Structure Of Target Protein Research Articles

In silico screening of protein-binding peptides with an application to developing peptide inhibitors against antibiotic resistance

Abstract The field of therapeutic peptides is experiencing a surge, fueled by their advantageous features. These include predictable metabolism, enhanced safety profile, high selectivity, and reduced off-target effects compared to small molecule drugs. Despite progress in addressing limitations associated with peptide drugs, a significant bottleneck remains: the absence of a large-scale in silico screening method for a given protein target structure. Such methods have proven invaluable in accelerating small molecule drug discovery. The high flexibility of peptide structures and the large diversity of peptide sequences greatly hinder the development of urgently needed computational methods. Here, we report a method called MDockPeP2_VS to address these challenges. It integrates molecular docking with structural conservation between protein folding and protein-peptide binding. Briefly, we discovered that when the interfacial residues are conserved, a sequence fragment derived from a monomeric protein exhibits a high propensity to bind a target protein with a similar conformation. This valuable insight significantly reduces the search space for peptide conformations, resulting in a substantial reduction in computational time and making in silico peptide screening practical. We applied MDockPeP2_VS to develop peptide inhibitors targeting the TEM-1 β-lactamase of E. coli, a key mechanism behind antibiotic resistance in gram-negative bacteria. Among the top ten peptides selected from in silico screening, TF7 (KTYLAQAAATG) showed significant inhibition of β-lactamase activity with a Ki value of 1.37 ± 0.37 µM. This fully automated, large-scale structure-based in silico peptide screening software is available for free download at https://zougrouptoolkit.missouri.edu/mdockpep2_vs/download.html.

In silico analysis of Arbacia lixula-derived peptides and plasmid construction for recombinant anti-aging therapies

Skin aging is one of the degenerative processes influenced by tyrosinase, elastase, collagenase, hyaluronidase, and matrix metalloproteinase-9 (MMP9) activity. One promising avenue for discovering antiaging therapeutics is the peptides from the Arbacia lixula spine. The aim of this study was to explore the potential of peptides from A. lixula spine as a multitarget inhibitor for recombinant antiaging therapies through in silico approaches. The crystal structure of peptides previously identified in A. lixula spine was visualized using the UCSF Chimera. The protein data bank (PDB) database was used to obtain the crystal structures of protein targets. The webservers Innovagen, AllerTop, and ToxinPred were utilized to predict the peptide's water solubility, toxicity, and allergenicity. MOE application was used to prepare all ligands and proteins, molecular docking, and visualization. Molecular dynamics simulations were carried out on the protein-ligand complexes on Yasara Dynamics application. The Benchling website was used to perform virtual electrophoresis and reconstruct the recombinant plasmid (Psb1c3). Based on the molecular docking results, peptide REGSPDLLE has the potential as a multitarget inhibitor of tyrosinase (-9.07 kcal/mol), hyaluronidase (-10.57 kcal/mol), elastase (-9.32 kcal/mol), collagenase (-10.57 kcal/mol), and MMP9 (-10.43 kcal/mol). Peptide REGSPDLLE was selected due to its strong binding affinity on the active site of each target protein and exhibits non-toxic, non-allergenic, and good water-soluble as indicated by Support Vector Machine score <0. Molecular dynamics simulations confirmed stable interactions with receptor proteins. Peptide REGSPDLLE was successfully inserted into the recombinant pSB1C3 plasmid, confirmed by virtual electrophoresis with bands at ~2000 bp and ~150 bp. Further in vitro and in vivo studies are necessary to verify the anti-aging efficacy of peptide REGSPDLLE.

Highly Tunable, Nanomaterial-Functionalized Structural Templating of Intracellular Protein Structures Within Biological Species.

Inside living organisms, proteins are self-assembled into diverse 3D structures optimized for specific functions. This structure-function relationship can be exploited to synthesize functional materials through biotemplating and depositing functional materials onto protein structures. However, conventional biotemplating faces limitations due to the predominantly intracellular existence of proteins and associated challenges in achieving tunability while preserving functionality. In this study, Conversion to Advanced Materials via labeled Biostructures (CamBio), an integrated biotemplating platform that involves labeling target protein structures with antibodies followed by the growth of functional materials, ensuring outstanding nanostructure tunability is proposed. Protein-derived plasmonic nanostructures created by CamBio can serve as precise quantitative tools for assessing target species is demonstrated. The assessment is achieved through highly tunable and efficient surface-enhanced Raman spectroscopy (SERS). CamBio enables the formation of dense nanogap hot spots among metal nanoparticles, templated by diverse fibrous proteins comprising densely repeated monomers. Furthermore, iterative antibody labeling strategies to adjust the antibody density surrounding targets, amplifying the number of nanogaps and consequently improving SERS performance are employed. Finally, cell-patterned substrates and whole meat sections as SERS substrates, confirming their easily accessible, cost-effective, scalable preparation capabilities and dimensional tunability are incorporated.

