Assessing the performance of MM/PBSA and MM/GBSA methods. 9. Prediction reliability of binding affinities and binding poses for protein-peptide complexes

Characterizing the Cooperative Effect of PROTAC Systems with End-Point Binding Free Energy Calculation

Proteolytic targeting chimeras (PROTACs), as an emerging type of drug, function by proximity-based modalities that narrow the distance between a target protein and the E3 ubiquitin ligase to facilitate the ubiquitination labeling of the target protein for degradation. Although it is evidenced that the cooperativity of the PROTAC ternary interaction is one of the key factors affecting the degradation rate of a target protein, PROTAC design utilizing this indicator is still challenging because of the complicated/flexible interactions in a target-PROTAC-E3 ternary system. Therefore, developing reliable and practicable computational methods is of great interest for PROTAC design. Hence, in this study, we investigate the feasibility of using the end-point binding free energy calculation method, represented by molecular mechanics/Poisson-Boltzmann (generalized-Born) surface area (MM/PB(GB)SA), for characterizing cooperativity (including the stabilization and hook effects) of the PROTAC systems. The result shows that MM/GBSA is a good predictor in characterizing these effects under a relatively long molecular dynamics adjustment (50-100 ns) and low dielectric constant (εin = 1), with the Pearson correlation coefficient (rp) > 0.5 and 0.6 for the stabilization and hook effect, respectively. This study provides a feasible strategy for characterizing the cooperativity of the PROTAC systems, facilitating the rational design of PROTAC molecules.

Inhibitor binding and disruption of coupled motions in MmpL3 protein: Unraveling the mechanism of trehalose monomycolate transport

Mycobacterial membrane protein Large 3 (MmpL3) of Mycobacterium tuberculosis (Mtb) is crucial for the translocation of trehalose monomycolate (TMM) across the inner bacterial cell membrane, making it a promising target for anti-tuberculosis (TB) drug development. While several structural, microbiological, and in vitro studies have provided significant insights, the precise mechanisms underlying TMM transport by MmpL3 and its inhibition remain incompletely understood at the atomic level. In this study, molecular dynamic (MD) simulations for the apo form and seven inhibitor-bound forms of Mtb MmpL3 were carried out to obtain a thorough comprehension of the protein's dynamics and function. MD simulations revealed that the seven inhibitors in this work stably bind to the central channel of the transmembrane domain and primarily forming hydrogen bonds with ASP251, ASP640, or both residues. Through dynamical cross-correlation matrix and principal component analysis analyses, several types of coupled motions between different domains were observed in the apo state, and distinct conformational states were identified using Markov state model analysis. These coupled motions and varied conformational states likely contribute to the transport of TMM. However, simulations of inhibitor-bound MmpL3 showed an enlargement of the proton channel, potentially disrupting coupled motions. This indicates that inhibitors may impair MmpL3's transport function by directly blocking the proton channel, thereby hindering coordinated domain movements and indirectly affecting TMM translocation.

Molecular mechanism of Dang-Shen-Yu-Xing decoction against Mycoplasma bovis pneumonia based on network pharmacology, molecular docking, molecular dynamics simulations and experimental verification

Mycoplasma bovis pneumonia is a highly contagious respiratory infection caused by Mycoplasma bovis. It is particularly prevalent in calves, posing a significant threat to animal health and leading to substantial economic losses. Dang-Shen-Yu-Xing decoction is often used to treat this condition in veterinary clinics. It exhibits robust anti-inflammatory effects and can alleviate pulmonary fibrosis. However, its mechanism of action remains unclear. Therefore, this study aimed to preliminarily explore the molecular mechanism of Dang-Shen-Yu-Xing decoction for treating mycoplasma pneumonia in calves through a combination of network pharmacology, molecular docking, molecular dynamics simulation methods, and experimental validation. The active components and related targets of Dang-Shen-Yu-Xing decoction were extracted from several public databases. Additionally, complex interactions between drugs and targets were explored through network topology, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. Subsequently, the binding affinity of drug to disease-related targets was verified through molecular docking and molecular dynamics simulation. Finally, the pharmacodynamics were verified via animal experiments. The primary network topology analysis revealed two core targets and 10 key active components of Dang-Shen-Yu-Xing decoction against Mycoplasma bovis pneumonia. Kyoto Encyclopedia of Genes and Genomes enrichment analysis showed that the mechanism of Dang-Shen-Yu-Xing decoction for treating mycoplasma bovis pneumonia involved multiple signaling pathways, with the main pathways including PI3K-Akt and IL17 signaling pathways. Moreover, molecular docking predicted the binding affinity and conformation of the core targets of Dang-Shen-Yu-Xing decoction, IL6, and IL10, with the associated main active ingredients. The results showed a strong binding of the active ingredients to the hub target. Further, molecular docking dynamics simulation revealed three key active components of IL10 induced by Dang-Shen-Yu-Xing decoction against Mycoplasma bovis pneumonia. Finally, animal experiments confirmed Dang-Shen-Yu-Xing decoction pharmacodynamics, suggesting that it holds potential as an alternative therapy for treating mycoplasma bovis pneumonia.

Isolation, Structure Elucidation, and Biological Activity of the Selective TACR2 Antagonist Tumonolide and its Aldehyde from a Marine Cyanobacterium

The macrocyclic tumonolide (1) with enamide functionality and the linear tumonolide aldehyde (2) are new interconverting natural products from a marine cyanobacterium with a peptide-polyketide skeleton, representing a hybrid of apratoxins and palmyrolides or laingolides. The planar structures were established by NMR and mass spectrometry. The relative configuration of the stereogenically-rich apratoxin-like polyketide portion was determined using J-based configuration analysis. The absolute configuration of tumonolide (1) was determined by chiral analysis of the amino acid units and computational methods, followed by NMR chemical shift and ECD spectrum prediction, indicating all-R configuration for the polyketide portion, as in palmyrolide A and contrary to the all-S configuration in apratoxins. Functional screening against a panel of 168 GPCR targets revealed tumonolide (1) as a selective antagonist of TACR2 with an IC50 of 7.0 μM, closely correlating with binding affinity. Molecular docking studies established the binding mode and rationalized the selectivity for TACR2 over TACR1 and TACR3. RNA sequencing upon treatment of HCT116 colorectal cancer cells demonstrated activation of the pulmonary fibrosis idiopathic signaling pathway and the insulin secretion signaling pathway at 20 μM, indicating its potential to modulate these pathways.

A deep attention model for wide-genome protein-peptide binding affinity prediction at a sequence level

Peptides are pivotal in numerous biological activities by engaging in up to 40 % of protein-protein interactions in many cellular processes. Due to their exceptional specificity and effectiveness, peptides have emerged as promising candidates for drug design. However, accurately predicting protein-peptide binding affinity remains a challenging. Aiming at the problem, we develop a prediction model PepPAP based on convolutional neural network and multi-head attention, which relies solely on sequence features. These features include physicochemical properties, intrinsic disorder, sequence encoding, and especially interface propensity which is extracted from 16,689 non-redundant protein-peptide complexes. Notably, the adopted regression stratification cross-validation scheme proposed in our previous work is beneficial to improve the prediction for the cases with extreme binding affinity values. On three benchmark test datasets: T100, a series of peptides targeting to PDZ domain and CXCR4, PepPAP shows excellent performance, outperforming the existing methods and demonstrating its good generalization ability. Furthermore, PepPAP has good results in binary interaction prediction, and the analysis of the feature space distribution visualization highlights PepPAP's effectiveness. To the best of our knowledge, PepPAP is the first sequence-based deep attention model for wide-genome protein-peptide binding affinity prediction, and holds the potential to offer valuable insights for the peptide-based drug design.

Multi-epitope peptide vaccines targeting dengue virus serotype 2 created via immunoinformatic analysis

The Middle East has witnessed a greater spread of infectious Dengue viruses, with serotype 2 (DENV-2) being the most prevalent form. Through this work, multi-epitope peptide vaccines against DENV-2 that target E and nonstructural (NS1) proteins were generated through an immunoinformatic approach. MHC class I and II and LBL epitopes among NS1 and envelope E proteins sequences were predicted and their antigenicity, toxicity, and allergenicity were investigated. Studies of the population coverage denoted the high prevalence of NS1 and envelope-E epitopes among different countries where DENV-2 endemic. Further, both the CTL and HTL epitopes retrieved from NS1 epitopes exhibited high conservancies' percentages with other DENV serotypes (1, 3, and 4). Three vaccine constructs were created and the expected immune responses for the constructs were estimated using C-IMMSIM and HADDOCK (against TLR 2,3,4,5, and 7). Molecular dynamics simulation for vaccine construct 2 with TLR4 denoted high binding affinity and stability of the construct with the receptor which might foretell favorable in vivo interaction and immune responses.

