Study Of Protein-ligand Interactions Research Articles

Exploring novel ligands for mGluR5: Design, computational analysis, and protein-ligand interaction studies

Exploring novel ligands for mGluR5: Design, computational analysis, and protein-ligand interaction studies

A robust CETSA data analysis automation workflow for routine screening

The Cellular Thermal Shift Assay (CETSA) enables the study of protein-ligand interactions in a cellular context. It provides valuable information on the binding affinity and specificity of both small and large molecule ligands in a relevant physiological context, hence forming a unique tool in drug discovery. Though high-throughput lab protocols exist for scaling up CETSA, subsequent data analysis and quality control remain laborious and limit experimental throughput. Here, we introduce a scalable and robust data analysis workflow which allows integration of CETSA into routine high throughput screening (HT-CETSA). This new workflow automates data analysis and incorporates quality control (QC), including outlier detection, sample and plate QC, and result triage. We describe the workflow and show its robustness against typical experimental artifacts, show scaling effects, and discuss the impact of data analysis automation by eliminating manual data processing steps.

Differential Scanning Calorimetry: A Powerful and Versatile Tool for Analyzing Proteins and Peptides

Differential Scanning Calorimetry (DSC) is an efficient and versatile analytical technique widely used to study the thermal properties and stability of proteins. This review article provides a comprehensive overview of the principles and applications of DSC in protein research. DSC measures the heat flow associated with temperature-induced conformational changes in proteins, allowing for the direct determination of key thermodynamic parameters such as melting temperature (Tm), enthalpy change (ΔH), and heat capacity change (ΔCp). These parameters provide valuable insights into protein and peptide stability, folding mechanisms, and interactions with ligands. The review discusses the methodological aspects of DSC, including sample preparation, experimental setup, and data analysis techniques. It also highlights the application of DSC in various aspects, such as drug discovery, biopharmaceutical development, and the study of protein-ligand interactions. By elucidating the thermal behavior of proteins, DSC contributes significantly to our understanding of protein and peptide structure, function, and stability, making it an indispensable tool in biochemical and biophysical research.

Applications of Surface Plasmon Resonance (SPR) to the Study of Diverse Protein-Ligand Interactions.

Functional characterization of enzymes/proteins requires determination of the binding affinity of small molecules or other biomolecules with the target proteins. Several available techniques, such as proteomics and drug discovery strategies, require a precise and high-throughput assay for rapid and reliable screening of potential candidates for further testing. Surface plasmon resonance (SPR), a well-established label-free technique, directly measures biomolecular affinities. SPR assays require immobilization of one interacting component (ligand) on a conductive metal (mostly gold or silver) and a continuous flow of solution containing potential binding partner (analyte) across the surface. The SPR phenomenon occurs when polarized light excites the electrons at the interface of the metal and the dielectric medium to generate electromagnetic waves that propagate parallel to the surface. Changes in the refractive index due to interaction between the ligand and analyte are measured by detecting the reflected light, providing real-time data on kinetics and specificity. A prominent use of SPR is identifying compounds in crude plant extracts that bind to specific molecules. Procedures that utilize SPR are becoming increasingly applicable outside the laboratory setting, and SPR imaging and localized SPR (LSPR) are cheaper and more portable alternative for in situ detection of plant or mammalian pathogens and drug discovery studies. LSPR, in particular, has the advantage of direct attachment to test tissues in live-plant studies. Here, we describe three protocols utilizing SPR-based assays for precise analysis of protein-ligand interactions. © 2024 Wiley Periodicals LLC. Basic Protocol 1: SPR comparison of binding affinities of viral reverse transcriptase polymorphisms Basic Protocol 2: SPR screening of crude plant extract for protein-binding agents Basic Protocol 3: Localized SPR-based antigen detection using antibody-conjugated gold nanoparticles.

Machine Learning for Sequence and Structure-Based Protein-Ligand Interaction Prediction.