Impairing protein-protein interactions in an essential tRNA modification complex: An innovative antimicrobial strategy against Pseudomonas aeruginosa.

Protein-protein interactions (PPIs) have been recognized as a promising target for the development of new drugs, as proved by the growing number of PPI modulators reaching clinical trials. In this context, peptides represent a valid alternative to small molecules, owing to their unique ability to mimic the target protein structure and interact with wider surface areas. Among the possible fields of interest, bacterial PPIs represent an attractive target to face the urgent necessity to fight antibiotic resistance. Growing attention has been paid to the YgjD/YeaZ/YjeE complex responsible for the essential t6A37 tRNA modification in bacteria. We previously identified an α-helix on the surface of Pseudomonas aeruginosa YeaZ, crucial for the YeaZ-YeaZ homodimer formation and the conserved YeaZ-YgjD interactions. Herein, we present our studies for impairing the PPIs involved in the formation of the YeaZ dimers through synthetic peptide derivatives of this helical moiety, both in vitro with purified components and on P. aeruginosa cells. Our results proved the possibility of targeting those PPIs which are usually essential for protein functioning and thus are refractory to mutational changes and antibiotic resistance development.

Computational investigations of potential inhibitors of monkeypox virus envelope protein E8 through molecular docking and molecular dynamics simulations

The World Health Organization (WHO) has declared the monkeypox outbreak a public health emergency, as there is no specific therapeutics for monkeypox virus (MPXV) disease. This study focused on docking various commercial drugs and plant-derived compounds against the E8 envelope protein crucial for MPXV attachment and pathogenesis. The target protein structure was modeled based on the vaccinia virus D8L protein. Notably, maraviroc and punicalagin emerged as potential ligands, with punicalagin exhibiting higher binding affinity (− 9.1 kcal/mol) than maraviroc (− 7.8 kcal/mol). Validation through 100 ns molecular dynamics (MD) simulations demonstrated increased stability of the E8-punicalagin complex, with lower RMSD, RMSF, and Rg compared to maraviroc. Enhanced hydrogen bonding, lower solvent accessibility, and compact motions also attributed to higher binding affinity and stability of the complex. MM-PBSA calculations revealed van der Waals, electrostatic, and non-polar solvation as principal stabilizing energies. The binding energy decomposition per residue favored stable interactions between punicalagin and the protein’s active site residues (Arg20, Phe56, Glu228, Tyr232) compared to maraviroc. Overall study suggests that punicalagin can act as a potent inhibitor against MPXV. Further research and experimental investigations are warranted to validate its efficacy and safety.

PungentDB: Bridging traditional Chinese medicine of medicine food homology and modern food flavor chemistry

Merging traditional Chinese medicine's (TCM) principles of medicine-food homology with modern flavor chemistry, this research unveils PungentDB (http://www.pungentdb.org.cn/home), a database documenting 205 unique pungent flavor compounds from 231 TCMs. It provides detailed insights into their chemical attributes, biological targets (including IC50/EC50 values), and molecular structures (2D/3D), enriched with visualizations of target organ distribution and protein structures, exploring the pungent flavor space with the help of a feature-rich visual interface. This collection, derived from over 3249 sources and highlighting 9129 targets, delves into the compounds' unique pungent flavors—taste, aroma, and thermal sensations—and their interaction with taste and olfactory receptors. PungentDB bridges ancient wisdom and culinary innovation, offering a nuanced exploration of pungent flavors' role in enhancing food quality, safety, and sensory experiences. This initiative propels flavor chemistry forward, serving as a pivotal resource for food science advancement and the innovative application of pungent flavors.

Combining bioinformatics, network pharmacology and artificial intelligence to predict the target genes of S-ketamine for treating major depressive disorder.

