Protein Binding Sites Research Articles

CavitOmiX Drug Discovery: Engineering Antivirals with Enhanced Spectrum and Reduced Side Effects for Arboviral Diseases.

Advancing climate change increases the risk of future infectious disease outbreaks, particularly of zoonotic diseases, by affecting the abundance and spread of viral vectors. Concerningly, there are currently no approved drugs for some relevant diseases, such as the arboviral diseases chikungunya, dengue or zika. The development of novel inhibitors takes 10-15 years to reach the market and faces critical challenges in preclinical and clinical trials, with approximately 30% of trials failing due to side effects. As an early response to emerging infectious diseases, CavitOmiX allows for a rapid computational screening of databases containing 3D point-clouds representing binding sites of approved drugs to identify candidates for off-label use. This process, known as drug repurposing, reduces the time and cost of regulatory approval. Here, we present potential approved drug candidates for off-label use, targeting the ADP-ribose binding site of Alphavirus chikungunya non-structural protein 3. Additionally, we demonstrate a novel in silico drug design approach, considering potential side effects at the earliest stages of drug development. We use a genetic algorithm to iteratively refine potential inhibitors for (i) reduced off-target activity and (ii) improved binding to different viral variants or across related viral species, to provide broad-spectrum and safe antivirals for the future.

Investigation on synergistic inhibition of protein-bound heterocyclic amines in sarcoplasmic and myofibrillar model systems by amino acid combinations

The inhibition of amino acids on the formation of protein-bound HAs was assessed in both model systems and roast beef patties, and the synergism between these amino acids was also investigated. The amino acids can promote the formation of protein-bound HAs at low addition amount, and the total content of protein-bound HAs increased from 444.05 ± 4.98 ng/g of the control group to 517.36 ± 16.51 ng/g when 0.05 % cysteine was added. Amino acid combinations exhibited stable inhibitory effects, with the maximum inhibitory rate of 64 % in the treatment with histidine-proline combination (1:4). The synergistic inhibition may be caused by simultaneously scavenging intermediates and competing for the binding sites of muscle proteins, and the reaction with protein-bound HAs to form adduct can serve as supporting factors to co-mitigate the promotion in protein-bound HAs from increased protein solubility. These findings proposed the potential mitigation strategies against protein-bound HAs formation.

Exploring an Intracellular Allosteric Site of CC-Chemokine Receptor4 from 3D Models, Probe Simulations, and Mutagenesis.

We applied our previously developed probe confined dynamic mapping protocol, which combines enhanced sampling molecular dynamics (MD) simulations and fragment-based approaches, to identify the binding site of GSK2239633A (N-[[3-[[3-[(5-chlorothiophen-2-yl)sulfonylamino]-4-methoxyindazol-1-yl]methyl]phenyl]methyl]-2-hydroxy-2-methylpropanamide), a selective CC-chemokine receptor type 4 (CCR4) negative allosteric modulator, using CCR4 homology and AlphaFold models. By comparing the performance across five computational models, we identified conserved (K3108.49 and Y3047.53) and non-conserved (M2436.36) residue hotspots for GSK2239633A binding, which were validated by mutagenesis and bioluminescence resonance energy transfer assay. Further analysis of 3D models and MD simulations highlighted the pair of residues 6.36 and 7.56 that might account for antagonist selectivity among chemokine receptors. Our in silico protocol provides a promising approach for characterizing ligand binding sites in membrane proteins, considering receptor dynamics and adaptability and guiding protein template selection for ligand design.

Complex Formation and Hydrolytic Processes of Protected Peptides Containing the -SXH- Motif in the Presence of Nickel(II) Ion.

Interactions between metal ions and proteins are considered reversible, such as the coordination of a metal ion to a protein or enzyme, but irreversible processes like the oxidative reactions, aggregation or hydrolytic processes may occur. In the presence of Ni(II)-ions selective hydrolysis of the peptides containing the -SXH- or -TXH- motif was observed. Since the side chain of histidine serves as the metal ion binding site for many native proteins, and very often histidine is present in a -SXH- or -TXH- sequence, to study the complex formation and hydrolytic processes in presence of nickel(II) ion four peptides were synthesised: Ac-SKHM-NH2, A3SSH-NH2, A4SSH-NH2, AAAϵKSH-NH2. The Ni(II)-induced hydrolysis of Ac-SKHM-NH2 peptide occurs rapidly in alkaline medium already at room temperature. In two peptides containing -SSH- sequence on the C-termini, the N-terminal part is the major binding site for the nickel(II) ion, but the formation of dinuclear complexes was also observed. In the [Ni2LH-6]2- complex of hexapeptide, the coordination sphere of the metal ions is saturated with deprotonated Ser-O-, which does not result in hydrolysis of the peptide. For A4SSH-NH2, both Ni(II) ions fulfill the conditions for hydrolysis, which was confirmed by HPLC analyses at pH ≈8.2 and 25 °C.