Developing new drugs is too expensive and time -consuming. Accurately predicting the interaction between drugs and targets will likely change how the drug is discovered. Machine learning-based protein-ligand interaction prediction has demonstrated significant potential. In this paper, computational methods, focusing on sequence and structure to study protein-ligand interactions, are examined. Therefore, this paper starts by presenting an overview of the data sets applied in this area, as well as the various approaches applied for representing proteins and ligands. Then, sequence-based and structure-based classification criteria are subsequently utilized to categorize and summarize both the classical machine learning models and deep learning models employed in protein-ligand interaction studies. Moreover, the evaluation methods and interpretability of these models are proposed. Furthermore, delving into the diverse applications of protein-ligand interaction models in drug research is presented. Lastly, the current challenges and future directions in this field are addressed.

Graph Convolutional Capsule Regression (GCCR): A Model for Accelerated Filtering of Novel Potential Candidates for SARS-CoV-2 Based on Binding Affinity.

There has been a growing interest in discovering a viable drug for the new coronavirus (SARS-CoV-2) since the beginning of the pandemic. Protein-ligand interaction studies are a crucial step in the drug discovery process, as it helps us narrow the search space for potential ligands with high drug-likeness. Derivatives of popular drugs like Remdesivir generated through tools employing evolutionary algorithms are usually considered potential candidates. However, screening promising molecules from such a large search space is difficult. In a conventional screening process, for each ligand-target pair, there are time-consuming interaction studies that use docking simulations before downstream tasks like thermodynamic, kinetic, and electrostatic-potential evaluation. In this work, 'Graph Convolutional Capsule Regression' (GCCR), a model which uses Capsule Neural Networks (CapsNet) and Graph Convolutional Networks (GCN) to predict the binding energy of a protein-ligand complex is being proposed. The model's predictions were further validated with kinetic and free energy studies like Molecular Dynamics (MD) for kinetic stability and MM/GBSA analysis for free energy calculations. The GCCR showed an RMSE value of 0.0978 for 81.3% of the concordance index. The RMSE of GCCR converged around the iteration of just 50 epochs scoring a lower RMSE than GCN and GAT. When training with Davis Dataset, GCCR gave an RMSE score of 0.3806 with a CI score of 87.5%. The proposed GCCR model shows great potential in improving the screening process based on binding affinity and outperforms baseline machine learning models like DeepDTA, KronRLS, SimBoost, and other Graph Neural Networks (GNN) based models like Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT).

Identification, characterization, and CADD analysis of Plasmodium DMAP1 reveals it as a potential molecular target for new anti-malarial discovery

Developing drug resistance in the malaria parasite is a reason for apprehension compelling the scientific community to focus on identifying new molecular targets that can be exploited for developing new anti-malarial compounds. Despite the availability of the Plasmodium genome, many protein-coding genes in Plasmodium are still not characterized or very less information is available about their functions. DMAP1 protein is known to be essential for growth and plays an important role in maintaining genomic integrity and transcriptional repression in vertebrate organisms. In this study, we have identified a homolog of DMAP1 in P. falciparum. Our sequence and structural analysis showed that although PfDMAP1 possesses a conserved SANT domain, parasite protein displays significant structural dissimilarities from human homolog at full-length protein level as well as within its SANT domain. PPIN analysis of PfDMAP1 revealed it to be vital for parasite and virtual High-throughput screening of various pharmacophore libraries using BIOVIA platform-identified compounds that pass ADMET profiling and showed specific binding with PfDMAP1. Based on MD simulations and protein–ligand interaction studies two best hits were identified that could be novel potent inhibitors of PfDMAP1 protein. Communicated by Ramaswamy H. Sarma

Ligand based pharmacophore modelling and integrated computational approaches in the quest for small molecule inhibitors against hCA IX.