Ketamine has received attention owing to its rapid and long-lasting antidepressant effects; however, its clinical application is restricted by its addictiveness and adverse effects. S-ketamine, which is the S-enantiomer of ketamine, is considered safer and better tolerated by patients than ketamine. This study aimed to identify the key gene targets and potential signalling pathways associated with the mechanism of S-ketamine in major depressive disorder (MDD) treatment. The GSE98793 dataset was extracted from the Gene Expression Omnibus database, and differentially expressed genes were identified in blood samples from patients with MDD and healthy individuals. The hub genes among the differentially expressed genes were identified and enrichment analysis was performed. The therapeutic targets and related signalling pathways of S-ketamine in MDD treatment were analysed. The 3D structures of the target proteins were predicted using AlphaFold2, and molecular docking was performed to verify whether S-ketamine could be successfully docked to the predicted targets. A quantitative polymerase chain reaction was performed to determine the effect of ketamine on the screened targets. Among 228 target genes annotated using pharmacophore target gene analysis, 3 genes were identified and 2 therapeutic signalling pathways were discovered. S-ketamine exerts downregulatory effects on TGM2 and HSP90AB1 expression but exerts an up-regulatory effect on ADORA3 expression. The protein structures of the therapeutic targets were successfully predicted using AlphaFold2. S-ketamine may alleviate depression by targeting specific genes, including TGM2, HSP90AB1 and ADORA3, as well as signalling pathways, including the gonadotropin-releasing hormone and relaxin signalling pathways.

A tethering mechanism underlies Pin1-catalyzed proline cis-trans isomerization at a noncanonical site.

The prolyl isomerase Pin1 catalyzes the cis-trans isomerization of proline peptide bonds, a non-covalent post-translational modification that influences cellular and molecular processes, including protein-protein interactions. Pin1 is a two-domain enzyme containing a WW domain that recognizes phosphorylated serine/threonine-proline (pS/pT-P) canonical motifs and an enzymatic PPIase domain that catalyzes proline cis-trans isomerization of pS/pT-P motifs. Here, we show that Pin1 uses a tethering mechanism to bind and catalyze proline cis-trans isomerization of a noncanonical motif in the disordered N-terminal activation function-1 (AF-1) domain of the human nuclear receptor PPARγ. NMR reveals multiple Pin1 binding regions within the PPARγ AF-1, including a canonical motif that when phosphorylated by the kinase ERK2 (pS112-P113) binds the Pin1 WW domain with high affinity. NMR methods reveal that Pin1 also binds and accelerates cis-trans isomerization of a noncanonical motif containing a tryptophan-proline motif (W39-P40) previously shown to be involved in an interdomain interaction with the C-terminal ligand-binding domain (LBD). Cellular transcription studies combined with mutagenesis and Pin1 inhibitor treatment reveal a functional role for Pin1-mediated acceleration of cis-trans isomerization of the W39-P40 motif. Our data inform a refined model of the Pin1 catalytic mechanism where the WW domain binds a canonical pS/T-P motif and tethers Pin1 to the target, which enables the PPIase domain to exert catalytic cis-trans isomerization at a distal noncanonical site.

De Novo Design of Peptide Binders to Conformationally Diverse Targets with Contrastive Language Modeling.

Designing binders to target undruggable proteins presents a formidable challenge in drug discovery, requiring innovative approaches to overcome the lack of putative binding sites. Recently, generative models have been trained to design binding proteins via three-dimensional structures of target proteins, but as a result, struggle to design binders to disordered or conformationally unstable targets. In this work, we provide a generalizable algorithmic framework to design short, target-binding linear peptides, requiring only the amino acid sequence of the target protein. To do this, we propose a process to generate naturalistic peptide candidates through Gaussian perturbation of the peptidic latent space of the ESM-2 protein language model, and subsequently screen these novel linear sequences for target-selective interaction activity via a CLIP-based contrastive learning architecture. By integrating these generative and discriminative steps, we create a Peptide Prioritization via CLIP (PepPrCLIP) pipeline and validate highly-ranked, target-specific peptides experimentally, both as inhibitory peptides and as fusions to E3 ubiquitin ligase domains, demonstrating functionally potent binding and degradation of conformationally diverse protein targets in vitro. Overall, our design strategy provides a modular toolkit for designing short binding linear peptides to any target protein without the reliance on stable and ordered tertiary structure, enabling generation of programmable modulators to undruggable and disordered proteins such as transcription factors and fusion oncoproteins.