Stability and dynamics of extradenticle modulates its function

Extradenticle (EXD) is a partner protein of the HOX transcription factors and plays an important role in the development of Drosophila. It confers increased affinity and specificity of DNA-binding to the HOX proteins. However, the DNA-binding homeodomain of EXD has a significantly weaker affinity to DNA compared to the HOX homeodomains. Here, we show that a glycine residue (G290) in the middle of the EXD DNA-binding helix primarily results in this weaker binding. Glycine destabilizes helices. To probe its role in the stability and function of the protein, G290 was mutated to alanine. The intrinsic stability of the DNA-binding helix increased in the G290A mutant as observed by NMR studies and molecular dynamics (MD) simulation. Also, NMR dynamics and MD simulation show that dynamic motions present in the wild-type protein are quenched in the mutant. This in turn resulted in increased stability of the entire homeodomain (ΔΔGG→A of -2.6 kcal/mol). Increased protein stability resulted in three-fold better DNA-binding affinity of the mutant as compared to the wild-type protein. Molecular mechanics with generalized Born and surface area solvation (MMGBSA) analysis of our MD simulation on DNA-bound models of both wild-type and mutant proteins shows that the contribution to binding is enhanced for most of the interface residues in the mutant compared to the wild-type. Interestingly, the flexible N-terminal arm makes more stable contact with the DNA minor groove in the mutant. We found that the two interaction sites i.e. the DNA-binding helix and the unstructured N-terminal arm influence each other via the bound DNA. These results provide an interesting conundrum: alanine at position 290 enhances both the stability and the DNA-binding affinity of the protein, however, evolution prefers glycine at this position. We have provided several plausible explanations for this apparent conundrum. The function of the EXD as a HOX co-factor requires its ability to discriminate similar DNA sequences, which is most likely comprom.

Assessing the performance of MM/PBSA and MM/GBSA methods. 10. Prediction reliability of binding affinities and binding poses for RNA-ligand complexes

Ribonucleic acid (RNA)-ligand interactions play a pivotal role in a wide spectrum of biological processes, ranging from protein biosynthesis to cellular reproduction. This recognition has prompted the broader acceptance of RNA as a viable candidate for drug targets. Delving into the atomic-scale understanding of RNA-ligand interactions holds paramount importance in unraveling intricate molecular mechanisms and further contributing to RNA-based drug discovery. Computational approaches, particularly molecular docking, offer an efficient way of predicting the interactions between RNA and small molecules. However, the accuracy and reliability of these predictions heavily depend on the performance of scoring functions (SFs). In contrast to the majority of SFs used in RNA-ligand docking, the end-point binding free energy calculation methods, such as molecular mechanics/generalized Born surface area (MM/GBSA) and molecular mechanics/Poisson Boltzmann surface area (MM/PBSA), stand as theoretically more rigorous approaches. Yet, the evaluation of their effectiveness in predicting both binding affinities and binding poses within RNA-ligand systems remains unexplored. This study first reported the performance of MM/PBSA and MM/GBSA with diverse solvation models, interior dielectric constants (εin) and force fields in the context of binding affinity prediction for 29 RNA-ligand complexes. MM/GBSA is based on short (5 ns) molecular dynamics (MD) simulations in an explicit solvent with the YIL force field; the GBGBn2 model with higher interior dielectric constant (εin = 12, 16 or 20) yields the best correlation (Rp = -0.513), which outperforms the best correlation (Rp = -0.317, rDock) offered by various docking programs. Then, the efficacy of MM/GBSA in identifying the near-native binding poses from the decoys was assessed based on 56 RNA-ligand complexes. However, it is evident that MM/GBSA has limitations in accurately predicting binding poses for RNA-ligand systems, particularly compared with notably proficient docking programs like rDock and PLANTS. The best top-1 success rate achieved by MM/GBSA rescoring is 39.3%, which falls below the best results given by docking programs (50%, PLNATS). This study represents the first evaluation of MM/PBSA and MM/GBSA for RNA-ligand systems and is expected to provide valuable insights into their successful application to RNA targets.

Deep learning for advancing peptide drug development: Tools and methods in structure prediction and design

Peptides can bind challenging disease targets with high affinity and specificity, offering enormous opportunities for addressing unmet medical needs. However, peptides' unique features, including smaller size, increased structural flexibility, and limited data availability, pose additional challenges to the design process compared to proteins. This review explores the dynamic field of peptide therapeutics, leveraging deep learning to enhance structure prediction and design. Our exploration encompasses various facets of peptide research, ranging from dataset curation handling to model development. As deep learning technologies become more refined, we channel our efforts into peptide structure prediction and design, aligning with the fundamental principles of structure-activity relationships in drug development. To guide researchers in harnessing the potential of deep learning to advance peptide drug development, our insights comprehensively explore current challenges and future directions of peptide therapeutics.

Analysis of E2F1 single-nucleotide polymorphisms reveals deleterious non-synonymous substitutions that disrupt E2F1-RB protein interaction in cancer

Cancer is a medical condition that is caused by the abnormal growth and division of cells, leading to the formation of tumors. The E2F1 and RB pathways are critical in regulating cell cycle, and their dysregulation can contribute to the development of cancer. In this study, we analyzed experimentally reported SNPs in E2F1 and assessed their effects on the binding affinity with RB. Out of 46, nine mutations were predicted as deleterious, and further analysis revealed four highly destabilizing mutations (L206W, R232C, I254T, A267T) that significantly altered the protein structure. Molecular docking of wild-type and mutant E2F1 with RB revealed a docking score of -242 kcal/mol for wild-type, while the mutant complexes had scores ranging from -217 to -220 kcal/mol. Molecular simulation analysis revealed variations in the dynamics features of both mutant and wild-type complexes due to the acquired mutations. Furthermore, the total binding free energy for the wild-type E2F1-RB complex was -64.89 kcal/mol, while those of the L206W, R232C, I254T, and A267T E2F1-RB mutants were -45.90 kcal/mol, -53.52 kcal/mol, -55.67 kcal/mol, and -61.22 kcal/mol, respectively. Our study is the first to extensively analyze E2F1 gene mutations and identifies candidate mutations for further validation and potential targeting for cancer therapeutics.

Discovery of novel bromodomain-containing protein 4 (BRD4-BD1) inhibitors combined with 3d-QSAR, molecular docking and molecular dynamics in silico

Bromine-containing domain protein 4 (BRD4) plays a crucial role in regulating transcription and genome stability. Selective inhibitors of BRD4-BD1 can specifically target specific bromine domains to affect cell proliferation, apoptosis, and differentiation. In this work, 43 selective benzoazepinone BRD4-BD1 inhibitors were studied using molecular simulations and three-dimensional quantitative conformation relationships (3D-QSAR). A reliable 3D-QSAR model was established based on COMFA (Q2 = 0.532, R2 = 0.981) and COMSIA (S + E + H (Q2 = 0.536, R2 = 0.979) two different analysis methods. Through 3D-QSAR model prediction and quantum chemical analysis, 15 small molecules with stronger inhibitory activity than the template compounds were constructed, and 5 new compounds with higher predictive activity and binding affinity were screened by molecular docking and ADMET methods. According to the molecular dynamics simulation, the key residues that can interact with BRD4-BD1 protein and molecular docking results are consistent, including ASN140, MET132, GLN85, MET105, ASN135 and TYR97. From the MD trajectory, we calculated and analyzed RMSD, RMSF, free binding energy, FECM, DCCM and PCA, the loop region formed by amino acids VAL45∼PRO62 showed α-helix, β-folding and clustering towards the active center with the extension of simulation time. Further optimization of the structure of active candidate compounds A6, A11, A14, and A15 will provide the necessary theoretical basis for the synthesis and activity evaluation of novel BRD4-BD1 inhibitors.Communicated by Ramaswamy H. Sarma.

Substitution Models of Protein Evolution with Selection on Enzymatic Activity

Substitution models of evolution are necessary for diverse evolutionary analyses including phylogenetic tree and ancestral sequence reconstructions. At the protein level, empirical substitution models are traditionally used due to their simplicity, but they ignore the variability of substitution patterns among protein sites. Next, in order to improve the realism of the modeling of protein evolution, a series of structurally constrained substitution models were presented, but still they usually ignore constraints on the protein activity. Here, we present a substitution model of protein evolution with selection on both protein structure and enzymatic activity, and that can be applied to phylogenetics. In particular, the model considers the binding affinity of the enzyme-substrate complex as well as structural constraints that include the flexibility of structural flaps, hydrogen bonds, amino acids backbone radius of gyration, and solvent-accessible surface area that are quantified through molecular dynamics simulations. We applied the model to the HIV-1 protease and evaluated it by phylogenetic likelihood in comparison with the best-fitting empirical substitution model and a structurally constrained substitution model that ignores the enzymatic activity. We found that accounting for selection on the protein activity improves the fitting of the modeled functional regions with the real observations, especially in data with high molecular identity, which recommends considering constraints on the protein activity in the development of substitution models of evolution.

