteins are involved in a variety of functions in living organisms. The mutation of amino acid residues in a protein alters its structure, stability, binding, and function, with some mutations leading to diseases. Understanding the influence of mutations on protein structure and function help to gain deep insights on the molecular mechanism of diseases and devising therapeutic strategies. Hence, several generic and disease-specific methods have been proposed to reveal pathogenic effects on mutations. In this review, we focus on the development of prediction methods for identifying disease causing mutations in proteins. We briefly outline the existing databases for disease-causing mutations, followed by a discussion on sequence- and structure-based features used for prediction. Further, we discuss computational tools based on machine learning, deep learning and large language models for detecting disease-causing mutations. Specifically, we emphasize the advances in predicting hotspots and mutations for targets involved in cancer, neurodegenerative and infectious diseases as well as in membrane proteins. The computational resources including databases and algorithms understanding/predicting the effect of mutations will be listed. Moreover, limitations of existing methods and possible improvements will be discussed.

Biochemical and physiological characterization of Aedes aegypti midgut chymotrypsin

The Aedes aegypti mosquito is a vector of dengue, Zika, and chikungunya. The mosquito's reliance on blood facilitates the transmission of these viral pathogens to humans. Digestion of blood proteins depends on the biphasic expression of serine proteases, with trypsin-like activity contributing to most of the activity in the midgut. Other proteases found (serine collagenase- and chymotrypsin-like) are thought to contribute to digestion, but their roles are largely understudied. Thus, elucidating the activity and specific roles of all midgut proteases will help understand the complexity of the digestion process and help validate them as potential targets for the development of a new vector control strategy. Herein, we focused on characterizing the activity profile and role of Ae. aegypti chymotrypsin (AaCHYMO). Knockdown studies resulted in elimination and significant reduction of chymotrypsin-like activity in blood fed midgut extracts, while in vitro fluorescent and blood protein digestion assays revealed important substrate specificity differences. Interestingly, knockdown of AaCHYMO did not impact fecundity, indicating the presence of an intricate network of proteases working collectively to degrade blood proteins. Further, knockdown of the ecdysone receptor (EcR) led to a decrease in overall AaCHYMO expression and activity in the mosquito, which may play an important regulatory role.

Rapid transformation of nanobodies affinity based on AlphaFold2's high-accuracy predictions and interaction analysis for enrofloxacin detection in coastal fish

High-affinity antibodies are crucial in biosensors, disease diagnostics, therapeutic drug development, and immunological analysis, making the enhancement of antibody affinity a key research focus within the field. Computer-aided design is recognized as a time-saving and labor-efficient method for nanobodies in vitro affinity maturation. Compared to experimental mutagenesis techniques, it is advantageous due to the elimination of the need for laborious library construction and screening processes. However, these approaches are constrained by structural prediction since inaccuracy in structure could readily result in maturation failures. Herein, a novel nanobodies modification method for in vitro affinity maturation, utilizing the high accuracy prediction of AlphaFold2, was employed to rapidly transform a low affinity nanobody against enrofloxacin (ENR) into one with high affinity. The molecular docking results revealed a 1.5- to 2.5-fold increase in the number of noncovalent interactions of modified nanobodies, accompanied by a reduction in binding free energy ranging from 14.1 to 62.6%. The evaluation results from ELISA and BLI indicated that the affinity of the modified nanobodies had been enhanced by 6.2-91.6 times compared to the template nanobody. Furthermore, the modified nanobodies were employed for the detection of ENR-spiked coastal fish samples. In summary, this research proposed a nanobodies modification method from a new perspective, endowing its great application potential in biosensors, food safety, and environmental monitoring.

Protein allosteric site identification using machine learning and per amino acid residue reported internal protein nanoenvironment descriptors

Allosteric regulation plays a crucial role in modulating protein functions and represents a promising strategy in drug development, offering enhanced specificity and reduced toxicity compared to traditional active site inhibition. Existing computational methods for predicting allosteric sites on proteins often rely on static protein surface pocket features, normal mode analysis or extensive molecular dynamics simulations encompassing both the protein function modulator and the protein itself. In this study, we introduce an innovative methodology that employs a per amino acid residue classifier to distinguish allosteric site-forming residues (AFRs) from non-allosteric, or free residues (FRs). Our model, STINGAllo, exhibits robust performance, achieving Distance Center Center (DCC) success rate when all AFRs were predicted within pockets identified by FPocket, overall DCC, F1 score and a Matthews correlation coefficient (MCC) of 78 %, 60 %, 64 % and 64 % respectively. Furthermore, we identified key descriptors that characterize the internal protein nanoenvironment of AFRs, setting them apart from FRs. These descriptors include the sponge effect, distance to the protein centre of geometry (cg), hydrophobic interactions, electrostatic potentials, eccentricity, and graph bottleneck features.