Identification of Potential Lead Compounds against BCL-2 through In-silico Screening of Phytochemicals of Nigella sativa and Cuscuta reflexa for Oral Squamous Cell Carcinoma Management

Significance of the study: In silico analysis is frequently used in physicochemical characterisation, the discovery and improvement of novel compounds with affinity to a target, and the elucidation of absorption, distribution, metabolism, excretion, and toxicity features. Aim and Objective: The aim of this study is to assess the efficacy of Nigella sativa and Cuscuta reflexa in targeting Bcl2-antiapoptotic protein in Oral Squamous Cell Carcinoma through in silico analysis. Materials and Methods: The present study was designed to formulate a drug against Oral Squamous cell carcinoma against the target protein Bcl-2. Protein Database (PDB) was used to select and analyse the protein. Based on the literature search, the original molecules selected were thymoquinone, tannin pyrogallol, Cuscutin, kaempferol, nigellicine and the ligand structures were obtained ZINC15 database. Target protein structure was recognised through Protein sequence from PB (Protein Data Bank). Swiss ADME was used for assessing the properties of the molecules. Twenty new molecules were identified from zinc pharmer and docking was done for all the 20 using Swiss dock. Binding energy was assessed and compared with the original molecules.ZINC73690300 and ZINC73690304 had better binding energy than that of original molecules. Both molecules can be potential candidates for Bcl2 inhibition to prevent OSCC. Results: ZINC73690300 and ZINC73690304 had better binding energy than that of the original molecules. Both molecules can be potential candidates for Bcl-2 inhibition to prevent OSCC. Conclusion: The present study evaluated the binding energy and bioavailability of the combined extract, thus attempting to predict the target ligand binding between the compounds in OSCC before attempting in vitro studies.-

Advances in Computational Methods for Drug Design: A Revolution in Pharmaceutical Development

Computational methods of drug design involve the use of various software and algorithms to predict the properties of drug molecules, screen for potential targets, and optimize drug candidates for therapeutic efficacy, it lowering the cost of drug research and development time. The discovery and development of a novel medicine is a lengthy, complex, expensive, and high-risk process that has no commercial counterpart. CADD has previously been used to uncover drugs that have gone through clinical trials and become innovative medicines for many ailments. CADD approaches are widely divided into two categories: structure-based drug design (SBDD) and ligand-based drug design (LBDD). SBDD is used when the three-dimensional structures of target proteins are available, while LBDD design is used in the absence of receptor 3D information and relies on knowledge of molecules that bind to the biological target of interest. Some applications in CADD are Lead Optimization, Virtual screening (VS), ADMET prediction, and toxicity prediction. Medicinal chemists use a variety of computational approaches to modify the chemical structure of a compound to maximize its in vitro activity, drug discovery is driven by the idea that a ligand with higher binding affinity to a target should be more efficacious than that with lower binding affinity to the same target. Target flexibility is one of the key issues that still need to be resolved in drug discovery. The majority of molecular docking tools give the ligand high flexibility, but they fix or give the protein's residues close to or inside the active site only limited flexibility. It is very difficult to provide complete molecular flexibility to the protein as this increases the space and time complexity of the computation. In conclusion, computational methods have revolutionized the field of drug design by enabling faster, more cost-effective, and more efficient drug discovery

AutoDock-SS: AutoDock for Multiconformational Ligand-Based Virtual Screening.

Ligand-based virtual screening (LBVS) can be pivotal for identifying potential drug leads, especially when the target protein's structure is unknown. However, current LBVS methods are limited in their ability to consider the ligand conformational flexibility. This study presents AutoDock-SS (Similarity Searching), which adapts protein-ligand docking for use in LBVS. AutoDock-SS integrates novel ligand-based grid maps and AutoDock-GPU into a novel three-dimensional LBVS workflow. Unlike other approaches based on pregenerated conformer libraries, AutoDock-SS's built-in conformational search optimizes conformations dynamically based on the reference ligand, thus providing a more accurate representation of relevant ligand conformations. AutoDock-SS supports two modes: single and multiple ligand queries, allowing for the seamless consideration of multiple reference ligands. When tested on the Directory of Useful Decoys─Enhanced (DUD-E) data set, AutoDock-SS surpassed alternative 3D LBVS methods, achieving a mean AUROC of 0.775 and an EF1% of 25.72 in single-reference mode. The multireference mode, evaluated on the augmented DUD-E+ data set, demonstrated superior accuracy with a mean AUROC of 0.843 and an EF1% of 34.59. This enhanced performance underscores AutoDock-SS's ability to treat compounds as conformationally flexible while considering the ligand's shape, pharmacophore, and electrostatic potential, expanding the potential of LBVS methods.