Carbonic anhydrase IX is an important biomarker to fight hypoxic tumours in both initial and metastatic stages of many forms of cancer. Overexpression of hCA IX in the hypoxic environment, has an active role in pH maintenance and makes the hCA IX a better target for the inhibitors targeting specific types of cancer stages. Being a member of the carbonic anhydrase family and having sixteen isoforms, it is important to have a selective inhibition of hCA IX to limit the disruption in the biological and metabolic pathways where other isoforms of hCA are localised and to avoid the other toxicity and adverse effects we try to find selective hCA IX inhibitors from a natural derivative. In the process of finding selective hCA inhibitors we developed a pharmacophore model based on existing inhibitors with IC50 values of less than 50 nm, which is then validated with the external decoy set and used for database searching followed by virtual screening to identify the hits based on the pharmacophore fit score and RMSD. Molecular docking studies were performed to identify protein ligand interaction and molecular dynamics simulation studies to analyse the stability of the complex and DFT studies were carried out. The initial screening yielded 43 hits with the RMSD value less than 1, which when subjected to docking exhibited very good interaction with key residues ZN301, HIS94, HIS96 and HIS119. The top 4 compounds in the molecular dynamics simulation studies for 100 ns provided useful insights on the stability of the complex and the DFT studies confirmed the energy variation between HOMO and LUMO is within an acceptable range. An average binding score of -7.8 Kcal mol-1 for the lead compounds and high stability margin in the dynamics study concludes that these lead compounds demonstrated outstanding potential for hCA IX inhibitory action theoretically and that further experimental studies for selective inhibition are inevitable.

Nanomolar Protein Thermal Profiling with Modified Cyanine Dyes.

Protein properties and interactions have been widely investigated by using external labels. However, the micromolar sensitivity of the current dyes limits their applicability due to the high material consumption and assay cost. In response to this challenge, we synthesized a series of cyanine5 (Cy5) dye-based quencher molecules to develop an external dye technique to probe proteins at the nanomolar protein level in a high-throughput one-step assay format. Several families of Cy5 dye-based quenchers with ring and/or side-chain modifications were designed and synthesized by introducing organic small molecules or peptides. Our results showed that steric hindrance and electrostatic interactions are more important than hydrophobicity in the interaction between the luminescent negatively charged europium-chelate-labeled peptide (Eu-probe) and the quencher molecules. The presence of substituents on the quencher indolenine rings reduces their quenching property, whereas the increased positive charge on the indolenine side chain improved the interaction between the quenchers and the luminescent compound. The designed quencher structures entirely altered the dynamics of the Eu-probe (protein-probe) for studying protein stability and interactions, as we were able to reduce the quencher concentration 100-fold. Moreover, the new quencher molecules allowed us to conduct the experiments using neutral buffer conditions, known as the peptide-probe assay. These improvements enabled us to apply the method in a one-step format for nanomolar protein-ligand interaction and protein profiling studies instead of the previously developed two-step protocol. These improvements provide a faster and simpler method with lower material consumption.

Geometric graph learning with extended atom-types features for protein-ligand binding affinity prediction

Understanding and accurately predicting protein-ligand binding affinity are essential in the drug design and discovery process. At present, machine learning-based methodologies are gaining popularity as a means of predicting binding affinity due to their efficiency and accuracy, as well as the increasing availability of structural and binding affinity data for protein-ligand complexes. In biomolecular studies, graph theory has been widely applied since graphs can be used to model molecules or molecular complexes in a natural manner. In the present work, we upgrade the graph-based learners for the study of protein-ligand interactions by integrating extensive atom types such as SYBYL and extended connectivity interactive features (ECIF) into multiscale weighted colored graphs (MWCG). By pairing with the gradient boosting decision tree (GBDT) machine learning algorithm, our approach results in two different methods, namely sybylGGL-Score and ecifGGL-Score. Both of our models are extensively validated in their scoring power using three commonly used benchmark datasets in the drug design area, namely CASF-2007, CASF-2013, and CASF-2016. The performance of our best model sybylGGL-Score is compared with other state-of-the-art models in the binding affinity prediction for each benchmark. While both of our models achieve state-of-the-art results, the SYBYL atom-type model sybylGGL-Score outperforms other methods by a wide margin in all benchmarks. Finally, the best-performing SYBYL atom-type model is evaluated on two test sets that are independent of CASF benchmarks.