Dissecting the role of ALK double mutations in drug resistance to lorlatinib with in-depth theoretical modeling and analysis

Anaplastic lymphoma kinase (ALK) is implicated in the genesis of multiple malignant tumors. Lorlatinib stands out as the most advanced and effective inhibitor currently used in the clinic for the treatment of ALK-positive non-small cell lung cancer. However, resistance to lorlatinib has inevitably manifested over time, with double/triple mutations of G1202, L1196, L1198, C1156 and I1171 frequently observed in clinical practice, and tumors regrow within a short time after treatment with lorlatinib. Therefore, elucidating the mechanism of resistance to lorlatinib is paramount in paving the way for innovative therapeutic strategies and the development of next-generation drugs. In this study, we leveraged multiple computational methodologies to delve into the resistance mechanisms of three specific double mutations of ALKG1202R/L1196M, ALKG1202R/L1198F and ALKI1171N/L1198F to lorlatinib. We analyzed these mechanisms through qualitative (PCA, DCCM) and quantitative (MM/GBSA, US) kinetic analyses. The qualitative analysis shows that these mutations exert minimal perturbations on the conformational dynamics of the structural domains of ALK. The energetic and structural assessments show that the van der Waals interactions, formed by the conserved residue Leu1256 within the ATP-binding site and the residues Glu1197 and Met1199 in the hinge domain with lorlatinib, play integral roles in the occurrence of drug resistance. Furthermore, the US simulation results elucidate that the pathways through which lorlatinib dissociates vary across mutant systems, and the distinct environments during the dissociation process culminate in diverse resistance mechanisms. Collectively, these insights provide important clues for the design of novel inhibitors to combat resistance.

In-silico guided design, screening, and molecular dynamic simulation studies for the identification of potential SARS-CoV-2 main protease inhibitors for the targeted treatment of COVID-19

COVID-19, the disease responsible for the recent pandemic, is caused by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The main protease (Mpro) of SARS-CoV-2 is an essential proteolytic enzyme that plays a number of important roles in the replication of the virus in human host cells. Blocking the function of SARS-CoV-2 Mpro offers a promising and targeted, therapeutic option for the treatment of the COVID-19 infection. Such an inhibitory strategy is currently successful in treating COVID-19 under FDA's emergency use authorization, although with limited benefit to the immunocompromised along with an unfortunate number of side effects and drug-drug interactions. Current COVID vaccines protect against severe disease and death but are mostly ineffective toward long COVID which has been seen in 5-36% of patients. SARS-CoV-2 is a rapidly mutating virus and is here to stay endemically. Hence, alternate therapeutics to treat SARS-CoV-2 infections are still needed. Moreover, because of the high degree of conservation of Mpro among different coronaviruses, any newly developed antiviral agents should better prepare us for potential future epidemics or pandemics. In this paper, we first describe the design and computational docking of a library of novel 188 first-generation peptidomimetic protease inhibitors using various electrophilic warheads with aza-peptide epoxides, α-ketoesters, and β-diketones identified as the most effective. Second-generation designs, 192 compounds in total, focused on aza-peptide epoxides with drug-like properties, incorporating dipeptidyl backbones and heterocyclic ring motifs such as proline, indole, and pyrrole groups, yielding 8 hit candidates. These novel and specific inhibitors for SARS-CoV-2 Mpro can ultimately serve as valuable alternate and broad-spectrum antivirals against COVID-19.Communicated by Ramaswamy H. Sarma.

Computational Approach to Elucidating Insulin-Protamine Binding Interactions and Dynamics in Insulin NPH Formulations

Insulin NPH is an intermediate-acting insulin. Its protracted action profile is due to the formation of microcrystalline suspensions when insulin is complexed with a basic peptide protamine, zinc ion, and phenolic ligands. Despite advancements in analytical techniques, the binding epitope and binding mode of the protamine in the insulin-protamine complex are still unknown. In this study, we used bioinformatics tools such as molecular docking and molecular dynamics (MD) simulations to compute the binding sites and energetics of the insulin-protamine complex. We have taken four naturally occurring protamine peptides that are independently docked with the insulin R6 hexamer and subjected them to 200 ns MD simulations to observe the dynamics of the complexes and estimate the binding energies. The arginine-rich protamine peptides were found to bind on the surface of the insulin hexamer through hydrogen bonding, hydrophobic, and electrostatic interactions well supported by the calculated negative binding energies. The overall structure of the insulin hexamer was retained upon binding, highlighting its dynamic stability in the complex. Furthermore, the residues at the termini of the protamine peptides in the complex were seen to be highly dynamic, which stabilize toward the end of the simulation.

Dissecting the geometric and hydrophobic constraints of stapled peptides

Stapled peptides are a promising class of molecules with potential as highly specific probes of protein-protein interactions and as therapeutics. Hydrocarbon stapling affects the peptide properties through the interplay of two factors: enhancing the overall hydrophobicity and constraining the conformational flexibility. By constructing a series of virtual peptides, we study the role of each factor in modulating the structural properties of a hydrocarbon-stapled peptide PM2, which has been shown to enter cells, engage its target Mouse Double Minute 2 (MDM2), and activate p53. Hamiltonian replica exchange molecular dynamics (HREMD) simulations suggest that hydrocarbon stapling favors helical populations of PM2 through a combination of the geometric constraints and the enhanced hydrophobicity of the peptide. To further understand the conformational landscape of the stapled peptides along the binding pathway, we performed HREMD simulations by restraining the peptide at different distances from MDM2. When the peptide approaches MDM2, the binding pocket undergoes dehydration which appears to be greater in the presence of the stapled peptide compared with the linear peptide. In the binding pocket, the helicity of the stapled peptide is increased due to the favorable interactions between the peptide residues as well as the staple and the microenvironment of the binding pocket, contributing to enhanced affinity. The dissection of the multifaceted mechanism of hydrocarbon stapling into individual factors not only deepens fundamental understanding of peptide stapling, but also provides guidelines for the design of new stapled peptides.

An in silico analysis of the interaction of marine sponge-derived bioactive compounds with type 2 diabetes mellitus targets DPP-4 and PTP1B

Type 2 diabetes is a medical condition involving elevated blood glucose levels resulting from impaired or improper insulin utilization. As the number of type 2 diabetes cases increases each year, there is an urgent need to develop novel drugs having new targets and/or complementing existing therapeutic protocols. In this regard, marine sponge-derived compounds hold great potential due to their potent biological activity and structural diversity. In this study, a small library of 50 marine sponge-derived compounds were examined for their activity towards type 2 diabetes targets, namely dipeptidyl peptidase-4 (DPP-4) and protein tyrosine phosphatase 1B (PTP1B). The compounds were first subjected to molecular docking on protein models based on their respective co-crystal structures to assess binding free energies (BFE) and conformations. Clustering analysis yielded BFE that ranged from 24.54 kcal/mol to -9.97 kcal/mol for DPP-4, and from -4.98 kcal/mol to -8.67 kcal/mol for PTP1B. Interaction analysis on the top ten compounds with the most negative BFE towards each protein target showed similar intermolecular interactions and key interacting residues as in the previously solved co-crystal structure. These compounds were subjected to absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiling to characterize drug-likeness and combining the results from these analyses, (S)-6'-debromohamacanthin B was identified as a potential multi-target inhibitor of DPP-4 and PTP1B, having favorable protein interaction, no Lipinski violations, good gastrointestinal (GI) tract absorption, blood-brain barrier (BBB) penetration, and no predicted toxicity. Finally, the interaction of (S)-6'-debromohamacanthin B with the two proteins was validated using molecular dynamics simulations over 100 ns through RMSD, radius of gyration, PCA, and molecular mechanics Poisson-Boltzmann surface area (MMPBSA) confirming favorable interactions with the respective proteins.Communicated by Ramaswamy H. Sarma.

Rational Prediction of PROTAC-Compatible Protein-Protein Interfaces by Molecular Docking

Proteolysis targeting chimeras (PROTACs) are heterobifunctional ligands that mediate the interaction between a protein target and an E3 ligase, resulting in a ternary complex, whose interaction with the ubiquitination machinery leads to target degradation. This technology is emerging as an exciting new avenue for therapeutic development, with several PROTACs currently undergoing clinical trials targeting cancer. Here, we describe a general and computationally efficient methodology combining restraint-based docking, energy-based rescoring, and a filter based on the minimal solvent-accessible surface distance to produce PROTAC-compatible PPIs suitable for when there is no a priori known PROTAC ligand. In a benchmark employing a manually curated data set of 13 ternary complex crystals, we achieved an accuracy of 92% when starting from bound structures and 77% when starting from unbound structures, respectively. Our method only requires that the ligand-bound structures of the monomeric forms of the E3 ligase and target proteins be given to run, making it general, accurate, and highly efficient, with the ability to impact early-stage PROTAC-based drug design campaigns where no structural information about the ternary complex structure is available.

