Binding of small-molecule ligands to proteins: "what you see" is not always "what you get"

Absolute Binding Free Energies with OneOPES

The calculation of absolute binding free energies (ABFEs) for protein-ligand systems has long been a challenge. Recently, refined force fields and algorithms have improved the quality of the ABFE calculations. However, achieving the level of accuracy required to inform drug discovery efforts remains difficult. Here, we present a transferable enhanced sampling strategy to accurately calculate absolute binding free energies using OneOPES with simple geometric collective variables. We tested the strategy on two protein targets, BRD4 and Hsp90, complexed with a total of 17 chemically diverse ligands, including both molecular fragments and drug-like molecules. Our results show that OneOPES accurately predicts protein-ligand binding affinities with a mean unsigned error within 1 kcal mol-1 of experimentally determined free energies, without the need to tailor the collective variables to each system. Furthermore, our strategy effectively samples different ligand binding modes and consistently matches the experimentally determined structures regardless of the initial protein-ligand configuration. Our results suggest that the proposed OneOPES strategy can be used to inform lead optimization campaigns in drug discovery and to study protein-ligand binding and unbinding mechanisms.

Druglike Molecules Binding to Large Membrane Proteins: Absolute Binding Free Energy Computation

In this research, we employed the alchemical double-decoupling method alongside restraining potentials, coupled with the FEPMD method, to ascertain the standard binding free energy of a drug-like molecule termed BHQ and three analogous compounds engineered with progressive addition of bulky para-alkyl groups binding to SERCA (Ca2+-ATPase of skeletal muscle sarcoplasmic reticulum). Integral transmembrane proteins represent crucial drug targets in numerous therapeutic interventions, presenting computational challenges due to their considerable system sizes. Our approach integrated the generalized born potential method and the spherical solvent boundary potential method, allowing us to explicitly focus on the active binding site while treating the remainder of the system implicitly. We evaluated contributions to the standard binding free energy from distinct interaction potentials: electrostatic, repulsive, dispersive, and restraining potentials, computed separately. The resulting absolute binding free energy of BHQ (11.63 kcal/mol) closely aligns with experimental measurements (10.56 kcal/mol). Notably, an accurate estimation of the absolute binding free energy was achieved for the simplest analog, created with the addition of a single para-methyl group. However, the analog with two para-methyl groups exhibited the highest binding free energy, which disagreed with experimental results. Determining the binding free energy of the BHQ analog engineered with three para-methyl groups presented challenges in convergence and resulted in the lowest free energy among the three.

Accurate prediction of dynamic protein-ligand binding using P-score ranking

Protein-ligand binding prediction typically relies on docking methodologies and associated scoring functions to propose the binding mode of a ligand in a biological target. Significant challenges are associated with this approach, including the flexibility of the protein-ligand system, solvent-mediated interactions, and associated entropy changes. In addition, scoring functions are only weakly accurate due to the short time required for calculating enthalpic and entropic binding interactions. The workflow described here attempts to address these limitations by combining supervised molecular dynamics with dynamical averaging quantum mechanics fragment molecular orbital. This combination significantly increased the ability to predict the experimental binding structure of protein-ligand complexes independent from the starting position of the ligands or the binding site conformation. We found that the predictive power could be enhanced by combining the residence time and interaction energies as descriptors in a novel scoring function named the P-score. This is illustrated using six different protein-ligand targets as case studies.

Structure-based screening of FDA-approved drugs identifies potential histone deacetylase 3 repurposed inhibitor: molecular docking and molecular dynamic simulation approaches

Histone deacetylase 3 (HDAC3) is a member of the histone deacetylase family that has emerged as a crucial target in the quest for novel therapeutic interventions against various complex diseases, including cancer. The repositioning of FDA-approved drugs presents a promising avenue for the rapid discovery of potential HDAC3 inhibitors. In this study, we performed a structure-based virtual screening of FDA-approved drugs obtained from DrugBank. Candidate hits were selected based on their binding affinities and interactions with HDAC3. These promising hits were then subjected to a comprehensive assessment of their biological properties and drug profiles. Our investigation identified two FDA-approved drugs, Imatinib and Carpipramine, characterized by their exceptional affinity and specificity for the binding pocket of HDAC3. These molecules demonstrated a strong preference for HDAC3 binding site and formed interactions with functionally significant residues within the active site pocket. To gain deeper insights into the binding dynamics, structural stability, and interaction mechanisms, we performed molecular dynamics (MD) simulations spanning 300 nanoseconds (ns). The results of MD simulations indicated that Imatinib and Carpipramine stabilized the structure of HDAC3 and induced fewer conformational changes. Taken together, the findings from this study suggest that Imatinib and Carpipramine may offer significant therapeutic potential for treating complex diseases, especially cancer.

Structure-guided identification of potent inhibitors of ROS1 kinase for therapeutic development against non-small cell lung cancer

Proto-oncogene tyrosine-protein kinase ROS (ROS1) is a member of the sevenless receptor, which affects epithelial cell differentiation and is highly expressed in a variety of tumor cells. The elevated expression and dysfunction of ROS1 have been involved in various malignancies, such as non-small cell lung cancer (NSCLC), stomach cancer, ovarian, breast cancer, cholangiocarcinoma, colorectal cancer, adenosarcoma, oesophageal cancer, etc. ROS1 has been postulated as a potential drug target in anticancer therapeutics. In this study, we carried out a virtual screening of phytochemicals against ROS1 to identify its potential inhibitors. The virtual screening process was performed on the ROS1 structure, where two phytochemicals, Helioscopinolide C and Taiwanin C, were identified. These compounds resulted from filters like Lipinski rule of five, PAINS filter, binding affinities values, and all-atom molecular dynamics (MD) simulations followed by principal component analysis (PCA) and essential dynamics. The findings of this study highlight the role of ROS1 in multiple physiological candidates and its therapeutic targeting using phytochemicals. This study suggests Helioscopinolide C and Taiwanin C as potential compounds for therapeutic development targeting ROS1-associated non-small cell lung cancer for clinical applications. Further in vitro and in vivo experiments are required to validate these findings.Communicated by Ramaswamy H. Sarma.

The metagenome-derived esterase PET40 is highly promiscuous and hydrolyses polyethylene terephthalate (PET)

Polyethylene terephthalate (PET) is a widely used synthetic polymer and known to contaminate marine and terrestrial ecosystems. Only few PET-active microorganisms and enzymes (PETases) are currently known, and it is debated whether degradation activity for PET originates from promiscuous enzymes with broad substrate spectra that primarily act on natural polymers or other bulky substrates, or whether microorganisms evolved their genetic makeup to accepting PET as a carbon source. Here, we present a predicted diene lactone hydrolase designated PET40, which acts on a broad spectrum of substrates, including PET. It is the first esterase with activity on PET from a GC-rich Gram-positive Amycolatopsis species belonging to the Pseudonocardiaceae (Actinobacteria). It is highly conserved within the genera Amycolatopsis and Streptomyces. PET40 was identified by sequence-based metagenome search using a PETase-specific hidden Markov model. Besides acting on PET, PET40 has a versatile substrate spectrum, hydrolyzing δ-lactones, β-lactam antibiotics, the polyester-polyurethane Impranil® DLN, and various para-nitrophenyl ester substrates. Molecular docking suggests that the PET degradative activity is likely a result of the promiscuity of PET40, as potential binding modes were found for substrates encompassing mono(2-hydroxyethyl) terephthalate, bis(2-hydroxyethyl) terephthalate, and a PET trimer. We also solved the crystal structure of the inactive PET40 variant S178A to 1.60 Å resolution. PET40 is active throughout a wide pH (pH 4-10) and temperature range (4-65 °C) and remarkably stable in the presence of 5% SDS, making it a promising enzyme as a starting point for further investigations and optimization approaches.