In Silico Protein-Ligand Interaction Study of Selected Phytochemicals Against Monoamine Oxidase-B

Parkinson’s disease (PD) is a progressive neurodegenerative disease associated with the death of dopaminergic neurons in the substantia nigra pars compacta. Monoamine oxidase B (MAO-B) inhibitors lessen the degree of PD but still present various side effects. Natural products such as morin, piceatannol, resveratrol, and gallic acid are vital phytochemicals from plants with numerous health benefits. This in silico study aimed to identify potent natural MAO-B inhibitors that could serve as better alternatives to the known MAO-B inhibitors (rasagiline and selegiline). The crystal structure of MAO-B (PDB ID: 2C65) was retrieved from the Protein Data Bank (RSCB) and prepared for molecular docking via Discovery Studio 2020 software. Molecular docking between MAO-B and morin, piceatannol, resveratrol, gallic acid, rasagiline, and selegiline utilised the PyRx software. The Discovery Studio 2020 software was used for visualization. The SwissADME server was used to study the physiochemical properties (Lipinski rule of five), pharma-cokinetic parameters, and absorption, distribution, metabolism, elimination, and toxicity (ADMET) profiles of morin. Comparing results with binding affinity indicated that morin (-10.0 kcal/mol) has a superior potency against 2C65 than piceatannol (-9.0 kcal/mol), resveratrol (-807 kcal/mol), dopamine (-6.4 kcal/mol), gallic acid (-6.3 kcal/mol), rasagiline (-8.0 kcal/mol), and selegiline (-7.4 kcal/mol). Drug candidates comply with all five of Lipinski’s drug-likeness rules with appropriate ADMET properties. Overall, the molecular docking results suggest that morin may be considered a suitable therapeutic candidate for PD treatment.

Investigating the interaction between some of Bipolaris sorokiniana’s toxins and the Gα Subunit of the Wheat G-Protein using bioinformatics tools

Spot blotch disease of wheat, caused by the fungus Bipolaris sorokiniana (Sacc.) Shoem., produces several toxins like: prehelminthosporol, helminthosporol, helminthosporic acid, sorokinianin, Bipolaroxin. These toxins interact with the plant and thereby increase the symptoms of small dark lesions and huge yield losses in different regions around the world so there is an urgent need to decipher the molecular interaction between wheat and those toxins for in-depth understanding of host–pathogen interactions. In the present study, we have modeledthe three-dimensional structure of G-protein alpha subunit from Triticum aestivum as G-protein was shown that it is an important player behind the resistance to many plant diseases. Molecular docking studies were performed using the active site of the modeled G-protein alpha subunit from T. aestivumand some of fungus’s toxins followed by molecular dynamics (MD) simulation studies to explore the stability, conformational flexibility, and dynamic behavior. Protein-ligand interaction study revealed one H-bond formed by Lys302 and hydrophobic contacts formed by Tyr159, Gly162, Val167, Asp256, Gln257 and Ile298 with prehelminthosporol, Protein-ligand interaction study revealed H-bond formed three H-bonds formed by Tyr159, Gly162, and Asp256 and hydrophobic interactions formed by Ser160, Cys161, Ser162, Ile298, Lys302 and Val306 with helminthosporic acid. Protein-ligand interaction study revealed two H-bond formed by His172, Arg301 and hydrophobic interactions by Tyr159, Pro168, Asp169, Ile298 and Lys302 with sorokinianin. Protein-ligand interaction study revealed H-bond formed six H-bonds mainly formed by Glu29, Ser30, Lys32, and Ala177. In addition to H-bonds, hydrophobic contacts formed by Gly28, Gly31, Ser33, Thr34, Arg78, Val179, Thr181 and Gly209 with Bipolaroxin were also observed.