Study on mechanism of transdermal administration of eugenol for pain treatment by network pharmacology and molecular docking technology

The objective of this study was to explore the pharmacological mechanism of transdermal administration of eugenol (EUG) for pain treatment. Firstly, network pharmacology techniques were employed to identify the potential targets responsible for the analgesic effect of EUG. Subsequently, molecular docking technology was used to validate interactions between EUG and the crystal structure of the core target protein. Finally, the impact of EUG on the expression and activation of TRPV1 receptors in HaCaT cells was evaluated through in vitro experiments, thus confirming the analysis of network pharmacology. The study suggested that the transdermal administration of EUG for pain treatment might target the TRPV1 receptor. Molecular docking revealed that EUG could spontaneously bind to the TRPV1 receptor with a high binding ability. The analysis of Western blot (WB) and intracellular Ca2+ levels demonstrated that EUG could increase the expression of TRPV1 in HaCaT cells, activating TRPV1 to induce intracellular Ca2+ influx (P < 0.05). These findings suggested that the initial application of EUG would cause a brief stimulation of TRPV1 receptors and upregulation of TRPV1 expression. Upon continued exposure, EUG would act as a TRPV1 agonist, increasing intracellular Ca2+ levels that might be associated with desensitization of pain sensations.

A study of the molecular mechanism of action of Jiawei Guizhishaoyaozhimu Decoction during rheumatoid arthritis therapy based on basic of network pharmacology and experimental verification.

Rheumatoid arthritis (RA) is a chronic autoimmune disease, which primarily affects the joints. The aim of the present study was to predict the main active ingredients of Jiawei Guizhishaoyaozhimu Decoction (JWGZSYZMD) and potential targets of this treatment during RA therapy by using molecular docking and network pharmacology methods. In addition, another aim was to investigate the therapeutic effects and mechanism of JWGZSYZMD on joint inflammation in rat models of collagen Ⅱ-induced arthritis (CIA). JWGZSYZMD ingredients and targets and genes associated with RA first extracted from traditional Chinese medicine (TCM) Systems Pharmacology Database and Analysis Platform, Bioinformatics Analysis Tool of Molecular Mechanism-TCM and Genecards databases, which were then transferred to the STRING database to set up protein interaction networks. The crystal structures of target proteins were also downloaded from the Protein Data Bank before molecular docking of compounds onto the protein targets was performed using AutoDock Vina software. In addition, a drug compound target visualization network was constructed using Cytoscape 3.7.2 software, which was used to elucidate the main mechanism underlying the anti-RA effect of JWGZSYZMD. A CIA rat model was established and animals were divided into the control, CIA model, JWGZSYZMD treatment (low-, medium- and high-dose) and tripterygium glycoside groups. Compared with the rats in the CIA model group, the joint scores of the rats in the high-dose group of JWGZSYZMD were significantly lower after 21 days of treatment. The expression levels of IL-6, TNF-α, IL-1β and IL-17A in the synovial supernatant of the model rats were lower compared with those in the CIA group. Also, the expression of the aforementioned cytokines in the high-dose JWGZSYZMD group was significantly lower compared with those in the CIA model group. To conclude, using molecular docking combined with network pharmacology, the material basis and molecular mechanism underlying the effects of JWGZSYZMD during RA therapy were studied, which could potentially provide a reference for future clinical applications.

GnRH Induces Citrullination of the Cytoskeleton in Murine Gonadotrope Cells.