Probing the Activation Mechanisms of Agonist DPI-287 to Delta-Opioid Receptor and Novel Agonists Using Ensemble-Based Virtual Screening with Molecular Dynamics Simulations

Pain drugs targeting mu-opioid receptors face major addiction problems that have caused an epidemic. The delta-opioid receptor (DOR) has shown to not cause addictive effects when bound to an agonist. While the active conformation of the DOR in complex with agonist DPI-287 has been recently solved, there are still no FDA-approved agonists targeting it, providing the opportunity for structure-based virtual screening. In this study, the conformational plasticity of the DOR was probed using molecular dynamics (MD) simulations, identifying two representative conformations from clustering analysis. The two MD conformations as well as the crystal conformation of DOR were used to screen novel compounds from the ZINC database (17 million compounds), in which 69 drugs were picked as potential compounds based on their docking scores. Notably, 37 out of the 69 compounds were obtained from the simulated conformations. The binding stability of the 69 compounds was further investigated using MD simulations. Based on the MM-GBSA binding energy and the predicted drug properties, eight compounds were chosen as the most favorable, six of which were from the simulated conformations. Using a dynamic network model, the communication between the crystal agonist and the top eight molecules with the receptor was analyzed to confirm if these novel compounds share a similar activation mechanism to the crystal ligand. Encouragingly, docking of these eight compounds to the other two opioid receptors (kappa and mu) suggests their good selectivity toward DOR.

Staying Ahead of the Game: How SARS-CoV-2 has Accelerated the Application of Machine Learning in Pandemic Management

In recent years, machine learning (ML) techniques have garnered considerable interest for their potential use in accelerating the rate of drug discovery. With the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the utilization of ML has become even more crucial in the search for effective antiviral medications. The pandemic has presented the scientific community with a unique challenge, and the rapid identification of potential treatments has become an urgent priority. Researchers have been able to accelerate the process of identifying drug candidates, repurposing existing drugs, and designing new compounds with desirable properties using machine learning in drug discovery. To train predictive models, ML techniques in drug discovery rely on the analysis of large datasets, including both experimental and clinical data. These models can be used to predict the biological activities, potential side effects, and interactions with specific target proteins of drug candidates. This strategy has proven to be an effective method for identifying potential coronavirus disease 2019 (COVID-19) and other disease treatments. This paper offers a thorough analysis of the various ML techniques implemented to combat COVID-19, including supervised and unsupervised learning, deep learning, and natural language processing. The paper discusses the impact of these techniques on pandemic drug development, including the identification of potential treatments, the understanding of the disease mechanism, and the creation of effective and safe therapeutics. The lessons learned can be applied to future outbreaks and drug discovery initiatives.

Discovering Traditional Chinese Medicine (TCM) Formulas for Complex Diseases Based on a Combination of Reverse Systematic Pharmacology and TCM Meridian Tropism Theory: Taking COVID-19 as an Example

OBJECTIVE

Infections and death have been a part of our daily lives since the COVID-2019 pandemic outbreak in 2019, and the societal and economic consequences have lingered for an unanticipated duration. Novel and effective treatments are still desperately needed around the world to combat the infection. Here, we discovered a novel traditional Chinese medicine formula (TCMF) to potentially combat COVID-19 through reverse systematic pharmacology (disease → targets → TCMF → disease).

METHODS

Combining Integrative network pharmacology and the traditional Chinese medicine (TCM) theory, a TCMF for COVID-19 was identified. In silico physiological interactions between herbs and disease hub targets were validated by molecular docking and dynamics simulation.

RESULTS

Based on disease-related gene/pathway targets and a combination of reverse pharmacology and TCM meridian tropism theory, a COVID-19-associated herb database was constructed. A new TCMF, including Gancao, Baitouweng, Congbai, and Diyu (GBCD), was discovered for anti-COVID-19 therapy. The KEGG and GO analyses of 49 intersecting genes suggested that GBCD could combat COVID-19 through antiviral, antiinflammation, immunoregulation, and cytoprotection activities. Moreover, these possible effects were validated through docking and MD simulation.

CONCLUSIONS

To the best of our knowledge, this study is the first to combine reverse pharmacology and meridian tropism theories for TCMF development, and a novel herbal combination, GBCD, was discovered for anti-COVID-19 therapy.

Uncovering the Kinetic Characteristics and Degradation Preference of PROTAC Systems with Advanced Theoretical Analyses

Proteolysis-targeting chimeras (PROTACs), which can selectively induce the degradation of target proteins, represent an attractive technology in drug discovery. A large number of PROTACs have been reported, but due to the complicated structural and kinetic characteristics of the target-PROTAC-E3 ligase ternary interaction process, the rational design of PROTACs is still quite challenging. Here, we characterized and analyzed the kinetic mechanism of MZ1, a PROTAC that targets the bromodomain (BD) of the bromodomain and extra terminal (BET) protein (Brd2, Brd3, or Brd4) and von Hippel-Lindau E3 ligase (VHL), from the kinetic and thermodynamic perspectives of view by using enhanced sampling simulations and free energy calculations. The simulations yielded satisfactory predictions on the relative residence time and standard binding free energy (r_p > 0.9) for MZ1 in different Brd^BD-MZ1-VHL ternary complexes. Interestingly, the simulation of the PROTAC ternary complex disintegration illustrates that MZ1 tends to remain on the surface of VHL with the BD proteins dissociating alone without a specific dissociation direction, indicating that the PROTAC prefers more to bind with E3 ligase at the first step in the formation of the target-PROTAC-E3 ligase ternary complex. Further exploration of the degradation difference of MZ1 in different Brd systems shows that the PROTAC with higher degradation efficiency tends to leave more lysine exposed on the target protein, which is guaranteed by the stability (binding affinity) and durability (residence time) of the target-PROTAC-E3 ligase ternary complex. It is quite possible that the underlying binding characteristics of the Brd^BD-MZ1-VHL systems revealed by this study may be shared by different PROTAC systems as a general rule, which may accelerate rational PROTAC design with higher degradation efficiency.

The Mythimna separata general odorant binding protein 2 (MsepGOBP2) is involved in the larval detection of the sex pheromone (Z)-11-hexadecenal

BACKGROUND

Mythimna separata is a notorious pest causing crop damages at the larval stage. Gaining insight into larval olfaction mechanisms would provide knowledge for olfaction-based management of M. separata larvae.

RESULTS

In the present research, (Z)-11-hexadecenal (Z11-16: Ald), a major component of M. separata sex pheromone, was found to attract early-instar larvae of M. separata in a food context. Using a fluorescent binding assay, we found that M. separata general odorant binding protein 2 (MsepGOBP2) exhibited high binding affinity to Z11-16: Ald. Further, silencing of MsepGOBP2 resulted in a sharp reduction of the response to Z11-16: Ald, which could not be mitigated by increasing the concentration of Z11-16: Ald. Additionally, we employed molecular dynamics-based approaches to unravel the interaction details between MsepGOBP2 and Z11-16: Ald, specifically the binding of Z11-16: Ald to MsepGOBP2.

CONCLUSION

Z11-16: Ald is attractive to early-instar larvae of M. separata, and MsepGOBP2 is identified to be indispensable in the larval detection of Z11-16: Ald. These results could aid in the development of olfaction-based methods for controlling M. separata in the larval stage. © 2023 Society of Chemical Industry.

The XBB.1.5 slightly increase the binding affinity for host receptor ACE2 and exhibit strongest immune escaping features: molecular modeling and free energy calculation

Introduction: The current XBB variant of SARS-CoV-2 with the strongest immune escaping properties is currently the most dominant variant circulating around the world. With the emergence of XBB global morbidities and mortalities have raised again. In the current scenario, it was highly required to delineate the binding capabilities of NTD of XBB subvariant towards human neutralizing antibodies and to dig out the binding affinity of RBD of XBB subvariant with ACE2 receptor. Materials and Methods: The current study uses molecular interaction and simulation-based approaches to decipher the binding mechanism of RBD with ACE2 and mAb interaction with NTD of the spike protein. Results: Molecular docking of the Wild type NTD with mAb revealed a docking score of -113.2 ± 0.7 kcal/mol while XBB NTD docking with mAb reported -76.2 ± 2.3 kcal/mol. On the other hand, wild-type RBD and XBB RBD with ACE2 receptor demonstrated docking scores of -115.0 ± 1.5 kcal/mol and -120.8 ± 3.4 kcal/mol respectively. Moreover, the interaction network analysis also revealed significant variations in the number of hydrogen bonds, salt-bridges, and non-bonded contacts. These findings were further validated by computing the dissociation constant (KD). Molecular simulation analysis such as RMSD, RMSF, Rg and hydrogen bonding analysis revealed variation in the dynamics features of the RBD and NTD complexes due to the acquired mutations. Furthermore, the total binding energy for the wild-type RBD in complex with ACE2 reported -50.10 kcal/mol while XBB-RBD coupled with ACE2 reported -52.66 kcal/mol respectively. This shows though the binding of XBB is slightly increased but due to the variation in the bonding network and other factors makes the XBB variant to enter into the host cell efficiently than the wild type. On the other hand, the total binding free energy for the wildtype NTD-mAb was calculated to be -65.94 kcal/mol while for XBB NTD-mAb was reported to be -35.06 kcal/mol respectively. The significant difference in the total binding energy factors explains that the XBB variant possess stronger immune evasion properties than the others variants and wild type. Conclusions: The current study provides structural features for the XBB variant binding and immune evasion which can be used to design novel therapeutics.