Potential inhibitors of RPS6KB2 and NRF2 in head and neck squamous cell carcinoma

Among the major altered pathways in head and neck squamous cell carcinoma, AKT/mTORC1/S6K and NRF2/KEAP1 pathway are quite significant. The overexpression and overstimulation of proteins from both these pathways makes them the promising candidates in cancer therapeutics. Inhibiting mTOR has been in research from past several decades but the tumour heterogeneity, and upregulation of several compensatory feed-back mechanisms, encourages to explore other downstream targets for inhibiting the pathway. One such downstream effectors of mTOR is S6K2. It is reported to be overexpressed in cancers such as head and neck cancer, breast cancer and prostate cancer. In case of NRF2/KEAP1 pathway, nuclear factor erythroid 2-related factor 2 (NFE2L2 or NRF2) is overexpressed in ∼90% of head and neck squamous cell carcinoma (HNSCC) cases. It associates with poor survival rate and therapeutic resistance in HNSCC treatment. NRF2 pathway is the primary antioxidant pathway in the cell which also serves pro-tumorigenic functions, such as repression of apoptosis, cell proliferation support and chemoresistance. The aim of this work was to explore S6K2 and NRF2 and identify novel and potential inhibitors against them for treating head and neck squamous cell carcinoma. Since the crystal structure of S6K2 was not available at the time of this study, we modelled its structure using homology modelling and performed high throughput screening, molecular dynamics simulations, free energy calculations and protein-ligand interaction studies to identify the inhibitors. We identified natural compounds Crocin and Gypenoside XVII against S6K2 and Chebulinic acid and Sennoside A against NRF2. This study provides a significant in-depth understanding of the two studied pathways and therefore can be used in the development of potential therapeutics against HNSCC. Communicated by Ramaswamy H. Sarma

Effectiveness of estrogen and its derivatives over dexamethasone in the treatment of COVID-19

Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus and dexamethasone is a glucocorticoid widely used for its treatment. Dexamethasone is not used in non-severe cases due to its immunosuppressant action. So, considering this, Estrogen and Estetrol were tested for the treatment of COVID-19 as they all possess a common steroid ring and dislike dexamethasone, they are immunoenhancer. Virtual screening of test ligands was performed through molecular docking, MM-GBSA, simulations, in silico ADMET and drug-likeness prediction to identify their potential to inhibit the effects of SARS-CoV-2. Results showed that test ligands possess drug-like properties and they are safe as drug candidates. The protein-ligand interaction study revealed that they bind with the amino acid residues at the active site of the target proteins and the test ligands possess better binding potential than Dexamethasone. With protein Mpro, Estetrol and Estrogen showed docking score of −7.240 and −5.491 kcal/mol, and with protein ACE2, Estetrol and Estrogen showed docking score of −5.269 and −4.732 kcal/mol, respectively. Further, MD Simulation was carried out and most of the interactions of molecular docking are preserved during simulation. The prominent interactions that our test ligands showed during MD Simulation are similar to drugs that possess in vitro anticovid activity as shown in recent studies. Hence, our test ligands possessed potential for anticovid activity and they should be further tested through in vitro and in vivo studies for their activity against COVID-19. Communicated by Ramaswamy H. Sarma

Seeing the complete picture: proteins in top-down mass spectrometry.

Top-down protein mass spectrometry can provide unique insights into protein sequence and structure, including precise proteoform identification and study of protein-ligand and protein-protein interactions. In contrast with the commonly applied bottom-up approach, top-down approaches do not include digestion of the protein of interest into small peptides, but instead rely on the ionization and subsequent fragmentation of intact proteins. As such, it is fundamentally the only way to fully characterize the composition of a proteoform. Here, we provide an overview of how a top-down protein mass spectrometry experiment is performed and point out recent applications from the literature to the reader. While some parts of the top-down workflow are broadly applicable, different research questions are best addressed with specific experimental designs. The most important divide is between studies that prioritize sequence information (i.e., proteoform identification) versus structural information (e.g., conformational studies, or mapping protein-protein or protein-ligand interactions). Another important consideration is whether to work under native or denaturing solution conditions, and the overall complexity of the sample also needs to be taken into account, as it determines whether (chromatographic) separation is required prior to MS analysis. In this review, we aim to provide enough information to support both newcomers and more experienced readers in the decision process of how to answer a potential research question most efficiently and to provide an overview of the methods that exist to answer these questions.