Complete combinatorial mutational enumeration of a protein functional site enables sequence-landscape mapping and identifies highly-mutated variants that retain activity.

Understanding how proteins evolve under selective pressure is a longstanding challenge. The immensity of the search space has limited efforts to systematically evaluate the impact of multiple simultaneous mutations, so mutations have typically been assessed individually. However, epistasis, or the way in which mutations interact, prevents accurate prediction of combinatorial mutations based on measurements of individual mutations. Here, we use artificial intelligence to define the entire functional sequence landscape of a protein binding site in silico, and we call this approach Complete Combinatorial Mutational Enumeration (CCME). By leveraging CCME, we are able to construct a comprehensive map of the evolutionary connectivity within this functional sequence landscape. As a proof of concept, we applied CCME to the ACE2 binding site of the SARS-CoV-2 spike protein receptor binding domain. We selected representative variants from across the functional sequence landscape for testing in the laboratory. We identified variants that retained functionality to bind ACE2 despite changing over 40% of evaluated residue positions, and the variants now escape binding and neutralization by monoclonal antibodies. This work represents a crucial initial stride toward achieving precise predictions of pathogen evolution, opening avenues for proactive mitigation.

Studies on N-(6-Indazolyl) benzenesulfonamide Derivatives as Potential Anticancer Agents: Integrating Synthesis, In silico Docking, and Molecular Dynamics Simulations

A new series of benzenesulfonamide derivatives, namely N-(1-(2-chloropyrimidin-4-yl)-1[Formula: see text]-indazol-5-yl) compounds (4a-i) and N-(1-(2-chloropyrimidin-4-yl)-6-ethoxy-1[Formula: see text]-indazol-5-yl) derivatives (5a-i), were synthesized. This was achieved by reducing 1-(2-halopyrimidin-4-yl)-5-nitro-1[Formula: see text]-indazole (3) with SnCl2, followed by reacting with various aryl sulphonyl chlorides in pyridine. Structural confirmation was carried out through IR, 1H-NMR, [Formula: see text]C-NMR and mass spectral methods. Anticancer activity evaluation of compounds 4a-i and 5a-i revealed that N-(1-(2-chloropyrimidin-4-yl)-6-ethoxy-1H-indazol-5-yl)-2-nitrobenzenesulfonamide (5b) displayed exceptional efficacy. Molecular dynamics simulations were employed to rigorously assess the stability of ligand–protein complexes, highlighting a consistent and robust binding of the most potent compound within the binding sites of target proteins. The results unequivocally affirmed the remarkable anticancer activity, supported by comprehensive molecular docking analyses uncovering intricate interactions involving hydrogen bonds, electrostatic forces and hydrophobic contacts. These findings collectively offer invaluable insights into the complex molecular structure and dynamics of receptor target sites, establishing a robust foundation for the development of novel and potent anticancer agents with promising pharmaceutical implications.

Oligonucleotide-Based Photoaffinity Probes: Chemical Tools and Applications for Protein Labeling.

A variety of proteins interact with DNA and RNA, including polymerases, histones, ribosomes, transcription factors, and repair enzymes. However, the transient non-covalent nature of these interactions poses challenges for analysis. Introducing a covalent bond between proteins and DNA via photochemical activation of a photosensitive functional group introduced onto nucleic acids offers a means to stabilize these often weak interactions without significantly altering the binding interface. Consequently, photoactivatable oligonucleotides are powerful tools for investigating nucleic acid-protein interactions involved in numerous biological and pathological processes. In this review, we provide a comprehensive overview of the chemical tools developed so far and the different strategies used for incorporating the most commonly used photoreactive reagents into oligonucleotide probes or nucleic acids. Furthermore, we illustrate their application with several examples including protein binding site mapping, identification of protein binding partners, and in cell studies.