The catalytic mechanism of direction-dependent interactions for 2,3-dihydroxybenzoate decarboxylase

Benzoic acid decarboxylases offer an elegant alternative to CO2 fixation by reverse reaction-carboxylation, which is named the bio-Kolbe-Schmitt reaction, but they are unfavorable to carboxylation. Enhancing the carboxylation efficiency of reversible benzoic acid decarboxylases is restricted by the unexplained carboxylation mechanisms. The direction of reversible enzyme catalytic reactions depends on whether catalytic residues at the active center of the enzyme are protonated, which is subjected by the pH. Therefore, the forward and reverse reactions could be separated at different pH values. Reversible 2,3-dihydroxybenzoate acid decarboxylase undergoes decarboxylation at pH 5.0 and carboxylation at pH 8.6. However, it is unknown whether the interaction of enzymes with substrates and products in the forward and reverse reactions can be exploited to improve the catalytic activity of reversible enzymes in the unfavorable direction. Here, we identify a V-shaped tunnel of 2,3-dihydroxybenzoic acid decarboxylase from Aspergillus oryzae (2,3-DHBD_Ao) through which the substrate travels in the enzyme, and demonstrate that the side chain conformation of a tyrosine residue controls the entry and exit of substrate/product during reversible reactions. Together with the kinetic studies of the mutants, it is clarified that interactions between substrate/product traveling through the enzyme tunnel in 2,3-DHBD_Ao are direction-dependent. These results enrich the understanding of the interactions of substrates/products with macromolecular reversible enzymes in different reaction directions, thereby demonstrating a possible path for engineering decarboxylases with higher carboxylation efficiency. KEY POINTS: • The residue Trp23 of 2,3-DHBD_Ao served as a switch to control the entry and exit of catechol • A V-shaped tunnel of 2,3-DHBD_Ao for decarboxylation and carboxylation reactions was identified • The results provide a promising strategy for engineering decarboxylases with direction-dependent residues inside the substrate/product traveling tunnel of the enzyme.

Artificial intelligence revolutionizing drug development: Exploring opportunities and challenges

By harnessing artificial intelligence (AI) algorithms and machine learning techniques, the entire drug discovery process stands to undergo a profound transformation, offering a myriad of advantages. Foremost among these is the ability of AI to conduct swift and efficient screenings of expansive compound libraries, significantly augmenting the identification of potential drug candidates. Moreover, AI algorithms can prove instrumental in predicting the efficacy and safety profiles of candidate compounds, thus endowing invaluable insights and reducing reliance on extensive preclinical and clinical testing. This predictive capacity of AI has the potential to streamline the drug development pipeline and enhance the success rate of clinical trials, ultimately resulting in the emergence of more efficacious and safer therapeutic agents. However, the deployment of AI in drug discovery introduces certain challenges that warrant attention. A primary hurdle entails the imperative acquisition of high-quality and diverse data. Furthermore, ensuring the interpretability of AI models assumes critical importance in securing regulatory endorsement and cultivating trust within scientific and medical communities. Addressing ethical considerations, including data privacy and mitigating bias, represents an additional momentous challenge, requiring assiduous navigation. In this review, we provide an intricate and comprehensive overview of the multifaceted challenges intrinsic to conventional drug development paradigms, while simultaneously interrogating the efficacy of AI in effectively surmounting these formidable obstacles.

Landscape of In Silico Tools for Modeling Covalent Modification of Proteins: A Review on Computational Covalent Drug Discovery

Covalent drug discovery has been a challenging research area given the struggle of finding a sweet balance between selectivity and reactivity for these drugs, the lack of which often leads to off-target activities and hence undesirable side effects. However, there has been a resurgence in covalent drug design following the success of several covalent drugs such as boceprevir (2011), ibrutinib (2013), neratinib (2017), dacomitinib (2018), zanubrutinib (2019), and many others. Design of covalent drugs includes many crucial factors, where "evaluation of the binding affinity" and "a detailed mechanistic understanding on covalent inhibition" are at the top of the list. Well-defined experimental techniques are available to elucidate these factors; however, often they are expensive and/or time-consuming and hence not suitable for high throughput screens. Recent developments in in silico methods provide promise in this direction. In this report, we review a set of recent publications that focused on developing and/or implementing novel in silico techniques in "Computational Covalent Drug Discovery (CCDD)". We also discuss the advantages and disadvantages of these approaches along with what improvements are required to make it a great tool in medicinal chemistry in the near future.

Structural modeling of hERG channel-drug interactions using Rosetta

The human ether-a-go-go-related gene (hERG) not only encodes a potassium-selective voltage-gated ion channel essential for normal electrical activity in the heart but is also a major drug anti-target. Genetic hERG mutations and blockage of the channel pore by drugs can cause long QT syndrome, which predisposes individuals to potentially deadly arrhythmias. However, not all hERG-blocking drugs are proarrhythmic, and their differential affinities to discrete channel conformational states have been suggested to contribute to arrhythmogenicity. We used Rosetta electron density refinement and homology modeling to build structural models of open-state hERG channel wild-type and mutant variants (Y652A, F656A, and Y652A/F656 A) and a closed-state wild-type channel based on cryo-electron microscopy structures of hERG and EAG1 channels. These models were used as protein targets for molecular docking of charged and neutral forms of amiodarone, nifekalant, dofetilide, d/l-sotalol, flecainide, and moxifloxacin. We selected these drugs based on their different arrhythmogenic potentials and abilities to facilitate hERG current. Our docking studies and clustering provided atomistic structural insights into state-dependent drug-channel interactions that play a key role in differentiating safe and harmful hERG blockers and can explain hERG channel facilitation through drug interactions with its open-state hydrophobic pockets.

Facilitation of hERG Activation by Its Blocker: A Mechanism to Reduce Drug-Induced Proarrhythmic Risk

Modulation of the human Ether-à-go-go-Related Gene (hERG) channel, a crucial voltage-gated potassium channel in the repolarization of action potentials in ventricular myocytes of the heart, has significant implications on cardiac electrophysiology and can be either antiarrhythmic or proarrhythmic. For example, hERG channel blockade is a leading cause of long QT syndrome and potentially life-threatening arrhythmias, such as torsades de pointes. Conversely, hERG channel blockade is the mechanism of action of Class III antiarrhythmic agents in terminating ventricular tachycardia and fibrillation. In recent years, it has been recognized that less proarrhythmic hERG blockers with clinical potential or Class III antiarrhythmic agents exhibit, in addition to their hERG-blocking activity, a second action that facilitates the voltage-dependent activation of the hERG channel. This facilitation is believed to reduce the proarrhythmic potential by supporting the final repolarizing of action potentials. This review covers the pharmacological characteristics of hERG blockers/facilitators, the molecular mechanisms underlying facilitation, and their clinical significance, as well as unresolved issues and requirements for research in the fields of ion channel pharmacology and drug-induced arrhythmias.