In Silico Design of Peptide Inhibitors Targeting HER2 for Lung Cancer Therapy

BACKGROUND/OBJECTIVES

Human epidermal growth factor receptor 2 (HER2) is overexpressed in several malignancies, such as breast, gastric, ovarian, and lung cancers, where it promotes aggressive tumor proliferation and unfavorable prognosis. Targeting HER2 has thus emerged as a crucial therapeutic strategy, particularly for HER2-positive malignancies. The present study focusses on the design and optimization of peptide inhibitors targeting HER2, utilizing machine learning to identify and enhance peptide candidates with elevated binding affinities. The aim is to provide novel therapeutic options for malignancies linked to HER2 overexpression.

METHODS

This study started with the extraction and structural examination of the HER2 protein, succeeded by designing the peptide sequences derived from essential interaction residues. A machine learning technique (XGBRegressor model) was employed to predict binding affinities, identifying the top 20 peptide possibilities. The candidates underwent further screening via the FreeSASA methodology and binding free energy calculations, resulting in the selection of four primary candidates (pep-17, pep-7, pep-2, and pep-15). Density functional theory (DFT) calculations were utilized to evaluate molecular and reactivity characteristics, while molecular dynamics simulations were performed to investigate inhibitory mechanisms and selectivity effects. Advanced computational methods, such as QM/MM simulations, offered more understanding of peptide-protein interactions.

RESULTS

Among the four principal peptides, pep-7 exhibited the most elevated DFT values (-3386.93 kcal/mol) and the maximum dipole moment (10,761.58 Debye), whereas pep-17 had the lowest DFT value (-5788.49 kcal/mol) and the minimal dipole moment (2654.25 Debye). Molecular dynamics simulations indicated that pep-7 had a steady binding free energy of -12.88 kcal/mol and consistently bound inside the HER2 pocket during a 300 ns simulation. The QM/MM simulations showed that the overall total energy of the system, which combines both QM and MM contributions, remained around -79,000 ± 400 kcal/mol, suggesting that the entire protein-peptide complex was in a stable state, with pep-7 maintaining a strong, well-integrated binding.

CONCLUSIONS

Pep-7 emerged as the most promising therapeutic peptide, displaying strong binding stability, favorable binding free energy, and molecular stability in HER2-overexpressing cancer models. These findings suggest pep-7 as a viable therapeutic candidate for HER2-positive cancers, offering a potential novel treatment strategy against HER2-driven malignancies.

Bioinformatics design of peptide binding to the human cardiac troponin I (cTnI) in biosensor development for myocardial infarction diagnosis

Cardiovascular disease has reached a mortality rate of 470,000 patients each year. Myocardial infarction accounts for 49.2% of these deaths, and the cTnI protein is a crucial target in diagnosing myocardial infarction. A peptide-based bioreceptor design using a computational approach is a good candidate to be developed for a rapid, effective, and selective detection method for cTnI although it is still lacking in study. Hence, to address the scientific gap, we develop a new candidate peptide for the cTnI biosensor by bioinformatics method and present new computational approaches. The sequential point mutations were made to the selected peptide to increase its stability and affinity for cTnI. Next, molecular docking was performed to select the mutated peptide, and one of the best results was subjected to the molecular dynamics simulation. Finally, the results showed that the best peptide showed the lowest affinity and good stability among other mutated peptide designs for interacting with the cTnI protein. In addition, the peptide has been tested to have a higher specificity towards cTnI than its major isomer, sTnI, through molecular docking and molecular dynamics simulation. Therefore, the peptide is considered a good potential bioreceptor for diagnosing myocardial infarction diseases.

Polymers for Disrupting Protein-Protein Interactions: Where Are We and Where Should We Be?

Protein-protein interactions (PPIs) are central to the cellular signaling and regulatory networks that underlie many physiological and pathophysiological processes. It is challenging to target PPIs using traditional small molecule or peptide-based approaches due to the frequent lack of well-defined binding pockets at the large and flat PPI interfaces. Synthetic polymers offer an opportunity to circumvent these challenges by providing unparalleled flexibility in tuning their physiochemical properties to achieve the desired binding properties. In this review, we summarize the current state of the field pertaining to polymer-protein interactions in solution, highlighting various polyelectrolyte systems, their tunable parameters, and their characterization. We provide an outlook on how these architectures can be improved by incorporating sequence control, foldability, and machine learning to mimic proteins at every structural level. Advances in these directions will enable the design of more specific protein-binding polymers and provide an effective strategy for targeting dynamic proteins, such as intrinsically disordered proteins.