Peptidylarginine deiminases (PADs or PADIs) catalyze the conversion of positively charged arginine to neutral citrulline, which alters target protein structure and function. Our previous work established that gonadotropin-releasing hormone agonist (GnRHa) stimulates PAD2-catalyzed histone citrullination to epigenetically regulate gonadotropin gene expression in the gonadotrope-derived LβT2 cell line. However, PADs are also found in the cytoplasm. Given this, we used mass spectrometry (MS) to identify additional non-histone proteins that are citrullinated following GnRHa stimulation and characterized the temporal dynamics of this modification. Our results show that actin and tubulin are citrullinated, which led us to hypothesize that GnRHa might induce their citrullination to modulate cytoskeletal dynamics and architecture. The data show that 10 nM GnRHa induces the citrullination of β-actin, with elevated levels occurring at 10 min. The level of β-actin citrullination is reduced in the presence of the pan-PAD inhibitor biphenyl-benzimidazole-Cl-amidine (BB-ClA), which also prevents GnRHa-induced actin reorganization in dispersed murine gonadotrope cells. GnRHa induces the citrullination of β-tubulin, with elevated levels occurring at 30 min, and this response is attenuated in the presence of PAD inhibition. To examine the functional consequence of β-tubulin citrullination, we utilized fluorescently tagged end binding protein 1 (EB1-GFP) to track the growing plus end of microtubules (MT) in real time in transfected LβT2 cells. Time-lapse confocal microscopy of EB1-GFP reveals that the MT average lifetime increases following 30 min of GnRHa treatment, but this increase is attenuated by PAD inhibition. Taken together, our data suggest that GnRHa-induced citrullination alters actin reorganization and MT lifetime in gonadotrope cells.

Target-specific novel molecules with their recipe: Incorporating synthesizability in the design process

Application of Artificial intelligence (AI) in drug discovery has led to several success stories in recent times. While traditional methods mostly relied upon screening large chemical libraries for early-stage drug-design, de novo design can help identify novel target-specific molecules by sampling from a much larger chemical space. Although this has increased the possibility of finding diverse and novel molecules from previously unexplored chemical space, this has also posed a great challenge for medicinal chemists to synthesize at least some of the de novo designed novel molecules for experimental validation. To address this challenge, in this work, we propose a novel forward synthesis-based generative AI method, which is used to explore the synthesizable chemical space. The method uses a structure-based drug design framework, where the target protein structure and a target-specific seed fragment from co-crystal structures can be the initial inputs. A random fragment from a purchasable fragment library can also be the input if a target-specific fragment is unavailable. Then a template-based forward synthesis route prediction and molecule generation is performed in parallel using the Monte Carlo Tree Search (MCTS) method where, the subsequent fragments for molecule growth can again be obtained from a purchasable fragment library. The rewards for each iteration of MCTS are computed using a drug-target affinity (DTA) model based on the docking pose of the generated reaction intermediates at the binding site of the target protein of interest. With the help of the proposed method, it is now possible to overcome one of the major obstacles posed to the AI-based drug design approaches through the ability of the method to design novel target-specific synthesizable molecules.

Antivirals for monkeypox virus: Proposing an effective machine/deep learning framework.

Monkeypox (MPXV) is one of the infectious viruses which caused morbidity and mortality problems in these years. Despite its danger to public health, there is no approved drug to stand and handle MPXV. On the other hand, drug repurposing is a promising screening method for the low-cost introduction of approved drugs for emerging diseases and viruses which utilizes computational methods. Therefore, drug repurposing is a promising approach to suggesting approved drugs for the MPXV. This paper proposes a computational framework for MPXV antiviral prediction. To do this, we have generated a new virus-antiviral dataset. Moreover, we applied several machine learning and one deep learning method for virus-antiviral prediction. The suggested drugs by the learning methods have been investigated using docking studies. The target protein structure is modeled using homology modeling and, then, refined and validated. To the best of our knowledge, this work is the first work to study deep learning methods for the prediction of MPXV antivirals. The screening results confirm that Tilorone, Valacyclovir, Ribavirin, Favipiravir, and Baloxavir marboxil are effective drugs for MPXV treatment.

ProBID-Net: a deep learning model for protein-protein binding interface design.

Protein-protein interactions are pivotal in numerous biological processes. The computational design of these interactions facilitates the creation of novel binding proteins, crucial for advancing biopharmaceutical products. With the evolution of artificial intelligence (AI), protein design tools have swiftly transitioned from scoring-function-based to AI-based models. However, many AI models for protein design are constrained by assuming complete unfamiliarity with the amino acid sequence of the input protein, a feature most suited for de novo design but posing challenges in designing protein-protein interactions when the receptor sequence is known. To bridge this gap in computational protein design, we introduce ProBID-Net. Trained using natural protein-protein complex structures and protein domain-domain interface structures, ProBID-Net can discern features from known target protein structures to design specific binding proteins based on their binding sites. In independent tests, ProBID-Net achieved interface sequence recovery rates of 52.7%, 43.9%, and 37.6%, surpassing or being on par with ProteinMPNN in binding protein design. Validated using AlphaFold-Multimer, the sequences designed by ProBID-Net demonstrated a close correspondence between the design target and the predicted structure. Moreover, the model's output can predict changes in binding affinity upon mutations in protein complexes, even in scenarios where no data on such mutations were provided during training (zero-shot prediction). In summary, the ProBID-Net model is poised to significantly advance the design of protein-protein interactions.