Diabetic wound healing of aloe vera major phytoconstituents through TGF-β1 suppression via in-silico docking, molecular dynamic simulation and pharmacokinetic studies

To restore the integrity of the skin and subcutaneous tissue, the wound healing process involves a complex series of well-orchestrated biochemical and cellular events. Due to the existence of various active components, accessibility and few side effects, some plant extracts and their phytoconstituents are recognised as viable options for wound healing agents. To find possible inhibitors of diabetic wound healing, four main constituents of aloe vera were identified from the literature. TGF-β1 and the compounds were studied using molecular docking to see how they interacted with the active site of target protein (PDB ID: 6B8Y). The pharmacokinetics investigation of the aloe emodin with the highest dock score complied with all the Lipinski's rule of five and pharmacokinetics criteria. Conformational change in the docked complex of Aloe emodin was investigated with the Amber simulation software, via a molecular dynamic (MD) simulation. The MD simulations of aloe emodin bound to TGF-β1 showed the significant structural rotations and twists occurring from 0 to 200 ns. The estimate of the aloe emodin-TGF-β1 complex's binding free energy has also been done using MM-PBSA/GBSA techniques. Additionally, aloe emodin has a wide range of enzymatic activities since their probability active (Pa) values is >0.700. 'Aloe emodin', an active extract of aloe vera, has been identified as the key chemical in the current investigation that can inhibit diabetic wound healing. Both in-vitro and in-vivo experiments will be used in a wet lab to confirm the current computational findings.Communicated by Ramaswamy H. Sarma.

Applying molecular docking to pesticides

Pesticide creation is related to the development of sustainable agricultural and ecological safety, and molecular docking technology can effectively help in pesticide innovation. This paper introduces the basic theory behind molecular docking, pesticide databases, and docking software. It also summarizes the application of molecular docking in the pesticide field, including the virtual screening of lead compounds, detection of pesticides and their metabolites in the environment, reverse screening of pesticide targets, and the study of resistance mechanisms. Finally, problems with the use of molecular docking technology in pesticide creation are discussed, and prospects for the future use of molecular docking technology in new pesticide development are discussed. © 2023 Society of Chemical Industry.

Pharmacophore Model-Based Virtual Screening Workflow for Discovery of Inhibitors Targeting Plasmodium falciparum Hsp90

Plasmodium falciparum causes the most lethal and widespread form of malaria. Eradication of malaria remains a priority due to the increasing number of cases of drug resistance. The heat shock protein 90 of P. falciparum (PfHsp90) is a validated drug target essential for parasite survival. Most PfHsp90 inhibitors bind at the ATP binding pocket found in its N-terminal domain, abolishing the chaperone's activities, which leads to parasite death. The challenge is that the NTD of PfHsp90 is highly conserved, and its disruption requires selective inhibitors that can act without causing off-target human Hsp90 activities. We endeavored to discover selective inhibitors of PfHsp90 using pharmacophore modeling, virtual screening protocols, induced fit docking (IFD), and cell-based and biochemical assays. The pharmacophore model (DHHRR), composed of one hydrogen bond donor, two hydrophobic groups, and two aromatic rings, was used to mine commercial databases for initial hits, which were rescored to 20 potential hits using IFD. Eight of these compounds displayed moderate to high activity toward P. falciparum NF54 (i.e., IC50s ranging from 6.0 to 0.14 μM) and averaged >10 in terms of selectivity indices toward CHO and HepG2 cells. Additionally, four compounds inhibited PfHsp90 with greater selectivity than a known inhibitor, harmine, and bound to PfHsp90 with weak to moderate affinity. Our findings support the use of a pharmacophore model to discover diverse chemical scaffolds such as FM2, FM6, F10, and F11 exhibiting anti-Plasmodium activities and serving as valuable new PfHsp90 inhibitors. Optimization of these hits may enable their development into potent leads for future antimalarial drugs.

Fluctuation-Dominated Ligand Binding in Molten Globule Protein

A molten globule (MG) state is an intermediate state of protein observed during the unfolding of the native structure. In MG states, milk protein α-lactalbumin (aLA) binds to oleic acid (OLA). This MG-aLA-OLA complex, popularly known as XAMLET, performs cytotoxic activities against cancer cell lines. However, the microscopic understanding of ligand recognition ability in the MG state of the protein has not yet been explored. Motivated by this, we explore the binding of bovine aLA with OLA using all-atom molecular dynamics (MD) simulations. We find the binding mode between MG-aLA and OLA using the conformational thermodynamics method. We also estimate the binding free energy using the umbrella sampling (US) method for both the MG state and the neutral state. We find that the binding free energy obtained from US is comparable with earlier experimental results. We characterize the dihedral fluctuations as the ligand is liberated from the active site of the protein using steered MD. The low energy fluctuations occur near the ligand binding site, which eventually transfer toward the Ca2+-binding site as the ligand is taken away from the protein.

Homology modeling of human BAP1 and analysis of its binding properties through molecular docking and molecular dynamics simulation

BRCA1-associated protein 1 (BAP1) is a nuclear-localized Ubiquitin C-terminal hydrolase (UCH) that functions as a tumour suppressor, and although BAP1 has been linked to cancer, the molecular mechanism by which BAP1 regulates cancer and its crystal structure have not been elucidated. In this study, computational approaches were used to identify the protein model of BAP1 and its potential inhibitors. The structure of the BAP1 model was constructed through homology modeling and the generated BAP1 model was observed to exhibit good quality protein model as the distribution of its amino acids in the Ramachandran's plot corresponded to 87.7% in the most favoured regions. Docking and simulating of the ubiquitin on the BAP1 model structure revealed the rearrangement of F228, F50, and H169 residues of the BAP1 and switching of BAP1's conformation into a productive state. Our screening results of potential BAP1 inhibitors against the FDA approved drugs shortlisted two potential inhibitors, which are FDA1065 and FDA755. We then performed molecular dynamics simulations and Molecular mechanics Poisson-Boltzmann surface area (MMPBSA) analysis on both inhibitors and found that only the BAP1-FDA755 formed a stable complex and the FDA755 ligand remained its position inside the active site of the BAP1 with a total binding energy of (-51.77 ± 3.49 kcal/mol). We speculate that the presence of methyl group in FDA755 play an important role in stabilizing the BAP1-FDA755 complex.Communicated by Ramaswamy H. Sarma.