Is the TCA cycle malate dehydrogenase-citrate synthase metabolon an illusion?

This review discusses the intriguing yet controversial concept of metabolons, focusing on the malate dehydrogenase-citrate synthase (MDH-CISY) metabolon as a model. Metabolons are multienzyme complexes composed of enzymes that catalyze sequential reactions in metabolic pathways. Metabolons have been proposed to enhance metabolic pathway efficiency by facilitating substrate channeling. However, there is skepticism about the presence of metabolons and their functionality in physiological conditions in vivo. We address the skepticism by reviewing compelling evidence supporting the existence of the MDH-CISY metabolon and highlighting its potential functions in cellular metabolism. The electrostatic interaction between MDH and CISY and the intermediate oxaloacetate, channeled within the metabolon, has been demonstrated using various experimental techniques, including protein-protein interaction assays, isotope dilution studies, and enzyme coupling assays. Regardless of the wealth of in vitro evidence, further validation is required to elucidate the functionality of MDH-CISY metabolons in living systems using advanced structural and spatial analysis techniques.

Co-localization and co-expression of Olfml3 with Iba1 in brain of mice

Olfml3 is a microglia-specific protein whose role in neuroinflammation is elusive. In silico analysis was conducted to characterize the Olfml3 protein, followed by molecular docking and MD simulation to check possible interaction with Iba1. Further, expression and co-localization analysis was performed in the LPS-induced neuroinflammatory mice brains. Results suggest that Olfml3 physically interacts with Iba1. Olfml3 and Iba1 expression increases during neuroinflammation in mice brains. Olfml3 was observed to co-localize with Iba1, and the number of Olfml3 and Iba1 dual-positive cells increased in the brain of the neuroinflammatory mice model. Thus, Olfml3 could potentially participate in microglia functions by interacting with Iba1.

Cold Spot SCANNER: Colab Notebook for predicting cold spots in protein-protein interfaces

BACKGROUND

Protein-protein interactions (PPIs) are conveyed through binding interfaces or surface patches on proteins that become buried upon binding. Structural and biophysical analysis of many protein-protein interfaces revealed certain unique features of these surfaces that determine the energetics of interactions and play a critical role in protein evolution. One of the significant aspects of binding interfaces is the presence of binding hot spots, where mutations are highly deleterious for binding. Conversely, binding cold spots are positions occupied by suboptimal amino acids and several mutations in such positions could lead to affinity enhancement. While there are many software programs for identification of hot spot positions, there is currently a lack of software for cold spot detection.

RESULTS

In this paper, we present Cold Spot SCANNER, a Colab Notebook, which scans a PPI binding interface and identifies cold spots resulting from cavities, unfavorable charge-charge, and unfavorable charge-hydrophobic interactions. The software offers a Py3DMOL-based interface that allows users to visualize cold spots in the context of the protein structure and generates a zip file containing the results for easy download.

CONCLUSIONS

Cold spot identification is of great importance to protein engineering studies and provides a useful insight into protein evolution. Cold Spot SCANNER is open to all users without login requirements and can be accessible at: https://colab.

RESEARCH

google.com/github/sagagugit/Cold-Spot-Scanner/blob/main/Cold_Spot_Scanner.ipynb .

In vitro combination effects of plant-derived quercetin with synthetic bicalutamide on prostate cancer and normal cell lines: in silico comparison

Prostate cancer is the second most frequent and the fifth greatest cause of death in men. Although diet has been connected to the prevalence of cancer in addition to other factors, the relation between cancer and prevention is weak. Treatment options are at risk due to cell resistance. To identify new combinations, we tried plant-derived quercetin with bicalutamide on cell lines. To determine the cytotoxicity and apoptotic potential of plant-derived quercetin and its combination, MTT [3-(4,5-dimethylthiazol-2-yl)-2,5 diphenyl tetrazolium bromide] and dual stain assays were performed. In silico protein-ligand interaction was performed to support the in vitro findings. A thin layer, column, and high-performance chromatography were used to purify quercetin along with an authentic sample. In the cytotoxic study, quercetin was minimized by 80% similar to bicalutamide and a combination of quercetin and bicalutamide by 50% when compared to controls by 2%. Quercetin and bicalutamide showed a similar binding affinity for androgen receptors (9.7 and 9.8), hub genes (10.8 and 10.0), and a few other PCa-related genes (9.4 and 9.1). We propose to conclude that the combination of quercetin plus bicalutamide can be used for chemotherapy if additional in vivo studies are conducted. The intake of foods high in polyphenolic compounds can help to prevent prostate cancer. Examination of quercetin on several cell lines will provide a definite conclusion to combat cancers.