Tetraenone A: A New β-Ionone Derivative from Tetraena aegyptia.

In this study, the chemical investigation of Tetraena aegyptia (Zygophyllaceae) led to the identification of a new megastigmene derivative, tetraenone A ((2S, 5R, 6R, 7E)-2-hydroxy-5,6-dihydro-β-ionone) (1), along with (3S, 5R, 6S, 7E)-3-hydroxy-5,6-epoxy-5,6-dihydro-β-ionone- (2), 3,4-dihydroxy-cinnamyl alcohol-4-glucoside (3), 3β,19α-dihydroxy-ursan-28-oic acid (4), quinovic acid (5), p-coumaric acid (6), and ferulic acid (7), for the first time. The chemical structures of 1-7 were confirmed by analysis of their 1D and 2D NMR and HRESIMS spectra and by their comparison with the relevant literature. The absolute configurations of 1 and 2 were assigned based on NOESY interactions and ECD spectra. Conformational analysis showed that 1 existed exclusively in one of the two theoretically possible chair conformers with a predominant s-trans configuration for the 3-oxobut-1-en-1-yl group with the ring, while the half-chair conformer had a pseudo-axial hydroxy group that was predominant over the other half-chair conformation. Boat conformations were not among the most stable conformations, and the s-trans isomerism was in favor of s-cis configuration. In silico investigation revealed that 1 and 2 had more favorable binding interactions with Mpro rather than with TMPRSS2. Accordingly, molecular dynamic simulations were performed on the complexes of compounds 1 and 2 with Mpro to explore the stability of their interaction with the target protein structure. Compounds 1 and 2 might offer a possible starting point for developing covalent inhibitors of Mpro of SARS-CoV-2.

In silico evaluation of the pharmacodynamic component of the interaction of S-alkyl derivatives of 5-methyl-4-(p-tolyl)-1,2,4-triazole-3-thiol with some biological targets

Derivatives of 1,2,4-triazole open wide opportunities for modern and progressive scientists in the development of innovative medicines. These compounds are known for their variability and structural flexibility, which allows scientists to experiment and create new molecules with unique properties. The use of 1,2,4-triazole derivatives in the creation of drugs is based on their ability to interact with biological systems and molecular targets. These compounds can be aimed at regulating physiological processes, reducing manifestations of pathological conditions or enhancing necessary biological reactions. Directed modification of the structure of 1,2,4-triazole derivatives allows to create biologically active compounds with improved properties. The aim of the work was to study in silico and to evaluate the possible interaction of a virtual series of S-alkyl derivatives of 5-methyl-4-(p-tolyl)-1,2,4-triazole-3-thiol with some enzyme systems. Materials and methods. A computer method (molecular docking) for predicting and evaluating the interaction between a ligand molecule and a target protein structure. Ligand preparation was performed using MarvinSketch 6.3.0, Hyper Chem 8, and AutoDockTools-1.5.6 programs. Enzyme preparation involved the use of Discovery Studio 4.0 and AutoDockTools-1.5.6 software packages. Direct molecular docking was performed using the Vina program. Results. A virtual series of S-alkyl derivatives of 5-methyl-4-(p-tolyl)-1,2,4-triazole-3-thiol with the potential possibility of creating a biologically active substance has been constructed. Using the Vina software tool, the nature and number of amino acid residues of the active centers of model enzymes, with which the proposed ligands coordinate and bind, were determined. According to the results of docking studies, the predicted affinity for lanosterol-14α-demethylase was determined. The effect on the receptor tyrosine kinase of anaplastic lymphoma is somewhat inferior in terms of qualitative and quantitative indicators. Conclusions. Using the method of molecular docking, it was established that S-alkyl derivatives of 5-methyl-4-(p-tolyl)-1,2,4-triazole-3-thiol have a fairly significant potential for the manifestation of antifungal activity, which justifies the further synthesis of these compounds and more in-depth study of fungistatic and fungicidal properties. Docking results for anaplastic lymphoma kinase show little promise in the development of anticancer agents.