Study of SQ109 analogs binding to mycobacterium MmpL3 transporter using MD simulations and alchemical relative binding free energy calculations

N-geranyl-N΄-(2-adamantyl)ethane-1,2-diamine (SQ109) is a tuberculosis drug that has high potency against Mycobacterium tuberculosis (Mtb) and may function by blocking cell wall biosynthesis. After the crystal structure of MmpL3 from Mycobacterium smegmatis in complex with SQ109 became available, it was suggested that SQ109 inhibits Mmpl3 mycolic acid transporter. Here, we showed using molecular dynamics (MD) simulations that the binding profile of nine SQ109 analogs with inhibitory potency against Mtb and alkyl or aryl adducts at C-2 or C-1 adamantyl carbon to MmpL3 was consistent with the X-ray structure of MmpL3 - SQ109 complex. We showed that rotation of SQ109 around carbon-carbon bond in the monoprotonated ethylenediamine unit favors two gauche conformations as minima in water and lipophilic solvent using DFT calculations as well as inside the transporter's binding area using MD simulations. The binding assays in micelles suggested that the binding affinity of the SQ109 analogs was increased for the larger, more hydrophobic adducts, which was consistent with our results from MD simulations of the SQ109 analogues suggesting that sizeable C-2 adamantyl adducts of SQ109 can fill a lipophilic region between Y257, Y646, F260 and F649 in MmpL3. This was confirmed quantitatively by our calculations of the relative binding free energies using the thermodynamic integration coupled with MD simulations method with a mean assigned error of 0.74 kcal mol^-1 compared to the experimental values.

Molecular dynamics simulations on interactions of five antibiotics with luciferase of Vibrio Qinghaiensis sp.-Q67

A large number of antibiotics have been used in the medical industry, agriculture, and animal husbandry industry in recent years. It may cause pollution to the aquatic environment and ultimately threaten to human health due to their prolonged exposure to the environment. We aim to study the toxicity mechanism of enrofloxacin (ENR), chlortetracycline hydrochloride (CTC), trimethoprim (TMP), chloramphenicol (CMP), and erythromycin (ETM) to luciferase of Vibrio Qinghaiensis sp.-Q67 (Q67) by using toxicity testing combined with molecular docking, molecular dynamics, and binding free energy analysis. The curve categories for ENR were different from the other four antibiotics, with ENR being J-type and the rest being S-type, and the toxicity of these five antibiotics (pEC50) followed the order of ENR (7.281) > ETM (6.814) > CMP (6.672) > CTC (6.400) > TMP (6.123), the order of toxicity value is consistent with the the magnitude of the binding free energy (ENR (-47.759 kcal/mol), ETM (-46.821 kcal/mol), CMP (-42.905 kcal/mol), CTC (-40.946 kcal/mol), TMP (-28.251 kcal/mol)). The van der Waals force provided the most important contribution to the binding free energy of the five antibiotics in the binding system with Q67 luciferase. Therefore, the dominant factor for the binding of antibiotics to luciferase was shape compensation. The face-to-face π-π stacking interaction between the diazohexane structure outside the active pocket region and the indoles structure of Phe194 and Phe250 in the molecular structure was the main reason for the highest toxicity value of antibiotic ENR. The hormesis effect of ENR has a competitive binding relationship with the α and β subunits of luciferase. Homology modeling, molecular docking, molecular dynamics simulations and binding free energy calculations were used to derive the toxicity magnitude of different antibiotics against Q67, and insights at the molecular level. The conclusion of toxicological experiments verified the correctness of the simulation results. This study contributes to the understanding of toxicity mechanisms of five antibiotics and facilitates risk assessment of antibiotic contaminants in the aquatic environment.

Orthogonal threading-through-β-sheet design of lung cancer EGFR extracellular domain-derived peptidic mimotopes binding to anti-EGFR antibody

Human epidermal growth factor receptor (EGFR) has been established as a therapeutic target of lung cancer and other diverse tumors. The antibody drug Cetuximab has been developed to target the third subdomain III (TSDIII) of EGFR extracellular domain (ECD) by competitively inhibiting epidermal growth factor binding. In this study, we performed systematic investigation on the crystal complex structure of EGFR ECD domain with Cetuximab to create a residue importance profile for the TSDIII subdomain, based on which a number of U-shaped, double-stranded linear peptides were derived and cyclized to orthogonally thread through most hotspot residues and many responsible residues within the TSDIII β-sheet plane; they represent mimotopes of the key antibody-recognition site of TSDIII subdomain. Computational analyses revealed that these linear peptides cannot spontaneously fold to the desired conformation in free state due to their intrinsic flexibility. Cell-free assays confirmed that the stapling can considerably improve the binding affinity of linear peptides to Cetuximab by up to 18-fold. The cOrt1 [3-18] cyclic peptide was measured to have the highest affinity in all designed linear and cyclic peptides.

Insights on the interaction of SARS-CoV-2 variant B.1.617.2 with antibody CR3022 and analysis of antibody resistance

BACKGROUND

The existence of mutated Delta (B.1.617.2) variants of SARS-CoV-2 causes rapid transmissibility, increase in virulence, and decrease in the effectiveness of public health. Majority of mutations are seen in the surface spike, and they are considered as antigenicity and immunogenicity of the virus. Hence, finding suitable cross antibody or natural antibody and understanding its biomolecular recognition for neutralizing surface spike are crucial for developing many clinically approved COVID-19 vaccines. Here, we aim to design SARS-CoV-2 variant and hence, to understand its mechanism, binding affinity and neutralization potential with several antibodies.

RESULTS

In this study, we modelled six feasible spike protein (S1) configurations for Delta SARS-CoV-2 (B.1.617.2) and identified the best structure to interact with human antibodies. Initially, the impact of mutations at the receptor-binding domain (RBD) of B.1.617.2 was tested, and it is found that all mutations increase the stability of proteins (ΔΔG) and decrease the entropies. An exceptional case is noted for the mutation of G614D variant for which the vibration entropy change is found to be within the range of 0.133-0.004 kcal/mol/K. Temperature-dependent free energy change values (ΔG) for wild type is found to be - 0.1 kcal/mol, whereas all other cases exhibit values within the range of - 5.1 to - 5.5 kcal/mol. Mutation on spike increases the interaction with the glycoprotein antibody CR3022 and the binding affinity (CLUSpro energy =  - 99.7 kcal/mol). The docked Delta variant with the following antibodies, etesevimab, bebtelovimab, BD-368-2, imdevimab, bamlanivimab, and casirivimab, exhibit a substantially decreased docking score (- 61.7 to - 112.0 kcal/mol) and the disappearance of several hydrogen bond interactions.

CONCLUSION

Characterization of antibody resistance for Delta variant with respect to the wild type gives understanding regarding why Delta variant endures the resistance boosted through several trademark vaccines. Several interactions with CR3022 have appeared compared to Wild for Delta variant, and hence, it is suggested that modification on the CR3022 antibody could further improve for the prevention of viral spread. Antibody resistance decreased significantly due to numerous hydrogen bond interactions which clearly indicate that these marketed/launched vaccines (etesevimab) will be effective for Delta variants.

Computational design of cyclic peptides to inhibit protein-peptide interactions

Many protein-protein interactions result from the binding of one folded protein with one short peptide segment, such as complexes formed by SH3 or PDZ domains. These transient protein-peptide interactions are notably involved in cellular signaling pathways and generally have low affinities, which opens the possibility to design competitive inhibitors of these complexes. We present and assess here our computational approach, called Des3PI, to design de novo cyclic peptides with potential high affinity for protein surfaces involved in interactions with peptide segments. The results were not conclusive for two receptors, the αVβ3 integrin and the CXCR4 chemokine receptor, but were promising in the case of SH3 and PDZ domains: For the former, Des3PI was able to find at least one cyclic sequence with six hotspots that binds a SH3 domain with a better theoretical affinity to the known proline-rich RLP2 peptide. For the latter, Des3PI could identify at least four cyclic sequences with four or five hotspots that have lower binding free energies computed by the MM-PBSA method than the reference peptide GKAP.