Dissecting the Thermodynamics of ATP Binding to GroEL One Nucleotide at a Time.

Variable-temperature electrospray ionization (vT-ESI) native mass spectrometry (nMS) is used to determine the thermodynamics for stepwise binding of up to 14 ATP molecules to the 801 kDa GroEL tetradecamer chaperonin complex. Detailed analysis reveals strong enthalpy-entropy compensation (EEC) for the ATP binding events leading to formation of GroEL-ATP7 and GroEL-ATP14 complexes. The observed variations in EEC and stepwise free energy changes of specific ATP binding are consistent with the well-established nested cooperativity model describing GroEL-ATP interactions, viz., intraring positive cooperativity and inter-ring negative cooperativity (Dyachenko A.; Proc. Natl. Acad. Sci. U.S.A.2013, 110, 7235-7239). Entropy-driven ATP binding is to be expected for ligand-induced conformational changes of the GroEL tetradecamer, though the magnitude of the entropy change suggests that reorganization of GroEL-hydrating water molecules and/or expulsion of water from the GroEL cavity may also play key roles. The capability for determining complete thermodynamic signatures (ΔG, ΔH, and -TΔS) for individual ligand binding reactions for the large, nearly megadalton GroEL complex expands our fundamental view of chaperonin functional chemistry. Moreover, this work and related studies of protein-ligand interactions illustrate important new capabilities of vT-ESI-nMS for thermodynamic studies of protein interactions with ligands and other molecules such as proteins and drugs.

Structural insights into the interactions of glutathione transferases with a nitric oxide carrier and sodium nitroprusside

Glutathione transferases are detoxification enzymes with multifaceted roles, including a role in the metabolism and scavenging of nitric oxide (NO) compounds in cells. Here, we explored the ability of Trametes versicolor glutathione transferases (GSTs) from the Omega class (TvGSTOs) to bind metal-nitrosyl compounds. TvGSTOs have been studied previously for their ligandin role and are interesting models to study protein‒ligand interactions. First, we determined the X-ray structure of the TvGSTO3S isoform bound to the dinitrosyl glutathionyl iron complex (DNGIC), a physiological compound involved in the storage of nitric oxide. Our results suggested a different binding mode compared to the one previously described in human GST Pi 1 (GSTP1). Then, we investigated the manner in which TvGSTO3S binds three nonphysiological metal-nitrosyl compounds with different metal cores (iron, ruthenium and osmium). We assayed sodium nitroprusside, a well-studied vasodilator used in cases of hypertensive crises or heart failure. Our results showed that the tested GST can bind metal-nitrosyls at two distinct binding sites. Thermal shift analysis with six isoforms of TvGSTOs identified TvGSTO6S as the best interactant. Using the Griess method, TvGSTO6S was found to improve the release of nitric oxide from sodium nitroprusside in vitro, whereas the effects of human GST alpha 1 (GSTA1) and GSTP1 were moderate. Our results open new structural perspectives for understanding the interactions of glutathione transferases with metal-nitrosyl compounds associated with the biochemical mechanisms of NO uptake/release in biological systems.

Bifunctional Lipid-Derived Affinity-Based Probes (AfBPs) for Analysis of Lipid-Protein Interactome.