Mudskipper detects combinatorial RNA binding protein interactions in multiplexed CLIP data

The uncovering of protein-RNA interactions enables a deeper understanding of RNA processing. Recent multiplexed crosslinking and immunoprecipitation (CLIP) technologies such as antibody-barcoded eCLIP (ABC) dramatically increase the throughput of mapping RNA binding protein (RBP) binding sites. However, multiplex CLIP datasets are multivariate, and each RBP suffers non-uniform signal-to-noise ratio. To address this, we developed Mudskipper, a versatile computational suite comprising two components: a Dirichlet multinomial mixture model to account for the multivariate nature of ABC datasets and a softmasking approach that identifies and removes non-specific protein-RNA interactions in RBPs with low signal-to-noise ratio. Mudskipper demonstrates superior precision and recall over existing tools on multiplex datasets and supports analysis of repetitive elements and small non-coding RNAs. Our findings unravel splicing outcomes and variant-associated disruptions, enabling higher-throughput investigations into diseases and regulation mediated by RBPs.

Genetic variant rs11568818 of matrix metalloproteinase MMP7 associated with newborn weight in pregnant women with fetal growth restriction

Background: Fetal growth restriction is a common complication of pregnancy that has been associated with a variety of adverse perinatal outcomes. The condition carries significant risks of neonatal mortality, major and minor morbidity, and long term health sequelae. The aim of the study: The aim of the study was to study the associations of matrix metalloproteinases gene polymorphism with newborn weight in pregnant women with fetal growth restriction and to evaluate their functional effects. Materials and methods: 98 cases of women with fetal growth restriction were selected as the experimental group. All pregnant women were performed genotyping of 5 SNPs of genes of matrix metalloproteinase (rs1799750 MMP-1, rs243865 MMP-2, rs3025058 MMP-3, rs11568818 MMP-7, rs17577 MMP-9). Results: We found associations of the rs11568818 polymorphism of the MMP-7 gene with newborn weight in women with fetal growth restriction within a recessive model (β = 0.32±0.13, p=0.016 pperm=0.022). This polymorphic locus possesses important functional effects. It is localized in an evolutionarily conserved region, a binding site for regulatory proteins (TBP, CFOS, CJUN), a region of DNase hypersensitivity, and a site of modified histones marking enhancers and promoters in mesenchymal and hematopoietic stem cells, osteoblast cells, adipocytes, and fibroblast cell line lungs, various parts of the brain, lungs, etc., determines the sensitivity of DNA to four transcription factors (Foxa, GR, PLZF, Pou5f1), is associated with the level of mRNA expression of the MMP-7 gene in the lungs, liver, and skeletal muscles. Conclusion: The rs11568818 polymorphism of the MMP-7 gene is associated with a newborn weight.

Regulation of RHOV signaling by interaction with SH3 domain-containing adaptor proteins and phosphorylation by PKA

RHOV and RHOU are considered atypical Rho-family small GTPases because of the existence of N- and C-terminal extension regions, abnormal GDP/GTP cycling, and post-translational modification. Particularly, RHOV and RHOU both have a proline-rich (PR) motif in the N-terminal region. It has been reported that the PR motif of RHOU interacts with GRB2, a SH3 domain-containing adaptor protein, and regulates its activity through EGF receptor signaling. However, it is unknown whether RHOV, like RHOU, interacts with SH3 domain-containing adaptor proteins. In this study, we investigated the interactions between RHOV and SH3 domain-containing adaptor proteins, including GRB2 and NCK2. The RHOV-induced serum response factor (SRF)-dependent gene transcriptional activity was attenuated in cells co-expressing either GRB2 or NCK2 compared to cells expressing RHOV alone. From the results of experiments using various gene mutants of RHOV and GRB2, it appears that the PR motif of the N-terminal region of RHOV is the crucial binding site for the SH3 domain-containing proteins. Furthermore, we found that Ser25 in the N-terminal region of RHOV is phosphorylated by PKA and that its phosphorylation is suppressed by interaction with NCK2 but not GRB2. We have found a novel regulatory mechanism for the phosphorylation of RHOV and its interaction with SH3 domain-containing adaptor proteins.