Structural investigation of CDCA3-Cdh1 protein-protein interactions using in vitro studies and molecular dynamics simulation

The anaphase-promoting complex/cyclosome (APC/C) ubiquitin ligase and its cofactor, Cdh1, regulate the expression of several cell-cycle proteins and their functions during mitosis. Levels of the protein cell division cycle-associated protein 3 (CDCA3), which is functionally required for mitotic entry, are regulated by APC/CCdh1 . CDCA3 is an intrinsically disordered protein and contains both C-terminal KEN box and D-box recognition motifs, enabling binding to Cdh1. Our previous findings demonstrate that CDCA3 has a phosphorylation-dependent non-canonical ABBA-like motif within the linker region bridging these two recognition motifs and is required for efficient binding to Cdh1. Here, we sought to identify and further characterize additional residues that participate within this ABBA-like motif using detailed in vitro experiments and in silico modeling studies. We identified the role of H-bonds, hydrophobic and ionic interactions across the CDCA3 ABBA-like motif in the linker region between KEN and D-box motifs. This linker region adopts a well-defined structure when bound to Cdh1 in the presence of phosphorylation. Upon alanine mutation, the structure of this region is lost, leading to higher flexibility, and alteration in affinities due to binding to alternate sites on Cdh1. Our findings identify roles for the anchoring residues in the non-canonical ABBA-like motif to promote binding to the APC/CCdh1 and regulation of CDCA3 protein levels.

Development and test of highly accurate endpoint free energy methods. 1: Evaluation of ABCG2 charge model on solvation free energy prediction and optimization of atom radii suitable for more accurate solvation free energy prediction by the PBSA method

Accurate estimation of solvation free energy (SFE) lays the foundation for accurate prediction of binding free energy. The Poisson-Boltzmann (PB) or generalized Born (GB) combined with surface area (SA) continuum solvation method (PBSA and GBSA) have been widely used in SFE calculations because they can achieve good balance between accuracy and efficiency. However, the accuracy of these methods can be affected by several factors such as the charge models, polar and nonpolar SFE calculation methods and the atom radii used in the calculation. In this work, the performance of the ABCG2 (AM1-BCC-GAFF2) charge model as well as other two charge models, that is, RESP (Restrained Electrostatic Potential) and AM1-BCC (Austin Model 1-bond charge corrections), on the SFE prediction of 544 small molecules in water by PBSA/GBSA was evaluated. In order to improve the performance of the PBSA prediction based on the ABCG2 charge, we further explored the influence of atom radii on the prediction accuracy and yielded a set of atom radius parameters for more accurate SFE prediction using PBSA based on the ABCG2/GAFF2 by reproducing the thermodynamic integration (TI) calculation results. The PB radius parameters of carbon, oxygen, sulfur, phosphorus, chloride, bromide and iodine, were adjusted. New atom types, on, oi, hn1, hn2, hn3, were introduced to further improve the fitting performance. Then, we tuned the parameters in the nonpolar SFE model using the experimental SFE data and the PB calculation results. By adopting the new radius parameters and new nonpolar SFE model, the root mean square error (RMSE) of the SFE calculation for the 544 molecules decreased from 2.38 to 1.05 kcal/mol. Finally, the new radius parameters were applied in the prediction of protein-ligand binding free energies using the MM-PBSA method. For the eight systems tested, we could observe higher correlation between the experiment data and calculation results and smaller prediction errors for the absolute binding free energies, demonstrating that our new radius parameters can improve the free energy calculation using the MM-PBSA method.

Molecular mechanism of ATP and RNA binding to Zika virus NS3 helicase and identification of repurposed drugs using molecular dynamics simulations

Congenital Zika virus syndrome has caused a public health emergency of international concern. So far, there are no drugs available to prevent or treat the infection caused by Zika virus. The Zika virus NS3 helicase is a potential protein target for drug discovery due to its vital role in viral genome replication. NS3 helicase unwinds the viral RNA to enable the reproduction of the viral genome by the NS5 protein. NS3 helicase has two crucial binding sites; the ATP binding site and the RNA binding site. We used molecular docking and molecular dynamics (MD) simulations to study the structural behavior of Zika virus NS3 helicase in its apo form and in the presence of ATP, single-stranded RNA, and both ATP-RNA to understand their potential implications in NS3 helicase activity. Further, we have carried out virtual screening of FDA approved drugs, followed by molecular docking to identify the ATP-competitive hit molecules as probable Zika virus NS3 helicase inhibitors. The MD simulations trajectories were analyzed using normal mode analysis and principal component analysis that reveals fluctuations in the R-loop. These findings aid in understanding the molecular mechanisms of the simultaneous binding of ATP and RNA, and guide the design and discovery of new inhibitors of the Zika virus NS3 helicase as a promising drug target to treat the Zika virus infection. Communicated by Ramaswamy H. Sarma.

Atomic Mechanisms of Transthyretin Tetramer Dissociation Studied by Molecular Dynamics Simulations

The dissociation of the transthyretin (TTR) tetramer into a monomer is closely related to various TTR amyloidoses in humans. While the tetramer dissociation has been reported to be the rate-limiting step for TTR aggregation, few details are known about the mechanism. Here, molecular dynamics (MD) simulations were performed by combining conventional MD and biased metadynamics to investigate the mechanism for the wild-type (WT) and mutant (T119M) structures. Both were found to have a great deal in common. Conventional MD simulations reveal that interfacial hydrophobic interactions contribute significantly to stabilize the tetramer. Interfacial residues including L17, V20, L110, and V121 with close contacts form a hydrophobic channel. Metadynamics simulations indicate that the mouth opening of the hydrophobic channel is the first and the most difficult step for dissociation. Interactions of V20 between opposing dimers lock four monomers into the tetramer, and disruption of the interactions is found to be involved in the final step. During the dissociation, an increasing extent of solvation was observed by calculating the radial distribution functions of water around interfacial hydrophobic residues, suggesting that water plays a role in driving the tetramer dissociation. Moreover, compared to T119, residue M119 has a longer side chain that extends into the hydrophobic channel, making solvation more difficult, consistent with a higher energy barrier for dissociation of the T119M tetramer. This result provides a good explanation for the protective role of the T119M mutation. Overall, this study can provide atomic-level insights to better understand the pathogenesis of TTR amyloidosis and guide rational drug design in the future.

Investigations on the binding specificity of β-galactoside analogues with human galectin-1 using molecular dynamics simulations

Galectin-1 (Gal-1) is the first member of galectin family, which has a carbohydrate recognition domain, specifically binds towards β-galactoside containing oligosaccharides. Owing its association with carbohydrates, Gal-1 is involved in many biological processes such as cell signaling, adhesion and pathological pathways such as metastasis, apoptosis and increased tumour cell survival. The development of β-galactoside based inhibitors would help to control the Gal-1 expression. In the current study, we carried out molecular dynamics (MD) simulations to examine the structural and dynamic behaviour Gal-1-thiodigalactoside (TDG), Gal-1-lactobionic acid (LBA) and Gal-1-beta-(1→6)-galactobiose (G16G) complexes. The analysis of glycosidic torsional angles revealed that β-galactoside analogues TDG and LBA have a single binding mode (BM1) whereas G16G has two binding modes (BM1 and BM2) for interacting with Gal-1 protein. We have computed the binding free energies for the complexes Gal-1-TDG, Gal-1-LBA and Gal-1-G16G using MM/PBSA and are -6.45, -6.22 and -3.08 kcal/mol, respectively. This trend agrees well with experiments that the binding of Gal-1 with TDG is stronger than LBA. Further analysis revealed that the interactions due to direct and water-mediated hydrogen bonds play a significant role to the structural stability of the complexes. The result obtained from this study is useful to formulate a set of rules and derive pharmacophore-based features for designing inhibitors against galectin-1.Communicated by Ramaswamy H. Sarma.

Molecular docking and molecular dynamics study Lianhua Qingwen granules (LHQW) treats COVID-19 by inhibiting inflammatory response and regulating cell survival

Purpose

2019 Coronavirus disease (COVID-19) is endangering health of populations worldwide. Latest research has proved that Lianhua Qingwen granules (LHQW) can reduce tissue damage caused by inflammatory reactions and relieve patients' clinical symptoms. However, the mechanism of LHQW treats COVID-19 is currently lacking. Therefore, we employed computer simulations to investigate the mechanism of LHQW treats COVID-19 by modulating inflammatory response.

Methods

We employed bioinformatics to screen active ingredients in LHQW and intersection gene targets. PPI, GO and KEGG was used to analyze relationship of intersection gene targets. Molecular dynamics simulations validated the binding stability of active ingredients and target proteins. Binding free energy, radius of gyration and the solvent accessible surface area were analyzed by supercomputer platform.