ConspectusAlthough lipids are not genetically encoded, they are fundamental building blocks of cell membranes and essential components of cell metabolites. Lipids regulate various biological processes, including energy storage, membrane trafficking, signal transduction, and protein secretion; therefore, their metabolic imbalances cause many diseases. Approximately 47 000 lipid species with diverse structures have been identified, but little is known about their crucial roles in cellular systems. Particularly the structural, metabolic, and signaling functions of lipids often arise from interactions with proteins. Lipids attach to proteins not only by covalent bonds but also through noncovalent interactions, which also influence protein functions and localization. Therefore, it is important to explore this lipid-protein "interactome" to understand its roles in health and disease, which may further provide insight for medicinal development. However, lipid structures are generally quite complicated, rendering the systematic characterization of lipid-protein interactions much more challenging.Chemoproteomics is a well-known chemical biology platform in which small-molecule chemical probes are utilized in combination with high-resolution, quantitative mass spectrometry to study protein-ligand interactions in living cells or organisms, and it has recently been applied to the study of protein-lipid interactions as well. The study of these complicated interactions has been advanced by the development of bifunctional lipid probes, which not only enable probes to form covalent cross-links with lipid-interacting proteins under UV irradiation, but are also capable of enriching these proteins through bioorthogonal reactions.In this Account, we will discuss recent developments in bifunctional lipid-derived, affinity-based probes (AfBP)s that have been developed to investigate lipid-protein interactions in live cell systems. First, we will give a brief introduction of fundamental techniques based on AfBPs which are related to lipid research. Then, we will focus on three aspects, including probes developed on the basis of lipidation, lipid-derived probes with different modification positions (e.g., hydrophobic or hydrophilic parts of a lipid), and, finally, in situ biosynthesis of probes through intrinsic metabolic pathways by using chemically modified building blocks. We will present some case studies to describe these probes' design principles and cellular applications. At the end, we will also highlight key limitations of current approaches so as to provide inspirations for future improvement. The lipid probes that have been constructed are only the tip of the iceberg, and there are still plenty of lipid species that have yet to be explored. We anticipate that AfBP-based chemoproteomics and its further advancement will pave the way for a deep understanding of lipid-protein interactions in the future.

Binary combinatorial scanning reveals potent poly-alanine-substituted inhibitors of protein-protein interactions

Establishing structure–activity relationships is crucial to understand and optimize the activity of peptide-based inhibitors of protein–protein interactions. Single alanine substitutions provide limited information on the residues that tolerate simultaneous modifications with retention of biological activity. To guide optimization of peptide binders, we use combinatorial peptide libraries of over 4,000 variants—in which each position is varied with either the wild-type residue or alanine—with a label-free affinity selection platform to study protein–ligand interactions. Applying this platform to a peptide binder to the oncogenic protein MDM2, several multi-alanine-substituted analogs with picomolar binding affinity were discovered. We reveal a non-additive substitution pattern in the selected sequences. The alanine substitution tolerances for peptide ligands of the 12ca5 antibody and 14-3-3 regulatory protein are also characterized, demonstrating the general applicability of this new platform. We envision that binary combinatorial alanine scanning will be a powerful tool for investigating structure–activity relationships.

Investigation of anti-microbial activity of imidazol [2, 1-B][1,3,4] thiadiazole by using molecular docking and ADMET studies

This report consists of molecular docking based on series of imidazol [2,1-b], , thiadiazole-benzimidazole derivative. Molecular docking is software which gives information about molecular modeling in which molecule fits into target binding sites and predict structure of intermolecular complex. These molecules were investigated by protein ligand binding score, protein ligand interaction and ADME studies. All the target molecules were analyzed against which is a gram positive bacteria found on skin and upper respiratory tract. The protein molecule selected for the analysis was PDB code 4LAE protein ligand. Basically it is a oxidoreductase inhibitor and its structure is based on 7(benzimidazole-1-yl)-2, 4-diaminoquinazolines. Out of all twenty nine compounds five compounds (5B,5G,5H,5N and 5Q) were estimated as most potent molecules as antibacterial agent.