Combining molecular modelling and experimental approaches to gain mechanistic insights into the LuxP drug target in Streptococcus pyogens

Autoinducer-2 can mediate inter- and intra-species communication signal between bacteria and these signals from AI-2 is noted from limited species of bacteria. In humans, S. pyogenes is a pathogen that causes a wide range of illnesses and can survive in the host system and transmit infection. The process by which S. pyogenes acquires the competence to live and disseminate infection remains unknown. We hypothesized that AI-2 and their receptors would play a significant role during infection, and for that present investigation provides the experimental and molecular insights. In the absence of details about the receptor LuxP and LuxQ, the screening approach provides supporting insights. The evolutionary relationship and similarities of the PBP domain (Spy 1535) and the signal transmission PDZ domain (Spy 1536) were studied in relation to their counterparts in other bacteria. Molecular docking and modeling confirmed the domain-enhanced specificity for AI-2 binding. In vitro studies showed that AI-2, which is present in the cell-free supernatant of S. pyogenes, regulates luminescence in P. luminous and biofilm development in E. coli using the LuxS reporter genes. Examination of S. pyogenes gene expression revealed modulation of virulence genes when the pathogen was exposed to V. harveyi HSL and AI-2. Therefore, S. pyogenes pathogenicity is sequentially regulated by AI-2 it acquires from other commensal bacteria. Overall, this study lays the groundwork for understanding the signalling mechanism from AI-2, which are critical to the pathogenic mechanism of S. pyogenes.Communicated by Ramaswamy H. Sarma.

Plasticity of the binding pocket in peptide transporters underpins promiscuous substrate recognition

Proton-dependent oligopeptide transporters (POTs) are promiscuous transporters of the major facilitator superfamily that constitute the main route of entry for a wide range of dietary peptides and orally administrated peptidomimetic drugs. Given their clinical and pathophysiological relevance, several POT homologs have been studied extensively at the structural and molecular level. However, the molecular basis of recognition and transport of diverse peptide substrates has remained elusive. We present 14 X-ray structures of the bacterial POT DtpB in complex with chemically diverse di- and tripeptides, providing novel insights into the plasticity of the conserved central binding cavity. We analyzed binding affinities for more than 80 peptides and monitored uptake by a fluorescence-based transport assay. To probe whether all 8400 natural di- and tripeptides can bind to DtpB, we employed state-of-the-art molecular docking and machine learning and conclude that peptides with compact hydrophobic residues are the best DtpB binders.

Design, in Silico Studies and Biological Evaluation of New Chiral Thiourea and 1,3-Thiazolidine-4,5-dione Derivatives

In this study, new chiral thiourea and 1,3-thiazolidine-4,5-dione derivatives were synthesized, it was aimed to evaluate the various biological activities and molecular docking of these compounds. Firstly, the new thioureas (1-16) were obtained by reacting 1-naphthylisothiocyanate with different chiral amines. Then, the chiral thioureas were cyclized with oxalyl chloride to obtain 1,3-thiazolidine-4,5-dione derivatives (17-32). All compounds were evaluated with several in vitro antioxidant and enzyme inhibition activities. Compound 30 was the most active compound against AChE, with a value of IC50 =8.09±0.58 μM. On the other hand, all compounds were tested in silico absorption, distribution, metabolism, and excretion (ADME) assays to better understand their bioavailability. These physicochemical properties, pharmacokinetics, and drug-likeness of all compounds were calculated using SwissADME. Furthermore, according to molecular docking analyses compound 30 exhibited significant binding affinities for all enzymes. Based on our overall observations, compound 30 could be recommended as a potential lead for the therapuetic of Alzheimer's.

Clustering Heterogeneous Conformational Ensembles of Intrinsically Disordered Proteins with t-Distributed Stochastic Neighbor Embedding

Intrinsically disordered proteins (IDPs) populate a range of conformations that are best described by a heterogeneous ensemble. Grouping an IDP ensemble into "structurally similar" clusters for visualization, interpretation, and analysis purposes is a much-desired but formidable task, as the conformational space of IDPs is inherently high-dimensional and reduction techniques often result in ambiguous classifications. Here, we employ the t-distributed stochastic neighbor embedding (t-SNE) technique to generate homogeneous clusters of IDP conformations from the full heterogeneous ensemble. We illustrate the utility of t-SNE by clustering conformations of two disordered proteins, Aβ42, and α-synuclein, in their APO states and when bound to small molecule ligands. Our results shed light on ordered substates within disordered ensembles and provide structural and mechanistic insights into binding modes that confer specificity and affinity in IDP ligand binding. t-SNE projections preserve the local neighborhood information, provide interpretable visualizations of the conformational heterogeneity within each ensemble, and enable the quantification of cluster populations and their relative shifts upon ligand binding. Our approach provides a new framework for detailed investigations of the thermodynamics and kinetics of IDP ligand binding and will aid rational drug design for IDPs.

Advanced cancer targeting using aptamer functionalized nanocarriers for site-specific cargo delivery

The non-specificity of standard anticancer therapies has profound detrimental consequences in clinical treatment. Therapeutic specificity can be precisely achieved using cutting-edge ligands. Small synthetic oligonucleotide-ligands chosen through Systematic evolution of ligands by exponential enrichment (SELEX) would be an unceasing innovation in using nucleic acids as aptamers, frequently referred to as "chemical antibodies." Aptamers act as externally controlled switching materials that can attach to various substrates, for example, membrane proteins or nucleic acid structures. Aptamers pose excellent specificity and affinity for target molecules and can be used as medicines to suppress tumor cell growth directly. The creation of aptamer-conjugated nanoconstructs has recently opened up innovative options in cancer therapy that are more effective and target tumor cells with minor toxicity to healthy tissues. This review focuses on a comprehensive description of the most capable classes of aptamer-tethered nanocarriers for precise recognition of cancer cells with significant development in proficiency, selectivity, and targetability for cancer therapy. Existing theranostic applications with the problems and future directions are also highlighted.

MELD-Bracket Ranks Binding Affinities of Diverse Sets of Ligands

Affinity ranking of structurally diverse small-molecule ligands is a challenging problem with important applications in structure-based drug discovery. Absolute binding free energy methods can model diverse ligands, but the high computational cost of the current methods limits application to data sets with few ligands. We recently developed MELD-Bracket, a Molecular Dynamics method for efficient affinity ranking of ligands [ JCTC 2022, 18 (1), 374-379]. It utilizes a Bayesian framework to guide sampling to relevant regions of phase space, and it couples this with a bracket-like competition on a pool of ligands. Here we find that 6-competitor MELD-Bracket can rank dozens of diverse ligands that have low structural similarity and different net charges. We benchmark it on four protein systems─PTB1B, Tyk2, BACE, and JAK3─having varied modes of interactions. We also validated 8-competitor and 12-competitor protocols. The MELD-Bracket protocols presented here may have the appropriate balance of accuracy and computational efficiency to be suitable for ranking diverse ligands from typical drug discovery campaigns.