Results

COVID-19 had 4628 gene targets, LHQW had 1409 gene targets, intersection gene targets were 415. Bioinformatics analysis showed that intersection targets were closely related to inflammation and immunomodulatory. Molecular docking suggested that active ingredients (including: licopyranocoumarin, Glycyrol and 3-3-Oxopropanoic acid) in LHQW played a role in treating COVID-19 by acting on CSF2, CXCL8, CCR5, NLRP3, IFNG and TNF. Molecular dynamics was used to prove the binding stability of active ingredients and protein targets.

Conclusion

The mechanism of active ingredients in LHQW treats COVID-19 was investigated by computer simulations. We found that active ingredients in LHQW not only reduce cell damage and tissue destruction by inhibiting the inflammatory response through CSF2, CXCL8, CCR5 and IFNG, but also regulate cell survival and growth through NLRP3 and TNF thereby reducing apoptosis.

Predicting the mutation effects of protein–ligand interactions via end-point binding free energy calculations: strategies and analyses

Protein mutations occur frequently in biological systems, which may impact, for example, the binding of drugs to their targets through impairing the critical H-bonds, changing the hydrophobic interactions, etc. Thus, accurately predicting the effects of mutations on biological systems is of great interests to various fields. Unfortunately, it is still unavailable to conduct large-scale wet-lab mutation experiments because of the unaffordable experimental time and financial costs. Alternatively, in silico computation can serve as a pioneer to guide the experiments. In fact, numerous pioneering works have been conducted from computationally cheaper machine-learning (ML) methods to the more expensive alchemical methods with the purpose to accurately predict the mutation effects. However, these methods usually either cannot result in a physically understandable model (ML-based methods) or work with huge computational resources (alchemical methods). Thus, compromised methods with good physical characteristics and high computational efficiency are expected. Therefore, here, we conducted a comprehensive investigation on the mutation issues of biological systems with the famous end-point binding free energy calculation methods represented by MM/GBSA and MM/PBSA. Different computational strategies considering different length of MD simulations, different value of dielectric constants and whether to incorporate entropy effects to the predicted total binding affinities were investigated to provide a more accurate way for predicting the energetic change upon protein mutations. Overall, our result shows that a relatively long MD simulation (e.g. 100 ns) benefits the prediction accuracy for both MM/GBSA and MM/PBSA (with the best Pearson correlation coefficient between the predicted ∆∆G and the experimental data of ~ 0.44 for a challenging dataset). Further analyses shows that systems involving large perturbations (e.g. multiple mutations and large number of atoms change in the mutation site) are much easier to be accurately predicted since the algorithm works more sensitively to the large change of the systems. Besides, system-specific investigation reveals that conformational adjustment is needed to refine the micro-environment of the manually mutated systems and thus lead one to understand why longer MD simulation is necessary to improve the predicting result. The proposed strategy is expected to be applied in large-scale mutation effects investigation with interpretation.

A proteomics-MM/PBSA dual approach for the analysis of SARS-CoV-2 main protease substrate peptide specificity

The main protease M^pro of SARS-CoV-2 is a well-studied major drug target. Additionally, it has been linked to this virus' pathogenicity, possibly through off-target effects. It is also an interesting diagnostic target. To obtain more data on possible substrates as well as to assess the enzyme's primary specificity a two-step approach was introduced. First, Terminal Amine Isobaric Labeling of Substrates (TAILS) was employed to identify novel M^pro cleavage sites in a mouse lung proteome library. In a second step, using a structural homology model, the MM/PBSA variant MM/GBSA (Molecular Mechanics Poisson-Boltzmann/Generalized Born Surface Area) free binding energy calculations were carried out to determine relevant interacting amino acids. As a result, 58 unique cleavage sites were detected, including six that displayed glutamine at the P1 position. Furthermore, modeling results indicated that M^pro has a far higher potential promiscuity towards substrates than expected. The combination of proteomics and MM/PBSA modeling analysis can thus be useful for elucidating the specificity of M^pro, and thus open novel perspectives for the development of future peptidomimetic drugs against COVID-19, as well as diagnostic tools.

Fast Prediction of Binding Affinities of SARS-CoV-2 Spike Protein and Its Mutants with Antibodies through Intermolecular Interaction Modeling-Based Machine Learning

Since the introduction of the novel SARS-CoV-2 virus (COVID-19) in late 2019, various new variants have appeared with mutations that confer resistance to the vaccines and monoclonal antibodies that were developed in response to the wild-type virus. As we continue through the pandemic, an accurate and efficient methodology is needed to help predict the effects certain mutations will have on both our currently produced therapeutics and those that are in development. Using published cryo-electron microscopy and X-ray crystallography structures of the spike receptor binding domain region with currently known antibodies, in the present study, we created and cross-validated an intermolecular interaction modeling-based multi-layer perceptron machine learning approach that can accurately predict the mutation-caused shifts in the binding affinity between the spike protein (wild-type or mutant) and various antibodies. This validated artificial intelligence (AI) model was used to predict the binding affinity (Kd) of reported SARS-CoV-2 antibodies with various variants of concern, including the most recently identified "Deltamicron" (or "Deltacron") variant. This AI model may be employed in the future to predict the Kd of developed novel antibody therapeutics to overcome the challenging antibody resistance issue and develop structural bases for the effects of both current and new mutants of the spike protein. In addition, the similar AI strategy and approach based on modeling of the intermolecular interactions may be useful in development of machine learning models predicting binding affinities for other protein-protein binding systems, including other antibodies binding with their antigens.

Interleukin-1α, Interleukin-1β and Interleukin-1 Receptor Antagonist Share a Common U-shaped Recognition Epitope on Interleukin-1 Receptor Surface

Interleukin-1 (IL-1) plays a central role in the regulation of immune and inflammatory responses. There are two forms of IL-1 agonists (IL-1α and IL-1β) and one form of IL-1 antagonist (IL-1Ra); they share a similar binding mode to IL-1 receptor (IL-1R) but exhibit opposite biological functions on the receptor. In this study, the intermolecular interactions of IL-1R receptor with IL-1α, IL-1β and IL-1Ra ligands were systematically investigated at structural, energetic and dynamic levels. It was found that the receptor primarily adopts a U-shaped, double-stranded and linear/conformational-hybrid epitope to commonly interact with the three ligands. The epitope covers a common protein segment (residues 107-127), which is fully located within in the C2T2 subsdomain of IL-1R extracellular domain (ECD) and contributes ~40% to the total binding energy of IL-1R/ligand association. The epitope is natively folded into an ordered conformation in IL-1R protein context but would become largely disordered out of the context. Here, we adopted a disulfide bridge to staple U-shaped epitope-derived peptides, which can be effectively constrained into a native-like conformation and thus exhibit an improved affinity to ligands as compared to their unstapled counterpart, with affinity increase by up to ~15-fold. These disulfide bridges were designed to point out of ligand/peptide complex interface and thus would not disrupt the direct complex interaction. Energetic decomposition imparted that the stapling has only a modest influence on the interaction enthalpy and desolvation effect of ligand/peptide binding, but can substantially reduce entropy penalty upon the binding. For a peptide, the stapling-addressed entropic reduction can be roughly regarded as a constant, which only improves peptide affinity to these ligands, but does not change peptide selectivity over different ligands. This article is protected by copyright. All rights reserved.

Characterizing the stabilization effects of stabilizers in protein-protein systems with end-point binding free energy calculations

Drug design targeting protein-protein interactions (PPIs) associated with the development of diseases has been one of the most important therapeutic strategies. Besides interrupting the PPIs with PPI inhibitors/blockers, increasing evidence shows that stabilizing the interaction between two interacting proteins may also benefit the therapy, such as the development of various types of molecular glues/stabilizers that mostly work by stabilizing the two interacting proteins to regulate the downstream biological effects. However, characterizing the stabilization effect of a stabilizer is usually hard or too complicated for traditional experiments since it involves ternary interactions [protein-protein-stabilizer (PPS) interaction]. Thus, developing reliable computational strategies will facilitate the discovery/design of molecular glues or PPI stabilizers. Here, by fully analyzing the energetic features of the binary interactions in the PPS ternary complex, we systematically investigated the performance of molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) and molecular mechanics generalized Born surface area (MM/GBSA) methods on characterizing the stabilization effects of stabilizers in 14-3-3 systems. The results show that both MM/PBSA and MM/GBSA are powerful tools in distinguishing the stabilizers from the decoys (with area under the curves of 0.90-0.93 for all tested cases) and are reasonable for ranking protein-peptide interactions in the presence or absence of stabilizers as well (with the average Pearson correlation coefficient of ~0.6 at a relatively high dielectric constant for both methods). Moreover, to give a detailed picture of the stabilization effects, the stabilization mechanism is also analyzed from the structural and energetic points of view for individual systems containing strong or weak stabilizers. This study demonstrates a potential strategy to accelerate the discovery of PPI stabilizers.