Structural analysis of the FERM domain of human protein tyrosine phosphatase non-receptor type 21.

Protein tyrosine phosphatase non-receptor type 21 (PTPN21) is a cytosolic protein tyrosine phosphatase that regulates cell growth and invasion. Due to its oncogenic properties, PTPN21 has recently emerged as a potential therapeutic target for cancer. In this study, the three-dimensional structure of the PTPN21 FERM domain was determined at 2.1 Å resolution by X-ray crystallography. The crystal structure showed that this domain harbors canonical FERM folding and consists of three subdomains that are tightly packed via highly conserved intramolecular hydrophobic interactions. Consistent with this, the PTPN21 FERM domain shares high structural homology with several other FERM domains. Moreover, structural superimposition demonstrated two putative protein-binding sites of the PTPN21 FERM domain, which are presumed to be associated with interaction with its binding partner, kinesin family member 1C. Thus, these data suggest that the FERM domain of PTPN21 serves as a module that mediates protein-protein interaction, like other FERM domains.

Splicing-specific transcriptome-wide association uncovers genetic mechanisms for schizophrenia

Recent studies have highlighted the essential role of RNA splicing, a key mechanism of alternative RNA processing, in establishing connections between genetic variations and disease. Genetic loci influencing RNA splicing variations show considerable influence on complex traits, possibly surpassing those affecting total gene expression. Dysregulated RNA splicing has emerged as a major potential contributor to neurological and psychiatric disorders, likely due to the exceptionally high prevalence of alternatively spliced genes in the human brain. Nevertheless, establishing direct associations between genetically altered splicing and complex traits has remained an enduring challenge. We introduce Spliced-Transcriptome-Wide Associations (SpliTWAS) to integrate alternative splicing information with genome-wide association studies to pinpoint genes linked to traits through exon splicing events. We applied SpliTWAS to two schizophrenia (SCZ) RNA-sequencing datasets, BrainGVEX and CommonMind, revealing 137 and 88 trait-associated exons (in 84 and 67 genes), respectively. Enriched biological functions in the associated gene sets converged on neuronal function and development, immune cell activation, and cellular transport, which are highly relevant to SCZ. SpliTWAS variants impacted RNA-binding protein binding sites, revealing potential disruption of RNA-protein interactions affecting splicing. We extended the probabilistic fine-mapping method FOCUS to the exon level, identifying 36 genes and 48 exons as putatively causal for SCZ. We highlight VPS45 and APOPT1, where splicing of specific exons was associated with disease risk, eluding detection by conventional gene expression analysis. Collectively, this study supports the substantial role of alternative splicing in shaping the genetic basis of SCZ, providing a valuable approach for future investigations in this area.

Exploration of Potential Broad-Spectrum Antiviral Targets in the Enterovirus Replication Element: Identification of Six Distinct 5' Cloverleaves.

Enterovirus genomic replication initiates at a predicted RNA cloverleaf (5'CL) at the 5' end of the RNA genome. The 5'CL contains one stem (SA) and three stem-loops (SLB, SLC, SLD). Here, we present an analysis of 5'CL conservation and divergence for 209 human health-related serotypes from the enterovirus genus, including enterovirus and rhinovirus species. Phylogenetic analysis indicates six distinct 5'CL serotypes that only partially correlate with the species definition. Additional findings include that 5'CL sequence conservation is higher between the EV species than between the RV species, the 5'CL of EVA and EVB are nearly identical, and RVC has the lowest 5'CL conservation. Regions of high conservation throughout all species include SA and the loop and nearby bases of SLB, which is consistent with known protein interactions at these sites. In addition to the known protein binding site for the Poly-C binding protein in the loop of SLB, other conserved consecutive cytosines in the stems of SLB and SLC provide additional potential interaction sites that have not yet been explored. Other sites of conservation, including the predicted bulge of SLD and other conserved stem, loop, and junction regions, are more difficult to explain and suggest additional interactions or structural requirements that are not yet fully understood. This more intricate understanding of sequence and structure conservation and variability in the 5'CL may assist in the development of broad-spectrum antivirals against a wide range of enteroviruses, while better defining the range of virus isotypes expected to be affected by a particular antiviral.