GB-score: Minimally designed machine learning scoring function based on distance-weighted interatomic contact features

In recent years, thanks to advances in computer hardware and dataset availability, data-driven approaches (like machine learning) have become one of the essential parts of the drug design framework to accelerate drug discovery procedures. Constructing a new scoring function, a function that can predict the binding score for a generated protein-ligand pose during docking procedure or a crystal complex, based on machine and deep learning has become an active research area in computer-aided drug design. GB-Score is a state-of-the-art machine learning-based scoring function that utilizes distance-weighted interatomic contact features, PDBbind-v2019 general set, and Gradient Boosting Trees algorithm to the binding affinity prediction. The distance-weighted interatomic contact featurization method used the distance between different ligand and protein atom types for numerical representation of the protein-ligand complex. GB-Score attains Pearson's correlation 0.862 and RMSE 1.190 on the CASF-2016 benchmark test in the scoring power metric. GB-Score's codes are freely available on the web at https://github.com/miladrayka/GB_Score.

In silico analysis of dietary polyphenols and their gut microbial metabolites suggest inhibition of SARS-CoV-2 infection, replication, and host inflammatory mediators

The outcome of SARS-CoV-2 infection ranges from asymptomatic to severe COVID-19 and death resulting from an exaggerated immune response termed cytokine storm. Epidemiological data have associated consumption of a high-quality plant-based diet with decreased incidence and severity of COVID-19. Dietary polyphenols and their microbial metabolites (MMs) have anti-viral and anti-inflammatory activities. Autodock Vina and Yasara were used in molecular docking and dynamics studies to investigate potential interactions of 7 parent polyphenols (PPs) and 11 MMs with the α- and Omicron variants of the SARS-CoV-2 spike glycoprotein (SGP), papain-like pro-tease (PLpro) and 3 chymotrypsin-like protease (3CLpro), as well as host inflammatory mediators including complement component 5a (C5a), C5a receptor (C5aR), and C-C chemokine receptor type 5 (CCR5). PPs and MMs interacted to varying degrees with residues on target viral and host inflammatory proteins showing potential as competitive inhibitors. Based on these in silico findings, PPs and MMs may inhibit SARS-CoV-2 infection, replication, and/or modulate host immunity in the gut or periphery. Such inhibition may explain why people that consume a high-quality plant-based diet have less incidence and severity of COVID-19.Communicated by Ramaswamy H. Sarma.

A heterobifunctional molecule system for targeted protein acetylation in cells

Protein acetylation is a vital biological process that regulates myriad cellular events. Despite its profound effects on protein function, there are limited research tools to dynamically and selectively regulate protein acetylation. To address this, we developed an acetylation tagging system, called AceTAG, to target proteins for chemically induced acetylation directly in live cells. AceTAG uses heterobifunctional molecules composed of a ligand for the lysine acetyltransferase p300/CBP and a FKBP12^F36V ligand. Target proteins are genetically tagged with FKBP12^F36V and brought in proximity with p300/CBP by AceTAG molecules to subsequently undergo protein-specific acetylation. Targeted acetylation of proteins in cells using AceTAG is selective, rapid, and can be modulated in a dose-dependent fashion, enabling controlled investigations of acetylated protein targets directly in cells. In this protocol, we focus on (1) generation of AceTAG constructs and cell lines, (2) in vitro characterization of AceTAG mediated ternary complex formation and cellular target engagement studies; and (3) in situ characterization of AceTAG induced acetylation of targeted proteins by immunoblotting and quantitative proteomics. The robust procedures described herein should enable the use of AceTAG to explore the roles of acetylation for a variety of protein targets.

Machine learning and molecular simulation ascertain antimicrobial peptide against Klebsiella pneumoniae from public database

Antimicrobial peptides (AMPs) are short peptides with a broad spectrum of antimicrobial activity. They play a key role in the host innate immunity of many organisms. The growing threat of microorganisms resistant to antimicrobial agents and the lack of new commercially available antibiotics have made in silico discovery of AMPs increasingly important. Machine learning (ML) has improved the speed and efficiency of AMP discovery while reducing the cost of experimental approaches. Despite various ML platforms developed, there is still a lack of integrative use of ML platforms for AMP discovery from publicly available protein databases. Therefore, our study aims to screen potential AMPs with antibiofilm properties from databases using ML platforms, followed by protein-peptide molecular docking analysis and molecular dynamics (MD) simulations. A total of 5850 peptides classified as non-AMP were screened from UniProtKB and analyzed using various online ML platforms (e.g., CAMPr3, DBAASP, dPABBs, Hemopred, and ToxinPred). Eight potential AMP peptides against Klebsiella pneumoniae with antibiofilm, non-toxic and non-hemolytic properties were then docked to MrkH, a transcriptional regulator of type 3 fimbriae involved in biofilm formation. Five of eight peptides bound more strongly than the native MrkH ligand when analyzed using HADDOCK and HPEPDOCK. Following the docking studies, our MD simulated that a Neuropeptide B (Peptide 3) bind strongly to the MrkH active sites. The discovery of putative AMPs that exceed the binding energies of the native ligand underscores the utility of the combined ML and molecular simulation strategies for discovering novel AMPs with antibiofilm properties.

The slow but steady rise of binding free energy calculations in drug discovery

Binding free energy calculations are increasingly used in drug discovery research to predict protein-ligand binding affinities and to prioritize candidate drug molecules accordingly. It has taken decades of collective effort to transform this academic concept into a technology adopted by the pharmaceutical and biotech industry. Having personally witnessed and taken part in this transformation, here I recount the (incomplete) list of problems that had to be solved to make this computational tool practical and suggest areas of future development.

ACES: Optimized Alchemically Enhanced Sampling

We present an alchemical enhanced sampling (ACES) method implemented in the GPU-accelerated AMBER free energy MD engine. The methods hinges on the creation of an "enhanced sampling state" by reducing or eliminating selected potential energy terms and interactions that lead to kinetic traps and conformational barriers while maintaining those terms that curtail the need to otherwise sample large volumes of phase space. For example, the enhanced sampling state might involve transforming regions of a ligand and/or protein side chain into a noninteracting "dummy state" with internal electrostatics and torsion angle terms turned off. The enhanced sampling state is connected to a real-state end point through a Hamiltonian replica exchange (HREMD) framework that is facilitated by newly developed alchemical transformation pathways and smoothstep softcore potentials. This creates a counterdiffusion of real and enhanced-sampling states along the HREMD network. The effect of a differential response of the environment to the real and enhanced-sampling states is minimized by leveraging the dual topology framework in AMBER to construct a counterbalancing HREMD network in the opposite alchemical direction with the same (or similar) real and enhanced sampling states at inverted end points. The method has been demonstrated in a series of test cases of increasing complexity where traditional MD, and in several cases alternative REST2-like enhanced sampling methods, are shown to fail. The hydration free energy for acetic acid was shown to be independent of the starting conformation, and the values for four additional edge case molecules from the FreeSolv database were shown to have a significantly closer agreement with experiment using ACES. The method was further able to handle different rotamer states in a Cdk2 ligand identified as fractionally occupied in crystal structures. Finally, ACES was applied to T4-lysozyme and demonstrated that the side chain distribution of V111χ1 could be reliably reproduced for the apo state, bound to p-xylene, and in p-xylene→ benzene transformations. In these cases, the ACES method is shown to be highly robust and superior to a REST2-like enhanced sampling implementation alone.