Generalized Methodology for the Quick Prediction of Variant SARS-CoV-2 Spike Protein Binding Affinities with Human Angiotensin-Converting Enzyme II

Variants of the SARS-CoV-2 virus continue to remain a threat 2 years from the beginning of the pandemic. As more variants arise, and the B.1.1.529 (Omicron) variant threatens to create another wave of infections, a method is needed to predict the binding affinity of the spike protein quickly and accurately with human angiotensin-converting enzyme II (ACE2). We present an accurate and convenient energy minimization/molecular mechanics Poisson-Boltzmann surface area methodology previously used with engineered ACE2 therapeutics to predict the binding affinity of the Omicron variant. Without any additional data from the variants discovered after the publication of our first model, the methodology can accurately predict the binding of the spike/ACE2 variant complexes. From this methodology, we predicted that the Omicron variant spike has a Kd of ∼22.69 nM (which is very close to the experimental Kd of 20.63 nM published during the review process of the current report) and that spike protein of the new "Stealth" Omicron variant (BA.2) will display a Kd of ∼12.9 nM with the wild-type ACE2 protein. This methodology can be used with as-yet discovered variants, allowing for quick determinations regarding the variant's infectivity versus either the wild-type virus or its variants.

Integrated unsupervised-supervised modeling and prediction of protein-peptide affinities at structural level

Cell signal networks are orchestrated directly or indirectly by various peptide-mediated protein-protein interactions, which are normally weak and transient and thus ideal for biological regulation and medicinal intervention. Here, we develop a general-purpose method for modeling and predicting the binding affinities of protein-peptide interactions (PpIs) at the structural level. The method is a hybrid strategy that employs an unsupervised approach to derive a layered PpI atom-residue interaction (ulPpI[a-r]) potential between different protein atom types and peptide residue types from thousands of solved PpI complex structures and then statistically correlates the potential descriptors with experimental affinities (KD values) over hundreds of known PpI samples in a supervised manner to create an integrated unsupervised-supervised PpI affinity (usPpIA) predictor. Although both the ulPpI[a-r] potential and usPpIA predictor can be used to calculate PpI affinities from their complex structures, the latter seems to perform much better than the former, suggesting that the unsupervised potential can be improved substantially with a further correction by supervised statistical learning. We examine the robustness and fault-tolerance of usPpIA predictor when applied to treat the coarse-grained PpI complex structures modeled computationally by sophisticated peptide docking and dynamics simulation. It is revealed that, despite developed solely based on solved structures, the integrated unsupervised-supervised method is also applicable for locally docked structures to reach a quantitative prediction but can only give a qualitative prediction on globally docked structures. The dynamics refinement seems not to change (or improve) the predictive results essentially, although it is computationally expensive and time-consuming relative to peptide docking. We also perform extrapolation of usPpIA predictor to the indirect affinity quantities of HLA-A*0201 binding epitope peptides and NHERF PDZ binding scaffold peptides, consequently resulting in a good and moderate correlation of the predicted KD with experimental IC50 and BLU on the two peptide sets, with Pearson's correlation coefficients Rp = 0.635 and 0.406, respectively.

Theoretical investigations on the effects of mutations in important residues of NS1B on its RNA-binding using molecular dynamics simulations

NS1B protein plays an important role in countering host antiviral defense and virulence of influenza virus B, considered as the promising target. The first experimental structure of the NS1B protein has recently been determined, was able to bind to double-stranded RNA (dsRNA). However, few studies attempt to investigate the RNA-binding mechanism of the NS1B. In this study, we provide our understanding of the structure-function relationship, dynamics and RNA-binding mechanism of the NS1B protein by performing molecular dynamics simulations combined and MM-GBSA calculations on the NS1B-dsRNA complex. 12 key residues are identified for RNA-binding by forming hydrogen bonds with the. Our results also demonstrate that mutations (R156A, K160A, R208A and K221A) can cause the local structure changes of NS1B CTD and the hydrogen bonds between NS1B CTD and RNA disappearance, which may be the main reasons for the decrease in RNA-binding affinity. These results mentioned will help us understanding the RNA-binding mechanism and could provide some medicinal chemistry insights chances for rational drug design targeting NS1B protein.

Sequence Effect of Peptide-Based Materials on Delivering Interferon-α (IFN-α): A Molecular Dynamic Perspective

Peptide-based biomaterials exhibit great potentials in developing drug delivery platforms due to their biocompatibility and biodegradability beyond poly(ethylene glycol). How different amino acids in peptides used for delivery play their roles is still unclear at the microscopic level. This work compared the assembly behaviors of a series of peptides around interferon-α (IFN-α). Through all-atom molecular simulations, the sequence effect of peptides on delivering interferon-α was quantitively characterized. The hydrophobic elastin-like peptide (VPGAG)_n preferred to self-aggregate into dense clusters, rather than encapsulate IFN-α. The hydrophilic zwitterionic peptides with repeating unit "KE" tended to phase-separate from IFN-α in the mixture. In contrast, peptides with a hybrid sequence, i.e., (VPKEG)_n, exhibited the highest contact preference, and the formed protective shell endowed IFN-α with better thermal stability and stealth property and achieved a subtle balance between protecting IFN-α and subsequent releasing. Further energy decomposition analysis revealed that the positively charged Lys contributed most to the binding affinity while the negatively charged Glu contributed most to the hydrophilic property of peptide-based materials. In summary, this article reveals why peptides composed of repeating hydrophobic and charged residues could be a potential choice for delivering therapeutic proteins in the form of solution.

Synthesis of benzimidazole/triphenylamine-based compounds, evaluation of their bioactivities and an in silico study with receptor tyrosine kinases

The antiproliferative activity of AL-1 against various cancer cells indicated the applicability of the BI-TPA-based compound as a potential multi-cancer inhibitor.

Discovery of microtubule stabilizers with novel scaffold structures based on virtual screening, biological evaluation, and molecular dynamics simulation

Disrupting the dynamics and structures of microtubules can perturb mitotic spindle formation, cause cell cycle arrest in G2/M phase, and subsequently lead to cellular death via apoptosis. In this investigation, the structure-based virtual screening methods, including molecular docking and rescoring, and similarity analysis of interaction molecular fingerprints, were developed to discover novel tubulin inhibitors from ChemDiv database with 1,601,806 compounds. The screened compounds were further filtered by PAINS, ADME/T, Toxscore, SAscore, and Drug-likeness analysis. Finally, 17 hit compounds were selected, and then submitted to the biologic evaluation. Among these hits, the P2 exhibited the strongest antiproliferative activity against four tumor cells including HeLa, HepG2, MCF-7, and A549. The in vitro tubulin polymerization assay revealed P2 could promote tubulin polymerization in a dose dependent manner. Finally, in order to analyze the interaction modes of complexes, the molecular dynamics simulation was performed to investigate the interactions between P2 and tubulin. The molecular dynamics simulation analysis showed that P2 could stably bind to taxane site, induced H6-H7, B9-B10, and M-loop regions changes. The ΔG_bind energies of tubulin-P2 and tubulin-paclitaxel were -68.25 ± 12.98 and -146.05 ± 16.17 kJ mol^-1, respectively, which were in line with the results of the experimental test. Therefore, P2 has been well characterized as lead compounds for developing new tubulin inhibitors with potential anticancer activity.

Interleukin-25 recognition by its unique receptor IL-17Rb via two discrete linear and cyclic epitopes

Interleukin-17 (IL-17) is a family of pro-inflammatory cytokines and has been involved in the pathogenesis of chronic inflammatory and autoimmune diseases. The IL-17E, also known as IL-25, is a distinct member of this family that binds to its unique receptor IL-17Rb to induce the activation of nuclear factor kappa-light-chain enhancer of activated B cells. Here, we systematically examined the intermolecular recognition and association of IL-25 with IL-17Rb and demonstrated that the IL-25 primarily adopts two discrete linear and cyclic epitopes to interact with IL-17Rb. The two epitopes are separately located in the monomers 1 and 2 of IL-25 homodimer and cover sequences ¹²⁵ DPRGNSELLYHN¹³⁶ and ⁷⁷ ELDRDLNRLPQDLY⁹⁰ . They totally contribute 71.6% binding energy to the full-length IL-25. The linear epitope targets a site spanning over the extracellular fnIIID1 and fnIIID2 domains of IL-17Rb, while the cyclic epitope primarily binds at the fnIIID1 domain. In addition, we also found that the linear and cyclic epitopes are natively folded into ordered single-stranded and double-stranded conformations in IL-25 protein context, respectively, but would become largely disordered when splitting from the context to be free peptides, which, however, cannot bind effectively to IL-17Rb as them in the native state. In this respect, we extended the cyclic epitope to cover the whole IL-25 double-stranded region and added a disulfide bridge across its two strands at three selected anchor residue pairs. It is revealed that the disulfide-stapled peptides can be constrained into a native-like conformation and thus exhibit an improved binding potency to IL-17Rb as compared to their unstapled counterpart.