Efficient synthesis and in-silico studies of pyrano[3,2-c]pyrones based glycohybrids

This work is comprising of designing, efficient synthesis and molecular docking studies of pyrano[3,2-c]pyrones based stereodivergent glycohybrids. The diverse pyrano[3,2-c]pyrones based glycohybrids were efficiently prepared through condensation of 4-hydroxycoumarin derivatives and 4‑hydroxy-6-methyl-2H-pyran-2-ones with various enantiopure 2,3-dideoxy-α,β-unsaturated carbohydrate enals using organo-catalyst, l-proline, in EtOAc at room temperature. 3,4,6-tri-O-acetyl-D-glucal derived acetyl-protected carbohydrate enals yielded highly diastereoselective glycohybrid products. Whereas, poor selectivity was observed with α,β-unsaturated carbohydrate enals obtained from 3,4,6-tri-O-acetyl-d-galactal. The in-silico investigations show the significant binding activity of the selected compounds in the active binding site of HCK protein (PDB:1QCF) with the better docking score of −7.13 kcal/mol as compared to the co-crystallized ligand PP1 (−6.77 kcal/mol). Additionally, MM/GBSA was calculated using the Prime module to determine the binding energy of the ligands. Selected compounds showed the maximum binding affinity of −74.18 kcal/mol compared to the co-crystallized ligand PP1 (−70.40 kcal/mol). ADMET screening of these compounds predicts the low toxicity and better metabolic profile of the synthesized compounds inside the body.

Pharmacophore modelling based virtual screening and molecular dynamics identified the novel inhibitors and drug targets against Waddlia chondrophila

Waddlia chondrophila is a possible cause of fetal death in humans. This Chlamydia-related bacterium is an emergent pathogen that causes human miscarriages and ruminant abortions, which results in financial losses. Despite the years of efforts, the underlying mechanism behind the pathogenesis of W. chondrophila is little known which hindered the development of novel treatment options. In the framework of current study, computational approaches were used to identify novel inhibitors (phytocompounds) and drug targets against W. chondrophila. At first, RNA polymerase sigma factor SigA and 3-deoxy-d-manno-octulosonic acid transferase were identified through subtractive proteomics pipeline. Afterwards, extensive docking and simulation analyses were conducted to optimize potentially novel phytocompounds by assessing their binding affinity to target proteins. A 100ns molecular dynamics simulation well complimented the compound's binding affinity and indicated strong stability of predicted compounds at the docked site. The calculation of binding free energies with MMGBSA corroborated the significant binding affinity between phytocompounds and target protein binding sites. The proposed phytocompounds may be a viable treatment option for patients infected with W. chondrophila; however, further research is required to ensure their safety.

Machine Learning in Enhancing Protein Binding Sites Predictions - What Has Changed Since Then?

Accurate identification of protein binding sites is pivotal for understanding molecular interactions and facilitating drug discovery efforts. However, the dynamic nature of proteinligand interactions presents a formidable challenge, necessitating innovative approaches to bridge the gap between theoretical predictions and experimental realities. This review explores the challenges and recent advancements in protein binding site prediction. Specifically, we highlight the integration of molecular dynamics simulations, machine learning, and deep learning techniques to capture the dynamic and complex nature of protein-ligand interactions. Additionally, we discuss the importance of integrating experimental data, such as structural information and biochemical assays, to enhance prediction accuracy and reliability. By navigating the intersection of classical and the onset of machine learning and deep learning approaches, we aim to provide insights into current state-of-the-art techniques and chart a course for future protein binding site prediction advancements. Ultimately, these efforts could unravel the mysteries of protein-ligand interactions and accelerate drug discovery endeavors.

Prediction of drug–target binding affinity based on multi-scale feature fusion

Accurate prediction of drug–target binding affinity (DTA) plays a pivotal role in drug discovery and repositioning. Although deep learning methods are widely used in DTA prediction, two significant challenges persist: (i) how to effectively represent the complex structural information of proteins and drugs; (ii) how to precisely model the mutual interactions between protein binding sites and key drug substructures. To address these challenges, we propose a MSFFDTA (Multi-scale feature fusion for predicting drug target affinity) model, in which multi-scale encoders effectively capture multi-level structural information of drugs and proteins are designed. And then a Selective Cross Attention (SCA) mechanism is developed to filter out the trivial interactions between drug–protein substructure pairs and retain the important ones, which will make the proposed model better focusing on these key interactions and offering insights into their underlying mechanism. Experimental results on two benchmark datasets demonstrate that MSFFDTA is superior to several state-of-the-art methods across almost all comparison metrics. Finally, we provide the ablation and case studies with visualizations to verify the effectiveness and the interpretability of MSFFDTA. The source code is freely available at https://github.com/whitehat32/MSFF-DTA/.