Prioritizing Small Sets of Molecules for Synthesis through in-silico Tools: A Comparison of Common Ranking Methods

Prioritizing molecules for synthesis is a key role of computational methods within medicinal chemistry. Multiple tools exist for ranking molecules, from the cheap and popular molecular docking methods to more computationally expensive molecular-dynamics (MD)-based methods. It is often questioned whether the accuracy of the more rigorous methods justifies the higher computational cost and associated calculation time. Here, we compared the performance on ranking the binding of small molecules for seven scoring functions from five docking programs, one end-point method (MM/GBSA), and two MD-based free energy methods (PMX, FEP+). We investigated 16 pharmaceutically relevant targets with a total of 423 known binders. The performance of docking methods for ligand ranking was strongly system dependent. We observed that MD-based methods predominantly outperformed docking algorithms and MM/GBSA calculations. Based on our results, we recommend the application of MD-based free energy methods for prioritization of molecules for synthesis in lead optimization, whenever feasible.

Structures Controlled by Entropy: The Flexibility of Strychnine as Example

To study the flexibility of strychnine, we performed molecular dynamics simulations with orientational tensorial constraints (MDOC). Tensorial constraints are derived from nuclear magnetic resonance (NMR) interaction tensors, for instance, from residual dipolar couplings (RDCs). Used as orientational constraints, they rotate the whole molecule and molecular parts with low rotational barriers. Since the NMR parameters are measured at ambient temperatures, orientational constraints generate conformers that populate the whole landscape of Gibbs free energy. In MDOC, structures are populated that are not only controlled by energy but by the entropy term TΔS of the Gibbs free energy. In the case of strychnine, it is shown that ring conformers are populated, which has not been discussed in former investigations. These conformer populations are not only in accordance with RDCs but fulfill nuclear Overhauser effect (NOE)-derived distance constraints and ³J_HH couplings as well.

General Theory of Specific Binding: Insights from a Genetic-Mechano-Chemical Protein Model

Proteins need to selectively interact with specific targets among a multitude of similar molecules in the cell. However, despite a firm physical understanding of binding interactions, we lack a general theory of how proteins evolve high specificity. Here, we present such a model that combines chemistry, mechanics, and genetics and explains how their interplay governs the evolution of specific protein-ligand interactions. The model shows that there are many routes to achieving molecular discrimination-by varying degrees of flexibility and shape/chemistry complementarity-but the key ingredient is precision. Harder discrimination tasks require more collective and precise coaction of structure, forces, and movements. Proteins can achieve this through correlated mutations extending far from a binding site, which fine-tune the localized interaction with the ligand. Thus, the solution of more complicated tasks is enabled by increasing the protein size, and proteins become more evolvable and robust when they are larger than the bare minimum required for discrimination. The model makes testable, specific predictions about the role of flexibility and shape mismatch in discrimination, and how evolution can independently tune affinity and specificity. Thus, the proposed theory of specific binding addresses the natural question of "why are proteins so big?". A possible answer is that molecular discrimination is often a hard task best performed by adding more layers to the protein.

Computationally Elucidating the Binding Kinetics for Different AChE Inhibitors to Access the Rationale for Improving the Drug Efficacy

Traditional drug discovery is based on a binding affinity (thermodynamics)-driven paradigm. Numerous examples, however, demonstrated that drug efficacy does not always depend only on binding affinity but positively correlates with binding kinetics, that is, the dissociation rate constant (k_off). Binding free energy landscape (BFEL) constructor is a computational binding kinetics prediction method, previously developed by us, that estimates the binding kinetics for ligand-protein based on their constructed binding free energy landscape, but it also reveals the detailed molecular mechanism of the binding event, hence, providing the position of transition states at the molecular level to modify/improve the binding kinetics. Acetylcholinesterase (AChE) is a well-known Alzheimer's disease (AD) target for which there is still not an ideal drug on the market. Therefore, to improve the drug design strategy for AD, the binding kinetics and binding molecular mechanisms of the four inhibitors of AChE, that is, E2020 (Aricept), HupA, Rivastigmine, and Galantamine, were studied. Also, the differentiation of the binding kinetics between mAChE and TcAChE was studied to evaluate the sensitiveness of BFEL constructor. The flexibility of molecules has a noticeable effect on the nature of BFEL. To the same target, flexible molecules (i.e., E2020 and Rivastigmine) which contain more rotatable bonds tend to have more complicated BFELs reflecting more complicated molecular action mechanisms than the rigid ones (i.e., HupA and Galantamine), which therefore could be more challenging to be optimized. The binding kinetics is highly dependent on the structure of the molecules, such as the length and the functional groups. Therefore, E2020 presents better binding kinetic and thermodynamic properties with either TcAChE or mAChE. Therefore, it is the most promising lead drug for binding kinetics-based drug design. In addition, the binding kinetics of a drug may present different values in the proteins of different organisms because the residue compositions of the binding gorges of the targets are variant, that is, E2020 shows lower binding affinity and association energy barrier in binding with mAChE than TcAChE. However, HupA presents a better binding property with TcAChE than mAChE.

Large-Scale Ligand Perturbations of the Protein Conformational Landscape Reveal State-Specific Interaction Hotspots

Protein flexibility is important for ligand binding but often ignored in drug design. Considering proteins as ensembles rather than static snapshots creates opportunities to target dynamic proteins that lack FDA-approved drugs, such as the human chaperone, heat shock protein 90 (Hsp90). Hsp90α accommodates ligands with a dynamic lid domain, yet no comprehensive analysis relating lid conformations to ligand properties is available. To date, ∼300 ligand-bound Hsp90α crystal structures are deposited in the Protein Data Bank, which enables us to consider ligand binding as a perturbation of the protein conformational landscape. By estimating binding site volumes, we classified structures into distinct major and minor lid conformations. Supported by retrospective docking, each conformation creates unique hotspots that bind chemically distinguishable ligands. Clustering revealed insightful exceptions and the impact of crystal packing. Overall, Hsp90α’s plasticity provides a cautionary tale of overinterpreting individual crystal structures and motivates an ensemble-based view of drug design.

Ligand-Enhanced Negative Images Optimized for Docking Rescoring

Despite the pivotal role of molecular docking in modern drug discovery, the default docking scoring functions often fail to recognize active ligands in virtual screening campaigns. Negative image-based rescoring improves docking enrichment by comparing the shape/electrostatic potential (ESP) of the flexible docking poses against the target protein’s inverted cavity volume. By optimizing these negative image-based (NIB) models using a greedy search, the docking rescoring yield can be improved massively and consistently. Here, a fundamental modification is implemented to this shape-focused pharmacophore modelling approach—actual ligand 3D coordinates are incorporated into the NIB models for the optimization. This hybrid approach, labelled as ligand-enhanced brute-force negative image-based optimization (LBR-NiB), takes the best from both worlds, i.e., the all-roundedness of the NIB models and the difficult to emulate atomic arrangements of actual protein-bound small-molecule ligands. Thorough benchmarking, focused on proinflammatory targets, shows that the LBR-NiB routinely improves the docking enrichment over prior iterations of the R-NiB methodology. This boost can be massive, if the added ligand information provides truly essential binding information that was lacking or completely missing from the cavity-based NIB model. On a practical level, the results indicate that the LBR-NiB typically works well when the added ligand 3D data originates from a high-quality source, such as X-ray crystallography, and, yet, the NIB model compositions can also sometimes be improved by fusing into them, for example, with flexibly docked solvent molecules. In short, the study demonstrates that the protein-bound ligands can be used to improve the shape/ESP features of the negative images for effective docking rescoring use in virtual screening.

Drug Discovery by Automated Adaptation of Chemical Structure and Identity

Computer-aided drug design offers the potential to dramatically reduce the cost and effort required for drug discovery. While screening-based methods are valuable in the early stages of hit identification, they are frequently succeeded by iterative, hypothesis-driven computations that require recurrent investment of human time and intuition. To increase automation, we introduce a computational method for lead refinement that combines concerted dynamics of the ligand/protein complex via molecular dynamics simulations with integrated Monte Carlo-based changes in the chemical formula of the ligand. This approach, which we refer to as ligand-exchange Monte Carlo molecular dynamics, accounts for solvent- and entropy-based contributions to competitive binding free energies by coupling the energetics of bound and unbound states during the ligand-exchange attempt. Quantitative comparison of relative binding free energies to reference values from free energy perturbation, conducted in vacuum, indicates that ligand-exchange Monte Carlo molecular dynamics simulations sample relevant conformational ensembles and are capable of identifying strongly binding compounds. Additional simulations demonstrate the use of an implicit solvent model. We speculate that the use of chemical graphs in which exchanges are only permitted between ligands with sufficient similarity may enable an automated search to capture some of the benefits provided by human intuition during hypothesis-guided lead refinement.

Characterizing Receptor Flexibility to Predict Mutations That Lead to Human Adaptation of Influenza Hemagglutinin

A key step in the emergence of human pandemic influenza strains has been a switch in binding preference of the viral glycoprotein hemagglutinin (HA) from avian to human sialic acid (SA) receptors. The conformation of the bound SA varies substantially with HA sequence, and crystallographic evidence suggests that the bound SA is flexible, making it difficult to predict which mutations are responsible for changing HA-binding preference. We performed molecular dynamics (MD) simulations of SA analogues binding to various HAs and observed a dynamic equilibrium among structurally diverse receptor conformations, including conformations that have not been experimentally observed. Using one such novel conformation, we predicted—and experimentally confirmed—a set of mutations that substantially increased an HA’s affinity for a human SA analogue. This prediction could not have been inferred from the existing crystal structures, suggesting that MD-generated HA–SA conformational ensembles could help researchers predict human-adaptive mutations, aiding surveillance of emerging pandemic threats.

Fisetin 8-C-glucoside as entry inhibitor in SARS CoV-2 infection: molecular modelling study

Coronaviruses are RNA viruses that infect varied species including humans. TMPRSS2 is gateway for SARS CoV-2 entry into the host cell. It causes proteolytic activation of spike protein and discharge of the peptide into host cell. The TMPRSS2 inhibition could be one of the approaches to stop the viral entry, therefore, interaction pattern and binding energies for Fisetin and TMPRSS2 have been explored in the present study. TMPRSS2 peptide was used for homology modelling and then for further study. Molecular docking score and MMGBSA Binding energy of Fisetin was better than Nafamostat, a known inhibitor of TMPRSS2. Post docking MM-GBSA free energy for Fisetin and Nafamostat was -42.78 and -21.11 kcal/mol, respectively. Fisetin forms H bond with Val 25, His 41, Lys 42, Lys 45, Glu 44, Ser186. Nafamostat formed H bonds with Lys 85, Asp 90, Asp 203. RMSD plots of TMPRSS2, TMPRSS2-Fisetin and TMPRSS2-Nafamostat complex showed stable profile with very small fluctuation during entire simulation of 150 ns. Significant decrease in TMPRSS2-Fisetin and TMPRSS2-Nafamostat complex fluctuation occurred around His 41, Glu 44, Gly 136, Ser 186 in RMSF study. During simulation Fisetin interaction was observed with residues Val 25, His 41, Glu 44, Lys 45, Lys 87, Gly 136, Gln 183, Ser 186 likewise interaction of Nafamostat with Lys 85, Asp 90, Asn 163, Asp 203 and Ser 205. Post simulation MM-GBSA free energy was found to be -51.87 ± 4.3 and -48.23 ± 4.39 kcal/mol for TMPRSS2 with Fisetin and Nafamostat, respectively.Communicated by Ramaswamy H. Sarma.

Side Group of Hydrophobic Amino Acids Controls Chiral Discrimination among Chiral Counterions and Metal-Organic Cages

The self-assembly of chiral Pd₁₂L₂₄ metal-organic cages (MOCs) based on hydrophobic amino acids, including alanine (Ala), valine (Val), and leucine (Leu), into single-layered hollow spherical blackberry-type structures is triggered by nitrates through counterion-mediated attraction. In addition to nitrates, anionic N-(tert-butoxycarbonyl) (Boc)-protected Ala, Val, and Leu were used as chiral counterions during the self-assembly of d-MOCs. Previously, we showed that l-Ala suppresses the self-assembly process of d-Pd₁₂Ala₂₄ but has no effect on l-Pd₁₂Ala₂₄, i.e., chiral discrimination. Here, we indicate when the amino acid used as the chiral counterion has a bulkier side group than the amino acid in the MOC structure, no chiral discrimination exists; otherwise, chiral discrimination exists. For example, Ala can induce chiral discrimination in all chiral MOCs, whereas Leu can induce chiral discrimination only in Pd₁₂Leu₂₄. Moreover, chiral anionic d- and l-alanine-based surfactants have no chiral discrimination, indicating that bulkier chiral counterions with more hydropohobic side groups can erase chiral discrimination.

Machine learning assessment of the binding region as a tool for more efficient computational receptor-ligand docking

We present a combined computational approach to protein-ligand binding, which consists of two steps: (1) a deep neural network is used to locate a binding region on a target protein, and (2) molecular docking of a ligand is performed within the specified region to obtain the best pose using Autodock Vina. Our in-house designed neural network was trained using the PepBDB dataset. Although the training dataset consisted of protein-peptide complexes, we show that the approach is not limited to peptides, but also works remarkably well for a large class of non-peptide ligands. The results are compared with those in which the binding region (first step) was provided by Accluster. In cases where no prior experimental data on the binding region are available, our deep neural network provides a fast and effective alternative to classical software for its localization. Our code is available at https://github.com/mksmd/NNforDocking.