The binding pattern of ferric iron and iron-binding protein in Botrytis cinerea

Iron-binding protein (Ibp) has protective effect on pathogen exposed to H2O2 in defense response of plants. Ibp in Botrytis cinerea (BcIbp) is related to its virulence. Bcibp mutation lead to virulence deficiencies in B. cinerea. BcIbp is involved in the Fe3+ homeostasis regulation. Recognition the binding site and binding pattern of ferric iron and iron-binding protein in B. cinerea are vital to understand its function. In this study, molecular dynamics (MD) simulations, gaussian accelerated molecular dynamics (GaMD) simulations, dynamic cross correlation analysis and quantum chemical energy calculation were used to explore binding pattern of ferric iron. MD results showed that the C-terminal region had little effect on the stability of residues in the Fe3+-binding pocket. Energy calculations suggested the most likely coordination pattern for ferric iron in iron-binding protein. These results will help to understand the binding of ferric iron to iron-binding protein and provide new ideas for regulating the virulence of B. cinerea.

B-cell immune deficiency in twin sisters expands the phenotype of MOPDI.

Microcephalic osteodysplastic primordial dwarfism type I (MOPDI) is a very rare and severe autosomal recessive disorder characterized by marked intrauterine growth retardation, skeletal dysplasia, microcephaly and brain malformations. MOPDI is caused by biallelic mutations in RNU4ATAC, a non-coding gene involved in U12-type splicing of 1% of the introns in the genome, which are recognized by their specific splicing consensus sequences. Here, we describe a unique observation of immunodeficiency in twin sisters with mild MOPDI, who harbor a novel n.108_126del mutation, encompassing part of the U4atac snRNA 3' stem-loop and Sm protein binding site, and the previously reported n.111G>A mutation. Interestingly, both twin sisters show mild B-cell anomalies, including low naive B-cell counts and increased memory B-cell and plasmablasts counts, suggesting partial and transitory blockage of B-cell maturation and/or excessive activation of naive B-cells. Hence, the localization of a mutation in stem II of U4atac snRNA, as observed in another RNU4ATAC-opathy with immunodeficiency, that is, Roifman syndrome (RFMN), is not required for the occurrence of an immune deficiency. Finally, we emphasize the importance of considering immunodeficiency in MOPDI management to reduce the risk of serious infectious episodes.

SOFB is a comprehensive ensemble deep learning approach for elucidating and characterizing protein-nucleic-acid-binding residues

Proteins and nucleic-acids are essential components of living organisms that interact in critical cellular processes. Accurate prediction of nucleic acid-binding residues in proteins can contribute to a better understanding of protein function. However, the discrepancy between protein sequence information and obtained structural and functional data renders most current computational models ineffective. Therefore, it is vital to design computational models based on protein sequence information to identify nucleic acid binding sites in proteins. Here, we implement an ensemble deep learning model-based nucleic-acid-binding residues on proteins identification method, called SOFB, which characterizes protein sequences by learning the semantics of biological dynamics contexts, and then develop an ensemble deep learning-based sequence network to learn feature representation and classification by explicitly modeling dynamic semantic information. Among them, the language learning model, which is constructed from natural language to biological language, captures the underlying relationships of protein sequences, and the ensemble deep learning-based sequence network consisting of different convolutional layers together with Bi-LSTM refines various features for optimal performance. Meanwhile, to address the imbalanced issue, we adopt ensemble learning to train multiple models and then incorporate them. Our experimental results on several DNA/RNA nucleic-acid-binding residue datasets demonstrate that our proposed model outperforms other state-of-the-art methods. In addition, we conduct an interpretability analysis of the identified nucleic acid binding residue sequences based on the attention weights of the language learning model, revealing novel insights into the dynamic semantic information that supports the identified nucleic acid binding residues. SOFB is available at https://github.com/Encryptional/SOFB and https://figshare.com/articles/online_resource/SOFB_figshare_rar/25499452.