Insights into substrate behavior in a solvent-free protein liquid to rationalize its reduced catalytic rate

When proteins are engineered with a polymer surfactant coating on their surface, they can form a liquid phase by themselves, without the need for a solvent, such as, say, water. However, such solvent-free protein liquids (SFPL), despite their capability to function at temperatures above those in aqueous solutions, exhibit much reduced catalytic rates. A comprehensive understanding of the nature of substrates in such liquids is crucial to reason out the reduced catalytic activity of enzymes as SFPL media, and thus identify the means to improve the same. Employing atomistic molecular dynamics simulations of lipase A from Bacillus subtilis in its SFPL form, we demonstrate that at low concentrations, the substrate molecules are located mostly in the hydrophilic layer of the surfactant shell that ensheaths the enzyme; substrates in this SFPL are present in various conformations with similar propensities as in the aqueous solution. Slower translational diffusion and reorientational dynamics, as well as the reduced tendency of a substrate molecule to closely interact with the enzymes in the SFPL medium have been identified herein as the contributing factors for the reduced activity of enzymes in this hybrid liquid. At high concentrations of substrates corresponding to those used in in vitro experiments, the formation of an enzyme-substrate complex is observed. Microscopic insights reported here can aid in the choice of surfactants to improve the catalytic rate of enzymes in SFPL.

Protein-Protein Binding Free Energy Predictions with the MM/PBSA Approach Complemented with the Gaussian-Based Method for Entropy Estimation

Here, we present a Gaussian-based method for estimation of protein-protein binding entropy to augment the molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) method for computational prediction of binding free energy (ΔG). The method is termed f5-MM/PBSA/E, where "E" stands for entropy and f5 for five adjustable parameters. The enthalpy components of ΔG (molecular mechanics, polar and non-polar solvation energies) are computed from a single implicit solvent generalized Born (GB) energy minimized structure of a protein-protein complex, while the binding entropy is computed using independently GB energy minimized unbound and bound structures. It should be emphasized that the f5-MM/PBSA/E method does not use snapshots, just energy minimized structures, and is thus very fast and computationally efficient. The method is trained and benchmarked in 5-fold validation test over a data set consisting of 46 protein-protein binding cases with experimentally determined dissociation constant K _d values. This data set has been used for benchmarking in recently published protein-protein binding studies that apply conventional MM/PBSA and MM/PBSA with an enhanced sampling method. The f5-MM/PBSA/E tested on the same data set achieves similar or better performance than these computationally demanding approaches, making it an excellent choice for high throughput protein-protein binding affinity prediction studies.

Ligand binding remodels protein side-chain conformational heterogeneity

While protein conformational heterogeneity plays an important role in many aspects of biological function, including ligand binding, its impact has been difficult to quantify. Macromolecular X-ray diffraction is commonly interpreted with a static structure, but it can provide information on both the anharmonic and harmonic contributions to conformational heterogeneity. Here, through multiconformer modeling of time- and space-averaged electron density, we measure conformational heterogeneity of 743 stringently matched pairs of crystallographic datasets that reflect unbound/apo and ligand-bound/holo states. When comparing the conformational heterogeneity of side chains, we observe that when binding site residues become more rigid upon ligand binding, distant residues tend to become more flexible, especially in non-solvent-exposed regions. Among ligand properties, we observe increased protein flexibility as the number of hydrogen bonds decreases and relative hydrophobicity increases. Across a series of 13 inhibitor-bound structures of CDK2, we find that conformational heterogeneity is correlated with inhibitor features and identify how conformational changes propagate differences in conformational heterogeneity away from the binding site. Collectively, our findings agree with models emerging from nuclear magnetic resonance studies suggesting that residual side-chain entropy can modulate affinity and point to the need to integrate both static conformational changes and conformational heterogeneity in models of ligand binding.

Identification of Hypericin as a Candidate Repurposed Therapeutic Agent for COVID-19 and Its Potential Anti-SARS-CoV-2 Activity

The COVID-19 pandemic has had an unprecedented impact on the global economy and public health. Its etiologic agent, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is highly transmissible, pathogenic and has a rapid global spread. Currently, the increase in the number of new confirmed cases has been slowed down due to the increase of vaccination in some regions of the world. Still, the rise of new variants has influenced the detection of additional waves of rising cases that some countries have experienced. Since the virus replication cycle is composed of many distinct stages, some viral proteins related to them, as the main-protease (Mpro) and RNA dependent RNA polymerase (RdRp), constitute individual potential antiviral targets. In this study, we challenged the mentioned enzymes against compounds pre-approved by health regulatory agencies in a virtual screening and later in Molecular Mechanics/Poisson–Bolzmann Surface Area (MM/PBSA) analysis. Our results showed that, among the identified potential drugs with anti-SARS-CoV-2 properties, Hypericin, an important component of the Hypericum perforatum that presents antiviral and antitumoral properties, binds with high affinity to viral Mpro and RdRp. Furthermore, we evaluated the activity of Hypericin anti-SARS-CoV-2 replication in an in vitro model of Vero-E6 infected cells. Therefore, we show that Hypericin inhibited viral replication in a dose dependent manner. Moreover, the cytotoxicity of the compound, in cultured cells, was evaluated, but no significant activity was found. Thus, the results observed in this study indicate that Hypericin is an excellent candidate for repurposing for the treatment of COVID-19, with possible inhibition of two important phases of virus maturation.

Disentangling the recognition complexity of a protein hub using a nanopore

WD40 repeat proteins are frequently involved in processing cell signaling and scaffolding large multi-subunit machineries. Despite their significance in physiological and disease-like conditions, their reversible interactions with other proteins remain modestly examined. Here, we show the development and validation of a protein nanopore for the detection and quantification of WD40 repeat protein 5 (WDR5), a chromatin-associated hub involved in epigenetic regulation of histone methylation. Our nanopore sensor is equipped with a 14-residue Win motif of mixed lineage leukemia 4 methyltransferase (MLL4_Win), a WDR5 ligand. Our approach reveals a broad dynamic range of MLL4_Win-WDR5 interactions and three distant subpopulations of binding events, representing three modes of protein recognition. The three binding events are confirmed as specific interactions using a weakly binding WDR5 derivative and various environmental contexts. These outcomes demonstrate the substantial sensitivity of our nanopore sensor, which can be utilized in protein analytics.

Nanopores are powerful tools for sampling protein-peptide interactions. Here, the authors convert a protein-based nanopore into a sensitive biosensor to characterize the complex binding of WDR5 protein to a 14-residue ligand.

In-Silico modulation of Interleukin-8 (IL8) for the therapeutic management of endodontic pulpitis

Emerging clinical evidences highlight the association of Interleukin-8 (IL8) with endodontic pulpitis. Relatively higher expression of IL8 has been found in the pulp samples of pulpitis patients with moderate/severe pain. It is speculated that IL8 can be considered as a potential target for therapeutics of endodontic pulpitis. A library consisting of 3072 small molecules from the ZINC database was used to identify potential lead molecules with drug-like properties against the IL8. Based on the in-silico structure-assisted drug designing involving molecular docking, MD simulations, and MMPBSA analyses, we found a small molecule ZINC14613097 inhibits IL8. This study provides a new lead molecule than can be further validated in in-vitro, in-vivo, and ongoing clinical studies for the therapeutic management of endodontic pulpitis.